sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
2b39f6ef52cfe70b85ec38a65bfe73960a2cc9528201a817c8d31d68cf27aeae | Julia | 45,109 | 1,325 | #############################################################
####### SIMULATION FUNCTIONS FOR WHOLE DATA SET #############
#############################################################
function create_covariate_dataset(Nid)
## base on distribution from validation dataset
d_debutalder = truncated(Norma... |
c0532a2c8c93aa18a26e5022d0ec0e6b6d46b37ed6a794d0498d0fd816f1921b | Julia | 97,164 | 2,720 | ### A Pluto.jl notebook ###
# v0.20.17
using Markdown
using InteractiveUtils
# This Pluto notebook uses @bind for interactivity. When running this notebook outside of Pluto, the following 'mock version' of @bind gives bound variables a default value (instead of an error).
macro bind(def, element)
#! format: off
... |
298d41e651e144862b1799155570d6b5cec74c1bb1a26cb5c34e672ab7dd058e | Jupyter | 16 | 5 | # %%
pwd
# %%
|
98798b009f1a649a6ad66a43a458e178198aca3f06f860a1ba6c5b068685dd7c | Jupyter | 125 | 17 | # %%
from joblib import load
# %%
data = load('./ESOL_attentiveFP.data')
# %%
len(data)
# %%
data[0]
# %%
data[1]
# %%
|
b044052025b0efddde3a563804d253183dbbd27ee09e86d3810dd77710ee8814 | Jupyter | 174 | 10 | # %%
from scipy.stats import binomtest
# Active = best
binomtest(17, 22, p=0.5, alternative="greater")
# %%
# Sham = worst
binomtest(15, 22, p=0.5, alternative="greater")
|
bf7f13f97113de1643f297af89e6c91d66241719d14b83736570cc48ea7f5094 | Jupyter | 193 | 15 | # %%
import pandas as pd
df = pd.read_pickle("pubmed_abstracts_miRNA.pkl")
print(f"Loaded {len(df)} entries")
print("Columns:", df.columns.tolist())
df.head()
# %%
df['abstract'][0]
# %%
|
6286a34cd6c60a5f05d02aa4e6e691cf9ce7628c6a0e0491d1aa4851e9b5ee82 | Jupyter | 200 | 19 | # %%
import pandas as pd
# %%
#pd.read_pickle('./descriptor_scale.cfg')
# %%
from __init__ import load_config
# %%
df = load_config(ftype='fingerprint', metric='correlation')
# %%
ls -lh
# %%
|
288423b7fc1a5d1d56df48fbc380773e2defa0581c66212f5aa2189de81603a3 | Jupyter | 243 | 19 | # %%
from joblib import load
# %%
train, valid, test = load('./ESOL_train_valid_test.data')
# %%
print(len(train), len(valid), len(test))
# %%
train.to_csv('./train.csv')
valid.to_csv('./valid.csv')
test.to_csv('./test.csv')
# %%
# %%
|
dd39b52eb5bc926a4d166c019a2a4bbdf005af5c2b96da47716d5fc6d3d5dba4 | Jupyter | 281 | 17 | # %%
import sys
sys.path.insert(0, '/home/shenwanxiang/Research/bidd-molmap/')
from molmap.feature.sequence.aas.local_feature.aai import load_index, load_all
from molmap.feature.sequence.nas.global_feature import nac
# %%
# %%
ls -lh ./result_data/
# %%
load_all().data
# %%
|
471061206e2bbee6619d1ab8c05170cd8cc8fa6a293e9b270b5d3ec66775b6ca | Jupyter | 400 | 13 | # %%
#Fetch ESM2
import torch
torch.hub.set_dir("checkpoints/esm2")
model, alphabet = torch.hub.load("facebookresearch/esm:main", "esm2_t6_8M_UR50D")
# %%
#Fetch DR-BERT
from transformers import AutoModelForTokenClassification, AutoTokenizer
checkpoint = "checkpoints/drbert"
tokenizer = AutoTokenizer.from_pretrained(... |
62fdd18268e7ac182f8b44b3010f34cc227e7f3020de5a607ba90b33cdb695b9 | Jupyter | 444 | 37 | # %%
import molmap
# %%
ftypes = ['descriptor', 'fingerprint']
methods = ['umap', 'tsne', 'mds']
metrics = ['cosine', 'correlation']
# %%
for ftype in ftypes:
for method in methods:
for metric in metrics:
mp = molmap.MolMap(ftype = ftype, metric= metric)
mp.fit(method = method)
... |
2382ca00bc8e3baccd1849838810a6aa774745fa7588d18d46fc6b9e7acc91a7 | Jupyter | 450 | 7 | # %%
#Dispatcher code for training. Ensure correct dataset is in "data" folder and named data.json
python scripts/dispatcher.py --config configs/protein_localization/full_prot_comp_pred.json --log_dir /home/shd-sun-lab/protgps/logs
# %%
#Remember to look into: /Synapse Navigation/protgps/datasets/protein_compartments.... |
0056a2082bfa78ecc23c58dfdaec201af77ccc82b90d5c50dc789f6c834e78d9 | Jupyter | 517 | 42 | # %%
%config Completer.use_jedi = False
# %%
from molmap import LocalAASeqMolMap
# %%
lamp = LocalAASeqMolMap()
# %%
lamp.df_index1
# %%
lamp.df_index2
# %%
lamp.df_index3
# %%
lamp.fit('MLMPKKNRIAIHELLFKEGVMVAKKDVHMPKHPELAD')
# %%
import seaborn as sns
# %%
sns.heatmap(lamp.get_matrices_aas_orders())
# %%
s... |
d2d045b6c4b9f745881aa9402682886a00f074b30de2b806f08c314f1e00f6d6 | Jupyter | 540 | 32 | # %%
import pandas as pd
import numpy as np
%config Completer.use_jedi = False
# %%
aas = '''MVADPPRDSKGLAAAEPTANGGLALASIEDQGAAAGGYCGSRDQVRRCLRANLLVLLTVVAVVAGVALGLGVSGAGGALALGPERLSAFVFPGELLLRLLRMIILPLVVCSLI'''
import seaborn as sns
# %%
from molmap import LocalAASeqMolMap
# %%
lamp = LocalAASeqMolMap()
# %%
lamp.fi... |
10a7ce864f26cbaf76145addbf1ce5756cace96850f5bac241ef05e33cb33d4d | Jupyter | 561 | 29 | # %% [markdown]
# # Stats for Figure 4
# %%
import pandas as pd
import numpy as np
import statsmodels.api as sm
import statsmodels.formula.api as smf
# %% [markdown]
# ### Load data
# %%
nmda = pd.read_csv('../data/DendEventTimes/nmda_spk_times.csv')
nmda
# %%
mean_fr = pd.read_csv('../data/Figure4a.csv')
mean_fr
... |
3024649184632bc6507c9346cdd1c2b7939ef397a9848cc1b161187b2c8c3c19 | Jupyter | 575 | 20 | # %% [markdown]
# ```{currentmodule} optimap
# ```
# %%
from optimap.utils import jupyter_render_animation as render
# %% [markdown]
# ```{tip}
# Download this tutorial as a {download}`Jupyter notebook <converted/smoothing.ipynb>`, or a {download}`python script <converted/smoothing.py>` with code cells.
