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|---|---|---|---|---|
e4ddcea904aa2b89b259b6ec72ff376c7fe3b724372a0e4370ded81b55bf447d | Jupyter | 16,236 | 359 | # %% [markdown]
# # Comparing model performances
#
# Our objective is to evaluate the performance of the seq#-classifier on simulations created using a range of evolutionary models. For this, we generate figures depicting the learning curves of the pre-trained models. Furthermore, we employ various metrics and sanity ... |
1f4ca06610115b294995fb2de0dc3904abeca44ef48125f78c8c1c3617fc0d15 | Jupyter | 16,285 | 415 | # %% [markdown]
# ## Cavalli (MAGIC Cohort) Data Analysis and Integration with GSVA
# Used for Figure 3
#
# Author: Max Gold
# %%
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import seaborn as sns
from sklearn.cluster import AgglomerativeClustering
from scipy import stats
import te... |
1e353b4de9f0df2464d7c22e022ad8ebea2d4c9cc66014d0e74ca37c36f99dd6 | Jupyter | 16,327 | 344 | # %% [markdown]
# # Clean Re-run — Notebook 1 of 3: Training + Accuracy (Ultralytics models)
#
# **Run this on an NVIDIA A100 GPU** (Runtime ▸ Change runtime type ▸ A100, High-RAM).
# Do **not** train on a T4 — too slow for 30 training runs. Keep the *same* GPU type for the whole notebook.
#
# This notebook trains **... |
b62e65decdf6de4eff98e2a660f1e0b59d7b5b732e7582cb8783ff3f8ecbc9d5 | Jupyter | 16,394 | 502 | # %%
import os
os.environ['R_HOME'] = '/Library/Frameworks/R.framework/Resources'
# %%
import numpy as np
import pandas as pd
from rpy2 import robjects
import statsmodels.api as sm
# %% [markdown]
# # Load results for each cohort
# %% [markdown]
# ### DIGPD
# %%
df_digpd = pd.read_csv(
'Revision/Johann/With int... |
afacd9f4508d44e01fd82b51686ae1e148401da6a2f56dfe7c569b2f01444a07 | Jupyter | 16,790 | 465 | # %%
import pandas as pd
import scipy.io as io
import matplotlib.pyplot as plt
import matplotlib.pylab as pylab
import numpy as np
from matplotlib import gridspec
import seaborn as sns
import datetime
from datetime import datetime, timedelta
import pickle
import glob
# %%
import matplotlib.dates as dates
from scipy im... |
90658d6fa8663ddaf5705738339c3237cfe7687f2976d43e58bf90c4803c567d | Jupyter | 16,951 | 501 | # %%
import torch
import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
from scipy.stats import linregress
import tqdm
import pandas as pd
import sys
sys.path.append('./../..')
from dynaphos import utils
# %%
# plt.rcParams.update({
# 'axes.labelsize': 'large',
# 'font.size':... |
598c71432096b96a79b91235cbbc05bd2cae9efe93f578c28f6002696385304c | Jupyter | 16,984 | 369 | # %% [markdown]
# # How to prepare your own dataset for training an Object Detector
#
# %% [markdown]
# ## Gathering data
# First of all you need data. To train a robust classifier, we need a lot of pictures which should differ a lot from each other. So they should have different backgrounds, random object, and varyi... |
90e94a08ff09d5a40584c3bb2f10fac382cb3c5bf441bbfa0cbe58fef0184588 | Jupyter | 17,108 | 232 | # %% [markdown]
# # SHIP: network building
#
# This tutorial will cover the essentials for building a network object in SHIP.
#
# ### Note on SHIP:
# Spiking (neural network) Hardware In PyTorch is an emulation platform intended for hardware-based SNNs, based on compact models to mimic the behavior and functionalitit... |
81d77d6fa0aa33fcbac87a1947fba4e470fefda1977518568b2096e908675627 | Jupyter | 17,165 | 413 | # %%
# predicts expression of the test data with the trained and random networks
# predicts expression of MelBrainSys data
# creates correlation plots: predicted and original expression:
# Figure-2-gene-expr-prediction-correl.png
# %%
library(ggplot2)
library(gridExtra)
library(parallel)
# %%
basePath = "/data/bcu_... |
9104edb89a70646a8b006442ed8fabb62b3dacd5caaf2024e679a676211aab6f | Jupyter | 17,273 | 481 | # %% [markdown]
# # FederatedRuntime 101: Quickstart with MNIST
# %% [markdown]
# Welcome to the first **FederatedRuntime** Tutorial !
# This tutorial demonstrates how to deploy Federated-Learning experiment based on workflow interface on a distributed computing infrastructure.
#
# Data scientists often start by dev... |
bbe0991a50252cec6ab2fdbb87424dd266100fabd85add1ea5167a8cf700537c | Jupyter | 17,279 | 544 | # %%
# %% [markdown]
# Todos:
#
# 1) dataset: splitting by folds, and traiing on particular folds only?
# vs.
# training on all data, irespective of fold. get the same training dataset as the pevious 2 papers. take the redo-database 19436 structures for training of which 300 are held out.
