sha256 stringlengths 64 64 | language stringclasses 27
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|---|---|---|---|---|
806c1595ed08f8408cb7093fe498e87d68209238bbf9821ba882d5c8dd89d8e6 | Jupyter | 12,322 | 386 | # %% [markdown]
# # 202 Generating the feature embeddings of original datasets and MolMM's outputs
# %%
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import pytorch_lightning as pl
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_light... |
0babcb470f78647c8d74e87a755be43548756a8dc40cea1198133668be4e0586 | Jupyter | 12,335 | 499 | # %%
import sys
sys.path.append('/Users/midani/OneDrive/proj/leap/manuscript')
import numpy as np
import pandas as pd
import gseapy as gs
import matplotlib.pyplot as plt
import seaborn as sns
from matplotlib.font_manager import FontProperties
import repo_code.lib_aux as lib_aux
import repo_code.lib_plots as lib_plot... |
35f3216897ceb27430d9e65f51848168b357f64a70a3c56bf416e45998eff70b | Jupyter | 12,335 | 258 | # %% [markdown]
# # Phase-dependent modulation of action potential threshold by rhythmic inhibition
#
# This notebook analyzes how rhythmic inhibition at different frequencies (beta ~16 Hz and gamma ~64 Hz) modulates the threshold voltage for action potential initiation in a simulated layer 5 pyramidal neuron. As desc... |
5f229fc66ce3bf1e7a44cc69d5d613cb437922db088ab157082366cd1cb90edb | Jupyter | 12,345 | 419 | # %% [markdown]
# # Plantspecies - ChemicalEntity 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 = BA... |
d9d8459b5f1aa9c663bc2b07c69c10641ba339076e69123e9fdb7b899472eccd | Jupyter | 12,361 | 300 | # %% [markdown]
# Calculate ACFs:
#
# on unit level:
# 1. Pearsonr trial average
# 2. STTC trial average
# 3. STTC trial concat
# 4. STTC trial concat with global normalisation
#
# on trial level:
# 1. ACF proper per trial
# 2. iSTTC per trial
# %%
import pandas as pd
import numpy as np
import csv
import sys
from... |
e5cf50c644cda6c77f12ec7b628293cbc697f723e9eb904c04790581f57c80c9 | Jupyter | 12,378 | 363 | # %%
# 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]
... |
dc493b666939ab50ac4cd227aba0754d33eb68745005205d99214d2fae068c17 | Jupyter | 12,424 | 301 | # %%
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... |
e4082b2b729c346825324d984f729bf04b309717e8367f0e3b6b1c39c2cd29f5 | Jupyter | 12,443 | 541 | # %%
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")
# %%
def plotdata(results, ave, selec):
selected_cols = np.random.choice(ave, selec, replace=False)
na = ... |
f7f71abcc506970dec89f0d2e1909cd4bfd8aa6c8e7cd55136dc1255bddb28e9 | Jupyter | 12,452 | 282 | # %% [markdown]
# # Classical Shadows in Pauli Basis
# %% [markdown]
# ## Overview
# %% [markdown]
# [Classical shadows](https://www.nature.com/articles/s41567-020-0932-7) formalism is an efficient method to estimate multiple observables. In this tutorial, we will show how to use the ``shadows`` module in ``TensorCir... |
6fe156bc4279bcd3fe75d4245a0a3991bce76477c02bea83daddece757a1df3e | Jupyter | 12,466 | 297 | # %%
# 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")
%run colab_setup.py
except ImportError:... |
31897ac23275680724a14c7157c99f4d7157e67606800ffbd7ce12cdf5d198fc | Jupyter | 12,594 | 351 | # %%
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... |
c0aa7f9b51b27f22f0bec93179bd140bba676fbd4f53fa9ceb95446b297f7830 | Jupyter | 12,642 | 327 | # %% [markdown]
# # Drosophila Chemical–Gene (STITCH) — Relation-Wise KG Triple Construction
#
# ## Purpose
#
# This notebook processes **Chemical–Protein interaction data** from the STITCH database for *Drosophila melanogaster* and transforms it into standardized Chemical–Gene relation-wise Knowledge Graph (KG) trip... |
eb3db8c38b785de3338ae152d674fe789a0c8ca1bc0b0ae8f0f0da6765892838 | Jupyter | 12,644 | 354 | # %%
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... |
1535cb084c2862e3a6e3c28539a001bdd8b9eea3d55be0c347b44e358b35e079 | Jupyter | 12,671 | 347 | # %% [markdown]
# # Motion Artefact Detection and Correction
# This notebook shows how to identify and correct motion-artefacts using xarray-based masks and cedalion's correction functionality.
