sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
bd2bc31f3fc7ca204b25678d7d044f6daf4851fcf40d845d0a7003f0561661b2 | Jupyter | 15,397 | 374 | # %%
import sys
sys.path.append('/Users/mahsazarei/Grantlab Brugada Dropbox/TerraMera/Gitlab/Mahsa/AA_APLID/Amino_Acid_manuscript/helper_functions')
from load_data import *
from utils import *
from models import *
# %% [markdown]
# # Step 1: Read AAM (spectra) and AA proportions (ground-truth)
# %%
root_dir = '/Users... |
ddab4dcf63c740ed50e156cb836e18f92283c3121dab48c2743afb01d999b38c | Jupyter | 15,409 | 381 | # %%
import os
os.chdir('C:\\Users\\arif\\OneDrive - Higher Education Commission\\HI Research\\pyimagesearch')
from sklearn.metrics import mean_squared_error, mean_absolute_error, explained_variance_score, r2_score
from timeseires.utils.to_split import to_split
from timeseires.utils.multivariate_multi_step import multi... |
862ad8cd25509bfddec90fbb2cff0c6545e9e4dd59dc634d003c709ea74b8fbd | Jupyter | 15,420 | 378 | # %%
import os
os.chdir('C:\\Users\\arif\\OneDrive - Higher Education Commission\\HI Research\\pyimagesearch')
from sklearn.metrics import mean_squared_error, mean_absolute_error, explained_variance_score, r2_score
from timeseires.utils.to_split import to_split
from timeseires.utils.multivariate_multi_step import multi... |
b4898eae20b4c51d4e98a4c5e7929ed287c53b8c9e5c1963d766e0257d4c368b | Jupyter | 15,454 | 381 | # %%
import os
os.chdir('C:\\Users\\arif\\OneDrive - Higher Education Commission\\HI Research\\pyimagesearch')
from sklearn.metrics import mean_squared_error, mean_absolute_error, explained_variance_score, r2_score
from timeseires.utils.to_split import to_split
from timeseires.utils.multivariate_multi_step import multi... |
10366930cb8ad7008e1c37291168aec6434ae5a3d4c1dae877bb7fb3ee377e88 | Jupyter | 15,466 | 466 | # %% [markdown]
#
# # More flexibility over the training loop and samplers
# %% [markdown]
# Note, you can find the original version of this notebook at [docs/advanced_tutorials/18_training_interface.ipynb](https://github.com/sbi-dev/sbi/blob/main/docs/advanced_tutorials/18_training_interface.ipynb) in the `sbi` repo... |
c3232e5f6aa8e992f1db81dc72a75c6b5962ba8cc6c8906ab36ad58e2af1ff73 | Jupyter | 15,474 | 395 | # %% [markdown]
# ### Imports
# %%
%load_ext autoreload
%autoreload 2
#%matplotlib notebook
%matplotlib inline
# %%
# Computational packages
import numpy as np
import matplotlib.pyplot as plt
import torch
import tqdm
# General packages
import datetime
import sys
import os
# custom packages
import ratsimulator
from ... |
7ad400bae29f01dc64aa8b0035f99f9f4506e3cece46dc69154ef8342a8fdb51 | Jupyter | 15,531 | 381 | # %%
import os
os.chdir('C:\\Users\\arif\\OneDrive - Higher Education Commission\\HI Research\\pyimagesearch')
from sklearn.metrics import mean_squared_error, mean_absolute_error, explained_variance_score, r2_score
from timeseires.utils.to_split import to_split
from timeseires.utils.multivariate_multi_step import multi... |
9e03d5fcd57e25118d5b3dce95f5a46c0b23542ab7732084b8226b8976f571f8 | Jupyter | 15,546 | 495 | # %%
# IMPORT STATEMENTS
import cv2
import re
import numpy as np
from matplotlib import pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable, axes_size
import matplotlib.patches as patches
import matplotlib.colors as colors
import PIL
import math
get_ipython().magic('matplotlib inline')
import pandas a... |
42eea994f7f7eea11e7380afd4ae041bc7883a64fdd57e416e05b128369d2f9a | Jupyter | 15,575 | 381 | # %%
import os
os.chdir('C:\\Users\\arif\\OneDrive - Higher Education Commission\\HI Research\\pyimagesearch')
from sklearn.metrics import mean_squared_error, mean_absolute_error, explained_variance_score, r2_score
from timeseires.utils.to_split import to_split
from timeseires.utils.multivariate_multi_step import multi... |
267b3973e34cbe511208d177ab49259abd290b016bbbde45e7cb5b0d6b77495c | Jupyter | 15,582 | 376 | # %% [markdown]
# # Test Specificity Filters
# %%
import os
from pathlib import Path
from oligo_designer_toolsuite.database import OligoDatabase, ReferenceDatabase
from oligo_designer_toolsuite.oligo_specificity_filter import (
ExactMatchFilter,
BlastNFilter,
BlastNSeedregionFilter,
BlastNSeedregio... |
42f66cd1c6ec02b0678aab36f0ba79aafb7403d259140f3395404da0d222acb0 | Jupyter | 15,646 | 383 | # %%
import os
os.chdir('C:\\Users\\Syed Hasnat\\OneDrive - Higher Education Commission\\HI Research\\pyimagesearch')
from sklearn.metrics import mean_squared_error, mean_absolute_error, explained_variance_score, r2_score
from timeseires.utils.to_split import to_split
from timeseires.utils.multivariate_multi_step impor... |
68c5d89a2bee475fa12f218c3bbc94b10d347bd9ff48e35eeedd37d64eb38103 | Jupyter | 15,845 | 385 | # %%
import os
os.chdir('C:\\Users\\Syed Hasnat\\OneDrive - Higher Education Commission\\HI Research\\pyimagesearch')
from sklearn.metrics import mean_squared_error, mean_absolute_error, explained_variance_score, r2_score
from timeseires.utils.to_split import to_split
from timeseires.utils.multivariate_multi_step impor... |
de8d3f1868331f71ee2771c41502d5450a4ae369d5e3f7dda9ac87fc5e56e4c6 | Jupyter | 16,013 | 383 | # %% [markdown]
# # SBI with iid data and permutation-invariant embeddings
#
# There are scenarios in which we observe multiple data points per experiment and we can assume that they are independent and identically distributed (iid, i.e., they are assumed to have the same underlying model parameters).
