sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
7422dba4b2317a07fa12799900b88122d86d0922599e9c624f86fd113825a1ec | Jupyter | 10,567 | 303 | # %% [markdown]
# # Testcase: Private Attributes
# %% [markdown]
# # Getting Started
# %% [markdown]
# Initially, we start by specifying the module where cells marked with the `#| export` directive will be automatically exported.
#
# In the following cell, `#| default_exp experiment `indicates that the exported fil... |
509f9cf7e49f702a1f20313d15496d9a9d74924ce14f1752709b9ee6cc24ac9a | Jupyter | 10,665 | 377 | # %%
import numpy as np
import os, sys
sys.path.append(os.getcwd()+"/..")
from rnn_scripts.train import *
from rnn_scripts.utils import *
from tasks.seqDS import *
import matplotlib.colors as colors
import matplotlib.pyplot as plt
%matplotlib inline
from mayavi import mlab
mlab.init_notebook()
_,_,luts1,luts2 = np.lo... |
edd0cfeb3a336c45822f94f2fc9a28de37ecf2f9c60f8836f0e514ffa79c99ff | Jupyter | 10,677 | 130 | # %% [markdown]
# $\textbf{Lead Author: Adele Myers}$
#
# Inspired by: Guigui, Miolane, Pennec, 2022. Introduction to Riemannian Geometry and Geometric Statistics: from basic theory to implementation with Geomstats.
# %% [markdown]
# Dear Learner,
#
# Welcome. This is a set of notebooks designed to teach you how to ... |
77eaa700c67903689ca8b365dfa5810b0a494ecef8e26119b59cd6989c8bcaa5 | Jupyter | 10,718 | 442 | # %% [markdown]
# # Introduction
#
# ### This notebook serves as a tutorial for using `gUtils` functions.
#
# ### gUtils is an R package developed to extend the functionality of GenomicRanges, the R package which stores genomic data and represents genomic locations in the Bioconductor project
# %% [markdown]
# ## ... |
ba6ea0fedc3ea6e2ef3a05026b9fda36a839402769ca8a615f90b1c653e69964 | Jupyter | 10,731 | 282 | # %%
import os
os.chdir('../')
import DeepPurpose.DTI as models
from DeepPurpose.utils import *
from DeepPurpose.dataset import *
# %%
X_drug, X_target, y = load_process_KIBA('./data/', binary=False)
drug_encoding = 'Morgan'
target_encoding = 'CNN'
train, val, test = data_process(X_drug, X_target, y,
... |
719ec42f6c85095e89720e4ff4c37c7ef72aee8a20f654d6d13a98b9468937c1 | Jupyter | 10,733 | 256 | # %% [markdown]
# # Samples per feature dependence
# %% [markdown]
# How do power, association strength as well as errors in weights, scores and loadings depend on sample size?
# %% [markdown]
# ## Setup
# %%
import numpy as np
import pandas as pd
import xarray as xr
import scipy.linalg
import scipy.stats
from scipy... |
0fb7620f760edcba43824379da35c1f78628e8ef9cce48e22c8a2db5e43380bd | Jupyter | 10,799 | 303 | # %% [markdown]
# # Testcase: Private Attributes Initialization with Both Options
# %% [markdown]
# # Getting Started
# %% [markdown]
# Initially, we start by specifying the module where cells marked with the `#| export` directive will be automatically exported.
#
# In the following cell, `#| default_exp experiment... |
0eae2eb8981a2c758c8219a38a0a6e4f7e85cdcf156bc6bc5589ed2cf1f48c46 | Jupyter | 10,855 | 442 | # %%
import os, glob
from tqdm import tqdm
import numpy as np
from skimage.io import imread, imsave
import matplotlib.pyplot as plt
from matplotlib import rc
plt.rcParams.update({'font.size': 20})
# plt.style.use('dark_background')
rc('pdf', fonttype=42)
import funcs
import imp
imp.reload(funcs)
# %%
folder = os.pat... |
851ab6fc59b5f25bb9925d74acc6244a4a8aae253445352c0abd2fd72e6e952e | Jupyter | 10,855 | 310 | # %%
import os
os.chdir('../')
import DeepPurpose.CompoundPred as property_pred
from DeepPurpose.utils import *
from DeepPurpose.dataset import *
from sklearn.metrics import mean_squared_error, roc_auc_score, average_precision_score, f1_score
import numpy as np
from ax.plot.contour import interact_contour, plot_co... |
e5f17cc1cadfd5dcefe4fc158c98ad86d7f501798ba4124d4cae2c369b23d975 | Jupyter | 11,056 | 301 | # %%
# calculates for each metastases pair
# average impacts log-ratios (brain/extra-cranial) on pathways
# median impact log-ratios of all altered genes on all genes
# average impacts log-ratios on each gene
#
#
# from the altered genes of all met. pairs
# as basis for other scripts
# saved for later: metPa... |
4408769d5755c10f538ead09f09e0ba65e66de5d0feb61ec872176debcdefc81 | Jupyter | 11,076 | 304 | # %% [markdown]
# ### This notebook is for reproducing the paper results' subsection "Optimal $\gamma$-fused structure-function modular organization" and generate the panels of the figure S1. The goal of the notebook is to test different initial parcellations (iPAs), and comparing the results. We will select for the re... |
af35acb4032141884eb4333f882eddea18f589f396ab03387c981cfc1622f43c | Jupyter | 11,106 | 272 | # %% [markdown]
# # Churn Prediction - Predicting when your customers will churn
# ---
# %% [markdown]
# ## 1 - Introduction
#
# A software as a service (SaaS) company provides a suite of products for Small-to-Medium enterprises, such as data storage, Accounting, Travel and Expenses management as well as Payroll mana... |
2e4bb2d3dd0ef2d73c1ba54c2d727c1c48c51f02562a069aa9083380e419279b | Jupyter | 11,223 | 306 | # %%