#
# - sample sequences ran... |
20172fe339cc5e3f32595fbed2f7e8341103b49a4a776b7773853de43e3ce15e | Jupyter | 17,361 | 368 | # %%
%matplotlib inline
# %% [markdown]
# # Import modules
# %%
from skan.pre import threshold
from tqdm.notebook import tqdm
from skimage.io import imread
import os, re, glob
from scipy.spatial.distance import cdist
import pandas as pd
from math import sqrt
import numpy as np
import skimage.io
import matplotlib
impo... |
ef00b35a02c7b9eaa71c613cd512c6703fe5f10f6d452bc0bf0d3148dd740cbb | Jupyter | 17,365 | 484 | # %%
# creates
# - connectivity table for paper - SupplTable-S5-connectivity-stats.xls
# - DotPlot of connections - SupplFigure-S5-source-target-interactions.png
# - known melanoma driver gene ranking in networks - SupplTable-S6-key-driver-ranks.xls
# %%
library(ggplot2)
library(gridExtra)
library(parallel)
librar... |
00d52fc986c81e0747fb581287dade9fa7afcee1598daa25d01de405bf937a83 | Jupyter | 17,385 | 495 | # %%
# %%
# %%
# creates
# - connectivity table for paper - SupplTable-S5-connectivity-stats.xls
# - DotPlot of connections - SupplFigure-S3-source-target-interactions.png
# - known melanoma driver gene ranking in networks - SupplTable-S6-key-driver-ranks.xls
# %%
library(ggplot2)
library(gridExtra)
library(... |
2c1ea9481f5f00b570b0814c3561c46a0dee15acd04b98c55ba3a36ed1f3b28f | Jupyter | 17,486 | 506 | # %%
# 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 numpy ... |
96a989d2e758d5d004606b19c44935650ffcbcb5089711a6145d5dbbe0246890 | Jupyter | 17,703 | 438 | # %% [markdown]
# # Workflow Interface
# ## Fine-tuning neural-chat-7b-v3 using Intel(R) Extension for Transformers and OpenFL
# [](https://colab.research.google.com/github/intel/openfl/blob/develop/openfl-tutorials/experimental/workflow/LLM/neur... |
47cf3ac6164a22afa84bfc5f6e2d5e8726229adec053b6e681b74f5f63f10f98 | Jupyter | 17,760 | 460 | # %% [markdown]
# # Computing with triangular shapes in Kendall framework
# %% [markdown]
# Lead author: Elodie Maignant.
#
# David G. Kendall first took interest into triangular shapes while studying random alignments of points in archeology and related fields [[KK1980]](#References). Following this statistical stud... |
b1f0646039bda246af6fb264f959fe0bc695607eee5c602762f431ad5a650e01 | Jupyter | 17,773 | 372 | # %% [markdown]
# # Clean Re-run — Notebook 2 of 3: D-Fine-Nano (official repo)
#
# **Run on an NVIDIA A100** (same as Notebook 1). D-Fine uses its own repo + COCO format, so it can't share
# the Ultralytics environment — run it in a **separate Colab session** from Notebook 1.
#
# Produces D-Fine's Phase-1 metrics, p... |
d7083f8a8323484d691513906210f1e75937ce66d1825f8f0b10fe66aaa89f1d | Jupyter | 17,777 | 571 | # %%
import numpy as np
import os
import pandas as pd
from scipy.stats import median_abs_deviation, chi2
import seaborn as sns
import statsmodels.api as sm
# %% [markdown]
# # Data preprocessing
# %%
df_digpd = pd.read_excel('DIGPD data/dig_pd_2019_11.xlsx')
df_pca_digpd = pd.read_csv('Ancestry/Genetic ancestry (pri... |
115d2b01a40e95ec5c4b9358c2ac7f3b6b852e7a6d5f11baf9ccbf565e1261df | Jupyter | 18,014 | 511 | # %% [markdown]
# ### This notebook is for reproducing a part of the paper results' subsection "Multi-scale hierarchical $\gamma$-fused structure-function modular organization" and generate the panels of the figures S2 and S3. The objective of the notebook is to represent the tree of the optimal parcellation and compu... |
39be05f432a59622b6a49551c7c0d6d10136629e065450f4ba5d4835f646552a | Jupyter | 18,020 | 627 | # %%
%load_ext rpy2.ipython
# %%
# %%R
# install.packages('MuMIn', repos='http://cran.us.r-project.org')
# %%
import pandas as pd
import numpy as np
import pickle
import matplotlib.pyplot as plt
import matplotlib.pylab as pylab
from matplotlib import gridspec
import seaborn as sns
import pingouin as pg
import copy
im... |
e4f9a722b7fb1dd66402ceee8d8640c0c4c7e8f08727a7c88215b23776817bc0 | Jupyter | 18,062 | 589 | # %% [markdown]
# # Implement your own Riemannian Geometry
# %% [markdown]
# Lead author: Nina Miolane.
#
# Geomstats provides several Riemannian manifolds in its `geometry` folder. Yet, the manifold that you are interested in might not be available there.
