sha256 stringlengths 64 64 | language stringclasses 28
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|---|---|---|---|---|
a56cbb5da7a3bb2c6c578fe00f7c138dd59561ec556cd352d80144c9b2bde47f | Jupyter | 37,035 | 800 | # %%
import os
import glob
import matplotlib.pyplot as plt
from nilearn import image as nimg
from nilearn import plotting as nplot
from bids.layout import BIDSLayout
import numpy as np
import nibabel as nib
from nibabel import load
import pandas as pd
from nilearn.image import resample_to_img
import pickle
import seabo... |
864ba8812ef235521cca40bc46938582423fc8407b4dbe9e45d716d549c25271 | Jupyter | 37,095 | 1,171 | # %% [markdown]
# # Habituation analysis
#
# Habituation metrics (swim speed, thigmotaxis, trial success) across sessions.
# %%
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from scipy import stats
from matplotlib.cm import get_cmap
import pingouin as pg
import os
from cichlid_omr.config impo... |
5a3c66024b739351554ae635421fcbf6bd4fb8df9ebfbdbd550adc614e206b54 | Jupyter | 39,474 | 1,092 | # %% [markdown]
# # Fine-tuning on Pre-trained Model for Cell-type Annotation
# In this tutorial, we demonstrate how to fine-tune a pre-trained model on a new dataset for the cell type annotation task. We use the Multiple Sclerosis dataset as an example and fine-tune on the pre-trained whole-body model. Please download... |
d6b724efbbaefa86fbfe899cb2636ddea089e63351f1418e38f02c019fde0964 | Jupyter | 40,146 | 1,374 | # %% [markdown]
# # SegCT/MRI Gradio Colab — ROI候補検索対応版 v2
#
# TotalSegmentatorをColab上で実行し、ROI候補を検索・複数選択して、STL / SegRef3D互換label PNG / 体積CSVなどを出力します。
#
# **主な改善点**
#
# - ROI名を手入力しなくても、taskに応じた候補から検索・複数選択できます。
# - 添付の `resources/roi_catalog_*.txt` 相当のカタログをノートブック内に埋め込み済みです。
# - STL、SegRef3D互換label PNG、preview PNG、体積CS... |
168546a67d6062fc6960ac3d7c821ccc1aea64fc482eec05b35b509c41d5dba9 | Jupyter | 41,179 | 658 | # %% [markdown]
# Import Libraries
# %%
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from skimage.filters import gaussian
from skimage.morphology import remove_small_objects
from skimage.measure import label, regionprops
from skimage import exposure
from skimage import measure
from ... |
5dd08f2bbc5b70bb41beb066c1c358b5bc26519de5ced8748dc15796e7d85f5e | Jupyter | 41,360 | 1,081 | # %%
import os
import glob
import matplotlib.pyplot as plt
from nilearn import image as nimg
from nilearn import plotting as nplot
from bids.layout import BIDSLayout
import numpy as np
import nibabel as nib
from nibabel import load
import pandas as pd
from nilearn.image import resample_to_img
import pickle
import seabo... |
b2cdda342e7023584dc78c07aa2df46e5e2547d9e6403fda86199bfc95891464 | Jupyter | 41,587 | 1,276 | # %%
## benchmark 方法：NicheCompass， GraphST， STAGATE， Cellcharter
## benchmark 数据集：
## metrics ref https://genomebiology.biomedcentral.com/articles/10.1186/s13059-024-03361-0
# %% [markdown]
# ### seqFish 小鼠胚胎发生 data
# %%
import os
import torch
import pandas as pd
import scanpy as sc
from sklearn import metrics
import... |
00c90ebe617938670017da66efb8574f6077a1726b508c65bd61ee3be48eebef | Jupyter | 41,753 | 988 | # %% [markdown]
# In this notebook, I'll show you how to train a BPReveal model on ATAC-seq data.
#
# If you already have a dataset you want to work with, you still have to go through the first section, since an important part of our pipeline involves shifting the ends of the reads.
# %% [markdown]
# ## My workflow
#... |
2f08e7e49d4b6e20a7f7338df1c7d5bf24f33fdc945780297b1d46f909c16eee | Jupyter | 41,866 | 1,119 | # %%
import os
import numpy as np
import scipy
import matplotlib as mpl
import matplotlib.pyplot as plt
import pandas as pd
from scipy.spatial import cKDTree
from scipy.spatial.distance import cdist
from scipy.ndimage import gaussian_filter1d
from mpl_toolkits.axes_grid1 import make_axes_locatable
import time
import sk... |
61ce0b58e887445237d296ce32f87ea99ce0f4df0f0410ed731e8574ee3a9d9e | Jupyter | 42,407 | 991 | # %% [markdown]
# # Demo: Plot Combined Manuscript Source Data
#
# This notebook reads only `data/spacestream_manuscript_source_data.xlsx`. Each worksheet is one manuscript figure panel; panels with multiple source tables are parsed from stacked sections within that worksheet.
# %%
from pathlib import Path
import os
... |
7032956aef50684469b573b933e56b706b6babf6610cae8e9be3f391027efc6e | Jupyter | 43,383 | 1,235 | # %% [markdown]
# # Compression Parameters
# %% [markdown]
# In this section, we examine different compressors with different compression configuration (N/E1/E2). For each configuration, we train the model with MSE error (reconstruction error).
