sha256 stringlengths 64 64 | language stringclasses 27
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|---|---|---|---|---|
5c96f8f1dc73305d5cfdc4f5bb649137539fa9fc6f3a1944bf4c50a2dc41e47a | Jupyter | 17,250 | 528 | # %% [markdown]
# # ChemicalEntity ↔ Gene Relation-Wise Merge
# %% [markdown]
# ## 0. Configuration
# %%
import pandas as pd
import numpy as np
import re
# ── Base directories ──────────────────────────────────────────────────────────
BASE_DIR = '/storage/Arushi/090526_EvoAge/kg_formation/'
PROC_DIR = BASE_DIR +... |
2c3e96b64f943d2c02ac82d93c833c04f97ed47966712388664173d3e464a3d9 | Jupyter | 17,278 | 408 | # %%
import numpy as np
import pandas as pd
import pickle
import os
from isttc.scripts.cfg_global import project_folder_path
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
mpl.rcParams['pdf.fonttype'] = 42
mpl.rcParams['ps.fonttype'] = 42
plt.rcParams['svg.fonttype'] = 'none'
# %%
da... |
2c8ea93ab7fcc45f332a5c34e0d2e1789669dc8a186655314e7e7f390e463db0 | Jupyter | 17,441 | 516 | # %%
# Load the required libraries and set a seed
library(Seurat)
library(Signac)
library(reshape2)
library(dplyr)
library(ggplot2)
library(caret)
library(glue)
set.seed(1234)
setwd("~/projects/deepscore")
source("R/deepscore.R")
source("R/marker_analysis.R")
# %%
# Recommended way to install Keras in R
install.packa... |
6f88f36b3fc45ed16b6a8603190767b9c4ba524cc18e05727e7296d10dcee6b3 | Jupyter | 17,493 | 572 | # %% [markdown]
# # S5: DOT - Image Reconstruction
# %% [markdown]
# ## Learning objectives
#
# In this notebook you will learn to:
#
# - Reconstruct HbO/HbR images from channel-space data using the DOT forward model
# - Visualise 3-D activation images on the brain surface
# - Project vertex-space images onto anatom... |
8ca25967bd52ef895fabef6297952a640fcfd650589c6e45493016dfc32a6de1 | Jupyter | 17,556 | 475 | # %% [markdown]
# Group and sum by metaprogram problem solved with: https://stackoverflow.com/questions/39650749/group-by-sparse-matrix-in-scipy-and-return-a-matrix
# %% [markdown]
# ## All programs
# %% [markdown]
# #### Load modules
# %%
import numpy as np
import pandas as pd
import numpy as np
import matplotlib.p... |
813e867c329a583691ee73bbfc8d67b4ee247741a3cfc42416a666e605c331dc | Jupyter | 17,580 | 437 | # %% [markdown]
# # Optode Registration: Spring-Relaxation vs. Snap-to-Scalp
#
# When fNIRS data are recorded, optode positions are typically digitized in a
# probe-specific coordinate system. Before any head-model-based analysis —
# image reconstruction, sensitivity mapping, parcellation-based averaging —
# those po... |
9c6d5b2953327ff5f9a53485e5c9f051fc7d4561bd2d7759f80e4bf6992d5846 | Jupyter | 17,612 | 493 | # %% [markdown]
# # Using Channel Variance as Proxy for Measurement Noise and as a Weight for Global Physiology Removal
#
# To improve statistics, channel pruning might not always be the way.
# An alternative is to use channel weights in the calculation of averages (e.g. across subjects) or image reconstruction.
# O... |
0e4040af9d619cae0a95e305b185d40faefc5d14dd04f57cd58f938429b46e07 | Jupyter | 17,679 | 456 | # %%
import sys
import os
import pandas as pd
from sklearn import preprocessing
from tqdm import tqdm
import fm
import torch
from torch import nn
from torch import optim
from torch.utils.data import DataLoader
import numpy as np
import random
def seed_torch(seed=0):
random.seed(seed)
os.environ['PYTHONHASHSEED... |
df0adff3f286362b9b88ea80cf80dc510aa649e3ecca4e99d3204347727bf80e | Jupyter | 17,698 | 381 | # %%
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
# %%
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')
DBN_UNS... |
ee9eb8739d11890a0259fdee43dbe59790d630834bf173ae5787cb865e512fb0 | Jupyter | 17,770 | 595 | # %%
from molmap import loadmap
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
import seaborn as sns
from rdkit import Chem
from rdkit.Chem.Draw import IPythonConsole
#IPythonConsole.ipython_useSVG = True
import numpy as np
import pandas as pd
from tqdm import tqdm
from collections import defaul... |
1720343ba4fb156d5237004d63412b8960a20063c2fbcff78b25e6479ea4051f | Jupyter | 17,807 | 461 | # %%
import numpy as np
import pandas as pd
import pickle
import json
import matplotlib as mpl
from datetime import datetime
import matplotlib.pyplot as plt
import seaborn as sns
from matplotlib.colors import TwoSlopeNorm
from isttc.scripts.cfg_global import project_folder_path
from isttc.tau import fit_single_exp, fi... |
2e5d803b91b150c049c0a325c8ee5ba5d286441bbe41165e2ed63312784850b1 | Jupyter | 17,910 | 487 | # %% [markdown]
# # Calculate relationship between AP and dendritic spikes with poisson excitation and rhythmic inhibition with variable frequency
#
# The simulations had either:
# 1. Rhythmic inhibition at the soma (64 Hz) or dendrites (16 Hz)
# 2. Poisson excitation at the soma and dendrites
#
# Here we calculate... |
7973a954c6f0117983d4fbb0a705dcb9b8442b5f58c06217e465d004cec1efe3 | Jupyter | 17,934 | 487 | # %% [markdown]
# # Calculate relationship between AP and dendritic spikes with poisson excitation and rhythmic inhibition where rhythms are reversed
#
# The simulations had either:
# 1. Rhythmic inhibition at the soma (16 Hz) or dendrites (64 Hz)
# 2. Poisson excitation at the soma and dendrites
#
# Here we calcul... |
d90be4b79156099b3e9d61c0b193d729b02296119a4e12ad1ee4ac90ba3f8e44 | Jupyter | 18,071 | 413 | # %%
import logging
import os
import sys
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from pnet.data_processing import filter_variants, prostate_data_loaders, utils
sys.path.insert(0, '../..') # add project_config to path
import project_config
try:
import wandb
_wandb_available = T... |
6d1e8d8d7be01cf3b1794aeb55e2b6a0c5d73a10c9592e6c3e7cb811bdb08be1 | Jupyter | 18,150 | 444 | # %% [markdown]
# ```{currentmodule} optimap
# ```
# %%
from optimap.utils import jupyter_render_animation as render
# %% [markdown]
# ```{tip}
# Download this tutorial as a {download}`Jupyter notebook <converted/io.ipynb>`, or as a {download}`python script <converted/io.py>` with code cells.
