sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
bd1f3c2e013796d15121faae7545b897996060eb9c962a89df46f3c086660d6c | Jupyter | 11,821 | 254 | # %% [markdown]
# # Tutorial: Yeast TYE7 bulk calling cards data
# %% [markdown]
# In this tutorial, we analyze a calling card experiment that mapped the binding of the yeast transcription factor TYE7. The dataset is from [Shively CA, PNAS. (2020)](https://www.pnas.org/doi/10.1073/pnas.1818015116?url_ver=Z39.88-2003&r... |
fbd7c401af8d0b8aa1252aa1002a06facb72b6a68afe943aab57073c26018843 | Jupyter | 11,857 | 229 | # %% [markdown]
# # Example
#
# The following example demonstrates how to the use the `pymatch` package to match [Lending Club Loan Data](https://www.kaggle.com/wendykan/lending-club-loan-data). Follow the link to download the dataset from Kaggle (you'll have to create an account, it's fast and free!).
#
# Here we ma... |
8279ea5b052ef3f8b4279938cbfdf8b7540871e07ce9b28339ecbf732f3d6da9 | Jupyter | 11,911 | 246 | # %% [markdown]
# # XGBOOST parameter exploration
# This notebook is a template for finding the XGBOOST model best suited for your needs
# %%
import os
import matplotlib.pyplot as plt
import numpy as np
from xgboost import XGBClassifier
import sys
parent_dir=os.path.dirname(os.getcwd())
sys.path.insert(0,parent_dir )
... |
3372c1d58c0fed5a73de0b71314a80e36ec7db16208ce4d50714bed576f25751 | Jupyter | 11,984 | 492 | # %% [markdown]
# Don't forget to run IRkernel::installspec() in R in order to let the jupyter recongize the R kernel before running the notebook.
# %% [markdown]
# ## Visualisation of HTODemux result
# %%
library(data.table)
library(plyr)
library(Seurat)
library(ggplot2)
library(repr)
library(stringr)
# %%
# Use M... |
f2554eb0f2eafc7034c0eacb9aa8b85a2fb1339104a253f2f662ffff7a090b16 | Jupyter | 12,007 | 299 | # %% [markdown]
# # PCGrimAge
# %% [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-... |
cb640ef189efbf6e2bf95bbc9aeb88ae16145e3c2f8e9dca3059ea797c1c6cff | Jupyter | 12,022 | 342 | # %% [markdown]
# # LP Sweep Results — Pixel Error vs Ensemble Std Dev
# %%
from pathlib import Path
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
# %% [markdown]
# ## Utility functions
# %%
def standardize_scorer_level(df, new_scorer="standard_scorer"):
"""Normali... |
08dca95aa969cc790ef8afa8b1268409839081585946cf0c741a4bf4be6dc51c | Jupyter | 12,037 | 320 | # %%
from matplotlib import pyplot as plt
from matplotlib import rc
rc('font',**{'family':'sans-serif','sans-serif':['Arial']})
rc('text', usetex=False)
import scipy.stats as stats
from sklearn.preprocessing import minmax_scale
from pymer4.models import Lmer, Lm
from statsmodels.tsa.ar_model import AutoReg
import seabo... |
005ae182196f2bbd11f53536b43fa5a3a2c7b84cc80fe378e9214c0348a1ad19 | Jupyter | 12,043 | 261 | # %% [markdown]
# ## Part1. Compare GNNs
# %%
import os
import pandas as pd
import numpy as np
from hgnn.load_data import load_data
from hgnn.features import Graph_smiles
from hgnn.trainers import train_gnn
from hgnn.model import HGNNPredictor
from contrast_gnns.GraphVAE import *
from contrast_gnns.GraphTransformers ... |
27ef2e7146cc7ae679af2e1d7d08ce27e76c14f26e8ade7c6ef6d327ba901c7e | Jupyter | 12,081 | 300 | # %%
#%% Transitive Inference - Single model analysis #########################
from __future__ import division
import numpy as np
import task_train_test as tk
import nn_models as nm
import nn_analyses as nna
import ti_functions as ti
import postproc as pp
datadir ... |
bb2813ed08e939ce9a3cb9b3e6577beaa32ec9fc5e0a6bffe5fd985d5e52c830 | Jupyter | 12,099 | 288 | # %% [markdown]
# [](https://colab.research.google.com/github/PTB-MR/mrpro/blob/main/examples/notebooks/pgd_wavelet_reconstruction.ipynb)
# %%
import importlib
if not importlib.util.find_spec('mrpro'):
%pip install mrpro[notebooks]
# %% [m... |
cac358c4162b164f22d611a413af88c7b55f9b02ce308d0554f4cd882ddc8c12 | Jupyter | 12,232 | 279 | # %%
library('officer')
library('R.matlab')
library(dplyr)
# %% [markdown]
# # Read in data
# %%
data=read.csv('/dagher/dagher11/filip/MAPT_OB//data/dataset_excluded_nooutlierdexclusions.csv', header=T, quote='"')
# %%
results=matrix(ncol=6,nrow=length(c(
grep('thickness_of',colnam... |
2c78648854cbdb6c33f69ef44a601ceec3cd0cf4702febf9731e781d129a6cf2 | Jupyter | 12,336 | 370 | # %%
!{sys.executable} -m pip install seaborn
# %%
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 ipywidge... |
5b6e19f90450e87ccd85e512e06a1d629a19b759d754dce710661f86fbcc6475 | Jupyter | 12,370 | 296 | # %% [markdown]
# # `StratifiedGOAnalysis` class
# - this class undertakes analyses related to GO
# - this class calls wrapper functions (`gprofiler` and `get_go` in `tools/_go.py`) that calls R pkgs such as `AnnotationDBi` and `gprofiler2` from python env (See **Appendix** for details).
