sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
58deb72b1b8a9fed806c9cdf532f7ccbb312c95334f4906f3567f5fdd6c61560 | Jupyter | 9,312 | 246 | # %%
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
import sys
import scipy as sp
import h5py
from functools import reduce
import matplotlib.lines as mlines
sys.path.append('../methods/')
def legend_title_left(leg):
c = leg.get_children()[0]
title = c.ge... |
05eb2582d0f7bd2c5c43a40df16c7f8f44528e68e7bee4034be9c29d85825e81 | Jupyter | 9,321 | 241 | # %%
import pandas as pd
import numpy as np
def get_statistics_data(data_type):
path=f'{data_type}_Summary.xlsx'
data=pd.read_excel(path)
data.describe().to_csv(f'data/new_data/{data_type}_statistic.csv',encoding='utf-8-sig')
print(data['Fasting Plasma Glucose (mg/dl)'][data['Fasting Plasma Glucose (mg/... |
650a017f8608c8bbe1b623bf422e105acdf3ee23046d946e7fdfe9242695bee6 | Jupyter | 9,324 | 375 | # %%
import sys
sys.path.append('/Users/midani/OneDrive/proj/leap/manuscript')
import os
import re
import numpy as np
import pandas as pd
from typing import Tuple
from functools import reduce
import repo_code.lib_aux as lib_aux
subsetDf = lib_aux.subsetDf
remove_zero_cols = lib_aux.remove_zero_cols
remove_zero_rows... |
7aed6edcbc187e3d4b0142234b8a4d2ba524185d17b4c8b062e0ce3c7ecb63b0 | Jupyter | 9,327 | 270 | # %%
import numpy as np
import pandas as pd
import pickle
%matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
from pathlib import Path
import re
import scipy.stats as stats
# %%
matplotlib.rcParams.update({'font.size': 10})
matplotlib.rcParams['pdf.fonttype'] = 42
matplotlib... |
cb086bcc0fdedc99917def462fc18aa95cbc3a08b4acbbd81ce803fd0aac8c45 | Jupyter | 9,336 | 341 | # %% [markdown]
# # Data Loading and Preprocessing
# %%
# 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 i... |
a290f90db4b6696e1ff58a784c5f5e4708fa609d4177a38bac099b7afd8b2999 | Jupyter | 9,346 | 240 | # %%
%load_ext autoreload
%autoreload 2
import numpy as np
import pandas as pd
from PIL import Image
import tifffile
import napari
from matplotlib import pyplot as plt
# %%
from scribbles_testing.FoodSeg103_data_handler import load_food_batch, load_food_data
from scribbles_testing.convpaint_helpers import generate_co... |
e010d499c997b794d82c5fcd6802e0f0e739187e34fb831d01a14e71f31dcb84 | Jupyter | 9,411 | 288 | # %% [markdown]
# # BiologicalProcess → BiologicalProcess Relation Pipeline
#
# Builds a unified, deduplicated edge table for the **BiologicalProcess–BiologicalProcess** relation
# by ingesting processed files from multiple KG sources, normalising identifiers
# if needed, and writing the final triple table to disk... |
d8290b2bb9d042a9bf35a6ac89dac8d1d07e528e53679a3ed38465d64458f6aa | Jupyter | 9,460 | 279 | # %% [markdown]
# # ChemicalEntity ↔ Pathway Relation-Wise Merge
#
# Merges Chemical–Pathway triples from Monarch and iBKH; resolves chemical names via
# PubChem and pathway names via Reactome; assigns `tail_id_is` based on pathway ID prefix
# (Reactome vs KEGG); deduplicates by `(head, relation, tail)`; and saves the... |
5bd54fbcfef766c29191566dcefe2065f50bc138761d0b9f4da200ab8ca20625 | Jupyter | 9,476 | 243 | # %%
import numpy as np
from matplotlib import cm
import matplotlib.pyplot as pl
from matplotlib import rcParams
from matplotlib import rc
from matplotlib.lines import Line2D
from mpl_toolkits import mplot3d
import pandas
# This bit is for that figure formatting. Change font and font size if desired
font = {'family' :... |
68c157e4dc7124b6454020dd33afedb0eb49de6b4596fd29e5161337a88548e9 | Jupyter | 9,551 | 284 | # %% [markdown]
# # Examining and thresholding sensitivity of a probe to the cortex using the Schaefer parcellation scheme
#
# This notebook shows how to examine the theoretical sensitivity of a probe on a headmodel to brain areas (here we use parcel coordinates from the Schaefer 2018 atlas), and how to identify parce... |
87c72e24502feb11fa457c5bc2108eb43f9252926ae76cc4795cd292efc1c228 | Jupyter | 9,573 | 230 | # %% [markdown]
# # Calculate phase dependent modulation of AP generation in response to spatially diffuse or concentrated poisson excitation and rhythmic inhibition
#
# The simulations had either:
# 1. Rhythmic inhibition at the soma (64 Hz) or dendrites (16 Hz)
# 2. Diffuse or concentrated synaptic excitation at a... |
ad680d06b7d80f6a4c8c4edee20e1a0274a22f4c1c9f86824c9a51407ed6650f | Jupyter | 9,575 | 284 | # %%
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
from solver.ODE.special_ode_cases import get_ode_sepcial_case
from utils.plots_ode import get_std_pred
# %%
HOME = os.getcwd()
case = '_H'
ext_n... |
bba7084dece3d88939528930e8d69b8e6c7b939f571a6cac9583733ebae54a8f | Jupyter | 9,598 | 323 | # %%
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=1.3)
def adjust_box_widths(g, fac):
"""
Adjust the withs of a seaborn-generated boxplot.
