sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
f8720859bac33ba1b19fcd442cf52607e811552482832f1d5b721684fa04dd82 | Jupyter | 19,808 | 300 | # %%
import pandas as pd
import numpy as np
import json
# %%
results = """
Camera v1
{'mae': 0.27153275613021877, 'rmse': 0.38624637618598856, 'max': 1.7149375548548547, 'failure_rate': 2.816812454605741, 'distance': 7315.052901123929, 'distance_per_intervention': 166.2512022982711, 'interventions': 44, 'whiteness': 4... |
5ba8a121340ea2fce0ee0506f3a6130c7e222ac663d8647b777fa3581c3dc992 | Jupyter | 19,863 | 630 | # %% [markdown]
# # Find the best parameters for the RL model and fit
# %%
%load_ext autoreload
%autoreload 2
%matplotlib inline
# %%
from tqdm.auto import tqdm
from rl_analysis.util import count_transitions
from rl_analysis.io.df import dlight_exclude
from rl_analysis.models_rl.models import simulate
from rl_analysi... |
d71f10249683d62ccf37e532928ee297e6c0d6a45612de4237dfaa3f44f89683 | Jupyter | 20,094 | 490 | # %%
import shutil
import time
from typing import final
from openai import AsyncOpenAI
import json
import os
import asyncio
import pandas as pd
# OPENAI - TEXT ---------------------------------------------
async def OpenAI_QA_FunctionCall_general(message_content:str, model_name: str, openai_api,
... |
47f9fa224b6728deba6e03c95990fc16d0f3588a055e82a6fb48223664bb849c | Jupyter | 20,097 | 587 | # %% [markdown]
# <a href="https://colab.research.google.com/github/Poirazi-Lab/dendrify/blob/main/paper_figures/Fig4_notebook.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# ## Overview
# > *In this interactive Python notebook, we... |
4d76ef46b3ecef4be35f6f26dc4ce3d05d6e0a6b31ed5abcc383516c4bb66d29 | Jupyter | 20,118 | 588 | # %% [markdown]
# <a href="https://colab.research.google.com/github/Poirazi-Lab/dendrify/blob/main/paper_figures/Fig4_notebook.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# ## Overview
# > *In this interactive Python notebook, we... |
0c8487c24dc0923befc2b0895217d1f9d0055f78d02121d49e4518d28ccd8520 | Jupyter | 20,124 | 640 | # %% [markdown]
# ##### This notebook creates STDNN-FP (pytorch) for classification prediction on RTECS
#
# RTECS dataset has been commented out.
#
# Notebook shows results for seed = 124, but we also ran on seed 122, 123.
#
# Before use define desired pathways to save models,:
# - path variable, in "Create checkp... |
20c42fef12c5d2b728e9516a964c349f5d68b8a3ce6f490b14c1ecf32f32a1be | Jupyter | 20,138 | 394 | # %% [markdown]
# # Repoerting MNI/PPMI/ADNI data demographics and initial test for L2 based matching
# This is the notebook for dataset demographics visualziation and choosing the propor testing the matching the control subjects with our ET subjects to creat new subsamples that is
# ## Data:
# - 1. MNI demograph... |
3f6a9d07be8722c08f414a04949133892a10ff0acca67f8daa10b252e70658c7 | Jupyter | 20,168 | 649 | # %% [markdown]
# ##### This notebook creates STDNN-SE (pytorch) for classification prediction on RTECS
#
# RTECS dataset has been commented out.
#
# Notebook shows results for seed = 124, but we also ran on seed 122, 123.
#
# Before use define desired pathways to save models,:
# - path variable, in "Create checkp... |
448238a50dd4af26b549829b8e92e01b961f0d24b2f30197affe9129ff988481 | Jupyter | 20,283 | 446 | # %% [markdown]
# # Synthetic generation
# Notebook used to make whole stacks of synthetic data based on single step results (synthetic_tests.ipynb).
#
# **Note that there are a lot of hard coded numbers below. They are/have been empirically tuned by visual comparison to real data.**
# **Note:** the script `run_ge... |
4396d25382a05a3d61622f0a61b375a2836fc97591cd20321e5b3f1ac4c95aa5 | Jupyter | 20,286 | 742 | # %%
%load_ext autoreload
%autoreload 2
%matplotlib inline
# %%
from rl_analysis.plotting import savefig, setup_plotting_env, clean_ticks, geno_palette
from rl_analysis.io.df import dlight_exclude_toml
from rl_analysis.photometry.snippet import get_shuffle_average
from functools import partial
from tqdm.auto import tq... |
4fbd920f156bf78106006a9543b6aded2e56a34f3c8d3501a7a332c321ab03ae | Jupyter | 20,320 | 575 | # %% [markdown]
# # Annotate experiments
# Notebook used to create binary annotations for experiments using a computer vision approach, or deep networks.
# Manual tuning and correction are used to improve the quality of the detection, before it is used as ground truths.
# %%
%matplotlib inline
import os, sys, time,... |
4b98178250a57d9593bffb4173c5399672090af1f6305d6f8c1e64dc94655693 | Jupyter | 20,332 | 666 | # %% [markdown]
# # Extracting Reaction Networks from RHEA
#
# This notebook shows how to extract edges connecting RHEA reactions based on shared non-currency metabolites. The resulting network can be used to analyze the connectivity of RHEA reactions.