# %%
from tools import Stratif... |
e9b500264c8c0293f180406c265ec293ce4eded37ce2857fd670567d67a20ad2 | Jupyter | 12,425 | 398 | # %% [markdown]
# [](https://colab.research.google.com/github/lucascamillomd/pyaging/blob/main/tutorials/tutorial_dnam.ipynb) [](https://nbviewer.jupyter.org/github/lucasc... |
c2e1c9592889ba4069967300fd685bf377ef47031d0fb8d78f642e86cb7a65e4 | Jupyter | 12,458 | 260 | # %% [markdown]
# # GrimAge2
# %% [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-i... |
0cbfa9d3bb2945e36571f612edd60b4cfa2c2679295c352e18550664a42fa6ad | Jupyter | 12,544 | 338 | # %% [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... |
018ff6a4642f0ec272967fd0846335bc025c7800832e966ac107967d8cabb97e | Jupyter | 12,557 | 219 | # %% [markdown]
# # Running EVcouplings pipeline jobs
#
#
# ------
#
# ## Content
#
# This notebook demonstrates how to run EVcouplings jobs, and covers the following topics:
#
# (1) __Configuration files__: how to setup and specify settings
#
# (2) __Running jobs__: executing the config file (via the EVcouplings... |
364b63b9ba0fb31e8c21614859384c10a18146c9e28c16a9ecd9b0e3f7660d66 | Jupyter | 12,655 | 335 | # %%
import os.path as op
from glob import glob
import gzip
import pickle
import matplotlib.pyplot as plt
import nibabel as nib
import numpy as np
# from utils import plot_gradient, plot_subcortical_gradient, plot_meta_maps
# %%
result_dir = op.abspath("../results")
data_dir = op.abspath("../data")
output_dir = op.j... |
20ba1b32d3febc9d8dbd71389b6dee67efb6a26275f2d8e1173b9c39b94a6558 | Jupyter | 12,656 | 464 | # %% [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... |
c6c93381694a81ca49e98bd77a2292c6e7353bce9270a0051200d4ea179ac541 | Jupyter | 12,770 | 416 | # %% [markdown]
# ### Check GPU hardware
# %%
!nvidia-smi
# %% [markdown]
# ### Download datasets and embeddings
# %%
!wget https://raw.githubusercontent.com/anhvt00/MCAPS/master/data/Independent-testsets/multi-species/dict.tsv
!wget https://raw.githubusercontent.com/anhvt00/MCAPS/master/data/Independent-testsets/mu... |
072f81622470c75b3720481e748387e55e83e7a250b4b0e7a4492a107ea5a808 | Jupyter | 12,857 | 362 | # %%
import matplotlib
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from helper import *
from tqdm import tqdm_notebook
import brainconn as bc
import pandas as pd
import seaborn as sns
import nilearn.plotting as plotting
import palettable as pal
import matplotlib.colors as mcolors
import matpl... |
e1619978fde9e7312dfc1fcefec3d89d40b35155098217eaae8a35acc59ece8c | Jupyter | 12,894 | 356 | # %%
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... |
c5517afb1a47d492e5ae74088b8a5fa10e701c7ddc7bede2fb118f352e130f64 | Jupyter | 12,942 | 369 | # %% [markdown]
# # KDMAge
# %% [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-int... |
6ac82571ef954496c160f839ac630b5d6eb4e4e0dc7fc07675beef5f36f83f6c | Jupyter | 12,948 | 300 | # %% [markdown]
# This notebook illustrates how to compute the Imbalance Gain measure described in "Robust inference of causality in high-dimensional dynamical processes from the Information Imbalance of distance ranks" (V. Del Tatto, G. Fortunato, D. Bueti, A. Laio, https://arxiv.org/abs/2305.10817), using the illustr... |
f4638cf44e248f3cc844200f4997a8f11dde680c07db6fffdbaae658d2310f54 | Jupyter | 12,972 | 327 | # %% [markdown]
# # Exporting calcium dynamics associated with epileptiform activity
# Below are definitions of values in each of the output tables
#
# ## Cell Output Table
#
# - IIS_corr: r value, IIS amplitude vs. calcium change
# - HFO_corr: r value, HFO amplitude vs. calcium change
# - IIS_ave: Average calcium ch... |
c1b757bac6d6271e1dac3014aa807b5544762a5782943d8ad0364c7f1012262b | Jupyter | 13,048 | 307 | # %%
import numpy as np
import sys
import os
import pandas as pd
sys.path.append('../../')
from script.Model_run import Speech_analysis
# %%
def monkey_patch_openpyxl():
'''Openpyxl has a bug with workbooks that have wrong cell styling information.