# calculates for each metastases pair
# average impacts log-ratios (brain/extra-cranial) on pathways
# median impact log-ratios of all altered genes on all genes
# average impacts log-ratios on each gene
#
#
# from the altered genes of all met. pairs
# as basis for other scripts
# saved for later: metPa... |
69b31c273267605f054b5013969b11623cca48e962c46687766019cfc4aca2d5 | Jupyter | 11,338 | 240 | # %% [markdown]
# # Workflow Interface 104: Working with Keras
# [](https://colab.research.google.com/github/intel/openfl/blob/develop/openfl-tutorials/experimental/workflow/104_Keras_MNIST_with_GPU.ipynb)
# %% [markdown]
# In this tutorial, we ... |
4b913f226d52dd8eede7df6dcb8967e6ad31fce0b581aa71554e793d14ba1418 | Jupyter | 11,361 | 280 | # %% [markdown]
# ## Yeast gene trees in BHV space
#
# One of the biggest challenges in phylogenetics is that different data sets based on different genes can yield different phylogenetic trees. In an article published in *Nature* in 2003, Rokas et al. found that when data sets contain a small number of genes, it is e... |
9e032452603fbbd159513dade67b7dab38cf8e2c72654934d4e5d101cd5b19f9 | Jupyter | 11,362 | 265 | # %% [markdown]
# # Samples per feature is a key parameter
# %% [markdown]
# We have used "samples per feature" as an effective sample size parameter to account for the fact that datasets can have hugely varying number of features. To see if this is justified we here plot sample-per-feature dependencies of several met... |
6db2c30715d8ea129bf4d50612242f7f10c16941d162dc7997cb5f77cc729701 | Jupyter | 11,547 | 297 | # %%
# violin plots of impact log2-ratios, Suppl. Figure S4
# cluster heatmaps of log-ratio impacts on pathways, Figure 3
# Suppl. Table S7: log-impact and log-expression per pathway
# Suppl. Table S9: expression impact quadrants shared pathways
# %%
library(ggplot2)
library(gridExtra)
library(pheatmap)
library(scales... |
24ac9cbbb1146e155172c48535fe4603547ea5a09f7847188f712de6907053bb | Jupyter | 11,550 | 271 | # %% [markdown]
# # Clean Re-run — Notebook 3 of 3: Latency + Export benchmark (T4)
#
# **Run on an NVIDIA T4** (Runtime ▸ Change runtime type ▸ T4 GPU) — the deployment proxy. Run *after*
# Notebook 1 has saved weights to `CLEAN_RERUN/phase1/*/run/weights/best.pt`.
#
# For every format this reports latency **broken ... |
8b75a60e0af71e8d98c92badda95396b08d4a86a410e67bcc234249808b9e5f5 | Jupyter | 11,554 | 297 | # %%
# violin plots of impact log2-ratios, Suppl. Figure S4
# cluster heatmaps of log-ratio impacts on pathways, Figure 3
# Suppl. Table S7: log-impact and log-expression per pathway
# Suppl. Table S9: expression impact quadrants shared pathways
# %%
library(ggplot2)
library(gridExtra)
library(pheatmap)
library(scales... |
50dfbdc31a6a536c8a246099938f42b85fa6f308f18d85ca1ed6060f6bda565e | Jupyter | 11,635 | 310 | # %% [markdown]
# # Experiment 2: NHP Mapping (2D)
#
# In this tutorial, we will use the `optim-neurostim` library to perform a larger 2D optimization.
# We will use a non-human primate (NHP) dataset, where stimuli delivered in the NHP motor cortex are associated with arm/hand muscles responses.
# We will optimize the... |
d6128b2df4e91abfbd2e3c72fdffd11b113c49ed0fc23ece7e6aa75d2ecfea8b | Jupyter | 11,725 | 308 | # %%
import sys
sys.path.append('../../')
import os
from tqdm import tqdm
import numpy as np
import pandas as pd
import seaborn as sns; sns.set(style='ticks', context='paper',
rc={'font.size': 6,
'axes.titlesize': 7,
'... |
215278f9f8f7729864eff1dcd80bac6c361dc14dd3ad0609dd243165b8c57b5f | Jupyter | 11,744 | 285 | # %% [markdown]
# # Credit Risk - Computing the speed of repayment of loans
# %% [markdown]
# ---
# %% [markdown]
# ## 1 - Introduction
#
# Credit Risk refers to the likelihood that a borrower will not be able to repay a loan contracted by a lender. Thus throughout the years, financial institutions have developed va... |
071820878a9c1aaf5baad14f78ada697dbae5d9c1b92a9d9adb80c0df97fd640 | Jupyter | 11,752 | 216 | # %% [markdown]
# # Density based retrieval relevance
#
# An important aspect of using embeddings-based retreival systems like Chroma is knowing whether there are relevant results to a given query in the existing dataset. As application developers, we would like to know when the system doesn't have enough information ... |
ec8182a9b606534db1e1e3f47a9b26027c3c2b5fa0f571f3dc7266960aa36a24 | Jupyter | 11,758 | 352 | # %%
# Author: Guoqing Bao
# School of Computer Science, The University of Sydney
# Date: 2019-12-12
# GitHub Project Link: https://github.com/guoqingbao/Pathofusion
# Please cite our work if you found it is useful for your research or clinical practice
import os
# os.environ["CUDA_VISIBLE_DEVICES"]="0"
import gc
gc.e... |
1d6e651ffe5110d0662dc8ad4a4f8b08afdae8fc3c4e665106da65a7d9bc5ba0 | Jupyter | 11,773 | 283 | # %% [markdown]
# # Dimensionality Reduction of VGG16
# In this tutorial we will present how to create a reduced version of VGG16 using the techniques described in the article
#
# ''A Dimensionality Reduction Approach for Convolutional Neural Networks'', Meneghetti L., Demo N., Rozza G., https://arxiv.org/abs/2110.09... |
d4f9fd3ea2ff2162534ea3df7aac41cd90666de30d9095643bc52dd3a9859083 | Jupyter | 11,905 | 295 | # %% [markdown]
# # Experiment 4: Spinal stimulation (4D)
#
# In this tutorial, we will use the `optim-neurostim` library to perform a 4D spinal stimulation spatio-temporal optimization.