# For example, i... |
3cb6f751a883011a6036522176291f3116e21e259e6c29b3da5ad582236fc6be | Jupyter | 16,209 | 418 | # %% [markdown]
# # Channel Combination Performance Matrix (Recording-wise)
#
# This notebook generates heatmap visualizations comparing sleep staging performance
# across different EEG/EOG channel combinations for AnySleep vs U-Sleep models.
#
# ## Purpose
#
# Analyze how the choice of input channels affects model ... |
a7855808a24bb554deb1be3230a031ae63b9bd20164fa66b2fa993af0aea7d30 | Jupyter | 16,440 | 484 | # %%
import argparse
import numpy as np
import tensorflow as tf
import keras.backend as K
import matplotlib.pyplot as plt
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten, Conv2D, MaxPooling2D
from keras.regularizers import l2
import g... |
069d3423e0f785bf5499e046c5888a1bc9360c899a7a5914de3a7d35f83fc81c | Jupyter | 16,621 | 481 | # %% [markdown]
# # WHODAS 2.0 functionality prediction
#
# **Authors:** *Santiago de Leon*, *Jesús Herrera López*, *Emese Sükei*
#
# One-month long time-series data were summarized using statistical measures (minimum, maximum, mean, median, standard deviation, IQR), creating 64 features. We then applied a sequentia... |
1c8f57adce48f95c34d2f99ebf4933de4e4b6e1e6a96e057d6766783234b4a8a | Jupyter | 17,122 | 439 | # %% [markdown]
# # How to choose abstraction levels in sbi
# %% [markdown]
# `sbi` offers flexibility ranging from simple, high-level workflows to full control over neural networks and sampling. This guide shows:
#
# 1. **Four abstraction levels** for controlling the density estimator (common to NPE and NLE)
# 2. **... |
d84c472a84de21052b3e7fe35fb083f94c67d60237f20a3263850d000b882ab5 | Jupyter | 17,253 | 502 | # %% [markdown]
# <br><br>
# 事情是这样的<br>
# 我本来已经<br>
# 打算完结这个分析了<br>
# 但杜老师，居然<br>
# 竟然，也是不出意料<br>
# 叫我加一个参数<br>
# %% [markdown]
# # TBS
# %% [markdown]
# ## preparing
# %%
import pandas as pd
import matplotlib.pyplot as plt
import hddm
import numpy as np
import pickle
from patsy ... |
b62a8cc5a5eb0778f819fd77a771474fab7bce16c7b96f38bf7d59a64de05077 | Jupyter | 17,273 | 503 | # %% [markdown]
# <br><br>
# 事情是这样的<br>
# 我本来已经<br>
# 打算完结这个分析了<br>
# 但杜老师，居然<br>
# 竟然，也是不出意料<br>
# 叫我加一个参数<br>
# %% [markdown]
# # TBS
# %% [markdown]
# ## preparing
# %%
import pandas as pd
import matplotlib.pyplot as plt
import hddm
import numpy as np
import pickle
from patsy impo... |
35b32ddef7db8bc3617be29f5ba8a1a06e44afb5f4d8b2c8b06dd273cb07f153 | Jupyter | 17,282 | 598 | # %% [markdown]
# # Obtaining adversarials
# ## Attack methods: CW, BIM, FGSM
# %%
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from datetime import datetime
# %%
cd C:/Users/fjdur/Documents/utils/
# %%
import utils_guided_retraining as utils
# %%
from importlib import reload
utils =... |
e94bb1b9f5f529ec33fedfe0d270b15034e14df44df6db73387d0c00c2cdffea | Jupyter | 17,344 | 501 | # %% [markdown]
# <br><br>
# 事情是这样的<br>
# 我本来已经<br>
# 打算完结这个分析了<br>
# 但杜老师，居然<br>
# 竟然，也是不出意料<br>
# 叫我加一个参数<br>
# %% [markdown]
# # TBS
# %% [markdown]
# ## preparing
# %%
import pandas as pd
import matplotlib.pyplot as plt
import hddm
import numpy as np
import pickle
from patsy impo... |
fabf1cd1929edb80fe84cb9f477ffea98fc7a22f858d9df3b4b6772bd73f34c9 | Jupyter | 17,355 | 500 | # %% [markdown]
# <br><br>
# 事情是这样的<br>
# 我本来已经<br>
# 打算完结这个分析了<br>
# 但杜老师，居然<br>
# 竟然，也是不出意料<br>
# 叫我加一个参数<br>
# %% [markdown]
# # TBS
# %% [markdown]
# ## preparing
# %%
import pandas as pd
import matplotlib.pyplot as plt
import hddm
import numpy as np
import pickle
from patsy impor... |
12f2379b17574d301af8222c99c738c94460e3948fa4d0de62f1c8f147837351 | Jupyter | 17,357 | 395 | # %%
import pandas as pd
import numpy as np
import io
from rdkit import Chem
from rdkit.Chem import AllChem
from rdkit.Chem import Draw
from rdkit.Chem.Draw import rdMolDraw2D
import IPython.display
from collections import defaultdict
from tqdm import tqdm
import matplotlib.pyplot as plt
from matplotlib.colors import L... |
e222291909e27dcc4fbcfc44f395ff366d1b22d1da77c7185e54bbfc57eae97f | Jupyter | 17,370 | 506 | # %% [markdown]
# <br><br>
# 事情是这样的<br>
# 我本来已经<br>
# 打算完结这个分析了<br>
# 但杜老师，居然<br>
# 竟然，也是不出意料<br>
# 叫我加一个参数<br>
# %% [markdown]
# # TBS
# %% [markdown]
# ## preparing
# %%
import pandas as pd
import matplotlib.pyplot as plt
import hddm
import numpy as np
import pickle
from patsy ... |
3c084d117f50520ea24009229bbfe8650ffa2cdaa3008473dadd7855a43702f9 | Jupyter | 17,446 | 502 | # %% [markdown]
# <br><br>
# 事情是这样的<br>
# 我本来已经<br>
# 打算完结这个分析了<br>
# 但杜老师，居然<br>
# 竟然，也是不出意料<br>
# 叫我加一个参数<br>
# %% [markdown]
# # TBS
# %% [markdown]
# ## preparing
# %%
import pandas as pd
import matplotlib.pyplot as plt
import hddm
import numpy as np
import pickle
from patsy impo... |
c7498562aab707816b9c6982a416bb42bcdddc5d4f98259d851b164217f5bc02 | Jupyter | 17,465 | 624 | # %% [markdown]