# %%
# This cells setups the environment when executed in Google Colab.
try:
import google.colab
!curl -s https://ra... |
c69ce5d5ddc9a4a8a12ba63998994e899f9743d6f203b15d2968e598ee1995f1 | Jupyter | 12,687 | 438 | # %%
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
import seaborn as sns
from rdkit import Chem
from rdkit.Chem import Draw
from rdkit.Chem.Draw import IPythonConsole
from rdkit.Chem.MolStandardize import rdMolStandardize
from rdkit import RDLogger
#IPythonConsole.ipython_useSVG = True
import n... |
4b5f9259aa5d0375319c3144dbc5646cf81b5c1f32bd34b9f23727a01f4ae77e | Jupyter | 12,728 | 374 | # %%
#point the directory to your main directory for the project
PROTGPS_PARENT_DIR = "/home/shd-sun-lab/SynapseNavigator" # point to the protgps local repo
# %%
#see if GPU is available for PyTorch
import torch
print("CUDA available:", torch.cuda.is_available())
print("CUDA version (PyTorch built with):", torch.vers... |
f4242dd580b6945c0dd0e402fd57367983f497fb80373a666056166d7593b3e3 | Jupyter | 12,736 | 335 | # %% [markdown]
# # Lattice Geometries in TensorCircuit
#
# ## Quick Start Guide
#
# **📋 Available Lattice Types:**
#
# | Class | Description | Use Cases |
# |-------|-------------|-----------|
# | `SquareLattice` | 2D square grid with optional PBC | Spin models, quantum dots |
# | `ChainLattice` | 1D linear chain ... |
c5f2bc0dbadd2b4b6de4b23127b7a779bdc16d75cd91bd5c806a4871cb62fd0a | Jupyter | 12,864 | 186 | # %% [markdown]
# <div align="center">
#
# <a href="https://ultralytics.com/yolov5" target="_blank">
# <img width="1024", src="https://raw.githubusercontent.com/ultralytics/assets/master/yolov5/v70/splash.png"></a>
#
#
# <br>
# <a href="https://bit.ly/yolov5-paperspace-notebook"><img src="https://assets.pape... |
250a729fdd9385ffca3bbdccd049ebfa09cfa5aea5bc095a666fb2ef2f44b0a8 | Jupyter | 12,867 | 184 | # %% [markdown]
# <div align="center">
#
# <a href="https://ultralytics.com/yolov5" target="_blank">
# <img width="1024", src="https://raw.githubusercontent.com/ultralytics/assets/master/yolov5/v70/splash.png"></a>
#
#
# <br>
# <a href="https://bit.ly/yolov5-paperspace-notebook"><img src="https://assets.pape... |
0b6173c3ff2e4d7c4e113bf05ac55120fd87382fd047c846971d4c55db62286f | Jupyter | 12,913 | 320 | # %%
from IPython.display import display, HTML
display(HTML("<style>.container { width:75% !important; }</style>"))
display(HTML("<style>div.output_scroll { height: 44em; }</style>"))
# %%
#Import functions you will need for running this script
# %matplotlib widget
import os
import numpy as np
import numpy.matlib
impo... |
58bbdd41d4461669d0cd87863cbbb520dbc9d74c57d949e5c003ade7130ed24d | Jupyter | 12,923 | 323 | # %% [markdown]
# # 第五章 变分量子算法 （Variational quantum algorithms）
# %%
from matplotlib import pyplot as plt
import tensorflow as tf
import tensorcircuit as tc
K = tc.set_backend("tensorflow")
# %% [markdown]
# ## 1. 变分量子算法：框架和组件
# %% [markdown]
#   变分量子算法是一种适合“近期有噪声中等规模量子线路”（Noisy Intermediate-Scale Quantum... |
b48426ade342666857234ef85ccc56cbb614eee9d65f53e06422f4a4dab7b28a | Jupyter | 12,935 | 418 | # %%
import os # for operating system interactions
import re # for regular expressions
import glob # for file handling
import numpy as np # for numerical operations
import pandas as pd # for data manipulation
from scipy.stats import zscore # for z-score normalization
from scipy.spatial.distance import s... |
08d32b5176143b884594eba343efb14874e12e03dca97f923aeba6a9d30ea998 | Jupyter | 12,996 | 408 | # %% [markdown]
# # Figure 2 individual participant data for experiments 1, 2, and 3
#
# ___
# %%
import pickle
import numpy as np
import re
from pathlib import Path
import pandas as pd
import json
import pickle
import importlib
import IPython.display as ipd
%matplotlib inline
import matplotlib.pyplot as plt
im... |
bce2fbfe2dbe2ec407b9929714bcc51d40044b354f49d605c4baa20846a3739b | Jupyter | 13,056 | 310 | # %% [markdown]
# Plot ACFs
# %%
import matplotlib.pyplot as plt
import seaborn as sns
import pickle
import numpy as np
import pandas as pd
from isttc.scripts.cfg_global import project_folder_path
from isttc.tau import func_single_exp
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
... |
8d4a1bae9ec9180cbd0bbb35dbb8ba0d7c2285f94bee8c5699f0f95fec9f9173 | Jupyter | 13,091 | 321 | # %% [markdown]
# # Solving QUBO Problem using QAOA
#
# ## Overview
#
# In this tutorial, we will demonstrate how to solve quadratic unconstrained binary optimization (QUBO) problems using QAOA. There is a specific application for portfolio optimization and we will introduce it in another [tutorial](https://tensorcir... |
2ac6b25ecaf43d51bbea7cdbb2cc2ba41afa2ac31354cceae109af8a4b3e8ee5 | Jupyter | 13,250 | 370 | # %%
from napari_convpaint import ConvpaintModel
import numpy as np
import matplotlib.pyplot as plt
import skimage
