sha256 stringlengths 64 64 | language stringclasses 28
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|---|---|---|---|---|
4bd91a4381077c241e6ea765bfe6a322102241154d1117c9b1591fe880eb1082 | Jupyter | 4,871 | 157 | # %% [markdown]
# # PGExplainer on BA-Shapes dataset for 2-layer GCN
# %%
import os
import os.path as osp
import torch
from torch_geometric.data import DataLoader
from torch_geometric.data import download_url, extract_zip
from dig.xgraph.dataset import SynGraphDataset
from dig.xgraph.models import *
from dig.xgraph.... |
0670acb5cd6878180dc4e601167ca90031d9c359f698ba340c8e29dc7e264b9a | Jupyter | 4,882 | 170 | # %%
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import sys
import os
BASE_PATH = './../dynaphos-core/'
sys.path.append(BASE_PATH)
sys.path.append('./../..')
from dynaphos import utils
results = pd.read_csv('results/predicted_temporal_dynamics.csv')
activation, brightness = results['p... |
7e5449aefd3b03aac01eea21d256ca0d8066518d12ed1e09bfd405f04e030b23 | Jupyter | 4,944 | 211 | # %% [markdown]
# ## Quality Control for MALDI Data
# Used for Figure 6
#
# Author: Max Gold
# %%
import numpy as np
import os
import pandas as pd
import scanpy as sc
import matplotlib.pyplot as plt
import seaborn as sns
import math
from scipy import stats
from sklearn.cluster import KMeans
# %%
base_fold = '../... |
9b2aaa9f647d49ec2d9606bf0a4958c58a07cd447640f505e8d832847c5c0170 | Jupyter | 4,949 | 177 | # %% [markdown]
# # Plotting Electron Densities
#
# This notebook is meant to show how you can plot machine learned densities. It uses an example structure and pretrained model. You should use this as a template with your own structure(s) and model(s)
# %%
%load_ext autoreload
%autoreload 2
import sys
import os
impo... |
1c9a6d9a5d39e5c5cdaeaf505fd23a223d6fc8d51edf29ed8fe3fcd12c5e5369 | Jupyter | 4,995 | 168 | # %%
%load_ext autoreload
%autoreload 2
import math
import numpy as np
import torch
import torch_geometric
from utils import get_iso_permuted_dataset
from utils import flatten_list
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
#device = torch.device("cpu")
print (device)
torch.set_default_d... |
bece265d9d896cf5a97edb992602bae174c573a05fe4b9b1d91b53bc11c53868 | Jupyter | 5,015 | 127 | # %% [markdown]
# [](https://colab.research.google.com/github/RatInABox-Lab/RatInABox/blob/main/demos/extensive_example.ipynb)
# %% [markdown]
# ## Extensive RatInABox example
# In this example we:
# 1. Initialise environment. A rectangular env... |
936dd5d9cf0b3cff9eb7b99497eb07f174850aa4bb6eba2f49ab745b1317cdd2 | Jupyter | 5,073 | 145 | # %%
# General imports
import glob
import os.path
import numpy as np
from task2_regression.models.happyquokka import happyquokka
from task2_regression.models.adt_env import ADT
from task2_regression.models.vlaai import vlaai
from task2_regression.models.eegnet_env import EEGNet
from task2_regression.models.fcnn_env im... |
338272f9803a0a4bb6be0b9f1f5c13cefeccbffdb80695acbd40b59c71376070 | Jupyter | 5,097 | 115 | # %%
import sys
sys.path.append('../')
from scipy import stats
import pandas as pd
import seaborn as sns; sns.set(style='ticks', context='talk',
rc={'font.size': 6,
'axes.titlesize': 7,
'axes.labelsize': 7,
... |
6db3aa5db637bee254d66c412002bedc34cd15169180129675a68c933ee36322 | Jupyter | 5,133 | 193 | # %% [markdown]
# # CCA vs PLS errors
# %%
import numpy as np
import xarray as xr
from gemmr.data import load_outcomes, print_ds_stats
from gemmr.metrics import *
from gemmr.sample_size.interpolation import calc_n_required_all_metrics
from gemmr.util import subset_ds
import colorcet
import holoviews as hv
hv.extensi... |
878b28186d157415fd12f84cda5ba7e83502ab1ecd1f8bf3352aa7b51bc2758d | Jupyter | 5,162 | 219 | # %% [markdown]
# ## Joint Graphical Lasso Analysis for MALDI Data
# Used for Figure 6
#
# Author: Max Gold
# %%
from collections import Counter
import itertools
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
import networkx as nx
import os
import scanpy as sc
from scipy impo... |
caf51fce750b864d450cfd722a95a84870cb96a0bb171e39236c3491049f6442 | Jupyter | 5,166 | 107 | # %% [markdown]
# # Basic embedding retrieval with Chroma
#
# This notebook demonstrates the most basic use of Chroma to store and retrieve information using embeddings. This core building block is at the heart of many powerful AI applications.