# %% [markdown]
# ## Training
# %%
import TSA
import tools
PROJ_NAME = ... |
db0daff7dd6087a57bad03f969688c0dca41de4f58c6a5751f5ef6aabaeadef1 | Jupyter | 43,532 | 1,299 | # %% [markdown]
# **This notebook requires prior.ipynb to be fully executed first.**
# %% [markdown]
# # Training Phase
# %% [markdown]
# ## Full model
# %%
import TSA
import tools
PROJ_NAME = 'DualDataset'
TSA.setup_proj(PROJ_NAME, 'ab')
# %%
import keras as K
import tensorflow as tf
def load_prior(k_fold: int, ... |
d6cb7abc872553663b2e07b8534ae978e896d07419df4798517d2007175ee578 | Jupyter | 49,068 | 1,668 | # %%
!pip install torch matplotlib torchdiffeq
import torch
import torch.nn as nn
import matplotlib.pyplot as plt
from torchdiffeq import odeint
print("PyTorch:", torch.__version__)
print("CUDA :", torch.cuda.is_available())
if torch.cuda.is_available():
print("GPU name:", torch.cuda.get_device_name(0))
impor... |
a1544ffc275efffbdb4c97ba3b638c5d32eed74eadd851269f0acf7e6311c246 | Jupyter | 50,437 | 1,360 | # %%
%matplotlib inline
%reset
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import scikit_posthocs as sp
import seaborn as sns
import statsmodels.api as sm
import statsmodels.formula.api as smf
import statsmodels.stats.multitest as smm
from scipy import stats
from scipy.integrate import simp... |
22e4a47eecc294c9a6b36d03fc7911d55619b368c0f03a915d13b3b9b12f8b3c | Jupyter | 50,552 | 949 | # %% [markdown]
# # Code and updates for existing code to make xml's for whole slide inference masks
# %% [markdown]
# ## Overview
# - I improved the cv2_polygonize function
# - easy handling if processing spacing and saving output correctly to wsi spacing
# - allow to simplify polygons
# - correct removel... |
d67ff6335c3549efc91519e38e7812250bb0f7d93632889a80faa5ad19563a15 | Jupyter | 52,589 | 1,376 | # %% [markdown]
# In this notebook, I'll go through a full example of using BPReveal to analyze some chip-nexus data. These are the same data that were used in our original paper: Avsec, Ž., Weilert, M., Shrikumar, A. et al. Base-resolution models of transcription-factor binding reveal soft motif syntax. Nat Genet 53, ... |
679daa0d846497a0e8ed0f2d9689d61410a3c4af6eec38e88873d8219c8001ae | Jupyter | 53,114 | 1,726 | # %%
import loompy
import numpy as np
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import os
from cytograph.pipeline import load_config, PunchcardDeck
from cytograph.plotting import colorize
from cytograph.embedding import art_of_tsne
from cytograph.visualization import scatterm
from cytograph.... |
45d75e61268848b694d593f90be6b20f66a890b8336adfa45704f91518c0f64b | Jupyter | 54,246 | 1,739 | # %%
import torch
import torch.nn as nn
import matplotlib.pyplot as plt
import pandas as pd
from matplotlib.transforms import Bbox as BB
from google.colab import drive
from statistics import *
import os, math
drive.mount('/content/drive')
Repository_path = ""
trim_percent = 0 #[0, 2,5, 5, 7.5, 10]
# %% [markdown]
#... |
7bd6fe84b89e91f57f8a102fe549b703ab9cac9ec749d0cbbcd570b5f91e9dd4 | Jupyter | 54,829 | 1,745 | # %%
import torch
import torch.nn as nn
import matplotlib.pyplot as plt
import pandas as pd
from matplotlib.transforms import Bbox as BB
from google.colab import drive
from statistics import *
import os, math
drive.mount('/content/drive')
Repository_path = ""
# %% [markdown]
# # 1.実験データの読み込み&外れ値の削除
# %% [markdown]
... |
a1a055c6660155a96050deb9d7fd0c60a9972f6e922686bcbd67e04166681bc5 | Jupyter | 55,916 | 1,457 | # %%