# ```
# %% [... |
3529af8b4aee95b527cb49efadbecd35932487f0e4de857f25bd6b77198cabc3 | Jupyter | 598 | 20 | # %% [markdown]
# ```{currentmodule} optimap
# ```
# %%
from optimap.utils import jupyter_render_animation as render
# %% [markdown]
# ```{tip}
# Download this tutorial as a {download}`Jupyter notebook <converted/plotting.ipynb>`, or a {download}`python script <converted/plotting.py>` with code cells. We highly recom... |
95f64f90eb09279341aa64ce23b24fac747211e218672310f0ba752bee0b941d | Jupyter | 667 | 20 | # %% [markdown]
# ```{currentmodule} optimap
# ```
# %%
from optimap.utils import jupyter_render_animation as render
# %% [markdown]
# ```{tip}
# Download this tutorial as a {download}`Jupyter notebook <converted/cv.ipynb>`, or as a {download}`python script <converted/cv.py>` with code cells. We highly recommend usin... |
e37ffaa830be957b56a744644cf27356e078ae7f1d4a60b74c6dcf6c3083ecb0 | Jupyter | 685 | 20 | # %% [markdown]
# ```{currentmodule} optimap
# ```
# %%
from optimap.utils import jupyter_render_animation as render
# %% [markdown]
# ```{tip}
# Download this tutorial as a {download}`Jupyter notebook <converted/plotting.ipynb>`, or a {download}`python script <converted/plotting.py>` with code cells. We highly recom... |
af88e3bebbe556da6533a52acd9131967ca372599ff9986af61fe9535d3450cd | Jupyter | 745 | 33 | # %% [markdown]
# # AttentiveFP has bugs about splitting this dataset
#
# The 'ToxCast_attentiveFP.data' comes from [AttentiveFP](https://github.com/OpenDrugAI/AttentiveFP/blob/master/code/2_Physiology_or_Toxicity_ToxCast.ipynb)
#
# at line 14, we saved their train_df, valid_df and test_df
#
#
# ```python
# from jo... |
311d501e9f04612aa49ff0f573ac15432acf837592d55875af784c488f3b8087 | Jupyter | 790 | 72 | # %%
import pandas as pd
# %%
df = pd.read_csv('./bace.csv')
# %%
dfs = pd.read_csv('untitled.txt')
dff = df[df.smiles.isin(dfs.smiles)].reset_index(drop=True)
dff = dff.sort_values('Class')
dff = dff.sort_values('pIC50', ascending=False).reset_index(drop=True)
# %%
dff
# %%
# %%
dff.to_csv('./debug2.csv')
# %%
... |
83d56a3eff760383a17d8ed4800c72f3b7e958f8e9595d5548ba4346bc0be77e | Jupyter | 791 | 38 | # %% [markdown]
# # Stats for Figure 1
# %%
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# %% [markdown]
# ### Load data
# %%
fr_spon = pd.read_csv('../data/Figure1FR.csv')
fr_spon
# %% [markdown]
# ### Describe the properties of the firing rates
# %%
fr_spon['firing_rate'].describe()
# ... |
f6a5d2a12a5f818688daec6d4a1e18454e79e4be64981f847a835fdf481c5738 | Jupyter | 817 | 32 | # %% [markdown]
# # Counting multi-task model parameters in paper of :
# ### `Prediction of Human Cytochrome P450 Inhibition Using a Multitask Deep Autoencoder Neural Network`
# ### the autoencode part isn't included
# %%
from tensorflow.keras.utils import plot_model
from tensorflow.keras import Model, Input
from tens... |
fb448dd2103fcce7377ec7b03fa46832927c1377905e0eb60a4681523e528145 | Jupyter | 828 | 29 | # %%
import pandas as pd
from collections import defaultdict
# Load the DataFrame
dir_ids_seq = 'data_ids_seq/mirna_mirna_sequences_WITHIDS.pkl'
df = pd.read_pickle(dir_ids_seq)
# Dictionary: sequence → list of {"ID": ..., "Category": ...}
dict_seq_to_id_type = defaultdict(list)
# Iterate over both x and y columns
f... |
6affe4d7feba1b219d8f782f8373bdd7ea4fc58475501ed6b1871d79dc71fb95 | Jupyter | 831 | 40 | # %%
import molmap
import os
# %%
data_save_folder = '/raid/shenwanxiang/FP_maps'
# %%
ls -lh /raid/shenwanxiang/FP_maps
# %%
if not os.path.exists(data_save_folder):
os.makedirs(data_save_folder)
# %%
metric = 'cosine'
method = 'umap'
n_neighbors = 30
min_dist = 0.1
# %%
bitsinfo = molmap.feature.fingerprint.... |
15e43cdad617546deb0084033872e799411dcbb91b4e471315619f33bf87c5f0 | Jupyter | 834 | 28 | # %%
import numpy as np
import scipy.signal as ss
import matplotlib.pyplot as plt
# %%
def minmax(x):
"""min max normalizes the given array"""
return (x - np.min(x))/(np.max(x)-np.min(x))
B_old = [0.049922035, -0.095993537, 0.050612699, -0.004408786]
A_old = [1, -2.494956002, 2.017265875, -0.522189400]
B_... |
131dfffe8f69d62bec803e685bc319a35ded557cdef38065ef06aee3077089d5 | Jupyter | 837 | 26 | # %%
from parse_prt_file import parse_prt_file
conditions = parse_prt_file('/Users/alexandresayal/GitHub/phd-main-exp/prt/interhemisphericLocalizer340v.prt')
# Print the extracted data
for condition, trials in conditions.items():
print(f"Condition: {condition}")
for onset, offset in trials:
print(f" ... |
6fe6d01eee0de22e087910a2ac2b443ff781110c2e55c2d53a9dbf13832b78d0 | Jupyter | 874 | 30 | # %% [markdown]
# # Approve Hits
# %%
import boto3
# %%
aws_access_key_id = "AKIAJ7ODKKKLEQNUJYZA"
aws_secret_access_key = "XXXXX"