# # Obtaining adversarials
# ## Attack methods: CW, BIM, FGSM
# %%
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from datetime import datetime
# %%
pwd
# %%
cd ../../utils/
# %%
import utils_guided_retraining as utils
# %%
from importlib import reload
utils = reload(u... |
755abcfe8bbac71bbe7175a9cfe67f6097fd6d2f0bcf284dda0e081809468a56 | Jupyter | 17,468 | 468 | # %%
import seaborn as sns
import pandas as pd
from matplotlib.colors import LinearSegmentedColormap
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
import numpy as np
# %%
# Functions for custom normalization of colorbar
import matplotlib
def _forward(x):
return np.sqrt(x)
def _inverse(... |
0c86e5e2adc67ba7b37d1086c519c86ef7923f2a56007ddddbf52d4a156517ca | Jupyter | 17,473 | 503 | # %% [markdown]
# <br><br>
# 事情是这样的<br>
# 我本来已经<br>
# 打算完结这个分析了<br>
# 但杜老师，居然<br>
# 竟然，也是不出意料<br>
# 叫我加一个参数<br>
# %% [markdown]
# # TBS
# %% [markdown]
# ## preparing
# %%
import pandas as pd
import matplotlib.pyplot as plt
import hddm
import numpy as np
import pickle
from patsy impo... |
bdc0090814569840f5cec78c0a576948a9f5e2264dcdb2d04501f19e78bcdae2 | Jupyter | 17,479 | 539 | # %%
import numpy as np
import joblib
import skimage
import matplotlib as mpl
import matplotlib.pyplot as plt
import scipy
import tifffile as tif
from tqdm.notebook import tqdm
from pathlib import Path
from cycler import cycler
from mpl_toolkits.axes_grid1.anchored_artists import AnchoredSizeBar
from mpl_toolkits.axes... |
1524dfd0a023eb2dc57f59267ec28a3473811873a8df76d058f2c2c4ff089e55 | Jupyter | 17,487 | 512 | # %% [markdown]
# <br><br>
# 事情是这样的<br>
# 我本来已经<br>
# 打算完结这个分析了<br>
# 但杜老师，居然<br>
# 竟然，也是不出意料<br>
# 叫我加一个参数<br>
# %% [markdown]
# # TBS
# %% [markdown]
# ## preparing
# %%
import pandas as pd
import matplotlib.pyplot as plt
import hddm
import numpy as np
import pickle
from patsy i... |
7fe00d63af325212e5c0cbce91dd5fd5669287e4cbb920d4ab2a7da567df129c | Jupyter | 17,509 | 557 | # %%
import os
PODCAST_ROOT = os.environ.get("PODCAST_ROOT", "/engram/naplab/users/rs4613/Podcast") # set to your copy of the dataset
# %% [markdown]
# #### ChatGPT was used to assist writing code. The authors reviewed, edited, and take full responsibility for the content.
# %%
root_dir = f'{PODCAST_ROOT}'
base_dir... |
224dcaefd6361d0d44b28145147903d42f1bd8ccf4effcbd145a9261006ae215 | Jupyter | 17,571 | 545 | # %% [markdown]
# ### Need to first run `simulate_full.ipynb` to generate the data used in this notbeook
# %%
import numpy as np
import joblib
import skimage
import matplotlib as mpl
import matplotlib.pyplot as plt
import scipy
import tifffile as tif
from tqdm.notebook import tqdm
from pathlib import Path
from cycler... |
3a5b8080b5448c3355b6085f6c3ee7a2f8f7a1564ef593d9da81dc7013413ee3 | Jupyter | 17,595 | 469 | # %% [markdown]
# # SBI for decision-making models
#
# In [this
# tutorial](https://sbi.readthedocs.io/en/latest/advanced_tutorials/12_iid_data_and_permutation_invariant_embeddings.html),
# we showed how to use SBI with trial-based iid data. Such scenarios can arise,
# for example, in models of perceptual decision mak... |
869490271f0de942dff73e9c5e5d96b88a3b011b5ce9f8706de5c45e9b6846c8 | Jupyter | 17,685 | 620 | # %% [markdown]
# # Obtaining adversarials
# ## Attack methods: CW, BIM, FGSM
# %%
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from datetime import datetime
# %%
cd ../../utils/
# %%
import utils_guided_retraining as utils
# %%
from importlib import reload
utils = reload(utils)
# %... |
2752dd0e0982ee29e112c6cd0fc9237fbe7511e167973c6330b40dce281bc186 | Jupyter | 17,695 | 543 | # %% [markdown]
# <a href="https://colab.research.google.com/github/open-mmlab/mmpose/blob/master/demo/MMPose_Tutorial.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # MMPose Tutorial
#
# Welcome to MMPose colab tutorial! In this... |
4f1716554715e0d9a5fffb120abfa5f66aa2f2dd3d66c792dff355eda2437a97 | Jupyter | 17,810 | 569 | # %%
import pandas as pd
from matplotlib import pyplot as plt
import numpy as np
import dit
import admUI
import math
# %% [markdown]
# # Introduction
#
# Information and its theory are an important quantity that govern many fields, rangning from communication to machine learning. In this tutorial, we are going to d... |
8184a754454dc4fcbda148045c20ae0d04e92ed15f8fdd74e4bc9ad287edeca5 | Jupyter | 17,904 | 625 | # %% [markdown]
# # Obtaining adversarials
# ## Attack methods: CW, BIM, FGSM
# %%
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from datetime import datetime
# %%
cd C:/Users/fjdur/Documents/utils/
# %%
import utils_guided_retraining as utils
# %%
from importlib import reload
utils =... |
5a0bd4eab296f4075694964fa9abdfadf39c6d69d863c9ba36a9b9d864596702 | Jupyter | 18,155 | 362 | # %% [markdown]
# # How to configure the neural network
# %% [markdown]
# A **config object** describes the neural estimator and its settings. Pass it to a trainer to choose and configure the network.