%matplotlib inline
# NOTE: %reset wipes every variable in the current kernel session, and normally
# prompts "Proceed (y/[n])?" interactively when run in a live notebook - kept
# here to ensure a clean run, but will hang if this notebook is ever run
# non-interactively (e.g. via jupyter nbconvert --execute).
%res... |
4218bd07b6a924f18b3e403189a09e3b5f459a55c0a56fcaa684424ffc5cbbba | Jupyter | 56,091 | 1,622 | # %%
import json
import re
from dataclasses import dataclass
from pathlib import Path
from typing import Iterable, Optional, Dict, Any, Tuple, List
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
# ----------------------------
# Config
# ----------------------------
@dataclass(frozen=True)
cl... |
f11e530159b86dd239af33172bf1eaf51a1a80e4a938d18be7bb27e42b68debd | Jupyter | 56,421 | 1,610 | # %%
import numpy as np
from matplotlib import pyplot as plt
import matplotlib as mpl
import scipy.signal as signal
import pandas as pd
import scipy.stats as stats
import seaborn as sns
import scipy.ndimage as filters
import glob
import os
# %%
import pingouin as pgstats
# %%
import pickle
# %%
from ploting import *... |
79941931de7ee568527c03f63010deb18590b465b590d54edc48573df2c80b68 | Jupyter | 57,669 | 1,458 | # %% [markdown]
# # Install dependencies
# %%
import os
import numpy as np
import nibabel as nib
import matplotlib.pyplot as plt
from scipy.stats import linregress
from pathlib import Path
from scipy import stats
from sklearn.linear_model import LinearRegression
from scipy.stats import gaussian_kde
from matplotlib.li... |
07ff42592c173e797866f3f7012586758c9adcc5de1b7e9a6c0fa8103e91adc9 | Jupyter | 58,932 | 1,627 | # %% [markdown]
# This is the integration test for BPReveal. It trains up an OSKN model and runs a full suite of analysis on it.
# %% [markdown]
# # Setup
# %%
import os
os.environ["PATH"] = os.environ["PATH"] + ":/n/apps/CentOS7/bin/"
import bpreveal
print(bpreveal.__version__)
import bpreveal.utils as utils
from bp... |
25cce784603fb2a125053650ac6076ae5a757346da3a2866d832eca7ff71baa8 | Jupyter | 59,339 | 1,553 | # %%
import os
import numpy as np
import matplotlib.pyplot as plt
import time
import pandas as pd
from tqdm import tqdm
import warnings
import pandas as pd
from scipy.spatial.distance import cdist
import skimage as ski
from skimage.transform import resize
import warnings
import cmcrameri.cm as cmc
import matplotlib.tic... |
9e36c03557ca721b1b9bb3f182bb198f8d670fffd834d9e629903f9e017a03d1 | Jupyter | 59,597 | 1,324 | # %% [markdown]
# # 【使い方】
#
# ランタイム>すべてのセルを実行（**Ctrl+F9**）によりすべてのセルを実行し、セル[2]の最後に生成された**URL（Running on public URL）をクリック**して開いてください。（GUIが新しいタブで開かれる）
#
# ※セル[1]は実行完了までに約5分、セル[2]は5秒程度を要します。
#
# ※このcolabの画面（タブ）は閉じないでください。
#
#
#
#
# # [How to use]
#
# Run all cells by selecting Runtime > Run all cells (**Ctrl+F9**),... |
1efe102701d1133135e0cee1140b11a53730996857358b223154200e61d60310 | Jupyter | 59,742 | 1,323 | # %% [markdown]
# # 【使い方】
#
# ランタイム>すべてのセルを実行（**Ctrl+F9**）によりすべてのセルを実行し、セル[2]の最後に生成された**URL（Running on public URL）をクリック**して開いてください。（GUIが新しいタブで開かれる）
#
# ※セル[1]は実行完了までに約5分、セル[2]は5秒程度を要します。
#
# ※このcolabの画面（タブ）は閉じないでください。
#
#
#
#
# # [How to use]
#
# Run all cells by selecting Runtime > Run all cells (**Ctrl+F9**),... |
03c321fb013456da87a0e125b28f6627c764da32ac0727cbd28f45ec8640d77a | Jupyter | 59,901 | 1,330 | # %% [markdown]
# # 【使い方】
#
# ランタイム>すべてのセルを実行（**Ctrl+F9**）によりすべてのセルを実行し、セル[2]の最後に生成された**URL（Running on public URL）をクリック**して開いてください。（GUIが新しいタブで開かれる）
#
# ※セル[1]は実行完了までに約5分、セル[2]は5秒程度を要します。
#
# ※このcolabの画面（タブ）は閉じないでください。
#
#
#
#
# # [How to use]
#
# Run all cells by selecting Runtime > Run all cells (**Ctrl+F9**),... |
b4f9166f56854369aa698ee16fac5899f50a4989b6fb68e200ed78ab5269472b | Jupyter | 59,961 | 1,330 | # %% [markdown]