Monkey patch the library so it can handle these types of work... |
578daaaf0fc648539b1379aeee6f17eaf3c24f1f378d1111e7339bca710dcc9c | Jupyter | 13,069 | 431 | # %% [markdown]
# # Library
# %%
# My library
from molgraph.graphmodel import *
import pickle
import numpy as np
import pandas as pd
import scipy.stats
import seaborn as sns
import matplotlib.pyplot as plt
from matplotlib.pyplot import figure
from tqdm import tqdm
import os
# %% [markdown]
# # SINGLE
# %%
# Load mo... |
87921e28bd29cdb1eebe2d81bacd9bee3e160c17b5fbf3f10245f75564e3fefa | Jupyter | 13,087 | 358 | # %%
import dask
from dask.diagnostics import ProgressBar
from dask.distributed import Client
# %%
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.... |
1097824a3297cefc0085956d8aa85d92d880289691b927f5dad2baaa4bb6a359 | Jupyter | 13,150 | 340 | # %%
# finding mirror-symmetric units in trained/untrained networks
# useful packages:
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings("ignore")
import matplotlib.gridspec as gridspec
import torchlens as tl
%matplotlib inline
import matplotlib.patheffe... |
bade82473abd29391b2624afcd783e7deee4dc7bd223374bac7aa130698b4756 | Jupyter | 13,341 | 402 | # %% [markdown]
# # Gene analysis
# %% [markdown]
# ## Get genetics data
# %%
import abagen
atlas = abagen.fetch_desikan_killiany()
# %%
files = abagen.fetch_microarray(data_dir='/dagher/dagher9/filip/', donors='all', verbose=0)
# %%
expression_DKT = abagen.get_expression_data('/dagher/dagher11/filip/Downloads/OASI... |
d203027ab30e00f92afa45613984bd399851fb0c3441e4136651a7690ff8ed22 | Jupyter | 13,393 | 512 | # %% [markdown]
# # **High-Dimensional Diffusion MRI Reconstruction Tutorial**
#
# ### Introduction:
#
# * High-dimensional MRI (HD-MRI) refers to the acquisition of more than 2D imaging data;
# * Typical examples are dynamic MRI, quantitative parameter mapping, and **diffusion MRI**.
#
#
# * This notebook
# ... |
e61cdc86e25d245c6fc55d64ce41de2483303b0bdeb315feff4f0b6b5e48c05c | Jupyter | 13,420 | 286 | # %% [markdown]
# # A sinusoidal weight matrix can be learned by Oja's rule when forcing a sinusoidal input
#
# ## Fixing the activity and running the weight updates
# %%
import numpy as np
import numpy.lib.stride_tricks
import matplotlib.pyplot as plt
import matplotlib
import scipy.linalg
import matplotlib.scale
plt... |
021ae7f6848471ba2438f4f44ff0dac2104c7c033f033eac6eb4809d7b56bcd7 | Jupyter | 13,503 | 413 | # %%
"""Plot MED OFF vs. ON power (Figure 2)."""
import os
import sys
from pathlib import Path
import numpy as np
import copy
import pandas as pd
import pte_stats
import pte_decode
from matplotlib import pyplot as plt
from scipy.stats import sem
from matplotlib import pyplot as plt
from matplotlib.colors import Linear... |
eafec5ca79971de7ebcdd79af103a67a0455c12649fcea3399f416873871667d | Jupyter | 13,553 | 371 | # %% [markdown]