#
# Each model has its own config class. Unsupported or misspelled fields raise an error at construction.
#
# This g... |
e15b2713259c79a3944f1a3212bc6c2c047cfdcbb6186616ff60e26ececf2a4e | Jupyter | 18,302 | 562 | # %%
# IMPORT STATEMENTS
import re
import numpy as np
import matplotlib
from matplotlib import pyplot as plt
%matplotlib inline
import pandas as pd
import sys
sys.path.append("/booleanfs2/sahoo/Hegemon")
sys.path.append("../")
import StepMiner as smn
import HegemonUtil as hu
acolor = ["#00CC00", "#D8A03D","#EC008C",
... |
28c727984d55d09245e66145ebe36f24b2f31e2d727fa2802a51615aa96b4e18 | Jupyter | 18,350 | 309 | # %%
import glob
import json
import logging
import sys
from os.path import dirname
import matplotlib.pyplot as plt
import numpy as np
from sklearn.model_selection import train_test_split, StratifiedKFold
console_handler = logging.StreamHandler(sys.stdout)
file_handler = logging.FileHandler("logs/anysleep_dodo_vs_dodh... |
9b938b0cd4e020eb0ded9263da654de232a0454bb2b7608936d6d51c481b1c61 | Jupyter | 18,507 | 409 | # %%
import sys
if "../" not in sys.path: sys.path.append("../")
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
import scipy
from tqdm.notebook import tqdm
import seaborn as sns
sns.set(rc={'figure.facecolor':'white'})
plt.rcParams["figure.facecolor"] = "white"
sns.set... |
5e2e05be55bfb4dede42f66e79adb1ae1f2c7754078f1cd8208a8d6a3a641f2a | Jupyter | 18,622 | 536 | # %%
%matplotlib inline
# Load surface mask and distance matrix
import glob
import h5py
import numpy as np
from brainspace.gradient import GradientMaps
from brainspace.gradient import alignment
import matplotlib.pyplot as plt
import matplotlib.cm as cm
from sklearn.decomposition import PCA
import helpers
HCPFilename... |
2e0942c72ae75f2590f5587465a5cb3a774089e0bfcbe62b3a4ab656214661c2 | Jupyter | 18,800 | 431 | # %%
import glob
import json
import os
import re
import matplotlib.pyplot as plt
import numpy as np
from sklearn.metrics import cohen_kappa_score, f1_score
from tabulate import tabulate
score_order = ["Wake", "N1", "N2", "N3", "REM"]
# %%
reprod_results_paths = [
"../../logs/exp001/exp001a/sweep-2025-07-30_12-29... |
9f92f801ed41790c1f40a4b13e762cd5daaad446eb8f6c188a8094af52575c52 | Jupyter | 18,815 | 525 | # %%
"""
Basic settings...
"""
# -*- coding: utf-8 -*-
%load_ext autoreload
%autoreload 2
import os, time
# select GPU for training
os.environ["CUDA_VISIBLE_DEVICES"] = "3"
# import tensorflow.experimental.numpy as np
import numpy as np
import tensorflow as tf
import random
import scipy.io as scio
import h5py
impor... |
bdbd83286ca4daafe3fca55d99b833b4b83b27ce603e0fe0834bc52a7eba0722 | Jupyter | 19,004 | 545 | # %% [markdown]
# # Figure 4 and 5: Inference over distance-based rules
#
# This notebook contains the Python code for generating Figure 4 and 5 in the paper.
# The figure shows the results for performing SBI on the distance-based wiring rules.
#
# The training data and inference objects are pre computed and only lo... |
474a6644121034858f3c28ccfe92de5dc12972264833d292ccccad020827d0fc | Jupyter | 19,206 | 506 | # %% [markdown]
# ### Computation of sBTRF from processed EEG data and comparison with behavior
# See the other python scripts to see the pre-processing steps and computations of the individual multichannel binaural TRFs (mcBTRF). The preprocessing steps involve band-pass flitering the raw EEG, rejection of ocular proj... |
8dc037515aa4db98973b9a0cf3264b888535024b78b810a50f8d0e3ef5e6a041 | Jupyter | 19,217 | 411 | # %% [markdown]
# Importing relevant packages and modules
# %%
%load_ext autoreload
%autoreload 2
#%matplotlib notebook
%matplotlib inline
# %%
import sys
from pathlib import Path
# Plotting
import numpy as np
from matplotlib import pyplot as plt
plt.style.use('bioAI.mplstyle')
import copy
'''
CUSTOM PACKAGES
'''
#... |
84339e73b6b72b776b67c6ce2f1a87af2761a752e06cfba001c6482888928dcd | Jupyter | 19,223 | 659 | # %% [markdown]
# # Obtaining adversarials
# ## Attack methods: CW, BIM, FGSM
# %%
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from datetime import datetime
# %%
pwd
# %%
cd ../utils/
# %%
import utils_guided_retraining2 as utils
# %%
from importlib import reload
utils = reload(uti... |
3a2be21df38766779b8e852be5d56c3cbb18072edde3d65bc72fd3c06222303e | Jupyter | 19,339 | 448 | # %%
import pandas as pd
import numpy as np
import io
from rdkit import Chem
from rdkit.Chem import AllChem
from rdkit.Chem import Draw
from rdkit.Chem.Draw import rdMolDraw2D
import IPython.display
from collections import defaultdict
from tqdm import tqdm
import matplotlib.pyplot as plt
from matplotlib.colors import L... |