# ```
# %% [markdown]
#... |
629029bde8e34fb5cb0b6e160bd8abd52cc905951e209617e04723cbcd691c4f | Jupyter | 18,165 | 581 | # %% [markdown]
# ## Notebook to generate the panels for the Fig. 5 of Kadobianskyi et al., 2026
# %% [markdown]
# ### Registration and analysis of the morphological differences in male and female Danionella cerebrum
# %% [markdown]
# Load libraries, ants numpy matplotlib. Additional requirements: pandas, seaborn
# ... |
ed21b08ac821ad5b4e7c8fdd17cec8cab9a6612d2420c3b335d1c21aec243a17 | Jupyter | 18,380 | 638 | # %%
# Import library
import glob, json, pickle
import numpy as np
import pandas as pd
import mat73
from statsmodels.stats.multitest import fdrcorrection
from tqdm import tqdm
import scipy.stats as stats
# %%
# Original sample
x = np.array([5.1, 5.3, 5.8, 6.0, 5.6])
n_boot = 10000 # Number of bootstrap samples
boot_... |
3e4044d27eb9b6985752f53487d7867fc709ab4000bf0ca1361e43b37ae61f0a | Jupyter | 18,428 | 672 | # %% [markdown]
# # tensorcircuit SDK for QCloud（230220 ver）
# %% [markdown]
# ## import the package
#
# ``apis`` is temporarily as the entry point submodule for qcloud
# %%
import tensorcircuit as tc
from tensorcircuit.cloud import apis
from tensorcircuit.cloud.wrapper import batch_expectation_ps
from tensorcircuit... |
788b446718cd1656475c57f1988b65c48d7fea74b1174e49831a2fb0c153f6df | Jupyter | 18,659 | 460 | # %%
import numpy as np
import pandas as pd
import os
# %% [markdown]
# %%
BASE_PATH = "/storage/Arushi/090526_EvoAge/kg_formation/data_collection/"
OUT_PATH = "/storage/Arushi/090526_EvoAge/kg_formation/processed_data/agingatlas"
# ── derived sub-paths (do not edit below this line) ──────────────
# Inputs
PUBCHEM... |
89cc8238f72707be70c87153420a1aa55006e3c060faebd9b36c30788068aae1 | Jupyter | 18,732 | 502 | # %% [markdown]
# # Quantum Dropout for QAOA
# %% [markdown]
# ## Overview
# %% [markdown]
# Quantum Approximation Optimization Algorithm (QAOA) is a hybrid classical-quantum algorithm used for solving the combinatorial optimization problem, which is proposed by [Farhi, Goldstone, and Gutmann (2014)](https://arxiv.or... |
d63f5587acc988c2a0abfee9a6b653977b1f956e11acd26dfa9c1a7df2e60c03 | Jupyter | 18,735 | 592 | # %% [markdown]
# # Disease ↔ ChemicalEntity Relation-Wise Merge
#
# Merges Disease–Chemical triples from Monarch, PrimeKG (×3), PharmKG, and TARKG;
# resolves disease names via DO/MESH and chemical names via PubChem/DrugBank;
# deduplicates by `(head, relation, tail)`; and saves the result.
# %% [markdown]
# ## 0. C... |
de056fe3318b33059fd128cbfe2ad1d70701a080eebdc9984be0beff04ae969d | Jupyter | 18,944 | 691 | # %%
import sys
sys.path.append('/Users/midani/OneDrive/proj/leap/manuscript')
%load_ext autoreload
%autoreload 2
import importlib
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from matplotlib.ticker import MultipleLocator, FixedLocator
from matplotlib.transforms import... |
dd769b32e9d21380c7600b4cb0ffdf16acbbf972e83ff8c124c7121936807fa6 | Jupyter | 19,057 | 482 | # %%
import numpy as np
import pandas as pd
import pickle
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
from pathlib import Path
import re
import pickle
from src import util_analysis
# %%
import matplotlib
matplotlib.rcParams.update({'font.size': 10})
matplotlib.rcParams['pdf.fonttype'... |
9ed568ce873b07d8d3a6cf4bd2c8f32013954fe79b20ed67687823b633973343 | Jupyter | 19,088 | 469 | # %%
import numpy as np
import pandas as pd
import pickle
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
from pathlib import Path
import re
# get utils for thresholds
from src import util_analysis
from src import util_process_prolific as util_process
import importlib
from tqdm.auto import ... |
53650ec6ba34d83035226da2c39bf84ee0fbac159f339228922f195a4630e3ed | Jupyter | 19,128 | 449 | # %%
import pickle
import pandas as pd
import numpy as np
import matplotlib as mpl
from matplotlib import rcParams
import matplotlib.pyplot as plt
import h5py
import os
import tqdm
import scipy
from scipy import signal
from tqdm import tnrange
import seaborn as sns
from scipy.stats import norm,entropy,linregress
from s... |
2b86d90980065cfb58ffef7600792eb6457ee0e285e7b886fcdae0f0f8ae7846 | Jupyter | 19,378 | 443 | # %%
!pip install rdkit torch_geometric torch --quiet
# %%
#!pip install rdkit torch_geometric torch --quiet
import torch
import pandas as pd
from rdkit import Chem
import numpy as np
import networkx as nx
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
from rdkit import Chem
from rdkit.Chem impor... |
9293157a0b99e6bfc8dc66dd1644c855117d49b09404d4fd8cee27a59b9a8145 | Jupyter | 19,569 | 543 | # %% [markdown]
# # Quantum Approximation Optimization Algorithm (QAOA) for Not-all-equal 3-satisfiability (NAE3SAT)
# %% [markdown]
# ## Overview
# %% [markdown]
# Quantum Approximation Optimization Algorithm (QAOA) is a hybrid classical-quantum algorithm used for solving the combinatorial optimization problem, whic... |
5a515e61a3800ef3b05d471edc41c695d803965b30597fd48bac3444a4ec158c | Jupyter | 19,571 | 534 | # %% [markdown]
# # Photogrammetric Optode Coregistration
#
# Photogrammetry offers a possibility to get subject-specific optode coordinates. This notebook illustrates the individual steps to obtain these coordinates from a textured triangle mesh and a predefined montage.