#
# This notebook shows how to use Riemannian geometry on an... |
1ea59d53baf0be15a7a74e897119ee6c7644b9617d971865e807dc22b1dd89a8 | Jupyter | 18,084 | 405 | # %% [markdown]
# # Workflow Interface 104: MNIST XPU
# [](https://colab.research.google.com/github/intel/openfl/blob/develop/openfl-tutorials/experimental/workflow/104_MNIST_XPU.ipynb)
# %% [markdown]
# Welcome to the OpenFL Experimental Workfl... |
37695354502ffd3ed64a8558d4407bc668a63424d694b5b703b786ca9b8fb73e | Jupyter | 18,148 | 410 | # %% [markdown]
# # Interactive notebook on how to build and examine neurodynamic models in PyRates:
#
# ## Setting up a Jansen-Rit-like cortical microcircuit using the Montbrio population model
#
# In this example, it is your task to use PyRates to build a new model of a cortical microcircuit, fusing the architectu... |
538b5a99e38be520dfc8391daa5623edf675d6b19847e33f25a7a0ed0e4d9aca | Jupyter | 18,159 | 480 | # %%
# make figures for Thalamic model
# %%
import numpy as np
import scipy as sp
from scipy import stats
import matplotlib as mpl
import matplotlib.pyplot as plt
import pandas as pd
import brian2
import pickle
import copy
mpl.rcParams['pdf.fonttype'] = 42
mpl.rcParams['ps.fonttype'] = 42
mpl.rcParams['svg.fonttyp... |
eede07d65a461b3ae554f3992c3db2230028cfccf48ff1281d099715122a2635 | Jupyter | 18,261 | 554 | # %%
# gvp: implement gvp on pdb dataset for residue analysis
# %%
from constants import AA3_TO_AA1
# %%
from pdbHelper import get_atoms_neighbor
# %%
#
import numpy as np
import torch
import torch.nn as nn
from gvp import GVP, GVPConvLayer, LayerNorm
# %%
GVP
# %%
torch.cuda.is_available()
# %% [markdown]
# # ... |
255049d244601327f139baa3ac68eaf12feb6810d2baf3c9cdfa60462fa414e6 | Jupyter | 18,310 | 562 | # %% [markdown]
# ## SimpleForestPlot
# %%
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import statsmodels.api as sm
import statsmodels.formula.api as smf
import myforestplot as mfp
%load_ext autoreload
%autoreload 2
%load_ext watermark
%watermark -n -u -v -iv -w -p gra... |
e4fcc4dbc7dce4fc4f6115450b4f07587b24a096c2c4efb59a66a07299c5b9ca | Jupyter | 18,336 | 593 | # %% [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... |
fc7bf33ce22d8b224b44900dfc588718727be420478b122c811be33a9f059ff5 | Jupyter | 18,386 | 670 | # %%
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 ivac
import extq... |
472010a031f54b05f9a0183c376c5c94063ecc6567c918b93bca8f63e4ca7cd5 | Jupyter | 18,438 | 466 | # %% [markdown]
# # **SKIN DISEASE PREDICTION MODEL**
# ### *version4*
# %% [markdown]
# ## Importing necessary modules and libraries
# %%
%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.see... |
27734d3bb84d569e3e1f03321f54d4b7eb2fd396601ef9217df7e42102fe9dd2 | Jupyter | 18,441 | 285 | # %% [markdown]
# # SHIP: creating a model for a component
#
# This section focuses on the definition of a component's model.
#
# ### Note on SHIP:
# Spiking (neural network) Hardware In PyTorch is an emulation platform intended for hardware-based SNNs, based on compact models to mimic the behavior and functionalitit... |
8e049e64f5057591788306b4db0fa02ccdd4cc148c953cf29d9ae63982eac1aa | Jupyter | 18,567 | 446 | # %% [markdown]
# # Shape analysis of curves with the Square Root Velocity metric
# %% [markdown]
# Lead author: Alice Le Brigant.
#
# In this notebook, we demonstrate how to compute distances between shapes of curves in $\mathbb R^2$ and $\mathbb R^3$ using the [`discrete_curves.py`](https://github.com/geomstats/geo... |
f4b46a59376e71dc4f2bc257bcc1e41db36ce7a1d7145b102ca5ec4b9d060fe3 | Jupyter | 18,743 | 423 | # %% [markdown]
# # Interactive notebook on how to build and examine neurodynamic models in PyRates:
#
# ## Setting up a Jansen-Rit-like cortical microcircuit using the Montbrio population model
#
# In this example, it is your task to use PyRates to build a new model of a cortical microcircuit, fusing the architectu... |
fcfb4c60ad9701b1632c6c447dd5e2fabad1eef5e2c22056c0c387e566820694 | Jupyter | 18,821 | 538 | # %%
import os, pandas as pd, numpy as np, json, matplotlib.pyplot as plt
from natsort import natsorted
from tqdm import tqdm
os.chdir("./src/lib/hand_predictor")
from utils.hand.api import ffmpeg4format, mp_kpts_generator, mp_kpts_preprocessing, hand_pos_inference, model_pred_severity
from utils.hand.api import hand_... |
71828f8f98699581701b61da124e51ad3aff1c6c0c9a48f37a036b56bbe725a3 | Jupyter | 18,854 | 568 | # %%
import os
os.chdir('../')
import DeepPurpose.CompoundPred as models
from DeepPurpose.utils import *
from DeepPurpose.dataset import *
from sklearn.metrics import mean_squared_error, roc_auc_score, average_precision_score, f1_score
