sha256 stringlengths 64 64 | language stringclasses 27
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|---|---|---|---|---|
97640679f945ed3bb3e01169e229d92cf2ca0a0d17ee16071132e34ca82f4d1a | Jupyter | 7,437 | 231 | # %% [markdown]
# # 梯度计算效率比较
# %% [markdown]
# ## 概述
#
# 在本教程中，我们比较了通过 TensorCircuit 提供的自动微分框架和 Qiskit 提供的传统参数平移框架对梯度和类梯度对象（例如量子费雪信息）进行估测的效率。
# %% [markdown]
# ## 设置
#
# 我们从 Qiskit 和 TensorCircuit 导入必要的包和模块。
# %%
import time
import numpy as np
from functools import reduce
from operator import xor
from qiskit.opfl... |
db5737edb287d88a69049a8a9c642090e7b6ea7b75adb183d6190f255a6c76cc | Jupyter | 7,459 | 237 | # %% [markdown]
# # Protein ↔ Pathway Relation-Wise Merge
#
# Merges Protein–Pathway triples from CrossBAR (×2) and TARKG; fills protein head names
# from UniProt; deduplicates by `(head, relation, tail)`; and saves the result.
# %% [markdown]
# ## 0. Configuration
# %%
import os
import pandas as pd
import numpy as ... |
4dc7fd48d02f21a1f0f894bad1c707055fe4ef840da65871bf132b69f74f917f | Jupyter | 7,471 | 264 | # %% [markdown]
# # QML in PyTorch
# %% [markdown]
# ## Overview
#
# In this tutorial, we show the MNIST binary classification QML example with the same setup as [mnist_qml](mnist_qml.ipynb). This time, we use the PyTorch machine learning pipeline to build the QML model.
# Again, this note is not about the best QML p... |
3a7a2e01d44f97423162d09e063766576cab2747ddc8f529cc5ca429b84e259c | Jupyter | 7,475 | 299 | # %%
import pandas as pd
import numpy as np
import glob
import os
from tqdm import tqdm
# %%
!pwd
# %% [markdown]
# %%
# Monarch/Monarch_final/Mouse/Gene_Mouse_Phenotype.csv
# hmdhp/hmdhp_MOUSE_GENE_PHENOTYPE.csv
# mgi_do/mgido_MOUSE_GENE_PHENOTYPE.csv
# %%
BASE_DIR = '/storage/Arushi/090526_EvoAge/kg_formatio... |
2c8c8b737874c8c95b80e7d7fde3f5b8e73e9e26e229dbaab64e31e0edf314ae | Jupyter | 7,485 | 220 | # %% [markdown]
# # Regression 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.projects... |
c18c48e1d22de831f16f7c1c96446e214a2d04b9e5f845b7c2edb17c5cc899cc | Jupyter | 7,490 | 292 | # %%
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... |
35f6c234120b73cd172e0476285852976a989908fabb26797fde58eb6108f620 | Jupyter | 7,517 | 237 | # %%
import pandas as pd
import numpy as np
import re
# %%
!pwd
# %% [markdown]
# # Mappings
# %%
your_path_here = '/storage/Arushi/090526_EvoAge/kg_formation/data_collection/'
# %%
# %%
Pubchem = pd.read_pickle(f'{your_path_here}databases_for_mapping/pubchem/combined_df.pkl')
# %%
## Gprofiler_protein to gene
... |
48bea27d57f5b211e02f0c79af6f285da30c5749353a18abebf24c88af1b1e26 | Jupyter | 7,530 | 272 | # %% [markdown]
# # multi-task alternate training strategy--clearence prediction
# %%
from molmap import model as molmodel
import molmap
import matplotlib.pyplot as plt
import pandas as pd
from tqdm import tqdm
from joblib import load, dump
tqdm.pandas(ascii=True)
import numpy as np
import tensorflow as tf
import o... |
fab7647e8bb2fbeb7a9729fa37e156bd36ce41f6b1d6ae120fbc9b631cc93301 | Jupyter | 7,533 | 230 | # %% [markdown]
# # CellularComponent → CellularComponent Relation Pipeline
#
# Builds a unified, deduplicated edge table for the **CellularComponent–CellularComponent** relation.
#
# **Output schema:** `head | relation | tail | head_type | relation_type | tail_type | kg_source | head_id_is | tail_id_is | head_detail... |
6ac6b9596419db0a2d808e9e55c45edf3b2acad24d3fbb1229d6cf32e11931c3 | Jupyter | 7,572 | 199 | # %% [markdown]
# # Build surface
# ###### Last updated 2024-04-24 (ASH)
# ###### Updated by ETU on 2024-05-06
# This notebook walks though constructing a surface using a single long chain of bead type "A". This can be used to build homogenous surfaces in PIMMS.
#
# ### Approach
# Broadly, the approach here is to:
#
... |
ea6a0862b6d58a0a749e24cc1a41619b16c9ec77203d8f02721231b35721971b | Jupyter | 7,572 | 304 | # %% [markdown]
# # Phenotype ↔ Disease Relation-Wise Merge
#
# Merges Phenotype–Disease triples from PrimeKG; resolves disease tail names via DO;
# deduplicates by `(head, relation, tail)`; and saves the result.
#
# %% [markdown]
# ## 0. Configuration
# %%
import os
import pandas as pd
BASE_DIR = '/storage/Arush... |
735a56ec261d61ce638390c4f326dd4570792e9ebb4b442fa599c193dffa8109 | Jupyter | 7,606 | 234 | # %% [markdown]
# # Evaluation on Pauli String Sum
# %% [markdown]
# ## Overview
# %% [markdown]
# We need to evaluate the sum of many Pauli string terms on the circuit in various quantum algorithms, the ground state preparation of a Hamiltonian $H$ in VQE is a typical example.