#
#
# %%
# TAXID = "9606" # Hs
TAXID = "7227" # Dmel
OUTDIR =... |
85216fefc19a495525f5073eb9a4710b264c76307b29c1bd04bb3a31a4f3de5b | Jupyter | 20,334 | 643 | # %% [markdown]
# ##### This notebook creates STDNN-FP (pytorch) for classification prediction on ClinTox
#
# Using split data already saved.
#
# Notebook shows results for seed = 124, but we also ran on seed 122, 123.
#
# Before use define desired pathways to save models,:
# - path variable, in "Create checkpoint"... |
8fc749d7696538e1ad72ff7b43ade329ae83d3fdf12d49845d06ef323c50a835 | Jupyter | 20,343 | 648 | # %% [markdown]
# ##### This notebook creates STDNN-SE (pytorch) for classification prediction on Tox21
#
# Using split data already saved.
#
# Notebook shows results for seed = 124, but we also ran on seed 122, 123.
#
# Before use define desired pathways to save models,:
# - path variable, in "Create checkpoint" s... |
de37713389d595cbfac1af9abe5b2da233c580aae3c71fa62ec96058dce994ff | Jupyter | 20,460 | 645 | # %% [markdown]
# ##### This notebook creates STDNN-FP (pytorch) for classification prediction on Tox21
#
# Using split data already saved.
#
# Notebook shows results for seed = 124, but we also ran on seed 122, 123.
#
# Before use define desired pathways to save models,:
# - path variable, in "Create checkpoint" s... |
1e525fd13a57cd46423c2823ea0641e2cc197abd9721835b9e115cf0c35ad442 | Jupyter | 20,466 | 657 | # %% [markdown]
# ##### This notebook creates STDNN-SE (pytorch) for classification prediction on ClinTox
#
# Using split data already saved.
#
# Notebook shows results for seed = 124, but we also ran on seed 122, 123.
#
# Before use define desired pathways to save models,:
# - path variable, in "Create checkpoint"... |
e40a17278917cff28ecc800fdb5e7314fab535bed9b2f54b14e79d37392c5817 | Jupyter | 20,512 | 603 | # %% [markdown]
# ## NoteBook to Train and Visualize the Features Network
# %%
# Importing bunch of libraries
import os
import cv2 as cv
import time
import random
import warnings
import numpy as np
import matplotlib.pyplot as plt
from tqdm import tqdm
from itertools import chain
from skimage.io import imread, imshow... |
3d269fd1860a84ad531f84632d73449e9f9e9862ebd41f6f03b848c949a05b97 | Jupyter | 20,638 | 691 | # %%
"""
jupyter-nbconvert report_arch.ipynb --to python
python -u report_arch.py --expt_dir ~/plume/plumezoo/latest/fly/memory/
"""
# %%
from __future__ import division
import os
import glob
import pickle
from natsort import natsorted
import argparse
import os
import sys
import numpy as np
import tqdm
import pandas a... |
ee1ba6a661270396d08b4ad10ee9d9de1c696720652d414bcf3c5632806a94b5 | Jupyter | 20,925 | 555 | # %%
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib as mpl
import pickle
mpl.rcParams.update({'font.size': 14, 'axes.labelsize': 'medium', 'axes.titlesize': 'medium', 'legend.fontsize': 12})
# %%
def fold_analysis(run_name):
dfs_fold = []
for fol... |
bee436aef35a38e7017174c1b331aed9ab481e4ada801437b1a8d22f159540ca | Jupyter | 21,164 | 643 | # %%
import pymesh
# %%
import numpy as np
import sys
sys.path.append("../../../source/")
# %%
ground_truth_dir = 'ground_truth/'
masif_pred_dir = 'masif_pred/'
transient_interactions_file = 'testing_transient.txt'
# List of sppider_predictions
sppider_pred_dir = 'sppider_pred/'
# List of intpred_predictions
intpre... |
ab896ecfecc097de5a44a67de11bf4d88b180b9db35812c1186dd23f4e569031 | Jupyter | 21,202 | 837 | # %%
%load_ext autoreload
%autoreload 2
%matplotlib inline
# %%
use_platform = "cpu"
# %%
if use_platform == "cpu":
import numpyro
import multiprocessing as mp
numpyro.set_host_device_count(mp.cpu_count() * 10)
numpyro.set_platform("cpu")
# %%
import jax.numpy as jnp
import jax
from jax.example_libra... |
0358bc8fac6b5f4e24b97999535fcd4eb56d544f2bfa9eff092e7865d90bedcd | Jupyter | 21,224 | 575 | # %% [markdown]
# # Tracking test
# Perform test for the neuron tracking (identities).
# Inputs are RGB and binary detection frames, and the tracker outputs frames similar to the binary segmentation with a different values for each neuron identity:
# * 0: background pixels
# * 1: pixels of the axon n°1
# * 2:... |
5b6b64dad63fcba54828aa8131b9e798014ae377bdd21f4d7aed392b892480e3 | Jupyter | 21,449 | 487 | # %% [markdown]
# # Hsp70 protein Notebook
# The Hsp70 protein is chaperone protein; they can assist folding and assembly of newly synthesized proteins, trigger refolding cycles of misfolded proteins, transport unfolded proteins through organelle membranes, and when necessary, deliver non-functional proteins to the pro... |
30b504d691eee5f6fdb74cd084f664edd3dfee1386cfb7a9f4d61fa2b57a6922 | Jupyter | 21,465 | 685 | # %% [markdown]
# ##### This notebook creates MTDNN-SE (pytorch) for classification prediction on ClinTox and RTECS
#
# Using split data already saved.
#
# RTECS dataset has been commented out.
#
# Notebook shows results for seed = 124, but we also ran on seed 122, 123.
#
# Before use define desired pathways to s... |
c58b8bb54cd4b6a55e7746d46cd84cbae8437d1385f06bb81f304588144847c1 | Jupyter | 21,513 | 671 | # %%
from functools import partial
import json
import os
import pickle
from datetime import datetime
import ee
import geemap
from multiprocessing.dummy import Pool as ThreadPool
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from tqdm.notebook import tqdm
import sys
sys.path.append('../')
from... |
506842d8f5874ee9c7e3a5f40b0c5658051fd4e92b3ac5f231dd152d9f45c9f6 | Jupyter | 21,677 | 682 | # %% [markdown]
