sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
acd05d73cbfe866d0c4c1226d01554ca2fd69b98cc3a294c1ff391183fe97193 | Jupyter | 16,952 | 547 | # %%
import pandas as pd
pd.set_option('display.max_rows', 200) # or 1000
from scipy.stats import zscore
import matplotlib.pyplot as plt
import matplotlib as mpl
mpl.rcParams['figure.dpi'] = 150
mpl.rcParams['pdf.fonttype'] = 42
mpl.rcParams['ps.fonttype'] = 42
import seaborn as sns
import plotly.graph_objects as go
... |
3ef85c9135b641af72659b842605f37b3c3f1373f16023c4b98bf1bb8421e255 | Jupyter | 17,037 | 562 | # %% [markdown]
# # IntrinClock
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base model](#Load-weight... |
1df114cab2f53e26c1e3ce6831fcb7804b58061c21f17767acfab4839589b52e | Jupyter | 17,072 | 490 | # %% [markdown]
# # Imports
# %%
import numpy as np
import tensorflow as tf
from neuroxai.utils.process import get_last_layer, get_last_conv_layer
from examples.classification_model import get_classification_model, get_xai_classification_model, load_images_2d, ResNet50V2
from examples.segmentation_model import get_d... |
4d595761c4f5e1f03014909aa710524dcb27e1c3a01bb5496a88e1153f2404e2 | Jupyter | 17,111 | 429 | # %%
import matplotlib.pyplot as plt
%matplotlib inline
import pandas as pd
import numpy as np
import sys
# change pkg_path variable to the directory storing the package
pkg_path = '/Users/jake/Code/'
sys.path.insert(0, pkg_path)
from photometry_analysis import fp_dat, tfc_dat, fp_viz
import seaborn as sns
import stats... |
75dc053c348b1edeec3dca45369e3ba349a5de865ac099be8cf3a80ec654fc14 | Jupyter | 17,119 | 549 | # %% [markdown]
# # TraSE-IN pipeline
# %% [markdown]
# This notebook has been developped under Ubuntu, with miniconda and python3.10.
#
# For visualization, we mostly use `opencv` and `matplotlib` which gives us cross platform GUI.
#
# Sadly, we have noticed than GDK3 (the usual GUI backend used by opencv on Linux... |
5b985793fce0a1ce4b5b8c5329eb9dc7455c4d5d829839f5f2142ab9cbfe445a | Jupyter | 17,297 | 601 | # %% [markdown]
# # Figs. 3J, 3K, 3L
# ## ROI-based analyses
#
# For "Network-level encoding of local neurotransmitters in cortical astrocytes" (Cahill et al. 2024)
# %% [markdown]
# ---
# %% [markdown]
# ## Setup
# %% [markdown]
# #### Imports
# %%
import numpy as np
import pandas as pd
import matplotlib.pyplot ... |
dfe454eb35e8ce34891776587d26f19d62ce2856b6227927fcf6526ead671aa0 | Jupyter | 17,385 | 433 | # %% [markdown]
# (autocorr)=
#
# # Autocorrelation analysis & convergence
#
# In this tutorial, we will discuss a method for convincing yourself that your chains are sufficiently converged.
# This can be a difficult subject to discuss because it isn't formally possible to guarantee convergence for any but the simple... |
9f65a5c8c3552e1fe7f5579f373dd0959e951b0c4f2ae38defde8316bfee829b | Jupyter | 17,479 | 384 | # %% [markdown]
# # Preprocessing and clustering
# %%
# Core scverse libraries
from __future__ import annotations
import anndata as ad
# Data retrieval
import pooch
import scanpy as sc
# %%
sc.settings.set_figure_params(dpi=50, facecolor="white")
# %% [markdown]
# The data used in this basic preprocessing and clu... |
755807984bd85f2f0e59d3f21ef89ac1b2df5c820a2f9435b055b177c59275fc | Jupyter | 17,539 | 560 | # %% [markdown]
# # Simpler pipeline
#
# Contrarily to TRASE-IN pipeline we rely only on GCaMP images. This supports 3D tracking & analysis.
#
# We rely on Wavelet + SKT + EMC2 instead of StarDist + eMHT + EMC2 for tracking
# %% [markdown]
# This notebook has been developped under Ubuntu, with miniconda and python3.... |
3a4604bf1dea3b5a492db31872b98eaf12830656fbfe731a8d2f72bbc8f3b504 | Jupyter | 17,545 | 465 | # %% [markdown]
# ## Factor analysis
# %%
# calculate features
from rdkit import Chem
from analysis.factor.descriptors import all_descriptors
index_filepath = os.path.join(base_dir, 'dataset/bitter_sweet_nonbitter/secondary_bsn.csv')
df = pd.read_csv(index_filepath, header=0, sep=',')
descriptor_names = []
with open(o... |
f3f293bd9ac0b8fb3f1c191edfa9876eda514c477164a70ed594785d5ce055a3 | Jupyter | 17,657 | 450 | # %%
# Copyright (c) 2012-2023, NECOTIS
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
#
# - Redistributions of source code must retain the above copyright notice,
# this list of conditions a... |
17ef14b29e899132fc3705a55459e20969356a6effc0dce7520ddb41d02d0e77 | Jupyter | 17,701 | 444 | # %%
import igraph as ig
import numpy as np
from statistics import mean
import sympy.combinatorics as combinatorics
import sage.groups.perm_gps.permgroup as sage_permgroup
import sage.groups.perm_gps.permgroup_named as sage_named_groups
#from functions import *
get_names_from_list_of_ids = lambda ig_graph, ids : [i... |
641bd0b8a1215ed1152e72a28e98a97b56ba541492c4a65c2b529e2fa33c5269 | Jupyter | 17,732 | 456 | # %%
# from neuprint import Client
# c = Client('https://neuprint-cns.janelia.org/', dataset='optic-lobe', token= TBD)
import sys
from pathlib import Path
import numpy as np
import plotly.graph_objects as go
from dotenv import load_dotenv, find_dotenv
load_dotenv()
PROJECT_ROOT = Path(find_dotenv()).parent
sys.... |
adc2b83905b0a9038571424c3ab2c5aff47eeb2ef942206d03a94763677c2e66 | Jupyter | 17,778 | 663 | # %% [markdown]
# # Figurs S3J, S5C, S5D, S5E
# ## Distribution characteristics
#
# For "Network-level encoding of local neurotransmitters in cortical astrocytes" (Cahill et al. 2024)
# %% [markdown]
# ---
# %% [markdown]
# ## Setup
# %% [markdown]
# #### Imports
# %%
# Others
from pathlib import Path
import num... |
a494fbc0a8fce682a7aebf741968665c1562eddf3a2b82af01cf690864726a44 | Jupyter | 17,840 | 479 | # %% [markdown]
# # Computation of individual phenotypes from shap profiles
#
# The core function to generate shap values is `get_shaps()` below. Some notes on its usage:
#
# - abundance data should be a pandas dataframe consisting of abundance data in wide form, i.e. `dat` should have:
# - first column: `subject_i... |
1d5149cffb4e2bd1fc0335bf04b75bbab08a006dc8c301cdd1612f3b82a9ff19 | Jupyter | 18,244 | 468 | # %% [markdown]
# # HBR with Normal likelihood
#
# Welcome to this tutorial notebook that will go through the fitting and evaluation of Normative models with a Hierarchical Bayesian Regression (HBR) model using a Normal likelihood.