#
# Here we will use a toy dataset based on real data. Stimuli delivered in the rat spinal cord, once at each step, are associated ... |
8211dd45dd6bff14c6dc432c3fe6f70d31167d1006eaabc1179471bdb2b9a7ed | Jupyter | 11,972 | 384 | # %%
import scipy.io as io
import matplotlib.pyplot as plt
import matplotlib.pylab as pylab
import numpy as np
import seaborn as sns
# %%
import matplotlib.pyplot as plt
font = {'family': 'serif', 'serif': ['Helvetica']}
plt.rc('font', **font)
plt.rc('text', usetex=False)
# %%
label_fontsize = 16
title_fontsize = 18... |
9472afd504514a5f767a48fda1b91f02646dc74d83f772f519979cf83ae35cfd | Jupyter | 12,004 | 353 | # %%
import numpy as np
import pandas as pd
# %%
# -*- coding: utf-8 -*-
# ----------------------------------------------------------------------------------------------------------------------------------
# A program for computing the CLB distance and the Jaccard index for different solutions and comparing them.
# A... |
427896710b8e922bd835374cbd9ba746e8548c66872f7ad46ab0de4374b4bb6e | Jupyter | 12,024 | 386 | # %%
import os, glob, tqdm
from skimage.io import imread, imsave
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from matplotlib import rc
import matplotlib as mpl
import scipy.ndimage as ndi
from skimage.metrics import structural_similarity as ssim
from skimage.metrics impo... |
306f0a1fd3c422ecefd0e053b165b7c8ad7f4775b1b9fe7a5ccda0b8607b0a23 | Jupyter | 12,070 | 352 | # %%
import glob
import importlib
import os
import random
import sys
from itertools import combinations
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import prettypyplot as pplt
import scipy
import seaborn as sns
from matplotlib import ticker
import extq
# %%
sys.path.insert(1, "../../.... |
9aab8a1f3b2ef594c697694cb1b02dbbf48f2fad9dad8826f478265d203bb44d | Jupyter | 12,159 | 266 | # %% [markdown]
# ### Full implementation of GP-BO optimization of neurostimulation
# %% [markdown]
# @file OptimizeNeurostim.ipynb
# %% [markdown]
# @authors Rose Guay-Hottin https://github.com/RoseGH20
# Marco Bonizzato https://github.com/mbonizzato
# @version 1.0 / June 2021
# %% [markdown]
# The cod... |
8e428ea05667223211d41376dd49aa8460e81f28d61ee7e29903e6bf43530f04 | Jupyter | 12,197 | 347 | # %% [markdown]
# # Scikit-learn Linear Regression Tutorial using Workflow Interface with Ridge Regularization
#
#
# This tutorial demonstrates how to train a linear regression model using scikit-learn with Ridge regularization on a dataset, leveraging the new OpenFL Workflow Interface. The Workflow Interface provide... |
58e24bc273abbedace1af0721752bcacd840087ef9ed5dbca87b5a11effae061 | Jupyter | 12,226 | 379 | # %% [markdown]
# # Testcase: Reference
# %% [markdown]
# # Getting Started
# %% [markdown]
# Initially, we start by specifying the module where cells marked with the `#| export` directive will be automatically exported.
#
# In the following cell, `#| default_exp experiment `indicates that the exported file will be... |
4cdaffcb5762f294c4220f51895b695afc73c966f9735728d2780f9150d280f3 | Jupyter | 12,260 | 289 | # %%
import pandas as pd
import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
from matplotlib.lines import Line2D
from copy import copy
from itertools import product
import sys
from pathlib import Path
sys.path.append(str(Path.cwd().parents[1] / 'project_utils'))
# sys.path.... |
b2fd60511963bc897ec59386ad92956d244ae2b0298d290966f5d6a89448db6b | Jupyter | 12,284 | 310 | # %% [markdown]
# # Experiment 3: Cortical Stimulation on Treadmill (2D)
#
# In this tutorial, we will use the `optim-neurostim` library for a 2D optimization. We will use a rat dataset, where stimuli delivered in the rat motor cortex are associated with leg muscles responses. We will optimize the xy position of the s... |
1e1d9ec636089bbf26829cbda98243b47eef754efefa1ba1d1cd3987df33ccb7 | Jupyter | 12,317 | 408 | # %% [markdown]
# # Small network (one E- and one I-populations)
#
# We use this notebook to generate the data and plots focusing on the generation of gamma oscillations through E-I coupling for the revision of the eLife manuscript.