# %%
# %%
def run_Morgan(fold_n, balanced):
train = pd.read_csv('./aic... |
c91d09ad82c77f5c71ed5e72524783e2345f59984e205de10459e44f42bdf14a | Jupyter | 18,949 | 746 | # %% [markdown]
# # EDA and data split for Propedia
#
# Positive data downloaded from http://bioinfo.dcc.ufmg.br/propedia/download
# %%
import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
# %%
DATA_DIR = '/home/ec2-user/SageMaker/efs/data/Propedia'
# %%
os.path.di... |
55f6ff3117aa51a37b56052d19f02252148fb503711c2f25d5dbb64bd94d51b5 | Jupyter | 19,048 | 578 | # %%
import copy
import glob
import os
import sys
from typing import Any, Callable, NamedTuple, Sequence
import dill as pickle
# %%
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
# %%
import flax
import flax.linen as nn
import jax
import matplotlib as mpl
import matplotlib.pyplot as plt
import mdtraj as md
import numba as... |
db35906795fbb332d2fc1ea3af4ad0497b3fcd3c611e4443e17b9752ccd3bb57 | Jupyter | 19,143 | 605 | # %%
import numpy as np
import pandas as pd
import leidenalg
import scipy.sparse as sparse
from scipy.io import mmread
from scipy.stats import pearsonr, pointbiserialr
from anndata import AnnData
import anndata
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
from matpl... |
7ca6c917fa1d93225be1401ba71d1fd82fdfc90a43745f753086931836fd4b3f | Jupyter | 19,308 | 533 | # %%
from google.colab import drive
drive.mount('/content/drive')
# %%
# load data
import numpy as np
from numpy import genfromtxt
path = '/content/drive/MyDrive/DeepOmics/Project1/Proposed/Simulations/Linear/S1/'
X1Train = genfromtxt(path + 'X1Train.csv', delimiter=',')
X1Test = genfromtxt(path + 'X1Test.csv', delimi... |
9bbe4c747d3beac0fb5ef2849ec278ba455f9d1faa7a296e2d899840c4b45912 | Jupyter | 19,562 | 616 | # %%
import numpy as np
import os
import pandas as pd
from scipy.stats import median_abs_deviation, chi2
import seaborn as sns
import statsmodels.api as sm
# %% [markdown]
# # Data preprocessing
# %%
df_iceberg = pd.read_excel('ICEBERG data/export_iceberg_31052021.xlsx', sheet_name='Neuropsy')
df_pca_iceberg = pd.re... |
1da8c2a475c1ea704c045b1e08ec4355f0e83f038fd2730a925a6831fb890096 | Jupyter | 19,630 | 542 | # %% [markdown]
# # Towards biologically plausible phosphene simulation for the differentiable optimization of visual cortical prostheses
# %% [markdown]
# This notebook showcases some of the functionalities and applications of the phosphene simulator
# %% [markdown]
# ## 0. Imports
# %% [markdown]
# ##### Import s... |
711f65829d994a4d22bbad67892bec9ea8833eb5fea5f671b9441297cb9928b8 | Jupyter | 19,648 | 558 | # %% [markdown]
# # Analysis of prior literature
# %% [markdown]
# Here, we look into CCAs reported in the literature. We analyze sample-size dependence of reported association strengths and estimate the corresopnding weight errors.
# %% [markdown]
# ## Setup
# %%
import numpy as np
import pandas as pd
import xarray... |
d258d384677f598f01d45a1a9ed9532aaec8ea66ab3e7edf44607033c38b280d | Jupyter | 19,701 | 413 | # %%
import numpy as np
import pickle
import os.path
from os.path import isfile, join
from os import path,listdir
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from scipy import stats
import matplotlib.patches as patches
from pathlib import Path
from tifffile import imread, imsave
!pip insta... |
05ffb0c76dc9dc5330cdaafe27a8029625d25ec660e9a73ca5f569baa64a17db | Jupyter | 19,855 | 612 | # %%
import numpy as np
from matplotlib import pyplot as plt
from scipy.optimize import curve_fit
from scipy.stats import t, sem
from scipy.stats import linregress
import scipy.signal
from tqdm.autonotebook import tqdm as notebook_tqdm
import pandas as pd
import seaborn as sns
import sys
sys.path.append('../..')
imp... |
9e4a42e0233ac9db6642235fe8ae50e54be32cf1de77f7f1d119a1fd61797e00 | Jupyter | 19,991 | 574 | # %%
# run locally on mac
# or on O2 with dd_tf2_mamba
# %% [markdown]
# # to sample from gvp
# %% [markdown]
# # 1 sample sitewise from gvp
# %%
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import random
import sys
import importlib
sys.path.append('/n/groups/marks/users/david/github/pare... |
76fa79b6e944ba1c6421cf9117bec24cbc7152cc8d21e0cd039bd4a19c704e5f | Jupyter | 20,122 | 716 | # %%
import numpy as np
import os, sys
sys.path.append(os.getcwd()+"/..")
from rnn_scripts.train import *
from rnn_scripts.utils import *
from rnn_scripts.bifurcations import *
from tasks.seqDS import *
from scipy.cluster.vq import kmeans2
import matplotlib.pyplot as plt
%matplotlib inline
import matplotlib.patches as... |
df541053d8a931d04060eadae7fb24f4fad48eb0d9a2ea1409c3740ed50662ae | Jupyter | 20,199 | 525 | # %% [markdown]