#
# Let's jump right in.
# %% [markdown]
# ### Imports
# %%
import logging
import w... |
f0e018d0b4e0771c262508426f0b7f3819a9ec7f86d4207749a6a1b6e56faaea | Jupyter | 18,315 | 518 | # %% [markdown]
# # HBR with Beta likelihood
#
# Welcome to this tutorial notebook that will go through the fitting and evaluation of Normative models with a Hierarchical Bayesian Regression (HBR) model using a Beta likelihood.
#
# Let's jump right in.
# %% [markdown]
# ### Imports
# %%
import logging
import warni... |
fb8cb7f1e8f615b83fd817f0ebf709fcdba958df229d78b2f066c88495c14a0b | Jupyter | 18,329 | 394 | # %% [markdown]
# # Vacuole volumetry Analysis (WebKnossos .csv export)
# This notebook reads .csv files downloaded from WebKnossos and performs data visualization and hyptothesis testing for the presence and distribution of neurodegenerative vacuoles.
#
# Before you begin, organize the .stl files as follows -
#
# ... |
488365fdaa0c1bdb30d48f349cd1760bb6cf4466b036981dbe300c0e0f4c6aee | Jupyter | 18,366 | 490 | # %% [markdown]
# # ALIGNN/ALIGNN-d graph representation for molecular structures
#
# This notebook will guide you through encoding molecular structures into the ALIGNN/ALIGNN-d graph representation.
#
# You will need to install `ase` for reading molecular structure files and creating molecular graphs.
# %%
import n... |
f8a2984f77906ae14eac5fa469f1e585b7ceda9b1c8b35a4f6410a6051949eba | Jupyter | 18,384 | 596 | # %%
import pandas as pd
import numpy as np
#import plotly.express as px
import matplotlib.pyplot as plt
%matplotlib inline
low_memory=False
path = input("Enter path to B-SOID ouput pose labels file:")
file = input("Enter filename:")
df = pd.read_csv(path + "/" + file)
#fig = px.line(df, x = 'Frame', y = 'Labels', t... |
380daf9cc1f8301c64ff0459f145a0449c39fbc1b3fa46f0731d02aa4fa9578b | Jupyter | 18,397 | 540 | # %%
from samalg import SAM
import pandas as pd
from matplotlib import pyplot as plt
import numpy as np
import scanpy as sc
from matplotlib.pyplot import rc_context
# %%
print(sc.__version__)
# %%
samsporo=SAM()
# %%
samsporo.load_data('sporocysts3.stem.counts_2.csv')
# %%
samsporo.preprocess_data()
# %%
samsporo.... |
13c5190ea5a96c112a19598955d9336ff131ab4b2d75902c40389a649699367e | Jupyter | 18,484 | 398 | # %%
import pandas as pd
import numpy as np
import seaborn as sns
import pingouin as pg
import dabest
import matplotlib as mpl
mpl.rcParams['figure.dpi'] = 150
mpl.rcParams['pdf.fonttype'] = 42
mpl.rcParams['ps.fonttype'] = 42
mpl.rcParams['figure.figsize'] = 11.7,8.27
import matplotlib.pyplot as plt
from statsmodels... |
a45423bb8b5669b385b886c251cd513fcd47e75e2f552c90c4442cfd4b88b54d | Jupyter | 18,484 | 339 | # %% [markdown]
# # Making a connectivity matrix from the Fruit Fly connectome
#
# Using Janelia's data [here](https://storage.cloud.google.com/hemibrain/v1.2/exported-traced-adjacencies-v1.2.tar.gz) (explained [here](https://dvid.io/blog/release-v1.2/#downloads))
#
# We get a list of all neuron names, types and ids,... |
138baf6cba010a60ce774fc825b9734dfd3c73a9a9588d2c43d72e509f26f339 | Jupyter | 18,511 | 505 | # %%
import os
import glob
import sys
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.manifold import TSNE
from umap import UMAP
import seaborn as sns
sys.path.append('../')
import src.utils as my_u
from src.utils import df_cp
from src.utils import df_log
from src.utils import df_t... |
6db6866606ae97ad659c5761e775eb375fd5efe0dd04cfcfb4ebd114bc74f873 | Jupyter | 18,519 | 474 | # %% [markdown]
# # HomeostaticDysregulation
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base model]... |
cff1f5ac62553ad9b3e65b5b713e130cdec553a6d6d4c10a45e70a22fbd268e5 | Jupyter | 18,530 | 428 | # %%
import scipy as sp
import scipy.io
import scipy.stats
import os
import numpy as np
import pandas as pd
import glob
import random as rand
from tqdm import tnrange, tqdm_notebook
import sys
sys.path.append(os.getcwd()+'\\..')