# %%
# This cells setups the environment when... |
ab61886922c97a19927ec6b42600e4818d84197b0732832bf7186a0b38f7fa30 | Jupyter | 19,685 | 530 | # %% [markdown]
# ## Identify mimetic TFs implicated in mimetic TECs
# ### 1. Select EPCAM+ spots from the spatial section
# ### 2. Get average expression profile of the cell types in scRNA-seq
# ### 3. Selecting the TF that are expressed in the TEC spots
# ### 4. Determination of TF expression that is specific to TEC... |
d0d5c3062681cd2bc0432ad00eaf329dbf33eabe657fdd15309ace0aff9ecad3 | Jupyter | 19,798 | 470 | # %% [markdown]
# # AdEx double pool SDE system
# %%
#export
# Initialization
import numpy as np
# %% [markdown]
# ## Derivatives of transfer functions with respect to firing rates
# %%
#export
def diff_fe(TF, fe, fi ,XX, df=1e-5):
return (TF(fe+df/2., fi,XX)-TF(fe-df/2.,fi,XX))/df # deltaTF... |
721598830be85374db7c13862454880f8747d1348228fa8e450b78376719336a | Jupyter | 19,953 | 434 | # %% [markdown]
# todo: here I compare fit quaility on the unit level - so far I have pearsonr trial avg and sttc trial avg, no sttc concat
# %%
import pandas as pd
import numpy as np
# from scipy.optimize import curve_fit, OptimizeWarning
# from sklearn.metrics import r2_score
# from scipy import stats
import matplot... |
69c529f08de8d1712205b90b3ef423c98dea82a2ce614f2b265d106c762a10b4 | Jupyter | 20,001 | 870 | # %%
import tensorflow as tf
import matplotlib.pyplot as plt
import numpy as np
import tensorcircuit as tc
from models import *
from datar import *
from poison_unlearn import *
K = tc.set_backend("jax")
tc.set_dtype("complex128")
tc.set_contractor("cotengra")
# %% [markdown]
# ## plot
# %%
def plotdata(results, ave... |
09468f8d0c9831eb00a9e773a40d1666a9e897a8844c7736e69c66472f6fb78c | Jupyter | 20,288 | 443 | # %% [markdown]
# # Figure 4: Distinct Excitation/Inhibition Balance Effects of Perisomatic and Distal Dendritic Inhibition
#
# This notebook analyzes simulations from [Headley et al. (eLife, 2026): "Spatially targeted inhibitory rhythms differentially affect neuronal integration"](https://doi.org/10.7554/eLife.95562)... |
0f9945201c9bbd7d344a6901e49693b6742aee5cb468fd1d46e26208efebb146 | Jupyter | 20,356 | 528 | # %%
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib_venn import venn2, venn3
import seaborn as sns
import numpy as np
import statsmodels.api as sm
path_result = 'results/'
def legend_title_left(leg):
c = leg.get_children()[0]
title = c.get_children()[0]
hpack = c.get_children()[1]
... |
daebea32a23d5003e6684e9ecfc55c1086f1c3230dcf8703f989bab401427cb0 | Jupyter | 20,366 | 429 | # %% [markdown]
# # Figure 7: Frequency specific effects of rhythmic inhibition on neuronal integration
#
# This analysis examines frequency-dependent effects of inhibitory rhythms on the distal dendrites and perisomatic region. We varied the frequency of rhythmic inhibition between 0.5 and 80 Hz on either the perisom... |
627047b1e53f754e26fd678921a2e9276289fcb0c7633bc40945f66c3d377136 | Jupyter | 20,416 | 550 | # %%
# Load packages for data analysis
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from datetime import datetime, timedelta
# Load packages for Big Query
from google.cloud import bigquery
import os
# %% [markdown]
# ### Set-up
# %% [markdown]
# **Set-up: GCP interface**
# %% [markdown]
... |
3268e5b032426e71bec1018459a45be2a331864166dde8a438fa038a3d10810e | Jupyter | 20,568 | 708 | # %% [markdown]
# # Adding Synthetic Hemodynamic Reponses to Data
#
# This example notebook illustrates the functionality in `cedalion.sim.synthetic_hrf`
# to create simulated datasets with added activations.
# %%
# This cells setups the environment when executed in Google Colab.
try:
import google.colab
!cur... |
0d1b0620ddc29fbc44dd63ba46dda5561687ad7a79524b950f20345318825ed5 | Jupyter | 20,591 | 512 | # %% [markdown]
# # ChemicalEntity ↔ ChemicalEntity Relation-Wise Merge
#
# Merges Chemical–Chemical triples from Monarch, DRKG, PrimeKG (×2), PharmKG, Hetionet,
# CrossBAR, iBKH, DtiNet, STITCH, and pheknowlator; resolves chemical names via PubChem and DrugBank;
# assigns `head_id_is` / `tail_id_is` based on ID prefi... |
354862bf5030a2be28224a8b9d2c31ad1993960cc273a0a3e3632e34c1d2e053 | Jupyter | 20,654 | 655 | # %% [markdown]