# # Federated Fine-Tuning of Phi-4 Using OpenFL
# %% [markdown]
#
# In this tutorial, we demonstrate how to fine-tune Microsoft's Phi-4 model in a federated learning workflow.
#
# We will fine-tune **Microsoft's [Phi4](https://huggingface.co/microsoft/phi-4)** model using a diverse dataset such as [M... |
ffa668de3d664330884fd482b4f4c1e640343b581b45fa9bce60624c08a0e389 | Jupyter | 20,237 | 680 | # %%
%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
import cloudvolume as cv
import matplotlib.pylab as plt
import torch
import tq... |
6743ba8f734cc93231ac50285f1f33ab81d40abc38e982be849b4c4b3d6b598a | Jupyter | 20,362 | 554 | # %%
# Fig S2-S3-S4, 4
# %% [markdown]
# # Datasets Class and Data Analysis
# %% [markdown]
# In our work we investigated 5 datasets that contain several types of movements and diverse brain areas recorded (for more information about datasets check our paper or table "**Datasets Used in Study**" in github repositor... |
d47546732bf8ebf60400f443fedff7963014ad89bc0e46424f9c05d7b488f3c4 | Jupyter | 20,383 | 526 | # %% [markdown]
# # setup
# %%
%matplotlib inline
import eelbrain as eel
import numpy as np
import scipy, pathlib, importlib, mne, time, os, statsmodels, statsmodels.stats.multitest
from tqdm import tqdm
import matplotlib.pyplot as plt
import preprocessing as pre
import models as md
import plotting
from pathnames impo... |
5e98a79b33e12a155d68da259da18645cd02516422416c68af6d637e82ef9941 | Jupyter | 20,395 | 664 | # %% [markdown]
# # Weight errors
# %% [markdown]
# How much do estimated CCA and PLS weights vary across repetitions, depending on sample size?
# %% [markdown]
# ## Setup
# %%
import warnings
from matplotlib import MatplotlibDeprecationWarning
warnings.simplefilter('ignore', MatplotlibDeprecationWarning)
warnings.f... |
a2a1da867dfc9e15af72ff14c734489faf6295c5e054a24f225fc3c12b341a9c | Jupyter | 20,428 | 491 | # %%
### 0.0 loading package
import numpy as np
import pandas as pd
#import modin.pandas as modin_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 ... |
026520a6c722a55a86d87a20f818500227deb7136adcbfd447d74edee7a7bc60 | Jupyter | 20,470 | 607 | # %%
BASE_PATH = '../../dynaphos-core/'
import os
import sys
sys.path.append(BASE_PATH)
import logging
import dynaphos
import torch
import matplotlib.pyplot as plt
import numpy as np
import cv2
import ipywidgets as widgets
from dynaphos import utils
from dynaphos.cortex_models import get_visual_field_coordinates_fr... |
24cc15cab58a21e2d265c1a7ffb0fe29c09b77508f058d8c55f042ef414c6791 | Jupyter | 20,484 | 641 | # %%
%matplotlib inline
%load_ext autoreload
%autoreload 2
import matplotlib
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 json
import sys
import operator
from scipy import ndimage
from pdb import... |
eacb063facf59cee6607d0efc3fd24e6559d2c94090580f3ca75a48a236397e8 | Jupyter | 20,563 | 474 | # %% [markdown]
# # Hyperbolic Embedding of Graphs and Clustering
# %% [markdown]
# Lead authors: Thomas Gerald and Hadi Zaatiti.
#
# ## Introduction
# %% [markdown]
# From social networks to parse trees, knowledge graphs to protein interaction networks, Graph-Structured Data is endemic to a wide variety of natural ... |
6ea28175b67d819bcddf4db854e6829805d70a5d652b0f2d18903f8c3dba4815 | Jupyter | 20,747 | 537 | # %% [markdown]
# # **SKIN DISEASE PREDICTION MODEL**
# ### *version4*
# %% [markdown]
# ## Importing necessary modules and libraries
# %%
%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.see... |
45be5eaeb73142a51155c2ed000d82d98edf291499523ba911818bf6d1209b41 | Jupyter | 20,775 | 517 | # %%
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
import astropy.stats.circstats as astropy_circstats
sys.path.insert(0, CODE_RE... |
d1fbebdbf8341bf14d0b3ab0c9be78bdf2de0b0b50eca2b8cbc9e578be411458 | Jupyter | 20,807 | 764 | # %% [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 example GRanges and data.table objects
# %%
## insta... |
fe166b023306e437b5a6be483e1aa417926b89882da94bc979ea80726bae7486 | Jupyter | 20,823 | 662 | # %%
import numpy as np
import pandas as pd
import leidenalg
import scipy.sparse as sparse
from scipy.io import mmread
from scipy.stats import pearsonr, pointbiserialr
from anndata import AnnData
import anndata
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
from matpl... |
e52de91dc67913cef3f2f1f4d96c568cffccd3f5b5750fbbf9d3b3ecb5c921e7 | Jupyter | 20,888 | 675 | # %%
%matplotlib inline
import sys
import os
import shutil
import time
import traceback
from pathlib import Path
import h5py
import json
import pickle
from types import SimpleNamespace
from importlib import reload
from scipy import signal, stats, spatial, ndimage
from scipy.interpolate import interp1d
from sklearn im... |
53bb903186141f01a576f6784bfb7219f4e70a5547bdbe2707a9d789a0ca46dd | Jupyter | 20,898 | 607 | # %% [markdown]
# # 1. Data organization
# %%
!pip install numpy matplotlib pandas pymare myforestplot pypcurve scipy
# %%
import numpy as np
import matplotlib.pyplot as plt
import pickle, os, gzip, pandas as pd
data_dir = '../Data'
# %% [markdown]
# ## 1.1 Study characteristics
# %%
df_study = pd.read_excel(f'{dat... |
14c53f181eeee29e6721d399f47bcfb6f1ad58059c79a71f20a5e38f0f608fb4 | Jupyter | 20,959 | 631 | # %%
BASE_PATH = '../../../dynaphos-core/'
import os
import sys
sys.path.append(BASE_PATH)
import logging
import dynaphos
import torch
import matplotlib.pyplot as plt
import numpy as np
import cv2
import ipywidgets as widgets
from dynaphos import utils
from dynaphos.cortex_models import get_visual_field_coordinate... |
aa61cdf68db6ea9053a31afdd647281bdf5e404aac85b1b6058de063ba9dabf9 | Jupyter | 20,998 | 866 | # %%
%matplotlib inline
import sys
import os
import shutil
import time
import traceback
from pathlib import Path
import h5py
import json
import pickle
from types import SimpleNamespace
from scipy import signal, stats, spatial
from scipy.interpolate import interp1d
from sklearn import linear_model as lm
import nump... |
031ba6fe4c337360d7e0bae92b9a03b2fc7593f921fdf5aaf35b580cebe79c9a | Jupyter | 21,689 | 665 | # %%
import pandas as pd
import scipy.io as io
import matplotlib.pyplot as plt
import matplotlib.pylab as pylab
import numpy as np
from matplotlib import gridspec
import seaborn as sns
import pingouin as pg
import copy
# %%
from scipy import stats
from scipy import sparse
# %%
from functools import partial
# %%
impo... |
caeb1ad5ef875892c838d20286960fbed2a8a77bafd180a52942c0383358af88 | Jupyter | 21,767 | 587 | # %% [markdown]
# ## Forking
# ChromaDB now supports forking. Below is an example using forking to chunk and embed a github repo, fork off of the collection for a new github branch, and apply diffs to the new branch.