sys.path.append(os.getcwd()+'\\..\\..\\..')
import cmocean
from utils import utils
from ut... |
ff55134941e80a90747e5a94bc0d333f5f8be0f3652ef0a1d3ba3276c375651f | Jupyter | 18,543 | 495 | # %%
# %%
# path_to_predictions = "/home/jupyter-n.mekkes@gmail.com-f6d87/clinical_history/final_predictions/ALL_clinical_trajectories_dictionary_rules_of_thumb_yearly_2023-01-31.pkl"
path_to_predictions = "/home/jupyter-n.mekkes@gmail.com-f6d87/clinical_history/final_predictions/ALL_clinical_trajectories_dictionary_... |
a371a4af1d0f481e824318a6fd5a769d314a01c6685561c1cf4a33d9c4141e9d | Jupyter | 18,651 | 507 | # %%
import os
import glob
import sys
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.manifold import TSNE
from umap import UMAP
import seaborn as sns
sys.path.append('../')
import src.utils as my_u
from src.utils import df_cp
from src.utils import df_log
from src.utils import df_t... |
fd5944851f8b7d4674a19524a76d545bc3697ec7f3f1b4e0abb5af62289ba7ca | Jupyter | 18,683 | 509 | # %%
import os
import glob
import sys
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.manifold import TSNE
from umap import UMAP
import seaborn as sns
sys.path.append('../')
import src.utils as my_u
from src.utils import df_cp
from src.utils import df_log
from src.utils import df_t... |
0282ce56bf90146a10cc550916f332fe9fd8333e159a4896973385e282dcebde | Jupyter | 18,739 | 514 | # %%
import os
import glob
import sys
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.manifold import TSNE
from umap import UMAP
import seaborn as sns
sys.path.append('../')
import src.utils as my_u
from src.utils import df_cp
from src.utils import df_log
from src.utils import df_t... |
43f03271480d5c62fbae7753aad12dbb942dda912367a433aa4158676f7e1b9c | Jupyter | 18,778 | 420 | # %% [markdown]
# # Interative code for analyzing epileptic LFP from imaging sessions.
# Below are definitions of values in each of the output tables
#
# ## HFO Table:
# - HFO_time: time of HFO in seconds, relative to the start and end of the recording
# - HFO_frame: frame at which the HFO occured
# - HFO_duration: ... |
b929b13b3cedad02906ecd08754f1f3e6e125e4676de220c8d7a0bffbf419af7 | Jupyter | 18,898 | 536 | # %% [markdown]
# # Netherlands Neurogenetics Database
# Author: Nienke Mekkes <br>
# Date: 21-Sep-2022. <br>
# Correspond: n.j.mekkes@umcg.nl <br>
#
# ## Script: clinical history NLP models: SVC
# Objectives: optimization of SVC based model <br>
# Based on: https://towardsdatascience.com/multi-label-text-classificati... |
c60c2b2aae2cd5bad3e170e54173dc06740aacb89006211999edf501cb039aad | Jupyter | 19,111 | 522 | # %% [markdown]
# # Tabular_Pancreas + Baron
# %%
import os
import glob
import sys
import pandas as pd
import numpy as np
import random
import matplotlib.pyplot as plt
from sklearn.manifold import TSNE
from umap import UMAP
import seaborn as sns
sys.path.append('../')
import src.utils as my_u
from src.utils import d... |
c67a33e51073d5676425bd6fdcbde90ab193fa2f763e0a33962a9b101683c6dd | Jupyter | 19,114 | 536 | # %%
"""Example for plotting gradient data"""
import os.path as op
from glob import glob
import pickle
import matplotlib.image as mpimg
from matplotlib.gridspec import GridSpec
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import seaborn as sns
import ptitprince as p... |
85e4e69b7d993d4aaa6ebe0309c2c1a6dff9121622769b8cabe2481ff00f9ad8 | Jupyter | 19,136 | 552 | # %%
import pandas as pd
import numpy as np
from pathlib import Path
# %%
code_dir=Path.cwd()
project_dir=code_dir.parent
input_dir=project_dir/"input"
output_dir=project_dir/"output/pls/"
tmp_dir=project_dir/"tmp"
output_dir.mkdir(exist_ok=True, parents=True)
# %%
# Load data
y_ukb = pd.read_csv(input_dir/"y_ukb.c... |
c166e6720810dcafba5b5d8b432776f2bc669848e4445eaf117757a8573abf4b | Jupyter | 19,160 | 525 | # %% [markdown]
# # Setup
# %%
import numpy as np
import pandas as pd
from pathlib import Path
import nibabel as nib
import matplotlib.pyplot as plt
from scipy.stats import zscore
%matplotlib inline
# %%
code_dir=Path.cwd()
project_dir=code_dir.parent
input_dir=project_dir/"input"
output_dir=project_dir/"output/surfa... |
b55e461901e1d76c618c640efeeca210c7115da7b9f5813de8c315c7a8c63a10 | Jupyter | 19,193 | 575 | # %%
import pandas as pd
df_weights_scaled_subs = pd.read_pickle("df_weights_scaled_subs_fapar_model_trg_v20230918.pkl")
# %%
df_weights_scaled_subs
# %%
covs_rfe = ['hfp',
'pop_count',
'bio10',
'bio11',
'bio12',
'bio13',
'bio14',
'bio15',
'bio16',
'bio17',
'bio1',
'bio2',
'bio3',
'bio4',
'bio5',
'bio... |
60e7bc5fc8cd65ef1deee76e88b4e2bb2e4c70067a69339f10c319d3812c9591 | Jupyter | 19,214 | 450 | # %% [markdown]
# [](https://colab.research.google.com/github/PTB-MR/mrpro/blob/main/examples/notebooks/cartesian_reconstruction.ipynb)
# %%
import importlib
if not importlib.util.find_spec('mrpro'):
%pip install mrpro[notebooks]
# %% [mar... |
e187615f0c2087203e44038233416b60cad839eeea2bb1bba327418bb454dcb0 | Jupyter | 19,326 | 469 | # %%
import scipy as sp
import scipy.io
import os
import numpy as np
import pandas as pd
import glob
import csv
import random as rand
from tqdm import tnrange, tqdm_notebook
from collections import Iterable
import matplotlib.pylab as mpl
import random as rand
from ipywidgets import *
import colorlover as cl
from scipy ... |
8ab40236fffc11945e19baf02629afa76cd67edc4eecabb062b9b8f59900765d | Jupyter | 19,349 | 470 | # %% [markdown]