#
# There are several cases:
# * [Disconnected](#disconnected_case) network of E-I, r... |
28227345ef2c28dd31f789813567542c2e3c2ac16eee5ce23053661a82be7405 | Jupyter | 12,606 | 287 | # %%
import sys
sys.path.append('../')
from scipy import stats
import pandas as pd
import seaborn as sns; sns.set(style='ticks', context='paper',
rc={'font.size': 6,
'axes.titlesize': 7,
'axes.labelsize': 7,
... |
260d35adce09a2c81b78fe9b159a21ebe7a95b67ec7eab624aace2136f3e83a1 | Jupyter | 12,663 | 291 | # %% [markdown]
# # Analysing the frequencies of introgressed alleles in *D. sechellia* using IntroUNET
#
# This notebook takes us through the steps of our analysis of the results from running IntroUNET on our *D. simulans* and *D. sechellia* data set. Before we start here, we have already run IntroUNET on these data ... |
147059834b7d553a90c58d6f9d90cfd61296c0ef10875d61845d17f87cae07c2 | Jupyter | 12,744 | 372 | # %% [markdown]
# # Data on Manifolds
# %% [markdown]
# Lead author: Nina Miolane.
# %% [markdown]
# We import the backend that will be used for geomstats computations and set a seed for reproducibility of the results.
# %%
import geomstats.backend as gs
gs.random.seed(2020)
# %% [markdown]
# Finally, we import th... |
9d05d742f19d49cbe939ec2ec3098129bc0d06cb86e8fcb30f5a9907d84ef682 | Jupyter | 12,853 | 268 | # %% [markdown]
# # # Workflow Interface 104: Working with Keras on CPU
# [](https://colab.research.google.com/github/intel/openfl/blob/develop/openfl-tutorials/experimental/workflow/104_Keras_MNIST_with_CPU.ipynb)
#
# ## Training a CNN on CPU u... |
54dbc16d55f62dc1a5d11226141be157843efc5eb1ae097f451d9036e56cf275 | Jupyter | 13,000 | 453 | # %%
from scanpy_recipes import sc
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
# %% [markdown]
# # *Aggr :
# ## RV18001 - RV18002 - RV18003
# ## RV19001 - RV19002 - RV19004 - RV19005 - RV19006 - RV19007 - RV19008 - RV19009
# %% [markdown]
# ## Loading data
# %%
ac =... |
948795300f1516760f5432a4f3a64b2c9d0b7160d68f185ab635a580c8a8759b | Jupyter | 13,010 | 392 | # %% [markdown]
# # Sample size calculator
# %% [markdown]
# We develop a sample size calculator for CCA and PLS. We had previously generated datasets for a variety of parameters and determined the corresponding required sample sizes to bound statistical power and error metrics. To circumvent the computational cost as... |
728b93105bafd5af31fc90d9281c5943d5d49a34a69a91df6b56c3c4557b274a | Jupyter | 13,034 | 338 | # %%
%matplotlib inline
from pathlib import Path
import h5py
from types import SimpleNamespace
from joblib import delayed, Parallel
import numpy as np
import pandas as pd
from scipy import signal
pd.set_option('display.max_rows', 30)
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(style='whitegrid', pa... |
ad6fa7c25316985046b92bb0329f47c44691ba2879a516d1cd073f2f9256aebf | Jupyter | 13,042 | 447 | # %%
# Before Starting to run the code, please upload following files:
# - embed_proteins.py
# - rnn_gru.pt
# - data_BacLAB.csv #
# - data_nonBacLAB.txt #
# - data_BacLAB_plus_nonBacLAB.txt #
# - List_kmers.csv #
# - model_classifier.h5 #
# - model_classifier.json #
# %%... |
5786e5d2dd94d8d04488d19904858419f84fd8d67890e77e50b69e8b195cc84d | Jupyter | 13,116 | 280 | # %% [markdown]
# Here we shall learn the stochastic dynamics of the one-dimensional Fredrickson-Andersen (FA) model, consisting of binary spins on a lattice, through observation of a single trajectory. The trajectory of length $T$, obtained through kinetic (or continuous-time) Monte Carlo, consists of configurations a... |
c99daefa9321e074486ec109961f50b8666153ba5148c22543deed99c590f335 | Jupyter | 13,120 | 381 | # %% [markdown]
# # Federated PyTorch UNET Tutorial using Workflow API
# %%
# Install dependencies if not already installed
%pip install torch
%pip install matplotlib
%pip install ray
# %%
# Import installed modules
import PIL
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as op... |
aa1828f8f54f304cbecdd4ee6c5abf366376fdf821219dd3bcece211e0fb8dde | Jupyter | 13,147 | 558 | # %%
import sys
import importlib
import gc
import glob
import random
from itertools import combinations
import numpy as np
import sklearn
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib import ticker
import seaborn as sns
import prettypyplot as pplt
import pyemma
import mdtraj as md
import M... |
b5b8bb240090c712db6c0e51ca0ba27230060d445b11cbf3f31b76958eacb4ba | Jupyter | 13,150 | 397 | # %% [markdown]
# # Loadings and Cross-loadings
# %% [markdown]
# We explore how many samples are necessary to obtain stable cross-loadings, and compare to loadings.
# %% [markdown]
# ## Setup
# %%
import warnings
from matplotlib import MatplotlibDeprecationWarning
warnings.simplefilter('ignore', MatplotlibDeprecati... |
011483e06845df3ee20af0ad066dc913da644183f75733e02c4991936f20ef9c | Jupyter | 13,184 | 404 | # %% [markdown]
# # Notebook to illustrate fish identification with a classifying CNN
# %% [markdown]
# Showing that neural networks can distinguish between individuals in a population of fish. We will use miniCARP, a restricted version of a library of fish images prepared by the lab as part of **[idtracker.ai](http:/... |
254f75324f23b45f5e7a4c114320ed1c9d0b856448d069767438a2f1d9c57cdf | Jupyter | 13,222 | 354 | # %% [markdown]