aws_region = "us-east-1"
client = boto3.client('mturk', region_name= "us-east-1", aws_access_key_id=aws_access_key_id, aws_secret_access_key=aws_secret_access_key)
# %%
client.approve_as... |
1ef24549285ae92fca3312e71b38681e231a20368d539c11b1a92dea0273393e | Jupyter | 878 | 52 | # %%
from molmap import dataset
from molmap import loadmap
import molmap
import matplotlib.pyplot as plt
from joblib import dump, load
from tqdm import tqdm
import pandas as pd
import numpy as np
tqdm.pandas(ascii=True)
# %% [markdown]
# # list of various types of fingerprint
# %%
bitsinfo = molmap.feature.fingerpri... |
4ab3b564149f0ca92c9b8bea9087aca7c93e009e3fc0e6a5dfe9006a408068cd | Jupyter | 884 | 53 | # %% [markdown]
# ## In case you need to tune the hyperparameter about the feature map object, you can rearrange your feature values X by the rearrangement method, you don't need to extract the feature values again
# %%
import molmap
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
# %%
mp_cosin... |
0249c8c90d57958ad8ac42a4cc2df8a3a174e515aba546f20432e6383edb75e1 | Jupyter | 913 | 43 | # %%
%config Completer.use_jedi = False
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from molmap import PDB2Fmap
# %% [markdown]
# ## transofrm self properties
# %%
pm = PDB2Fmap(embd_grain='all', fmap_shape=None)
pm.fit(pdb_file='./1a1e/1a1e_protein.pdb', embd_chain='... |
b85dc2778a44e0ea8e61d559b98a3c805a2e755428b33b601fdb70ca6abf52fd | Jupyter | 916 | 55 | # %%
from molmap import dataset
from molmap import loadmap
import molmap
import matplotlib.pyplot as plt
from joblib import dump, load
from tqdm import tqdm
import pandas as pd
import numpy as np
tqdm.pandas(ascii=True)
# %%
molmap.feature.fingerprint.Extraction({'RDkitFP':{}}).bitsinfo
# %% [markdown]
# # list of v... |
6ad856809339ceefb14d31b15e8f1ca22011121ace541ba66833431b21a1a78c | Jupyter | 917 | 36 | # %%
pip install brainsmash
# %%
import numpy as np
import time
from datetime import datetime, timedelta
from brainsmash.mapgen.base import Base
from scipy.io import savemat
num_genes = 763
num_surrogates = 1000
num_regions = 213
dist_mat_file = "distance_matrix.txt"
surrogate_genes = np.zeros((num_surrogates, num_r... |
f8e2388c2db7248a79c8f0aee03fecdb1edfe37b91a138dc2fd4825fd686a101 | Jupyter | 930 | 54 | # %%
from molmap import dataset
from molmap import loadmap
import molmap
import matplotlib.pyplot as plt
from joblib import dump, load
from tqdm import tqdm
import pandas as pd
import numpy as np
tqdm.pandas(ascii=True)
# %% [markdown]
# # list of various types of fingerprint
# %%
bitsinfo = molmap.feature.fingerpri... |
b72cde0a07888303e1685f8b50fc5b04ce967840350194c5242302cfdf13a0ef | Jupyter | 936 | 41 | # %% [markdown]
# %%
import os
from src.my_settings import settings
from src.utils import apply_mask
sett = settings()
# %%
task_run_labels = [
"task-loc_run-1",
"task-nf_run-1",
"task-nf_run-2",
"task-sham_run-1",
"task-sham_run-2",
]
# %%
# apply functional mask to each run of a subject
for ... |
0460781070215b408bde8a536a35fe97a50d84815302bbd7c88683ca61b17883 | Jupyter | 965 | 71 | # %%
%config Completer.use_jedi = False
# %%
from molmap import GlobAASeqMolMap
# %%
ps0 = 'MLMPKKNRIAIHELLFKEGVMVAKKDVHMPKHPELAD'
ps1 = 'MQSLKSMLMPKKNRIAIHELLFKNVPNLHVMKA'
ps2 = 'KEGVMVAKKDVHMPKHPELADKNVPNLHVMKAMQSLK'
ps3 = 'MQSLKSMLMPKKNRIAIHVPNLHVMKANLHVMK'
ps4 = 'KEKKDVHMPKHPELADKNVPNLHVMKAMQSLK'
ps5 = 'MPKHPELAD... |
00551127517b701dd381a1c01b8e90d4349e677ff08d701965ad24b1f1185e42 | Jupyter | 987 | 47 | # %% [markdown]
# ## Imports
# %%
from skimage import io
import numpy as np
from arcos4py.tools import track_events_image
import matplotlib.pyplot as plt
# %% [markdown]
# ## Use track_events_image to track objects in a binary image
# %%
img1 = io.imread("sample_data/pix/2_crossing.tif")
img2 = io.imread("sample_dat... |
a47ebbea598f8d7af34ebe7d94f877ca8afcb6268d4ce1336e9f143e4ee8d499 | Jupyter | 1,018 | 38 | # %% [markdown]
# # Attach PZA PDB to PncA AlphaFold Predicted Structures PDBs
# %%
from tqdm import tqdm
import os
# %%
def concat_pdb(file1, file2, output_file):
with open(file1, "rb") as f1, open(file2, "rb") as f2:
lines1 = f1.readlines()
lines1 = lines1[:-1]
data2 = f2.read()
#... |
7b8cb5ff8d66df097f0fbcc8d5f0371b7884f128ccdec62ecb48c437600f7638 | Jupyter | 1,049 | 33 | # %%
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
# %%
log_file = 'ESOL_Wed_Jan_29_11-02-29_2020.log'
df = pd.read_csv(log_file)
n_neighbors_list = df.n_neighbors.unique()
min_dist_list = df.min_dist.unique()
x = df.valid_best_rmse.values.reshape(len(n_neighbors_list), len(min_dist_list))
... |
82bd7b5bdcbdd71407634f9f34de7cb75e961b6f5ef58686da88fb960c9e2cf7 | Jupyter | 1,066 | 50 | # %% [markdown]