ad1b21ce7f086ed4824e296df9cd27ff01154daa9a95e982f754673bd7b884a6 | Jupyter | 19,596 | 343 | # %%
import glob
import json
import logging
import sys
from functools import partial
import matplotlib.pyplot as plt
import numpy as np
from sklearn.model_selection import train_test_split, StratifiedKFold
console_handler = logging.StreamHandler(sys.stdout)
file_handler = logging.FileHandler("logs/anysleep_mnc_nt1_sf... |
107895efeb37f540df13329329f8d9fe2e02fc30aa48668219074122b88d9c78 | Jupyter | 19,633 | 603 | # %% [markdown]
# ## Code to reproduce Figure 4 of PMID: 37516087
# ### Alternatively you can just click on [this link](https://colab.research.google.com/drive/18tmFN7gJS9eKhN88AU2a9eKsuM8hRNP3?usp=sharing) to copy this code from Google Colab
# ### and click on the "Run all" button on the top
# %%
import requests
url ... |
7842c08d018ff5c7ae785ef3a12fe0a0e8db048f5008ce769367411bbf2613db | Jupyter | 19,882 | 576 | # %% [markdown]
# # AI-guided Discovery of a Barrier-Protective Therapy in Inflammatory Bowel Disease
#
# Authors: Debashis Sahoo1-3†*, Lee Swanson4†, Ibrahim M. Sayed5†$, Gajanan D. Katkar4, Stella-Rita Ibeawuchi5, Yash Mittal6, Rama F. Pranadinata4, Courtney Tindle4, Mackenzie Fuller4, Dominik L. Stec4, John T. Chan... |
0e5f0227d7eea543744122a65424bff8ca156cb823d781f93a3dfcffc3720980 | Jupyter | 20,188 | 442 | # %%
import sys
if "../" not in sys.path: sys.path.append("../")
from os import path
import numpy as np
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
from scipy import ndimage as ndi
from scipy import stats
import seaborn as sns
# sns.set_palette("deep")
sns.set_theme(style="whitegrid")
sns.... |
44050ee3f4dc34873ff17e1f127aa757863878cd824f066f44f1276b7eb3cc59 | Jupyter | 20,224 | 492 | # %%
import sys
if "../" not in sys.path: sys.path.append("../")
from os import path
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from scipy import ndimage as ndi
from scipy import stats
from tqdm.notebook import tqdm
import random
import pickle
import seaborn as sns
# sns.set_palette("de... |
2a8725085932f5a1a5948eaf94695943c7357da7f0e439dd510c331fc0694bf6 | Jupyter | 20,533 | 469 | # %%
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
import scipy
import statsmodels.stats.multitest
import glob
import warnings
warnings.filterwarnings("ignore")
import matplotlib as mpl
import matplotlib.font_manager
mpl.rcParams['pdf.fonttype'] = 42
font = {'size' : 12}... |
fea9d0b58da559dbe00fccef4b5a3e48de42db3157d62bac915e5edaff15b122 | Jupyter | 20,621 | 556 | # %% [markdown]
# # API of implemented methods
#
# This notebook spells out the API for all algorithms implemented in the `sbi` toolbox:
#
# - [Neural Posterior estimation (NPE)](https://sbi.readthedocs.io/en/latest/tutorials/16_implemented_methods.html#neural-posterior-estimation-npe)
#
# - [Neural Likelihood estim... |
442dc91fb922a408d2ddb668c882586e578252d9430a2e4e7f457176784a86e3 | Jupyter | 20,670 | 504 | # %%
import os
os.chdir('C:\\Users\\Syed Hasnat\\OneDrive - Higher Education Commission\\HI Research\\pyimagesearch')
from sklearn.metrics import mean_squared_error, mean_absolute_error, explained_variance_score, r2_score
from timeseires.utils.to_split import to_split
from timeseires.utils.multivariate_multi_step impor... |
18b716208e4b57066bb760bc46ae639b6bc0cc5c883d6570616a10c11345d3d7 | Jupyter | 20,830 | 645 | # %% [markdown]
# ### EDA
#
# The output files of this notebook can be found under **eda** file.
# %%
import pandas as pd
import numpy as np
import statsmodels.api as sm
import scipy.stats as st
import matplotlib.pyplot as plt
import seaborn as sn
import matplotlib.mlab as mlab
from sklearn.preprocessing import Stand... |
f10d72fea363aaef505d3820bbed4766ffe564dc9afc91dfcefc7dc3afa28c5f | Jupyter | 20,917 | 653 | # %% [markdown]
# ### Generate the post hoc dataset for analysis
# %%
import math
import time
import parmap
import pickle
import multiprocessing
from imagen_posthocloader import *
import matplotlib.pyplot as plt
import seaborn as sns
from plot_results_posthoc import *
%matplotlib inline
# %%
num_cores = multiprocessi... |
ae55729342fd361326567862fa171f0e8d3bae0f1feb549870cb212f499e1a14 | Jupyter | 21,851 | 684 | # %%
import cv2
import re
import numpy as np
from matplotlib import pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable, axes_size
import matplotlib.patches as patches
import matplotlib.colors as colors
import PIL
import math
get_ipython().run_line_magic('matplotlib', 'inline')
import pandas as pd
imp... |
abe4d91f86842e396f61bd1a81a19e91e95060b486504549eda2464aed0e53f8 | Jupyter | 22,706 | 607 | # %% [markdown]