#
# ## What are embeddings?
#
# Embeddings are the A.I-native way to rep... |
8adfc1b9391ff8b80a615b1533a41137f948ec030b83c658c3ff2ab6f7b36bee | Jupyter | 5,213 | 137 | # %% [markdown]
# # inferring amino acid preference predictions from structure
# - take PDB file and convert to LMDB dataset (following https://github.com/drorlab/atom3d/tree/master/atom3d)
# - then take the trained RES-GVP model to find atom predictions
#
# ### Inputs needed:
# - the equivariant graph neural network ... |
8ef873a92e49f62fcbcd6fd9cf31c5d14a367f35a4d5363b9ee20232cc149056 | Jupyter | 5,213 | 165 | # %%
from sklearn.preprocessing import normalize
from sklearn.feature_selection import chi2,mutual_info_regression
import scipy.cluster.hierarchy as shc
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.metrics import silhouette_score,calinski_harabasz_score,davies_bouldin_score
# %%
... |
1cbaa01bf85f165aea7ff8e8120af52fb5f4e891f2fba5dbc477ecc67fc29716 | Jupyter | 5,318 | 165 | # %%
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 numpy as np
import scipy as sc
import pandas as pd
sys.path.insert(0, CODE_REPO_PATH)
import read_data as rd
import pfl3_stim as p... |
2ddf25c7f4581cbe4c7ed1ffb26842a803ab8c81daf3ef1be67460c2f59c8285 | Jupyter | 5,343 | 191 | # %% [markdown]
# # Testcase: Subset of Collaborators
# %% [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 exporte... |
0f1f9cee13a8f14bfb8fc78e52af7ca2d8bc75f06a89c42a73023e1f38d36ecb | Jupyter | 5,357 | 131 | # %%
import sys
sys.path.append('../')
import numpy as np
import pandas as pd
from sklearn.linear_model import LinearRegression
import seaborn as sns; sns.set(style='ticks', context='paper',
rc={'font.size': 6,
'axes.titlesize': 7,
... |
4e678f7955811f07690bc782468c1f572e02de83d8b59e53dbefb596aeb1d21a | Jupyter | 5,376 | 178 | # %%
import pandas as pd
import matplotlib.pyplot as plt
# %%
df1 = pd.read_csv ('EpochAccuracy1300.csv')
# %%
df1
# %%
epochs = df1. iloc[:, 0]
accuracy = df1.iloc[:, 1]
# %%
fig = plt.figure(figsize=(5,5))
fig.suptitle('OptLSTM')
plt.plot(epochs ,accuracy)
plt.xlabel('Training Epochs')
plt.ylabel('Validation Accu... |
13787d258d9c4665f3f9393fd26bd78095183eb6bf3b7adf79984f8e08f53f37 | Jupyter | 5,387 | 225 | # %% [markdown]
# # Fréchet Mean and Tangent PCA
#
# Lead author: Nina Miolane.
#
# This notebook shows how to compute the Fréchet mean of a data set.
# Then it performs tangent PCA at the mean.
# %%
import matplotlib.pyplot as plt
import geomstats.backend as gs
import geomstats.visualization as visualization
from ... |
1f9ed0cb85f82b6f2e59f11c62315a0739a8ae0a43d2f0097b2f3851d5fb1ef9 | Jupyter | 5,413 | 195 | # %% [markdown]
# # Prepare full/intact complexes for the PDBBind dataset
# %%
import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import torch
# %%
from tqdm import tqdm
import pickle
# %%
from rdkit import Chem
from Bio.PDB import PDBParser, MMCIFParser
from Bio.P... |
0eb0b4ae6de142caedbb49cb2562792eb201efeb02bf553d8eeb34382977c52c | Jupyter | 5,459 | 196 | # %%
import cloudknot as ck
import itertools
import numpy as np
import pandas as pd
# %%
input_dirs = ["b0-tensorfa-dwiqc"]
# %%
gold_standard = pd.read_csv("expert-ratings/expert_ratings.csv")
report_subs = ["sub-" + sub for sub in gold_standard["subject"]]
len(report_subs)
# %%
def create_tfrecs(s3_input_dir, seed... |
848aefda979269693041b393db8a5192107343d42da14a66d126412cd040813a | Jupyter | 5,465 | 170 | # %% [markdown]
# # Quickstart
# %%
from typing import Union, Optional, List, Dict, Tuple, Any
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 watermark
%watermark -n -u -... |
4f11d0095f9d1bd93e242c5dae626bda69d4c4f3142a9bd0fdc9a530dd95510a | Jupyter | 5,483 | 168 | # %% [markdown]
# # Quickstart
# %%
from typing import Union, Optional, List, Dict, Tuple, Any
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 watermark
%watermark -n -u -... |
54c955d29f829137c622419547596d0d7fbf659d40fc6e7d1e04e92bc3163c4f | Jupyter | 5,490 | 238 | # %% [markdown]
# # 211220 Ground truth
# - 211102 synapse annotation
# - 211119 synapse annotation
# %%
import numpy as np
import tifffile as tif
from cloudvolume import CloudVolume
import itertools
import operator
# %%
def chunk_bboxes_2d(vol_size, chunk_size, overlap=(0, 0), offset=None, mip=0):
if mip > 0:... |
ef9c5f2f8e4985af1ff39c7fac05cf5ccb593dd49c2f6686eef0172a7b7eb8a2 | Jupyter | 5,531 | 193 | # %%
%matplotlib inline
%load_ext autoreload
%autoreload 2
# %%
from configuration import *
import pandas as pd
import spikeinterface.full as si
from spikeinterface.sortingcomponents.benchmark.benchmark_motion_interpolation import BenchmarkMotionInterpolationMearec
from figure_waveforms_distortion import plot_templ... |
37a122e1d06c3eb621e145a89df222dd0e050a6fe07f6dd254c388421198fc9b | Jupyter | 5,533 | 217 | # %%
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... |
a472cc9f6162c265fbc7059ff4ff883ea8f182058e31440ed82934200236ca9f | Jupyter | 5,613 | 151 | # %%
import numpy as np
import os,sys
sys.path.append(os.getcwd()+"/..")