# ##### This notebook creates MTDNN-FP (pytorch) for classification prediction on ClinTox and RTECS
#
# Using split data already saved.
#
# RTECS dataset has been commented out.
#
# Notebook shows results for seed = 124, but we also ran on seed 122, 123.
#
# Before use define desired pathways to s... |
29c99792218f8a0f84c991e976cda5e8339eadc236c07c694b4bf14fc9c106e0 | Jupyter | 21,753 | 685 | # %% [markdown]
# # Setup
#
# %% [markdown]
# ## Get files / dependencies
# %%
%tensorflow_version 1
# %%
!python --version
# %%
!git clone https://github.com/google-research/proteinfer
%cd proteinfer
!pip3 install -qr requirements.txt
import pandas as pd
import tensorflow
import inference
import parenthood_l... |
62273d95d5681f8c673e054bb56ac9c0c3b9584845acc1858e585a785f46288e | Jupyter | 21,783 | 693 | # %% [markdown]
# ##### This notebook creates MTDNN-SE (pytorch) for classification prediction on Tox21 and RTECS
#
# Using split data already saved.
#
# RTECS dataset has been commented out.
#
# Notebook shows results for seed = 124, but we also ran on seed 122, 123.
#
# Before use define desired pathways to sav... |
d827ae6a9adb18583cb4a3c77dbf08db0aa38eaaecce76b03d1b2d8485f6aef0 | Jupyter | 21,874 | 553 | # %% [markdown]
# ### This notebook takes the reader through all the test data used throughout the manuscript and creates clean and usable files for each panel
#
# For each saved file, the fields correspond to :
# <pre>
# `sequence` : the sequence
# `Measured Expression` : the measured expression (or expression change... |
71a0bf5ce3e87db81c1981a2229a1297802f7d2f20a5de321b760b0e11ca2bc3 | Jupyter | 21,951 | 820 | # %%
%load_ext autoreload
%autoreload 2
%matplotlib inline
# %%
from tqdm.auto import tqdm
from rl_analysis.plotting import (
setup_plotting_env,
clean_ticks,
clean_plot_labels,
format_pval,
savefig
)
from rl_analysis.regression import model_fit
from rl_analysis.io.df import dlight_exclude
from rl_... |
440b09a10b7348f0d6d15212925a09e1f7c724c368304cb7adb8c8f9d3003d11 | Jupyter | 21,977 | 692 | # %% [markdown]
# ##### This notebook creates MTDNN-SE (pytorch) for classification prediction on Tox21, ClinTox, and RTECS
#
# Using split data already saved.
#
# Notebook shows results for seed = 124, but we also ran on seed 122, 123.
#
# Before use define desired pathways to save models,:
# - path variable, in "... |
d746200118abbb61ec4374443c1057f4b8d76327deed92bde7924038b4aae5cd | Jupyter | 22,005 | 689 | # %% [markdown]
# ##### This notebook creates MTDNN-FP (pytorch) for classification prediction on Tox21 and RTECS
#
# Using split data already saved.
#
# RTECS dataset has been commented out.
#
# Notebook shows results for seed = 124, but we also ran on seed 122, 123.
#
# Before use define desired pathways to sav... |
9f6f17c5940c934b92b89c2683c1c5365b5a8362f6b1a83c3944bc21e2d6d032 | Jupyter | 22,102 | 688 | # %% [markdown]
# ##### This notebook creates MTDNN-FP (pytorch) for classification prediction on Tox21 and ClinTox
#
# Using split data already saved.
#
# Notebook shows results for seed = 124, but we also ran on seed 122, 123.
#
# Before use define desired pathways to save models,:
# - path variable, in "Create c... |
5c7aaa4570bc16f0a9c8a9b33105ba5690f8e5e9f8bd9e90bad4c12468377fcb | Jupyter | 22,223 | 519 | # %% [markdown]
# ## 1: Import required packages & functions <a class="anchor" id="chapter1"></a>
# %%
print('Importing libraries')
%reset -f
import warnings
warnings.filterwarnings("ignore", category=FutureWarning)
# clear all variables
for i in list(globals().keys()):
if(i[0] != '_'):
exec('del {}'.fo... |
5c612c0ce47686d0bed89d51f224c0c23b51cd3f138728a0e0d810c4d59ecf2c | Jupyter | 22,248 | 608 | # %% [markdown]
# ## NoteBook to Train and Visualize the U-Net Network
# %%
# Importing bunch of libraries
import os
import sys
import time
import random
import warnings
import cv2 as cv
import numpy as np
import matplotlib.pyplot as plt
from tqdm import tqdm_notebook as tqdm
from skimage.io import imread, imshow
fro... |
7b8737301b981a5745592c6fa6dc019e9057ab351cf7dbc7e04a25acbf7a83a1 | Jupyter | 22,306 | 621 | # %%
from __future__ import division
import os
import math
import random
import scipy.stats as stats
import numpy as np
import seaborn as sns
import pymworks
import pickle
from collections import Counter
import matplotlib.pyplot as plt
%matplotlib inline
# %%
sns.set_style('ticks')
# %%
pickle_files_have_phases = Fal... |
5da9a068a6825414047dbf1f92740ccb8078c3d6c513589935e0e9765512a45d | Jupyter | 22,316 | 717 | # %%
%pylab inline
%config InlineBackend.figure_format = 'retina'
#pip install pytorch-lightning
# pip install ipywidgets
# %%
gpu_info = !nvidia-smi
gpu_info = '\n'.join(gpu_info)
if gpu_info.find('failed') >= 0:
print('Not connected to a GPU')
else:
print(gpu_info)
# %%
#install pyg
import os
import torch
os.... |
a96f92b8b6b73b3888a2fcdcadb66a33b219b771c2538f2ae987149e5c86e14b | Jupyter | 22,471 | 632 | # %% [markdown]
# # Train a Patch Classifier via Weak Labeling
# %% [markdown]
# This notebook takes strongly labeled data, generates an ensemble of patch classifiers, trains an SVM, then uses all of this to generate weak labels on a ton of unlabeled data. This unlabeled data with weak labels is then combined with the... |
9e7184b5bbf51d5e75e09a78fdbe92b93033500ee12ff7c1dcf01f1e02e70e0e | Jupyter | 22,706 | 711 | # %% [markdown]