# %% [markdown]
# # Using Convpaint programmatically (API)
# %% [markdown]
# ## Loading a saved model and using it in a loop
# %% [markdown]
# In one example workflow you might have interactively traine... |
0728063fbb6c0238fd54b6f6a4532fb9d29ea02f0da10a56654cdcf9cea4baa5 | Jupyter | 13,282 | 535 | # %%
import sys
sys.path.append('/Users/midani/OneDrive/proj/leap/manuscript')
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.ticker as mticker
import seaborn as sns
from itertools import product
from scipy.stats import mannwhitneyu, spearmanr, pearsonr
from statsmo... |
edc3edeb65234211683522b2b77fc7502a533adf1228aa0f49180f06e0282abb | Jupyter | 13,352 | 447 | # %%
import os
import tifffile
from aicsimageio import AICSImage
from aicsimageio.readers import CziReader
from scipy.ndimage import median_filter
from skimage.filters import threshold_otsu
from skimage.feature import canny
from skimage.measure import regionprops, label
from skimage.transform import rotate
import numpy... |
87ee697e7e39aa89f2e631e83da1ba77abcfec1d5ee6218eb258324bbc22b0f9 | Jupyter | 13,357 | 471 | # %%
import sys
sys.path.append('/Users/midani/OneDrive/proj/leap/manuscript')
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from matplotlib.ticker import MultipleLocator
from scipy.stats import mannwhitneyu
from statsmodels.stats.multitest import multipletests... |
0f61f852c7686dd5b0d2e00e93218970d252ebc2b8e151225c40ffc86d9a27d2 | Jupyter | 13,388 | 426 | # %%
from sklearn.metrics import roc_auc_score, precision_recall_curve
from sklearn.metrics import auc as calculate_auc
from sklearn.metrics import mean_squared_error
from sklearn.metrics import accuracy_score
from scipy.stats.stats import pearsonr
import os
import subprocess
import pandas as pd
import numpy as np
ran... |
96d4491c5357d299caaab4f874ba555bdf57fb9a22fbb42791664d28832a0546 | Jupyter | 13,390 | 293 | # %% [markdown]
# ## Setup
# %%
import MEArec as mr
import numpy as np
import scipy.optimize
import os
import sys
import re
import ast
import matplotlib.pyplot as plt
import pandas as pd
import pickle
import time
from collections import defaultdict
from joblib import Parallel, delayed
import spikeinterface as si
impo... |
2e30bc66c094612d632b48dcc654c88356791fc9a82155b0b8d747da84953bd3 | Jupyter | 13,444 | 406 | # %% [markdown]
# Generating trials dataset:
#
# * using resampling procedure (Number of resampling iterations: 100 (based on bootstrapping stability analysis), Number of trials per resampling: N = 40 (based on data in monkey dataset so the number of trials is from experiments))
# * using different number of trials (n... |
8ffca12106ba5eed7cece2e45da6683756356e841259ce16fd1a94dd90d3d6a4 | Jupyter | 13,506 | 485 | # %%
import sys
sys.path.append('/Users/midani/OneDrive/proj/leap/manuscript')
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib.ticker as mticker
import statsmodels.formula.api as smf
from statsmodels.stats.multitest import fdrcorrection
import ... |
8fbd4be342e6c171fcf9b72dfc75c5e61445c5afa0235df331aeb058386b27cf | Jupyter | 13,574 | 518 | # %% [markdown]
# # RNA-FM Tutorial
# %% [markdown]
# ### Workflow of our tutorial
#
# **Preparation**
# 1. install the RNA-FM package
# 2. load the necessary libraries
#
# **Task 1. RNA family clustering**
#
# Goal: to demonstrate that RNA-FM embeddings are biologically meaningful
#
# 1. read RNA sequences for ea... |
f3b88160726ab23942de4e93a886a0356346a686a65144adbc648ad4dceaba64 | Jupyter | 13,583 | 466 | # %% [markdown]
# # Protein ↔ Disease Relation-Wise Merge
#
# Merges Protein–Disease triples from CKG (×2), CrossBAR (×2), and DtiNet;
# assigns `tail_id_is` by disease ID prefix (DOID vs MESH); fills protein head names
# from UniProt; deduplicates by `(head, relation, tail)`; and saves the result.
# %% [markdown]
# ... |
c67dc7d16568f2f9c4a35d16272e3c88a2525567544f7b19ba99b6770c621bc4 | Jupyter | 13,620 | 407 | # %% [markdown]
# # Gene → BiologicalProcess Relation Pipeline
#
# Builds a unified, deduplicated edge table for the **Gene–BiologicalProcess** relation.
#
# **Output schema:** `head | relation | tail | head_type | relation_type | tail_type | kg_source | kg_type | head_id_is | tail_id_is | head_detail_name | tail_det... |
fa836791bb23b4a429a346beeff8c0467a68eac59dba74138e639cbcf0839243 | Jupyter | 13,766 | 378 | # %% [markdown]
# # Channel Quality Assessment and Pruning
#
# This notebook sketches how to prune bad channels
# %%
# 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_set... |
4dd43ff5ea4ba98453573ee022182eb3b44fd61398bdb5559c049c68f04d2083 | Jupyter | 13,934 | 351 | # %% [markdown]
# # Calculate phase dependent modulation of AP threshold poisson excitation and rhythmic inhibition
#
# 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 voltage threshold for action p... |
8af59332fdaec05a3c8073be09928cc5d68d7f09c81a3c36f66fee89adb05b6f | Jupyter | 13,947 | 284 | # %% [markdown]
# # Imports
# %%
import time
start_time = time.time()
# %%
import sys
sys.path.append('..')