# # Model Evaluation & Benchmarking (scATAC-seq)
#
#
# Model evaluation and benchmarking (scATAC-seq)
#
# Compare iAODE with scVI-family models using latent space evaluation metrics.
#
# Dataset: 10X Mouse Brain 5k scATAC-seq (HVP subset)
#
# **Converted from:** `examples/model_evaluation_atac.py`
... |
e24c99972e192155470c2928c5f7303b89096074d6d777d39ff52a25bef74cf7 | Jupyter | 20,678 | 475 | # %% [markdown]
# Loads data and generate plots:
# 1. raster plot for each unit
# 2. summary plot - number of units per area and number of trials per units
# 3. summary plot fixation period - number of units per area and number of trials per units
# 4. summary plot fixation period - number of units per area and number ... |
bac9babd490aba4d6a4f5aa3caae205111380f2ab5d00280abdd6f1fec9bae5d | Jupyter | 20,683 | 578 | # %% [markdown]
# # 00 settings
# %%
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import seaborn as sns
from scipy.stats import pearsonr
from scipy import stats
from scipy import signal
import pymannkendall as mk
from oasis.functions import deconvolve
# settings
bu... |
7a24815832da651ed22902d6894232e407851c8290dd104bc8802f441ffe5acc | Jupyter | 20,834 | 614 | # %% [markdown]
# # Fitting a GLM with Gaussian Kernels
# %% [markdown]
# ## Overview
#
# This notebook extends the basic GLM analysis shown in [32_glm_fingertapping_example](./32_glm_fingertapping_example.ipynb). It covers:
#
# 1. **Advanced design matrices** — Gaussian kernel basis functions with cosine drift regr... |
7e1329a68d873f775b39ee39a6973a33e6bb42a690436e10ac525ac32f436453 | Jupyter | 20,989 | 432 | # %% [markdown]
# # 第三章 量子线路 (Quantum Circuit)
# %% [markdown]
# ## 1 逻辑门与电路
#
#   量子计算通过量子电路来实现。量子电路的本质是幺正变换和测量的组合。在物理上，我们无法直接实现过分复杂的幺正变换，所以期望通过一些容易实现的幺正变化来产生更复杂的幺正变换，这些较容易实现的变换则称为量子门。这个过程就类似于通过最基本的逻辑操作（与非门）来搭建大规模的数字电路一样。在本小节，我们将学习最基本的量子门，并简单了解一下如何通过她们来搭建复杂的量子电路。
#
# ### 1.1 经典逻辑门与电路
#
#   在了解量... |
ed1762d26e74cd9a6c7c281e71efe3ac69befd0d24e6cd97fb8b8881315d5ef6 | Jupyter | 21,015 | 634 | # %%
import os
import glob
import pandas as pd
from aicsimageio import AICSImage
from cellpose import models
import pyclesperanto_prototype as cle
import numpy as np
from skimage.filters import threshold_otsu, gaussian
from skimage.segmentation import watershed
from skimage.morphology import disk, erosion, remove_small... |
9038157343443a77133af3d72e4d8658fdbf6229123598d45f4499c50e356919 | Jupyter | 21,138 | 625 | # %%
import os
import sys
# Get the absolute path of the current notebook's directory
notebook_dir = os.getcwd()
parent_dir = os.path.abspath(os.path.join(notebook_dir, ".."))
sys.path.append(parent_dir) # Add parent directory to sys.path
# model
import torch
import torch.nn as nn
import torch.nn.functional as F
imp... |
171250582dd1c334bc2065407146cdbb29fc0bc37ec672c81fc226b11b69c6a6 | Jupyter | 21,286 | 944 | # %%
import matplotlib.pyplot as plt
import matplotlib as mpl
import matplotlib
from matplotlib.colors import DivergingNorm
from matplotlib.colors import to_rgba_array
import numpy as np
from joblib import dump, load
from tqdm import tqdm
import pandas as pd
tqdm.pandas(ascii=True)
import seaborn as sns
import os
fro... |
a61f25d80ec205abb5ac4c9a7b09c89557eae330ebe247c0d036ebb4ecf82278 | Jupyter | 21,363 | 452 | # %% [markdown]
# # GenAge → Knowledge Graph (KG) Builder
#
# **Source:** [GenAge](https://genomics.senescence.info/genes/) — Model Organisms database (`genage_models.csv`)
# **Species covered:** *Saccharomyces cerevisiae*, *Caenorhabditis elegans*, *Drosophila melanogaster*, *Mus musculus*, *Homo sapiens*
#
#
# #... |
a865642ca102218e399917055d2be2d219e9c5fde14073c9fd329c1a88b14250 | Jupyter | 21,432 | 592 | # %% [markdown]
# # S2: Photogrammetric Optode Co-Registration
#
# Photogrammetry offers a possibility to get subject-specific optode coordinates. This notebook illustrates the individual steps to obtain these coordinates from a textured triangle mesh and a predefined montage.
# %% [markdown]
# ## Learning objectives... |
2bdbae82ca7fc01a240cbac7a3a9c5f801db43863dcea85e2ca4c019aa07ad9b | Jupyter | 21,638 | 670 | # %% [markdown]
# # Bootstrapping Evaluation
# %%
import sys
import os
import pandas as pd
import numpy as np
from tqdm import tqdm
import torch
from scipy import stats
import matplotlib.pyplot as plt
from matplotlib.patches import Patch, Rectangle
import seaborn as sns
from torch_geometric.data import DataLoader
pat... |
a846262eff6a4bb6a55620bd1c8adf7f182c34db08c1c319893b6f6627bf3c8b | Jupyter | 21,692 | 477 | # %% [markdown]
# # Zebrafish Gene–Phenotype Knowledge Graph Pipeline (*Danio rerio*)
#
# ## Purpose
#
# This notebook implements the **complete end-to-end pipeline** for constructing Gene–Phenotype Knowledge Graph (KG) triples for Zebrafish (*Danio rerio*) from raw ZFIN data. It processes raw phenotype annotations, ... |
c70f1e542c8596ce1d13300aa8bb40633888b947e160701430b1eff1af3527fc | Jupyter | 21,695 | 642 | # %%
import os
import sys
# Get the absolute path of the current notebook's directory
notebook_dir = os.getcwd()
parent_dir = os.path.abspath(os.path.join(notebook_dir, ".."))