from rnn_scripts.train import load_rnn
from rnn_scripts.utils import su_stats
from matplotlib.colors import colorConverter as cc
from matplotlib.lines import Line2D
import matplotlib.pyplot as plt
%matplotlib inline
# %%
# Load sweep
# ----------... |
2f3aa0fde347859fc5107dc25a61c5a7326b659b8979dd2c3cc45715379b31a6 | Jupyter | 5,633 | 176 | # %%
# читаем датасет
import numpy as np
import pandas as pd
# load dataset from file csv
# f = 'D:/koltsov_python_scripts/Tomotopy/preprocessing_data/20news_lem.csv'
f = 'C:/koltcov_python_scripts/Word_embeding project/coherence/lenta_lem_2_utf8.csv'
#f = 'D:/koltsov_python_scripts/Tomotopy/preprocessing_data/WoS11967... |
a4c09e1d28064c008724edc5a1754359333283668da08accd01b9487590fbe55 | Jupyter | 5,686 | 127 | # %% [markdown]
# This example aims to demonstrate our detection method step-by-step for the user, so they have a firm understanding of how the process works and can easily run it with their own data. In it we detect defects in a single segmented image where the centroid cordinates and cell orientations have already be... |
5192871ff1d76dc1eb68ea1f993751b5bc0f5839d566317a56fb3480b304a146 | Jupyter | 5,725 | 127 | # %% [markdown]
# # Conjuctive grid cells [](https://colab.research.google.com/github/RatInABox-Lab/RatInABox/blob/main/demos/conjunctive_gridcells_example.ipynb)
#
# In this demo we'll show a very simple way one could model conjuctive grid cel... |
2163b1a5e0e5493960e1ca7a84848bcac61dffe5a83c34a8577da79efaf2dd19 | Jupyter | 5,765 | 218 | # %% [markdown]
# # Parse protein complexes from datasets and count the number of nodes/edges, and atoms.
#
# Then filter out complexes with too many atoms
# %%
from rdkit import Chem
from transformers import T5Tokenizer, T5EncoderModel
import pytorch_lightning as pl
from pytorch_lightning.callbacks.early_stopping i... |
027de5adb03ced010922fdbb8df8e2b861f58551f6e540f99965d03dd0ffa0b8 | Jupyter | 5,797 | 136 | # %% [markdown]
# # Use Roboflow with Chroma
#
# With [Roboflow Inference](https://inference.roboflow.com), you can calculate image embeddings using CLIP, a popular multimodal embedding model. You can then store these embeddings in Chroma for use in your application.
#
# In this guide, we are going to discuss how to ... |
e844ce8fbdaf67be9ec52a179831dfa83536edf6663be45a65f94e7204fb5301 | Jupyter | 5,798 | 227 | # %%
import matplotlib.pyplot as plt
import matplotlib.colors as colors
import nibabel as nib
import numpy as np
import os
import os.path as op
from glob import glob
from nilearn import plotting
# %%
nifti_dir = "../data/osfstorage/deep-learning-qc/integrated-gradients"
confusion_classes = ["true_pos", "true_neg", "f... |
d1337664bfcd147b7a9bf162701d76432bc152471de9a81e1ac57e68bd177fb3 | Jupyter | 5,828 | 173 | # %%
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from os import listdir
from covesTools import read_sampled_mut_key
from scipy.sparse.linalg import lsmr
from copy import deepcopy
import sys
sys.path.append('../')
import importlib
import myTools
importlib.reload(myTools)
from myTools import... |
7328a236ee3d19b7881951b1cc960b9bcdf4940bcc2a6c20063635f2ab1a26e8 | Jupyter | 5,835 | 162 | # %% [markdown]
# ## Graph Space
# $\textbf{Lead Author: Anna Calissano}$
# %% [markdown]
# Dear Learner,
#
# in this notebook we are going to see gpca and regression in graph space in action.
# %%
import matplotlib.pyplot as plt
import networkx as nx
import geomstats.backend as gs
import geomstats.datasets.utils a... |
9ba90053c794ac323eaae29ff621c012315be5c1cadad3e529681e642a6678f3 | Jupyter | 5,839 | 210 | # %%
import os, sys
import pandas as pd
import numpy as np
from scipy import stats
from matplotlib import pyplot as plt
import seaborn as sns
from biotite.structure.io.pdb import PDBFile
from biotite import structure as struct
from foldingdiff import plotting
# %%
sctm_table = pd.read_csv(
"/home/wukevin/projec... |
fd348142c5122635e0248020dfd28f5e3e79133fa5acd1fb0e8a0a9b1188b662 | Jupyter | 5,985 | 196 | # %% [markdown]
# # Multimodal Retrieval
#
# Chroma supports multimodal collections, i.e. collections which contain, and can be queried by, multiple modalities of data.
#
# This notebook shows an example of how to create and query a collection with both text and images, using Chroma's built-in features.