# # 【使い方】
#
# ランタイム>すべてのセルを実行（**Ctrl+F9**）によりすべてのセルを実行し、セル[2]の最後に生成された**URL（Running on public URL）をクリック**して開いてください。（GUIが新しいタブで開かれる）
#
# ※セル[1]は実行完了までに約5分、セル[2]は5秒程度を要します。
#
# ※このcolabの画面（タブ）は閉じないでください。
#
#
#
#
# # [How to use]
#
# Run all cells by selecting Runtime > Run all cells (**Ctrl+F9**),... |
d7d37ae1a9b2544688436cba8572f808962f485c4f3c09e890c0c360b6305e62 | Jupyter | 59,971 | 1,330 | # %% [markdown]
# # 【使い方】
#
# ランタイム>すべてのセルを実行（**Ctrl+F9**）によりすべてのセルを実行し、セル[2]の最後に生成された**URL（Running on public URL）をクリック**して開いてください。（GUIが新しいタブで開かれる）
#
# ※セル[1]は実行完了までに約5分、セル[2]は5秒程度を要します。
#
# ※このcolabの画面（タブ）は閉じないでください。
#
#
#
#
# # [How to use]
#
# Run all cells by selecting Runtime > Run all cells (**Ctrl+F9**),... |
b2e53df6ce269dda3017706c2f0a848a054ce9f36c808e0b9a9bb152dea67a63 | Jupyter | 60,272 | 1,700 | # %%
import os
import pandas as pd
import numpy as np
import openai
import requests
import pandas as pd
import openai
import json
import logging
import time
from typing import List, Dict, Any, Optional
from enum import Enum
from dataclasses import dataclass
import openrouter
# %%
# Import the openrouter module
#... |
ad8bff16ea3f7edcdc80b0b10497c64f788dcd51172e986c75499271d3fa17cd | Jupyter | 60,872 | 1,358 | # %% [markdown]
# # 【使い方】
#
# ランタイム>すべてのセルを実行（**Ctrl+F9**）によりすべてのセルを実行し、セル[2]の最後に生成された**URL（Running on public URL）をクリック**して開いてください。（GUIが新しいタブで開かれる）
#
# ※セル[1]は実行完了までに約5分、セル[2]は5秒程度を要します。
#
# ※このcolabの画面（タブ）は閉じないでください。
#
#
#
#
# # [How to use]
#
# Run all cells by selecting Runtime > Run all cells (**Ctrl+F9**),... |
bc2874620d458e3fc879c460890a1c5b6524207db59aa69e963e6dd9f6f60a36 | Jupyter | 62,983 | 1,366 | # %% [markdown]
# # [drosophila_body_orientation_predictor](https://github.com/nehalsinghmangat/drosophila_body_orientation_predictor) Notebook
# This notebook serves to clean, correct, and augment the experimental data described in the paper [Plume-Tracking Behavior of Flying Drosophila Emerges from a Set of Distinct... |
8617c1677ec43c45abaa3140b51fcd0e487e79c539c062dcfaf6f0340b30711c | Jupyter | 63,619 | 1,376 | # %% [markdown]
# # Load experimental data
# %%
import h5py as h5
import numpy as np
import matplotlib.pyplot as plt
import io_dict_to_hdf5 as ioh5
fname = '../data/parker_abe/ModelData_all.h5'
GLM_Data = ioh5.load(fname)
# %%
date_mouse_id_list = GLM_Data.keys()
for data_ani in date_mouse_id_list:
print(data... |
4eef2866a301778ba330b27ba7ea74e523a97a8fb7e49872d7e16953d6b15cee | Jupyter | 64,241 | 1,441 | # %% [markdown]
# # 【使い方】
#
# ランタイム>すべてのセルを実行（**Ctrl+F9**）によりすべてのセルを実行し、セル[2]の最後に生成された**URL（Running on public URL）をクリック**して開いてください。（GUIが新しいタブで開かれる）
#
# ※セル[1]は実行完了までに約5分、セル[2]は5秒程度を要します。
#
# ※このcolabの画面（タブ）は閉じないでください。
#
# ※エラー発生などは https://github.com/SatoruMuro/SAM2GUIfor3Drecon/issues にお知らせいただけると助かります。
#
#
#
# ... |
42febd026a6cb3d72522721b7faac87d86d9b479ba922f8710e40ba1693233de | Jupyter | 64,734 | 2,137 | # %% [markdown]
# # The Light Atom Curriculum
#
# We use curriculum learning in OneQMC to get the most gradient steps on our model for the lowest cost. The aim is to do as many gradient steps on the smallest possible molecules, increasing molecule size only to "teach" new concepts.
#
# The currently proposed levels a... |
90096f6efb018fa167178bce1499c2b0466f8c2ed1729b4b5888b7266014ef02 | Jupyter | 65,159 | 1,465 | # %% [markdown]
# # 【使い方】
#
# ランタイム>すべてのセルを実行（**Ctrl+F9**）によりすべてのセルを実行し、セル[2]の最後に生成された**URL（Running on public URL）をクリック**して開いてください。（GUIが新しいタブで開かれる）
#
# ※セル[1]は実行完了までに約5分、セル[2]は5秒程度を要します。
#
# ※このcolabの画面（タブ）は閉じないでください。
#
# ※エラー発生などは https://github.com/SatoruMuro/SAM2GUIfor3Drecon/issues にお知らせいただけると助かります。
#
#
#
# ... |
f313cd0aa38fb2d0e460127088f490ffe9266e010c140b8543287db3a9ec4d17 | Jupyter | 67,466 | 1,947 | # %% [markdown]
# ## Supplementary notebook for Brachyury expression levels predict lineage potential and axis-forming ability of in vitro derived neuromesodermal progenitors.