# # Topological Data Analysis (TDA) of PDGFRβ+ labeled nuclei (FISH).
# %% [markdown]
# ## Preview
#
# This notebook reports the topological data analysis (TDA) for PDGFRβ+ nuceli (labeled with fluorescence in situ hybridization) in three ROIs of the ischemic hemisphere. Please note that this notebook... |
b4fff8470719ad0cfa3db65667e275c34fdd9c9b13f9ed85fcf2d5dfec0a65af | Jupyter | 23,105 | 427 | # %% [markdown]
# # Simulation-based Calibration in SBI
#
# After a density estimator has been trained with simulated data to obtain a posterior,
# the estimator should be made subject to several **diagnostic tests**. This needs to be
# performed before being used for inference given the actual observed data. _Posteri... |
14b4f0b3d373bf77e2990270ccc8fb56e599434208758e9e5a11457ce7f36cfa | Jupyter | 23,219 | 651 | # %%
# IMPORT STATEMENTS
import cv2
import re
import numpy as np
from matplotlib import pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable, axes_size
import matplotlib.patches as patches
import matplotlib.colors as colors
import PIL
import math
get_ipython().magic('matplotlib inline')
import pandas a... |
38bd152fcb6c5ba046ada1ec2b8c53bf7b37796bba74f464241812c69c2ff472 | Jupyter | 23,640 | 575 | # %%
import matplotlib.pyplot as plt
import scipy.io as spio
import pandas as pd
import numpy as np
import os
import seaborn as sns
from my_tools import *
from ranked_structural_connectivity import *
import pickle
from IPython.display import Image
import matplotlib.lines as mlines
from scipy import spatial
from matplo... |
e1ba308fa6e9ba709e37fda78fade8417e94e2588b9bf7ac8ff3cd09868fd7cd | Jupyter | 24,320 | 715 | # %%
#options: cc200, dosenbach160, aal
p_ROI = "cc200"
p_fold = 10
p_center = "Stanford"
p_mode = "whole"
p_augmentation = True
p_Method = "ASD-DiagNet"
# %%
parameter_list = [p_ROI,p_fold,p_center,p_mode,p_augmentation,p_Method]
print("*****List of patameters****")
print("ROI atlas: ",p_ROI)
print("per Center or who... |
781bc034ab4811a2ad76609b60baee23e0ba6634c282fe3c621d1c9fa4ddd597 | Jupyter | 24,914 | 788 | # %% [markdown]
# # Code for "Mapping the landscape of magnetic field effects on neural repair: a systematic review, mathematical model, and meta-analysis"
# ### Written by: Meghan McGraw and Gabrielle Gilmer
# %% [markdown]
# # Imports
# ### Import packages into Python
# %%
import numpy as np
%matplotlib inline
im... |
3a89665fecd99dea52e3e1827714b4f2a81db28916d97f1e1975c6d26d9e5098 | Jupyter | 24,917 | 854 | # %%
# IMPORT STATEMENTS
import cv2
import re
import numpy as np
import matplotlib
from matplotlib import pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable, axes_size
import matplotlib.patches as patches
import matplotlib.colors as colors
import PIL
import math
get_ipython().magic('matplotlib inline... |
66f619731b02e8810127f1d78d30d0076ef4584f83e0c8a38f0fefae4e78b2d5 | Jupyter | 25,044 | 868 | # %%
import numpy as np
import pandas as pd
from pandas import read_csv
import pickle
from sklearn.preprocessing import MinMaxScaler, StandardScaler
from sklearn.preprocessing import OneHotEncoder
# %%
df=pd.read_csv('C:\\Users\\Syed Hasnat\\OneDrive\\Journal Paper\\Code\\Hasant code\\dataset_plots_code\\Dataset\\7_Da... |
aa3cbaa1e5d6ca55b77fa3dd40ec83e9007367884b4c1f51868b88fb5e376d63 | Jupyter | 25,616 | 568 | # %% [markdown]
# # SENSITIVITY ANALYSIS
# The preliminary results in our IMAGEN paper advocates for a more in-depth understanding of what contributes to the significant performance of the ML models for the three time-points. Here we will throw several _what-if_ and _why_ questions at the results to help us understand ... |
f1b4028053815d10ee51c2cd4b84788ef0728b94a8d2653026b2c71c90f86feb | Jupyter | 25,665 | 777 | # %%
pip install tensorflow==2.5
# %%
pip install keras==2.5
# %%
pip install cv2
# %%
pip install opencv-python
# %%
from keras.datasets import mnist
from keras.utils import np_utils
import numpy as np
import keras
# %%
from matplotlib import pyplot
# %%
from sklearn.model_selection import train_test_split
imp... |
ea81d3c65f9ace78a4ce658768798ba93b3b6496b7bdca1edcb2e751cc6040b9 | Jupyter | 26,221 | 774 | # %% [markdown]
# # Initialisation
# %%
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
import os
import pyabf
import seaborn as sns
import shelve
import sys
from scipy.stats import zscore
from sklearn.cluster import AgglomerativeClustering
from scipy.cluster.hierarchy import... |
4136e5193c0c221b7ece7b2c4622bdbf299cc4c9c814963f998d588cbd38c3a8 | Jupyter | 26,259 | 731 | # %% [markdown]
# # chx10 recruitment analysis during MLR electrical stimulation
#
# ----------
#
#
#
#
# %%
import numpy as np
import pandas as pd
import os
import sys
import pickle
import logging
from matplotlib import cm
import matplotlib.pyplot as plt
sys.path.insert(1, '../utils/')
from import_data im... |
7127a8dc33e300b82a76666732de221691be3df4a08f8a990752e0b060dd8221 | Jupyter | 27,100 | 1,279 | # %% [markdown]
# # Ordering by metrics and retraining phase
#
# ## Dataset: MNIST
# %% [markdown]
# ## Experiment configuration 3
# %%
pip install --user tensorflow==2.5
# %%
import argparse
import numpy as np
import tensorflow as tf
import tensorflow.keras.backend as K
import matplotlib.pyplot as plt
from tens... |
16413572eda19b5bcaf0d2ecd9235721e32bbcd398ab54a4e6f6f9445bb84661 | Jupyter | 27,359 | 572 | # %% [markdown]
# # Vector Field Methods: FMPE and NPSE
#
# `sbi` incorporates recent algorithms based on Flow Matching and Score Matching generative models, which are also referred to as Continuous Normalizing Flows (CNF) and Denoising Diffusion Probabilistic Models (DDPM), respectively.