"""
# iterating throu... |
ef6349bf9abb8d45de0d360f4ecc719a8d46b620e665ea0b19d627fea5fbe966 | Jupyter | 9,605 | 300 | # %% [markdown]
# # Protein ↔ Protein Relation-Wise Merge
#
# Merges Protein–Protein triples from Monarch, CKG (×3), CrossBAR, TARKG, DtiNet, and STITCH;
# fills missing head/tail names from UniProt; deduplicates by `(head, relation, tail)`;
# and saves the result.
# %% [markdown]
# ## 0. Configuration
# %%
import p... |
e762ca3ba2a3d283966664dfd11a93939c93f6fc57c09d299f157d68a6b3e1c2 | Jupyter | 9,609 | 310 | # %%
import numpy as np
import pandas as pd
from tqdm import tqdm
tqdm.pandas(ascii=True)
from rdkit import Chem
import seaborn as sns
from sklearn.cluster import AgglomerativeClustering, DBSCAN, SpectralClustering
from scipy.stats import ks_2samp, chisquare, power_divergence
import tmap, os
from faerun import Faer... |
5ee1b4171a32f6ab652632c74fc546d54c1971d06e4bfc6ad384ed1336e62b14 | Jupyter | 9,797 | 300 | # %% [markdown]
# # Gene ↔ Pathway Relation-Wise Merge
#
# Merges Gene–Pathway triples from Monarch, TARKG, iBKH, and Harmonizome; resolves
# missing gene head names via NCBI synonyms; normalises ID-type labels; deduplicates by
# `(head, relation, tail)`; and saves the result.
# %% [markdown]
# ## 0. Configuration
#... |
23d5f44604748da0cd9599b35a1bc65e7b35c0ee4c875a45a8aeb124771441c0 | Jupyter | 9,798 | 290 | # %% [markdown]
# # Precomputed forward model results
#
# We provide precomputed fluence and sensitivity files for the example datasets. These are created by this notebook and can be obtained through `cedalion.data.get_precomputed_sensitivity`.
#
# The second part of the notebook visualizes the currently available se... |
31f0e9df72c31286295c0e7b03a324a1fe835df3c458e26bb2669ea4d6452cf6 | Jupyter | 9,815 | 207 | # %% [markdown]
# # MetaboAge → Knowledge Graph (KG) Builder
# %%
# ! wget https://www.metaboage.info/static/website/variation-data.xlsx
# ! wget https://www.metaboage.info/static/website/chemical-modeling.xlsx
# %% [markdown]
# ---
# ## 0 · Configuration — edit ONLY these two lines
# %%
import os
import re
import p... |
66f551adcc62fc99375e414a8700ccd9a773d3725babcb25db6610391af7ca35 | Jupyter | 9,854 | 297 | # %% [markdown]
# # ANATOMY ↔ GENE Relation-Wise Merge
#
# Merges ANATOMY–GENE triples from multiple KG sources (DRKG, PrimeKG, Hetionet, TARKG),
# aligns to a common schema, deduplicates by `(head, relation, tail)`, and saves the result.
# %% [markdown]
# ## 0. Configuration
# %%
import pandas as pd
import numpy as... |
186ef8ca33f48f7d222e8aaaf39fddf3fcb47413dee0633f0a259893cf96f58d | Jupyter | 9,874 | 268 | # %%
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... |
b00fcac0721f6c19bf9aa26ce5bc34f96a9b81db03c7db56915f5c1d6d59429b | Jupyter | 9,933 | 373 | # %% [markdown]
# # AlphaFold Predicted Structures Analysis
# %%
from Bio.PDB import PDBParser, Superimposer, PPBuilder
import numpy as np
import os
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
plt.rcParams['axes.labelsize'] = 12
plt.rcParams['figure.dpi'] = 300
# %%
parser = PDBParser(... |
88f97a56bb6c08d244145bffdccb8ffba2bdce7c465f09f1381c789829c5a61d | Jupyter | 9,934 | 266 | # %%
import napari
import numpy as np
import seaborn as sns
from napari.utils.notebook_display import nbscreenshot
from IPython.display import Image
# %% [markdown]
# # Multichannel IMC data
# %% [markdown]
# ### Working with Imaging Mass Cytometry (IMC) data
# %% [markdown]
# Here we demonstrate how Convpaint can e... |
32122337ab618069f5883cfa7d71a24106caf413bce8e5b314ae8b2b23be629f | Jupyter | 9,956 | 240 | # %% [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
... |
ebf99dfe717c5d1d8fc2e801cde9d1ef302f558153017244e028c633c6daadc0 | Jupyter | 9,956 | 369 | # %% [markdown]