#
# Anahí Binagui-Casas$^{1,*,\#}$, Anna Granés$^{1,*}$, Alberto Ceccarelli$^{2,\$}$, Matthew French$^{3,\$}$, Filip J. Wymeersch$^{4}$, Rosa P... |
1bfa68e59985676eb7c49aa7a2723f8c415252082534db5ad0182d09d9a45820 | Jupyter | 67,745 | 1,672 | # %% [markdown]
# # Confounds in functional MRI
# As one of our reviewers noted, most decoding analyses (at least in cognitive neuroscience)
# are done on functional MRI, as opposed to structural MRI like we used as our main example in
# our paper (i.e., decoding gender from VBM/TBSS data). In this notebook, we simulat... |
44360b6c751482d23ae12898a2a5a362814d7d42afdb4687f80ca1c83f389181 | Jupyter | 68,372 | 1,719 | # %%
%matplotlib inline
# NOTE: %reset wipes every variable in the current kernel session, and normally
# prompts "Proceed (y/[n])?" interactively when run in a live notebook - kept
# here to ensure a clean run, but will hang if this notebook is ever run
# non-interactively (e.g. via jupyter nbconvert --execute).
%res... |
63ccfb6d39df8e29c3cede1428ad7655a9791e76f3818db81f1de774751ffce3 | Jupyter | 68,794 | 1,484 | # %% [markdown]
# # Load experimental data with no eye movement correction
# %%
import h5py as h5
import numpy as np
import matplotlib.pyplot as plt
import io_dict_to_hdf5 as ioh5
fname = '../data/parker_abe/ModelData_all.h5'
GLM_Data = ioh5.load(fname)
# %%
# Model Variables Documentation
# model_nsp - Neural sp... |
c6f263b5ed7cb5093dc9a3264b80ca0b817cd6c00bc02324a38143d7334a6b3a | Jupyter | 74,043 | 1,725 | # %% [markdown]
# # 【使い方】
#
# ## 重要：セルを実行する前にT4 GPUを選択してください
#
# Colabメニューの **ランタイム → ランタイムのタイプを変更 → T4 GPU → 保存** の順に選択してください。T4以外のGPUが割り当てられている場合は、そのGPUも使用できます。CPUのままでも起動できますが、SAM2の処理に非常に時間がかかります。
#
# ランタイム>すべてのセルを実行（**Ctrl+F9**）によりすべてのセルを実行し、セル[2]の最後に生成された**URL（Running on public URL）をクリック**して開いてください。（GUIが新しいタブで開か... |
6432620956b0f446bbc39a202d2b90cf50a82cf7051c19beb6d0630ce50a5c0d | Jupyter | 74,817 | 1,619 | # %% [markdown]
# # Notebook with empirical analyses on the influence of confounds in MVPA
# In this notebook, we evaluate different strategies to deal with confounds in MVPA on empirical data (see [this notebook](LINK) for analyses of simulated data). Here, we try to predict gender (male/female) from structural MRI da... |
c32e2236ee3fce4294e4aed583423e8fd52eb4ca693b37f4152e7892825d486d | Jupyter | 76,262 | 2,406 | # %% [markdown]
# ## Setup
# %% [markdown]
# ### Imports
# %%
%load_ext autoreload
%autoreload 2
import os
from os.path import join as ospj
from glob import glob
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
from tqdm.notebook import tqdm
from ... |
8ab21753d0835b7effcbfd2ad88043b108848453f37a8da0a14d276aa1d8bcc4 | Jupyter | 78,521 | 1,942 | # %%
%matplotlib inline
# NOTE: %reset wipes every variable in the current kernel session, and normally
# prompts "Proceed (y/[n])?" interactively when run in a live notebook - kept
# here to ensure a clean run, but will hang if this notebook is ever run
# non-interactively (e.g. via jupyter nbconvert --execute).
%res... |
b9899b5a14b2d0f146d18511e381eac1628217472c8592c11c49abe6a03d95fc | Jupyter | 82,140 | 2,569 | # %% [markdown]
# ## Setup
# %% [markdown]
# ### Imports
# %%
%load_ext autoreload
%autoreload 2
import os
from os.path import join as ospj
from glob import glob
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
from tqdm.notebook import tqdm
from ... |
65783a8cc9c067401b202600db0cee210c4630876d95a1677acaf816b110df54 | Jupyter | 88,791 | 2,159 | # %%
%matplotlib inline
# NOTE: %reset wipes every variable in the current kernel session, and normally
# prompts "Proceed (y/[n])?" interactively when run in a live notebook - kept
# here to ensure a clean run, but will hang if this notebook is ever run
# non-interactively (e.g. via jupyter nbconvert --execute).
%res... |
69c053c6f1ef937f3fcf61715fa3a6f2e855abe35b80a06bc44893df38c4910a | Jupyter | 90,238 | 1,833 | # %% [markdown]
# # Load Data
# %%
'''
This script optimizes logistic nonlinearities for a variety of Gaussian distribtuions and plots the optimal parameters.
Author: Jonathan Gant
Date: 29.08.2024
'''
import numpy as np
import matplotlib.pyplot as plt
from utilities import logistic_func, calc_MI, calc_entropy
from t... |
cbac73be1757a2780739686b6efc90763f74718f4e5363f277c35c017a5bfb9d | Jupyter | 92,369 | 2,323 | # %% [markdown]
# # Data Preparation
# %% [markdown]
# - **Creator**: Sebastian Birk (<sebastian.birk@helmholtz-munich.de>)
# - **Date of Creation:** 01.10.2022
# - **Date of Last Modification:** 26.07.2024
# %% [markdown]
# ## 1. Setup
# %% [markdown]
# ### 1.1 Import Libraries
# %%
import gc
import os
import warn... |
d164806c488100348ba683a9e5e4e2046ee461f2b6a006ed3761df4f9b9aa418 | Jupyter | 95,059 | 1,921 | # %% [markdown]
# # Load Data
# %%
'''
This script optimizes logistic nonlinearities for a variety of Gaussian distribtuions and plots the optimal parameters.