# # Experiment 1: Rat Mapping (2D)
#
# In this tutorial, we will use the `optim-neurostim` library for a 2D optimization.
# We will use a rat dataset, where stimuli delivered in the rat motor cortex are associated with leg muscles responses.
# We will optimize the xy position of the stimulation in orde... |
6f19034de57afef0aeee43bfb14a02d50785a28adc2857970ed66a308292eab8 | Jupyter | 13,292 | 352 | # %% [markdown]
# # Notebook to illustrate fish identification with contrastive learning
# %% [markdown]
# We will demonstrate that an embedding CNN (ResNet18) can be trained to differentiate fish images from various animals. For this, we will use miniCARP, a simplified version of a fish image library developed by the... |
d40ba89a1f5be5045fc1b9e0dadf39c8a261eeffeaca81c34bebdd5bcaf625ff | Jupyter | 13,327 | 294 | # %% [markdown]
# # notebook to score all the datasets with the CoVES vs others
# - create table of unsupervised methods (CoVES, ESM-IF, proteinMPNN, BLOSUM, EvCouplings)
# - scoring of AT 3pos, AT 10pos, GFP, AAV, GRB2
# %%
from os import listdir
import matplotlib.pyplot as plt
import matplotlib.gridspec as gs
from s... |
3f980d6fc74fd5b2ec842b904c012b090f85e02bfb1bc8aa244175a1463e18bd | Jupyter | 13,337 | 424 | # %%
CODE_REPO_PATH='/YOUR_PATH/Converting_allo_goal_to_ego_steering/analysis/'
import warnings
warnings.filterwarnings('ignore')
from importlib import reload
import sys
import os
import pandas as pd
import scipy as sc
import numpy as np
sys.path.insert(0, CODE_REPO_PATH)
import read_data as rd
import behaviour as ... |
c0ab9d2ebde937c02dc1b1abb672bbab6629e8d639dc34acba17f314fe1c544b | Jupyter | 13,347 | 365 | # %% [markdown]
# # Predictive Maintenance - Predicting when a machine will break
# %% [markdown]
# ---
# %% [markdown]
# ## 1 - Introduction
#
# We will consider that a manufacturing company uses many machines to build their final products. The factory is using IoT technologies via smart sensors to measure and save... |
3936687a9854f0ab742903ba5370a6519e63d3b8619b60db1937e2007064dc98 | Jupyter | 13,365 | 594 | # %%
import pandas as pd
import scipy.io as io
import matplotlib.pyplot as plt
import matplotlib.pylab as pylab
from matplotlib.ticker import MaxNLocator
import numpy as np
from matplotlib import gridspec
import seaborn as sns
import pingouin as pg
import copy
import glob
import os
# %%
from datetime import datetime, ... |
e1bfeef70decdfd26c658f72c2dfc9f9f1a3890f0bca346393517bce4068d034 | Jupyter | 13,474 | 340 | # %%
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.manifold import TSNE
import argparse
import torch
import torch.nn.functional as F
import torch.nn as nn
import numpy as np
import copy
import random
import time
import math
import utils
import models
# %%
def setup_seed(seed):... |
326c3e290be0e7a1d93c3c072277ed294534f72674a5a758ae869468736f5721 | Jupyter | 13,495 | 351 | # %% [markdown]
# # Workflow Interface 201: Using Ray to request exclusive GPUs
# [](https://colab.research.google.com/github/intel/openfl/blob/develop/openfl-tutorials/experimental/workflow/201_Exclusive_GPUs_with_Ray.ipynb)
# %% [markdown]
# I... |
9ce9dd249d986dd1570ad82061dc959e5498b89c7eec259d5c0f22edaf344c98 | Jupyter | 13,507 | 457 | # %% [markdown]
# # Onslow et al. 2014
#
# ### A Canonical Circuit for Generating Phase-Amplitude Coupling
#
# Replicating the Onslow model, consisting of a set of coupled E-I populations generating theta-nested gamma oscillations. The authors used the Wilson-Cowan formalism. Equations and parameters are given in the... |
9f954be4a621f7fcc147e9d18c96a099aa0ecd72b1811cf190064c2e3e1264b4 | Jupyter | 13,512 | 386 | # %%
%matplotlib inline
import matplotlib.pyplot as plt
import time
import numpy as np
from six import iteritems
import shelve
from scipy.ndimage.measurements import label
import h5py
import PIL
import os
from IPython.display import Image, display
from IPython.html.widgets import interact, fixed
from IPython.html impo... |
7ef228ecdd67eaa623a9a1e1da74bc24d22eb161045cf4987f403f586956ec7c | Jupyter | 13,523 | 358 | # %%
library(parallel)
library(pheatmap)
library(xlsx)
# %%
basePath = "/data/bcu_projects/MelBrainSys_PostdocProject_Gruetzmann/publications/2022-my-MelBrainSys-paper/scripts-etc-for-publication/"
myPath = paste0(basePath,"regNet/validation-cohort/")
setwd(myPath)
# %%
localRlibs = paste0(basePath,"conda/lib/R/libra... |
2539547156be961102cbda4fcb63692895712be9e1154da30a72eee31ef81d74 | Jupyter | 13,535 | 358 | # %%
library(parallel)
library(pheatmap)
library(xlsx)
# %%
basePath = "/data/bcu_projects/MelBrainSys_PostdocProject_Gruetzmann/publications/2022-my-MelBrainSys-paper/scripts-etc-for-publication/"
myPath = paste0(basePath,"regNet/validation-cohort/")
setwd(myPath)
# %%
localRlibs = paste0(basePath,"conda/lib/R/libra... |
2bda7bcb045dc50e1105ceebdcb7b29de1a3b10a6fe0de68e916e49f8b6ef461 | Jupyter | 13,639 | 387 | # %% [markdown]
# [](https://colab.research.google.com/github/RatInABox-Lab/RatInABox/blob/main/demos/decoding_position_example.ipynb)
# %% [markdown]
# # Decoding position from firing rates
#
# In this example we show how position can be deco... |
40e4a859aaa4565dcc661c5623ce136cb95185a7b0f8cbacc31efb14a7cb30a4 | Jupyter | 13,643 | 410 | # %% [markdown]
# # Load packages
# %%
suppressWarnings(suppressMessages(library(gUtils)))