# # Disease ↔ Phenotype Relation-Wise Merge
#
# Merges Disease–Phenotype triples from Monarch and CrossBAR; resolves disease names
# via DO/MESH; deduplicates by `(head, relation, tail)`; and saves the result.
# %% [markdown]
# ## 0. Configuration
# %%
import pandas as pd
import numpy as np
BASE_DIR... |
52dc426abfca24fbc30542e3d1a6e5b062cd46c638f39b73b79900feb9c26811 | Jupyter | 10,077 | 330 | # %% [markdown]
# # Basic scATAC-seq Example
#
#
# Basic scATAC-seq example: dimensionality reduction with iAODE
#
# Train iAODE on scATAC-seq with peak annotation, TF-IDF normalization,
# and UMAP-based visualization.
#
# Dataset: 10X Mouse Brain 5k scATAC-seq
# %% [markdown]
# ## Setup
# %%
import sys
from path... |
d050e996154447893b1d85080c674355165038192878478d44939560a9cab132 | Jupyter | 10,095 | 235 | # %%
# correlation maps visiualization
import os
import pickle
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import ttest_ind
# 定义文件路径
paths = [
r"C:\Users\12770\Desktop\project_ym\SVCA\Result\m010iso2\400s-900s(awake)\neuron_correlation_statistics",
r"C:\Users\12770\Desktop\project_ym\S... |
7f0a49bab7a8490578b70c76b65a2ab50708f8b5aacff73129aad0365bc0ab55 | Jupyter | 10,121 | 283 | # %%
# 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]
... |
87f54ac00ce67c6e70dffc32b840c872286827838ba797c7a72468b247d5a19f | Jupyter | 10,243 | 316 | # %%
from deepscore import DeepScore
from preprocessing import *
import scanpy as sc
import episcanpy as epi
import anndata as ad
from tensorflow import keras
import os, gc
%load_ext rpy2.ipython
%load_ext tensorboard
os.chdir("/home/pab/projects/ESPACE/ESPACE_multiome")
sc.settings.set_figure_params(dpi=80, color_ma... |
3877d68f695f503c92b3d2b8b7cf6b29cd0443230e2f5488018504cdf1ec21d7 | Jupyter | 10,246 | 310 | # %%
import sys
import os
# Add the parent directory of `notebook/` to sys.path
sys.path.append(os.path.abspath(".."))
import torch
palette = ['#43AA8B', '#F8961E', '#F94144']
sub = str.maketrans("0123456789", "₀₁₂₃₄₅₆₇₈₉")
# data pre-processing and visualization
import numpy as np
import matplotlib as mpl
import mat... |
abe023786f2bd1c7703c40734b7532921809b2aeb5e7043941c2fd6cf9ebdb88 | Jupyter | 10,327 | 322 | # %% [markdown]
# # Misc Figures
# %%
import sys
import os
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
import warnings
warnings.filterwarnings('ignore')
path = os.path.join('..', '.')
if path not in sys.path:
sys.path.append(os.path.abspath(path))
plt.rcParams['figure.dpi'] = 30... |
943b86df86b99dba78b890757ba217aa3951c6b36cac2195c2232ec0539e7169 | Jupyter | 10,367 | 320 | # %% [markdown]
# # ChemicalEntity ↔ Mutation Relation-Wise Merge
#
# Merges Chemical–Mutation triples from CKG and EvoAGE; resolves chemical names via
# PubChem, DrugBank (standard + extended); falls back to raw head value for any
# remaining unresolved IDs; deduplicates by `(head, relation, tail)`; and saves the res... |
02b1c315aef83e775077ed61efe21704b7c6242a968bf3eb3f8c47304c95bd9c | Jupyter | 10,370 | 247 | # %%
import pandas as pd
from matplotlib import pyplot as plt
import seaborn as sns
import plotly.express as px
import numpy as np
# %%
DBN_synt_ADNI=pd.read_csv('../results/DeepBrainNet/DBN_ADNI.csv')
DBN_synt_UNSAM=pd.read_csv('../results/DeepBrainNet/DBN_UNSAM.csv')
DBN_synt_RRIB=pd.read_csv('../results/DeepBrainNe... |
cd3c4864838f97d3a5d444756ef890797036dbb4ba897b0dbf593bdeca0ca9cf | Jupyter | 10,413 | 307 | # %% [markdown]
# # 102 Dismonstrating the classification performance for MolMM
# * including comparison and ablation study
# %%
import pandas as pd
import numpy as np
import torch
import os
import itertools
from collections import OrderedDict, defaultdict
# %%
def show_result(path,begin,count,stage,start='test'):
... |
1dde28d46e1b735be8bd6ce92dc8e2c09012be558cee410fe445d0ef07edf20d | Jupyter | 10,434 | 272 | # %% [markdown]
# # Relationship between APs and dendritic spikes across an oscillatory burst
#
# The simulations had either:
# 1. Bursts of rhythmic inhibition either at the soma (64 Hz) or dendrites (16 Hz)
# 2. Poisson excitation at the soma and dendrites
#
# Here we calculate the spike-triggered average between ... |
c072d35a9cf9bbb23c6e5b9fad2e3d105f75b8d32671608fb7f391e8697fb771 | Jupyter | 10,470 | 265 | # %%
import os
import time
from ase.visualize.plot import plot_atoms
from ase import Atoms
from ase.units import kB
import numpy as np
from scipy.optimize import minimize_scalar
from scipy.special import erf
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
import seaborn as sns
import os, h5py, json,... |
976babb443d464f833164925a7dbf9134739451306357de24bbbdbcbc445ef97 | Jupyter | 10,492 | 271 | # %% [markdown]