# %% [markdo... |
c9b53dc1f3e38d3001056229104de4a1de87fbe43cd4c4005d29a4c40ecf9439 | Jupyter | 5,995 | 218 | # %% [markdown]
# # Within-subject MVPA
# %%
### Import packages
### Please install nltools first if you have not installed it before. See tutorials for nltools: http://neurolearn.readthedocs.io/en/latest/
from nltools.mask import expand_mask, collapse_mask
from nltools.data import Brain_Data
import seaborn as sns
im... |
8fab54f67e77b7e19fd7b4ec2c6fc39be8c5adc69a415a93b907ffdda1d61235 | Jupyter | 6,023 | 201 | # %%
%matplotlib inline
%load_ext autoreload
%autoreload 2
import os
from visualize import PyramidVisualizer, get_dataset, display_image, simple_visualizer
from metroem.aligner import Aligner
from corgie import residuals
# %%
#os.environ["CUDA_VISIBLE_DEVICES"]="0"
#os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
#impor... |
ebef86d65939b4be0eb3032ad0f5ad6d47e8dce5ae18d5dc8b2d44e336bc31f3 | Jupyter | 6,028 | 169 | # %%
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... |
74058555fd51c75f21aa3ac81e23cbb76c3112b209d17d3c6c6b5dc61ed45dce | Jupyter | 6,042 | 231 | # %% [markdown]
# ## Explore data and analysis.
# %% [markdown]
# Here we will reproduce a figure from the manuscript. We will start by loading and analyzing some files. We will then make some plots.
# %%
%load_ext autoreload
%autoreload 2
import numpy as np
import matplotlib.pyplot as plt # just to show plots duri... |
9f4338ca60d189436ee158a2714b6610087ddba4f7dadad2d5b5644a20892a4e | Jupyter | 6,052 | 232 | # %%
import numpy as np
import matplotlib.pyplot as plt
import os, sys
sys.path.append(os.getcwd()+"/..")
from rnn_scripts.utils import*
from rnn_scripts.model import *
from rnn_scripts.train import *
from rnn_scripts.coupled_oscillators import *
from tasks.seqDS import *
import matplotlib.pylab as pl
%matplotlib inlin... |
977f8467f1d659b4c74b138c33d27bb553ee89585999cd50e3fb697c2c02a611 | Jupyter | 6,062 | 138 | # %%
import numpy as np
import pandas as pd
from scipy.spatial.transform import Rotation
import seaborn as sns; sns.set(style='white', context='paper',
rc={'font.size': 7,
'axes.titlesize': 9,
'axes.labelsize': 8,
... |
ea7b6a082c1bdbee1ba90ba4f83ae7ae332111949a47ac3511cddf5d3de529cc | Jupyter | 6,074 | 167 | # %%
import os
os.chdir('../')
# %%
import DeepPurpose.DTI as models
from DeepPurpose.utils import *
from DeepPurpose.dataset import *
# %%
from time import time
t1 = time()
X_drug, X_target, y = load_process_KIBA('./data/', binary=False)
drug_encoding = 'CNN'
target_encoding = 'Transformer'
train, val, test = dat... |
e67e5be64991619e4ae4dfc03f029dfb9a8cb118d5cb953f4503e603d504eb07 | Jupyter | 6,133 | 180 | # %%
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib as mpl
import sys
sys.path.append('./../..')
from dynaphos import utils
# %%
plt.style.use('../_figurestyle/small.mplstyle')
PLOT_ZERO_ACTIVATIONS = 'standard' # choose: 'standard', 'ignore', 'crosses... |
aac2027adb00a503f250a043821b3cd12180e90d79741b1e3883695ac19ca849 | Jupyter | 6,197 | 166 | # %%
# 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>
# ----------------------------------... |
2d18125e25132698ac453e74cb56187008b2c193676ca299d8522a9626f41fc9 | Jupyter | 6,291 | 140 | # %% [markdown]
# # Quickstart
#
# In this guide, we will train a simple Convolutional Neural Network (CNN) on MNIST handwritten digits dataset. We will simulate a Federated Learning experiment between two collaborators, orchestrated by an aggregator, via few CLI commands.
#
# OpenFL must be installed for this tutori... |
de83ff2f476e4a4384cec14f8b56c395e6beb1f4023e1be9d97ce4ef5d322843 | Jupyter | 6,308 | 222 | # %%
import cloudknot as ck
import itertools
ck.set_region('us-west-2')
# %%
import AFQ.data as afqd
study = afqd.S3BIDSStudy(
"hbn-curated",
"fcp-indi",
"data/Projects/HBN/BIDS_curated/derivatives/qsiprep",
subjects="all",
)
# %%
def afq_hbn(subject):
import AFQ
print(AFQ.__version__)
... |
1c3375d93efd70d177f6f7d343747845d663517d30f7ecbf1e4454e838d4a622 | Jupyter | 6,327 | 194 | # %%
!pip install git+https://github.com/securefederatedai/openfl.git
!pip install -r ../workflow_interface_requirements.txt
!pip install torch
!pip install torchvision
# %%
from copy import deepcopy
import numpy as np
import torch
import torchvision
from time import time
from torchvision import datasets, transforms
f... |
f8f63f660dce0082950b4368e447796694a4ac2110d1f5dc6866e046a31b864f | Jupyter | 6,372 | 136 | # %%
import sys
sys.path.append('../')
import pandas as pd
import seaborn as sns; sns.set(style='ticks', context='paper',
rc={'font.size': 6,
'axes.titlesize': 7,