#
# At the highest level, yo... |
081c94b07f1adcbd6a1b648e3637ee37dce3e5f9e0f45b067e822b5cc5ec7e0a | Jupyter | 27,449 | 1,285 | # %% [markdown]
# # Ordering by metrics and retraining phase
#
# ## Dataset: GTSRB
# %% [markdown]
# ## Experiment configuration 3
# %%
pip install --user tensorflow==2.5
# %%
import argparse
import numpy as np
import tensorflow as tf
import keras.backend as K
import matplotlib.pyplot as plt
from keras.utils imp... |
2a1589bf1d898aae79a1e704a109687455d5e14f3bf97de9a954713586602e1b | Jupyter | 27,510 | 1,285 | # %% [markdown]
# # Ordering by metrics and retraining phase
#
# ## Dataset: MNIST
# %% [markdown]
# ## Experiment configuration 3
# %%
pip install --user tensorflow==2.5
# %%
import argparse
import numpy as np
import tensorflow as tf
import keras.backend as K
import matplotlib.pyplot as plt
from keras.utils imp... |
5a20b47f270dbdbd260fb01e070f23cf791aff35dc400f92adb50b5c9445bc91 | Jupyter | 28,729 | 770 | # %% [markdown]
# ### Organise file structure to BIDS and create a holdout split
#
# Reference documentation links:
# * IMAGEN's native documentation: https://imagen-europe.com/resources/imagen-dataset/documentation/
# * Our local documentation: https://github.com/ritterlab/deepMRI/wiki/IMAGEN
# * Their github reposit... |
fb8f5e945aa5e6d3128d579df5a175dc7bc9852d6e9115c4a7648dd6b69f8ed3 | Jupyter | 30,001 | 1,340 | # %% [markdown]
# # Ordering by metrics and retraining phase
#
# ## Dataset: CIFAR-10
# %% [markdown]
# ## Experiment configuration 3
# %%
pip install --user tensorflow==2.5
# %%
import argparse
import numpy as np
import tensorflow as tf
import keras.backend as K
import matplotlib.pyplot as plt
from keras.utils ... |
38cd24c1d7037bb41b368bc5bf75c0ab73e8f0fb8c39f91d5968e40df03cf3d0 | Jupyter | 30,083 | 1,472 | # %% [markdown]
# # Ordering by metrics and retraining phase
#
# ## Dataset: GTSRB
# %% [markdown]
# ## Experiment configuration 2
# %%
pip install --user tensorflow==2.5
# %%
import argparse
import numpy as np
import tensorflow as tf
import keras.backend as K
import matplotlib.pyplot as plt
from keras.utils imp... |
2f57e6d4e6ba1d5fb23d71be624d4f378e2cd3c2a4945ae2f6befa039c8bb416 | Jupyter | 30,244 | 1,473 | # %% [markdown]
# # Ordering by metrics and retraining phase
#
# ## Dataset: GTSRB
# %% [markdown]
# ## Experiment configuration 1
# %% [markdown]
# ## Configuration 1
# 1. Incremental guided retraining starting from scratch using the new adversarial inputs and original training set
# %%
pip install --user tensorf... |
5382e3cb259fa8b0d322acce73546566e35530c7903f1f196f71ba632aa21d72 | Jupyter | 31,043 | 1,372 | # %% [markdown]
# # Ordering by metrics and retraining phase
#
# ## Dataset: Intel
# %% [markdown]
# ## Experiment configuration 3
# %%
pip install --user tensorflow==2.5
# %%
import argparse
import numpy as np
import tensorflow as tf
import keras.backend as K
import matplotlib.pyplot as plt
from keras.utils imp... |
8fe80319a67142d3e636195410dd46e167872d8d8e0fb3fa1f7ffe8fa322f1a4 | Jupyter | 31,060 | 852 | # %%
# IMPORT STATEMENTS
import sys
sys.path.append("Hegemon/")
sys.path = ["BoNE/"] + sys.path
import matplotlib
matplotlib.rcParams['pdf.fonttype'] = 42
matplotlib.rcParams['ps.fonttype'] = 42
try:
reload # Python 2.7
except NameError:
try:
from importlib import reload # Python 3.4+
except Imp... |
4c66cf7b74d7bf75bbf6b61ebe54f7e6fcef7c842668e6ee36ce92ce156323a0 | Jupyter | 31,094 | 860 | # %%
%load_ext autoreload
%autoreload 2
# %%
import pandas as pd
import numpy as np
from glob import glob
from os.path import join
import os
from scikits.bootstrap import ci
import matplotlib.pyplot as plt
import seaborn as sns
from statsmodels.stats.multitest import fdrcorrection
sns.set(style="whitegrid", contex... |
305d517083bedee8a7ed1c29ffd06af0ffe73889d9d3b3729dbf3e3e5dbf398c | Jupyter | 31,153 | 1,347 | # %% [markdown]
# # Ordering by metrics and retraining phase
#
# ## Dataset: MNIST
# %% [markdown]
# ## Experiment configuration 2
# %%
import argparse
import numpy as np
import tensorflow as tf
import keras.backend as K
import matplotlib.pyplot as plt
from keras.utils import np_utils
from keras.models import Seq... |
2ddaa6638323e3ebdc85fda1fb541e43e37b52e8d2acc87cb345ea00e86e6e4f | Jupyter | 31,253 | 805 | # %% [markdown]
# # Model Misspecification in SBI
#
# In this tutorial, we would describe how to detect prior misspecification, one instance of model misspecification in Simulation-based Inference (SBI), and demonstrate it on two simulators: a toy 2d Gaussian mean inference task, and the [Hodgkin-Huxley
# model](https... |
c29acf6b9fb47f4cf5dee64f3d396112c388fb395aec1dae21b54bf95fd86086 | Jupyter | 31,314 | 1,375 | # %% [markdown]
# # Ordering by metrics and retraining phase
#
# ## Dataset: CIFAR-10
# %% [markdown]
# ## Experiment configuration 1
# %% [markdown]
# ## Configuration 1
# 1. Incremental guided retraining starting from scratch using the new adversarial inputs and original training set
# %%
pip install --user tens... |
dac3a5b4adfc6e9dd5cb39bffc2d19708d82dd0ed69301f6dd4d1c9e62d91424 | Jupyter | 31,460 | 1,361 | # %% [markdown]
# # Ordering by metrics and retraining phase
#
# ## Dataset: MNIST
# %% [markdown]
# ## Experiment configuration 1
# %% [markdown]
# ## Configuration 1
# 1. Incremental guided retraining starting from scratch using the new adversarial inputs and original training set
# %%
#pip install --user tensor... |