# # Calculating the Scalp Coupling Index
#
# This notebook calculates the Scalp Coupling Index[1] metric for assessing the signal quality of a recording.
#
#
# [1] L. Pollonini, C. Olds, H. Abaya, H. Bortfeld, M. S. Beauchamp, and J. S. Oghalai, “Auditory cortex activation to natural speech and simul... |
2ad12ad49d1b626d0d780c09b7a72107a87b9e3def5fba32719fd13af425465b | Jupyter | 10,518 | 357 | # %% [markdown]
# # Mutation → Disease Relation Pipeline
#
# Builds a unified, deduplicated edge table for the **Mutation–Disease** 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_detail_name`
# ... |
644b9d14dcc74e891f9cdd4600967ee01c61f0a1bea663a5ed6c65ed53966e47 | Jupyter | 10,529 | 273 | # %%
import sys
sys.path.append('/Users/midani/OneDrive/proj/leap/manuscript')
import numpy as np
import pandas as pd
from pandas.api.types import CategoricalDtype
import repo_code.lib_excel as lib_excel
# %% [markdown]
# # Demographic and birth factors
# %%
data_demographics = pd.read_csv('./inputs/MicrobiomeBra... |
429a501567e4b99580b258de48f696d7b73a243e10097a4deb12771dba885770 | Jupyter | 10,551 | 330 | # %%
import pickle
import numpy as np
import h5py
import matplotlib.pyplot as plt
import scipy as sc
from statistics import median, mean, stdev, mode
from scipy.signal import find_peaks, peak_prominences, peak_widths
import scipy.integrate as integrate
import scipy.special as special
import seaborn as sns
import os
fro... |
a2511126528ab89d925e72f751a7a13319896132ac1043388ba23b4cde46a261 | Jupyter | 10,555 | 239 | # %%
import numpy as np
import pandas as pd
import pickle
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'
# %%
dataset_fold... |
b5600c07963ae381e1576a0dc97241ba99b9b6ddbe3c6e87e6362d290bc069da | Jupyter | 10,603 | 232 | # %% [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
... |
7c3da37bcbef3b3efb957fa4522b576f86c024d621223d972d275a156679c764 | Jupyter | 10,618 | 302 | # %%
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... |
6cf5e2c7b5847995ff64b7a5de20ec631eb9ca905c871bbae6a9ec95d8cf3e02 | Jupyter | 10,630 | 294 | # %%
import sys
sys.path.insert(0, '/home/sxh/Research/AttentiveFP/code',)
# %%
import os
os.environ["CUDA_VISIBLE_DEVICES"] ="0"
# %%
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... |
a8ebe7086aea4fa4c6cb1619b29104c33a39a3074f2fadfb10888d8eb49c2cbe | Jupyter | 10,642 | 391 | # %% [markdown]
# # POMS Automatic Scoring
# %%
import os
import pandas as pd
from src.my_settings import settings
import seaborn as sns
import matplotlib.pyplot as plt
from statannotations.Annotator import Annotator
sett = settings()
# %%
tsv_path = os.path.join(sett["git_path"], "data", "POMS_Responses.tsv")
df =... |
05d35b268aa0991f15dc41d04c6f651c449ba7c293e7df90fdeec4e27c7db913 | Jupyter | 10,645 | 407 | # %%
import pandas as pd
import numpy as np
# %%
BASE_PATH = "/storage/Arushi/090526_EvoAge/kg_formation/data_collection/"
OUT_PATH = "/storage/Arushi/090526_EvoAge/kg_formation/processed_data/string"
# %%
# %% [markdown]
# # NCBI GENE
# %%
NCBI_Yeast_gene = pd.read_csv(f'{BASE_PATH}databases_for_mapping/ncbi/Sacc... |
02fecf9ab248bcc721bdb3370184ae55c63a7ecb1c634af894adc94073eff08a | Jupyter | 10,697 | 356 | # %% [markdown]
# # STITCH & STRING (Human) — KG Processing
# **Project:** MetaboGlue / EvoAge KG | **Species:** *Homo sapiens*
# **Notebook:** `STITCH_for_EvoKG_Processing_human.ipynb`
#
# **Outputs:**
#
# | Section | Relation | Output file |
# |---------|----------|-------------|
# | §4 — STITCH Chem... |
fe1e367d859b0e2aa98804bee00544cd63e124bbb0c1aabd6589b7a3133a5ebd | Jupyter | 10,704 | 428 | # %%
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"... |
3709afa3681a5ba960b19f084a56a9348d10b1f41714169fd3e3dcbaa0cc0b52 | Jupyter | 10,719 | 312 | # %%
import sys
sys.path.insert(0, '/home/sxh/Research/AttentiveFP/code',)
# %%
ll /home/sxh/Research/AttentiveFP/
# %%
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
imp... |
f61f9331b5cbf273daebbf62fb75410f1060f69b0be3177a9daba59df092c47a | Jupyter | 10,756 | 312 | # %%
import sys
sys.path.insert(0, '/home/sxh/Research/AttentiveFP/code',)
# %%
import os
os.environ["CUDA_VISIBLE_DEVICES"] ="0"