'axes.labelsize': 7,
... |
dde3445500427c3c161f07a009c56701ae13e153e18495219349c0562fabd6ae | Jupyter | 6,407 | 187 | # %% [markdown]
# # sample sequences based on local structure
# use the residue preferences learned from structural microenvironment to sample combinatorial variants with Boltzmann function
#
# ### inputs:
# - the structure based residue preferences
# - a set of positions and mutations to be sampled
#
# ### outputs:
... |
0dbc01540fa5eb632ee95e2bc3f71c4327672aaa6371b6bebf3ccf720b8adace | Jupyter | 6,419 | 216 | # %%
import os, sys
import functools
import tempfile
import multiprocessing as mp
from typing import *
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from tqdm.auto import tqdm
from biotite import structure as struct
from biotite.structure.io.pdb import PDBFile
from fol... |
02372244b8587bbb859a31f2eeed301e693c9bb4acf322183ff045bd9d7f1ca5 | Jupyter | 6,433 | 224 | # %% [markdown]
# Computational Neurophysiology Laboratory, Brain Institute, UFRN, Brazil<br>
# Rodrigo MM Santiago<br>
# 2023
# %% [markdown]
# # WFbC
# *Waveform-based classification of dentate spikes*
# %% [markdown]
# ### Function import
# %% [markdown]
# **Custom functions**
# %%
import dentatespike as ds
# %... |
3d90ba01d49e451b8f883f101d3086405e862033dd0f669abdab8aa9ccbfc0ad | Jupyter | 6,455 | 170 | # %% [markdown]
# # Example to use the pipeline with your own model
# %%
import os
import torch
import torch.nn.functional as F
from torch_geometric.nn import GINEConv
from molx.dataset import Molecule3D
from molx.mol3d import Mol3DTrainer, eval3d
from molx.model import make_mask
device = torch.device('cuda' if torc... |
7be9c05397494030021047f61463c07a21fcfbbd047799c22d1ac0715abb94c8 | Jupyter | 6,477 | 219 | # %%
%matplotlib inline
%load_ext autoreload
%autoreload 2
import os
from visualize import PyramidVisualizer, get_dataset, display_image, simple_visualizer
from metroem.aligner import Aligner
from corgie import residuals
# %%
#os.environ["CUDA_VISIBLE_DEVICES"]="0"
#os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
#impor... |
323f0f4e8265a21462039d99f63cb954eaaa8450d21de0c1471399bee12e62f5 | Jupyter | 6,510 | 245 | # %% [markdown]
# # Testing for modules impacted by diet
# %%
#load packages
pkgs <- c("mgcv", "lme4", "ggplot2", "vroom", "dplyr", "forcats", "tidyr", "Seurat", "SingleCellExperiment", "gratia", "MASS", "fitdistrplus", "furrr", "tidyverse")
vapply(pkgs, library, logical(1), character.only = TRUE, logical.return = TRU... |
3b19291ff9fd791649a8c416d084fca108c3cb46d678071c480407c4ca555f72 | Jupyter | 6,521 | 247 | # %%
import AFQ.data as afqd
import cloudknot as ck
import importlib
import s3fs
import json
import os.path as op
import numpy as np
import pandas as pd
# %%
study = afqd.S3BIDSStudy(
"hbn_curated-0",
bucket="fcp-indi",
s3_prefix="data/Projects/HBN/BIDS_curated",
subjects=1,
)
qsiprep_study = afqd.S3B... |
3f13c04b7a69fe07d6fd76388e47af618a5967d5a0c7301b12ef1a211228187e | Jupyter | 6,532 | 249 | # %% [markdown]
# # Testing for modules impacted by diet
# %%
#load packages
pkgs <- c("mgcv", "lme4", "ggplot2", "vroom", "dplyr", "forcats", "tidyr", "Seurat", "SingleCellExperiment", "gratia", "MASS", "fitdistrplus", "furrr", "tidyverse")
vapply(pkgs, library, logical(1), character.only = TRUE, logical.return = TRU... |
fb7352c0c3f4d9cee36a5fc4be697627f43c7ac50fd6c0a01740cad0462f14c6 | Jupyter | 6,542 | 246 | # %% [markdown]
# # Testing for modules impacted by diet
# %%
#load packages
pkgs <- c("mgcv", "lme4", "ggplot2", "vroom", "dplyr", "forcats", "tidyr", "Seurat", "SingleCellExperiment", "gratia", "MASS", "fitdistrplus", "furrr", "tidyverse")
vapply(pkgs, library, logical(1), character.only = TRUE, logical.return = TRU... |
3fac0a264ce90100bdce77f4473cfeabbecccb712252a23e02f624c76e335832 | Jupyter | 6,574 | 216 | # %% [markdown]
# ## GSVA Consensus Clustering
# Generally used for Figure 2
#
# Author: Max Gold
# %%
import matplotlib
import matplotlib.colors as mcolors
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import seaborn as sns
from scipy import stats
... |
03c70394d6ca4ea90dabbee47cf88f06402999999a7b38dda671b60bc5c1f716 | Jupyter | 6,599 | 157 | # %% [markdown]
# # Custom Dataset for image classification
# In this tutorial, we will describe how to create and use a custom dataset for the aim of image classification, following https://www.kaggle.com/basu369victor/pytorch-tutorial-the-classification.
# %% [markdown]
# ## Custom dataset
# First of all, you need ... |
9c27875a925f3f7e427af25601f7fe73a9979ce33b8e870b3ab9ceae0559000d | Jupyter | 6,623 | 224 | # %% [markdown]