# We need to calculate the expectation ... |
763a8d16949924a4a643613dc47ea37349d622676d2e16c212941ebb5c47e73a | Jupyter | 7,623 | 231 | # %% [markdown]
# # Gradient Evaluation Efficiency Comparison
# %% [markdown]
# ## Overview
#
# In this tutorial, we compare the efficiency of gradient and gradient-like object (such as quantum Fisher information) evaluation via automatical differentiation framework provided by TensorCircuit and the traditional param... |
eac79fc9c4a831e352ef581bfe702e6e9a5cc9abb135848abcf99a181574df05 | Jupyter | 7,633 | 275 | # %% [markdown]
# Spike trains are generated using univariate, self‐exciting Hawkes point process with an exponential kernel, using Ogata’s thinning algorithm.
# %%
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_haw... |
ae4ab0358392c6f342bea724698968f46b823624df7e5b31c01efd8d4879ceb6 | Jupyter | 7,650 | 221 | # %%
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Feb 5 14:35:35 2025
laminar analysis
@author: yuhui
"""
import os
import glob
import numpy as np
import matplotlib.pyplot as plt
import scipy.stats as stats
from statsmodels.stats.multitest import multipletests
import pandas as pd
from statsmodels.... |
6de755ca7ce9bac5e33db5c171cfa55b6ef45e3c06bc065e54ed93915cd8230b | Jupyter | 7,651 | 289 | # %%
import cedalion.dot
from pathlib import Path
import trimesh
import pymeshlab
import pyvista as pv
import cedalion.vis.blocks as vbx
import numpy as np
import nibabel.freesurfer
import nibabel
from collections import Counter
import matplotlib.pyplot as p
import cedalion.dataclasses as cdc
import cedalion.geometry.s... |
2049c052300b21c7127c362a347dbc6082e2b17bf29648273927b47a8c0e2b06 | Jupyter | 7,667 | 226 | # %% [markdown]
# # Phenotype ↔ Phenotype Relation-Wise Merge
#
# Merges Phenotype–Phenotype triples from PrimeKG, BOCK, and TARKG;
# deduplicates by `(head, relation, tail)`; and saves the result.
# %% [markdown]
# ## 0. Configuration
# %%
import os
import pandas as pd
import numpy as np
BASE_DIR = '/storage/Aru... |
a5759aef0720943f2259e0c0b0bb542ba852e5853a083ce720205be49e1acf83 | Jupyter | 7,678 | 246 | # %% [markdown]
# # Probing Many-body Localization by Excited-state VQE
# %% [markdown]
# ## Overview
# %% [markdown]
# This tutorial introduces a new method to probe many-body localization (MBL) by excited-state VQE. The model hosting MBL transition considered here is the interacting Aubry-Andr$\acute{e}$ model whic... |
d91aee70a7fe3eee713efed28c3baabfc87753f7d15da7f79e9044b3db1aa96f | Jupyter | 7,746 | 206 | # %%
from neurovelo.train import Trainer
from neurovelo.utils import ModelAnalyzer,latent_data,evaluate,decode_gene_velocity,vector_fields_similarity
import scvelo as scv
import scanpy as sc
import glob
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
# %%
adata = scv.datasets.gastrulation_eryt... |
50f325bad7cb90ee9c285c0935391e8ca29cf587296c9e451c474c9412bf62c0 | Jupyter | 7,751 | 281 | # %% [markdown]
# # Set Up
# %%
#import packages
import pandas as pd
from nilearn import surface
import numpy as np
import os
import glob
import usefulFunctions as uf
import matplotlib.pyplot as plt
import seaborn as sns
from matplotlib.patches import PathPatch
from matplotlib.collections import PatchCollection
# %... |
1edcad55ad52971eb9d3dcd369901651526fb393728c2a65fadad7ed468dbc9f | Jupyter | 7,768 | 251 | # %%
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
from sklearn.decomposition import PCA
from scipy.io import loadmat
from scipy.sparse import vstack
import os
from scipy.optimize import curve_fit
# %%
# Initialize lists to store PCA results
cum_vars = []
weights = []
# L... |
1309d64f9ed246d8a8ff5e97aeff74f179134c1ec890d6ab9a33ca392868d9d5 | Jupyter | 7,777 | 204 | # %% [markdown]
# # 分子上的变分量子本征求解器 (VQE)
# %% [markdown]
# ## 概述
#
# VQE 是一种变分算法，用于计算满足 $H \left|\psi_g\right> =E_g\left|\psi_g\right>$ 的给定哈密顿 H 的基态，我们称之为 $\psi_g$。对于任意归一化波函数 $\psi_f$，期望值 $\left<\psi_f|H|\psi_f \right>$ 总是不低于基态能量，除非 $\psi_f = \psi_g$ (这里我们假设基态没有简并）。基于这个事实，如果我们使用参数化波函数 $\psi_\theta$，例如由具有参数 $\theta$ 的... |
79b830639a94a8cf2b28cc832b38d1bcc46aca533917587445d20c4581ee4975 | Jupyter | 7,783 | 234 | # %% [markdown]
# # <center> Neural Network encoded Variational Quantum Eigensolver (NN-VQE)
# %% [markdown]
# ## Overview
#
# In this tutorial, we'll show you a general framework called neural network encoded variational quantum algorithms (NN-VQAs) with TensorCircuit. NN-VQA feeds input (such as parameters of a Ham... |
72077dad9999aab437024c03ef10be56aa8a9a10df9ed8cc6b243d0237c4e07d | Jupyter | 7,801 | 254 | # %%