# ##### This notebook creates MTDNN-SE (pytorch) for classification prediction on Tox21, ClinTox, and RTECS
#
# Using split data already saved.
#
# RTECS dataset has been commented out.
#
# Notebook shows results for seed = 124, but we also ran on seed 122, 123.
#
# Before use define desired pathw... |
17ef9799657bfac78d0e492375b33bef52f6f8a5d6a92ed1c2b3714ffe2322c8 | Jupyter | 22,878 | 647 | # %% [markdown]
# # Knowledge Assembly
# %%
from network import Network
from helpers import fit_sigmoid, mean_squared_error, rotate, load_behavioural_data
from plotting import plotting_init, matrix_plot, mds_plot
import torch
import numpy as np
from scipy.special import expit
from sklearn import manifold
from sklearn... |
5cfc46766ba7f3f27fbdfb9e8bda0e644750e6bbf7b254aff56d99b4265ce3f3 | Jupyter | 22,953 | 715 | # %% [markdown]
# ##### This notebook creates MTDNN-FP (pytorch) for classification prediction on Tox21, ClinTox, and RTECS
#
# Using split data already saved.
#
# RTECS dataset has been commented out.
#
# Notebook shows results for seed = 124, but we also ran on seed 122, 123.
#
#
# Before use define desired pa... |
2c0828bafb61b1273ef1700ee681c50fdc18f298bbd7994a31739cc1c8a7d3da | Jupyter | 22,985 | 902 | # %%
%load_ext autoreload
%autoreload 2
%matplotlib inline
# %%
from tqdm.auto import tqdm
from rl_analysis.plotting import (
setup_plotting_env,
geno_palette,
syllable_aliases,
savefig,
plot_pval,
clean_ticks
)
from rl_analysis.batch import apply_parallel_joblib
from rl_analysis.behavior.util ... |
0cf3f962052e436f569bc333d65eb12adb273f45e1bccc2952741fbea0e16b60 | Jupyter | 23,108 | 635 | # %% [markdown]
# ## NoteBook to Optimize the Parameters for the U-Net Network
# %%
# Importing bunch of libraries
import os
import sys
import time
import random
import warnings
import cv2 as cv
import numpy as np
import matplotlib.pyplot as plt
from tqdm import tqdm_notebook as tqdm
from skimage.io import imread, i... |
239b861e501954471dc6bdb17bbfa1191f26669f1d94bfd9b8dca03070cf1fab | Jupyter | 23,138 | 537 | # %% [markdown]
# # Weighted Margin Classifier (QNN)
# *Reformulation of [PennyLane's Margin Classifier](https://pennylane.ai/qml/demos/tutorial_multiclass_classification.html). Disabled random batch sampling from training set per iteration.*
# - **Weighted**: weighted loss function to handle class imbalance
# - **Marg... |
1826f58e213b4851df32aa56e7c1d4847895cfc5df1ffd06c1759d457417f81c | Jupyter | 23,274 | 785 | # %%
"""
jupyter-nbconvert report_correlations.ipynb --to python; python -u report_correlations.py
MDIRS=$(ls -d trained_models/ExptDynamic20210521/*VRNN*/)
for MDIR in $MDIRS; do
python -u report_correlations.py --model_dir $MDIR
done
"""
# %%
from __future__ import division
import os
import glob
import pickle
fro... |
679f01463fa451fcc5907402a4aa537bfdfca8000eae54550f7aeb0518d66d12 | Jupyter | 23,279 | 618 | # %%
import numpy as np
import pandas as pd
import os
from pathlib import Path
import selfies as sf
from rdkit import Chem
import pandas as pd
# %% [markdown]
# # Molecule retrieval from Zinc20 smi files
# %%
Is_data_prepared = True
if not Is_data_prepared:
tranche_dirs = ['FK', 'DC', 'BB', 'JA', 'HE', 'GA'... |
72b72d872ce7d8845cc7b5661c9bae7cb30ab562e43b11472df15364005822f8 | Jupyter | 23,322 | 396 | # %% [markdown]
# # Getting Started with Selene
#
# This tutorial explores the core components of Selene, and should teach you everything you need to know to train a simple model on biological sequence data.
# Before starting this tutorial, you need to install Selene.
# Instructions for installation are available [he... |
fb2cfa451a3d2124ad829f193278a977ecfd90cf0caa84100f1a5094d05ab284 | Jupyter | 23,353 | 475 | # %% [markdown]
# # Where Ontology Similarity and Text-Embedding Similarity Disagree
#
# For a sample of term pairs in each of several ontologies, we compare:
#
# - **ontology similarity**, computed from the terms' ancestor closures:
# 1. Jaccard over `is_a` ancestors;
# 2. Jaccard over `is_a` + `part_of` ancesto... |
11c988af8b986ed0ee34e9689d5e8412d6e912a1d2634a950c7a34ba6d4a551b | Jupyter | 23,416 | 692 | # %%
# Change directory to VSCode workspace root so that relative path loads work correctly. Turn this addition off with the DataScience.changeDirOnImportExport setting
# ms-python.python added
import os
try:
os.chdir(os.path.join(os.getcwd(), '../..'))