# # Classifying hands poses with Kendall shape spaces
# %% [markdown]
# Lead author: Nina Miolane.
#
# In this tutorial, we show how to use geomstats to perform a shape data analysis. Specifically, we aim to study the difference between two groups of data:
# - hand poses that correspond to the action ... |
c90f8bb16e681edeb32748ba8f33ca667014559f089725d80c6cbaefe34bed24 | Jupyter | 6,624 | 179 | # %% [markdown]
# <a href="https://colab.research.google.com/github/armiro/Covid19-Detection/blob/master/unet_lung_segmentation.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %%
from tensorflow.keras.layers import Conv2D, Conv2DTranspose, MaxPool... |
f10ccbe4c5fc5759eb3646101d858c11ac838eb112148a7128e7bc388347d56c | Jupyter | 6,633 | 172 | # %% [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 figure 2. The goal of the notebook is to represent how the node strength of the $\gamma$-fused structure-functio... |
e30a5097909113a6aad45ee3b22ca2d1cb3956afcffb2998be26f265aa1e675b | Jupyter | 6,635 | 149 | # %% [markdown]
# # Scikit-learn Compatibility
# %% [markdown]
# ---
# %% [markdown]
# ## 1 - Introduction
#
# `pysurvival` estimator models can be adapted to be used with [`scikit-learn`](https://scikit-learn.org/)'s cross-validation, hyperparameter optimization, and pipeline functionalities. This notebook explains... |
5a41e5cbf4d2e779860a0444184e07a4fd7fd8e4e1e216f6cff9e853f7d73e2d | Jupyter | 6,680 | 229 | # %%
%load_ext autoreload
%autoreload 2
import sys
if "/home/alee/psi4_mod/build/stage/lib" not in sys.path:
sys.path.append("/home/alee/psi4_mod/build/stage/lib")
print(sys.path)
import psi4
import math
import numpy as np
import torch
import torch_geometric
from utils import get_iso_permuted_dataset
from u... |
38ac0308ce49bbcb40abea5545c08bde9a46cc4ff23804423e4ef66cf0613a58 | Jupyter | 6,732 | 211 | # %% [markdown]
# # Akam et al. 2012
#
# ### Oscillators as Neural Masses
#
# We will make a toy model of Wilson-Cowan oscillators that exhibit phase reset. The parameters are given in the paper (Methods, subsection _Wilson-Cowan model_).
# %%
sum2 = sum # backup for plotting later
from brian2 import *
# %%
def g... |
6b7619d1ab8b16b7d239234b01c35a472012986883f549d861c8f750fc80e2db | Jupyter | 6,734 | 194 | # %% [markdown]
# # Align all and Compute for Graphs
#
# $\textbf{Lead Author: Anna Calissano}$
# %% [markdown]
# Dear learner,
#
# the aim of the current notebook is to introduce the align all and compute as a learning method for graphs. The align all and compute allows to estimate the Frechet Mean, the Generalize... |
b67b6c358258f82aab007823168b8bca9b97509c065c118373ee76f34e4b9249 | Jupyter | 6,761 | 208 | # %% [markdown]
# расчет coherence базируется на двух типов данных
# (бибилиотека из gensim)
# 1. датасет
# 2. тематическое решение
# %%
# читаем датасет
import numpy as np
import pandas as pd
# load dataset from file csv
# level1 - lenta
#f = 'C:/koltcov_python_scripts/Word_embeding project/lenta/level1/ETM_lenta1_d... |
f4a9b881d0c4ce73196da990247f1b935ba295dd9cfb5b09ad72c1bffbabf4b5 | Jupyter | 6,772 | 264 | # %%
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.pyplot as plt
%matplotlib inline
from mayavi import mlab
mlab.init_notebook()
cls = green_blue_colours()
_,_,lut1,lut2 = np.load('../da... |
6d6f9d014e0cf870e5383ab2165e661ea2477bc1dc1a071dbcecdcb41e16443f | Jupyter | 6,780 | 208 | # %% [markdown]
# # Partially denoise items in test set
#
# To get a better sense of how we do on different length structures, we take test set items, and ask our model to denoise partially noised versions of them. We can then compare overall reconstruction error as a function of length.
# %%
import os, sys
import mu... |
1566486ef64c6c2e78c1740c9524ebfccde9fc8bf7c7d4a842c68ec9e76746c2 | Jupyter | 6,783 | 254 | # %%
import logging
from glob import glob
from live2p.workers import RealTimeQueue
from live2p.utils import (get_nchannels, get_nvols,
get_tslice, ptoc, slice_movie,
tic, get_true_mm3d_range)
from live2p.offline import run_plane_offline
# logging setup
# change fo... |
350c56013a67c49144e8a4d820a61cf19ec55c441cd5c06f29a6e2908ceec25f | Jupyter | 6,785 | 247 | # %%
%matplotlib inline
%load_ext autoreload
%autoreload 2
from visualize import PyramidVisualizer, get_dataset, simple_visualizer
from metroem.aligner import Aligner
from collections import defaultdict
import random
# %%
import modelhouse
import json
iter_count = 500
model_path = "gs://corgie/models/pyramid_m4m6m9... |
fbc610dedd210973bb04c81297ad47d9ad52e4104469e470b9630c83c878511a | Jupyter | 6,808 | 156 | # %% [markdown]
# # Tutorial for sslgraph
# %% [markdown]
# In this hand-on code tutorial, we will show how to reproduce and develop self-supervised learning (SSL) methods using our DIG library. Specifically, we show how to implement existing SSL methods, how to develop and evaluate your own methods, and how to extrac... |
698de437410a8f4583efac391fd897296fa4e45fc3ab73f6103a61ec06c9eb98 | Jupyter | 6,832 | 128 | # %% [markdown]