# %%
! pip install chromadb --quiet
! pip install tree-sitter --quiet
! pip install numpy --quiet
! pi... |
40cf2ba94d3b0a6d1b9b5943d8fc929b990dbac36f2e98046b52e9c1f15ec5cb | Jupyter | 22,247 | 434 | # %%
## 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... |
71ebede7f59c243434f16b16a5844e5c19e4cedcf57ede8edcfcf3298dbdcc69 | Jupyter | 22,309 | 913 | # %% [markdown]
# ## second order invariance
#
# Figure 1 and 2
# %%
import matplotlib
from tqdm import tqdm
import secorder as sec
import numpy as np
import matplotlib.pyplot as plt
import scipy as scp
import scipy.stats as st
import torch
from torch import optim
np.set_printoptions(precision=4)
import pandas as pd... |
a0bc6c490148d758c4c036ee9a45aad185eba15ddc3cde6752e4783dcb768e39 | Jupyter | 22,344 | 686 | # %%
import pandas as pd
import scipy.io as io
import matplotlib.pyplot as plt
import matplotlib.pylab as pylab
import numpy as np
from matplotlib import gridspec
import seaborn as sns
import pingouin as pg
import copy
# %%
from scipy import stats
from scipy import sparse
# %%
from functools import partial
# %%
impo... |
bbcdb2cdb9f3713d9066bf068a73823053c99456b457a8cdbb62812b0ad7e029 | Jupyter | 22,463 | 643 | # %%
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 wind_task as ... |
3f8090c57ddc3379874b13c13a52fbcacf7440c7d15f9d08c58ab82e7769be9d | Jupyter | 22,496 | 729 | # %%
%load_ext rpy2.ipython
# %%
import pandas as pd
import scipy.io as io
import matplotlib.pyplot as plt
import matplotlib.pylab as pylab
import numpy as np
from matplotlib import gridspec
import seaborn as sns
from datetime import datetime, timedelta
import pickle
import glob
from tabulate import tabulate
from tqdm... |
ce2546e35fe922947c75d911edf689df764ea04901bff39de39c37f9760d48a3 | Jupyter | 22,539 | 638 | # %%
# extend the vanilla gvp with partial charge information, hydrogen atoms.
# %% [markdown]
# # get atom3d res dataset
# %%
import gvp
from atom3d.datasets import LMDBDataset
import torch_geometric
from functools import partial
import gvp.atom3d
import torch.nn as nn
import tqdm, torch, time, os
import numpy as... |
d6ec8d23713002789251b1ab91dfdc5e5ef2c578d1cd2095eb8b9a0d070747c1 | Jupyter | 22,654 | 609 | # %%
import os
import nibabel as nib
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import time
import pickle
import random
from scipy import ndimage
from skimage.filters import threshold_otsu
from skimage.measure import label, regionprops
from skimage import transform
from skimage.transform ... |
a653c4514a169750f4a071ac2d199b0428af848eb45af49f77cff8e110b65b0a | Jupyter | 22,815 | 501 | # %% [markdown]
# # Workflow Interface 103 - Cyclic Institutional Incremental Learning
# [](https://colab.research.google.com/github/intel/openfl/blob/develop/openfl-tutorials/experimental/workflow/103_Cyclic_Institutional_Incremental_Learning.ip... |
d026d901ca1916f2b252ffdaeb433924bfa9ef376d6ba900cc144fd39f3ed8ed | Jupyter | 23,224 | 567 | # %% [markdown]
# # Hierarchical Classification with COVID-CXNet
#
# %% [markdown]
# ## Importing libraries and run custom functions
# %%
import glob, numpy as np, cv2, matplotlib.pyplot as plt, tensorflow as tf, copy
from tensorflow.keras.preprocessing.image import load_img, img_to_array, ImageDataGenerator
from t... |
58357aa5afc9ac100f0eb8269ef7f44ca666e1e66ae84049b67eb7215aceb879 | Jupyter | 23,254 | 469 | # %%
## 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... |
e9930b6def99c0e808564b624a16043ef030f07964244cbb7e09cb914d6b7b81 | Jupyter | 23,351 | 599 | # %%
# Copyright (C) 2022-2024 TU Darmstadt
# SPDX-License-Identifier: Apache-2.0
# -----------------------------------------------------------
# Primary author: Phillip Rieger <phillip.rieger@trust.tu-darmstadt.de>
# Co-authored-by: Torsten Krauss <torsten.krauss@uni-wuerzburg.de>
# ----------------------------------... |
2c6f29b196a0da52e68d4803eb1afda36cac038e2cd8f73ef3a0bd98e03ff7d2 | Jupyter | 23,388 | 694 | # %% [markdown]
# # Imagenet results
#
# + classification performance metrics
# + AUROC
# + AUPRC
# + Accuracy
# + Sensitivity
# + specificity
# %%
import os
import pickle
import time
import gc
import torch
import torch.nn as nn
import torchvision.models as models
import torchmetrics
from t... |
b78f7f30215ae1f10b9d399857b32041475cc3007236d2676b3f64ec55379074 | Jupyter | 23,645 | 653 | # %%
# Importing necessary libraries
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.utils import Sequence
from keras_nlp.layers imp... |
f1544834b6e48e98f826f0b053266c6f70173b0a2d33f33fcf19d447ae3a5fcc | Jupyter | 23,679 | 511 | # %% [markdown]
# # SHIP: inference
#
# This part of the tutorial explains in more details how to use SHIP to perform the system simulation after building a network.
