sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
f547dc1fbc3d47b7ec17d2b993a8201ee0b657d8c8b35a2a14a4a3c321222759 | Jupyter | 2,586 | 76 | # %% [markdown]
# ### Figure 4D Heatmap Generator
# - Reads in data from file specified by `INPUT_CSV` and outputs heatmap to destination specified by `OUTPUT_PATH`
# - `FIG_SIZE` determines dimension of final plot width, height in inches
# - If `FILTER_CPM` is set to true, genes without at least one expression point >... |
0955490681926572fa9413a401c763dedf7157bd240cebf96e55ff909f545930 | Jupyter | 2,595 | 108 | Omnibus for RACE chi2=54.11935146185722
p=2.2274480814469196e-09
dof=7
expected_lt5=True
RACE,col AmericanIndianOrAlaskanNative:
chi2=4.547126962489126
p=0.2637922502218775
dof=1
expected_lt5=True
proportion ADNI vs NACC: 0.0 vs 0.011811023622047244
counts for ADNI vs NACC: 544 vs 508
RACE,col Asian:
chi... |
5272326eb3ec9129340e861dd540f05e47252afe9c59f0656057f1081531635e | Jupyter | 2,617 | 120 | # %% [markdown]
# ## TODO:
#
# - Add noise to individual diffraction patterns.
# %%
%load_ext autoreload
%autoreload 2
# %%
import numpy as np
import matplotlib.pyplot as plt
from pathlib import Path
import matplotlib as mpl
import skimage
from skimage import filters
from tqdm import tqdm
import tifffile as tif
impo... |
bc0eb886286c82393d2494097d5a2fb14955b78dc3ad039f2778475ce4aade87 | Jupyter | 2,635 | 66 | # %%
import glob
import json
from os.path import dirname
import numpy as np
ood_path = "../../logs/exp001/exp001a/sweep-2026-04-28_17-42-26_ood_test_predictions/"
ood_labels_glob = f"{ood_path}/*/labels.npz"
ood_preds_glob = f"{ood_path}/*/predictions.npz"
ood_labels_files = sorted(glob.glob(ood_labels_glob), key=la... |
129d7a11e4f7832dabbb658f49505adadcbb0212bff086ce25edd389e372450d | Jupyter | 2,637 | 66 | # %%
import glob
import json
from os.path import dirname
import numpy as np
ood_path = "../../logs/exp002/exp002a/sweep-2026-03-30_17-16-31_ood_test_predictions/"
ood_labels_glob = f"{ood_path}/*/labels.npz"
ood_preds_glob = f"{ood_path}/*/predictions.npz"
ood_labels_files = sorted(glob.glob(ood_labels_glob), key=la... |
805d3638412d82ebb7ea35c54fb61473363861fcfbf5928fbd5c8e11ef281599 | Jupyter | 2,773 | 93 | # %%
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... |
090c0908c64331d796624463331a52e9e6d051719a1556a06a9491bb02045a7c | Jupyter | 2,805 | 104 | # %%
import os
import pandas as pd
from rdkit import Chem
from rdkit.Chem import AllChem
from rdkit.Chem.Draw import IPythonConsole
IPythonConsole.ipython_useSVG=True
IPythonConsole.drawOptions.addAtomIndices=True
from matplotlib import pyplot as plt
from openff.toolkit.topology import Molecule, Topology
from openff.... |
43f0106b4f9a8ccea3497092e107ad28a2c372f583166021ebb5bbd0f3a18070 | Jupyter | 2,851 | 83 | # %% [markdown]
# ### Figure 5F Cell Markers Heatmap Generator
# - Reads in data from file specified by `INPUT_CSV` and outputs heatmap to destination specified by `OUTPUT_PATH`
# - `FIG_SIZE` determines dimension of final plot width, height in inches
# - If `FILTER_CPM` is set to true, genes without at least one expre... |
c714e9262c6b03d20802566ee6aa0f7a66229c5746fe50ec662d0cd16290f832 | Jupyter | 2,852 | 83 | # %% [markdown]
# ### Figure 5I Ion Channels Heatmap Generator
# - Reads in data from file specified by `INPUT_CSV` and outputs heatmap to destination specified by `OUTPUT_PATH`
# - `FIG_SIZE` determines dimension of final plot width, height in inches
# - If `FILTER_CPM` is set to true, genes without at least one expre... |
bdc0b39c996d6f76ec5153b99a9b379280598e8d3cc7ae6bd58ccee3ffe2a446 | Jupyter | 2,857 | 83 | # %% [markdown]
# ### Figure 4G Neruogenesis Heatmap Generator
# - Reads in data from file specified by `INPUT_CSV` and outputs heatmap to destination specified by `OUTPUT_PATH`
# - `FIG_SIZE` determines dimension of final plot width, height in inches
# - If `FILTER_CPM` is set to true, genes without at least one expre... |
d0df6e64bf69a97a71f8e8338c57b5d95740e4fbbbc30ec4ea720417cfbb0346 | Jupyter | 2,858 | 83 | # %% [markdown]
# ### Figure 4G Growth Factors Heatmap Generator
# - Reads in data from file specified by `INPUT_CSV` and outputs heatmap to destination specified by `OUTPUT_PATH`
# - `FIG_SIZE` determines dimension of final plot width, height in inches
# - If `FILTER_CPM` is set to true, genes without at least one exp... |
9a058a1e16c10addb496c497646eeef772e01ec583935159e2a02534ab6568f5 | Jupyter | 2,861 | 83 | # %% [markdown]
# ### Figure 4G Axon Guidance Heatmap Generator
# - Reads in data from file specified by `INPUT_CSV` and outputs heatmap to destination specified by `OUTPUT_PATH`
# - `FIG_SIZE` determines dimension of final plot width, height in inches
# - If `FILTER_CPM` is set to true, genes without at least one expr... |
18df68e316e70ddbdce22e1d85e511ba927fa6a64c5053257042ad388a8c5768 | Jupyter | 2,873 | 103 | # %%
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.