# # 1. Exploring the transport properties database
#
# 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).
#
# This... |
4390158e048c8eb9120c17d8814d7024a100a8797c49beed8b5af55bdfff8110 | Jupyter | 6,858 | 217 | # %%
import sys
import os
BASE_PATH = './../dynaphos-core/'
sys.path.append(BASE_PATH)
import time
import cv2
import numpy as np
import torch
from dynaphos.image_processing import sobel_processor, canny_processor
from dynaphos.simulator import GaussianSimulator
from dynaphos.utils import load_params, load_coordinate... |
71d220a34478286dbe4bb2f59b6c029e6d32f51c7d072e3b175f95da9eccab66 | Jupyter | 6,866 | 206 | # %% [markdown]
# # Practical Examples
# %%
from typing import Union, Optional, List, Dict, Tuple, Any
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
%autorelo... |
e6575f263fb2983d8b80adb78daf1d3ce02eecf5fe6368590114e44c1b56db59 | Jupyter | 6,871 | 198 | # %%
# to be run locally on macbook with evcouplings installed
# %%
import evcouplings
from evcouplings.couplings import CouplingsModel
import matplotlib.pyplot as plt
from scipy.stats import pearsonr, spearmanr
plt.style.use('/Users/davidding/PycharmProjects/pareSingleLibrary2/codebase/src/paper_style1.mplstyle')
d... |
a6abf57e488c97216617414c1591f4c9bd4e5bb95304bba664cae97be5d4e986 | Jupyter | 6,907 | 210 | # %%
import scanpy as sc
import pandas as pd
import decoupler as dc
import scipy
import copy
import numpy as np
sc.settings.verbosity = 3 # verbosity: errors (0), warnings (1), info (2), hints (3)
sc.logging.print_header()
sc.settings.set_figure_params(dpi=80, facecolor='white')
# %%
l = sc.read_mtx("snRN... |
f22b9161d22095e2a51552075ef09a525315a7ad75961bbb3dff92f2e2cbd7c6 | Jupyter | 6,914 | 237 | # %%
import cloudknot as ck
import itertools
import numpy as np
# %%
def create_tfrecs(sites, seed):
import nobrainer
import numpy as np
import os
import os.path as op
import pandas as pd
import re
from glob import glob
from s3fs import S3FileSystem
fs = S3FileSystem()
buc... |
8b354419d18d9e6c08c2a94cddc43d72b802c80206f3e10200884621186f3cba | Jupyter | 6,945 | 201 | # %%
# defines and prepares altered genes, i.e. differential promoter methylation + expression tendency
# saved in altered-genes-per-patient.rds
# overlap of these between metastases pairs
# %%
library(pheatmap)
library(ggplot2)
# %%
basePath = "/data/bcu_projects/MelBrainSys_PostdocProject_Gruetzmann/publications/20... |
e6909a28ca9729c773fb527c60c796b9018d51558b9f890ee763b8393fd0cd79 | Jupyter | 6,949 | 201 | # %%
# defines and prepares altered genes, i.e. differential promoter methylation + expression tendency
# saved in altered-genes-per-patient.rds
# overlap of these between metastases pairs
# %%
library(pheatmap)
library(ggplot2)
# %%
basePath = "/data/bcu_projects/MelBrainSys_PostdocProject_Gruetzmann/publications/20... |
7c2a11d79fee7af83c2bd0d100be62caa22d24aee71fedf0f5962444ea4e259b | Jupyter | 6,956 | 178 | # %%
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,
'... |
5d9151d5866107c0aaf2ecac1d50f612d515df98b8d348b973807ecacbf591c6 | Jupyter | 7,006 | 267 | # %%
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... |
2deeaa0fad7163f3b28977c6b08fcec12464c416c1ecf50b61bf8ad17066dcea | Jupyter | 7,038 | 186 | # %%
import sys
import os
BASE_PATH = './../dynaphos-core/'
sys.path.append(BASE_PATH)
import time
import cv2
import numpy as np
import torch
from dynaphos.image_processing import sobel_processor, canny_processor
from dynaphos.simulator import GaussianSimulator
from dynaphos.utils import load_params, load_coordinate... |
7cdd14a74dcf16f7860e6f32f35ad46869a6211af5fda767cc4b4d725ce024e5 | Jupyter | 7,048 | 157 | # %% [markdown]
# ## Trained model and optimal hyperparameters
#
# We have provided trained model and hyperparameters of SphereNet on QM9 and MD17 [here](https://github.com/divelab/DIG_storage/tree/main/3dgraph).
# %% [markdown]
# ## Example of 3D Graph
#
# Here we provide the example code for SphereNet on QM93D and... |
83d8f9887b5c2bbb08eaaa0863d319eb2e6268dba0776da16c1a035c7927cf99 | Jupyter | 7,054 | 220 | # %% [markdown]
# ## Jaccard index
# Predicts the species of each binary flash sequence by computing the Jaccard index with literature or population references.
# %%
%matplotlib inline
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import itertools, math
from sklearn.metri... |
551479f6f34e0503a20473ef6851f45ad811ab9b54f17ff7de2c4d9848ed2861 | Jupyter | 7,055 | 248 | # %%
import sys
sys.path.append('./..')
import logging
import dynaphos
import torch
import matplotlib.pyplot as plt
import numpy as np
import cv2
import os
import pandas as pd
import time
import pickle
from dynaphos import utils
from dynaphos.cortex_models import get_visual_field_coordinates_from_cortex_full, Map, ... |
de2c4176df16f5543447a926d31c65b511ac4138e7982865e28de1ee60566752 | Jupyter | 7,058 | 248 | # %%
import sys
sys.path.append('./../..')
import logging
import dynaphos
import torch
import matplotlib.pyplot as plt
import numpy as np
import cv2
import os
import pandas as pd
import time
import pickle
from dynaphos import utils
from dynaphos.cortex_models import get_visual_field_coordinates_from_cortex_full, Ma... |
b8745b8f5f31ee0faf70dcd25d1b3d0a41e2fc9063983a62cc439a0de8427bf8 | Jupyter | 7,125 | 224 | # %% [markdown]
# ## SVM
# Classifies in the 3-dimensional space of flash parameters (flash duration, number of flashes, interflash gap) using SVM.