# %%
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... |
56a2dbe60dbac7a1b36d43afe06ae5f4b20854237c4f1a9644fe8d57d8abe9d1 | Jupyter | 10,763 | 277 | # %% [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 interpretationsm
# %%
import numpy as np
from matplotlib import cm
import matplotlib.pyplot as pl
from matplotl... |
a477efbc2b93b26bc554251b2283d92e9ed5ace42287891bde44ca11d7ac0784 | Jupyter | 10,763 | 284 | # %% [markdown]
# <a href="https://colab.research.google.com/github/sokrypton/af2bind/blob/main/test/af2bind_gamma.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# ### AF2BIND: Prediction of ligand-binding sites using AlphaFold2
#
... |
de46841f30378b51aaa5906adb8512a1133fff7534420527b29f9098b3f89caa | Jupyter | 10,815 | 295 | # %%
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... |
d74eae0b2fee5a12833c58d7fb6506701a3f8d5fbd76f2aba93d400a6286bba3 | Jupyter | 10,836 | 300 | # %%
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... |
38bf75e7ac1db2f310d1bc19bc59229d75eebacc6e955075bbf48fb2f60dd77e | Jupyter | 10,839 | 274 | # %% [markdown]
# # Drosophila Gene–Gene (STRING) — Relation-Wise KG Triple Construction
#
# ## Purpose
#
# This notebook processes **Protein–Protein interaction data** from the STRING database for *Drosophila melanogaster* and transforms it into standardized Gene–Gene relation-wise Knowledge Graph (KG) triples. FlyB... |
76b342d64e7ce47ce87a6070161674d231aad162ce00385c46477019ddcbbe23 | Jupyter | 10,854 | 303 | # %%
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
import numpy as np
import gc
import sys
sys.setrecursionlimit(50000)
import pickle
torch.backends.cudnn.benchmark = True
torch.set_... |
c93a67ed3fbe9bdbe97dc2150fb2a11fe49a7a05f4978cfd22d994783832d10c | Jupyter | 10,870 | 329 | # %% [markdown]
# # Basic single trial fNIRS finger tapping classification
#
# This notebook sketches the analysis of a finger tapping dataset with multiple subjects. A simple Linear Discriminant Analysis (LDA) classifier is trained to distinguish left and right fingertapping.
#
# **PLEASE NOTE:** For simplicity's ... |
5425195bb8080f8aee41c477aeb910fd6c7bf6edc7b074d7d410d7cf4487f8a5 | Jupyter | 10,916 | 306 | # %% [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... |
d34be1dcbc6f67652c4c9a5d4725ddbbc388930ef52a654a5d321c5380f7673d | Jupyter | 10,954 | 287 | # %%
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 scipy.signal import resample
from tqdm import tnrange
import seaborn as sns
from scipy.stats imp... |
d234e10ad06bce3f9d0592f86c10b785dfff2368477ee831b4a25da34db2a074 | Jupyter | 10,964 | 339 | # %%
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "3"
os.environ["TF_USE_NVLINK_FOR_PARALLEL_COMPILATION"] = "0"
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
ENV = {"TF_FORCE_UNIFIED_MEMORY":"1", "XLA_PYTHON_CLIENT_MEM_FRACTION":"4.0"}
for k,v in ENV.items():
os.environ[k] = v
# %%
import numpy as np
import pickle
DA... |
5e1334abfa8bae5e2f553c356c4ced422c76ce7e6cef10eca37be24cb77300a1 | Jupyter | 11,006 | 300 | # %%
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
import sys
import scipy as sp
import h5py
import matplotlib.lines as mlines
from functools import reduce
sys.path.append('../methods/')
def legend_title_left(leg):
c = leg.get_children()[0]
title = c.g... |
95c4f76cbe708ebd126100cd5fc889db10e05cdd430c0ef43f42d197bee25770 | Jupyter | 11,052 | 315 | # %% [markdown]
# Using abcTau to fit ACFs for trials (Figure 2 from the paper).
#
# Three options to do that:
# * use abcTau package for both ACF and fitting
# * use ACF calculated before using acf function
# * use ACF calculated before using iSTTC concat function
# %%
import matplotlib.pyplot as plt
import seaborn... |
d80dc7f2e2c96c9d7b7b7079c2d167c12d7a15c80cba156f92a942a2a4c4f0ba | Jupyter | 11,109 | 412 | # %%
import os
import sys
import MDAnalysis as mda
import numpy as np
from sklearn.cluster import KMeans
import matplotlib
from matplotlib import pyplot as plt
import pandas as pd
import nglview as nv
import gumpy
import copy
from collections import defaultdict
from pprint import pprint
path = os.path.join('..', '.')