# ## Data preprocessing
# Let's first load the packages that will be necessary for the analysis and download the data from Nanostring's webiste.
# %%
import anndata as ad
import pandas as pd
import scanpy as sc
import squidpy as sq
import numpy as np
import os
import matplotlib.pyplot as plt
# %%
int_... |
d6251fc2b4334f36db54b63f8c88a68843e970b62f9673b02c0e776b9ce492bd | Jupyter | 1,066 | 45 | # %%
import numpy as np
import holoviews as hv
from numpy.linalg import svd
from holoviews import opts
hv.extension('matplotlib')
# %%
ap_na = np.load('apical_na.npy')
ap_nmda = np.load('apical_nmda.npy')
ba_na = np.load('basal_na.npy')
ba_nmda = np.load('basal_nmda.npy')
# %%
def PlotMat3d(mat):
return hv.Imag... |
4070395dedf247c287b2811347400e42f94ea214282f7074e5b09ede8f03f444 | Jupyter | 1,089 | 35 | # %% [markdown]
# # Chronological Diagnostic Algorithm for Parkinsonism
#
# A machine learning-based diagnostic tool that predicts neuropathology in patients with parkinsonism using chronological clinical presentations.
#
# **Key Features:**
# - Achieves 0.83 AUROC for predicting 9 diagnostic categories at 3 years po... |
adac53c0fba0a4e46633bbfaa145e5fd2599f05ba5a421caf6638501b0db61fa | Jupyter | 1,099 | 65 | # %%
import pandas as pd
# %%
df1 = pd.read_csv('./MolMap-OOB/bace_bbbp_hiv.csv')
df1
# %%
df2 = pd.read_csv('./MolMap-OOB/sider_toxcast_tox21.csv')
df2
# %%
df3 = pd.read_csv('./MolMap-OOB/freesolv_esol_malaria.csv')
df3
# %%
model_name = 'MMNB-OOTB'
# %%
df1['test_metric'] = 'ROC_AUC'
df1['test_performance'] = d... |
b369423cf0b066cbe7024d8eeda20295901272aa972e8ff8ee2595c0a6fc6e55 | Jupyter | 1,101 | 34 | # %%
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
# %%
log_file = 'Tox21_search_Mon_Feb__3_11-00-12_2020.log'
df = pd.read_csv(log_file)
n_neighbors_list = df.n_neighbors.unique()
min_dist_list = df.min_dist.unique()
x = df.valid_best_auc.values.reshape(len(n_neighbors_list), len(min_dist_... |
ff559ff3fbe2ff9259a37de80dbfe9a5dd93fecfb082dd1a0eaf621cc6649457 | Jupyter | 1,110 | 60 | # %% [markdown]
# # Counting multi-task model parameters in paper of :
# ### `Predictive Multitask Deep Neural Network Models for ADME-Tox Properties: Learning from Large Data Sets`
# %% [markdown]
# ## Single/multi task
# %%
from tensorflow.keras.utils import plot_model
from tensorflow.keras import Model, Input
from... |
e8811ccf48fb60e3d73b9cd697b5f529e263e7a661de0c45f0c855cf3edd5e0d | Jupyter | 1,113 | 56 | # %%
import torch
import glob
import os
import cv2
import matplotlib.pyplot as plt
import random
from torchvision import transforms
# %%
from google.colab import drive
drive.mount('/content/drive')
# %%
train_image_path = os.path.join(
'..',
'content',
'drive',
'MyDrive',
'ResNetModel',
'inpu... |
6a4c49ef001db0889ef36d56cd2ae7c3943a8fb5f927b5d962110a0fd773aa61 | Jupyter | 1,128 | 54 | # %%
from molmap import loadmap
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
np.random.seed(123)
# %%
aspirin = 'CC(=O)OC1=CC=CC=C1C(O)=O' #aspirin
smiles_list = [aspirin]
mp1 = loadmap('./descriptor.mp')
mp2 = loadmap('./fingerprint.mp')
# %%
X1 = mp1.batch_transform(smiles_list)
X2 = mp... |
1f38fb0c859dc5a6914b10988ebc5c1925cf646d48e871a2f169a48a1b8a1503 | Jupyter | 1,185 | 34 | # %% [markdown]
# # Using a custom masker
#
# Here we show how to provide a custom Masker to any of the SHAP model agnostic explanation methods. Masking can often be domain dependent and so it often helpful to consider alternative ways to perturb your data beyond the default ones included with SHAP.