from main_functions import *
# %%
import numpy as np
from matplotlib import pyplot as plt
import seaborn as sns
import pandas as pd
sns.set_theme()
# %% [markdown]
# # Simulate spatial data
# %%
# Simulati... |
c265224395ff8916720c36108018f5dba1de706b12aa71c52437b92f9357b822 | Jupyter | 14,066 | 326 | # %%
import numpy as np
import pandas as pd
import pickle
import json
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
from isttc.scripts.cfg_global import project_folder_path
from isttc.tau import fit_single_exp, fit_single_exp_2d, func_single_exp
# %%
mpl.rcParams['pdf.fonttype'] = 42
... |
3da4d78e8047e2638ee859955a8e75edc2da63c978b551dd13ae0e468b022361 | Jupyter | 14,071 | 503 | # %% [markdown]
# # Disease ↔ Anatomy Relation-Wise Merge
#
# Merges Disease–Anatomy triples from Monarch, DRKG, Hetionet, and TARKG;
# resolves disease names via DO/MESH, anatomy names via UBERON;
# deduplicates by `(head, relation, tail)`; and saves the result.
# %% [markdown]
# ## 0. Configuration
# %%
import pan... |
3b2deb178aecf86e9e0b708e566c5f7b10b25a48e960b6c3c73ea3bd17c25dfa | Jupyter | 14,148 | 392 | # %% [markdown]
# # Group Difference Pipeline
# %% [markdown]
# This notebook serves as a template for performing a group difference comparison using BFP and BrainSync. The steps in this pipeline can be easily customized to suite your study. Here, we use data from ADHD200 dataset available through http://fcon_1000.pr... |
b0652b0d8bb24cee6c506e93558813f56510060d5ec9cddc2d9316a36127c410 | Jupyter | 14,162 | 341 | # %% [markdown]
# # Convert a fNIRS dataset to BIDS
#
# <span style="font-size: large; color: red;"> Do not run all cells at once. Carefully read the comments before each code cell — some steps require you to manually modify certain files (e.g., the mapping CSV) before proceeding.<br> Make sure all required edits are ... |
4aeab6be93efafcbe6c672c7805782ea2765aedabb3194b27bf52c7f03a194a1 | Jupyter | 14,167 | 376 | # %% [markdown]
# # GLM Fingertapping Example
# %% [markdown]
# ## Overview
#
# This notebook demonstrates a complete GLM-based fNIRS analysis pipeline:
#
# 1. **Load and preprocess** — load a BIDS finger-tapping dataset, convert to haemoglobin concentration, and apply bandpass filtering.
# 2. **Build the design mat... |
9d75146242f7db61f41f09ee10534eaa80342315a7d3d763eca1e64e48adca2c | Jupyter | 14,226 | 295 | # %%
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 h5py
from scipy.special import softmax
import numpy as np
from statsmodels.distributions.empirical_distribution import ECDF
#uncorrected mode... |
5570b1fd746cc180890fe75314e87eb22df47562129e4b823bb82d8119bdcd79 | Jupyter | 14,307 | 290 | # %% [markdown]
# # Explaining Image Captioning (Image to Text) using Azure Cognitive Services and Partition Explainer
#
# This notebook demonstrates how to use SHAP for explaining output of image captioning models i.e. given an image, model outputs a caption for the image.
#
# Here, we are using Azure Cognitive Ser... |
49b6c97caec9195d389427ff01bb11e054e6cd24b10c1c52124ce1c086ec67aa | Jupyter | 14,364 | 438 | # %% [markdown]
# # Code to generate RDMs for time averaged stimuli for review rebuttal
# %%
import numpy as np
import h5py
from pathlib import Path
import IPython.display as ipd
import pickle
%matplotlib inline
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import scipy.stats as stats
... |
cf7d72e338e0c11bed3e1a5e5ff9675d28777fa2e87ec418eb46b75b40d1761e | Jupyter | 14,378 | 272 | # %% [markdown]
# # Figure 5: Phase-dependent effects of beta and gamma rhythmic inhibition on dendritic spikes
#
# **Paper:** Headley DB, Latimer B, Aberbach A, Nair SS (2026). Spatially targeted inhibitory rhythms differentially affect neuronal integration. *eLife*. https://doi.org/10.7554/eLife.95562
#
# ## Overvi... |
6f510b97c89de9ed86211365a3273f5566f2cf183c9278182ea18e4fbb332e58 | Jupyter | 14,400 | 485 | # %% [markdown]
# # scATAC-seq Peak Annotation Pipeline
#
#
# scATAC-seq peak annotation example
#
# End-to-end pipeline for annotating scATAC-seq peaks to genes using
# genomic features derived from GTF files.
#
# Data are automatically downloaded and cached in ~/.iaode/data/ on first run.
#
# **Converted from:**... |
3faf7ce7197b6b3e585245a8cb35a5f0a7b153585e76b70cc6a9d895c1d80fb2 | Jupyter | 14,450 | 390 | # %%
import h5py
import numpy as np
import matplotlib.pyplot as plt
import os
if os.name == 'posix':
print("This is a UNIX system.")
UNIX = True
elif os.name == 'nt':
print("This is a Windows system.")
UNIX = False
else:
print("Unknown operating system.")