print(os.getcwd())
os.chdir("/Users/bpedigo/JHU_code/maggot_mod... |
415e03f3aeb0681c1540bdd670d2db5157d9472eaa731f8c7adbf37bdf549f02 | Jupyter | 23,598 | 717 | # %%
import numpy as np
from tensorflow import keras
from tensorflow.keras import layers
# %%
import pickle
import pandas as pd
import matplotlib.pyplot as plt
import os
from sklearn.preprocessing import normalize as sklearn_normalize
from sklearn.utils import shuffle
from tqdm.notebook import tqdm
from matplotlib imp... |
4bd6a744315516e4cf542a10b85ce57b5dace016dac92fd9bfac4af80ee3df73 | Jupyter | 23,814 | 779 | # %%
import os
import sys
module_path = os.path.abspath(os.path.join('..'))
if module_path not in sys.path:
sys.path.append(module_path)
# %%
from visualization_utils import (
ImageType, generate_aim_spec, align_aims,
read_image_to_numpy, single_axis_image_and_masks
)
import numpy as np
import pandas as ... |
bbc174803816a81e043e6075d8b6d88da047ddb96855be54d535f5704fe4ce0e | Jupyter | 23,945 | 894 | # %%
%load_ext autoreload
%autoreload 2
%matplotlib inline
# %%
from tqdm.auto import tqdm
from rl_analysis.plotting import (
setup_plotting_env,
clean_plot_labels,
savefig,
clean_ticks,
plot_pval,
format_pval,
geno_palette,
)
from sklearn import model_selection
from copy import deepcopy
fr... |
d0264e22cd618f66a03a74162ce91389a6464123450df9c41ccbe5adcdd61488 | Jupyter | 23,976 | 880 | # %%
%load_ext autoreload
%autoreload 2
%matplotlib inline
# %%
from rl_analysis.plotting import (
setup_plotting_env,
geno_palette,
syllable_aliases,
lighten_color,
clean_ticks,
savefig
)
from collections import defaultdict
from scipy.stats import pearsonr
from sklearn.utils import shuffle
fro... |
861147935e4631e3f75fc53674703eb44f45bf1175bea05688f3f6ed55082b87 | Jupyter | 24,695 | 772 | # %% [markdown]
# # Setup
#
# %% [markdown]
# ## Get files / dependencies
# %%
%tensorflow_version 1
# %%
!git clone https://github.com/google-research/proteinfer
%cd proteinfer
!pip3 install -qr requirements.txt
import pandas as pd
import tensorflow
import inference
import parenthood_lib
import baseline_utils... |
25668952d7a844a25ffccae5ea3b91c68b051165c26e361b307c91357f704e13 | Jupyter | 24,855 | 639 | # %%
import numpy as np
import pandas as pd
from scipy.io import loadmat
import glob
import os
import sys
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(style="white")
from scipy import stats
from mne.viz import circular_layout, plot_connectivity_circle
import statsmodels.api as sm
from statsmodels.form... |
c36eeb6a07505b09dab320a2d19884ef3946de62c1946b3b7689775be221b5d5 | Jupyter | 25,002 | 954 | # %%
from manager import ModelManager
from tables import Cells, Model_Waveforms, Models, Cell_Closest_Ephyz_Clusters
from pandas import DataFrame
import numpy as np
import pandas
from matplotlib import pyplot as plt
import string
from scipy.stats import pearsonr
import statsmodels.formula.api as smf
import hdbscan
fr... |
10cc4911f659a7f15847ddee792a188b2199798e2c403675b4b9dab5a31ab6d2 | Jupyter | 25,285 | 786 | # %% [markdown]
# # Libraies
# %%
import warnings
import os
import sys
import sqlite3
import time
import numpy as np
import random
import pandas as pd
import math
import matplotlib.pyplot as plt
from sklearn.model_selection import StratifiedKFold
from sklearn.metrics import auc
from sklearn.metrics import roc_auc_sco... |
272d589690ed0c00a22e0705bf2f22b7687c33892cc802285bfeb9a132951dbd | Jupyter | 25,797 | 593 | # %%
import ipywidgets as wg
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import igraph
import copy
from mpl_toolkits.mplot3d import Axes3D
from IPython.display import Image
from jie.demo import mock_experiment, error_types, polymer_model, polymer_skip, find_polymer
from jie.aligner import ... |
c027ad7225b83bbb2f640c833e0c2982190f0b67c7253d731bb75ba5ed1dfaba | Jupyter | 26,464 | 545 | # %% [markdown]
# # About the Model
#
# TODO (advantages, disadvantages, some pictures from the application note, ...)
#
# ## Data Preparation
#
# In this tutorial, we assume you already extracted the required data and models with the download utility as described in the [README](README.md) file. They should be loca... |
6424e681d6394a0d53fc2a282c2b35879e7cd2866dd67297fd86128c9ab0e1fa | Jupyter | 26,484 | 359 | # %% [markdown]
# # Merging volumentric ROI measures and freesurfer surface measures.
# - Cortical thickness/volume/aseg/wmparc data generated by "../tab_data/freesurfer_stats2table.sh" from fMRIPrep_20.2.0 freesurfer_6.0.1
# - Cerebellar segmetations in both SUIT34 and MDTB10 spaces.
# - Uses participants data (../tab... |
d9ba244f652c7f0675ee467f8a9baaaf6f01e18118dbfd101c0157d8ce6d41c7 | Jupyter | 26,534 | 796 | # %% [markdown]
# # Plot yeast RBD DMS escape maps
# %% [markdown]
# ## Import modules and read data
# Import Python modules:
# %%
import itertools
import os
import altair as alt
import numpy
import pandas as pd
import sklearn.manifold
# %% [markdown]
# Disable max rows specifier for Altair:
# %%
_ = alt.data_t... |
319250ee6016ffd4402a1391b11c1b06908da920e0ac9c9c05b4b739130132b5 | Jupyter | 27,486 | 740 | # %%
""" If not already in your environment, install necessary packages."""