# %%
import xgboo... |
6395640dbc0cd0c8dbf270207a08bdbac66ef1d32776d386de424420875fa69f | Jupyter | 1,191 | 58 | # %%
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
# %%
# %%
# %%
log_file = 'BACE_bace_search_Wed_Sep_16_13-06-16_2020.log'
df = pd.read_csv(log_file)
n_neighbors_list = df.n_neighbors.unique()
min_dist_list = df.min_dist.unique()
len(n_neighbors_list), len(min_dist_list)
x = df.va... |
700e0a64d01afeb4596100233a4f82683bbf7cc3a101a4f3e13e5393ff0fa1ab | Jupyter | 1,234 | 48 | # %% [markdown]
# # ConvpaintModel Class
# %% [markdown]
# <img src='../images/CPM_architecture.png' style='width: 800px' />
# %%
from bs4 import BeautifulSoup
from IPython.display import display, HTML
# %%
fix_css = """
<style>
.doc.doc-object.doc-class {
background: transparent !important;
border: none !im... |
5632f5de265dc43ec85cf1b7c89cc3bae833a1e192b974510726784c55d91f69 | Jupyter | 1,266 | 42 | # %%
import torch
import scanpy as sc
sn_data = sc.read_h5ad('/cluster/home/sunyk/deeplearning/.sun_algo/test_data/reference.h5ad')
st_data = sc.read_h5ad('/cluster/home/sunyk/deeplearning/.sun_algo/test_data/query.h5ad')
sn_data.var_names = sn_data.var['features']
st_data.var_names = st_data.var['features']
# %%
im... |
543e45ad4f5f58ed34b6aa350751ebf5bcc57412a22e1098f0559c4d62d7e494 | Jupyter | 1,267 | 48 | # %% [markdown]
# # 00 settings
# %%
import numpy as np
# %% [markdown]
# # 01 find burst periods
# %%
def func_find_burst(test_signal):
BurstOrNot = np.logical_or(test_signal > 1.5*np.sqrt(np.var(test_signal)),
test_signal < -1.5*np.sqrt(np.var(test_signal)))
smooth_Burst... |
58bff0971b4f84b2079ba37b5d776bbf1a03f952c5cd97251d912581fc71cb59 | Jupyter | 1,286 | 46 | # %%
import sys
sys.path.insert(0, '/home/shenwanxiang/Research/bidd-molmap/')
from molmap.feature.sequence.nas.global_feature.nac import Kmer, RevcKmer, IDkmer
# kmer = nac.Kmer(k=5, normalize=True, upto=True) #4**5 + 4**4 + 4**3 + 4**2 + 4**1
# revkmer = nac.RevcKmer(k=5, normalize=True, upto=True)
# idkmer = nac.IDk... |
2630576356140f6206c41ac9ca241b7e26022effc820bb87a8f85fd89fb96e15 | Jupyter | 1,296 | 62 | # %%
import anndata as ad
import squidpy as sq
import cellcharter as cc
import pandas as pd
import scanpy as sc
import numpy as np
import matplotlib.pyplot as plt
# %% [markdown]
# ## Load data
# %%
path = "analysis/adata_obj/"
fig_path = 'figures/scatter_plots_k60/'
adata = sc.read_h5ad(path + "adata.h5ad")
batch_ke... |
fba9a26c30440738be485d7b9605626adea3315062d05e1eb472b86841ffbed2 | Jupyter | 1,296 | 47 | # %% [markdown]
# # Run from Database Example
# %%
from pathlib import Path
from sqlalchemy import create_engine
from cali.runner import CaliRunner
from cali.sqlmodel import AnalysisSettings
from cali.sqlmodel._model import Experiment
from cali.sqlmodel._visualize_experiment import print_cali_results
# %%
database_... |
a5d60eaa60c52ecc02c61f9cf2254c0d9af32a94e256604dfa6e3f4633c12028 | Jupyter | 1,325 | 47 | # %%
import pandas as pd
from glob import glob
# %%
csvs = glob('./results/*.csv')
r = []
for csv in csvs:
df = pd.read_csv(csv, index_col = 0)
df['model'] = csv.split('results_')[1].split('_')[0]
r.append(df)
# %%
dfres = pd.concat(r)
# %%
def format_groud(df):
res = {
'train_rmse': '%... |
3214f2c0ac41f51c99a9dfb20026f60a6d11c6d2f5377f8f982e7536793e26fb | Jupyter | 1,366 | 42 | # %%
from rlign import Rlign
from neurokit2.ecg import ecg_simulate
import numpy as np
import matplotlib.pyplot as plt
# %%
X = ecg_simulate(sampling_rate=100, heart_rate=60, noise=0.35, heart_rate_std=15).reshape(1, 1, 1000)
# %%
X.shape
# %%
plt.title("Input ECG")
plt.plot(X[0, 0], color="black", alpha=.8)
plt.xti... |
f54cb65dcabea3ce8a05e66d261c5d2be1714d34872ab0c7fbd0d5569945f855 | Jupyter | 1,369 | 59 | # %% [markdown]
# # Second Level GLMs
# For the Localizer, NF, and Sham Runs.
# %%
from src.my_settings import settings
from src.glm import secondlevel
from nilearn import plotting as nlp
from nilearn.glm import threshold_stats_img
sett = settings()
alpha = 0.05
hc = 'bonferroni'
ct = 10
# %% [markdown]
# ## NF Run... |
3d610a157d594165e2e4219ff0407ab65ff21df78295e78a9715c18410334072 | Jupyter | 1,393 | 58 | # %%
import numpy as np
from numpy.linalg import svd
import matplotlib.pyplot as plt
import h5py
# %%
temp = h5py.File("Y:\DendCompOsc\\16Hzapical_exc_mod\output_16Hz_dend_inh_0deg_exc_10p\\v_report.h5", "r")
# %%
print(temp['report']['biophysical']['data'])
# %%
test1 = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]).res... |
f0dce5f6cf986d6ae60ea811e35c42a0ec9b53cd6dd4b64716adc6ae5afe4e37 | Jupyter | 1,403 | 42 | # %% [markdown]
# # `image` plot
#
# This notebook is designed to demonstrate (and so document) how to use the `shap.plots.image` function.
# %%
import json
from tensorflow.keras.applications.resnet50 import ResNet50, preprocess_input
import shap
# load pre-trained model and choose two images to explain
model = Re... |
e58280e749ae92a1f55aaaac627548eccc54b9a1c7c4c3bbaa30122a5571b616 | Jupyter | 1,407 | 63 | # %%
import anndata as ad
import pandas as pd
import scanpy as sc
import scvi
import numpy as np
from lightning.pytorch import seed_everything
seed_everything(12345)
scvi.settings.seed = 12345
# %%
out_dir = "analysis/adata_obj/"
adata = sc.read_h5ad(out_dir+"/adata.h5ad")
# %%
scvi.model.SCVI.setup_anndata(
ada... |
9bef495e1f1fefc024281a6370f6976beed7dcd9c7e368077e91deeb17c6987c | Jupyter | 1,451 | 61 | # %% [markdown]
# # Testing out how to extract f/I curves from recordings
# %%
import os
import sys
sys.path.append('..') # have to do this for relative imports in jupyter
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import h5py
from src.load_spike_h5 import load_spike_h5
# %%
# files to pu... |
7c941c5489a1b1b07d0d5b28a403fe4e8fd42e96c5ac3365b50f3abff229d00e | Jupyter | 1,476 | 61 | # %% [markdown]