# (line)=
#
# # Fitting a model to data
#
# If you're reading this right now then you're probably interested in using
# emcee to fit a model to some noisy data.
# On this page, I'll demonstrate how you might do this in the simplest
# non-trivial model that I could think of: fitting a line to data when... |
5706c43fccf5390caa85e10e5e8106912f8496ac9027fb4a20a85545891e4130 | Jupyter | 13,577 | 441 | # %%
import igraph as ig
import numpy as np
import multiprocessing as mp
from sklearn.metrics import jaccard_score
from scipy.sparse import csr_matrix, coo_matrix, csc_matrix
import ipywidgets as widgets
from ipywidgets import interact
import seaborn as sns
# %%
# Core scverse libraries
import scanpy as sc
import annd... |
e9983d0edea8875253b88ce40776f097a210073f6a10ae360ae4e6634df8f992 | Jupyter | 13,662 | 282 | # %% [markdown]
# [](https://colab.research.google.com/github/PTB-MR/mrpro/blob/main/examples/notebooks/motion_corrected_reconstruction.ipynb)
# %%
import importlib
if not importlib.util.find_spec('mrpro'):
%pip install mrpro[notebooks]
# ... |
bfb683f77366e7ff4351cc8ff8ccd810c5f4e36c8110987e4ca56f0180c679e1 | Jupyter | 13,712 | 430 | # %%
# CIFAR10 dataset, digit recognition
import torch
import torchvision
from torchvision import transforms, utils
import numpy as np
from torch.utils.data.sampler import SubsetRandomSampler
import matplotlib.pyplot as plt
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from matplotli... |
043a6ec19406b5a1fedc57d4c8e7d3be3475ef38bb166ec7c5b83434ddd98a25 | Jupyter | 13,716 | 435 | # %%
# SVHN dataset, digit recognition
import torch
import torchvision
from torchvision import transforms, utils
import numpy as np
from torch.utils.data.sampler import SubsetRandomSampler
import matplotlib.pyplot as plt
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from matplotlib i... |
b99d7f1835cb0fd41ed34532af1cfbe7683f39b125b2482c30df61c2db84bd3f | Jupyter | 13,816 | 360 | # %%
import matplotlib.pyplot as plt
from msmfcode.core.config import *
from msmfcode.evaluation.plot import plot_error
from msmfcode.evaluation.data import load_experimental_data, load_experimental_config
from msmfcode.models.cann import DMSMF, FMSMF, Grid, SSSF
plt.rcParams.update({'font.size': 21})
plt.rcParams['te... |
01256a4ca4f79a6b9c599ed56a1b1264575c2ebf9090dd7a999cd070e0ca041b | Jupyter | 13,817 | 287 | # %% [markdown]
# <a href="https://colab.research.google.com/github/seanrjohnson/esmologs/blob/main/notebooks/Data_analysis.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Sensitive remote homology search by local alignment of sm... |
5e29fdee027ba0f125029981a8e744107c2f6a02cd49e1f6590b1f91cf4f6af2 | Jupyter | 13,818 | 320 | # %% [markdown]
# # Vacuole Volumetry Analysis (WebKnossos .stl export)
# This notebook reads .stl 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 -
#
# ... |
b4e72be2c35f95500e2f184ad915d2eb5555146cca24dc050660a889a87be568 | Jupyter | 13,860 | 335 | # %%
import pandas as pd
import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
import math
from sklearn.metrics import roc_curve, auc, RocCurveDisplay, precision_recall_curve, PrecisionRecallDisplay
import matplotlib as mpl
from IPython import display
from collections import OrderedDict
from pathlib ... |
ae5d977b29dd1533d2ab5102168ba70db45e384c05ad94567cdb517ad08aa229 | Jupyter | 13,867 | 321 | # %% [markdown]
# ## **SIMS Tutorial**
# In this tutorial, we will walk through the [SIMS (Scalable, Interpretable Machine Learning for Single Cell)](https://www.cell.com/cell-genomics/fulltext/S2666-979X(24)00165-4) pipeline step by step. SIMS is a deep learning-based tool built on TabNet, a transformer-based model op... |
33f01bd670597979a2bef78c0c2abdc08a1d4568d7e345c5d1aaccecda0797bd | Jupyter | 13,876 | 490 | # %% [markdown]
# ## This notebook is used to generate the finalized version of the classifier, to simply feature transformation into the final form, and to test that the results are the same
#
# Most of the code comes from operational_classifier.
# %%
import pandas as pd
import numpy as np
import pickle
import sys
#... |
1890f341a421d77fdea6faad4c72e5ee663402f9091a4ea9dbde5c0f00448cd3 | Jupyter | 13,909 | 204 | # %%
#hide
from rxn_yields.core import *
# %% [markdown]
# # Predicting Chemical Reaction Yields
#
# > Predicting the yield of a chemical reaction from a reaction SMILES using Transformers
# %% [markdown]
# Artificial intelligence is driving one of the most important revolutions in organic chemistry. Multiple platfo... |
91cdb76efa414ee166d52790e7a5dc55db2491613f5cf556268ea87ee37e43af | Jupyter | 13,932 | 477 | # %% [markdown]
# # Auxiliary Notebook: Ramping cell analysis
#
# Determination of ramping status for individual astrocytes.
# %% [markdown]
# ---
# %% [markdown]
# ## Setup
# %% [markdown]
# #### Imports
# %%
# Others
from pathlib import Path
from tqdm import tqdm
import numpy as np
import pandas as pd
import ... |
ba67392de5096b4ae272eef9c397e9c1d6e5a4c982cb7ed12e3a5c092af7857d | Jupyter | 13,953 | 369 | # %% [markdown]
# # Crossmodal project: Figure 2
# %%
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 ipywi... |
0fe4888d62d7a3e63614b47a2a733901ee4f2d596c00b78afbf76be7b76e6435 | Jupyter | 13,961 | 351 | # %% [markdown]
# # PhenoAgeSaoPaulo
# %% [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-w... |
86e63caaa002f629ad12f9f6c6cce9bb85a53ee3668fbf2228b11ae47c7042f0 | Jupyter | 14,018 | 575 | # %% [markdown]