# # A sinusoidal weight matrix is most robust to noise
#
# * If we want a circulant network to have stable activity with no input
# * and the network is operating in the linear regime
# * and the network needs to encode 2 variables (vector direction and magnitude)
# * and the neurons can have negative ... |
ea9ba504217682669a4efad1515c10928cb3691908f4f3723c21d5b5e108c0b1 | Jupyter | 19,462 | 418 | # %%
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import pingouin as pg
import dabest
from scipy.stats import chi2_contingency
import matplotlib as mpl
mpl.rcParams['figure.dpi'] = 150
mpl.rcParams['pdf.fonttype'] = 42
mpl.rcParams['ps.fonttype'] = 42
from matplotlib_venn import venn3
impor... |
0d855ba5a2c683177799c39fc517527ab12ec8968284c7347a7022c852b02a1e | Jupyter | 19,502 | 513 | # %%
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import pandas as pd
import numpy as np
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import seaborn as sns
from scipy.stats import pearsonr
from statsmodels.stats.multitest import fdrcorrection
from nilearn import datasets as ds
from... |
dbd38e84248d49dae3fb17f09742b13bc0298c67a3948128dfd21478e2d2613a | Jupyter | 19,506 | 456 | # %% [markdown]
# # Digital Twins of Microbiomes and General Ecosystems
#
# https://www.science.org/doi/10.1126/sciadv.adj0400
#
# Digital twins as a concept is increasingly relevant for modeling, exploring, understanding and exploiting complex natural systems. We demonstrate a new approach, that distills a generativ... |
30078eac8535faddfac9d6ad6aa2f49d241a4c3ad7051bd4b4d7c95078ce20d4 | Jupyter | 19,508 | 573 | # %%
import numpy as np
import torch
from pytorch_lightning import Trainer
from torch.distributions import normal, uniform
from rgan.utils import TorchStandardScaler, \
SamplesDataset, NormToBoundsScaler, \
MechanisticModelStochastic
from rgan.rgan_stochastic import rGAN... |
c6cfedb9d598599dbabde1fbfbae124556dbfb28e5e76d567cf04a2f72d860c9 | Jupyter | 19,581 | 673 | # %% [markdown]
# # Feature extraction: extraction of delta and theta frequency bands
# %% [markdown]
# Import packages
# %%
import io
import os
import mne
import copy
import glob
import array
import matplotlib
import numpy as np
import pandas as pd
import sklearn.metrics
import seaborn as sns
import scipy.io as sio
... |
52d70aed7d7773bac2455056abed98da385ac444ee581b27f667b7e447cc7e1b | Jupyter | 19,686 | 522 | # %%
import os
import glob
import sys
import pandas as pd
import numpy as np
import random
import matplotlib.pyplot as plt
from sklearn.manifold import TSNE
from umap import UMAP
import seaborn as sns
sys.path.append('../')
import src.utils as my_u
from src.utils import df_cp
from src.utils import df_log
from src.uti... |
1d0f84655e93b3bdd8fc32bb3213effec25c827bbedf888da6af1218905876f7 | Jupyter | 19,853 | 481 | # %%
import os
import glob
import sys
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.manifold import TSNE
from umap import UMAP
import seaborn as sns
sys.path.append('../')
import src.utils as my_u
from src.utils import df_cp
from src.utils import df_log
from src.utils import df_t... |
452f9aa4f96d5ab4cd5f670e1f34e24c665ee78a942b8556eb1db69aa1a69eed | Jupyter | 19,906 | 561 | # %% [markdown]
# # Netherlands Neurogenetics Database
# Author: Nienke Mekkes <br>
# Date: 21-Sep-2022. <br>
# Correspond: n.j.mekkes@umcg.nl <br>
#
# ## Script: clinical history NLP models: BOW
# Objectives: optimization of BOW based model <br>
# Based on: https://towardsdatascience.com/multi-label-classification-us... |
10a311bac383cdec27d50365f4955f8934b59241b68f165f8c6615dbb0ec3927 | Jupyter | 19,919 | 494 | # %%
import scipy as sp
import scipy.io
import os
import numpy as np
import pandas as pd
import glob
import csv
import random as rand
from tqdm import tnrange, tqdm_notebook
from collections import Iterable
import matplotlib.pylab as plt
import random as rand
from ipywidgets import *
from scipy import stats
import impo... |
624c7723466d635f3217b8bfa2db903683d04080e94c3c07819d42efefad8fcb | Jupyter | 20,032 | 576 | # %% [markdown]
# <html><head><meta content="text/html; charset=UTF-8" http-equiv="content-type"><style type="text/css">ol</style></head><body class="c5"><p class="c0 c4"><span class="c3"></span></p><p class="c2 title" id="h.rrbabt268i6e"><h1>CaImAn’s Demo pipeline</h1></p><p class="c0"><span class="c3">
# %%
im... |
8bf21f2baa36a6ab75f5b90094580cd483d5a72e3531b4f56f75e7ff2bf0a625 | Jupyter | 20,092 | 422 | # %% [markdown]
# # tAge mortality
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base model](#Load-wei... |
c25a0e4d6fa9e804718df2d507a273f6de24ae05224156a4ca600a69ea1d357e | Jupyter | 20,142 | 554 | # %% [markdown]
# # Digital Twins of Microbiomes and General Ecosystems
#
# https://www.science.org/doi/10.1126/sciadv.adj0400
#
# Digital twins as a concept is increasingly relevant for modeling, exploring, understanding and exploiting complex natural systems. We demonstrate a new approach, that distills a generativ... |
3bca52ea242509f09d72e86ee10a35429a4876f6b87d23bdd05f41adf18b10c7 | Jupyter | 20,176 | 563 | # %% [markdown]
# # twixtools: Example Code
#
# ### Table of Contents
# 1. <a href='#section1'>`read_twix`: Direct access to the raw data</a>
# 2. <a href='#section2'>`map_twix`: high-level raw data access via a numpy-like twix-array</a>
# 3. <a href='#section3'>`map_twix`: EPI reconstruction example</a>
# 4. <a href=... |
ab7923b7075255e110d9cf0a6bde48e10769a91fa016f9b6abc2ecbeddc2a584 | Jupyter | 20,178 | 743 | # %% [markdown]
# # Depression and Anxiety classification with ERPs
# %% [markdown]
# Conceptual replication of the study by [Cavanagh et al. (2019)](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6515849/) on own dataset.