# # Training Curves
# %%
import wandb
import pandas as pd
import matplotlib.pyplot as plt
# %%
plt.rcParams['figure.dpi'] = 300
plt.rcParams['axes.labelsize'] = 12
# %% [markdown]
# Training data obtained from WandB run.
#
# CSV saved in data for reproducibility.
# %%
# wandb.login()
# api = wandb.... |
a5cf5ff234c5fff9a533c5ef41d7921c907cae386cdf5859e8f87c49b8770078 | Jupyter | 1,476 | 45 | # %%
import os
import numpy as np
import scipy.io
from yass.evaluate.visualization import ChristmasPlot
from yass.evaluate.util import main_channels
# %% [markdown]
# # Create Some Fake Entires That demonstrates Plotting
#
# In the constructor, give a title, number of total datasets that you want to plot side by sid... |
bfa54408d01ecaadaba546004156e6a2b7c32d4a3d7743df5b03b9a9988073dc | Jupyter | 1,479 | 43 | # %% [markdown]
# # Changing feature extractor (DINOv2) to use Convpaint for animal tracking
# %% [markdown]
# With the pretrained vision transformer DINOv2 as feature extractor, Convpaint is remarkable at detecting animal body parts - or even actions such as closing/opening eyes.
#
# Here is a sample frame from a mo... |
e437902ab206d41cbe9a7a0dde0e79636b5e682e7d057182edfa1f9977c6f10c | Jupyter | 1,500 | 94 | # %%
import molmap
# %% [markdown]
# ## Hyper parameters setting
# %%
metric = 'cosine'
method = 'umap'
n_neighbors = 30
min_dist = 0.1
# %% [markdown]
# # 1.descriptor map
# %%
mp_name = './descriptor.mp'
mp1 = molmap.MolMap(ftype = 'descriptor', metric = metric, flist = [])
mp1.fit(method = method, n_neighbors = ... |
c701cd6341977f2a22f154223fdf36a5830d541662e463fce9c849aad55fc422 | Jupyter | 1,506 | 54 | # %% [markdown]
# # Text Data Explanation Benchmarking: Abstractive Summarization
# %% [markdown]
# This notebook demonstrates how to use the benchmark utility to benchmark the performance of an explainer for text data. In this demo, we showcase explanation performance for partition explainer on an Abstractive Summari... |
e093de2325323cc2035deff2dfe2412ee4c4ee4f01c85a4c74743dee164f50a1 | Jupyter | 1,507 | 62 | # %%
from tdc.single_pred import ADME
from tdc.benchmark_group import admet_group
from molmap.model import RegressionEstimator,MultiClassEstimator
from molmap import loadmap
def fix_seed(seed = 42):
import tensorflow as tf
import os, random
import numpy as np
os.environ['PYTHONHASHSEED']=str(seed)
... |
6894187166e8d259501598413e37acb2189f46f4674ad3f0da915231020b544d | Jupyter | 1,614 | 49 | # %% [markdown]
# # Migrating to the new "Explanation" API
#
# This notebook demonstrates some of the changes to the shap API that were introduced in shap `v0.36.0`.
# %%
# An example dataset and model
import xgboost
import shap
X, y = shap.datasets.adult(n_points=100)
model = xgboost.XGBClassifier().fit(X, y)
expl... |
7db2d37ec05a9efc6fb112994491be746cec1cc187c7801050cb8c0a4fdce4b5 | Jupyter | 1,644 | 64 | # %%
import os
import numpy as np
import glob
import csv
import random
test_array = np.repeat(np.arange(6), 12)
print(test_array)
# %%
np.random.shuffle(test_array)
flag = np.hstack(([False], test_array[:-1] == test_array[1:]))
flag_array = test_array[flag]
num_ocurrences = np.sum(test_array[:-1] == test_array[1:])
... |
a0f57427ad18cfba3701e408553443719ee7bcea47ae7e5a7fb08004fca9e730 | Jupyter | 1,655 | 52 | # %% [markdown]
# # 102 Checking the information of dataset, devices
# * geting the dataset size.
# %%
from sklearn.ensemble import RandomForestClassifier,RandomForestRegressor
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split,StratifiedKFold
from sklearn.metrics import *
impo... |
332d323b6ea72dfc814adf0f0361b834f54723a5f450ca23bbf12f807c48e6c1 | Jupyter | 1,664 | 55 | # %% [markdown]
# # Plot from Database Example
# %%
import matplotlib.pyplot as plt
from sqlalchemy import create_engine
from sqlmodel import Session, select
from cali.sqlmodel import ROI, Traces
from cali.sqlmodel._model import CaliResult
# %%
database_path = "tests/test_data/data_and_db_for_tests/test_db.cali"
eng... |
6527b1f6041206b051a905b48935094db43303b01653408817583fda6f43ff66 | Jupyter | 1,675 | 85 | # %%
import pandas as pd
import numpy as np
import seaborn as sns
%matplotlib inline
# %%
res = []
for i in ['ic', 'ki', 'ec', 'kd']:
df = pd.read_csv('./%s.csv' % i,sep=';')
# drop the compounds that has no pChEMBL Value
df = df.iloc[df['pChEMBL Value'].dropna().index]
df = df[["Molecule ChEMBL ID",... |
7bfe94b535365e7a0e1dc0ad0753738b4526b44892ced1926b2b53f12c6af7df | Jupyter | 1,702 | 55 | # %%
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
# %%
BAN_UNSAM=pd.read_csv('./BrainAgeNeXt/BAN_UNSAM.csv')
BAN_ADNI=pd.read_csv('./BrainAgeNeXt/BAN_ADNI.csv')
BAN_RRIB=pd.read_csv('./BrainAgeNeXt/BAN_RRIB.csv')