sys.path.append(parent_dir) # Add parent directory to sys.path
# model
import torch
import torch.nn as nn
import torch.nn.functional as F
imp... |
3875f3e6a8dc19a4c66f781358956ab01675ae04265b0abe050387e47f06f442 | Jupyter | 21,759 | 554 | # %% [markdown]
# # S1: Head models and Forward Modelling
#
# This notebook introduces how Cedalion handles head models and forward modelling for diffuse optical tomography.
# %% [markdown]
# ## Learning objectives
#
# In this notebook you will learn to:
#
# - Understand the `TwoSurfaceHeadModel` structure (segment... |
2858883e7885f3750506a2e458a8885f858fed4c52c32254ed54d6d3848f8679 | Jupyter | 21,774 | 644 | # %% [markdown]
# # MagnetDB — Raw Data Processing for Knowledge Graph
# **Project:** MetaboGlue / EvoAge KG | **Contributor:** Arushi
#
# All input files are read from `BASE_PATH`.
# All output files are written to `OUT_PATH` (main) or `OUT_PATH + "EvOlf/"` (EvOlf).
# %% [markdown]
# ## 0. Path Configuration
# ... |
2e3941062069cc947dc9873fa7e227bed225e04826f8bf141ee9d6452a262987 | Jupyter | 21,975 | 817 | # %% [markdown]
# # Plot Second Level GLM Maps
# 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
from nilearn.datasets import load_mni152_brain_mask
from nilearn.image import ... |
83cab4d985baea1e527167147e0de5cc419d8373e462ecfdd85f86f1d70a6dc6 | Jupyter | 21,978 | 365 | # %% [markdown]
# # 第二章 量子力学 (Quantum Mechanics)
# %% [markdown]
# ## 1. 量子力学基础
#
#   量子计算顾名思义，是使用量子力学规律进行计算的全新范式。目前理论和实验已经揭示，量子计算在计算能力上有远远超过传统计算机（也称经典计算）的潜力。量子力学是描述微观物理的最精确的理论，迄今为止得到了海量实验的验证。从数学上来说，量子力学的本质是希尔伯特空间（Hilbert space）及作用于其上的算子。当空间维数有限的情况下等价于在复数域上的线性空间。在本节中，我们将考虑有限维的线性空间及量子计算的基础。 相关线性代数的基础知识在附录中给出... |
d40f645b2b5a9ff00c57c4e70fbb3d6bd65ac34e5a934f9d8fd3f366302a6ab8 | Jupyter | 22,169 | 530 | # %% [markdown]
# Estimate parameters for resampling procedure (trial generation).
#
# * Number of resampling iterations: M is 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)
#
# #### Bootstrapping Sta... |
d8aecbf98fde2903ca84107854c65ebce518feb934a1c43afb7afd4ee3a6c0ab | Jupyter | 22,196 | 538 | # %% [markdown]
# # PharmKG → Knowledge Graph (KG) Builder
#
# **Source:** PharmKG-180k (`raw_PharmKG-180k.csv`)
# **Species:** *Homo sapiens*
#
# ## Relation types processed
#
# | Relation | Head ID | Tail ID |
# |---|---|---|
# | Gene_Gene | NCBI Symbol (via fullname + synonym map) | NCBI Symbol |
# | Gene_Disea... |
4a333e2a2c42437d2deb2cb847045c5c18cc61196e2ce65f8c7ed0b47e8f628c | Jupyter | 22,216 | 229 | # %% [markdown]
# ```{currentmodule} optimap
# ```
# %%
from optimap.utils import jupyter_render_animation as render
# %% [markdown]
# ```{tip}
# Download this tutorial as a {download}`Jupyter notebook <converted/motion_compensation.ipynb>`, or as a {download}`python script <converted/motion_compensation.py>` with co... |
1702b81b07d346967bf00cef5b8c6622158a6e1f84ddd4106f35003a1e2c7fe9 | Jupyter | 22,365 | 509 | # %% [markdown]
# # Head Models: MRI Segmentation and TwoSurfaceHeadModel
#
# This notebook documents how to build and load head models for use with Cedalion's DOT pipeline.
#
# **A head model** in Cedalion is a `TwoSurfaceHeadModel` that wraps:
# - **Tissue segmentation masks** — voxel-wise labels for scalp, skull, ... |
4cc45b930a8e21a420d876c50937824a5a25b8fecbcd52ed27211ff9bf959e5c | Jupyter | 22,667 | 689 | # %% [markdown]
# Generate plots
# %%
import numpy as np
import pandas as pd
import pickle
import joypy
from pathlib import Path
from isttc.scripts.cfg_global import project_folder_path
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib.colors import TwoSlopeNorm
import seaborn as sns
mpl.rcPa... |
8441885bc0e559796186cd12d217e84a59debda1953abf3512f2f0dc2b1d11a0 | Jupyter | 22,730 | 562 | # %% [markdown]