# %% [markdown]
# # Mel-spectrogram predic... |
238ac7a2ed5d1a1e02917b8af257366405d3620c64da8e7d69b396547d8e935f | Jupyter | 2,876 | 82 | # %% [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
'''
#... |
00478dfba75993a6681bf9965e930e044dd25a7a6ebdad7cada2cb7d977ca7b2 | Jupyter | 2,877 | 103 | # %%
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.
# %% [markdown]
# # Mel-spectrogram predic... |
8c6166abacc777d17ecfe24fc0a88694af938fe641d57fcd1a6a55fa186fd71d | Jupyter | 2,921 | 104 | # %%
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.
# %% [markdown]
# # Mel-spectrogram predic... |
66a618924de4e3f42ec6273923907a08b8b446fb785746748636b7edfcede351 | Jupyter | 3,005 | 75 | # %% [markdown]
# # How to use embedding nets for high-dimensional observations
# %% [markdown]
# Many simulators return high-dimensional outputs such as time-series or images. To efficiently learn the posterior given such simulation outputs, `sbi` can employ _embedding networks_ which reduce these high-dimensional ou... |
0ccc164454a69370a08e5064e6c311ec6bfc453b28fe5cb069c5263af561a211 | Jupyter | 3,113 | 71 | # %% [markdown]
# # How to use permutation-invariant embeddings for iid observations (for NPE)
# %% [markdown]
# > Important: This how-to guide is only relevant if you have iid observations, are planning to use NPE, **and** want to use the default `sample_with="direct"` method. However, standard NPE can natively handl... |
4208db7d5bc7e0bde0d7594bf5f59130f451c12d1cb9684b9d478e5a0f9ed5eb | Jupyter | 3,128 | 92 | # %%
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from rdkit import Chem
from rdkit.Chem.Draw import SimilarityMaps
from rdkit.Chem import Draw
from rdkit.Chem import rdDepictor
from rdkit.Chem.Draw import IPythonConsole
IPythonConsole.ipython_useSVG = True
IPythonConsole.ipython_sho... |
8c55114a540ae2e287335cf649cefe2624c38588c7e03e0baa4b3ca5df892300 | Jupyter | 3,168 | 98 | # %% [markdown]
# # Example 1: Load data
#
# This example illustrates the data preprocessing steps needed to bin grid cell activity and location before processing using GP regression. (We will fit the regression in example 2).
#
# Neuron `r2405_051216b_cell1816` was recorded by Dr. Marius Bauza and the Krupic lab; t... |
9228c873031fa08f0e508a1201d4197cafa6e33829c5afa836ba2be762be9704 | Jupyter | 3,181 | 88 | # %%
import scanpy as sc
import scvelo as scv
import numpy as np
import latentvelo as ltv
# %%
adata = sc.read('../data/DentateGyrus/10X43_1.h5ad')
# %%
adata
# %%
scv.pp.filter_and_normalize(adata, min_shared_counts=20, n_top_genes=2000)
# %%
scv.pp.pca(adata)
scv.pp.neighbors(adata, n_pcs=30, n_neighbors=30)
scv.... |
fb1b44822f6b99e85c1ad4a585a76c478d27fcd70c7fb8ac505a5c5b47e0da8a | Jupyter | 3,197 | 109 | # %%
from copy import deepcopy
from moleculekit.molecule import Molecule
from moleculekit.periodictable import periodictable
import numpy as np
import torch as pt
from torch.utils.benchmark import Timer
from torchmdnet.models.model import create_model
# TensorNet
model_1 = create_model({
'embedding_dimension': 32... |
8ef2d8d5d7086bc2be5e576b247497856531181e4b6420b7b07c4d1290a56841 | Jupyter | 3,216 | 90 | # %%
import scanpy as sc
import scvelo as scv
# %%
adata = sc.read('../../../../../retina/retina.h5ad')
# %%
adata
# %%
scv.pp.filter_and_normalize(adata, min_shared_counts=20, n_top_genes=2000)
# %%
scv.pp.pca(adata)
scv.pp.neighbors(adata, n_pcs=30, n_neighbors=30)
scv.pp.moments(adata, n_pcs=None, n_neighbors=No... |
24ec0a54602d1fde4bced5f940bd82ee5ffaf413a7932c3b4b3dc00d91b0a54f | Jupyter | 3,243 | 103 | # %%
import latentvelo as ltv
import numpy as np
import scanpy as sc
import scvelo as scv
import torch as th
# %% [markdown]
# # Read and plot data
# %%
adata = sc.read('../../scEUseq_organoids/scEU_organoid.h5ad')
cell_mapper = {
'1': 'Enterocytes',
'2': 'Enterocytes',
'3': 'Enteroendocrine',
'4': '... |
0c6f3de52bd3d89f2997c4f1711f7691b365d673bea0a653942a908dc833b9e3 | Jupyter | 3,251 | 85 | # %% [markdown]
# # How to run TARP
# %% [markdown]
# TARP is an alternative calibration check proposed recently in https://arxiv.org/abs/2302.03026.
#
# In contrast to SBC (Talts et al.) and expected coverage based highest posterior density regions
# (Deistler et al.,), TARP provides a necessary *and sufficient* co... |
17520677722abb34b5c143225f347414f6ba48e84916da4cec5270e71b5083ff | Jupyter | 3,254 | 97 | # %% [markdown]