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... |
112dd8de0b73a98809f381d2179cffa655b59bbb4f9821c59bc8c5dfc6dcfa68 | Jupyter | 7,809 | 246 | # %% [markdown]
# # MolecularFunction_MolecularFunction
# %%
import pandas as pd
import numpy as np
# %% [markdown]
# # Monarch KG
# %%
Monarch_MolFxn_MolFxn = pd.read_csv('/16Tbdrive2/arushis/070225/Data_compile/Monarch/Processed/Human/Human_MolecularActivity_MolecularActivity_Monarch.csv')
Monarch_MolFxn_MolFxn.co... |
e2a36df37398bd8716f2b034f9c1f70b41169a913583dc66c1274e405f99b04a | Jupyter | 7,811 | 234 | # %% [markdown]
# # <center> 神经网络编码的变分量子本征值求解器（NN-VQE）
# %% [markdown]
# ## 概述
#
# 在本教程中，我们将使用TensorCircuit展示一个量子计算通用框架——神经网络编码的变分量子算法（neural network encoded variational quantum algorithms，NN-VQAs）。NN-VQA将一个给定问题的参量（如哈密顿量的参数）作为神经网络的输入，并使用其输出来参数化标准的变分量子算法（variational quantum algorithms，VQAs）的线路拟设（ansatz circuit）。在本文中，我... |
c1fdfe121fe169d9a7481f1679107980b6da451f9e551f808d48bf225358d67d | Jupyter | 7,835 | 180 | # %% [markdown]
# # TensorCircuit SDK with TianYan Quantum Cloud
#
# This tutorial follows the provider-agnostic TensorCircuit cloud workflow and uses the shared `tc.cloud.apis` entry point to connect to the China Telecom TianYan quantum computing platform. It covers device discovery, task lifecycle management, cloud ... |
f0165e8076e5190ea1e678bc2adba435576052bbe7a1ec4b9e5246aefd10e23e | Jupyter | 7,842 | 156 | # %% [markdown]
# ```{currentmodule} optimap
# ```
# %%
from optimap.utils import jupyter_render_animation as render
# %% [markdown]
# ```{tip}
# Download this tutorial as a {download}`Jupyter notebook <converted/mask.ipynb>`, or as a {download}`python script <converted/mask.py>` with code cells. We highly recommend ... |
94637b3615318a7cf1dcd4c55a87d03496cbe016ca915d9c8557f8526e1ad518 | Jupyter | 7,878 | 217 | # %%
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Control‐ROI analysis for benson14_v1 across three contrasts:
• Stake_comp(Low‐ vs High‐stake)
• GL_comp (A GAIN sh vs A LOSS sh)
• RT_comp (Fast vs Slow RT)
For each: compute mean±SEM, run paired t-test, print stats, and plot as a paired bar.
fig s4 in... |
dc77d3bce6d90414d37cb7f24e23c02f9c4e618eaa7797d2f6866e6772e38423 | Jupyter | 7,927 | 200 | # %% [markdown]
# # 密度矩阵和混态演化
# %% [markdown]
# ## 概述
# TensorCircuit 提供了两种含噪声、混态量子演化的方法。
# $n$ 量子比特的全密度矩阵模拟是通过使用 ``tc.DMCircuit(n)`` 提供的，然后将量子操作——包括幺正门以及由 Kraus 算子指定的一般量子操作——添加到电路中。
# 相对于通过 ``tc.Circuit`` 对 $n$ 个量子比特进行纯态模拟，全密度矩阵模拟会占据两倍内存，因此可模拟的最大系统大小将是纯态情况下可以模拟的一半。
# 内存需求较小的选项是使用标准的 ``tc.Circuit(n)`` 对象并通过蒙特卡罗轨迹方法随机模... |
2c1bb6f1543472d21af4a470cfc258e0518790f92b6d8233102788d22e7faa99 | Jupyter | 7,936 | 293 | # %% [markdown]
# # QML on MNIST Classification
# %% [markdown]
# ## Overview
#
# The aim of this tutorial is not about the machine learning perspective on better design of QML method for MNIST classification. Instead, we use a simple parameterized circuit and demonstrate the QML-related technical ingredients of ``te... |
d4bc3292058297f8806530e03610204c976882ac737a1f44d63c0c4e1084ef35 | Jupyter | 7,940 | 321 | # %%
import sys
sys.path.append('/Users/midani/OneDrive/proj/leap/manuscript')
import os
import numpy as np
import pandas as pd
import repo_code.lib_aux as lib_aux
subsetDf = lib_aux.subsetDf
remove_zero_cols = lib_aux.remove_zero_cols
# %%
path_output = "../output"
# %%
def norm_log2_data(df):
"""Normalizatio... |
8a9f3758834083c8a760b752f52db4fdfeec585be68beae3b7c7b65806117e5b | Jupyter | 7,972 | 229 | # %%
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Feb 5 14:35:35 2025
@author: yuhui
"""
import os
import glob
import numpy as np
import matplotlib.pyplot as plt
import scipy.stats as stats
from statsmodels.stats.multitest import multipletests
import pandas as pd
from statsmodels.stats.anova impor... |
17351bdd9cb36ea88b61870f5b4ee4905e943ec9dd771f77584b993599500136 | Jupyter | 8,000 | 241 | # %% [markdown]
# # Set Up
# %%
# import necessary packages
import glob
import os
import usefulFunctions as uf
import matplotlib.pyplot as plt
import numpy as np
# %% [markdown]
# # Plot overlap of ALL labels
# %%
#set paths
base_dir = f'{os.path.dirname(os.getcwd())}/'
roi_dir = f'{base_dir}data/labels/average/allD... |
2f755a4a0d1c4eca6d79dff28cc7bfb13d188b770d2cadfb5870f18a1c4f364d | Jupyter | 8,003 | 292 | # %% [markdown]
# # MNIST 分类的量子机器学习
# %% [markdown]
# ## 概述
#
# 本教程的目的不是从机器学习的角度来更好地设计用于 MNIST 分类的量子机器学习方法。相反，我们使用一个简单的参数化电路，并演示 ``tensorcircuit``的量子机器学习相关的技术组件。此外，这个 jupyter notebook 绝不代表是量子机器学习的好的实践。
# [WIP note]
# %% [markdown]
# ## 设置
# %%
from functools import partial