# # HALD → Knowledge Graph (KG) Builder
#
# **Source:** HALD (Human Aging and Longevity Database) — `Entities.csv` + `Roles.csv`
# **Species:** *Homo sapiens*
#
# ## What this notebook does
#
# 1. Loads HALD entity and triple files, maps IDs to names and types.
# 2. Normalises entity types: Carbohy... |
0a4d716db46fae5744c132ebd32709285642e9809a3e252a147144e10d869979 | Jupyter | 22,744 | 506 | # %% [markdown]
# # DrugAge → Knowledge Graph (KG) Builder
#
# **Source:** [DrugAge](https://genomics.senescence.info/drugs/) database — two source files processed in parallel
#
# %%
# ! wget https://genomics.senescence.info/drugs/dataset.zip
# https://genomics.senescence.info/drugs/browse.php
# %% [markdown]
# ... |
e689d01a09c994ce0fb6ac24dbfff07134336392874610644f514b6ff68e72f5 | Jupyter | 22,791 | 577 | # %%
import math
import h5py
import pickle
import random
import pysam
import numpy as np
import pandas as pd
import os
import pickle as pkl
import viz_sequence
from scipy.stats import spearmanr, pearsonr
import matplotlib.pyplot as plt
from scipy.spatial.distance import jensenshannon
import numpy as np
from scipy.stats... |
dcf0e7b8cd2b2f8c85f50069c4ae0e8bd930aae4784c3ad5fb6ff283e076b3b5 | Jupyter | 22,949 | 550 | # %% [markdown]
# # Human Thymus Spatial Cartography
# ## Integration of fetal and pediatric Spatial Stereoseq transcriptome data
# %%
## Call all functions
%run integrate_niches_functions.ipynb
# %%
# %%
save_folder="/data/Combined_Analysis/Integration/FetPed_SelectedGenes_8samples/"
# %%
custom_marker = pd.rea... |
ab194ea9cb0707de3963d03b97fcfcb7d0c4d027565c2f64d22b0acebeeae69c | Jupyter | 22,997 | 686 | # %% [markdown]
# # Import Dependencies
# %%
import time
import sys
import os
print(sys.version)
import matplotlib.pyplot as p
from matplotlib.lines import Line2D
import numpy as np
import xarray as xr
import gzip
import pickle
import pyvista as pv
pv.set_jupyter_backend('server')
#pv.set_jupyter_backend('static')
... |
c15a3a0c6721a76091216bd97a09bca37e5a0a6280a8919229db228ab4ee9196 | Jupyter | 23,144 | 550 | # %%
import pandas as pd
import numpy as np
import os
# %% [markdown]
# # Mapping Setup
# %%
BASE_PATH = "/storage/Arushi/090526_EvoAge/kg_formation/data_collection/"
OUT_PATH = "/storage/Arushi/090526_EvoAge/kg_formation/processed_data/"
# %%
# ── 1. UniProt ─────────────────────────────────────────────────────────... |
5a1ec845671ee3f012234f432fe6b0f38cb000a213df35b9aeeaafbe1f73119d | Jupyter | 23,258 | 654 | # %%
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import seaborn as sns
from scipy.stats import pearsonr
from scipy import stats
from scipy import signal
import pymannkendall as mk
# %% [markdown]
# # 00 settings
# %%
# path
res_path = 'D:/sorting/data/m005/'
print... |
b7c0ea27f5fa128748cc541b8bbce6f210c4300d724561caf0c97ef6c185dff2 | Jupyter | 23,339 | 640 | # %% [markdown]
# ## LPS-induced inflammation differentially affects endogenous Ca2⁺ activity in mouse and human iPSC-derived astrocytes
# ### Franziska E. Müller, Flavian Ivanov, Anne-Catharine Studt, Ida Nitzsche, Frauke S. Bahr, Anna-Lena Krüger, Josephine Labus, Ghanendra Singh, Evgeni G. Ponimaskin, Kerstin Lenk* ... |
e09bbe92cc391d65b92beb3df01b7c7541171ef3eed586cc8558d26ff43d9e32 | Jupyter | 23,418 | 590 | # %%
%matplotlib inline
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch import Tensor,optim
import numpy as np
import scipy.io
import pickle
import h5py
import matplotlib.pyplot as plt
import matplotlib as mpl
import seaborn as sns
import tqdm
import itertools
from scipy.signal import ... |
040be4b0f2be2844719fae42c94d18e5ecbc40a565e95fc5eb52008083a3bf08 | Jupyter | 23,419 | 652 | # %% [markdown]
# # 00 settings
# %%
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import seaborn as sns
from scipy.stats import pearsonr
from scipy import stats
from scipy import signal
import pymannkendall as mk
# %%
# path
res_path = 'xxx/data_analysis_res_m010/'... |
ab06da2a60abb0b3a545a3183f1d15048a416c116e7d572156925c1426546d58 | Jupyter | 23,425 | 541 | # %%
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import scipy.stats as stats
# %%
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('./Brain... |
f2e1b4dc9ffe0cd3699eb88bba620a6c70ba8496fbab4bb74dab1bef3ec7b7b5 | Jupyter | 23,565 | 562 | # %%
import os
import jax
import numpy as np
import jax.numpy as jnp
import matplotlib.pyplot as plt
from scipy.stats import norm
from utils.utils import PyTree, uncertainty
# %%
x = jnp.linspace(1, 2.5, 100)
y = x*(x<=1.4) + (3.5+((1.1-1.4)/(1.6-1.4))*x)*(x>1.4)*(x<1.6) + (0.5*x+.28)*(x>=1.6)
plt.plot(x,y, 'o-')
# ... |
da3af0d5b1fabf449ccbeadea6bf329bf4cb697e034adc7148826abde6e1f2d6 | Jupyter | 23,769 | 525 | # %% [markdown]
# # Welcome to the AIMS Jupyter Notebook!
# # As a refresher, hit ctrl + enter to run each cell
# I tried to add comments and other markdown cells like this one where appropriate to help with interpretations. This first cell is just loading in all of the necessary python modules (which you should have a... |
24cfdd9770f1971fac89f41ba5521e8bfbd1ea85ebabdeebf571b25fa4460615 | Jupyter | 23,922 | 569 | # %% [markdown]