# %% [markdown]
# Import packages
# %%
import io
import os
import mne
import copy
import glob
im... |
d269cc3a745eb755dac2e648a620e2a7c75061a73804b1769959b09e1fca16ef | Jupyter | 20,200 | 539 | # %% [markdown]
# # HBR with SHASH likelihood
#
# Welcome to this tutorial notebook that will go through the fitting and evaluation of Normative models with a Hierarchical Bayesian Regression (HBR) model using a SHASH likelihood.
#
# Let's jump right in.
# %% [markdown]
#
# ### Imports
# %%
import logging
import ... |
dba4c23893926058b5a5d887fbd4c8c49a4d29db2d8a073e385ee658f04a91b2 | Jupyter | 20,386 | 411 | # %%
import pandas as pd
import numpy as np
import os
import matplotlib.pyplot as plt
# %%
gwas_dir = "/cluster/projects/p33/users/elizabept/multimodal/discovery/plink/results"
ref_bim = "/cluster/projects/p33/users/elizabept/multimodal/discovery/genos/results/UKB34886_T1_QCed_230222.bim"
# %%
bim = pd.read_csv(ref_b... |
5314aec5b26804568258f744eecf5af84b94294c0951c8a883155f7b93f9991f | Jupyter | 20,630 | 536 | # %% [markdown]
# # Bayesian Linear Regression
#
# Welcome to this tutorial notebook that will go through the fitting and evaluation of Normative models with Bayesian Linear Regression (BLR).
#
# Let's jump right in.
# %% [markdown]
# ## Imports
# %%
import logging
import warnings
import matplotlib.pyplot as plt
i... |
888102f3566adc299ecfb54b3dc78985186368f73063e53b4eab88498e56ad4e | Jupyter | 20,716 | 481 | # %%
%load_ext autoreload
%autoreload 2
import re
import scipy as sp
import scipy.io
import os
import numpy as np
import pandas as pd
import glob
import csv
import random as rand
from tqdm import tnrange, tqdm_notebook
from collections import Iterable
import matplotlib.pylab as mpl
import random as rand
from ipywidgets... |
ceb717a328b83fc9e021ce117a58f86240ed3bbd57abb731028e5fef390db015 | Jupyter | 20,823 | 361 | # %% [markdown]
# # Forward Operators
#
# In computational imaging, the ability to model the acquisition system accurately is a cornerstone for solving inverse problems. The `LinOp` class in Pyxu serves as a foundational abstraction for defining linear forward operators, essentially mapping the original signal or imag... |
a61f1e39e5a91fe6c90b21743ef0271429cd158a52e8bb5101f91dc58cc605be | Jupyter | 21,064 | 772 | # %% [markdown]
# # Figs. 4H, 4K, 4L
# # Ext. Data Figs. 6E, 6F
# ## Responder cells
#
# For "Network-level encoding of local neurotransmitters in cortical astrocytes" (Cahill et al. 2024)
# %% [markdown]
# ---
# %% [markdown]
# ## Setup
# %% [markdown]
# #### Imports
# %%
# Typing
from typing import (
Option... |
31f00b65defeb0132f52c9010883b17337251c0f3092450fa69f9360354ea1f3 | Jupyter | 21,065 | 687 | # %%
import json
def contar_lineas(filename):
count = 0
with open(filename, "rb") as f:
for bloque in iter(lambda: f.read(1024*1024), b""):
count += bloque.count(b">")
return count
#tabla informativa de Trembl
TABLE_file = "../Data2/Trembl/table"
#print(contar_lineas(filename))
#253,0... |
b123b536ec8d648d6d5a53a03aa99a0ff3d33b66fa54ef5e3e14c72265e94173 | Jupyter | 21,250 | 410 | # %% [markdown]
# # Standard outputs of EVcouplings pipeline
#
#
# This notebook explains the contents of the most commonly used output files of EVcouplings. It also demonstrates how to work with the files using the EVcouplings module, if applicable.