suppressWarnings(suppressMessages(library(BSgenome.Hsapiens.UCSC.hg19)))
Sys.setenv(DEFAULT_BSGENOME = 'BSgenome.Hsapiens.UCSC.hg19::Hsapiens')
# %% [markdown]
# # Create GRanges for examples
# %%
## instantiate GRanges object... |
80639ae51567f2f2260da8ec4b4e0162f0c31dfa82ff8c4c5480dd7fe93a599f | Jupyter | 13,688 | 390 | # %% [markdown]
# # 1: Import required packages & functions <a class="anchor" id="chapter1"></a>
# %%
# clear all variables
for i in list(globals().keys()):
if(i[0] != '_'):
exec('del {}'.format(i))
#suppress future warnings -- not really a good idea
from warnings import simplefilter
# ignore all future ... |
c6517d4ce2f9aee38d31cf2288983ab2de15f86ba7b7d81239421f1f53bcffe3 | Jupyter | 13,690 | 390 | # %%
library(tidyverse)
# %%
preprocessed_list = qs::qread('preprocessed_list.qs')
outliers_list = qs::qread('outliers_list.qs')
# %%
outliers_tbl <- outliers_list %>%
enframe(name = "Name", value = "Sample") %>%
unnest(Sample) %>%
rename(name = Sample, failed = Name)
outliers_tbl
# %%
do_regional_pca_plot =... |
a4892324700bb313151834da8102e5815df72a5f2e395dac69d3a94ed496e17b | Jupyter | 13,727 | 345 | # %%
import pickle
import matplotlib.pyplot as plt
import numpy as np
# %%
def obtain_TPR(TP,FN):
TPRs = []
for i in range(len(TP)):
TPR = TP[i]/(TP[i]+FN[i])
TPRs.append(TPR)
return TPRs
def obtain_TNR(TN, FP):
TNRs = []
for i in range(len(TN)):
TNR = TN[i]/(TN[i]+FP[i])
... |
e3cffbbb9910e0dcdb420d4a46aac297af34f7aa45f9ccdb62fcd2e180d7c355 | Jupyter | 13,838 | 438 | # %% [markdown]
# <a href="https://colab.research.google.com/github/katemartian/Photometry_data_processing/blob/master/Photometry_data_processing.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# <h1><center>Photometry data processin... |
d25d7ed7b5907ddece9cbf94fe49f4ee6f1cd2bf4116fbaef4547620e39bf431 | Jupyter | 13,902 | 309 | # %% [markdown]
# # Workflow API Tutorial: Keras MNIST
# %% [markdown]
# This tutorial demonstrates how to train a model using TensorFlow Keras on the MNIST dataset, leveraging the new OpenFL Workflow Interface. The Workflow Interface provides a novel way to compose federated learning experiments with OpenFL, enabling... |
38fd9b7a744291671b41f2bced3f1764b33b239479429b7f68e5f107b281b5f9 | Jupyter | 13,930 | 231 | # %% [markdown]
# # DeepPurpose Deep Dive
# ## Tutorial 1: Training a Drug-Target Interaction Model from Scratch
# #### [@KexinHuang5](https://twitter.com/KexinHuang5)
#
# In this tutorial, we take a deep dive into DeepPurpose and show how it builds a drug-target interaction model from scratch.
#
# Agenda:
#
# - Pa... |
3f9e3cc0a982e0c4b8071a57aff7f53317b5adea0313d2778bb0e0070c50949e | Jupyter | 14,024 | 484 | # %% [markdown]
# # Supervised logistic regression fit
#
# This notebook fits a per-residue mutation effect model to combinatorial variant effect datasets.
#
# ## Inputs:
# - measured effects of combinatorial protein variants, ie.
# - X is a one-hot encoding of the amino acid variant
# - Y is the measured v... |
baff4306d29632b4540c5de39e5ff11d6c5a4897237e3b346559616b3e8e0912 | Jupyter | 14,142 | 385 | # %%
library(tidyverse)
library(readxl)
library(lubridate)
library(patchwork)
# Set the print width to 120 characters
options(width = 120)
# %%
meta_0010 = read_xlsx('../data/metadata/0010/20190130 BW, FI - Collab with Tune Pers Group.xlsx', range = "B1:I19") %>%
# Fill in missing Box IDs
fill(`Box ID`) %>%
# ... |
44c12cff3b6171aeac3f406fdae715684be020d8f19b848f7edc1a7fbbf740d1 | Jupyter | 14,253 | 497 | # %%
import sys
import os
import importlib
import random
from itertools import combinations
import glob
import numpy as np
import scipy
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib import ticker
import seaborn as sns
import prettypyplot as pplt
import mdtraj as md
import MDAnalysis as mda... |
b6928f82e27c94cfb498025c5c93eaf5cd7fc8d4b3b18194027b1e165d16bf04 | Jupyter | 14,296 | 398 | # %% [markdown]
# ## Trees, Trees, more Trees, oh my!!
#
# In this notebook, we demonstrate how one might use some of the learning algorithms implemented in geomstats to apply to a non-Euclidean geodesic metric space.
#
# We choose BHV space, which is the space of unrooted phylogenetic trees without pendant edges.
# ... |
3a254234fb23a89b58de7157d2756edce26550b68fa86cdc1679c0830244cc80 | Jupyter | 14,298 | 290 | # %% [markdown]
# # Workflow Interface 102 - Held out aggregator validation
# [](https://colab.research.google.com/github/intel/openfl/blob/develop/openfl-tutorials/experimental/workflow/102_Aggregator_Validation.ipynb)
# %% [markdown]
# In this... |
7500fd0e477222960b99dac0037fbb98f2dd428dd8d42da8a084132b277895fd | Jupyter | 14,386 | 425 | # %% [markdown]
# # Image classification with Swin Transformers
# %%
!pip install -U tensorflow-addons
# %% [markdown]
# ## Setup
# %%
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
import tensorflow_addons as tfa
from tensorflow import keras
from tensorflow.keras import layers
import os
... |
29d708065a785cb7d61b15de0663513b2d3ae49ad1cfc1c0323eb1a3f50a7ffb | Jupyter | 14,416 | 453 | # %% [markdown]
# # this notebook is for the aggregation of all the clusters across subjects
# The idea, then is to create a data frame containing each clusted unit, the snr, av. fr, isi viol, unique id within for the animal, an unique id within the cross session match. Other identifiers, session, tetrode, depth.