... |
6514c6d7fd3a2ed924b6ae899157c7b0fedb6a70aa9092fcdc8ac625e0abd889 | Jupyter | 11,110 | 299 | # %% [markdown]
# <a href="https://colab.research.google.com/github/sokrypton/af2bind/blob/main/af2bind.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# ### AF2BIND: Prediction of ligand-binding sites using AlphaFold2
#
# AF2BIND i... |
0495ac72f44dd65318c2687cb76c010d2065422552bc9c925e93b2854b3b887d | Jupyter | 11,167 | 281 | # %% [markdown]
# # C. elegans Chemical–Gene (STITCH) — Relation-Wise KG Triple Construction
#
# ## Purpose
#
# This notebook processes **Chemical–Protein interaction data** from the STITCH database for *C. elegans* and transforms it into standardized Chemical–Gene relation-wise Knowledge Graph (KG) triples. Protein ... |
340cde29822e65a38dcc08ec5a70b2ee869b1752eeb81b6b11ca0f065c34661d | Jupyter | 11,238 | 291 | # %% [markdown]
# Loads data and generate plots for the paper:
# 1. summary plot fixation period 0-1000ms - number of units per area and number of trials per units (only trials with at least 1 spike per trial in fixation period)
# 2. firing rate
# %%
import pickle
import numpy as np
import csv
import pandas as pd
imp... |
0055964083135ee103404a5268c184cfa54e65ae990bf2998e8e05ca3c6acf29 | Jupyter | 11,254 | 263 | # %% [markdown]
# # Case-control analysis: SEA-AD gene-expression differences
#
# This tutorial compares excitatory-neuron pseudo-bulk expression between disease-status groups in the [SEA-AD Middle Temporal Gyrus dataset](https://doi.org/10.1038/s41593-024-01774-5), accessed through [CellxGene Census](https://chanzuck... |
a01bdd32fc34702d44a442669705ab281a8d57e87fae21e595a6df2c9b7a5728 | Jupyter | 11,257 | 175 | # %% [markdown]
# # Feature Extractor descriptions
# %% [markdown]
# Convpaint utilizes a **variety of pre-trained models for feature extraction**, allowing users to choose the most suitable model for their specific task. These models are designed to capture different aspects of the input data, enabling the most effec... |
91810daa5181f87f16f659612012bf9cfc28ae98b05ea86c6a62f67d5e7af80a | Jupyter | 11,267 | 368 | # %% [markdown]
# # Demo on TensorCircuit SDK for Tencent Quantum Cloud
#
# This notebook is not served as a full user manual for TC SDK for QCLOUD. Instead，it only highlighted a limited subset of features that TC enabled, mainly for live demo and tutorials.
#
# ## Import and Setup
# %%
import tensorcircuit as tc
#... |
746af2f24769172aff0a7657fc82b22ac001e0640cd2f35ec8c3e4ca2759536f | Jupyter | 11,296 | 361 | # %% [markdown]
# # Gene ↔ Gene Relation-Wise Merge
#
# Merges Gene–Gene triples from Monarch, DRKG, PrimeKG, PharmKG, Hetionet, BOCK, TARKG,
# iBKH, Harmonizome (×8), and hald; resolves missing head/tail gene names via NCBI;
# deduplicates by `(head, relation, tail)`; and saves the result.
# %% [markdown]
# ## 0. Co... |
c09fe411420e35a404e98b172279ee7dcd8a83a002164134f75aaf155d5d3d68 | Jupyter | 11,388 | 222 | # %% [markdown]
# Calculate taus:
#
# on unit level:
# 1. Pearsonr trial average
# 2. STTC trial average
# 3. STTC trial concat
#
# on trial level (not calculated yet):
# 1. Pearsonr per trial
# 2. ACF proper per trial
# 3. iSTTC per trial
# %%
import pandas as pd
import numpy as np
import matplotlib as mpl
impor... |
b79e9d6a0ba2efd46727c52a56f450ab2e26f98077af8b4a56320bcbff9b32c0 | Jupyter | 11,408 | 163 | # %% [markdown]
# ```{currentmodule} optimap
# ```
# %%
from optimap.utils import jupyter_render_animation as render
# %% [markdown]
# ```{tip}
# Download this tutorial as a {download}`Jupyter notebook <converted/ratiometry.ipynb>`, or a {download}`python script <converted/ratiometry.py>` with code cells.
# ```
#
# ... |
17cbd3ca87ec7cde4aa681b2427bc9b6d49bcb8a0eb62c88016cb65f2ce0c7fe | Jupyter | 11,438 | 271 | # %%
# 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:... |
179576be38ae9af5f77eb00e2b8927a84747d8951d1949b8135b624c1ddf9291 | Jupyter | 11,459 | 310 | # %% [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... |
0f290b1b5749ea2310d5c96285ac8051c996c8e1539194d1ae86cae761e88fac | Jupyter | 11,541 | 266 | # %% [markdown]
# # Detecting calcium signaling waves in timelapse movies
# This notebook demonstrates the workflow for detecting calcium signalling waves in microscopy images using [ARCOS](https://doi.org/10.1083/jcb.202207048), a tool to detect spatio-temporal signaling patterns.