#!pip install pickle, scipy
# %%
from common_functions import *
from plotting_functions import *
# %%
import math
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
import pickle
from random import randint
# %%
p_val... |
1ae1cf68d8faab8fb472d8a69ebaf13084e1c03115f852281dd9c36616fcfb48 | Jupyter | 28,259 | 728 | # %% [markdown]
# ## LLMs
# %%
#%pip install shap %pip install xgboost
import pandas as pd
import shap
import matplotlib.pyplot as plt
import openpyxl
import numpy as np
# %%
excel_path= r"C:\Users\LEGION\Documents\GIT\Tehran_COVID_Cohort\DO_NOT_PUBLISH\LLM_X_test_dataset.xlsx"
ground_truth_col = 'Inhospital Mortalit... |
d06d007980213c43ef190a0ac57f5ec1b972c10bdbe00e5c775199a211de59c8 | Jupyter | 28,580 | 496 | # %% [markdown]
# ## Power analysis for: Reproducibility of cerebellum atrophy involvement in advanced ET.
#
# 1. Working with only MNI dataset will result in underpowered research:
# posthoc power analysis with alpha=0.05, et=38, nc=32 and effect size 0.61 (obtained from literature median, both 1-sided and 2... |
aaf9d78883dc0937ba8770ef5f3e54c8691b9b157d5314be08e5954af0cf3ce1 | Jupyter | 29,185 | 837 | # %% [markdown]
# <a href="https://colab.research.google.com/github/Poirazi-Lab/dendrify/blob/main/paper_figures/Fig5_notebook.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# ## Overview
# > *In this interactive Python notebook, we... |
3d061ed9c991862dc560b21490f1ad05f7b080e9c6b1d3f9bb4e05ea355c2e90 | Jupyter | 29,206 | 838 | # %% [markdown]
# <a href="https://colab.research.google.com/github/Poirazi-Lab/dendrify/blob/main/paper_figures/Fig5_notebook.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# ## Overview
# > *In this interactive Python notebook, we... |
d5b3bff0a23f4e77d53c3d901e4b09412926ce9ddae56b81372952e506d8f3de | Jupyter | 29,343 | 826 | # %% [markdown]
# ### Getting started
# %%
import warnings
warnings.filterwarnings("ignore", message="Boolean inputs to the `inclusive` argument are deprecated infavour of `both` or `neither`.")
import numpy as np
import pandas as pd
import copy
import tqdm
import matplotlib.pyplot as plt
from collections import de... |
3970cc0ef462b23e0eb7197b89915c5115da4a6bfc9539758223b0ef7b8a3504 | Jupyter | 29,483 | 684 | # %% [markdown]
# ## Script to parse behavioral data from pickle files
#
# The purpose of this notebook is to parse the behavioral data in the appropriate pickle files into a format that is easily usable for analysis and plotting.
#
# The pickle files for each animal contain dictionaries where the keys are session na... |
fd0a46e415bd4ebc8045516a29ef51bc2d435149bb27e64e5510efb83ea05120 | Jupyter | 29,501 | 808 | # %% [markdown]
# ### Getting started
# %%
import warnings
warnings.filterwarnings("ignore", message="Boolean inputs to the `inclusive` argument are deprecated infavour of `both` or `neither`.")
import numpy as np
import pandas as pd
import copy
import tqdm
import matplotlib.pyplot as plt
from collections import de... |
2ee5a16f690e59806eea36f1e08ccf914118925f02fbe8fb03a1d41e62f81960 | Jupyter | 29,829 | 1,170 | # %% [markdown]
# # CHEBI Slimmer
#
# Creates a simplified version of CHEBI by conflating all members of a conjugate clique.
# %% [markdown]
# ## Initial setup
#
# Imports and use OAK to get an adapter to CHEBI sqlite database.
# %%
from typing import Optional, List
from collections import defaultdict
import panda... |
b61dc0b41da2dee94f370300452ab3dbeb536a0431f4980fc40cd90b0a2902b9 | Jupyter | 30,429 | 1,101 | # %%
%load_ext autoreload
%autoreload 2
%matplotlib inline
# %%
from tqdm.auto import tqdm
from rl_analysis.plotting import (
setup_plotting_env,
clean_ticks,
savefig,
)
from scipy.stats import mannwhitneyu
from rl_analysis.photometry.decoding.models import exp_smoother
from rl_analysis.photometry.decodi... |
2eae73e083153c6ae3bb337959bd2656d18b9290d02f2264575347fb00a29976 | Jupyter | 31,392 | 617 | # %%
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import pickle
import scipy.stats
import plotting_functions as pf
import analysis_functions as af
import sklearn
pf.set_fontsize(font_size=12)
%load_ext autoreload
%autoreload 2
plt.rcParams['font.family'] = 'sans-serif'
plt.rcParams['font.sans-... |
a18a32f8843c869daff795fcd11b8c7f519f0b86b42c0950935c4a7cb33d81b8 | Jupyter | 31,686 | 1,187 | # %%
import math
import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
import pytorch_lightning as pl
import torch.nn.functional as F
from pytorch_lightning import Trainer
from torchnlp.nn.attention import Attention
# %%
from blitz.modules imp... |
0fe9c275b6ef4ed6bc0e7d20623a95659bcc22952cc987ea4765c7f18e450760 | Jupyter | 31,704 | 1,078 | # %% [markdown]
# # This notebook plots figures that are used to analyse CASM dataset.
#
# Smaple data is provided if the size is < 25 mb. Otherwise, the relevant script used to create the necessary input data file on the cloud is referenced.