a149962b809350fc63fa91aa390a60738f9e1220522eb58fb4a2153698e54a5f | Jupyter | 31,875 | 1,380 | # %% [markdown]
# # Ordering by metrics and retraining phase
#
# ## Dataset: MNIST
# %% [markdown]
# ## Experiment configuration 2
# %%
import argparse
import numpy as np
import tensorflow as tf
import tensorflow.keras.backend as K
import matplotlib.pyplot as plt
#from keras.utils import np_utils
from tensorflow.... |
ca5b0da5eb731b6f28f85efb5b515dc38f342959b861c3c647f2155ccb5eec02 | Jupyter | 31,881 | 1,420 | # %% [markdown]
# # Ordering by metrics and retraining phase
#
# ## Dataset: CIFAR-10
# %% [markdown]
# ## Experiment configuration 1
# %% [markdown]
# ## Configuration 1
# 1. Incremental guided retraining starting from scratch using the new adversarial inputs and original training set
# %%
#pip install --user ten... |
50a0f08c4db1a755f440f9d929510d47ce7f1066f5727d70d456f7ddb138e061 | Jupyter | 32,301 | 1,440 | # %% [markdown]
# # Ordering by metrics and retraining phase
#
# ## Dataset: Intel
# %% [markdown]
# ## Experiment configuration 1
# %% [markdown]
# ## Configuration 1
# 1. Incremental guided retraining starting from scratch using the new adversarial inputs and original training set
# %%
pip install --user tensorf... |
0b09ab2fc2ea80fccf46575a70848b88c24d5ebabd8a383da70cdf97cf02ea1f | Jupyter | 32,359 | 952 | # %%
from Bio.SeqUtils import MeltingTemp as mt
from Bio.SeqUtils import Seq
from oligo_designer_toolsuite.oligo_property_calculator import calc_tm_nn, calc_gc_content
# %%
import numpy as np
import pandas as pd
from scipy.sparse import lil_matrix
class OligoOverlapCalculatorNew:
def _get_overlapping_matrix(sel... |
395f54a8f6cab78edf4053cfe849cf05969dd506d1b9e2d6cb094a9eb567b42f | Jupyter | 32,531 | 1,415 | # %% [markdown]
# # Ordering by metrics and retraining phase
#
# ## Dataset: CIFAR-10
# %% [markdown]
# ## Experiment configuration 2
# %%
pip install --user tensorflow==2.5
# %%
import argparse
import numpy as np
import tensorflow as tf
import keras.backend as K
import matplotlib.pyplot as plt
from keras.utils ... |
2f5a7bfe9f73558e5cbcd5be7cc44a01438dd40a4042f8aeac0016ae844c6056 | Jupyter | 33,239 | 605 | # %% [markdown]
# # Local Classifier Two-Sample Tests (L-C2ST)
#
#
# After a density estimator has been trained with simulated data to obtain a posterior, the estimator should be made subject to several diagnostic tests. This diagnostic should be performed before the posterior is used for inference given the actual ... |
50044174996ba2c7d1e11eddbe942f80bd37eeb6c668d34e17b06bf5c98a5b9e | Jupyter | 33,355 | 1,472 | # %% [markdown]
# # Ordering by metrics and retraining phase
#
# ## Dataset: Intel
# %% [markdown]
# ## Experiment configuration 2
# %%
pip install --user tensorflow==2.5
# %%
import argparse
import numpy as np
import tensorflow as tf
import keras.backend as K
import matplotlib.pyplot as plt
from keras.utils imp... |
6a572521e2a6a986490c5823f1a5604bdf671ccd6272c53e00a28c9db593709d | Jupyter | 33,591 | 1,140 | # %%
%matplotlib inline
import glob
import h5py
import numpy as np
from brainspace.gradient import GradientMaps
from brainspace.gradient import alignment
import matplotlib.pyplot as plt
import matplotlib.cm as cm
from sklearn.decomposition import PCA
import helpers
HCPFilenamesList = glob.glob('../IndSubHCP/*.mat')
HC... |
04a663c3a8553b400ac2aebb355fe31b40fbf325acae555597ee74238dc6e704 | Jupyter | 34,268 | 744 | # %%
# This notebook is for generating results for all subjects with cross-validation, and saving necessary variables for mapping
# %%
# load necessary libraries
# -*- coding: utf-8 -*-
%load_ext autoreload
%autoreload 2
import os, time
# select GPU for training
os.environ["CUDA_VISIBLE_DEVICES"] = "2"
# import ten... |
ac0373c40dd22353e28c920d5ced4012d18d5c74bf9d8420e2adc179f3733bb7 | Jupyter | 34,529 | 1,208 | # %% [markdown]
# ## Molecular Signatures for Microbe-Associated Colorectal Cancers
# %%
# IMPORT STATEMENTS
import cv2
import re
import numpy as np
import matplotlib
matplotlib.rcParams['pdf.fonttype'] = 42
matplotlib.rcParams['ps.fonttype'] = 42
from matplotlib import pyplot as plt
from mpl_toolkits.axes_grid1 impor... |
20201cc5be6ad5ba25813ea41bdb015650b0a138baf943e3e44a8624ced583a4 | Jupyter | 34,881 | 1,036 | # %%
pip install tensorflow==2.5
# %%
pip install keras==2.5
# %%
pip install cv2
# %%
pip install opencv-python
# %%
from keras.datasets import mnist
from keras.utils import np_utils
import numpy as np
import keras
# %%
from matplotlib import pyplot
# %%
from sklearn.model_selection import train_test_split
imp... |
df239d3302039e57e310033f63a51122dfe46b5bf8bc764223d9e27b3dfb32bc | Jupyter | 36,007 | 954 | # %% [markdown]
# # Linear Discriminative Analysis (LDA) Feature Selection (FS) Framework
# %%
import os
import sys
import pandas as pd
import numpy as np
import math
from scipy.signal import savgol_filter
from itertools import combinations
import matplotlib.pyplot as plt
from cycler import cycler
import matplotlib... |
c8117146a6d39c98969104c4b7b60564fcc25686a9771225ce39005eae7c517d | Jupyter | 37,296 | 1,196 | # %%
import os
PODCAST_ROOT = os.environ.get("PODCAST_ROOT", "/engram/naplab/users/rs4613/Podcast") # set to your copy of the dataset
# %% [markdown]
# #### ChatGPT was used to assist writing code. The authors reviewed, edited, and take full responsibility for the content.
# %%
root_dir = f'{PODCAST_ROOT}'
res_dir ... |
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