# %%
%matplotlib inline
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import itertools, math
from sklearn.metrics import roc... |
7d8256bffcc85f7de6f34d8057ac4f9e044768641cfad1ada1fe1108362a36ad | Jupyter | 7,129 | 220 | # %% [markdown]
# # Dot product
# Predicts the species of each binary flash sequence by taking the dot product with literature or population references.
# %%
%matplotlib inline
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import itertools, math
from sklearn.metrics impor... |
a78b3dc5915f543b675782e10a9fe4db2aec24cc8a6cc44c790d834d4bd696c0 | Jupyter | 7,141 | 206 | # %%
import neo
import numpy as np
import quantities as pq
import matplotlib.pyplot as plt
import seaborn as sns
from pathlib import Path
import sys
sys.path.append(str(Path.cwd().parents[1] / 'project_utils'))
from project_paths import project_paths
import cobrawap.pipeline
sys.path.append(str(Path(cobrawap.pipeline._... |
814b33f13b5f21c50ae0a62f7b7d4951be13bde6a4db30e4019206aced1be77c | Jupyter | 7,177 | 183 | # %%
import os
import cellrank as cr
import scanpy as sc
import anndata
import numpy as np
import warnings
warnings.filterwarnings("ignore")
import scvelo as scv
scv.settings.verbosity = 3
scv.settings.set_figure_params("scvelo")
cr.settings.verbosity = 2
# %%
datapath = "/hwfssz1/ST_BIOINTEL/P20Z10200N0039/06.groups... |
36fdb2ae24281de36e480fbe5f72bbe89a93aa7fc7a9c60be9fd5784a6a37be2 | Jupyter | 7,188 | 261 | # %%
from Bio import SeqIO
import torch
import numpy as np
from torch.utils import data
import torch.nn as nn
# %%
### preprocess the fasta data, keep the correspondence in the dictionary fasta_data
file_in = "zika-fasta1.fa"
fasta_sequences = SeqIO.parse(open(file_in),'fasta')
fasta_data = {}
seq_string = []
for... |
08c13bd5671eaab6b9aac266d278e498617c026ff7a95b35b2c9743688d548c6 | Jupyter | 7,214 | 176 | # %%
# mainSim code
# thalamic model
# AUG 9 2021
# %%
import numpy as np
import scipy as sp
import matplotlib as mpl
import matplotlib.pyplot as plt
import pandas as pd
import brian2
import pickle
import copy
import seaborn as sns
# %%
# load helper functions
from helper import generate_pref, plot_pref
import mod... |
b910415441c84332e2af2cbbe428b3040aba8ce6aeb568d6d11c676da7969b50 | Jupyter | 7,219 | 182 | # %%
import os
os.chdir('../')
# %%
import DeepPurpose.oneliner as oneliner
from DeepPurpose import dataset
import time
# %%
X_repurpose, drug_names, drug_CID = dataset.load_antiviral_drugs('./data')
# %%
start = time.time()
target, target_name = dataset.load_SARS_CoV2_Protease_3CL()
oneliner.repurpose(target = targ... |
37d585fc4cc46121b6f6eeb79e6ead81df64ead3a61db6a46ea8f7ff695280cd | Jupyter | 7,235 | 271 | # %% [markdown]
# # Neurodesign comparison of design generators
#
# In this notebook, we will compare 3 methods to generate an experimental design:
# - a design optimised using the genetic algorithm
# - a design optimised using simulations
# - a randomly drawn design
#
# We will do so using simulations: what is the r... |
21e2a86f1af1bcfd338cc589e24ab8e80e3c833c22f5ed9ccdf1eb34085e0e48 | Jupyter | 7,255 | 153 | # %% [markdown]
# # Tutorial for 3D graphs
#
# In this hand-on code tutorial, we will show how to learn representations and predict properties of 3D graphs using our DIG library. Specifically, we show how run experiments **on QM9 and MD17 using the implemented SphereNet**. You can easily replace SphereNet with SchNet,... |
f0476ec7390a62a7cd7cc3bdfb3311ac6fd64a12bd18d619110648428022dcd7 | Jupyter | 7,264 | 246 | # %%
# plotting the ROC curve for GFP oracle classification into half maximal fluorescence on 90-10% training-test
# %%
import importlib
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from os import listdir
import covesTools
importlib.reload(covesTools)
from covesTools import read_sampled_mut_... |
07583b5a11955318bed81790b4b4dc54b2b3ea5a246e1548e2790be90af58e0f | Jupyter | 7,275 | 156 | # %% [markdown]
# # Learning on Tangent Data
# %% [markdown]
# Lead author: Nicolas Guigui.
#
# In this notebook, we demonstrate how any standard machine learning algorithm can be used on data that live on a manifold yet respecting its geometry. In the previous notebooks we saw that linear operations (mean, linear we... |
21a64360dce77cac027932728486b7504c1ad8cb310c9ff95bf75080262720c1 | Jupyter | 7,348 | 212 | # %%
import neo
import numpy as np
import quantities as pq
import pandas as pd
from pathlib import Path
import sys
sys.path.append(str(Path.cwd().parents[1] / 'project_utils'))
from project_paths import project_paths
from cobrawap.pipeline.utils.neo_utils import time_slice
from cobrawap.pipeline.utils.io_utils import l... |
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