Author: Jonathan Gant
Date: 29.08.2024
'''
import numpy as np
import matplotlib.pyplot as plt
from utilities import logistic_func, calc_MI, calc_entropy
from t... |
8ad7ce7591cbc61d5443d000eb8733093d11f4a045933dc0927630d3fdbf92dd | Jupyter | 95,804 | 1,704 | # %% [markdown]
# # [drosophila_body_orientation_predictor](https://github.com/nehalsinghmangat/drosophila_body_orientation_predictor) — Data Correction and Model Training
#
# This notebook cleans, corrects, and augments the experimental data from [van Breugel et al. (2014)](https://www.sciencedirect.com/science/artic... |
2f5d310a83923234e2758a4e2441daff67c822f9e0f9c7cded4eebd81207d602 | Jupyter | 106,360 | 2,766 | # %%
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import os
import cmcrameri.cm as cmc
import skimage as ski
import seaborn as sns
from scipy.ndimage import uniform_filter
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
from statsmodels.stats.multitest import multipletests
... |
ab9f79ddec9de3d61496199379f9c5fe973bacab7a28ffeeb7a8b003f746bcdd | Jupyter | 116,680 | 2,339 | # %%
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
# %%
# close all figures
plt.close('all')
# %% [markdown]
# # Filter visualization
# %%
data_dir = 'PCA_then_ICA'
# mixing_matrix_high_tol = np.load(data_dir + "/mixing_matrix_picard_tol_1e-12_unit_var.npy")
# ic_components_... |
23f4c20b5625c316c765e145b4a15126578ab497b717e6ae2c12f1256ff1662e | Jupyter | 121,456 | 2,875 | # %% [markdown]
# # Simulation confound strategies
# This notebook evaluates the effectiveness of "confound strategies", i.e. their effectiveness of removing the influence of a known confound on machine learning analyses of (simulations of) mutivoxel pattern data. This notebook is meant as a supplement to the [article]... |
315fba4740a5fb987146617bde7cf719ed15a8e9f86ece1b7c184f1a35d2ca74 | Jupyter | 126,101 | 2,590 | # %% [markdown]
# # Load Data
# %%
'''
This script optimizes logistic nonlinearities for a variety of Gaussian distribtuions and plots the optimal parameters.
Author: Jonathan Gant
Date: 29.08.2024
'''
import numpy as np
import matplotlib.pyplot as plt
from utilities import logistic_func, calc_MI, calc_entropy
from t... |
afa8150e796f6e40a4a40e82b6af5625c9d81f8388529c92fc0dbafef991a1d7 | Jupyter | 127,583 | 3,471 | # %% [markdown]
# # DLC fin tracking
#
# Fin kinematics from DeepLabCut pose estimates → `DLC_closeup_data.json`, plus per-genotype figures.
# %%
import json
import os
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from matplotlib.cm import get_cmap
from matplotlib.collections import LineColl... |
1c70872d58f9d573fb091846f61db368004194f80438f36781ee0adf45144e0c | Jupyter | 142,788 | 4,296 | # %%
# compute protein-level + segment-level characterization (MODEL ONLY)
# and plot as BOX PLOTS + HISTOGRAMS (no ECDF, no random controls)
from pathlib import Path
import sys
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
# -----------------------------
# 0) Imports from your repo
# -------... |
9e597c2b6d4a943cdd1bbf9bbfc83a3d8fbcf1e1d2e1a9800e0d635e47c09c79 | Jupyter | 143,526 | 3,123 | # %% [markdown]
# # Post-hoc mechanistic tests
#
# Downstream analyses following:
# - `grpDotChangeSurfParcel.ipynb` and `grpDotContrastsSurfParcel.ipynb` — group-averaged HRFs and vs-baseline t-tests
# - `indDotTfceTTestSurfParcelSpatiotemporal4fcpt.ipynb` — individual-level TFCE tests, masking, and group-level direc... |
fee1777f3b9d20573e51e59123c525b1ae3c6b5c4183a2fee286b16731201a29 | Jupyter | 143,601 | 3,131 | # %% [markdown]
# # Post-hoc mechanistic tests
#
# Downstream analyses following:
# - `grpDotChangeSurfParcel.ipynb` and `grpDotContrastsSurfParcel.ipynb` — group-averaged HRFs and vs-baseline t-tests
# - `indDotTfceTTestSurfParcelSpatiotemporal4fcpt.ipynb` — individual-level TFCE tests, masking, and group-level direc... |
8715e91d3210f5f964a934acdde9df30faa82ea859f59c06c0eff1acd373c913 | Jupyter | 151,940 | 3,675 | # %% [markdown]
# # Wireless High-Density E-textile Eutectogel Electrode Arrays for Spatio-Temporal Machine Learning in Cutaneous Electrophysiology
# %% [markdown]
# Ruben Ruiz-Mateos Serrano <br>
# Start date : 10/01/2025
# %% [markdown]
# ## BSPM & ML
# %% [markdown]
# ### Test 16 channel BSPM recordings (with INT... |
d375a23856cf7936befe0cc694bce1c77dcd4e97342c1638629a6fca5708bd19 | Jupyter | 172,093 | 1,986 | # %% [markdown]