# This notebook analyzes replicate 1 for the cell-fitness assay for KCNE1.
# The sort was done
#
# The sequencing project was 7704
# %%
cd /scratch2/glazeram/7704-AG/
# %%
setwd("/scratch2/glazeram/7704-AG/")
library(data.table)
library(dplyr)
library(ggplot2)
library(tidyr)
library(RColorBrewer)
l... |
ecc2ed5a7c924e44d0641e43fd8ee9d48bc3af29126295bc51104393bea570d4 | Jupyter | 14,059 | 336 | # %% [markdown]
# # Getting started with longitudinal normative modelling
#
# A normative model tells you where a single scan sits relative to a reference population (a z-score). With two or more visits per subject, we can ask a further question:
#
# > Did this subject change *more than expected* between visits?
#
#... |
7db16cea3b50b8884464ceb7d6e1edc799ebc12289c4ff0b24d52c2e51fae036 | Jupyter | 14,093 | 392 | # %%
#!/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 import_dataset import import_dataset, import_UKB_subs, dataset_to_data
from nilearn.input_data import NiftiLabelsMasker
i... |
78d9d7faad943f3a3b9d0c4a70add20ae716a9de4fd3fb001a55f32fb2299f85 | Jupyter | 14,105 | 370 | # %% [markdown]
# # Netherlands Neurogenetics Database
# Author: Nienke Mekkes <br>
# Date: 21-Sep-2022. <br>
# Correspond: n.j.mekkes@umcg.nl <br>
#
# ## Script: clinical history labeled training data: cleaning & exploration
# Objectives: load and clean training data, do some basic data exploration
#
#
# ### Input ... |
f3ece65146afe11424dff7da25a585930ac9d58944c634600a9543ce5b4efea7 | Jupyter | 14,218 | 328 | # %%
import nibabel as nib
from neuromaps.datasets import available_annotations
from neuromaps import datasets
from neuromaps.resampling import resample_images
from neuromaps import parcellate
from nilearn.datasets import fetch_atlas_surf_destrieux
from neuromaps import datasets, images, nulls, resampling, plotting
fro... |
f917fd5f23777abe60628257bf270523489ea59feb82fc437344093d776c6606 | Jupyter | 14,288 | 331 | # %% [markdown]
# # Training with MJLab (MuJoCo Warp) and rl_games
#
# [MJLab](https://github.com/mujocolab/mjlab) provides Isaac Lab's manager-based task API
# on top of MuJoCo Warp — GPU-parallel robotics environments with no Isaac Sim dependency.
# rl_games ships a native wrapper (`env_name: mjlab_*`), shipped conf... |
6433cee950ed1a76663124b39f87dad98fffd10f068010d169426a62f9410a2a | Jupyter | 14,330 | 276 | # %% [markdown]
# # dPFT (DDR-based Pulmonary Function Test) Tutorial
#
# This tutorial is to preprocess a dicom file of DXR acquired images, and run SLEAP-based tracking
#
# dPFT is built to work with mp4s. There are ways to convert from dicom or AVI to mp4. Depending on your data/DXR machine, there might be convers... |
77043003aa50a09df50f8768314a78c4eb40b90d3e197f1c2cfe0d7e129c1bc3 | Jupyter | 14,338 | 450 | # %%
# asymSVHN dataset, digit recognition
import torch
import torchvision
from torchvision import transforms, utils, datasets
import numpy as np
from torch.utils.data.sampler import SubsetRandomSampler
import matplotlib.pyplot as plt
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
fro... |
1c6dfe603c1097298b176bf06ab080fb2e297dbc58cec2b79f8d1132689e9cb4 | Jupyter | 14,353 | 353 | # %% [markdown]
# In this notebook we will demonstrate some more advanced visualizations of MiniSom results.
# %%
from minisom import MiniSom
# %% [markdown]
# For this example we will again load the <a href="https://archive.ics.uci.edu/ml/datasets/seeds">seeds</a> dataset dataset using pandas:
# %%
import pandas as... |
4a2fe1cbbc13e10bf8d61d3f3794972f4ad1856c0b39e1f0bca24ccac9bef8a4 | Jupyter | 14,412 | 455 | # %% [markdown]
# ## MODEL - Simulating Cavity Expansion for *Alasaadi et al.*
# %% [markdown]
# ### Notes
#
# - **Model**
# - Use `simple_cell_sim` particle modeling engine to simulate cavity expansion and its effect on surrounding cells
# - Place cells in ring-shaped band around a single large central "cell... |
f981d6e5a9d2e89e5653507fc6c38a462fa0c33e325faed22af4bb88336fe196 | Jupyter | 14,727 | 469 | # %%
# symSVHN dataset, digit recognition
import torch
import torchvision
from torchvision import transforms, utils
import numpy as np
from torch.utils.data.sampler import SubsetRandomSampler
import matplotlib.pyplot as plt
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from matplotli... |
dbd51ce071e150fc8ed9dd6ec81054b1ec5fa314e1789f8baafae88b166308ec | Jupyter | 14,798 | 353 | # %%
"""Plot time delay estimates (Figure 3D)."""