#
# For each output file, there is a "output key" which can be use... |
d2f3574716ef83b4e3d6fa37937662011242eb2209023a432bc6a38295051470 | Jupyter | 21,519 | 438 | # %%
import numpy as np
import scipy as sp
import matplotlib.pyplot as plt
from scipy.misc import face
import skimage.io as skio
plt.style.use("seaborn-darkgrid")
plt.rcParams["figure.figsize"] = [9, 6]
plt.rcParams["figure.dpi"] = 150
plt.rcParams["axes.grid"] = False
plt.rcParams["image.cmap"] = "viridis"
rng = np.... |
4fa564c4fd1f9769cd1ea12a9485f0b02c082c17a253f6bd6fc5347225fed493 | Jupyter | 21,595 | 583 | # %% [markdown]
# # Netherlands Neurogenetics Database
# Author: Nienke Mekkes <br>
# Date: 21-Sep-2022. <br>
# Correspond: n.j.mekkes@umcg.nl <br>
#
# ## Script: clinical history NLP models: PubMedBERT
# Objectives: optimization of PubMedBERT based model <br>
# Based on:
#
#
# ### Input files:
# - File (excel or p... |
759a417e042b789e97b2460141beecf1babd30baa151cfcc341305576802ac11 | Jupyter | 21,613 | 443 | # %% [markdown]
# # tAge
# %% [markdown]
# ## Index
# 1. [Instantiate model class](#Instantiate-model-class)
# 2. [Define clock metadata](#Define-clock-metadata)
# 3. [Download clock dependencies](#Download-clock-dependencies)
# 4. [Load features](#Load-features)
# 5. [Load weights into base model](#Load-weights-into-... |
4d1f5a85cda1372dca03ebdf419783392adf9860a2dbbffbe2d5ff87bdf34146 | Jupyter | 21,634 | 432 | # %% [markdown]
# # Instructions
# The following code was designed in order to track the location of a single animal across the course of a single video session. After initally loading in the video, the user is able to crop the video frame using a rectangle selection tool. A background reference frame is then specifi... |
6fd0475bdb03b1230b38a91bcf5e43fab9f7e89d6122615d48f79b0ab30869db | Jupyter | 21,758 | 651 | # %% [markdown]
# ### Imports and Configuration
# %%
import copy
import os
import statistics
from functools import reduce
from copy import deepcopy
import matplotlib.pyplot as plt
import numpy as np
if os.environ.get('DISPLAY') is not None:
%matplotlib tk
import msmfcode_src
from msmfcode.core.logging import lo... |
ed110780b3f708d9c2821255613cf43e246657fe94fb91be790f3e8249460e6a | Jupyter | 21,768 | 695 | # %% [markdown]
# # Setup
# %%
# from google.colab import drive
# drive.mount('/content/drive')
# %%
!pip install pandas numpy jax sklearn selfies rdkit exmol kdeepensemble tqdm pyyaml h5py
!pip install -q tensorflowjs
!nvidia-smi
# %%
# regular libraries
import pandas as pd
import numpy as np
import os
# ML
from s... |
992279324546416bd353d704bc9598ffedd5f5589767dea52201b229f6e0a620 | Jupyter | 21,983 | 623 | # %% [markdown]
# # 2021-03 LC GcAMP Tone Only Analysis
# %%
import matplotlib.pyplot as plt
%matplotlib inline
import pandas as pd
import numpy as np
import sys
# change pkg_path variable to the directory storing the package
pkg_path = '/Users/jake/python_scripts/wiltgenlab/'
sys.path.insert(0, pkg_path)
from photome... |
3a6209ccddc522b334a578a4cff0448ac02c30167f521fe04fdac7abd25a9fce | Jupyter | 22,101 | 445 | # %% [markdown]
# # Instructions
# The following code was designed in order to track the location of a single animal across the course of a single video session. After initally loading in the video, the user is able to crop the video frame using a rectangle selection tool. A background reference frame is then specifi... |
c90d4af5e052110286c4ec1478bc198edc92ea905ad331b47da0af19e49dd764 | Jupyter | 22,282 | 329 | # %% [markdown]
# # kCSD-python package tutorial
#
# In this tutorial we illustrate the use of kCSD-python package for CSD reconstruction in the simplest case of a regular 2D square grid. This is a simplified version of a slice on a microelectrode array [Ness et al., 2015], or a planar silicone probe within the brain,... |
bf7877f574f43830b89080644d20f28022bd541363ad4fa18d90cd25dafb87c8 | Jupyter | 22,443 | 460 | # %% [markdown]
# #TWristAR Dataset Demo.ipynb
# Loads the 3 subject e4 wristband HAR dataset from Zenodo repository, converts the data into numpy arrays of X (data), y(labels), sub (subject numbers), then performs a hold-one-subject-out evaluation of a 1D-CNN.
#
# >X = (samples, time steps per sample, accel_x/y/z/tot... |
812fdd112f913c42518a1e646d0573d4c109d03de3232ae90c8564b00c6b9aa5 | Jupyter | 22,476 | 612 | # %%
#load libraries
import os
import glob
from eumap import parallel
from eumap.parallel import TilingProcessing
from eumap.raster import read_rasters, save_rasters
from eumap.misc import ttprint
from eumap.misc import find_files
import re
import pandas as pd
import numpy as np
import rasterio
from pathlib import Path... |
6715fd91245b8fe40ed2be86065f544e5a4109e4e9b336c817304877a202b0a2 | Jupyter | 22,625 | 579 | # %%
import scipy as sp
import scipy.io
import os
import numpy as np
import pandas as pd
import random as rand
from tqdm import tnrange, tqdm_notebook
from collections import Iterable
import matplotlib.pylab as mpl
import colorlover as cl
from scipy import stats
import importlib
import sys
sys.path.append(os.getcwd()+'... |
c4a83447c13db8ac848bbc54ee16330081ede534665717e1710f90630fe36d48 | Jupyter | 22,892 | 621 | # %%
import os
import glob
import sys
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.manifold import TSNE
from umap import UMAP
import seaborn as sns
sys.path.append('../')
import src.utils as my_u