# The data was originally acquired by... |
ec7027d9ed46cc7e1f5a25261840d3d5af2727b7c7fd6e26bfcd42359d46dc8e | Jupyter | 11,593 | 315 | # %%
import pandas as pd
import os
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
# %%
UNSAM_LC = "IQM_UNSAM_LC.csv"
JUK = "IQM_JUK.csv"
RRIB = "IQM_RRIB.csv"
ADNI="IQM_ADNI.csv"
# boxplot de cnr y efc para cada dataset
iqm_unsam = pd.read_csv(UNSAM_LC)
iqm_juk = pd.read_csv(JUK)
iqm_rrib = ... |
44a5d47075fb74123a70f2c1e0c7e41c2031c38ef460756fbb4e685889183023 | Jupyter | 11,601 | 305 | # %%
import h5py
import matplotlib.pyplot as plt
import os
import importlib
from pid_functions import *
import numpy as np
from scipy import signal
from scipy.io import savemat
import pandas as pd
import seaborn as sns
sns.set_context('poster')
# %%
def arrays_to_excel(arr1: np.ndarray, arr2: np.ndarray, filename: str... |
08148d133cf3194c4a9f216d36f1c159a178c1ad21fe4b2191a79e108d3769d4 | Jupyter | 11,625 | 259 | # %% [markdown]
# TSNE and UMAP run with the 4 groups (HDACs/SIRTS, HATS, TFs, and Ion Channels)
# %%
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import sklearn
from sklearn import manifold
import seaborn as sns
import math
import scipy
# %%
data = pd.read_csv('MaleFemalePheno.csv')
# %%
H... |
682d47b5832707a2d6c3cd017290afb8b2e58b6045af9591f405029ca4a63e30 | Jupyter | 11,627 | 273 | # %%
import os
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import warnings
from rdkit import Chem
from rdkit import RDLogger
from rdkit.Chem.Draw import IPythonConsole
from rdkit.Chem.Draw import MolsToGr... |
b10d9a888e99993b153726d007cfb1b374d4329b57d779ffd9555b8f74dad2d4 | Jupyter | 11,669 | 279 | # %%
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'... |
cd419fb10b29984ff7d1c5f776153159b50b8a9d6bc8e0fb3c31d15ace003bf2 | Jupyter | 11,742 | 477 | # %%
import os
import numpy as np
import scipy.stats as st
import pandas as pd
import scikit_posthocs
import iqplot
import bokeh.io
import bokeh.plotting
import bokeh.layouts
bokeh.io.output_notebook()
# %% [markdown]
# ## Exploratory Data Analysis
# %% [markdown]
# 1. Uploading the whole excel file to read from al... |
609d75d606e32a6f19ede9b82c0b540e01c6529d5f2843d3ecb12863c899c19a | Jupyter | 11,752 | 334 | # %%
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/"
# %% [markdown]
# ## PubChem
# %%
import pandas as pd
Pubchem_Syn_fil... |
65c734daf0828357265923e29d7790785d24286638509560303900b78a56f649 | Jupyter | 11,776 | 283 | # %% [markdown]
# # The Recording Container: Cedalion's main data structure and a guide to indexing
#
# This example notebook introduces the main data classes used by cedalion, and provides examples of how to access and index them.
#
# ## Overview
#
# **The class `cedalion.dataclasses.Recording` is Cedalion's main... |
672edf6384dff0d11380b4cc9b80585b2f108d9e4963ec8ddf3a19b49d1a8427 | Jupyter | 11,789 | 417 | # %% [markdown]
# # Disease ↔ Phenotype Relation-Wise Merge
#
# Merges Disease–Phenotype triples from Monarch and CrossBAR; resolves disease names
# via DO/MESH; deduplicates by `(head, relation, tail)`; and saves the result.
# %% [markdown]
# ## 0. Configuration
# %%
import pandas as pd
import numpy as np
BASE_DIR... |
2527d4d576bc7ce4faa0995a4bb7d360610542b346a3a97741316da3cd491683 | Jupyter | 11,790 | 257 | # %% [markdown]
# # Figures 2 & 3: Relationship Between Dendritic and Somatic Spikes
#
# 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).
#
# ## Background
# Dendritic spikes... |
26df637430dcfc43d0588b05eba2c31ed6054a1794197d57598f060a9e2a2eda | Jupyter | 11,839 | 263 | # %% [markdown]
# # Portfolio Optimization
#
# In this tutorial, we demonstrate the transformation of financial portfolio optimization into a quadratic unconstrained binary optimization (QUBO) problem. Subsequently, we employ the Quantum Approximate Optimization Algorithm (QAOA) to solve it. We will conduct a comparat... |
d93cd1ab434dd3049506b2f97473badd6c9dd8599a098564901b593f8777da1f | Jupyter | 11,852 | 322 | # %%
import numpy as np
import sklearn as sk
# from skopt import gp_minimize, forest_minimize
from src.layers import padding as pad_utils
from src.spatial_attn_lightning import BinauralAttentionModule
import yaml
# %% [markdown]
# # Second pass architecture search using v10 dataset (final version)
#
# This will use... |
6c486d6241b1cde06a58e5301a53a0bd31f7fe6ce9bc2bb7d47a9bf90a613848 | Jupyter | 11,855 | 272 | # %% [markdown]
# # Distributed Circuit Simulation and TensorNetwork Contraction
#
# ## Overview
#
# Simulating large quantum circuits or computing expectation values for complex Hamiltonians often involves contracting a massive tensor network. The computational cost (both time and memory) of this contraction can be ... |
943383b5b67821277bed9c876832242039f94c453647495b046c7c9bede26c91 | Jupyter | 11,866 | 325 | # %% [markdown]