import numpy as np
import tensorflow as tf... |
17eb918eb563b1f6f66d9f6f1fc70ab1e698b23ba816b60666e3c64642d821db | Jupyter | 8,005 | 269 | # %%
import pandas as pd
import numpy as np
import glob
import os
from tqdm import tqdm
# %%
!pwd
# %%
BASE_DIR = '/storage/Arushi/090526_EvoAge/kg_formation/'
MAPPING_DIR = BASE_DIR + 'data_collection/databases_for_mapping/'
PROC_DIR = BASE_DIR + 'processed_data/'
# ── Required output schema ─────────────... |
9732f1bdfe9de3d1668bf986058bab3ddbb73c61ffb5b0d5db3aba2d5cd528ac | Jupyter | 8,019 | 273 | # %%
import pandas as pd
import numpy as np
import glob
import os
from tqdm import tqdm
# %%
!pwd
# %%
BASE_DIR = '/storage/Arushi/090526_EvoAge/kg_formation/'
MAPPING_DIR = BASE_DIR + 'data_collection/databases_for_mapping/'
PROC_DIR = BASE_DIR + 'processed_data/'
# ── Required output schema ─────────────... |
2b41a931c7ec74d06c0a7385bccae7d8dad31f65f25280ee16faae994d458cbe | Jupyter | 8,024 | 223 | # %%
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Feb 5 14:35:35 2025
Compare signal changes between gain and loss in SH
fig 4 in the manuscript
@author: yuhui
"""
import os
import numpy as np
import scipy.stats as stats
import pandas as pd
import matplotlib.pyplot as plt
import glob
from statsmod... |
b013db8625b0677379b381f0097eecb75d71297564a76711f5fc064ee4ef5253 | Jupyter | 8,038 | 321 | # %%
import sys
sys.path.append("../fractal")
import fractal
import utils
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
# %% [markdown]
# # Sanity check
# %%
xs=utils.gen_koch(6,shape='curve')
scale=np.mean(np.linalg.norm(xs[:-1,:]-xs[1:,:],axis=1))
# %% [markdown]
# oversampling method retu... |
3962ba6698e6464b519d40308b693ae854439cde9c7eea759ccd272be4732e53 | Jupyter | 8,055 | 240 | # %% [markdown]
# # Fermion Gaussian State (FGS) Simulator
#
# This tutorial demonstrates how to use the Fermion Gaussian State (FGS) simulator implemented in `tensorcircuit-ng`. The FGS simulator allows for efficient simulation of non-interacting fermionic systems, which is particularly useful for studying free fermi... |
9f73d9f055f5b78f16051c7a3a867f7a6756ba108129b374d9b96faf1d61227b | Jupyter | 8,074 | 276 | # %%
import pandas as pd
import numpy as np
import glob
import os
from tqdm import tqdm
# %%
!pwd
# %%
BASE_DIR = '/storage/Arushi/090526_EvoAge/kg_formation/'
MAPPING_DIR = BASE_DIR + 'data_collection/databases_for_mapping/'
PROC_DIR = BASE_DIR + 'processed_data/'
# ── Required output schema ─────────────... |
8de42cfe1202679a96074502b4babef1f21f3148930511af9ec051105650a1d0 | Jupyter | 8,180 | 183 | # %% [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
import matplotlib.pyplot as plt
import seaborn as sns
from isttc.scripts.cfg_global impo... |
df4cfebdc7e5b6cda524ff52155dab2b66635a053077ac5f77ea0c231adef5ea | Jupyter | 8,181 | 313 | # %%
import pandas as pd
import numpy as np
import glob
import os
from tqdm import tqdm
# %%
!pwd
# %%
BASE_DIR = '/storage/Arushi/090526_EvoAge/kg_formation/'
MAPPING_DIR = BASE_DIR + 'data_collection/databases_for_mapping/'
PROC_DIR = BASE_DIR + 'processed_data/'
# ── Required output schema ─────────────... |
a8069bbb1c3d5923ba0b98c1684048f2f60c3d41bafc895225285f81bee72bb4 | Jupyter | 8,192 | 282 | # %%
# Load packages for data analysis
import pandas as pd
import numpy as np
from tableone import TableOne
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 ... |
8eb19231eff34dd5baf89e0b0312b72271e36ddd19649e0eebabaeb3c41ebab4 | Jupyter | 8,217 | 235 | # %%
from Bio.Seq import Seq
from Bio.Alphabet import IUPAC
from Bio import pairwise2
from Bio.pairwise2 import format_alignment
import numpy as np
import regex
import pandas
from progress.bar import Bar
# %% [markdown]
# # NEED TO NOTE OFF OF THE RIP, THIS PROGRAM IS CURRENTLY HEAVILY CUSTOMIZED FOR ADIMAB PNAS PAPER... |
a5e9bfe80766bcb4db1e8a691bbde38d715d98f981e790456bdc3172e7a9a9df | Jupyter | 8,217 | 295 | # %%
import scanpy as sc
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import anndata
# %% [markdown]
# # Brain
# %%
pd_atlas = sc.read_h5ad('/Pech_Janssens_PD_atlas/scRNA_PD_annotated.h5ad')
# %%
ctrl_young_raw = pd_atlas[(pd_atlas.obs.genotype == 'W1118CS') & (pd_atla... |
6eec5197256b048ebc0b15607cd8ddc2c356c3389dc67a83f32989299105451c | Jupyter | 8,219 | 317 | # %% [markdown]
# ### Import all required libraries and set constants for server connection
# %%
import os, sys
import numpy as np
import pickle
import time
import matplotlib.pyplot as plt
while os.getcwd()[-6:] != 'inkube':
os.chdir('..')