# Long-term reliability and stability of parameterised resting state EEG
# ======================================================================
#
# Analysis pipeline for 5-year test-retest reliability study.
#
# Data: Dortmund Vital Study (Getzmann et al., 2024)
# https://doi.org/10.18112/open... |
31dd1811959ac7869f0b8fd840d096d944384f2e1f14830e6d6047517153db37 | Jupyter | 200,000 | 37 | # %% [markdown]
# ##First, mount google drive.
# %%
from google.colab import drive,files
drive.mount('/content/gdrive')
# %% [markdown]
# # Testing Wind Direction ANN: Datasets
#
# ### This notebook will help us test our trained wind direction networks.
#
# The ANN used in the paper\* is:
# `network_construct_1hidd... |
987bcfdfbb7cd762b2ea6e4c83ce5f94a41a41a8a7adcee911d3ce77fa181655 | Jupyter | 200,000 | 6 | # %% [markdown]
# #**ANN to learn the wind direction from PFNs**
#
# This notebook contains a pipeline for designing and training an artificial neural network for estimating ambient wind direction adapted from Ben Cellini's BRAID workshop ANN tutorial. Here I use only columnar CX neuron (PFN) representations as inputs... |
e671c045fc9177495b147b7e22261c194aa90a683fe27c2e93d5cba36bd9b424 | Jupyter | 200,005 | 243 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# benchmarking on omnipath\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",... |
dbf067b02ef91a2638e8ff07e46708e9df60da0ce82c3db35ba68953c24c2a7e | License | 237 | 6 | Code (all .py files): MIT License
Data, results and metadata: Creative Commons Attribution 4.0 International (CC-BY 4.0)
Copyright (c) 2026 David Lima Azevedo
Institute of Physics, University of Brasilia (UnB)
ORCID 0000-0002-3456-554X
|
866d89cbf299323640d2ff76a5695e9813fded3a8aeed676c260583763767f17 | License | 302 | 8 | Creative Commons Attribution 4.0 International (CC BY 4.0)
You are free to share and adapt this dataset for any purpose, provided that
appropriate credit is given, a link to the license is provided, and changes
are indicated.
License information:
https://creativecommons.org/licenses/by/4.0/
|
046fd690a3e6734a5661a8fe077e4a153f45f24fcda1c79d728d450a8e28af61 | License | 568 | 14 | Copyright 2022 - University of Copenhagen
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, ... |
d1ace00bbe1c8017da2927001b6209e3f0631bc3118eca1242457dec6772842d | License | 589 | 13 | Copyright [2024] [Yeyi Cai]
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in wr... |
67b988ee4ce269af7abfba85869fbdaf2f5dfec7369dfc7fd74644081b1889b7 | License | 613 | 15 | #######
LICENSE
#######
Copyright (C) 2026 Ferreira LK, Westman E, Wahlund O and Mohanty R
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License or
any lat... |
19dc75e14ef278ee842a22d1eb202245751df7b573e6613b24773ad080e85a54 | License | 795 | 18 | PEER License
/*************************************************************************
Copyright (c) Oliver Stegle, Leopold Parts, Matias Piipari (PEER project).