# # How to use time-series embeddings
#
# This short guide shows how to use **time-series embedding networks**:
# - `CausalCNNEmbedding`
# - `TransformerEmbedding`
#
# We’ll use a simple `torch.randn` simulator to demonstrate the workflow.
#
# > In real use, replace `torch.randn` with a dynamical sim... |
0c4c8c17f9b28d6b4b51fccb370e029df7e52eae871da18520940c4e3811b66a | Jupyter | 3,301 | 91 | # %%
import scanpy as sc
import scvelo as scv
import numpy as np
import latentvelo as ltv
# %%
adata = sc.read('../../../..//Pancreas/endocrinogenesis_day15.5.h5ad')
# %%
adata
# %%
scv.pp.filter_and_normalize(adata, min_shared_counts=20, n_top_genes=2000)
# %%
scv.pp.pca(adata)
scv.pp.neighbors(adata, n_pcs=30, n_... |
0f654061d0fd63d14c79650527e54dbdedf9673af63b10bcfd10a20189edb166 | Jupyter | 3,328 | 101 | # %%
%load_ext autoreload
%autoreload 2
import sys; sys.path.append('../../'); from config import *
configure_pylab()
rc('text.latex',preamble=r"\usepackage{amsmath}")
matplotlib.rc('text', usetex = True)
params= {'text.latex.preamble' : r'\usepackage{amsmath}'}
plt.rcParams["xtick.major.size"] = 0
plt.rcParams["ytick... |
d341b19df4b578d8c99efe29237a55b2969f5b4c06eb042dfdc775b4c19716a2 | Jupyter | 3,328 | 79 | # %% [markdown]
# # How to use GPUs
# %% [markdown]
# `sbi` supports GPU training. GPU will speed up training if you use a very large
# batch-size, if you use a large embedding network, or if your simulation outputs are
# high-dimensional. This guide shows you how to train and perform inferencce on GPU.
# %% [markdow... |
a669c2830a118d76a1d0904ebc978e813102bc78ada1f7215b404b7b1899ad93 | Jupyter | 3,331 | 98 | # %%
%load_ext autoreload
%autoreload 2
import sys; sys.path.append('../../'); from config import *;
configure_pylab()
# %%
L = 128
fn = datafiles[6] #3
fn = 'r2405_051216b_cell1816.mat'
fn = 'r2288_180515b_tet2_cell2_GC.mat' # Best
nsamples = 4000
upsample = 2
Nh = 90
hh = linspace(0,2*pi,Nh+1)[:-1]
... |
8f19b98419821fecf241d1f8a26a98e00b85ae59a4191b86106f410a2a5f05e9 | Jupyter | 3,338 | 89 | # %%
import re
import numpy as np
log_file = "logs/anysleep_mnc_nt1_sfs.log"
# srs = [1, 2, 4, 8, 16, 32, 64, 128, 256, 384, 640, 960, 1920, 3840]
srs = [1, 128]
n_classes = 5
possible_triplets = [f"{i}{j}{k}"
for i in range(n_classes)
for j in range(n_classes)
... |
5e6bc02c81deb4a0b3d380996f9db4d409933cfe7337be93180e76e8d88a97a7 | Jupyter | 3,342 | 89 | # %%
import re
import numpy as np
log_file = "logs/anysleep_cap_insomnia_sfs.log"
# srs = [1, 2, 4, 8, 16, 32, 64, 128, 256, 384, 640, 960, 1920, 3840]
srs = [1, 32]
n_classes = 5
possible_triplets = [f"{i}{j}{k}"
for i in range(n_classes)
for j in range(n_classes)
... |
2fea984bc37c8eb84fcb79d09c7f217ee0eea23c2bea2c1a3f329babdd34a6ee | Jupyter | 3,343 | 89 | # %%
import re
import numpy as np
log_file = "logs/anysleep_dodo_vs_dodh_sfs.log"
# srs = [1, 2, 4, 8, 16, 32, 64, 128, 256, 384, 640, 960, 1920, 3840]
srs = [1, 256]
n_classes = 5
possible_triplets = [f"{i}{j}{k}"
for i in range(n_classes)
for j in range(n_classes)
... |
ef19c9972f2606f6c3253356625f54a55a34563b85869dc8baeb3d27275b35a4 | Jupyter | 3,343 | 72 | # %%
from imagen_post_hoc_helper import *
%matplotlib inline
# %%
DATA_DIR = r"/ritter/share/data/IMAGEN"
SAVE_DIR = r"/ritter/share/jihoon/imagen_ml/posthoc_analysis/figures"
posthoc = IMAGEN_posthoc()
# %%
for tp in ['FU3', 'FU2', 'BL']:
f, axes = plt.subplots(7,1, figsize=(10,20), sharex=True, sharey=True)
... |
d8c224dacfc7656b2accd52fe7fc97c19e61f0951496bced462c2af1548c203a | Jupyter | 3,348 | 97 | # %% [markdown]
# # Example 3: Optimized hyperparameters
#
# In this example, we optimize the period and variance of the GP prior using the evidence lower bound.
#
# Neuron `r2405_051216b_cell1816` was recorded by Dr. Marius Bauza and the Krupic lab; They have kindly shared this data to evaluate these GP methods. Ple... |
9ba5317f7ce105688fd86dd0c0441a4de7a0d614cae3489505efb274d11ae5ef | Jupyter | 3,365 | 82 | # %%
import pandas as pd
import numpy as np
import scipy.stats
# %%
survey_df = pd.read_csv('../data/duke_study-202112/survey-202112 - Data.csv')
print(survey_df)
# %%
property_types = ['image_quality', 'acquisition_ease', 'software', 'tactile', 'overall_onsd']
#[x.replace('-butterfly', '') for x in survey_df.columns... |
577552d085a70f1ec32ef00c5547b3165ab1e2f4fd3512003808b25ccb5994cd | Jupyter | 3,374 | 90 | # %% [markdown]
# # Example 4: Confidence intervals
#
# This example approximates confidence intervals for peak location using either sampling within each grid-field's Voronoi region, or a Quadratic approximation at the peak.
#
# Neuron `r2405_051216b_cell1816` was recorded by Dr. Marius Bauza and the Krupic lab; Th... |
2060219bad7ca917228f3890bc948c87aa654001e262c322c9798fd334f6260e | Jupyter | 3,401 | 120 | # %%
%load_ext autoreload
%autoreload 2
import sys; sys.path.append('../../'); from config import *;
configure_pylab()
# %%
# Plot helpers
def quickpeaks(s):
return (
(s>roll(s, 1,1))&
(s>roll(s,-1,1))&
(s>roll(s, 1,2))&
(s>roll(s,-1,2))&
(s>roll(s, 1,(1,2)))&
(s>rol... |
604b86941ba64caddffca3e789b89d87700312ea4a024c0334e4f69def5cd66a | Jupyter | 3,426 | 111 | # %%
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.