#
# ### Note on SHIP:
# Spiking (neural network) Hardware In PyTorch is an emulation platform intended for hardware-based SNNs, based on compact models ... |
9b831c3fd599aff7757626decef650e233f8f9748a12023131c40e5fda3026e6 | Jupyter | 23,739 | 565 | # %%
# produces
# Figure 5: circos plots of source and target genes
# SupplTable S10 target genes mean impact and expr. ratios per group
# SupplFigure S8 target gene candidates scatterplot ompact vs. expression
# %%
library(reshape2)
library(ggplot2)
library(circlize)
library(scales) # for alpha semi-transparency
libr... |
dd0d4baffbc13f8dd4f540c0266d4f24bc7b0bdb666ee96abdb39f820d55e379 | Jupyter | 23,746 | 568 | # %%
# produces
# Figure 5: circos plots of source and target genes
# SupplTable S10 target genes mean impact and expr. ratios per group
# SupplFigure S6 target gene candidates scatterplot ompact vs. expression
# %%
# %%
library(reshape2)
library(ggplot2)
library(circlize)
library(scales) # for alpha semi-transparen... |
7c195bc43c2655751395f447aec253f717cd9bb0c8c596f6b0b8dbf0eaaa7c41 | Jupyter | 23,792 | 573 | # %% [markdown]
# <a href="https://colab.research.google.com/github/armiro/COVID-CXNet/blob/master/multiclass_covid_cxnet.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Multiclass Classification with COVID-CXNet
#
# %% [markdo... |
51a067fbb26f1ebba737e7bd54798557801831964a279c8af82ef2e90f815bac | Jupyter | 23,893 | 704 | # %%
%load_ext rpy2.ipython
# %%
import pandas as pd
import scipy.io as io
import matplotlib.pyplot as plt
import matplotlib.pylab as pylab
import numpy as np
from matplotlib import gridspec
import seaborn as sns
import datetime
from datetime import datetime, timedelta
import pickle
import itertools
import glob
# %%
... |
68263f389a2435fd6f1523f7e04d9a253b8952c0ff43700bae78c00517338b16 | Jupyter | 23,905 | 704 | # %%
%load_ext rpy2.ipython
# %%
import pandas as pd
import scipy.io as io
import matplotlib.pyplot as plt
import matplotlib.pylab as pylab
import numpy as np
from matplotlib import gridspec
import seaborn as sns
import datetime
from datetime import datetime, timedelta
import pickle
import itertools
import glob
# %%
... |
2b4cd98d468d7b5088828c7b48ea281fdd6f3e28c56317a6df00b36c8784c2ba | Jupyter | 24,123 | 986 | # %% [markdown]
# # EDA and split for ProtCID data
# %%
import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
# %%
DATA_DIR = '/home/ec2-user/SageMaker/efs/data/ProtCID/JaredJanssen_Benchmark'
# %%
!ls -l {DATA_DIR}
# %%
# !ls -l {DATA_DIR}/non-physiol
# %%
# !wc -l... |
ffd73559b8c27e503f1f1e353f6fc1df48dd51dc71e5225cbaeccac307bed865 | Jupyter | 24,438 | 644 | # %% [markdown]
# # Workflow Interface 401: Synthetic non-IID Dataset with FedProx Optimizer
# [](https://colab.research.google.com/github/intel/openfl/blob/develop/openfl-tutorials/experimental/workflow/401_FedProx_with_Synthetic_nonIID.ipynb)
... |
05380bd289f7ef373dcf33ac0e372a50651a213eedc9d1765226c12fddd3b6a8 | Jupyter | 24,572 | 626 | # %%
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from matplotlib import cm
import yaml
import sys
sys.path.append('./../..')