print(os.getcwd())
sys.path.insert(0, './Communication')
from ControlPort i... |
7ecaa5e007894c2a6b379d49da29a1e07aed7662bb9f3fd6ca37937b4c2be3d0 | Jupyter | 8,228 | 176 | # %% [markdown]
# ```{currentmodule} optimap
# ```
# %%
from optimap.utils import jupyter_render_animation as render
# %% [markdown]
# ```{tip}
# Download this tutorial as a {download}`Jupyter notebook <converted/activation.ipynb>`, or a {download}`python script <converted/activation.py>` with code cells. We highly r... |
e8fec2fe7efbba54cd935a544cd7b4e4f649000f2b8e20abbf2b54308683fcab | Jupyter | 8,260 | 275 | # %% [markdown]
# # Scaling and Transforming Head Models
# %%
# 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" (def... |
f43297fe1c5f8468fa421cf421f91a34727b44b580ffe8a5e21ebab37ffac496 | Jupyter | 8,297 | 251 | # %% [markdown]
# # C. elegans Gene–Gene — Relation-Wise KG Triple Construction
#
# ## Purpose
#
# This notebook processes Gene–Gene interaction data for *C. elegans* from **two sources** and combines them into standardized relation-wise Knowledge Graph (KG) triples:
# 1. **STRING** — Protein–protein interactions (ma... |
34de319311e79d18182465870a5f7205b97fff76ff3a44aa6f0343141ad0ed17 | Jupyter | 8,324 | 211 | # %% [markdown]
# # Calculate phase dependent modulation of AP threshold 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 voltag... |
070b283e88f0325f72e9f0053df2eb7164bc17f46eab1a14eb90bc2164a34477 | Jupyter | 8,331 | 256 | # %%
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Feb 5 14:35:35 2025
Compare stake difference
fig s3 in the manuscript
@author: yuhui
"""
import os
import numpy as np
import scipy.stats as stats
import pandas as pd
import matplotlib
# matplotlib.use('Agg') # <- force non‐interactive, file‐outp... |
8a5906c2b4318297f57f7ce23d534d42557df1243b8ca3a3dc94fdee1e88bb94 | Jupyter | 8,334 | 249 | # %% [markdown]
# # Benchmark XGBoost explanations
# %% [markdown]
# This notebook compares several different explanation methods when applied to XGBoost models. These methods are compared across many different evaluation metrics. Explanation error is the primary metric we sort by, but we also compare across many othe... |
f4bed557b0e0b3c824ade0aaec3d038823f10add58daba1e0ee3eaef53f9a3ae | Jupyter | 8,353 | 324 | # %% [markdown]
# # 第一章 线性代数 (Linear Algebra)
# %%
# 本章代码通过numpy展示，导入numpy库
import numpy as np
from scipy import linalg as la
# %% [markdown]
# ## 1 向量
# ### 1.1 向量 （vector）
#
# 具有大小和方向的量，用 $\vert v\rangle$ 表示列向量，$\langle v\vert$ 表示行向量。
# %%
# 下面是一个二维向量的例子
print("行向量:")
print(np.array([2, 3]))
print()
print("列向量... |
db9d1cb8fe5f5f55623d90fd527a775cd309cb3e8c82b8d2934b6525fbaabac9 | Jupyter | 8,366 | 189 | # %% [markdown]
# # Simulation of Clifford Circuits
# %% [markdown]
# ## Overview
#
# Simulating quantum circuits on a classical computer is fundamentally hard. The memory required to store a quantum state vector for $n$ qubits grows as $2^n$, an exponential scaling that quickly becomes intractable. However, a specia... |
2e93109195ec3e75f614764196a51dd77a84233af7944662a233bdb2617bfd66 | Jupyter | 8,374 | 203 | # %% [markdown]
# # Variational Quantum Eigensolver (VQE) on Molecules
# %% [markdown]
# ## Overview
#
# VQE is a variational algorithm for calculating the ground state of some given hamiltonian H which we call it $\psi_g$ that satisfied $H \left|\psi_g\right> =E_g\left|\psi_g\right>$. For an arbitrary normalized wav... |
cb54955f7e06306f3ae207428895db9017114a16831cf57dcce38584e80e84e9 | Jupyter | 8,381 | 122 | # %% [markdown]
# # `text` plot
#
# This notebook is designed to demonstrate (and so document) how to use the `shap.plots.text` function. It uses a distilled PyTorch BERT model from the transformers package to do sentiment analysis of IMDB movie reviews.
#
# Note that the prediction function we define takes a list of... |
4f6eb86d913799e6f0e6add28a9dc89d09f40b8d116228238fe63270e30229ad | Jupyter | 8,465 | 215 | # %% [markdown]