# %% [markdown]
# List of the small files available to run this notebook:... |
289b693da62fab77e0422f6a639d065022d8411be33efdbdc4cd036bf4de0e21 | Jupyter | 32,123 | 825 | # %%
# If not already in your environment, install necessary packages.
#!pip install pickle, scipy
# %%
from common_functions import *
from plotting_functions import *
# %%
import math
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
import pickle
from scipy.stats import zscore, pearsonr, m... |
90ff046f0b5cc30e9b603f351d64ce8be5cb4b3c4b92502a1bb484ab457fa161 | Jupyter | 32,127 | 815 | # %% [markdown]
# ## 1: Import required packages & functions <a class="anchor" id="chapter1"></a>
# %%
print('Importing libraries')
%reset -f
import warnings
warnings.filterwarnings("ignore", category=FutureWarning)
# clear all variables
for i in list(globals().keys()):
if(i[0] != '_'):
exec('del {}'.fo... |
e5938cb3d4352d744eff4d03471ac37fa1ecbe05ad70e41732212877bb9947e8 | Jupyter | 32,693 | 823 | # %% [markdown]
# # Synthetic data test
# This notebook was used to perform tests for the synthetic data generation.
# **Note that there are a lot of hard coded numbers below. They are/have been empirically tuned by visual comparison to real data.**
# Steps:
# 1. Add neurons as gaussians (foreground)
# 2. Defor... |
100ae83a88366d258571efa15cb2c9b38086593faed7a75061ea79148da8f1a3 | Jupyter | 32,801 | 767 | # %%
import sys
import os
import glob
import collections
import shutil
import subprocess
import numpy as np
import pandas as pd
from openeye import oechem
from scipy import stats
from sklearn.metrics import roc_auc_score, roc_curve
from sklearn.utils import resample
from sklearn.linear_model import LinearRegression
... |
35caed593bc5e76da741bd39b32ccf8b5a4bc61fe04263bd8e301ba98b497688 | Jupyter | 33,682 | 1,007 | # %%
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
import numpy as np
import os
import pickle
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import normalize as sklearn_normalize
from sklearn.utils import shuffle
from tensorflow import keras
from tensorflow.keras i... |
8bb2733c361e454b9c5882402c7dbd97bc1869b1078a555efd88612849ae1570 | Jupyter | 34,169 | 970 | # %%
import numpy as np
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA
import copy
import torch
import torch.nn.functional as F
import sys
sys.path.append('/content/drive/MyDrive/neuro_research/stp_networks/')
import networks as nets
import net_utils as net_utils
import int_data as syn
import... |
0b40a0b7ef8c2bad1dbe185534c8f2758b10fbfe3dde665ccd71c112c7500b3d | Jupyter | 34,785 | 1,073 | # %% [markdown]
# # Gain of DRN populations and networks
#
# Shown in figs. 5, 6, 5S3.
# %%
from common import colors, pvalue_thresholds, savefig
from gain_util import (
infer_gaba_condition,
get_step_amplitudes,
GainSimulationVisualizer,
PSTHToGainIllustration,
)
# %%
from __future__ import division... |
e80872dc96c9f524d31506508adacf10450cf6b4cca3fbaadd20a7643985abd6 | Jupyter | 34,960 | 1,264 | # %%
%load_ext autoreload
%autoreload 2
%matplotlib inline
# %%
from tqdm.auto import tqdm
from rl_analysis.plotting import (
setup_plotting_env,
syllable_aliases,
clean_ticks,
format_pval,
savefig
)
from rl_analysis.util import whiten
from rl_analysis.photometry.encoding_average.util import run_in... |
3d91cc9c08acca1be4e0a078b462314b0042a7af9ede00ba837d5ef11e57c75f | Jupyter | 35,350 | 1,113 | # %% [markdown]
# # Import Packages
# %%
# Default packages
import os
from os.path import join
from datetime import date, datetime, timedelta
from joblib import load, dump
# Set current directory to "src"
os.chdir(join(os.getcwd(), os.pardir, "src"))
print(f"Current working directory: {os.getcwd()}")
# Installed pac... |
f97675450340e9458741abf7275c1bfc302349b0dd49f0179d98fa6b561035d8 | Jupyter | 35,921 | 563 | # %% [markdown]
# # Recurrent Neural Network
#
# Historically, [there have been many attempts](https://doi.org/10.3390/ijms22041676) to develop an algorithm that can generate chemical entities with desirable properties. Many of the early methods relied on evolutionary aglorithms, but today deep neural networks have be... |
743966b3b0bdad7d05041a918486d03c5f616d0616b829dd0a09d6bb9fad9bec | Jupyter | 36,030 | 682 | # %% [markdown]
# # Deception Detection supported by Machine Learning
# %% [markdown]
# ## Literature Review - Mind maps
# This notebook is devoted to show all the mind maps built as a summary of all the selected studies.
#
# Mind maps are ascendingly sorted by publishing year and title. Each section shows the articl... |
a61fc014d2a68c6a280ed893b3028941837dd64ac770c3095d3cf40ec225af5c | Jupyter | 36,059 | 854 | # %% [markdown]
# ## Script to parse behavioral data from pickle files
#
# The purpose of this notebook is to parse the behavioral data in the appropriate pickle files into a format that is easily usable for analysis and plotting.
#
# The pickle files for each animal contain dictionaries where the keys are session na... |
c168eebf6b19cf130b3fe32a6eb5b01c5179c03fec7cc73c83c410c978084949 | Jupyter | 36,086 | 1,316 | # %%
import matplotlib.pyplot as plt
import numpy as np
import torch
from cgtasknet.tasks.reduce import (
CtxDMTaskParameters,
DMTaskParameters,
DMTaskRandomModParameters,
GoDlTaskParameters,
GoDlTaskRandomModParameters,
GoRtTaskParameters,
GoRtTaskRandomModParameters,
GoTaskParameters,
... |
789b28808487d65a15e8480437ca10b18391987320ce0d2c08c7713f36e6352c | Jupyter | 36,207 | 892 | # %% [markdown]
# ## Script to parse behavioral data from pickle files
#
# The purpose of this notebook is to parse the behavioral data in the appropriate pickle files into a format that is easily usable for analysis and plotting.