from src.utils import df_cp
from src.utils import df_log
from src.utils import df_t... |
53cf10cdd6514c93a3ffbd433e635b48c4e17670e36daa5b5ee4e0323bae51e6 | Jupyter | 22,899 | 536 | # %%
import matplotlib
import matplotlib.pyplot as plt
import os
import pandas as pd
import scipy.stats as ss
import numpy as np
import seaborn as sns
from matplotlib.patches import Rectangle, Patch
from matplotlib.collections import PatchCollection
import re
import matplotlib.patches as patches
from matplotlib.colors ... |
418c85ea21f69fe8910c55f22697933f24b1d6348b1fb548a092b1bd28a8e4d0 | Jupyter | 23,063 | 646 | # %%
# Setting up environment and accessing database
import sys
from pathlib import Path
from dotenv import load_dotenv, find_dotenv
load_dotenv()
PROJECT_ROOT = Path(find_dotenv()).parent
sys.path.append(str(PROJECT_ROOT.joinpath('src')))
from utils import olc_client
c = olc_client.connect(verbose=True)
# %%
from n... |
ca980ccbd1a57e6c3a261a8931d60e4e04d40076bcdfc820ea916852a2fc7e1c | Jupyter | 23,280 | 596 | # %%
import scipy as sp
import scipy.io
import os
import numpy as np
import pandas as pd
import random as rand
from tqdm import tnrange, tqdm_notebook
from collections import Iterable
import matplotlib.pylab as mpl
import colorlover as cl
from scipy import stats
import importlib
import sys
sys.path.append(os.getcwd()+'... |
59cc7f59f63973e884c9f2431da3f05710437b14427adfe3bcde7fbad5f7db39 | Jupyter | 23,559 | 759 | # %% [markdown]
# # Figs. 4F, 4J; Ext. Data Figs. 6B, 6C
# ## Population-level event rate changes
#
# For "Network-level encoding of local neurotransmitters in cortical astrocytes" (Cahill et al. 2024)
# %% [markdown]
# ---
# %% [markdown]
# ## Setup
# %% [markdown]
# #### Imports
# %%
# Typing
from typing import... |
a1fedd4c406971ff5bcd7b4c49719b20678dd370d3808c7693a85b7ff3ba691b | Jupyter | 23,604 | 696 | # %% [markdown]
# # Tabular_Pancreas + Baron
# %%
import os
import glob
import sys
import pandas as pd
import numpy as np
import random
import matplotlib.pyplot as plt
from sklearn.manifold import TSNE
from umap import UMAP
import seaborn as sns
sys.path.append('../')
import src.utils as my_u
from src.utils import d... |
a0a681b61e8671975f0e381d1a78e2258939fba7426e44062f57ea1fd030bd19 | Jupyter | 23,643 | 596 | # %% [markdown]
# # Analysis code
# ## For Zarkali et al. Neuroimaging and plasma marker evidence for white matter macrostructure loss in Parkinson’s disease
#
# Author: Angelika Zarkali
#
# Date last updated: 15th November 2023
# %% [markdown]
# %% [markdown]
# ## Load libraries and data
# %%
#Import libraries
... |
7b574b7a6e1e5dfd29c7f738df0793153a0a2f93445e8184d587013eaf364cce | Jupyter | 24,401 | 683 | # %%
#load libraries
import os
import glob
from eumap import parallel
from eumap.parallel import TilingProcessing
from eumap.raster import read_rasters, save_rasters
from eumap.misc import ttprint
from eumap.misc import find_files
import re
import pandas as pd
import numpy as np
import rasterio
from pathlib import Path... |
491ff7c2af51f00759237fef38b8e32c8b96267ad0fa739f109f9f9c452b23e1 | Jupyter | 24,743 | 550 | # %%
import numpy as np
import sys
import os
import pandas as pd
sys.path.append('../../')
from script.Model_run import Speech_analysis
# %%
#data_home_dir = '/home/kvattis/Documents/data/'
data_home_dir = '/home/kvattis/Dropbox (Partners HealthCare)/Data_Raw_Biogen_SCA/'
# %%
speech_analyzer = Speech_analysis(print... |
769ea36f928a120f4da6ac94cda98c8d2fc1dc4c2d24d4324acd971a39849651 | Jupyter | 24,743 | 583 | # %%
import numpy as np
import sys
import os
import pandas as pd
sys.path.append('../../')
from script.Model_run import Speech_analysis
# %%
#Directory where the data are stored
data_home_dir = '/home/kvattis/Dropbox (Partners HealthCare)/Data_Raw_Biogen_SCA/'
data_home_dir_at = '/home/kvattis/Dropbox (Partners Healt... |
60f365e1ecbdf87d1cf157263fb10c0974927bd8243b3944cc62204d03d447ad | Jupyter | 24,807 | 700 | # %% [markdown]
# # CellPLATO | Cell Plasticity Analysis Tool (Trackmate version)
# %% [markdown]
# Step 1: Fill in the config file!
# %% [markdown]
# Also, make sure your data is set up in the following two tiered format:
#
# Master
# ├── Condition 1
# │ ├── Replicate 1
# ... |
1cddadca5635e54cac2138c2821239b8a83a9096604ac6bc88f0aff53d59452f | Jupyter | 25,110 | 559 | # %%
%load_ext autoreload
%autoreload 2
import re
import scipy as sp
import scipy.io
import os
import numpy as np
import pandas as pd
import glob
import csv
import random as rand
from tqdm import tnrange, tqdm_notebook
from collections import Iterable
import matplotlib.pylab as mpl
import random as rand
from ipywidgets... |
f7634d1608eaf2ba376d29417558a3d9a615b621a5797a0f245d2c84cf846062 | Jupyter | 25,154 | 818 | # %%
import numpy as np
import scanpy as sc
import pandas as pd
import os
# %%
sc.settings.verbosity = 3 # verbosity: errors (0), warnings (1), info (2), hints (3)
sc.logging.print_header()
sc.settings.set_figure_params(dpi=80, facecolor='white')
# %% [markdown]
# Read in the AnnData file.
# %%
os.chdi... |
53e41e10de546cbe8ceac5ef8f4e81660e0e2ef4332d8a794556bc4b77e5d28e | Jupyter | 25,155 | 818 | # %%
import numpy as np
import scanpy as sc
import pandas as pd
import os
# %%
sc.settings.verbosity = 3 # verbosity: errors (0), warnings (1), info (2), hints (3)
sc.logging.print_header()
sc.settings.set_figure_params(dpi=80, facecolor='white')