# # BOCK → Knowledge Graph (KG) Builder
#
# **Source:** BOCK `kg_rels.csv` + node CSV files
# **Species:** *Homo sapiens*
#
# ## What this notebook does
#
# 1. Loads BOCK node metadata files to build entity ID → label and ID → name lookup dictionaries.
# 2. Loads the full BOCK edge file (`kg_rels.c... |
b09792a1c09c56f0f59a00fbfa425ed22d86b6509f896dfe8fed6bc2b21b4351 | Jupyter | 24,028 | 607 | # %% [markdown]
# # GLM Illustrative Example
#
# This notebook explores cedalion's GLM functionality. Using simulated timeseries with a known 'ground truth' allows us to clearly see how the GLM works. We fit several different models to the timeseries and showcase the GLM statistics afforded by cedalion's statsmodels i... |
06f4a5a45320d3e0e6e57462792439728a65fd91941123cba07a6db80b57dc7e | Jupyter | 24,407 | 559 | # %% [markdown]
# # 第四章 量子算法（Quantum Algorithm）
# %% [markdown]
# 在第三章可逆计算里，给定$f(x)$，我们可以通过构造与辅助比特没有纠缠的可逆电路，从而系统地构造对应的量子电路。这样当输入态是$\frac{1}{2^{n/2}}\sum_{x= 0}^{2^n-1}|x\rangle$时，我们似乎可以“并行”运算$2^n$个输入的值$x$。但是由于测量的过程会随机塌缩到某一个输出态，例如$f(x_k)$，我们并不能真正同时得到所有的计算结果。因此，为了真正利用量子并行性，我们需要非常巧妙的算法设计来提取有用的计算结果，起到加速的效果。本节尽量以直观的方式介绍一些简... |
33db6d55cb63ab3031c1e1eae57fab257861a0bacb4173f213b92b980d1fd5e3 | Jupyter | 24,408 | 744 | # %%
import pandas as pd
import numpy as np
import re
import requests
from pathlib import Path
from collections import defaultdict
import warnings
warnings.filterwarnings('ignore')
# %% [markdown]
# ## 1. Configure Paths
# %%
BASE_DIR = Path("/home/aditya/synapse_motif_analysis/outputs/excitatory")
ATTRIBUTION_FILE... |
c2afabc6608603bf32513f800427683b0622057c8318514a43c680d4a658afc6 | Jupyter | 24,593 | 597 | # %% [markdown]
# # Calculate relationship between AP and dendritic spikes with poisson excitation and rhythmic inhibition with variable frequency
#
# The simulations had either:
# 1. Rhythmic inhibition at the soma (64 Hz) or dendrites (16 Hz)
# 2. Poisson excitation at the soma and dendrites
#
# Here we calculate... |
52cd883e36e5633cd24d397597711f88bfcaf27c2daac0ec2a5cb4e9cb732b87 | Jupyter | 24,734 | 629 | # %% [markdown]
# # AgeXtend — Combined Pipeline for EvoKG
#
#
# ## Full Pipeline Overview
#
# ```
# STEP 1 — Load shared reference files (PubChem, GO)
# STEP 2 — Process 9 hallmark TSV files → Chemical–Hallmark CSVs
# STEP 3 — Process Agextend's drug-aging data → per-species CSVs
# STEP 4 — Standar... |
a88d98599fe325988ce316a0521b6f58f26555ed60c653949c529fe934482b45 | Jupyter | 24,812 | 667 | # %%
import sys
sys.path.append('./')
sys.path.append('../')
sys.path.append('../..')
import os
import pandas as pd
from sklearn import preprocessing
import string
from typing import Sequence, Tuple, List, Union
from tqdm import tqdm
import fm
import torch
from torch import nn
from torch import optim
from torch.utils.... |
ba7d5ff1d7ad9bfe36af1a59ad9c099a4b66c2699445657732fd27bc0a5dfa11 | Jupyter | 24,820 | 539 | # %% [markdown]
# # Digital Ageing Atlas → Knowledge Graph (KG) Builder
#
# **Source:** [Digital Ageing Atlas](http://ageing-map.org/) database
# **Species covered:** *Homo sapiens*, *Mus musculus*
#
# ## What this notebook produces
#
# ### Module 1 — Gene → Tissue edges (`Gene_Tissue`)
# Maps DAA genes to BTO tis... |
cadb81ac659c9e0f810032358b9ea9e2645e794b31d4472b840b8165cc820ace | Jupyter | 24,887 | 605 | # %%
import numpy as np
import pandas
import matplotlib.pyplot as pl
import matplotlib as mpl
from matplotlib import cm
# THIS LAST BIT IS TO NAVIAGATE TO WHERE MY CUSTOM MODULES ARE LOCATED
import os
if os.getcwd()[-4:] != 'AIMS':
default_path = os.getcwd()[:-10]
os.chdir(default_path)
# %% [markdown]
# # Im... |
fcc81544c94d0c8b63f95daa60aaa77e0212550a45eb3127c75ac666a5755d75 | Jupyter | 25,192 | 708 | # %% [markdown]
# Generate plots: 10000 units from the dataset (100 000).
# %%
import numpy as np
import pandas as pd
import pickle
import joypy
from pathlib import Path
from isttc.scripts.cfg_global import project_folder_path
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib.colors import Tw... |
59c7f7cce3aa1eb3a901f9c6d9696077587e43710fc10b86a4c82034983be933 | Jupyter | 25,240 | 821 | # %% [markdown]
# # ChemicalEntity ↔ Disease Relation-Wise Merge
#
# Merges Chemical–Disease triples from Monarch, DRKG, CKG, PharmKG, Hetionet, TARKG,
# iBKH, PhytoChem, and EvoAGE; resolves chemical names via PubChem/DrugBank and disease
# names via MESH/DO; deduplicates by `(head, relation, tail)`; and saves the re... |
998bdf25e4c9c532dd8dc47a849ec66e52ae9bd69f6db738b0ad175aad4341c7 | Jupyter | 25,445 | 525 | # %%
"""
harmonizome_raw_to_csv.py (v3 — CORRECT)
==========================================
The Harmonizome gene_attribute_edges.txt files have this structure:
Columns : source | source_desc | source_id | target | target_desc | target_id | weight
Row 0 : GeneSym| <desc> | GeneID | <attr> | <attr_id> ... |
de15b786da527d15fc28aba2359dc352ede480c37dc8d919ebd4764575e1f56d | Jupyter | 25,519 | 986 | # %%
import sys
sys.path.append('/Users/midani/OneDrive/proj/leap/manuscript')
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import scipy.cluster.hierarchy as sch
import seaborn as sns
from itertools import product
from scipy.stats import spearmanr
from statsmodels.stats.multitest import fdrc... |
7fc3f03f28bebcf4060904f54b14f3efc16d166acad2b1fd76b6aec952ee30b9 | Jupyter | 25,833 | 666 | # %% [markdown]
# # S4: Model-driven (GLM) Analysis
#
# This tutorial demonstrates how to use a General Linear Model (GLM) to model the recorded time series as a superposition of hemodynamic responses and nuisance effects.