UNIX = False
# %%
if UNIX:
base = ... |
cb4e81d31c12c2afb080478c8033959bde04eaf897d9478eedbb6cfad58618b6 | Jupyter | 14,471 | 456 | # %% [markdown]
# # Effect of cue duration
# %%
import pandas as pd
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
from pathlib import Path
data_dir = Path("/om2/user/imgriff/projects/auditory_attention/data")
fname = data_dir / "experiment_1b_cue_duration_data_n_85_with_feature_gain_mod... |
9c0248189cb08d6dbc8fbacb13fb67db17e0497c53f26f3d27dfa051d5de6e27 | Jupyter | 14,482 | 378 | # %%
import numpy as np
import pandas
import matplotlib.pyplot as pl
import matplotlib as mpl
from matplotlib import cm
import seaborn as sns
# 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)
... |
eee3ab0feac39450098771480d36c7595c0012a9f1a3a97c7134a081ad4f16a8 | Jupyter | 14,532 | 392 | # %%
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 matplotlib
matplotlib.rcParams.update({'font.size': 10})
matplotlib.rcParams['pdf.fonttype'] = 42
matplotlib.rcParams['ps.fonttype'] = 42
matp... |
4c0ad9758ec20463e070f9d5eb6b5ebd6f29b3fdb22c52e34eb399348568db1a | Jupyter | 14,564 | 538 | # %% [markdown]
# # Image Reconstruction Options
#
# This notebook is a test bed to run and check all implemented image reconstruction methods.
# %%
# This cells setups the environment when executed in Google Colab.
try:
import google.colab
!curl -s https://raw.githubusercontent.com/ibs-lab/cedalion/dev/scrip... |
8b6b41d3747cafc9dcaf6b6800ab651cd7c1906005817e830a862a1c826bbf2d | Jupyter | 14,608 | 427 | # %%
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... |
26bbeb2528e885a65609e8bd0b6ff31206b9dddecb5605618a936b9a651333f5 | Jupyter | 14,633 | 342 | # %% [markdown]
# ## Plot Experiment 2
# %%
import pickle
import numpy as np
import re
from pathlib import Path
import pandas as pd
import json
%matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
from src import util_analysis
import re
# %%
matplotlib.rcParams.update({'f... |
03c03420c24f44ff0cefd4323b8516810d8addc10f1284b5c17c00f2c4ef486a | Jupyter | 14,675 | 387 | # %%
import sys
sys.path.insert(0, '/home/sxh/Research/AttentiveFP/code',)
import os
os.environ["CUDA_VISIBLE_DEVICES"] ="4"
# %%
import os
import torch
import torch.autograd as autograd
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.utils.data as Data
import time
impo... |
d9b9f65fd52c2e2daa6ab8d096a4ea26762ebd68494559190826280a4db52bc5 | Jupyter | 14,693 | 294 | # %% [markdown]
# # Figure 8: Frequency- and Phase-Dependent Effects of Rhythmic Inhibition on the Perisomatic Region
#
# This notebook analyzes how rhythmic inhibition delivered to perisomatic compartments (soma and dendrites within 100 µm) affects somatic membrane potential and action potential threshold across diff... |
354c465f3623d12faeb981095843f4703647510b2d5eedc5f54c13d3f7a7b873 | Jupyter | 14,705 | 431 | # %%
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... |
25e0962bc03c41ab766776c341e7c5d079f8feb804a759cded4599d372f4a019 | Jupyter | 14,781 | 440 | # %% [markdown]
# # STRING — Multi-Species Protein–Protein Interaction KG Processing
#
# **Species processed:**
#
# | Tax ID | Species | STRING file |
# |--------|---------|-------------|
# | 9606 | *Homo sapiens* | `9606.protein.links.detailed.v12.0.txt` |
# | 10090 | *Mus musculus* | `10090.protein.links.detailed.v... |
dcb24e3c0d77d65cae16f3091863ab4eaa801eb81f85f544be316e08f7109f7b | Jupyter | 14,796 | 368 | # %% [markdown]
# # Zebrafish Chemical–Gene (STITCH) — Relation-Wise KG Triple Construction
#
# ## Purpose
#
# This notebook processes **Chemical–Protein interaction data** from the STITCH database for Zebrafish (*Danio rerio*) and transforms it into standardized relation-wise Knowledge Graph (KG) triples. It include... |
d598c78da34627481779437d93a5bcbc897854832ddae80fbfe576d022f76ea2 | Jupyter | 14,839 | 601 | # %%
library(lme4)
library(lmerTest)
library(tidyverse)
library(broom.mixed)
library(car)
library(purrr)
# %% [markdown]
# ## Read metadata
# %%
path_meta <- "../seq-meta-data-tidy/outputs/mapping"
path_meta_bcm <- file.path(path_meta,"meta-data-bcm-all-sequencing.tsv")
df_bcm_meta <- read.csv(path_meta_bcm, sep="\t"... |
a920a1a2cc0d9d8d238a2068b52a084d36bbc7ecf5e44d23139477617684f747 | Jupyter | 14,880 | 293 | # %%
import sys
sys.path.append('../')
import numpy as np
import matplotlib.pyplot as plt
from utils_reconstruction import image_similarity as imsim