# %% [markdown]
# # Create GPT-2 embedding... |
c104592bdf151dca66a380b36578c9a7f9abdf396d04a7430fbc36d7879bc49c | Jupyter | 3,443 | 97 | # %% [markdown]
# # How to run expected coverage
# %% [markdown]
# Expected coverage provides a simple and interpretable tool to diagnose issues in the posterior. In comparison to other diagnostic tools such as L-C2ST, it requires relatively few additional simulations (~200) and it does not rely on any additional hype... |
1ad0d62fa05386d4e3bbed21fc6996b2162d300dfd169e8c72b34ad3a15b4d31 | Jupyter | 3,473 | 104 | # %% [markdown]
# # Training a ML Model using the serenityff-charge package
#
# ## Import necessary packages and set file paths:
#
# The [Trainer](../gnn/training/trainer.py) is a class that facilitates the training of an ML model and the [ChargeCorrectedNodeWiseAttentiveFP](../gnn/utils/model.py) is the model, that ... |
303f571a56e1e680d1a396e5ab04df33c711c220364c8a3f37d099572c56f752 | Jupyter | 3,497 | 90 | # %%
import scanpy as sc
import scvelo as scv
import latentvelo as ltv
import numpy as np
# %%
adata = sc.read('../../../../DeepVeloData/deepvelo_data/h5ad_files/Hindbrain_GABA_Glio.h5ad')
# %%
scv.pp.filter_and_normalize(adata, min_shared_counts=30, n_top_genes=2000)
# %%
scv.pp.pca(adata)
scv.pp.neighbors(adata, n... |
397d02f8c3f3e5ace0e804073aed13261cb90c9d1d0c817c4926382c8f0061ca | Jupyter | 3,508 | 77 | # %% [markdown]
# # Customizing the density estimator
# %% [markdown]
# `sbi` allows to specify a specific density estimator for each of the implemented methods.
# We support a variety of density estimators, e.g., mixtures of Gaussians, normalizing
# flows, and diffusion models. Some of the density estimators are impl... |
7cce6d2be728268d4daf663d949b9e06bce0a379e4d77164c1667ed28bf8634d | Jupyter | 3,569 | 89 | # %% [markdown]
# # How to run simulation-based calibration (SBC)
# %% [markdown]
# Similar to expected coverage, simulation-based calibration (SBC) provides a simple and interpretable tool to diagnose issues in the posterior. It also requires relatively few additional simulations (~200) and it does not rely on any ad... |
6c656acde73808d3d5bbe91ec4b8ff48fc4a4e41cc8d62f08b8ad25a0c9e53fa | Jupyter | 3,573 | 122 | # %%
%matplotlib inline
import scanpy as sc
import scrublet as scr
import scipy.io
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import os
import gzip
# %%
## rewrite 'load_genes' in scrublet to make it compatiable to gzip files
def load_genes(filename, delimiter='\t', column=0, skip_rows=0):
... |
d007f663d953366d02b543a9ae2967bd35f1444e2cc961aadc65a9bb2d249475 | Jupyter | 3,601 | 105 | # %%
import scanpy as sc
import scvelo as scv
import pandas as pd
import numpy as np
import latentvelo as ltv
# %%
labelling = sc.read('../../../../scNT-seq/Neu_one_shot.h5ad')
splicing = sc.read('../../../../scNT-seq/neuron_splicing_4_11.h5ad')
splicing.obs = labelling.obs.loc[splicing.obs.index, :]
meta = pd.read_... |
13af3b4572169ebc542800ad562a169e2a0013e800640d24901ec5cc260ca886 | Jupyter | 3,642 | 134 | # %% [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
plt.style.use('../bioAI.mplstyle')
# General packages
import datetime
import sys
import os
# custom... |
74349084f51784230f28558276e4bd61f7e3946bab7f1de802f336a260f39298 | Jupyter | 3,643 | 79 | # %% [markdown]