BAN_JUK=pd.read_csv('./BrainAgeNeXt/BAN_JUK.csv')
# %%
... |
41d9c227249d7facc36f72805e04c743f3af46aaa870be5d46f7028b5542276d | Jupyter | 1,709 | 28 | # %%
from src.model import initialize_system
from src.proposal import do_proposal
# %%
tokenizer, model_combined_place_backs, models_determiner, models_metadata_place_back = (
initialize_system(
path_data="./data/"
)
)
# %%
SEQUENCE_PROMOTER = "CATCTTGACCTTTTTCAGCGCCGTTAGGAGAAACCGCCTTACTAGCTCATTGCCGCC... |
2994185861924ac603fa881f12a193ba6e4f866a0382665ee84adf146c7f1033 | Jupyter | 1,710 | 61 | # %%
import pandas as pd
import os
import csv
# %%
ADNI_stats_folder = "/Users/parri/OneDrive/Documentos/Beca PEFI/brainage-models-benchmark/data/recon-all_stats/ADNI"
# %%
def extract_volumes_aseg(subjects_dir):
vols_lista = []
for subj in os.listdir(subjects_dir):
subj_path = os.path.join(subjects_... |
bb4f048c5eea647f96d09790a5b4865b8ce3148a1264527383bcb637969d0bdc | Jupyter | 1,714 | 50 | # %% [markdown]
# We show here how to construct time dependant GRNs as shown in the supp movie of NeuroVelo manuscript
# %%
import scvelo as scv
import glob
# %% [markdown]
# We give a trained NeuroVelo model and list of genes we want to observe
# %%
adata = scv.datasets.bonemarrow()
scv.pp.filter_and_normalize(adat... |
ab16f013c51665df10972ebf32985b046be971d17766a9aeb893a83717ac83a4 | Jupyter | 1,739 | 66 | # %% [markdown]
# # Set Up
# %%
# import necessary packages
import pandas as pd
import numpy as np
import os
# %%
# flag to save CSVs
save_csv = True
# set directories
base_dir = f'{os.path.dirname(os.getcwd())}/'
csv_dir = f'{base_dir}analysis/CSVs/'
# %% [markdown]
# # Combine group and individual roi PSC
# %%
#... |
10a3ed0ce331088549249d84f81605973467c576796f9baf5b493bb2138c4cd5 | Jupyter | 1,760 | 49 | # %%
%load_ext autoreload
%autoreload 2
import sys
sys.path.append("../../src/training")
from generators.batchgen_generator import *
import utils.batchgen_generator_utils as data_utils
bed_regions="/oak/stanford/groups/akundaje/projects/atlas/atac/caper_out/25b3429e-5864-4e8d-a475-a92df8938887/call-reproducibility_idr... |
66b0b09e3bac3dcac57adf31929c1471ff2de5c5be938ab1e966ece32f94079f | Jupyter | 1,764 | 63 | # %% [markdown]
# # Text Data Explanation Benchmarking: Machine Translation
# %% [markdown]
# This notebook demonstrates how to use the benchmark utility to benchmark the performance of an explainer for text data. In this demo, we showcase explanation performance for partition explainer on a Machine Translation model... |
45cb658e60ff74121b47d284893491b7daa81c9a47e5a983aa6088ef52ae5ba2 | Jupyter | 1,766 | 92 | # %%
import pandas as pd
import numpy as np
from Bio import Seq, SeqIO
from Bio import pairwise2
# %%
# %%
js1 = pd.read_json('./01-Pfizer_BNT-162b2.json',orient='index')[0].to_dict()
js2 = pd.read_json('./02-Moderna_mRNA-1273.json',orient='index')[0].to_dict()
# %%
s1 = Seq.Seq(js1['c1'])
s2 = Seq.Seq(js2['c2'])
... |
a27e1dd469a92cdb85cf634e38d413a311dd8af946f229435a39b493e7a1b034 | Jupyter | 1,806 | 61 | # %% [markdown]
# ## Import the necessary libraries from yass
# %%
import numpy as np
import scipy.io
from yass.evaluate import stability, util, visualization, analyzer
# %% [markdown]
# # Instantiating an analyzer (evaluation)
#
# Here for demonstration, we use retinal dataset that we have gold standard for.
#
# T... |
2a1b4ddba4e4123205096c566ac38b13de3ca430d1b7bdfbd6198d338cbaf694 | Jupyter | 1,822 | 65 | # %%
from __future__ import print_function
import rdkit
from rdkit import Chem
from rdkit.Chem import AllChem
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
%matplotlib inline
print("RDKit: %s"%rdkit.__version__)
# %%
# %%
def chemcepterize_mol(mol, embed=20.0, res=0.5):
dims = int(e... |
2ea81ee9f7142091729adcaa80f45073ebd1021b4e9d4a2cc1d34f4ef0b8db1e | Jupyter | 1,825 | 77 | # %%
import numpy as np
import matplotlib.pyplot as plt
from oasis.functions import deconvolve, estimate_parameters
from cali.extraction._util import calculate_dff
from cali.analysis._trace_analysis import compute_rising_edges
# %%
def plot_trace(y, b, c, s, thr: int = 0):
plt.figure(figsize=(20, 8))
plt.subpl... |
b380d0475e6812062cb24e9cc048e49b613bcc477f821d4864d3af291845fddf | Jupyter | 1,838 | 108 | # %%
import numpy as np
import pandas as pd
from tqdm import tqdm
from rdkit import Chem
import seaborn as sns
import tmap, os
from molmap import loadmap
from molmap.show import imshow_wrap
from sklearn.utils import shuffle
from joblib import load, dump
import numpy as np
import pandas as pd
import os
from sklearn... |
4561477b1f7d6cd2f083351778ff4ad852d97aa746041abdb7bacc94f2066e55 | Jupyter | 1,839 | 87 | # %% [markdown]