# Reproduce Fig2: Pearson based and STTC based area ACFs and timescales.
# %%
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 matplotlib.pyplot as plt
import seaborn as sns
from isttc.scrip... |
554ec041b7457ae468d08a3dbb84ae3da44ebd0177f02d0f2cbaefdd62c3ff2f | Jupyter | 8,475 | 223 | # %%
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'... |
cbfbaed9a2fddaeae12e20e19cee94a8a86b3867a734307bb75e407f4625a4ee | Jupyter | 8,480 | 278 | # %%
import os
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
import pickle
from tqdm import tnrange
from tqdm import tqdm
import seaborn as sns
from sc... |
f11d29ec121fa08861f02c3923a015ecb667064e732f26287269cdc238095953 | Jupyter | 8,482 | 284 | # %%
import pandas as pd
from collections import defaultdict
# Load the DataFrame
dir_ids_seq = 'data_ids_seq/mirna_mirna_sequences_WITHIDS.pkl'
df = pd.read_pickle(dir_ids_seq)
# Dictionary: sequence → list of {"ID": ..., "Category": ...}
dict_seq_to_id_type = defaultdict(list)
# Iterate over both x and y columns
f... |
76c6ea960c21cb5677c1121eccedc7d7298ffd2d60f6c9cc53d97ac86a6e9a2a | Jupyter | 8,488 | 235 | # %% [markdown]
# # Calculate relationship between AP and dendritic spikes with poisson excitation
#
# The simulations had either:
# 1. Poisson inhibition at the soma and dendrites
# 2. Poisson excitation at the soma and dendrites
#
# Here we calculate the spike-triggered average between dendritic spikes and action... |
785666e5c58fced49506d015364ec23e6ce0ba91241a557e0765fd86441e0827 | Jupyter | 8,512 | 246 | # %% [markdown]
# # Anatomy → Anatomy Relation Pipeline
#
# Builds a unified, deduplicated edge table for the **Anatomy–Anatomy** relation
# by ingesting processed files from multiple KG sources, normalising identifiers
# if needed, and writing the final triple table to disk.
#
# **Output schema:** `head | relati... |
28c1c5e0c9844106117def05f8032716ec82cbc1978017bd824b184a3c30a78d | Jupyter | 8,531 | 149 | # %% [markdown]
# # Demonstration of DCBC evaluation usage (Replication)
# This notebook shows an example of a Distance controlled boundary coefficient (DCBC) evaluation of a cortical parcellation using the Multi-domain task battery (MDTB) functional dataset. It replicates the results reported in the article
#
# - Zhi... |
3eafd886b6b90c6d19e84410033774d279b368a5fe3a2042f32be479f84cf173 | Jupyter | 8,577 | 189 | # %% [markdown]
# # Density Matrix and Mixed State Evolution
# %% [markdown]
# ## Overview
#
# TensorCircuit provides two methods of simulating noisy, mixed state quantum evolution. Full density matrix simulation of $n$ qubits is provided by using ``tc.DMCircuit(n)``, and then adding quantum operations -- both unita... |
732332d937883db3d908a3e58da7289779b8c87768db47c8249fe76a7f8269ca | Jupyter | 8,605 | 259 | # %% [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... |
b2859b11232b8674329fb77fe16f7296b92393907910ca789fb8faf2585c833d | Jupyter | 8,628 | 237 | # %% [markdown]
# Modified "Tutorial 1: How to use abcTau package to fit autocorrelations or PSDs" from the abcTau repo
# %%
import matplotlib.pyplot as plt
import seaborn as sns
import pickle
import numpy as np
import pandas as pd
from scipy import stats
# add the path to the abcTau package
import sys
#sys.path.ap... |
54ad69c6d20a319a24ee6eab630034cc3080e66ec672edd5ec5217dc875bbdcf | Jupyter | 8,650 | 284 | # %% [markdown]
# # 可微量子架构搜索
# %% [markdown]
# ## 概述
#
# 本教程演示了如何利用 TensorCircuit 提供的高级计算功能，例如 ``jit`` 和 ``vmap`` 来超级有效地模拟可微量子架构搜索（DQAS）算法，其中具有不同结构的量子电路的集合可以同时编译模拟。
# [WIP note]
# %% [markdown]
# ## 设置
# %%
import numpy as np
import tensorcircuit as tc
import tensorflow as tf
# %%
K = tc.set_backend("tensorflow")
... |
d2bd46f18fd71c2cf046bba83fc865993650b7b907439fa488f34dcf7f042b0f | Jupyter | 8,662 | 250 | # %%
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.cross_decomposition import PLSRegression
# %%
data = pd.read_csv('Male+Female.csv') #Load Data
filename = 'GroupRegulation_male.csv'
# %%
#define inputs and outputs for pls
inputs=['HDAC1','HDAC2','HDAC3','HDAC4','HDAC5','HDAC6',... |
5f56dc8786d389f58f77efd8a0e5c35bbf2af3afadd61682ea45a7342a1f24fd | Jupyter | 8,672 | 217 | # %% [markdown]
# Plot area taus for all sampling iterations.
# %%
import numpy as np
import pandas as pd
import pickle
import json
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib.ticker import FixedLocator
import seaborn as sns
from isttc.scripts.cfg_global import project_folder_path
from is... |
b1320dddfc9b728a12cdfe8149582ce0dc4362151b105c4b3eb90ec345ca4a15 | Jupyter | 8,678 | 251 | # %% [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... |
32dfe0ac60476d59289ccc156a75ec09708cf2cc4b5eba2f9c31195f504999ed | Jupyter | 8,714 | 221 | # %% [markdown]
# # Load modules
# %%
import numpy as np
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import scipy
import os
import scanpy as sc
import squidpy as sq
import scipy
from sklearn.preprocessing import LabelBinarizer
import warnings
warnings.filterwarnings... |
06c9ad25e337a7bf1b286985849b31336f97e47c0900da8d642eb038906994f6 | Jupyter | 8,744 | 310 | # %%
import nibabel as nib
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import TwoSlopeNorm
# %%
# Lista de archivos NIfTI
file_BAN_ADNI_MCI = "./BrainAgeNeXt/lrp_mean_ADNI_MCI.nii.gz"
file_BAN_ADNI_AD = "./BrainAgeNeXt/lrp_mean_ADNI_AD.nii.gz"
file_BAN_ANDI_CN = "./BrainAgeNeXt/lrp_mean_A... |
e69ffd5fd077cfdcd5847f7c69f263babd2a50cd9fb9e416fff819bf96a48f91 | Jupyter | 8,801 | 293 | # %% [markdown]