# # How to define custom neural nets
# %% [markdown]
# `sbi` allows you to specify a specific density estimator for each of the implemented methods.
# We support a variety of density estimators, e.g., mixtures of Gaussians, normalizing
# flows, and diffusion models. Some of the density estimators are i... |
c826823d42bb90b9a8d8706ac08722b3eda6754f072a0779513a360fdf9eec03 | Jupyter | 3,647 | 130 | # %%
%load_ext autoreload
%autoreload 2
# %%
import sys; sys.path.append('../../'); from config import *
configure_pylab()
plt.rcParams["xtick.major.size"] = 0
plt.rcParams["ytick.major.size"] = 0
# %%
L = 128
#fn = 'r2289_250515b_tet2_cell2_GC.mat'
fn = 'r2405_051216b_cell1816.mat'
data = Dataset.from_fi... |
96065b2187d67ba0f4e73ffe2429a6e18614dc8705f7352334d3b59ad82fc8c3 | Jupyter | 3,668 | 131 | # %%
import numpy as np
import matplotlib.pyplot as plt
# %% [markdown]
# # LatentVelo runtimes
# %%
names = ['mouse_erythroid', 'scnt', 'organoid', 'pancreas',
'mouse_hematopoiesis', 'bonemarrow', 'dentate_gyrus', 'retina', 'hindbrain', 'hindbrain_gaba']
nice_names = ['Mouse erythroid', 'scNT neurogenesis... |
d1fcde68edde747ba1ac5e696f1fb7f90f7c1f3ede5f6fdd9f7db81ddc897586 | Jupyter | 3,678 | 117 | # %%
!pip install split-folder
# %%
import os
import tensorflow as tf
from tensorflow import keras
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
import numpy as np
import pathlib
import cv2
#import splitfolders
# %%
train_dir = "data/raw/Original/TRAIN"
img_width = 48
img_heigh... |
ab06f8bc808b3a3f72e78820471a3f62eb12a392a41ee8ce048335728566baf1 | Jupyter | 3,690 | 212 | # %%
import matplotlib
%matplotlib inline
try:
reload # Python 2.7
except NameError:
try:
from importlib import reload # Python 3.4+
except ImportError:
from imp import reload # Python 3.0 - 3.3
import MacUtils as mut
reload(mut)
# %%
reload(mut)
wt1, l1 = mut.getCls13a14a3()
#wt1, l1 =... |
3afbb8a0323af06f537d5893a0db8c5b88757368c1cc3f51e84e8a1968d34844 | Jupyter | 3,746 | 94 | # %%
import mne
import os
import pandas as pd
import numpy as np
from scipy import signal, stats
import matplotlib.pyplot as plt
# %%
data_df = pd.read_excel(r'C:\Users\Lenovo\1代码 REM中beta升高\upload_data\figure1ef_data.xlsx',
index_col = [0])
# %%
freqs = np.array(data_df['freqs'])
# %%
dys_wak... |
2b13cd26fed5c6a74c8b4fe7d1556c1801749937535cf6b2c2f9f097b138e9b2 | Jupyter | 3,863 | 122 | # %% [markdown]
# # TorchMD API tutorial
# %% [markdown]
# ## System setup
# %% [markdown]
# We use the `moleculekit` library for reading the input topologies and starting coordinates
# %%
from moleculekit.molecule import Molecule
import os
testdir = "../tests/data/prod_alanine_dipeptide_amber/"
mol = Molecule(os.p... |
3acf243912b90edfa8bf5b4f76a187dfe869e95968a2d04d1edc5fc86466e8e6 | Jupyter | 3,874 | 127 | # %% [markdown]
# # Single Cell Analysis of D7, D14 and D21 RGC-iNs
# %%
import scanpy as sc
import pandas as pd
sc.settings.verbosity = 3 # hints
# %% [markdown]
# ### Data collection
# %%
### Read data into an AnnData objects (this step may take a few minutes the first time) ###
adata_7 = sc.read_10x_mtx(
f... |
85e8f0715ef6aee1495571dcbb0e4db99beb97f726980ab689805f85d9429d25 | Jupyter | 3,893 | 101 | # %%
import scanpy as sc
import scvelo as scv
import latentvelo as ltv
import numpy as np
# %%
adata = sc.read('../../../../Ranek_purvis2022/adata_nestorowa.h5ad')
scv.pp.filter_genes(adata, min_shared_counts=30)
ltv.utils.standard_clean_recipe(adata, spliced_key='spliced', unspliced_key='unspliced',
... |
38b668edf1ff6553387c099742b7ffcf6afb14a6ebc3d953d793f972b655b26a | Jupyter | 3,897 | 101 | # %%
import latentvelo as ltv
import scvelo as scv
import numpy as np
import scanpy as sc
np.random.seed(1)
# %%
adata = scv.datasets.pbmc68k()
scv.pp.remove_duplicate_cells(adata)
adata.obsm["X_tsne"][:, 0] *= -1
# %%
adata.obs['celltype_names'] = adata.obs.celltype.values
# %%
scv.pp.filter_genes(adata, min_shared... |
89be33cd3102e603efd630f94117629950cc2c7a4058178d171261e1af666df0 | Jupyter | 3,923 | 39 | # %% [markdown]
# # How to choose an inference method
# %% [markdown]
# `sbi` implements three main neural inference families:
#
# - `NPE` (Neural Posterior Estimation),
# - `NLE` (Neural Likelihood Estimation), and
# - `NRE` (Neural Ratio Estimation).
#
# Each family learns a different probability distribution from... |
8c122eab8ca5635b69244d0cb222c72195c92c3d8ad6669d7c81fa7cd17be53d | Jupyter | 3,939 | 158 | # %% [markdown]
# # Single Cell RGC Subtype Marker Expression Analysis in Day 21 RGC-iNs
# %%
import scanpy as sc
import matplotlib.pyplot as plt
sc.settings.verbosity = 3 # hints
# %% [markdown]
# ### Data Collection
# %%
# Read data into an AnnData object (this step may take a few minutes the first time)
adata =... |
0083995b91265c1736eb91e8a1f9805c319c13304666c1044e3b86b3e28efcb1 | Jupyter | 3,953 | 120 | # %%
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.