import os
import sys
from pathlib import Path
import numpy as np
import copy
import pandas as pd
from matplotlib import pyplot as plt
import pte_stats
import pte_decode
from scipy.stats import sem
from scipy.signal import find_peaks
cd_path = Path(os.getcwd()).absolut... |
329f0a651ea9cc131a8a96dc1d2e765fc16f19a313a515d7217f7e68ac0fdb4c | Jupyter | 14,811 | 353 | # %%
"""Plot time delay estimates (Figure 3D)."""
import os
import sys
from pathlib import Path
import numpy as np
import copy
import pandas as pd
from matplotlib import pyplot as plt
import pte_stats
import pte_decode
from scipy.stats import sem
from scipy.signal import find_peaks
cd_path = Path(os.getcwd()).absolut... |
0596240c12a512c08cd50667a8829c2fd596d0beb414965001b0be886dcb7323 | Jupyter | 14,905 | 466 | # %%
import numpy as np
import torch
from pytorch_lightning import Trainer
from torch.distributions import normal
from rgan.utils import trunc_normal_, TorchStandardScaler, \
SamplesDataset, NormToBoundsScaler, \
MechanisticModel
from rgan.rgan import rGAN, defaultNetsRG... |
dffff7525f6de622852f139821adabd7153d1127254e634ad7997765cae440a4 | Jupyter | 15,057 | 295 | # %% [markdown]
# # Tutorial: K562 HCT116 SP1 single-cell calling cards data.
# %% [markdown]
# In this tutorial, we will analyze the binding of the transcription factor Sp1 in K562 and HCT116 cell lines. These data are generated using single-cell calling cards (scCCs). The data is from [Moudgil et al., Cell. (2020)]... |
64741dea92ba6690433d947baa0c4c5fb2ea8891c037ade425abf8b368412da7 | Jupyter | 15,078 | 471 | # %%
import numpy as np
import torch
from pytorch_lightning import Trainer
from torch.distributions import normal
from rgan.utils import trunc_normal_, TorchStandardScaler, \
SamplesDataset, NormToTransformedBetaScaler, \
MechanisticModel
from rgan.rgan import rGAN, defa... |
6a8555171dd87534a1ac457d9bd7709e3e9e81302a219241118b832c6685610e | Jupyter | 15,282 | 420 | # %%
import scipy as sp
import scipy.io
from scipy import stats
import os
import numpy as np
import pandas as pd
import glob
from sklearn.metrics import roc_curve, auc
import matplotlib.pylab as mpl
from matplotlib import gridspec
import matplotlib.patches as patches
import sys
sys.path.append(os.getcwd()+'\\..')
from... |
f922e6d76943376204de2230ad911171f807fc56dc61bed439bd23f9a9c96ee4 | Jupyter | 15,285 | 435 | # %%
import warnings
warnings.filterwarnings("ignore")
import ipywidgets as widgets
from ipywidgets import interactive
from fmriprep_denoise.visualization import utils
path_root = utils.repo2data_path() / "denoise-metrics"
strategy_order = list(utils.GRID_LOCATION.values())
group_order = {
"ds000228": ["adult"... |
b1e71d5d6c2571d73e182e82a0fb0339457302c1b8e49b883c9573a7586478df | Jupyter | 15,345 | 433 | # %%
"""Example for plotting gradient data"""
import os.path as op
from glob import glob
import pickle
import numpy as np
import matplotlib.image as mpimg
from matplotlib.gridspec import GridSpec
import matplotlib.pyplot as plt
from nibabel import GiftiImage
from nibabel.gifti import GiftiDataArray
from surfplot.utils... |
89fbcf31db8a96494271b750175d48b75589248ede17a70ddc4917c9c165f22d | Jupyter | 15,376 | 341 | # %% [markdown]
# ## Example use of `chromosight quantify`
#
# In this notebook, we demonstrate how `chromosight quantify` can be used to compare chromatin loops between _S. cerevisiae_ cultures arrested in G1 phase vs metaphase. In this notebook, we re-analyse Hi-C data from [Garcia-Luis, J., Lazar-Stefanita, L., Gut... |
8372c9c9cbe50b3d7601c28b53d0fc86a53bfccd0cc4f8efd14567b766568c35 | Jupyter | 15,550 | 406 | # %%
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 ... |
09c46f15f36c370fb274eadba19d6bf23c1cf5eb1333760787b1a89f527a5114 | Jupyter | 15,617 | 361 | # %% [markdown]
# [](https://colab.research.google.com/github/Denys88/rl_games/blob/master/notebooks/mjlab_training_colab.ipynb)
#
# # MJLab (MuJoCo Warp) + rl_games
#
# Trains a **Unitree Go1 flat-terrain velocity policy** end-to-end on the Co... |
a08e001db616218ca42203f5237bead9286286782e78c5d6528df2809c2979bf | Jupyter | 15,627 | 387 | # %%