import tifffile
# %%
## load reconstruction .npy files
num_neurons = [7863, 7908, 8202, 7939, 8122]
mouse_names = [
"dynamic29515-10-12-Video-9b4f6a1a067fe51e15306b... |
a6ec0a132009ba13b5eff879100840d68213605e159782d60ebd021ce049cce6 | Jupyter | 14,935 | 448 | # %% [markdown]
# ## Plot Experiment 2
# %%
import sys
import pickle
import numpy as np
import re
from pathlib import Path
import pandas as pd
import json
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
from src import util_analysis
import re
# %%
import warnings
import numpy as np
impo... |
4d5494c30386464c035505f1b387e6e5f5c2bed3c20b841de21d08c76eae82f5 | Jupyter | 14,955 | 387 | # %%
import sys
sys.path.insert(0, '/home/sxh/Research/AttentiveFP/code',)
import os
os.environ["CUDA_VISIBLE_DEVICES"] ="4"
# %%
import os
import torch
import torch.autograd as autograd
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.utils.data as Data
import time
impo... |
789e47c1254098b08e33e518b00c48dccadf3944fc107f720f54baf909d53c2c | Jupyter | 14,997 | 397 | # %%
import sys
sys.path.insert(0, '/home/sxh/Research/AttentiveFP/code',)
import os
os.environ["CUDA_VISIBLE_DEVICES"] ="1"
# %%
import os
import torch
import torch.autograd as autograd
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.utils.data as Data
import time
impo... |
5b6e8e038016f81b9723d661ce9f31c4e3ce3af0652fe5ce96fdd6282deb1800 | Jupyter | 15,005 | 392 | # %%
import sys
sys.path.insert(0, '/home/sxh/Research/AttentiveFP/code',)
import os
os.environ["CUDA_VISIBLE_DEVICES"] ="7"
# %%
import os
import torch
import torch.autograd as autograd
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.utils.data as Data
import time
impo... |
3bad62cc04f62b563a44168d6a740baa1ae43ff9ff385f6a5bc483c0a34e0155 | Jupyter | 15,015 | 391 | # %%
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... |
b07aaffc0932b750814a1c11c5112aa0cc8b23591454a0700f2f5b0fb5cea84f | Jupyter | 15,052 | 221 | # %% [markdown]
# ```{currentmodule} optimap
# ```
# %%
from optimap.utils import jupyter_render_animation as render
# %% [markdown]
# ```{tip}
# Download this tutorial as a {download}`Jupyter notebook <converted/signal_extraction.ipynb>`, or a {download}`python script <converted/signal_extraction.py>` with code cell... |
007f204c86af3941901452b9db41cf7a1475232514cb8b97d002d2d1f0f9a47a | Jupyter | 15,080 | 357 | # %% [markdown]
# # Quantum Approximation Optimization Algorithm (QAOA)
# %% [markdown]
# ## Overview
# %% [markdown]
# QAOA is a hybrid classical-quantum algorithm that combines quantum circuits, and classical optimization of those circuits. In this tutorial, we utilize QAOA to solve the maximum cut (Max-Cut) combin... |
ba6d6ab79f6a4d3c3c40ebe79ba18ffa52a27d2a195ec95b61989ec289f3a697 | Jupyter | 15,090 | 391 | # %%
import numpy as np
import pickle
import h5py
from scipy import stats
from tqdm.auto import tqdm
from sklearn.metrics.pairwise import cosine_similarity
from scipy.stats import pearsonr
import pandas as pd
from pathlib import Path
import re
%matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
im... |
9b3eac5fd22cb7e72598625dd919504ddb1b02b3d2df3a3c74aa79e370020d46 | Jupyter | 15,099 | 330 | # %% [markdown]
# # Figure 10: Beta and Gamma Rhythms Bidirectionally Modulate Clustered Synaptic Drive in a Location-Dependent Manner
#
# This notebook analyzes how beta and gamma rhythmic inhibition affect the responsiveness of pyramidal neurons to clustered excitatory synaptic inputs at different dendritic location... |
4d9cac2ac4cfec88dd1c7f97ccff19c864335f77bc7f5ac97777eaa299fe984f | Jupyter | 15,114 | 297 | # %% [markdown]
# # Figure 5 Supplement 1: Phase-dependent effects on dendritic spikes of beta and gamma rhythmic inhibition delivered to opposite areas of the neuron
#
# **Paper:** Headley DB, Latimer B, Aberbach A, Nair SS (2026). Spatially targeted inhibitory rhythms differentially affect neuronal integration. *eLi... |
ee50ff09ee3cd583c9bc5f44ade12b97601078ad2235e8a6e13b8dce9b0a0143 | Jupyter | 15,133 | 509 | # %%
import seaborn as sns
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import PathPatch
sns.set(style='white', font='sans-serif', font_scale=1.5)
def adjust_box_widths(g, fac):
"""
Adjust the withs of a seaborn-generated boxplot.
"""
# iterating throu... |
759511298cecc618fd76d20fbda2fd233ae485bfd05422153c7df04aa8c48d84 | Jupyter | 15,145 | 433 | # %% [markdown]