# %% [markdown]
# # This script is for get... |
e2943e762eba061c78a3b4f29d7363a0bb8bbb76aa1c5cdd518c546325220778 | Jupyter | 4,006 | 115 | # %%
#import scib
import numpy as np
import scanpy as sc
import scvelo as scv
import latentvelo as ltv
# %%
adata = sc.read('../../../../scEUseq_organoids/scEU_organoid.h5ad')
cell_mapper = {
'1': 'Enterocytes',
'2': 'Enterocytes',
'3': 'Enteroendocrine',
'4': 'Enteroendocrine progenitor',
'5': 'Tu... |
3aa5bd8e843cf17d47571d2420e32d2104dbae371d73b30be8ec1f688feff993 | Jupyter | 4,049 | 117 | # %% [markdown]
#
# ### Implementation of sparse coding and learning with different regularizers
# #### Regularizers: ISTA, Hard thresholding, $\lambda_{1/2}$ thresholding, and CEL0
# %%
import numpy as np
import matplotlib.pyplot as plt
from tqdm.notebook import tqdm
import utils
from generativeModels import Sparse... |
83642545ce9e24a43a03eb40737af59389c9f1ee43a524d4125a56e54229e577 | Jupyter | 4,070 | 107 | # %%
import pickle
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import sys
sys.path.append('../')
from jax.config import config
import tensorflow as tf
import os
os.environ['XLA_PYTHON_CLIENT_PREALLOCATE'] = 'false'
tf.config.set_visible_devices([], device_type='GPU')
import u... |
5ecfaf4892b5feddb6e6116b135c9a095814581c0356729242d5db3639f2792f | Jupyter | 4,087 | 104 | # %%
import scanpy as sc
import scvelo as scv
import pandas as pd
# %%
adata = sc.read('../../../../hindbrain_pons/OligoPons_metafromSten.loom')
embedding= pd.read_csv('../../../../hindbrain_pons/embedding.csv', sep=',', index_col=0)
annotations = pd.read_csv('../../../../hindbrain_pons/annotations.csv', sep=',', inde... |
43695247fe2f319e34040bd69b97181ca9e774ce9c030e7fd7f8ca7382748330 | Jupyter | 4,098 | 96 | # %%
%load_ext autoreload
%autoreload 2
import sys; sys.path.append('../../'); from config import *
configure_pylab()
# %%
W = 100 # grid idth
H = 100 # grid height
P = 10 # cell period
L = (H,W)
# Plot helper
figure(0,(12,6),200)
nr = nc = 4; ip = 1
kernels = []
def im(k,t):
global nr,nc,ip,kernels
subplot(... |
b2a091759aec8b3f35042d7944a7d22052ddfd1e03982798bb1997bf3d4ea3d0 | Jupyter | 4,103 | 160 | # %%
import pandas as pd
import matplotlib.pyplot as plt
import hddm
import numpy as np
import pickle
from patsy import dmatrices, dmatrix, demo_data
# %% [markdown]
# # EXP2 iTBS
# %%
data = hddm.load_csv('exp2_DDM.csv')
# %% [markdown]
# ## Left consonant quiet
# %%
D_L=data.loc[data['turn_type'].isin(['Left iTBS... |
c1495a210542254a0f6d9fbf3c3bb4d39428d1f2ed077f573f4a1da51f69be45 | Jupyter | 4,109 | 157 | # %% [markdown]
# ### Imports
# %%
%load_ext autoreload
%autoreload 2
#%matplotlib notebook
%matplotlib inline
# %%
# Computational packages
import numpy as np
import torch
import tqdm
# Plotting
import matplotlib
from matplotlib import pyplot as plt
plt.style.use('bioAI.mplstyle')
# General packages
import datetim... |
ce03e1504bc4545481f05665365fd239a9bf80bf0c7f51c3503c52d3035a58fc | Jupyter | 4,118 | 131 | # %% [markdown]
# # BLIP: Inference Demo
# - [Image Captioning](#Image-Captioning)
# - [VQA](#VQA)
# - [Feature Extraction](#Feature-Extraction)
# - [Image Text Matching](#Image-Text-Matching)
# %%
# install requirements
import sys
if 'google.colab' in sys.modules:
print('Running in Colab.')
!pip3 install ... |
e9968babac5e60fec020c915358567edcca35805943fedf8bc8fefdf260d0f2b | Jupyter | 4,122 | 134 | # %%
import pandas as pd
import matplotlib.pyplot as plt
from clustering_traditional import TraditionalClustering
from clustering_statistics import get_cluster_statistics
from clustering_word2vec import Word2VecModel
# %%
# An example how to perform traditional list-based clustering of semantic VFT
# create the clust... |
3f80d9a65f4f36178bfa684f1bd412fb88d0d56b921b569048122e5a1dea2dab | Jupyter | 4,166 | 108 | # %%
import mne
import os
import pandas as pd
import numpy as np
from scipy import signal, stats
import matplotlib.pyplot as plt
# %%
data_df = pd.read_excel(r'C:\Users\Lenovo\1代码 REM中beta升高\upload_data\figure2adg_data.xlsx',
index_col = [0])
# %%
freqs = np.array(data_df['freqs'])
# %%
dys_wa... |
702c8a18ab38ece7cb550ab4225141298a96112b0ffb73b89c88c90c9c1f5d67 | Jupyter | 4,212 | 133 | # %%
from importlib import reload
import os
from rdkit import Chem
from copy import deepcopy
import numpy as np
import pandas as pd
import time
from serenityff.charge.tree_develop.develop_node import develop_node
from serenityff.charge.tree.tree import tree
from serenityff.charge.tree.node import node
from serenityff... |
abbd91ca7ef730c88d4de2b169803673710b888e8d14c036c2a94f036830412d | Jupyter | 4,230 | 108 | # %%
%load_ext autoreload
%autoreload 2
import os
base_path = os.path.abspath(os.path.join(os.getcwd(), '../'))
os.chdir(base_path)
# %%
from src.utils import *
import pandas as pd
# %%
batch_size = 4096
max_iter = 1000
learning_rate = 0.005
n_repeat = 10
model = tf.keras.models.load_model('models/siren')
# %%
a... |
9a219a9185bb42a77b0ad184c4a6665747e1a62464f62254646da80423c2e2fd | Jupyter | 4,249 | 90 | # %% [markdown]
# # 1. Oligo Sequences Generation
#
# The first step in building a pipeline is generating oligo sequences which can be loaded into an OligoDatabase. The `OligoSequenceGenerator` class can be used for generating new oligo sequences and allows the user to either design oligos from reference genomic seque... |
57bfd50863b96a6ec6b49db1a51673791d0a07ab8365980ae2ee11ca6048df1a | Jupyter | 4,266 | 115 | # %% [markdown]