# %... |
d47d142bd181bb297c18e73adbb69347832b49a1009864376434733e83227ffd | Jupyter | 14,422 | 321 | # %% [markdown]
# # notebook to score all the datasets with the CoVES vs others
# - create scoring of AT 3pos, AT 10pos, GFP, AAV, GRB2 using unsupervised methods (CoVES, ESM-IF, proteinMPNN, BLOSUM, EvCouplings)
# -
#
#
# ### Inputs:
# - the per protein residue preferences inferred from structure in order to calcul... |
e8fd859fc15743492d13b5fcc8e0430a158a9727f9b487c8d993a1f3827460d5 | Jupyter | 14,578 | 533 | # %%
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import os, sys
sys.path.append(os.getcwd()+"/..")
from rnn_scripts.utils import *
from rnn_scripts.model import RNN
from scipy.optimize import fsolve
from rnn_scripts.utils import green_blue_colours
cls = green_blue_colours()
# %%
# Load a mo... |
1a84cf6aa25fe3aad8cd2f9fb510aa1adaf4c0c951ad9ee33d83d1d95c61fa3d | Jupyter | 14,597 | 439 | # %%
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import os
from glob import glob
import seaborn as sns
from PIL import Image
np.random.seed(123)
from sklearn.preprocessing import label_binarize
from sklearn.metrics import confusion_matrix
import itertools
import keras
from... |
9f4833c3ce0d06705bda476cd08d245ba4ed273e01dd75226a9a1bceb6fb1e9e | Jupyter | 14,615 | 443 | # %%
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import os
from glob import glob
import seaborn as sns
from PIL import Image
np.random.seed(123)
from sklearn.preprocessing import label_binarize
from sklearn.metrics import confusion_matrix
import itertools
import keras
from... |
8627e6dbca89b86245bc1093ba00d72070616ed5702eb12c40fdd8c4fabfecab | Jupyter | 14,692 | 346 | # %% [markdown]
# <a href="https://colab.research.google.com/github/armiro/Covid19-Detection/blob/master/hands_on_covid_cxnet.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Up and running with COVID-CXNet
# %% [markdown]
# ## R... |
78076c1255ae3b73c5262d5178df9a95b339f7200dcccd433b334f64fe78c4b3 | Jupyter | 14,762 | 360 | # %% [markdown]
# # MNIST Example: Workflow API
# %% [markdown]
# Welcome to the first OpenFL Experimental Workflow Interface tutorial! This notebook introduces the API to get up and running with your first horizontal federated learning workflow. This work has the following goals:
#
# - Simplify the federated workflo... |
2b89caa07e976616a54d8f3fc54c2693a61421d2ed672f4f709aecb31e1befa9 | Jupyter | 14,846 | 442 | # %% [markdown]
# # Federated Runtime: 301_MNIST_Watermarking
# %% [markdown]
# This tutorial is based on the LocalRuntime example [301_MNIST_Watermarking](https://github.com/securefederatedai/openfl/blob/develop/openfl-tutorials/experimental/workflow/301_MNIST_Watermarking.ipynb). It has been adapted to demonstrate t... |
8058e5dc0b2b2efcbf5857c8e33a2cee74f4421581b04f2157a7813d634946e2 | Jupyter | 14,900 | 375 | # %% [markdown]
# # Federated Evaluation with MNIST
# %% [markdown]
# ### Introduction
#
# Welcome to the first OpenFL Federated Evaluation Workflow Interface tutorial! This notebook demonstrates OpenFL capability of running your first horizontal federated evaluation workflow. This work has the following goals:
#
# ... |
10b8647cd714ff9942f91989f2443ef08859c495353950bc3305d4d5aca755fb | Jupyter | 14,961 | 408 | # %%
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from sksurv.compare import compare_survival
from sksurv.nonparametric import kaplan_meier_estimator
# %%
MOCA_THRESHOLD = 24.5
MMSE_THRESHOLD = 28.5
# %%
significant_prs = [
'GRS__savage_2018__intelligence.txt',
'GRS__lee_2018__cognit... |
0e58d946231bf8b6ef30a981a0894d0f2a5c9e0e5ccb2e6b1db1bd987e45f267 | Jupyter | 14,982 | 487 | # %% [markdown]
# # TA generalization error estimate, and ROC curves
# - to rewrite
# %% [markdown]
# # TA linear and nonlinear fits
# %%
import tensorflow as tf
import keras
from scipy.sparse.linalg import lsmr
import statsmodels.api as sm
import random
from scipy.stats import pearsonr, spearmanr
import importlib... |
62530c4a4a03b2f1a15e773b3a87eaf2faa3f7fa3c7b272983893a4c0e9f8f97 | Jupyter | 14,994 | 630 | # %%
import sys
import importlib
import gc
import glob
import random
from itertools import combinations
import numpy as np
import sklearn
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib import ticker
import seaborn as sns
import prettypyplot as pplt
import pyemma
import mdtraj as md
import ... |
496b133cd97d153523741213f18b25846f7ef80a358356f837c81c804e3bdcbd | Jupyter | 15,066 | 289 | # %% [markdown]
# # 2. Loading the Transport Properties ANNs models
#
# This notebook is part of the Supporting Information of the article *Simulation and data-driven modeling of the transport properties of the Mie fluid* by Gustavo Chaparro and Erich A. Müller. (Submitted to Journal of Physical Chemistry B).