# # ChemicalEntity ↔ Protein Relation-Wise Merge
#
# Merges Chemical–Protein triples from CKG (×2), CrossBAR (×2), DtiNet, STITCH,
# and MetaboGlue (×3); resolves chemical names via PubChem/DrugBank and protein
# names via UniProt; deduplicates by `(head, relation, tail)`; and saves the result.
# %% [... |
00112da838ae89f983af8fdb622ef2049a178d27f84680612159ae7f5a2d56b7 | Jupyter | 15,171 | 234 | # %% [markdown]
# ```{currentmodule} optimap
# ```
# %%
from optimap.utils import jupyter_render_animation as render
# %% [markdown]
# ```{tip}
# Download this tutorial as a {download}`Jupyter notebook <converted/01_overview.ipynb>`, or a {download}`python script <converted/01_overview.py>` with code cells. We highly... |
06639176e05c93b52cf1ca7aaa9f8a12464b6b330d785b5e909efad7c2a6cf92 | Jupyter | 15,231 | 484 | # %% [markdown]
# # Trajectory Inference with Neural ODE (scRNA-seq)
#
#
# Trajectory inference example – Neural ODE (scRNA-seq)
#
# Trajectory inference using iAODE + Neural ODE on the paul15
# hematopoietic differentiation dataset, with velocity field
# visualization and summary statistics.
#
# Dataset: paul15 (h... |
ab79b011696d258f675519747858c233323d9fdd121cab442dd43753d7af9283 | Jupyter | 15,264 | 432 | # %%
import os
import sys
sys.path.append('../methods/')
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
path_base = '/home/jinandmaya/perturbseq/'
def legend_title_left(leg):
c = leg.get_children()[0]
title = c.get_children()[0]
hpack = c.get_children()[1]
... |
4fbd7d5f289e27e1f2fd92b171c075f05460c2f53237964878c2c3797ce540ac | Jupyter | 15,426 | 519 | # %% [markdown]
# # Disease ↔ Gene Relation-Wise Merge
#
# Merges Disease–Gene triples from DRKG, PrimeKG, PharmKG, Hetionet, TARKG, iBKH,
# and EvoAGE; resolves disease names via DO/MESH and gene names via NCBI;
# deduplicates by `(head, relation, tail)`; and saves the result.
# %% [markdown]
# ## 0. Configuration
... |
31d60cb872c094ee740d0e947b4ca52c0555ba5d499f61c303d66bf21c862214 | Jupyter | 15,481 | 441 | # %%
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
# %%
adnimerge=pd.read_csv('../data/ADNIMERGE_14Oct2024.csv')
unsam_data=pd.read_csv('../data/UNSAMparticipants.csv')
BAN_ADNI=pd.read_csv('../results/BrainAgeNeXt/BAN_ADNI.csv')
DBN_ADNI=pd.read_csv('../results/DeepBrai... |
7f9d8c8ae86562818e67ebf260ee58da7be45643932ec632b8e8eae56b8fb815 | Jupyter | 15,534 | 489 | # %% [markdown]
# # Trajectory Inference with Neural ODE (scATAC-seq)
#
#
# Trajectory inference with Neural ODE (scATAC-seq)
#
# Infer trajectories from chromatin accessibility using iAODE + Neural ODE
# with velocity field visualization.
#
# Dataset: 10X Mouse Brain 5k scATAC-seq (HVP subset)
#
# **Converted fro... |
b8da81f84055725710f9a37f27bbac607def1a3734fff88c7fcd85423ce47792 | Jupyter | 15,604 | 394 | # %%
import sys
sys.path.insert(0, '/home/sxh/Research/AttentiveFP/code',)
import os
os.environ["CUDA_VISIBLE_DEVICES"] ="6"
# %%
import os
import torch
import torch.autograd as autograd
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.utils.data as Data
import time
impo... |
debe5f39edc406d472adac77c0b6d3639426c784dcbba4194846e1aef2f03199 | Jupyter | 15,752 | 392 | # %%
import sys
sys.path.insert(0, '/home/sxh/Research/AttentiveFP/code',)
import os
os.environ["CUDA_VISIBLE_DEVICES"] ="2"
# %%
import os
import torch
import torch.autograd as autograd
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.utils.data as Data
import time
impo... |
46a20aa820194a06137a4ca709a016baa9e0d76df4b397c57738607f0ba7d4b1 | Jupyter | 15,775 | 512 | # %% [markdown]
# # Disease ↔ Disease Relation-Wise Merge
#
# Merges Disease–Disease triples from Monarch, DRKG, PrimeKG, PharmKG, Hetionet,
# TARKG, iBKH, and hald; resolves disease names via DO/MESH;
# deduplicates by `(head, relation, tail)`; and saves the result.
# %% [markdown]
# ## 0. Configuration
# %%
import... |
b26e7d87bfdcbd57f61b5bd5b41e5c44fb84ebd04900e59d20746f9b31917110 | Jupyter | 15,781 | 405 | # %% [markdown]
# Calculate and plot taus per area using full signal of different length. ACFs with NaNs are removed as preprocessing.
# %%
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 ... |
2b3dd67ae7c34fd22318220a1002680707058b8fa770c13e73d5367b85a54f6e | Jupyter | 15,806 | 399 | # %%
import sys
sys.path.insert(0, '/home/sxh/Research/AttentiveFP/code',)
import os
os.environ["CUDA_VISIBLE_DEVICES"] ="5"
# %%
import os
import torch
import torch.autograd as autograd
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.utils.data as Data
import time
impo... |
544bcb94ec807fb8b4cea93f0c0e3f7facbdfee01e6487a10e99ae1df6289ef0 | Jupyter | 15,934 | 463 | # %% [markdown]