# # Computational Requirements Comparison Across Architectures
#
# This notebook visualizes how computational cost scales with the number of input channels
# for different sleep staging architectures.
#
# ## Purpose
#
# Compare the computational complexity of four fusion strategies as the number of E... |
c755d30e86fded858f9a283b00924db12c5ef8b4760876e6c37babb8ac4b94eb | Jupyter | 4,302 | 129 | # %% [markdown]
# # Numerical simulation of FAST applied to a 2d film (see paper text for details)
# %%
%load_ext autoreload
%autoreload 2
# %%
import fast.s26_analysis.utils as utils
from fast.utils.generate_scan_pattern import generate_scan_pattern as gcn
import numpy as np
import matplotlib.pyplot as plt
from p... |
7702b7a81e219dae8ccfca63bc10959c8405dbfd14b45d581f1669c02307c9c3 | Jupyter | 4,315 | 126 | # %% [markdown]
# # High-Frequency Sleep Staging: Downstream Task Performance
#
# This notebook creates publication figures showing how downstream task performance
# varies with the temporal resolution of sleep stage predictions.
#
# ## Panels
#
# 1. **ISRUC Age Prediction** (left)
# - Task: Predict subject age f... |
a9857887a30a6b69f9dc7c20c74a162f896405f83f154269307ad0c36fb22316 | Jupyter | 4,355 | 106 | # %% [markdown]
# # How to detect model misspecification
# %% [markdown]
# "Model misspecification" means that the simulator can, for no parameter set, match the observation. In that case, all methods implemented in `sbi` will likely perform poorly. As such, it is essential to detect whether the model is misspecified ... |
2c33bc18edd517f724b51a16529cc6dfc636e5264d39fca4d75654b0de9c9908 | Jupyter | 4,383 | 91 | # %% [markdown]
# ## BayesDesign
#
# Protein design for stability and conformational specificity, maximizing the p(structure|sequence) objective.
#
#
# <figure>
# <img src="https://github.com/dellacortelab/bayes_design/blob/master/data/figs/bayes_design.png?raw=true" width="700">
# </figure>
#
#
#
# [Stern J., Fr... |
8d45876e2d904f5ce234215f611e590a1ad35560e904318fc343ee5101d93b0e | Jupyter | 4,390 | 205 | # %%
from keras.datasets import mnist
# %%
from keras.utils import np_utils
# %%
import numpy as np
import keras
# %%
(train_X, train_y), (test_X, test_y) = mnist.load_datad_data()
# %%
from matplotlib import pyplot
# %%
from matplotlib import pyplot
for i in range(9):
pyplot.subplot(330 + 1 + i)
pyplot.... |
02631925892089af7aa975121d01e0c3f1144be8bb131fe1aff3f32345f8cf60 | Jupyter | 4,414 | 119 | # %%
import os
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
# %%
df = pd.read_excel(r'C:\Users\Lenovo\1代码 REM中beta升高\upload_data\figure2bdefhi_data.xlsx')
# %%
nrem_all = df.groupby('state').get_group('nrem').reset_index(drop = True)
rem_all = df.groupby('state').get_gr... |
350472619c11b6c9864a128d32676f722887a06f3e060a1a6c032b6d66e46a05 | Jupyter | 4,474 | 165 | # %%
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.
# %%
base_dir = f"{PODCAST_ROOT}/Encoding_... |
03be2d9b762e3a2d5250e3a8f403161c88b844c8b08c67d4672cf279998516df | Jupyter | 4,523 | 120 | # %% [markdown]
# # How to run L-C2ST
#
# Tests like expected coverage and simulation-based calibration evaluate whether the posterior is **on average across many observations** well-calibrated. Unlike these tools, L-C2ST allows you to evaluate whether the posterior is correct **for a specific observation**. While thi... |
7d9348b50989c3d23d82a84d094ed9c84991ba86fbab8b21e2a65c4ccca7e0da | Jupyter | 4,537 | 174 | # %%
#%matplotlib widget
%load_ext autoreload
%autoreload 2
#%matplotlib notebook
#%matplotlib inline
%matplotlib widget
# %%
import tqdm
import torch
import numpy as np
import matplotlib.pyplot as plt
from ripser import ripser
from persim import plot_diagrams
import utils
plt.style.use("bioAI.mplstyle")
import... |
794ab5a133a1ad8949a9cb7b7ba7551f0915a9b29f0f237e8d90b2f49bcf7fe1 | Jupyter | 4,619 | 240 | # %%
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
# %%
df = pd.read_csv('C:\\Users\\arif\\OneDrive\\Journal Paper\\Code\\Irshad\\Dataset\\2_Dataset_cyclic_holi.csv', parse_dates=['Date'], index_col='Date')
# %% [markdown]
# # Days Counts (manually) in... |
631968a8db3d0b5eefa8d27d58717bdf63ae2692e922010c7358c6e70d1bec59 | Jupyter | 4,624 | 136 | # %% [markdown]
# # Numerical simulation of FAST applied to a generic image from scikit-image
# %%
%load_ext autoreload
%autoreload 2
# %%
import fast.s26_analysis.utils as utils
from fast.utils.generate_scan_pattern import generate_scan_pattern as gcn
import numpy as np
import matplotlib.pyplot as plt
from pathlib... |
c1b0b962055ffd6e7bf19ba5d44bd773a2dba8dd4bbc7f878f21bd6284dab638 | Jupyter | 4,626 | 161 | # %%
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
# %%
df = pd.read_csv('C:\\Users\\arif\\OneDrive\\Journal Paper\\Code\\Irshad\\Dataset\\6_Dataset_cyclic_holi.csv', parse_dates=['date_and_hour'], index_col='date_and_hour')
# %%
df
# %% [markdown]
# ... |
71c919c42b6abd9a4dea3d06cfbf9d9189a1ec783d7bb3c2727d08d45a01b645 | Jupyter | 4,688 | 151 | # %%
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
from matplotlib import cm
import sys
import os
from os import path
from scipy import stats, signal, ndimage
from tqdm.notebook import tqdm
import math
... |
7e6c1fadee8ed3a64a0796440b4b5573b698fa413634bd072b0316dc9391e33e | Jupyter | 4,703 | 113 | # %% [markdown]
# # How to tune hyperparameters with Optuna
# %% [markdown]
# This guide shows a minimal `optuna` ([documentation](https://optuna.org/)) loop for hyperparameter
# tuning in `sbi`. Optuna is a lightweight hyperparameter optimization library. You define
# an objective function that trains a model (e.g., ... |
c19274733a04e3add1f4fa49c0fb16de4e93c87c49b19d2ccea3ad52e8962a34 | Jupyter | 4,803 | 146 | # %%
import os
PODCAST_ROOT = os.environ.get("PODCAST_ROOT", "/engram/naplab/users/rs4613/Podcast") # set to your copy of the dataset
# %%
melspec.shape
# %%
"""
Check dimensions of mel-spectrogram and Whisper features before and after PCA reduction.