#
# The pickle files for each animal contain dictionaries where the keys are session na... |
5356ca0fccecc8c2d518c234302e815c51f21cc1e2176396f5b5b38f0060c221 | Jupyter | 36,360 | 1,054 | # %% [markdown]
# # TQIP Data Preprocessing
# %% [markdown]
# This notebook illustrates how we preprocessed the data for the paper: "Assesing the Utility of Deep Neural Networks in Dynamic Risk Prediction After Trauma"
#
# The TQIP database can be requested through the American College of Surgeons website at
# https... |
c98aa05ddc6f32c975806e42cac534bcf3bd75cbdb7167e82b14a9c1e10c29b1 | Jupyter | 37,175 | 1,013 | # %% [markdown]
# # AxoID with Optic Flow Warping
# Notebook used to test detection and tracking together to start the work for AxoID.
# This is the second iteration with use of Optic Flow Warping to get automatically good annotations.
#
# Workflow:
# 1. Detection:
# 1. Make a binary segmentation out of t... |
3ff2d591c351034fef70d58c8bc3be5ed32a088838f5383c7648961888b67c52 | Jupyter | 38,552 | 1,048 | # %% [markdown]
# # Whole-brain recordings of spontaneous activity of larval zebrafish
#
# Light-sheet fluorescence microscopy is a novel imaging method for simultaneously recording the activity of whole brains of small vertebrae (larval zebrafish) at neuron scale (~40K neurons).
# The measured patterns of spontaneou... |
e7c07bbbbd56e26b41d9e9aa171a77b2c648a63de686b3c984cdfce600581622 | Jupyter | 38,853 | 1,080 | # %% [markdown]
# This notebook is used to study model performance on the stability datasets from Rocklin et al. (2017) https://www.science.org/doi/10.1126/science.aan0693
# %%
import Bio.PDB
import numpy as np
import pandas as pd
import re
from matplotlib import pyplot as plt
from scipy.stats import ttest_rel
# %%... |
1f08aa4c2d058b579eb4749753af6355307a98fc46f27dc2af0f079748179aac | Jupyter | 39,067 | 1,009 | # %% [markdown]
# ## 1: Import required packages & functions <a class="anchor" id="chapter1"></a>
# %%
print('Importing libraries')
%reset -f
import warnings
warnings.filterwarnings("ignore", category=FutureWarning)
# clear all variables
for i in list(globals().keys()):
if(i[0] != '_'):
exec('del {}'.fo... |
2cda874894c615e98cc87b999e6009112a88d8dd3f03b87f5da6ffa1690fb7a7 | Jupyter | 39,614 | 1,087 | # %% [markdown]
# This is a notebook which combines the code from notebooks 1_Concat_scoring_method to 4_analyse_benchmark into one notebook for faster analysis, but even more condensed than the pipeline notebook for faster analysis, only computing correlation coefficients between experimental and predicted energies. T... |
5e8be01f130236976a6cc8e7912efb0caec7d1d1ed4fe1b6fe1abf1c166e5c14 | Jupyter | 39,919 | 1,012 | # %%
import torch
import torch.nn.functional as F
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
# %%
def sortcery_loss(etab, E_idx, data):
''' Compute the mean squared error between the etab's predicted energies for peptide-protein complexes and experimental energies derived from SORT... |
8ba705441a1a574eb464cf2b65524f4452ef3db870b703febdd5e90a0df36928 | Jupyter | 40,065 | 732 | # %% [markdown]
# # Cerebellar volumetry analysis with GLM
# ## Data:
# - 1. Freesurfer results: ICV. Lh/Rh_cerebellum_gm/wm (generated by "collect_volumes.sh" script);
# - Left/ Right cerebellum gray/white matter volumes
# - 2. Cerebellar SUIT segmentation with SUIT and MAGeT (MDTB) atlas;
# ... |
fabe4d062ca34ea5469985b0eed864a4bc1a07ee6a4e3c5d6841b27b552e32e5 | Jupyter | 40,575 | 1,298 | # %% [markdown]
# This is a notebook which combines the code from notebooks 1_Concat_scoring_method to 4_analyse_benchmark into one notebook for faster analysis. These notebooks are taken from Frappier et al. (2019) https://www.sciencedirect.com/science/article/pii/S0969212619300085
# %%
import glob
import re
import o... |
13867e1c56320fe7b58ec589fff5e69dd5b7d50da464ee3ecfa9354bc052e95c | Jupyter | 40,588 | 1,253 | # %% [markdown]
# ##### This notebook creates Transfer Learning (TL) experiment, with base model trained on RTECS, transferred to predict on ClinTox
#
# Using split data already saved.
#
# RTECS dataset has been commented out.
#
# Notebook shows results for seed = 124, but we also ran on seed 122, 123.
#
# To cre... |
cba3dc5a866f7c12a0999c9e4c339902f2b91a1143f4a868d56c1fa672160ff4 | Jupyter | 40,621 | 1,252 | # %% [markdown]
# ##### This notebook creates Transfer Learning (TL) experiment, with base model trained on Tox21, transferred to predict on ClinTox
#
# Using split data already saved.
#
# Notebook shows results for seed = 124, but we also ran on seed 122, 123.
#
# To create TL model. Base model was trained with a ... |
09b6e892bd62fd630b15fec84c69785065d00bc483e5f3e56a52102da11de8d5 | Jupyter | 41,085 | 329 | # %% [markdown]
# # Output neurons and learning
# In this tutorial we add Mushroom Body Output Neurons (MBONS) to the model and train the weights connecting them to the Kenyon Cells using a simple event-driven STDP rule.
#
# ## Install PyGeNN wheel from Google Drive
# Download wheel file
# %%
if "google.colab" in str... |
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