# # S3: Signal Processing
#
# This notebook demonstrates Cedalion's capabilities to assess signal quality and correct motion artifacts.
#
# Several signal quality metrics are implemented in the package `cedalion.sigproc.quality`. From these metrics
# boolean masks are created which indicate whether th... |
17d3f73b42155b35273f4dca6853a49101859ac95f0b3f2fbeab49c269b15694 | Jupyter | 15,987 | 565 | # %% [markdown]
# # Model Evaluation & Benchmarking (scRNA-seq)
#
#
# Model evaluation and benchmarking (scRNA-seq – paul15)
#
# Comprehensive comparison of iAODE vs. scVI-family models using
# Latent Space Evaluation (LSE) metrics on a trajectory dataset.
#
# Dataset: paul15 (hematopoietic scRNA-seq trajectory)
# ... |
b174f5b3cfcb43ad86a2e9cd35ca3ae6cc65a01ccc0987e3b1bd3ae04c4c5109 | Jupyter | 16,018 | 634 | # %%
import sys
sys.path.append('/Users/midani/OneDrive/proj/leap/manuscript')
import re
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from matplotlib.ticker import MultipleLocator
from scipy.cluster.hierarchy import linkage, dendrogram, leaves_list
from scipy.... |
872ea9b273973378c421512c51501b0d816a5d02f1f9bca3e0b0dba0c796c43f | Jupyter | 16,070 | 410 | # %%
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
from tqdm.auto import tqdm
%matplotlib inline
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
import matplotlib
# So that we can e... |
55f0e179dd86be066d55c1101e4788051625831f5146ce92af527fa8532e9c37 | Jupyter | 16,186 | 240 | # %% [markdown]
# ```{currentmodule} optimap
# ```
# %%
from optimap.utils import jupyter_render_animation as render
# %% [markdown]
# ```{tip}
# Download this tutorial as a {download}`Jupyter notebook <converted/basics.ipynb>`, or as a {download}`python script <converted/basics.py>` with code cells. We highly recomm... |
572bad535bba21d9d9f9624a0c2eda2ed597f6202fbd078ee8d6398f7071b4a1 | Jupyter | 16,372 | 392 | # %% [markdown]
# # Head Models in Cedalion: Getting Started
#
# This notebook gives a practical introduction to head models in Cedalion.
# It is intended for users who are new to DOT/fNIRS head modeling and want to understand
# what a head model contains, how to load it, and how to inspect and visualize its component... |
904c23ce060bcd4b9b83a255f2995594baeb19d53329e807b3b37cc8dda49ae2 | Jupyter | 16,510 | 357 | # %% [markdown]
# # Explaining Image Captioning (Image to Text) using Open Source Image Captioning Model and Partition Explainer
#
# This notebook demonstrates how to use SHAP for explaining output of image captioning models i.e. given an image, model outputs a caption for the image.
#
# Here, we are using a pre-tra... |
28e8afdadcacc02d56ac2330bf201fe9c17df3866504215ce67deeae37de07d7 | Jupyter | 16,584 | 364 | # %% [markdown]
# # GenDR → Knowledge Graph (KG) Builder
#
# **Source:** [GenDR](https://genomics.senescence.info/diet/) — Dietary Restriction Gene Manipulations (`cleaned_gendr_manipulations.csv`)
# **Species covered:** *Saccharomyces cerevisiae*, *Caenorhabditis elegans*, *Drosophila melanogaster*, *Mus musculus*
... |
8c597aaaba70917a11f5f2ee3bf37cff5917412420e3f3f823e001442b68311e | Jupyter | 16,618 | 334 | # %% [markdown]
# # Perturb-seq analysis (Python)
#
# This tutorial uses an excitatory-neuron subset from [Jin et al. (2020), *Science*](https://doi.org/10.1126/science.aaz6063), which studied gene perturbations in the developing mouse brain. Data are available from the [Broad Single Cell Portal](https://singlecell.br... |
078b8f84fb6fe9e332499bfadc0daeabbff176e620aae7520fbea61039c30d3a | Jupyter | 16,702 | 563 | # %%
# 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]
... |
16e037add4e727f4aaf3ad4c2c99cd3d4f2e019886db50f3589c9048b5916a1e | Jupyter | 16,759 | 421 | # %%
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... |
0263711966f69f3f64c6777a98f00928a32e11a01e3d4fcef604155672edcd2c | Jupyter | 16,950 | 561 | # %% [markdown]
# # Visualization Examples using Cedalion Plot Functions
#
# This notebook will be continuously extended to enable an easy look up of visualization functions.
#
# In each example cell, we un-necessarily re-import the plotting subpackages to clarify which of these are needed for the plots.
# %%
# Thi... |
340ab89068d0e5cbc261bef95a0fddfe97a734b7e10221e71b95ecab9fa3b22a | Jupyter | 17,145 | 574 | # %% [markdown]
# # Gene ↔ Disease Relation-Wise Merge
#
# Merges Gene–Disease triples from Monarch, DRKG, PharmKG, TARKG, Harmonizome (×7), and
# EvoAGE; resolves disease tail names/IDs via DO/MESH (incl. MONDO→MESH), gene head names
# via NCBI; deduplicates by `(head, relation, tail)`; and saves the result.
# %% [m... |
ae97d9dc4252410c232244b9c35f587cfa9bf1f9bcff1fe03056890888180a77 | Jupyter | 17,201 | 564 | # %% [markdown]
# # Optimizing QAOA by Bayesian Optimization (BO)
# %% [markdown]
# ## Overview
# %% [markdown]
# In this tutorial, we show how to use Bayesian optimization to optimize QAOA. For the introduction of QAOA, please refer to the [previous tutorial](qaoa.ipynb). Bayesian optimization in this tutorial is ba... |
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