Run this in Jupyter Notebook to see how PCA transforms your featu... |
1e271ac79279cad940864e4313c9faefa8b14ff26fe53478ef5be919ea6e1707 | Jupyter | 4,812 | 150 | # %%
import numpy as np
import torch.nn as nn
import torch.optim as optim
from lr_scheduler import CyclicCosineDecayLR
import matplotlib.pyplot as plt
import warnings
# pytorch warns when scheduler.step() is called before optimizer.step()
# ignore warnings in this notebook
warnings.filterwarnings('ignore')
# %%
cla... |
a49dfd4ecd7fd5fd754569e231e14b631c742f98e777e3e965cb1a0e40217bbf | Jupyter | 4,933 | 180 | # %% [markdown]
# # PQMF filter design example
#
# This notebook is for checking the filter response of PQMF.
# %%
import matplotlib.pyplot as plt
plt.rcParams["figure.figsize"] = (16, 9)
%matplotlib inline
from scipy.signal import freqz
from parallel_wavegan.layers import design_prototype_filter
# check prototype ... |
b76bea0333d02e1a4e99a1e54a7821a467771c6e0ede3a1e1ea8189b98739075 | Jupyter | 4,957 | 157 | # %%
import os
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib
import matplotlib.pyplot as plt
from scipy.stats import spearmanr
# %%
def heatmap(data, row_labels, col_labels, ax=None,
cbar_kw={}, cbarlabel="", **kwargs):
"""
Create a heatmap from a numpy array and tw... |
999733d6f594a5c596e070e42ca4d1c4311b85df96de7c71a2302f1c0831fe79 | Jupyter | 4,994 | 133 | # %%
%load_ext autoreload
%autoreload 2
import sys; sys.path.append('../../'); from config import *
configure_pylab()
# %% [markdown]
# # Posterior confidence via sampling
# %% [markdown]
# ### Retrieve model
# %%
L = 128
nsamples = 500 # Samples to draw
res = 1 # Upsampling resolution relative to gri... |
d56a81fe272aed45c4594f588c9ce0c18af0428ede65364953ce51de163d1e50 | Jupyter | 5,020 | 129 | # %% [markdown]
# # How to specify a custom prior (e.g., multiple independent priors)
# %% [markdown]
# `sbi` works with torch distributions only, so we recommend using those whenever
# possible. If you want a different prior in each parameter dimension, then you
# can use the `sbi` utility `MultipleIndependent`:
# %... |
6e0cc91ac2ea5c5f0eca4f6c02f7dafb1cdc1947ca9203f59c4391d6a3ef1461 | Jupyter | 5,043 | 135 | # %%
import numpy as np
import umap
import matplotlib.pyplot as plt
import matplotlib
from sklearn.decomposition import PCA
from ripser import ripser
from persim import plot_diagrams
# %%
def synth_grid(xx, yy, phase = 0, orientation = 0, f = 3):
""" Create ratemaps for n synthetic grid cells
params:
xx,... |
f3aca236c96fd39c9b855c4539b01bd0566efb0f9f641052766339b519fdf4a5 | Jupyter | 5,099 | 153 | # %% [markdown]
# # Example 5: Heading dependence
# %% [markdown]
# Inspect how grid maps change depending on the animal's heading.
# See `heading.py` for subroutines used in this notebook.
#
# Neuron `r2405_051216b_cell1816` was recorded by Dr. Marius Bauza and the Krupic lab; They have kindly shared this data to ... |
ca36ef91ef5d07a306d769d1228c08434df84aaf9ded614b4f568fdf616e74ee | Jupyter | 5,105 | 121 | # %% [markdown]
# # Multi-round inference
#
# In the previous tutorials, we have inferred the posterior using **single-round inference**. In **single-round inference**, we draw parameters from the prior, simulate the corresponding data, and then train a neural network to obtain the posterior. However, if one is intere... |
1ab96384e4bbb862f08fb6899a08abbce33a8a76ce0669b8e9259ff639799350 | Jupyter | 5,113 | 146 | # %%
import scanpy as sc
import scvelo as scv
import numpy as np
# %%
adata = sc.read('../../../../human_cd34_bone_marrow.h5ad')
# %%
adata
# %%
scv.pp.filter_and_normalize(adata, min_shared_counts=20, n_top_genes=2000)
# %%
scv.pp.pca(adata)
scv.pp.neighbors(adata, n_pcs=30, n_neighbors=30)
scv.pp.moments(adata, n... |
09c23c73571d9b7735b793b09861e35f74a5314368fc453e42b43b202dcbd91f | Jupyter | 5,137 | 156 | # %% [markdown]
# # Single Cell Analysis of D7, D14 and D21 RGC-iNs
# %%
import scanpy as sc
import pandas as pd
sc.settings.verbosity = 3 # hints
# %% [markdown]
# ### Data collection
# %%
### Read data into an AnnData objects (this step may take a few minutes the first time) ###
adata_7 = sc.read_10x_mtx(
f... |
f7a4f6c4315c0ebb1f3781dbc16d76ff5f5b4b460f670c76fee2bc4866256d01 | Jupyter | 5,137 | 138 | # %%
import numpy as np
run_folders_dict = {
"run_0": [
"../../logs/exp002/exp002a/sweep-2025-10-14_11-33-51_attention-weights/0",
"../../logs/exp002/exp002a/sweep-2025-10-14_11-38-43_attention-weights/0",
"../../logs/exp002/exp002a/sweep-2025-10-14_11-39-46_attention-weights/0",
".... |
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