# 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... |
7ee02263eaebcefa9f6b015224f710d0ca0bdfbb6c8481f0165e77418a5a7ce6 | Jupyter | 15,648 | 582 | # %% [markdown]
# # Fig. 4B
# # Ext. Data Figs. 4A, 4B
# ## Population-level mark differences
#
# For "Network-level encoding of local neurotransmitters in cortical astrocytes" (Cahill et al. 2024)
# %% [markdown]
# ---
# %% [markdown]
# ## Setup
# %% [markdown]
# #### Imports
# %%
# Typing
from typing import (
... |
48a1dc471625fff02df0a2069beca00158f5cc8383f8bfb569d38801b88d4620 | Jupyter | 15,684 | 391 | # %%
# 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... |
19ab19344e5be8d2a48f5fd1f58433ba854efe67c8e87ab97827272006e1a457 | Jupyter | 15,768 | 395 | # %%
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... |
595a7b6f9dbfdf40ead851ea4d607482374a877b69778414144e7d65eb94be70 | Jupyter | 15,782 | 387 | # %%
import torch
import torchvision
import os
from torch.utils.data import DataLoader, Dataset, TensorDataset
from PIL import Image, ImageOps
import numpy as np
import shutil
import random
from pathlib import Path
import albumentations as A
import cv2
from scipy.stats import norm, skewnorm
import skimage
# %%
def lis... |
6bec68828275d82d41a614c94a979313231a7d841014512c48190271e7ae0d6f | Jupyter | 15,817 | 499 | # %% [markdown]
# # Classification results visualization
# %% [markdown]
# Import packages
# %%
import io
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
import plotly.express as px
import matp... |
e4d0141491db6f3ac5f9a92a0eac64639dc3c12faa39ae18583f1095960b422f | Jupyter | 15,874 | 427 | # %% [markdown]
# # Crossmodal project: Figure 2
# %%
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 ipywi... |
d4c48478537effbdf1baea6d2c09d31ab7b1a48eb07835999e5be23878f0acca | Jupyter | 15,910 | 416 | # %%
#!pip install ipywidgets
#!jupyter nbextension enable --py widgetsnbextension
import scipy as sp
import scipy.io
import scipy.stats
import os
import numpy as np
import pandas as pd
import glob
import csv
import random as rand
from tqdm import tnrange, tqdm_notebook
import cmocean
import matplotlib.patches as patc... |
91ff6c1b01409b2e796b0042f53aadf39f9ac1d9e82bbcbc28b38ed7afceebe0 | Jupyter | 16,080 | 259 | # %% [markdown]
# # Tutorial: Brd4 bindings for Male/Female mouse bulk calling cards data.
# %% [markdown]
# We will analyze bulk calling cards datasets from [Kfoury et al., PNAS. (2021)](https://www.pnas.org/doi/10.1073/pnas.2017148118), which can be downloaded from [GEO](https://www.ncbi.nlm.nih.gov/geo/query/acc.... |
22e74bc858c6ee2466b9bae94fd9cbaac79ade77587edb7524b222cdab32371a | Jupyter | 16,122 | 519 | # %%
library(data.table)
library(plyr)
library(dplyr)
library(Seurat)
library(ggplot2)
library(repr)
library(stringr)
# %%
library(extrafont)
font_import(paths = "/Library/Fonts", prompt = FALSE)
loadfonts()
fonts()
# %%
plot_theme <- function() {
theme_bw() + theme(
panel.border = element_blank(),
... |
4351033578bbbed23be9d285c3ac5bfccf2d5d788cfc6d53323b8f0327ebce2a | Jupyter | 16,148 | 393 | # %%
import torch
import torchvision
import os
from torch.utils.data import DataLoader, Dataset, TensorDataset
from PIL import Image, ImageOps
import numpy as np
import shutil
import random
from pathlib import Path
import albumentations as A
import cv2
from scipy.stats import norm, skewnorm
import skimage
# %%
def lis... |
2ceffc5f218360affcb7f788f50f3b92581d8df1f3ae019f6f9036c503e89e8b | Jupyter | 16,295 | 341 | # %% [markdown]
# ## Part2. Compare Taste Predictors
# %%
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
PWD = os.path.abspath('.')+'/'
print(PWD)
# %%
bs_train_path = os.path.join(PWD, 'dataset/taste_dataset/bitter_sweet/bs_train.csv')
bn_train_path = os.path.join(PWD, 'dataset/tas... |
87bacb3d84f6bee584b5a487d0a16082f4e0740015567f4f5289b519c72a50a7 | Jupyter | 16,307 | 415 | # %%
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 matplotlib as mpl
import random as rand
from ipywidgets import *
import col... |
e0c8183df59b666ec44488ed9c9869bbfb6c7807f4c42a82cccd24c40fef0bcf | Jupyter | 16,358 | 398 | # %%