import os
BASE_PATH = './../dynaphos-core/'
sys.path.append(BASE_PATH)
from dynaphos import utils
import pandas as pd
import seaborn as sns
from scipy.optimize impo... |
1527b77d10851f8821573364f24a43cea4573ecde6235fcbb954714c11c7d2e8 | Jupyter | 24,993 | 687 | # %% [markdown]
# # Secure Aggregation with MNIST
# %% [markdown]
# ### Introduction
#
# In Federated Learning (FL), Secure Aggregation (SecAgg) is a technique that allows the participants to collaborate on the central model without revealing their individual contributions (local model updates). The goal is to allow ... |
847cbbea1155dd6dea72e1afe2bceef8f82a8aaf5dd41217c1a1f67521ef0e7e | Jupyter | 25,008 | 532 | # %%
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 random
from statsmodels.stats.multitest import fdrcorrection
import copy
from collections import Counter
im... |
66c296269506cee85cec4c4df634fab5ee6407d5d6f417648aa75121f99bcc4f | Jupyter | 25,400 | 546 | # %% [markdown]
# # Optimization of Sao Paulo traffic
# %% [markdown]
# Lead author: Jules Deschamps.
#
# This notebook presents a simple use case of *information geometry*, in the context of *traffic optimization* in Sao Paulo.
# We rely on a dataset listing all traffic jams in Sao Paulo for the past two decades (th... |
c21a64670954698d17fc19580cb091fffc336ab0fe2473940033a06f16afdf3d | Jupyter | 25,764 | 621 | # %% [markdown]
# ### 1: Import required packages & functions <a class="anchor" id="chapter1"></a>
# %%
# clear all variables
for i in list(globals().keys()):
if(i[0] != '_'):
exec('del {}'.format(i))
#suppress future warnings -- not really a good idea
from warnings import simplefilter
# ignore all futur... |
bc4b63a428395a03b4142c0ca31e4ada9fffd31c2002a3890e7a66fa4fcc44e6 | Jupyter | 25,808 | 446 | # %% [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]
# Note: Before you start to read this notebook, run the following code. This will impo... |
934444257e1f2e487478e3b412e58d9a84e402018e1bd0c1cdee9708a174901b | Jupyter | 26,075 | 724 | # %% [markdown]
# # notebook to visualize evaluating sampled antitoxin and GFP sequences
# %%
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from os import listdir
import os
import math
from collections import Counter
from scipy.sparse.linalg import lsmr
from copy import deepcopy
import tensorf... |
2a635536f41c3af0feebc2903cad78684a418e5ca5437f828bbdc7d931523a83 | Jupyter | 26,328 | 606 | # %%
# predicts expression of the test data with the trained and random networks
# predicts expression of MelBrainSys data
# creates correlation plots of these predicted and original expression:
# Figure-2-gene-expr-prediction-correl.png
# %%
library(ggplot2)
library(gridExtra)
library(parallel)
# %%
basePath = "/d... |
02674902596d0846bf17a39d218209d98e3cfd38f34c09e30c9e3f1e9e7af010 | Jupyter | 26,721 | 722 | # %% [markdown]
# # This is a usage example of simp_regress_test.py in pyhddmjags
#
# It follows a script in the [pyhddmjags](https://github.com/mdnunez/pyhddmjags) repository by Michael D. Nunez
#
# This script will run nicely on [Google Colab](https://colab.research.google.com/) without needing to install anything ... |
e33c3b969f04a7352edc81efef748c0117400c65ea5ea98dcc396c4cb7da42a6 | Jupyter | 26,763 | 813 | # %% [markdown]
# # creating predictions for a position given a pdb structure
#
# from https://arxiv.org/pdf/2012.04035.pdf
#
# Environments are extracted from a non-redundant subset of high-resolution structures from the PDB.
# Specifically, we use only X-ray structures with resolution <3.0 Å, and enforce a 60% sequ... |
65822b5e495ca53f4e43fe9f2427a71e52dac3b4005262361d94f827bf9f0f2c | Jupyter | 27,101 | 726 | # %% [markdown]
# # Workflow Interface 301: Watermarking
#
# [](https://colab.research.google.com/github/intel/openfl/blob/develop/openfl-tutorials/experimental/workflow/301_MNIST_Watermarking.ipynb)
# %% [markdown]
# This OpenFL Workflow Inter... |
d178376d1baee99ee715687f4ee6115b50b57f502028b3109762dc996c270071 | Jupyter | 27,218 | 746 | # %% [markdown]
# # Workflow Interface 402: Aggregator validation with a watermark dataset using Ray
#
# [](https://colab.research.google.com/github/intel/openfl/blob/develop/openfl-tutorials/experimental/workflow/402_MNIST_Aggregator_Validation... |
b7314f5af43941c861e831f2d1131a3e730120b60e8b2c6330e757eb817c016d | Jupyter | 27,296 | 735 | # %% [markdown]
# # This is a usage example of simpleCPP_test.py in pyhddmjags
#
# It follows a script in the [pyhddmjags](https://github.com/mdnunez/pyhddmjags) repository by Michael D. Nunez
#
# This script will run nicely on [Google Colab](https://colab.research.google.com/) without needing to install anything you... |
eb990251430d65f011d522e09f5cb721ea504a4cc18cd1a96668c0d4f2122f25 | Jupyter | 28,071 | 750 | # %% [markdown]
# # This is a usage example of generateCPP_test.py in pyhddmjags
#
# It follows a script in the [pyhddmjags](https://github.com/mdnunez/pyhddmjags) repository by Michael D. Nunez
#
# This script will run nicely on [Google Colab](https://colab.research.google.com/) without needing to install anything y... |
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