>>> SOURCE LICENSE >>>
This program is free software; you can redistribute it and/or modify
it under the terms of the GNU General Public License as publish... |
90a7e0377f19d33a1a4ec72c9d9d8633896fad34f5a7ef626e6865587d9b9c9f | License | 928 | 16 | MIT No Attribution
Copyright 2023 SpaceHack 2.0 Organizing Committee
Permission is hereby granted, free of charge, to any person obtaining a copy of this
software and associated documentation files (the "Software"), to deal in the Software
without restriction, including without limitation the rights to use, copy, mod... |
29d4e05f9e07c38cc4bcda02eb317e29073ded487724f7e736158be778890b12 | License | 1,051 | 7 | Copyright 2019 Kamil Bonna
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense... |
aa504543c3b42fd98e9a83cb50c5d00584976242420b43b351c288df400cc342 | License | 1,056 | 21 | MIT License
Copyright (c) QBiC
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, subli... |
a0d0920def9174af283b58023cf6d30d7f7fec3321717bb125537557039aff6c | License | 1,060 | 21 | MIT License
Copyright (c) 2025 GPL
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, s... |
16c81ce46aad4d4f4741458f0046ec9d3c5729dd8e39f1a36708c2cfe752d971 | License | 1,061 | 21 | MIT License
Copyright (c) 2023 RL4M
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, ... |
bd67c690c40e60a5849d7bd2eb524dd51a1e3fcd22fdbaee1206eade4bf3f3ca | License | 1,061 | 21 | MIT License
Copyright (c) 2025 Matt
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, ... |
1ceeacbed51e2890187425547bc2efd16c1b7ad45189b7dfb21e83a45a2e9d9e | License | 1,062 | 21 | MIT License
Copyright (c) 2022 suber
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute,... |
423fde8b633fa63af388ffcc58277cfa28c2fcde817f696a781867f4536c7655 | License | 1,062 | 21 | MIT License
Copyright (c) 2023 Yikun
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute,... |
2ca1cbdacf6831143122b13d861165705c177bbeb4fe6afa2e584aa5cd0d04c2 | License | 1,063 | 21 | MIT License
Copyright (c) 2025 CTPLab
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute... |
3acb4dbbadead5bee912ad4845f2f5be56692aeea108122f39dadfd01c5d1bd8 | License | 1,063 | 21 | MIT License
Copyright (c) 2024 jiya-m
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute... |
42e238c08d5ea1117b1fc76b6983f9e5ba1395845761ce80eba2520ef99333b6 | License | 1,063 | 21 | MIT License
Copyright (c) 2025 Han Yu
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute... |
6300cd0365e86eb5915b47c72eda51963c0225146747cb936ec9224134ab76ea | License | 1,063 | 21 | MIT License
Copyright (c) 2026 Nuo Xu
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute... |
802ea271a8d25d68313a423a58441d3c2cb6c8c2d140c00b67daf0ed9068e130 | License | 1,063 | 21 | MIT License
Copyright (c) 2023 王宁
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, su... |
993f771e9e455e5bd43a3e7ddc1025030e5b4439da0f257437b5d28775014694 | License | 1,063 | 21 | MIT License
Copyright (c) 2026 dwhhh3
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute... |
ad6c8bf818a3239d09d4d945db8235bbd8e12ac9bd2909c4a6e00bfb75c8a6f7 | License | 1,063 | 21 | MIT License
Copyright (c) 2025 btrex7
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute... |
af96932cf82f4d34291dfa2dcec11364274ba699bb09b4da3424f27499153688 | License | 1,063 | 21 | MIT License
Copyright (c) 2019 nkicsl
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute... |
f5a6182d30a85a43f4f99ee25c001801abb9d53c8b928b1330615423eea352ec | License | 1,063 | 21 | MIT License
Copyright (c) 2026 Shadou
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute... |
0f650564dc089389111e59d13af06a9a4657ee4e8344422fdc9a108425ead4cf | License | 1,064 | 21 | MIT License
Copyright (c) 2023 Xi Wang
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribut... |
1d7cb369ed2922da4cd67fb3656ed375a415ae6b38bc944a27d17e5edcc50036 | License | 1,064 | 21 | MIT License
Copyright (c) 2024 kkkayle
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribut... |
26680115cd3fc37100cbd51490943efbe7aa106883af16ba92debd62aa243cef | License | 1,064 | 21 | MIT License
Copyright (c) 2024 TRChote
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribut... |
2939543ceb8a3e1f88c14e902b41d8e3ea055001da512fb4bf48b510a5b719ed | License | 1,064 | 21 | MIT License
Copyright (c) 2019 Jie Hao
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribut... |
36eb8a3a4f4ede50cfa2d2a7297f31b44b0c289d1e595048be50234a0fa8288e | License | 1,064 | 21 | MIT License
Copyright (c) 2026 Jiaming
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribut... |
457d9e8d5bdf4ad409adf5e36d3d8120d98e855112431874b93df0b5f9015ae6 | License | 1,064 | 21 | MIT License
Copyright (c) 2024 tcapraz
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribut... |
84077c5734b82139658c3674b6dfb25037d2e4f449951277c764629bb18833c8 | License | 1,064 | 21 | MIT License
Copyright (c) 2022 fetpype
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribut... |
dda30269fe36e7213627d055d4a13143bd8e03d6988830d8b72e399d987e3d24 | License | 1,064 | 21 | MIT License
Copyright (c) 2018 Abi See
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribut... |
f6087ea7322d54e7eabb357163c34aa3eb5e73d6f003ed2c9b4f26eed67ff87b | License | 1,064 | 21 | MIT License
Copyright (c) 2026 GNACODE
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribut... |
36dd835d6f5d45492fb7d5d877db7bad0283f8b6ff8c023952355dc8d323b49c | License | 1,065 | 21 | MIT License
Copyright (c) 2020 PrismNet
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribu... |
3efdd0f0b6c3c95df505ee1a870514ff107e300266a8ddf0645df31b2aeec5db | License | 1,065 | 21 | MIT License
Copyright (c) 2019 Bo Liang
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribu... |
4900f66b2386aec2104aa6322a1b7ba77bea7bb76879fb01e6eca854fb54239c | License | 1,065 | 21 | MIT License
Copyright (c) 2026 yf-neuro
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribu... |
54fdd5bf7f9c03fe10c8d1b7eaa824eb3e844924517bf89ea916963efc2025c3 | License | 1,065 | 21 | MIT License
Copyright (c) 2026 wanyizju
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribu... |
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