# <a href="https://colab.research.google.com/github/sokrypton/af2bind/blob/main/af2bind_experimental.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# ### AF2BIND: Prediction of ligand-binding sites using AlphaFold2
#... |
bc3583a9f1001c8820f5495595d16e82a741493889da4043012d8b3b94071373 | Jupyter | 11,905 | 270 | # %% [markdown]
# # Figure 9: Phase-Dependent Effects of Gamma and Beta Bursts on Dendritic Spikes
#
# This notebook analyzes how oscillatory bursts of rhythmic inhibition affect dendritic spikes and action potentials. Based on Headley et al. (2026), *Spatially targeted inhibitory rhythms differentially affect neurona... |
072556c4a9d7c30d8d6b10abbe31376facde4b5726b8480c85542c0530a42e43 | Jupyter | 11,935 | 183 | # %% [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... |
974050eef50ec0be8f501e85b7873b2ca05bab6903ae4487d14962291dfc6189 | Jupyter | 11,948 | 313 | # %% [markdown]
# # Zebrafish Gene–Gene (STRING) — Relation-Wise KG Triple Construction
#
# ## Purpose
#
# This notebook processes **Protein–Protein interaction data** from the STRING database for Zebrafish (*Danio rerio*) and transforms it into standardized Gene–Gene relation-wise Knowledge Graph (KG) triples. Ensem... |
3351020e9e3c023a8222ff6bce031dd6769617cf79ceed67c25aa32ec19c9f3b | Jupyter | 11,964 | 479 | # %%
import os
import numpy as np
import scipy.stats as st
import pandas as pd
import scikit_posthocs
import iqplot
import bokeh.io
import bokeh.plotting
import bokeh.layouts
bokeh.io.output_notebook()
# %% [markdown]
# ## Exploratory Data Analysis
# %% [markdown]
# 1. Uploading the whole excel file to read from al... |
22c9be10656400d47a974789b41f65b1cb0241d5e2d41eb82cc89943be8c29e7 | Jupyter | 12,101 | 275 | # %% [markdown]
# # CellChat for identifying pathways enriched in each niche
# #### R script
# %%
library(data.table)
library(randomcoloR)
library(reshape2)
library(stringr)
n <- 25
palette <- distinctColorPalette(n)
n <- 65
palette_65 <- distinctColorPalette(n)
library('scales')
library(Seurat)
library(Matrix)
li... |
ea8a4891661a2080d027bf49c0313cad0ffb22aed6218fc2d7abaa7a9b09c766 | Jupyter | 12,185 | 228 | # %% [markdown]
# # Figure 6 Supplement 1: Phase-dependent effects on somatic excitability with reversed rhythm locations
#
# This analysis examines action potential threshold and membrane voltage modulation when the spatial targeting of beta and gamma rhythmic inhibition is reversed. Beta rhythmic inhibition (16 Hz) ... |
84904ffb4d1cc050ef5cf87366e1197f115a6137806e7ce056b8012e6f559054 | Jupyter | 12,188 | 380 | # %% [markdown]
# Generate examples:
# * of spike trains with varying firing rate, excitation strength and intrinsic timescale
# * of trials
# %%
import numpy as np
import pandas as pd
import pickle
from isttc.scripts.cfg_global import project_folder_path
from isttc.spike_utils import simulate_hawkes_thinning, get_tr... |
f51c52911a334a83eadbfc51ddd67dee9bc3ba55ca3324cc404a6831717edb36 | Jupyter | 12,191 | 432 | # %% [markdown]
# # GLM Fingertapping Example
# %%
import matplotlib.pyplot as p
import numpy as np
import pandas as pd
import xarray as xr
import cedalion
import cedalion.data
import cedalion.io
import cedalion.models.glm as glm
import cedalion.nirs
import cedalion.vis.blocks as vbx
import cedalion.vis.anatomy
impor... |
9cdd6b582b7f790d8940d14d16fcc8e57a9af6067c35f3b9d7ca98aafd4d3ade | Jupyter | 12,220 | 474 | # %%
import matplotlib.pyplot as plt
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 tensorflow as tf
import os
os.environ["CUDA_VISIBLE_DEVICES"]="1"
#tf.enable_eager_execution()
sns.set(style='white', font='sans-serif', ... |
5977b58cf79354d855ebd41c68d1a1ca3c99c13e786d5f172ab544f6afc652c9 | Jupyter | 12,257 | 312 | # %%
import sys
sys.path.append('../')
import numpy as np
import matplotlib.pyplot as plt
import scipy as sp
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-9b4f... |
c53f6b10c0c2eea31de02d750f8bab73e69f44c71918da0424aab8e055405390 | Jupyter | 12,283 | 365 | # %%
# %% [markdown]
# # Gene ↔ ChemicalEntity Relation-Wise Merge
#
# Merges Gene–Chemical triples from DRKG, PrimeKG, PharmKG, TARKG, Harmonizome, and hald;
# resolves chemical tail names via PubChem (and DrugBank for DB-prefixed IDs) and gene head
# names via NCBI; deduplicates by `(head, relation, tail)`; and sa... |
fcc1ccdea0c063c1645d98eb251089bad59a19621e3f1faeb48afba311bacd75 | Jupyter | 12,310 | 299 | # %% [markdown]
# # Calculate phase dependent modulation of AP threshold poisson excitation and rhythmic inhibition with reversed targeting of inhibitory rhythms to dendrites
#
# The simulations had either:
# 1. Rhythmic inhibition at the dendrites (64 Hz) or soma (16 Hz)
# 2. Poisson excitation at the soma and dend... |
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