import torch
import torchvision
import os
from torch.utils.data import DataLoader, Dataset, TensorDataset
from PIL import Image, ImageOps
import numpy as np
import shutil
import random
from pathlib import Path
import albumentations as A
import cv2
from scipy.stats import norm, skewnorm
import skimage
# %%
def lis... |
a9d9745b5017ef9da67c6d0492a20ac8da1c233c5348e119b87e04718cef66d4 | Jupyter | 16,359 | 399 | # %%
import torch
import torchvision
import os
from torch.utils.data import DataLoader, Dataset, TensorDataset
from PIL import Image, ImageOps
import numpy as np
import shutil
import random
from pathlib import Path
import albumentations as A
import cv2
from scipy.stats import norm, skewnorm
import skimage
# %%
def lis... |
11fd0f36e3fc76ee3bc4694d3b061d9ecc006403099cdadaf5a4d4e484919054 | Jupyter | 16,443 | 447 | # %% [markdown]
# # SystemsAgeLung
# %% [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... |
cbceb9a128e26c47abe2a4d83940befcca6022beaa349c333de4b54c887f1dc3 | Jupyter | 16,443 | 447 | # %% [markdown]
# # SystemsAgeBlood
# %% [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-we... |
ec280c61117534d44ba1616d766694663de0d740c4a1c4790bdafab9b5a12543 | Jupyter | 16,444 | 447 | # %% [markdown]
# # SystemsAgeLiver
# %% [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-we... |
a30e74da2659f78b8ada6fedbfe6cc2760fe7f93237a563d461519521333c826 | Jupyter | 16,445 | 447 | # %% [markdown]
# # SystemsAgeKidney
# %% [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-w... |
22be2aab9f3a5f811fe86f33b88d584d7092a09191dbf50ec0a29667a7f1e1d1 | Jupyter | 16,447 | 447 | # %% [markdown]
# # SystemsAgeImmune
# %% [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-w... |
888e5bf99201d0370e6aadc40f8dfc709015c13fe59d49ee39efc95fe2b565af | Jupyter | 16,456 | 447 | # %% [markdown]
# # SystemsAge
# %% [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... |
403f5bf5835b3a1f170d150a0c452d477628b01fc493c5f5039986718cdeee96 | Jupyter | 16,458 | 447 | # %% [markdown]
# # SystemsAgeHormone
# %% [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-... |
89ec25e09fefd8b043d55b5618aadb4e0ed9f8975a5f191f0937c4fc04b3cfbe | Jupyter | 16,458 | 447 | # %% [markdown]
# # SystemsAgeHeart
# %% [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-we... |
eb3350d33b8d82608b4f459a1812e3fc16cbb66911d8cee0c751088f2e55920b | Jupyter | 16,458 | 447 | # %% [markdown]
# # SystemsAgeBrain
# %% [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-we... |
9327d59d20008278927f66feb6fbacb61d7231e959e6a83cd9944e5e3ce47917 | Jupyter | 16,468 | 447 | # %% [markdown]
# # SystemsAgeMetabolic
# %% [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](#Loa... |
b6d3151426b38a981639385fc62964dbda9b7e5dacd70e9ac78839aaa2cf26a7 | Jupyter | 16,482 | 447 | # %% [markdown]
# # SystemsAgeInflammation
# %% [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](#... |
41eb0aefcb7693fb04bf2f5543b41861b618474ccaa635eda95428797fc9fcfd | Jupyter | 16,527 | 447 | # %% [markdown]
# # SystemsAgeMusculoSkeletal
# %% [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... |
d3c4bf8bc2d6323b84d7ab8cc7811561faaf63d8a0149e533b230a45ebd442fa | Jupyter | 16,678 | 425 | # %%
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()+'... |
58811a5f4429eeebd9622c885a3377bc83a9e6247383a9730a187dfd1f66269f | Jupyter | 16,681 | 370 | # %% [markdown]
# # System dynamics when both the weights and activity evolve
#
# Using Oja's rule the weights should always converge to have an L2 norm of 1.
#
# Our derivation involved integrating over a period of rotating activity before making a weight update, so we sum weight updates over each period before just... |
0db899380a69ab05407c9176daace4bfdae66accf901ca494c75f2b31abcde00 | Jupyter | 16,945 | 511 | # %%
import numpy as np
import torch
from pytorch_lightning import Trainer
from torch.distributions import normal, uniform
from rgan.utils import TorchStandardScaler, \
SamplesDataset, NormToBoundsScaler, \
MechanisticModel
from rgan.rgan import rGAN, defaultNetsRGAN
fro... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.