# %% [markdown]
# ## Learning objectives
#
# In this notebook you will learn to:
#
# - Build ... |
4299fce591d3b601fe9ba0bf83b3c298051a213bcdd2c01cc52d33ef1fec0309 | Jupyter | 25,867 | 696 | # %%
%matplotlib inline
import numpy as np
import pandas
import matplotlib.pyplot as pl
import math
import matplotlib as mpl
from sklearn.utils import resample
from matplotlib import cm
import multiprocessing as mp
import time
import sys
# THIS LAST BIT IS TO NAVIAGATE TO WHERE MY CUSTOM MODULES ARE LOCATED
import os
... |
1476948ec49985ee601217b0566e78e0127706cf45b6d22ad40a1347ea573168 | Jupyter | 25,878 | 649 | # %%
# Load packages for data analysis
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from datetime import datetime, timedelta
# Load packages for Big Query
from google.cloud import bigquery
import os
# %% [markdown]
# ### Set-up
# %% [markdown]
# **Set-up: GCP interface**
# %% [markdown]
... |
4401c8b8e97204c1f07eb80a22261991a721d47f3671e54565a22c99ed566543 | Jupyter | 25,885 | 987 | # %%
import sys
sys.path.append('/Users/midani/OneDrive/proj/leap/manuscript')
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import scipy.cluster.hierarchy as sch
import seaborn as sns
from itertools import product
from scipy.stats import spearmanr
from statsmodels.stats.multitest import fdrc... |
4840f6296da810d905e41486bcd409863b984a43e6f94c7dfdeb6020c5e37ee5 | Jupyter | 25,937 | 687 | # %% [markdown]
# # Calculate relationship between AP and dendritic spikes with poisson excitation with different E/I lags
#
# The simulations had either:
# 1. Poisson inhibition at the soma and dendrites, lagged at different delays w/r to the excitation
# 2. Poisson excitation at the soma and dendrites
#
# Here we... |
90ef7a46e904a92e2b766dfadf9ae0781e092b4d89113948c56884ff8a21cf2d | Jupyter | 26,033 | 600 | # %%
import pickle as pkl
import viz_sequence
from scipy.stats import spearmanr, pearsonr
import matplotlib.pyplot as plt
from scipy.spatial.distance import jensenshannon
import numpy as np
from scipy.stats import entropy
import matplotlib.pyplot as plt
import scipy.ndimage
import random
# %%
tf_prof = "tf_deepshap/SP... |
7fdee2499033c2dc875703951a365b99d22bab75e7d351bbc7f5e52b2378e32f | Jupyter | 26,068 | 808 | # %% [markdown]
# # Experiment 1 plots
#
# ### Plot analysis for speech-on-speech and speech-in-noise distractors, presented diotically
# ### Models run on all combinations of stimuli
# ___
# %%
import pickle
import numpy as np
import re
from pathlib import Path
import pandas as pd
import json
import pickle
import ... |
9c5d3da5de7041ebf49d0a5615ea09df53db117e12174451288036a3044c4d9e | Jupyter | 26,080 | 633 | # %% [markdown]
# # Generate figures for the joint simulation experiment
# Here, we analyze results of running models on simulated datasets with a combination of linear and nonlinear signal.
#
# Prerequisites:
# - you ran the joint simulation experiment
# - the results are saved as a CSV (you can generate this by run... |
8e3899acb3e2b90be0c5ccd555028d3c02e99acad78b62f04d4e9342f5e89f7c | Jupyter | 26,377 | 569 | # %% [markdown]
# # Welcome to the AIMS Jupyter Notebook (Peptide Version)!
# # As a refresher, hit ctrl + enter to run each cell
# I tried to add comments and other markdown cells like this one where appropriate to help with interpretations. This first cell is just loading in all of the necessary python modules (which... |
efd08b2f8f55b2c3cfa4d900b7315c7a7cdccbcf03ecb7e434dba05e99db8ca6 | Jupyter | 26,429 | 645 | # %% [markdown]
# # Tutorial 1: How to use abcTau package to fit autocorrelations or PSDs
#
# Details of the method are explained in:
# Zeraati, R., Engel, T. A., & Levina, A. (2022). A flexible Bayesian framework for unbiased estimation of timescales. Nature Computational Science, 2(3), 193-204. https://www.nature... |
3ce909ed88edf2bbaaa41aed4eaf09113767e190013bba4dee7a408c89f48cd9 | Jupyter | 26,613 | 779 | # %% [markdown]
# # Image Reconstruction
# %%
# This cells setups the environment when executed in Google Colab.
try:
import google.colab
!curl -s https://raw.githubusercontent.com/ibs-lab/cedalion/dev/scripts/colab_setup.py -o colab_setup.py
# Select branch with --branch "branch name" (default is "dev")
... |
394df6173103683edd6c4f025ee045079a277c0c7ad3ce94a810b5035e51ce39 | Jupyter | 26,699 | 995 | # %%
import sys
sys.path.append('/Users/midani/OneDrive/proj/leap/manuscript')
%load_ext autoreload
%autoreload 2
import os
import importlib
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from matplotlib.ticker import MultipleLocator
from matplotlib.colors import Boundar... |
26537a2b5f140a8ec2dcfb6c8db912b3e69f967c04257137acca799ec443112b | Jupyter | 26,742 | 1,007 | # %%
import sys
sys.path.append('/Users/midani/OneDrive/proj/leap/manuscript')
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import scipy.cluster.hierarchy as sch
import seaborn as sns
from itertools import product
from scipy.stats import spearmanr
from statsmodels.stats.multitest import fdrc... |
59325c58225400344c44a3f8922870cfb37218127c5ac9c40adf25c03e70a1b6 | Jupyter | 26,850 | 903 | # %% [markdown]
# # S7: Data Augmentation
#
# This example notebook illustrates the functionality in `cedalion.sim.synthetic_hrf` and `cedalion.sim.synthetic_artifact`
# to create simulated datasets with added activations and motion artifacts.
#
# It has two parts:
#
# 1. [Adding synthetic activations](#adding-synth... |
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