# # Gene ↔ MolecularFunction Relation-Wise Merge
#
# Merges Gene–MolecularFunction triples from Monarch, DRKG, Hetionet, BOCK, TARKG, and
# Harmonizome; resolves missing gene head names via NCBI synonyms; deduplicates by
# `(head, relation, tail)`; and saves the result.
# %% [markdown]
# ## 0. Configu... |
37ddc5eaf3b07eac7008e0b7d3e87bf13568c4be0809f9be3fa3c7f3c5a5fa70 | Jupyter | 8,802 | 249 | # %% [markdown]
# # BiologicalProcess → Gene Relation Pipeline
#
# Builds a unified, deduplicated edge table for the **BiologicalProcess–Gene** relation
# by ingesting processed files from multiple KG sources, normalising gene identifiers
# via NCBI/Ensembl mapping dictionaries, and writing the final triple table ... |
84d81c77378148ce25608fd54c72928c5d476ccfb67c4bd837e131941da2a705 | Jupyter | 8,842 | 188 | # %%
import os
import pandas as pd
import yaml
# %% [markdown]
# # Constructing single-gene spike-in simulated data
# First, run file in `src/data_processing/make_perturbed_genotype_datasets.py`. This will produce a folder containing all the perturbed dataset versions.
# Here, we build two config files so that we ca... |
82346ba7768bfb1c5d1e1102eb459e881ce72cfc48ef60c7b26064a636dc35c3 | Jupyter | 8,843 | 234 | # %%
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... |
27ebdfa816df6b074792928aa36f8e79b036ef911507aee0e40c4d1a3cd583f8 | Jupyter | 8,862 | 274 | # %% [markdown]
# # Gene–Gene Interactions — Mouse KG Processing
# **Project:** MetaboGlue / EvoAge KG | **Contributor:** Arushi
#
# **Sources processed:**
# - **File 1 (STRING):** `10090.protein.links.detailed.v12.0.txt` → Mouse Protein–Protein interactions, ENSMUSG IDs resolved to gene symbols via gProfi... |
64cec0bf7dfffe676247e97295ae506e0400368e6b7158d03f0f25596733ee4e | Jupyter | 8,895 | 233 | # %%
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
# %%
BAN_MAE_list = []
DBN_MAE_list = []
pyment_MAE_list = []
ENIGMA_MAE_list = []
BAN_ME_list = []
DBN_ME_list = []
pyment_ME_list = []
ENIGMA_ME_list = []
BAN_ASTD_list = []
DBN_ASTD_list = []
pyment_ASTD_list = []
ENIG... |
b0bc076e86c77126d86b1fcea39932472a23c1ce8fda93c2ecdf302592aa9c9e | Jupyter | 8,904 | 245 | # %% [markdown]
# # Using Convpaint & Python - Introduction
# %% [markdown]
# Here, we show you the basics of using Convpaint programmatically with Python. Concretely, we will present 3 ways to use the Convpaint GUI and/or API:
# - **a)** Using Convpaint **as a napari plugin (GUI)**, only accessing and processing ... |
dc5139c2b3be5d6f1a99cc290b654a59b8b5da2144611d7cf8db34c231f5cb1b | Jupyter | 8,922 | 285 | # %% [markdown]
# # Differentiable Quantum Architecture Search
# %% [markdown]
# ## Overview
#
# This tutorial demonstrates how to utilize the advanced computational features provided by TensorCircuit such as ``jit`` and ``vmap`` to super efficiently simulate the differentiable quantum architecture search (DQAS) algo... |
4750fd2ae2ee2591d40873631ef1e3b7a92da528ab71a83fb4fbdb4c8f23de32 | Jupyter | 8,939 | 252 | # %%
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Feb 5 14:35:35 2025
Compare signal changes between stakes
fig 1 in the manuscript
@author: yuhui
"""
import os
import numpy as np
import scipy.stats as stats
import pandas as pd
import matplotlib
# matplotlib.use('Agg') # <- force non‐interactiv... |
303d5222468f70435b7e432848e1ce018827f020a2f9d3816b34b317346a7d6c | Jupyter | 8,978 | 245 | # %% [markdown]
# # CellularComponent → Gene Relation Pipeline
#
# Builds a unified, deduplicated edge table for the **CellularComponent–Gene** relation
# by ingesting processed files from multiple KG sources, normalising gene identifiers
# via NCBI/Ensembl mapping dictionaries, and writing the final triple table ... |
ef63de515220b9cb10fe4f19d5c367ab401464024727b3ea3ab4dc5137ebdf22 | Jupyter | 8,984 | 278 | # %%
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
# %%
data = pd.read_csv('Male+Female.csv')
# %%
data = data.drop(['ID'], axis = 1)
Sex = data['Sex']
data_s = data.drop(['Sex'], axis = 1)
Male_Data = data[Sex==0]
Female_Data = data[Sex==1]
# %%
lut = dict(zip(Sex.uni... |
2857d1cc559119c5982b6d0ad521c54a970edb7961d858a2ce3b6838e9768aae | Jupyter | 9,016 | 258 | # %% [markdown]