#
# Thi... |
e0e8ec27c1f0b261a8eeb9312f8f7c3f825cd0306eb79f45083805303d8097c3 | Jupyter | 15,330 | 369 | # %% [markdown]
# # Workflow Interface 101: Quickstart
# [](https://colab.research.google.com/github/intel/openfl/blob/develop/openfl-tutorials/experimental/workflow/101_MNIST.ipynb)
# %% [markdown]
# Welcome to the first OpenFL Experimental Wor... |
e97bfaded5c36322aff68134906f545a038bcfc8fb36b9057e97a76fb0bfee6a | Jupyter | 15,548 | 759 | # %% [markdown]
# ## Population coding
#
# Figure 3
# %% [markdown]
# ### Data creation
# %%
import secorder as sec
import matplotlib.pyplot as plt
import numpy as np
import scipy.stats as stats
import os
path = os.getcwd() + '\\figures\\'
# %%
def smooth_data(nt, tau, scale):
x = scale*np.random.randn(nt+100)... |
b152ebffeaa8c5e6467e7d3db93474cf50b2bef8542aed22d75ff695411ccd4d | Jupyter | 15,549 | 527 | # %%
%matplotlib inline
%load_ext autoreload
%autoreload 2
import os
from visualize import PyramidVisualizer, get_dataset, display_image, simple_visualizer, visualize_residuals
from metroem.aligner import Aligner
from corgie import residuals
# %%
"hi"
# %%
checkpiont = "starge3_base"
#checkpoint_folder = "/usr/people... |
f083262fc9311cdece71cbad0d2d9e5e0b342f0ea84d1fd0e8d67e8515409872 | Jupyter | 15,741 | 453 | # %%
#### ASSOCIATED DATA CAN BE FOUND AT ZENODO REPO FROM https://doi.org/10.1186/s13059-023-02846-8 ####
import numpy as np
import math
import matplotlib.pyplot as plt
import csv
import pandas as pd
from scipy import stats
import re
from scipy.stats.stats import pearsonr
#import rpy2.robjects as robjects
import rand... |
cf9cddc2be0c8e607844e7dc7a09a9603e04e9ff6b609b338c664a27552c8e81 | Jupyter | 15,773 | 369 | # %% [markdown]
# # Dimensionality Reduction of SSD300
#
# In this tutorial we will present how to create a reduced version of SSD300 using the techniques described in the article :
#
# L. Meneghetti, N. Demo and G. Rozza, "A Proper Orthogonal Decomposition Approach for Parameters Reduction of Single Shot Detector Ne... |
837f5a717b58634ffd4ebe730f116a67ea4ad59e4f6beba6e02f16093f89aa5e | Jupyter | 15,795 | 397 | # %%
## loading package
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import pylab
import seaborn as sns
from sklearn.metrics.pairwise import cosine_similarity
from statannot import add_stat_annotation
import matplotlib as mpl
from scipy import stats, cluster
import glob
impo... |
3f61f8fa7b5cdb3b883945488baf62becc9fc175c584586f1de3a8d748eabbf4 | Jupyter | 15,908 | 417 | # %%
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.manifold import TSNE
import argparse
import torch
import torch.nn.functional as F
import torch.nn as nn
import numpy as np
import copy
import random
import time
import math
import utils
import models
# %%
def setup_seed(seed):... |
7e7a3748b75deea951736647d8cf6fa6553e4771d42fb096c194c69a327b6dfd | Jupyter | 15,958 | 417 | # %% [markdown]
# ### Linear Regression Tutorial using Workflow API
#
# This notebook demonstrates a linear regression tutorial using the Workflow Interface. The key steps include:
#
# 1. **Model Definition**:
# - Define a linear regression model with Mean Squared Error (MSE) as the loss function.
#
# 2. **Synth... |
0d4abd9a18a52674c5189d02b9feedcc36fa168eee6caba5b583616115cf9778 | Jupyter | 16,037 | 412 | # %%
library(DESeq2)
library(tidyverse)
library(ggplot2)
# %%
counts = read_csv('../03_build_meta_and_counts/counts.csv') %>% column_to_rownames('gene')
counts %>% head
# %%
meta = read_csv('../03_build_meta_and_counts/meta_long.csv') %>% filter(!is.na(name)) %>% column_to_rownames("name") %>% mutate(name = rownames(... |
7c4c8915d70bf9ec4045080532bdf75d7d7035113edeabde0b2e75c34cd0553f | Jupyter | 16,090 | 379 | # %% [markdown]
# # setup
# %%
%matplotlib inline
import eelbrain as eel
import numpy as np
import scipy, pathlib, importlib, mne, time, os, sys
from tqdm import tqdm
import matplotlib.pyplot as plt
import preprocessing as pre
import models as md
import plotting
from pathnames import *
importlib.reload(pre)
importlib.... |
f9e2803655c8db16c7b0f4c84f9070fd95b3ee7b9957ff24b2f1b0852f71fb5a | Jupyter | 16,138 | 380 | # %% [markdown]
# # Information geometry
# %% [markdown]
# Lead author: Alice Le Brigant.
#
# Disclaimer: this notebook requires the use of the ```numpy``` backend.
#
# ## Table of contents:
# * [Introduction](#sec1)
# * [Geometry of normal distributions](#sec2)
# * [Geometry of beta distributions](#sec3)
# * [Learn... |
45af17f0beb319bd96b215ec76f97ebcdf7061df071f34f91ab37e34dce1c1af | Jupyter | 16,163 | 497 | # %% [markdown]
# # Hand gesture classification with EMG data using Riemannian metrics
# %% [markdown]
# Lead author: Marius Guerard.
#
# In this notebook we are using EMG time series collected by 8 electrodes placed on the arm skin. We are going to show how to:
#
# - Process these kind of signal into covariance mat... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.