# Detailed description of run configuration could be found [here](../nablaDFT/README.md).
# %% [markdown]
# ## Test example
# %%
# model test example config
!cat ../config/gemnet-oc_test.yaml
# %%
!python ../run.py --config-name gemnet-oc_test.yaml
# %% [markdown]
# ## Inference on another dataset
... |
8e005cfe0dda3633f0a2e86874f9bd46249e2dbf7dc44f27508da59af665bfb8 | Jupyter | 1,850 | 77 | # %%
from molmap import loadmap
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
np.random.seed(123)
# %%
aspirin = 'CC(=O)OC1=CC=CC=C1C(O)=O' #aspirin
smiles_list = [aspirin]
mp1 = loadmap('../paper/descriptor.mp')
mp2 = loadmap('../paper/fingerprint.mp')
# %%
X1 = mp1.batch_transform(smiles... |
9ffaaaaa5b5a9c1e4ee6ddc67362cfed1c28dc74ad02215f6d96dcdca74bcbf3 | Jupyter | 1,853 | 72 | # %%
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from joblib import load, dump
import seaborn as sns
from molmap.feature.fingerprint import colormaps,colors
sns.set(style = 'white', font_scale = 2)
# %%
colors = sns.color_palette(palette = 'rainbow',n_colors=12)
# %%
df = pd.read_csv('./knn... |
36b7570af1dd64a65087bf1e213d9287acea928a91eac5566dbee32f36485491 | Jupyter | 1,870 | 61 | # %%
import pickle as pkl
import matplotlib.pyplot as plt
import os
# %%
model_25M="/srv/scratch/anusri/chrombpnet_paper/results/chrombpnet/ATAC/K562/4_4_shifted_ATAC_10.04.2021_bias_filters_500_subsample_25M/final_model_step3/unplug/"
model_500M="/srv/scratch/anusri/chrombpnet_paper/results/chrombpnet/ATAC/K562/4_4_s... |
8213b3abcd2ab3235b914ecd1e50e409f1fe30ffe9be3a29a315a657931ba38e | Jupyter | 1,884 | 88 | # %%
from molmap import show, loadmap, feature
import matplotlib.pyplot as plt
import seaborn as sns
import os
# %%
sns.set(style='white', font_scale = 2)
size = 20
# %%
cms = feature.fingerprint.colormaps
# %%
data_save_folder = '/raid/shenwanxiang/FP_maps'
# %%
aspirin = 'CC(=O)OC1=CC=CC=C1C(O)=O' #aspirin
NAC =... |
f1cf9afdae7ae362f098dd0dcc120ca5086182a56c8d876f23b22f33cc0f6a56 | Jupyter | 1,917 | 74 | # %%
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
# %% [markdown]
# # Import data
# %%
dane = pd.read_csv('analysis_dataset.csv')
# %% [markdown]
# Plot lines for each participant and for the average for the whole sample
# %%
fig, ax = plt.subplots(nrows=7, ncols=5, figsize=(14,10), ... |
448a8e896ba76eaf3282bb3cce2dba5ed2331f8fd57cf7dd42c49eeebde7b127 | Jupyter | 1,960 | 28 | # %% [markdown]
# # A note on annotations
# %% [markdown]
# Since the classifier behind Convpaint is learning specifically from features of the pixels that are annotated by the user, the **quality of the annotations is crucial for the performance of Convpaint**. While this dependence on good annotations is somewhat di... |
041ff9c617b83c82ffb8084b4879e165e2495912caec654240e606a59459dab9 | Jupyter | 1,961 | 129 | # %%
from rdkit import Chem
from rdkit.Chem.Draw import rdMolDraw2D, MolDraw2DSVG
from rdkit.Chem import Draw
from IPython import display
from base64 import b64decode
import io
import PIL.Image as Image
# %%
# %%
mol = Chem.MolFromSmiles('CC(=O)OC1=CC=CC=C1C(O)=O')
patt = Chem.MolFromSmarts('OC(*)=O')
# %%
hit_a... |
515f791464b86564626592c1cede543fc7ddac7889bbf3c5ebdb77643c6cee1c | Jupyter | 1,971 | 64 | # %% [markdown]
# # Quick script to incorporate apical nexus electrotonic attenuation into dendritic spike files
# %%
import pandas as pd
import numpy as np
# %% [markdown]
# ### List the files you want to process
# %%
# apical nexus attenuation file
nex_fpath = 'Z:\\DendOscSub\\Segments.csv'
# dend spike files
ds_... |
5eaafffb8c7b9c03d3784d9a402c0be180c9e067e41905980a3d024a4940c095 | Jupyter | 1,971 | 58 | # %% [markdown]
# # Training on multiple files
# %% [markdown]
# By default the plugin works on a "layer-level", i.e. annotations and training are done on a single layer (single image, RGB, stack etc.) However sometimes, one has for example a set of separate images that one wishes to segment in the same way. There mig... |
75e94a0db99e4419094fac63d2689113f271cb7336cdece967b2285e52db6874 | Jupyter | 1,982 | 116 | # %%
%config Completer.use_jedi = False
# %%
import numpy as np
import matplotlib.pyplot as plt
import cv2
%matplotlib inline
import pandas as pd
#reading the image
image = cv2.imread('p1.jpg')
#converting image to RGB
image = cv2.cvtColor(image,cv2.COLOR_BGR2RGB)
#plotting the grayscale image
r, g, b = cv2.split(... |
78d9a5d7ad027069b1711fb6c623f9dcb8b6e96490f1273ba508accbbeeb25a4 | Jupyter | 2,001 | 71 | # %% [markdown]
# Generating trials dataset using resampling procedure.
#
# * Number of resampling iterations: 100 (based on bootstrapping stability analysis)
# * Number of trials per resampling: N = 40 (based on data in monkey dataset so the number of trials is from experiments)
# %%
import csv
import pickle
import ... |
8985c13696131d6b11aab714880b89956dc9ac2cc7fd93fe91db787b34f4d2b4 | Jupyter | 2,009 | 30 | # %% [markdown]
# # Supplementary Material
#
# The paper is accompanied by seven Jupyter tutorial notebooks, included here in rendered form, along with a short Python primer for researchers new to the language.
#
# The notebooks can be explored interactively on Google Colab via the following links:
#
# - S0: Python... |
a7a34ba3bdc12be71a963001373d44bf7f84803585203ce27ef39f0345006e6b | Jupyter | 2,016 | 67 | # %% [markdown]
# # Title
# %% [markdown]
# ## Overview
#
# [Include a paragraph or two explaining what this example demonstrates, who should be interested in it, and what you need to know before you get started.]
# %% [markdown]
# ## Background
#
# [If the topic in this tutorial is involved, a background section ... |
502517ec3eefc2d87af396f46367e46cb3fd6970e33f02a8a519172ac55b4ff4 | Jupyter | 2,027 | 117 | # %%
# This scripts creates events.tsv for the main sample of subjects
# %%
import os
from src.utils import seq2tsv
# %% [markdown]
# # Settings
# %%
sub_id = "22"
# %%
bids_path = "/Volumes/T7/BIDS-MUSICNF"
subID_string = "sub-" + sub_id
sub_bids_path = f"{bids_path}/{subID_string}"
feedback_task_list = ["nf", "s... |
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