# # MolecularFunction → MolecularFunction Relation Pipeline
#
# Builds a unified, deduplicated edge table for the **MolecularFunction–MolecularFunction** relation.
#
# **Output schema:** `head | relation | tail | head_type | relation_type | tail_type | kg_source | kg_type | head_id_is | tail_id_is | h... |
566debe09bcbbca32ec18199ff13ade42d0f514b9d0b0da4c9b9458d52df3706 | Jupyter | 9,022 | 256 | # %% [markdown]
# # Gene → CellularComponent Relation Pipeline
#
# Builds a unified, deduplicated edge table for the **Gene–CellularComponent** 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... |
2edcadfda67cfaea3266a788a8c6b8ffd60d2f07fa53b46e34da496e102074fb | Jupyter | 9,028 | 403 | # %%
from pathlib import Path
from docutils.nodes import reference
from CrystalTracer3D.io import CrystalReader
import napari
in_img = Path(r'D:\Zuohan\Z9\raw\Nude32_Z9_2025_05_30__00_49_24(15).czi')
a = CrystalReader(in_img)
segchan = a.find_channel('ChS2-T1')
img = a.read(channel=segchan)
viewer = napari.Viewer()... |
6bee2735362fe1ad4cd0e398729ddad7703d8bfb19642f49625be5a2200ca11e | Jupyter | 9,053 | 227 | # %% [markdown]
# ## ================================================================
# ## BRIAN2 SIMULATION OF A RECURRENT EXCITATORY NEURONAL NETWORK
# ## ================================================================
# This notebook simulates current-driven excitatory neurons with
# spike-frequency adaptation (**A... |
2e2df36e7dc527756760449f7e25072c5bf81598c3911240f5821b9269b0f37c | Jupyter | 9,082 | 292 | # %% [markdown]
# # Gene ↔ Phenotype Relation-Wise Merge
#
# Merges Gene–Phenotype triples from BOCK, TARKG, Harmonizome (×5), and BioGrakn (×2);
# resolves phenotype tail names via HPO and missing gene head names via NCBI;
# deduplicates by `(head, relation, tail)`; and saves the result.
# %% [markdown]
# ## 0. Conf... |
f29b53f2f48cab6059eaec298dc374556f0cd2b0b8b59ab5ae474ce9cb8503fa | Jupyter | 9,131 | 224 | # %% [markdown]
# Modified "Tutorial 1: How to use abcTau package to fit autocorrelations or PSDs" from the abcTau repo
# %%
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
from scipy import stats
# add the path to the abcTau package
import sys
#sys.path.append('./abcTau')
sys.path.append('... |
e6edf00a0d25e76d56bd3197dde26a178afd36ad4cbc2053c169598cd7c4962f | Jupyter | 9,146 | 387 | # %%
import pandas as pd
import numpy as np
from collections import defaultdict
# %%
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 rdMolStan... |
43c797b43d112ff80ede03109cf1b74669aa97edb2670d1f480cb41d1643614a | Jupyter | 9,158 | 232 | # %%
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Calculate accept rate for each value condition, and also mean RT,
then run the ANOVA & paired t-tests.
note the valueH here refer to Magnitude Medium in the manuscript
Fig. S1 in the supplementary materials
"""
import os
import numpy as np
import scipy.stats as... |
87351929b01fa6fdd97cd45172d9fd629b3e68cf8b4bd98153fb7dc2589774c5 | Jupyter | 9,193 | 296 | # %% [markdown]
# # BiologicalProcess → ChemicalEntity Relation Pipeline
#
# Builds a unified, deduplicated edge table for the **BiologicalProcess–ChemicalEntity** relation
# by ingesting processed files from multiple KG sources, normalising identifiers
# via PubChem mapping dictionaries, and writing the final tri... |
ecbe1f16b508c031eef4bc5e83d7b772fc767be0a38292d89f408b7f94f1d5d9 | Jupyter | 9,213 | 295 | # %%
%matplotlib inline
import matplotlib.pyplot as plt
import os, sys, logging, torch
import pandas as pd
import numpy as np
from torch import nn, tensor
from functools import partial
sys.path.append(os.path.join(os.getcwd(), '..'))
from utils.create_batch import sort_atoms, momentum_transform, geom_transform, get_ba... |
c5eb22e979807330a679fb3a42adf4f3f758af2d9ce8842dbc276387003a15c2 | Jupyter | 9,251 | 208 | # %% [markdown]
# Calculate flags for every unit:
#
# 1. at least 20 completed trials
# 2. at least 1hz of mean activity during the fixation period
# 3. each 50-ms time bin during fixation with nonzero mean activity
#
# Flags are calculated based on binned data (to mimic the paper).
#
# Flags are calculated on the d... |
c4c237f9fd77001fa737e96ecc56b1f63598cd50ed8ea32fc3b58e6a5d3baeb8 | Jupyter | 9,268 | 300 | # %% [markdown]
# # GNN Explainer
#
# Generate edge explanations.
# %%
import sys
import os
import numpy as np
import pandas as pd
from tqdm import tqdm
import pickle as pkl
from collections import defaultdict
import matplotlib.pyplot as plt
import seaborn as sns
from matplotlib.patches import Patch
import ast
impo... |
2484282b3613fc5c402c635d19ad73b215b22cb39eaf81af1e549e582d10d954 | Jupyter | 9,283 | 226 | # %%
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... |
2f505a350928cde4af36f51c459a4bd5c2784faa9b4f9aff7513e40dda5680a6 | Jupyter | 9,288 | 241 | # %% [markdown]
# # Statsmodels Methods Overview
# %%
# 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 ... |
cb89640f437b54a2cd348754f45a9806de56dd8fa63a00e7f2a70ae279ac2504 | Jupyter | 9,289 | 231 | # %% [markdown]
# # `scatter` plot
#
# This notebook is designed to demonstrate (and so document) how to use the `shap.plots.scatter` function. It uses an XGBoost model trained on the classic UCI adult income dataset (which is a classification task to predict if people made over \\$50k in the 90s).
# %%
import xgboos... |
1b4e9f7fd0d84b822af2e6aa193688a1402b2b33e51d3d77fd2facebf505419e | Jupyter | 9,290 | 268 | # %%
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]
# ## Randomly generate architectures
#
# ### Required conditions:
# * N parameters < 20... |
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