# %% [markdown]
# Read in the AnnData file.
# %%
os.chdi... |
5a2a6c8dd01bfba43262cd5affe04387f69672df3f95b75476cd0f2e93620da3 | Jupyter | 25,432 | 737 | # %%
import os.path as op
import gc
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
# %%
from nimare.reports.figures import _reorder_matrix
# ['single', 'complete', 'average', 'weighted', 'ward']
def reorder_matrix(temp_data_df, flip_rows=False, flip_cols=False):
mat =... |
e048d611c2621615b49b4b54356d597e709f2050997dfcb5bcc0b770622cf9df | Jupyter | 25,494 | 576 | # %% [markdown]
# # Library
# %%
# My library
from molgraph.dataset import *
from molgraph.testingconsensus import *
from molgraph.visualize import *
from molgraph.interpret import *
from molgraph.experiment import *
# General library
import numpy as np
import pandas as pd
import scipy as sp
import os
# pytorch
import... |
7deec803496ea51ba772c85a4cc198b6db6b0be624ea53ebc1bf641f2e69f5c0 | Jupyter | 25,522 | 693 | # %% [markdown]
# ### Check GPU hardware
# %%
!nvidia-smi
# %% [markdown]
# ### Install requirements for ProtBert embedder
# %%
!pip install transformers sentencepiece h5py
# %%
!pip3 install torch==1.9.0+cu111 torchvision==0.10.0+cu111 torchaudio==0.9.0 -f https://download.pytorch.org/whl/torch_stable.html
# %% [... |
d2fdc40ea8bef456c1e25706e735951a070920034a353a7c4621a13e1bd324df | Jupyter | 25,569 | 694 | # %% [markdown]
# ### Check GPU hardware
# %%
!nvidia-smi
# %% [markdown]
# ### Install requirements for ProtBert embedder
# %%
!pip install transformers sentencepiece h5py
# %%
!pip3 install torch==1.9.0+cu111 torchvision==0.10.0+cu111 torchaudio==0.9.0 -f https://download.pytorch.org/whl/torch_stable.html
# %% [... |
72d656d5fb5ed15c3e6156747d3e018ff53c3891e7d9f9decceb8cf810607cc9 | Jupyter | 25,651 | 774 | # %%
import numpy as np
import scipy as sp
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation
from matplotlib.widgets import Slider
import skimage.io
import scipy.signal
from jupyter_compare_view import compare
import warnings
from bm3d import rgb_to, bm3d
from functools import partial
from ... |
b6cd3901eb6b47ec3f474d19cc22350540212907c2630dd7e8707f4245ee7d3d | Jupyter | 26,376 | 782 | # %% [markdown]
# # 2020-12 LC GcAMP TFC Analysis
# %%
import matplotlib.pyplot as plt
%matplotlib inline
import pandas as pd
import numpy as np
import sys
# change pkg_path variable to the directory storing the package
pkg_path = '/Users/jake/python_scripts/wiltgenlab/'
sys.path.insert(0, pkg_path)
from photometry_an... |
e2ebe573ceb61ee570bf2a16d0cd1a57d6dd0c8143cd46e2991d5f89d2bdd1ab | Jupyter | 26,404 | 813 | # %% [markdown]
# # Setup
# %%
# from google.colab import drive
# drive.mount('/content/drive')
# %%
!pip install pandas numpy jax sklearn selfies rdkit exmol kdeepensemble tqdm padelpy pyyaml h5py
!pip install -q tensorflowjs
!nvidia-smi
# %%
# regular libraries
import pandas as pd
import numpy as np
import os
# M... |
5fab6dbb1b83f8a0d5d071cf861cd226d6e414369498120bd95130ea2faa7d9c | Jupyter | 26,417 | 762 | # %% [markdown]
# # Tutorial Notebook - 3 February 2022
# %% [markdown]
# The purpose of this notebook is to provide an overview of the coordinate gathering process and training of the network via the toy problem of amino acid classification. The notebook has parts:
#
#
# 1. Establishing a list of pdb ids
# ... |
809abc83f33b7d198d08afb0bea9a7e7b411fa602896c4bc7f04928371cabf48 | Jupyter | 26,509 | 888 | # %% [markdown]
# # LC ChR2 2s Stim All Phases TFC Analysis
#
# ### Table of Contents
#
# 1. [Experiment Info](#ei)
# 2. [Imports and Set Up](#is)
# 3. [Training](#tr)
# 1. [Load and Clean Training Data](#lctr)
# 2. [Plot Training Data](#pltr)
# 4. [Tone Test](#to)
# 1. [Load and Clean Tone Test Data](#lc... |
d6860b0cfc175398041658e074aaafec90a25ad38c4d4454ecc59f3fd50d039a | Jupyter | 26,669 | 702 | # %% [markdown]
# # Figures to include in the paper
#
# This notebook generates all the figures to include in the paper for the Literature Review and save them
# %%
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
#import graph_tool.all as gt
import warnings
import itertool... |
eddf98cb110dee0889b35fd27ad24e1f3a6e3a9b360d2074704bf4b422d371b3 | Jupyter | 26,752 | 896 | # %% [markdown]
# # LC ChR2 2s Stim All Phases TFC Analysis
#
# ### Table of Contents
#
# 1. [Experiment Info](#ei)
# 2. [Imports and Set Up](#is)
# 3. [Training](#tr)
# 1. [Load and Clean Training Data](#lctr)
# 2. [Plot Training Data](#pltr)
# 4. [Tone Test](#to)
# 1. [Load and Clean Tone Test Data](#lc... |
7f953837bb40621f7d22a0966080b53e09d9edde081fdb17b2dc18f56b06caa9 | Jupyter | 26,799 | 653 | # %% [markdown]
# ### Compare-kreport-taxonomic-profiles.ipynb
#
# The goal of this Jupyter notebook is to facilitate easy comparisons of the taxonomic
# profiles of multiple metagenomic samples. These analyses require kraken-report
# (kreport) format files as inputs, and the analyses can be performed for any
# tax... |
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