sha256 stringlengths 64 64 | language stringclasses 27
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|---|---|---|---|---|
4b6106efd95b3119b3d200babd6b80ee89ca5aba35dc684b63269e33c81ec7c6 | Jupyter | 6,018 | 188 | # %% [markdown]
# # ChemicalEntity → BiologicalProcess Relation Pipeline
#
# Builds a unified, deduplicated edge table for the **ChemicalEntity–BiologicalProcess** relation.\n*Note: This strictly uses natively defined Chemical->Biological edges and includes kg_type classification.*
#
# **Output schema:** `head | rela... |
73f3dbb4837588ab729078d6d8658411c1fb5206c81dba289c43438c4cba31da | Jupyter | 6,018 | 207 | # %% [markdown]
# # Estimate the posterior probability of an interaction based on observed ERGs
#
# Quantify whether there is an interaction or not between two genes in Drosophila melanogaster PD models
#
#
# %%
import os.path
import pandas as pd
import yaml
import seaborn as sns
import arviz as az
import numpy as... |
dc88a2a283a051d549c3d3ee2bbcac1435a0749b0c3fefebc1e830f6b8156375 | Jupyter | 6,022 | 167 | # %%
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... |
a615c864ff523bcbf4ff35268df3f422a789ae863cbd5a5412765c096b3abf23 | Jupyter | 6,026 | 160 | # %%
import numpy as np
import pandas as pd
import pickle
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
from pathlib import Path
import re
# get utils for thresholds
from src import util_analysis
from src import util_process_prolific as util_process
import importlib
from tqdm.auto import ... |
0608f7212dcaafadd15045035d8183d03fe5be7e7cea62d8c07e55ed7b3e522a | Jupyter | 6,052 | 213 | # %%
from dataset import ProcessedLigandPocketDataset
from pathlib import Path
from torch.utils.data import DataLoader
import torch
# %%
train_dataset = ProcessedLigandPocketDataset(
Path('../01.data/full_h_single_separate/', 'test.npz'))
# %%
train_dataset.collate_fn
# %%
DataLoader(train_dataset, 2... |
0dac4f7ac25778d880be3757f8223c10a4b7da7b8cb4810ff47fe972f30e3485 | Jupyter | 6,062 | 223 | # %% [markdown]
# # Protein ↔ Tissue Relation-Wise Merge
# %% [markdown]
# ## 0. Configuration
# %%
import os
import pandas as pd
BASE_DIR = '/storage/Arushi/090526_EvoAge/kg_formation/'
PROC_DIR = BASE_DIR + 'processed_data/'
DB_DIR = BASE_DIR + 'data_collection/databases_for_mapping/'
OUT_PATH = BASE_DI... |
da2532cddf625fc14906af47fd76d40d05e3237277a13373e7c2eea550ec21e5 | Jupyter | 6,084 | 163 | # %%
import os, io, random
import string
import numpy as np
from Bio.Seq import Seq
from Bio.Align import MultipleSeqAlignment
from Bio import AlignIO, SeqIO
#import panel as pn
#import panel.widgets as pnw
#pn.extension()
from bokeh.plotting import figure, show
from bokeh.models import ColumnDataSource, Plot, Grid,... |
c275171277c3bb906ba9fbdc0858e5aee7da69f0485dc0540cf018794ba162ff | Jupyter | 6,101 | 226 | # %% [markdown]
# # Mutation ↔ Gene Relation-Wise Merge
#
# Merges Mutation–Gene triples from EvoAGE;
# deduplicates by `(head, relation, tail)`; and saves the result.
# %% [markdown]
# ## 0. Configuration
# %%
import os
import pandas as pd
BASE_DIR = '/storage/Arushi/090526_EvoAge/kg_formation/'
PROC_DIR = BAS... |
baef60b6ebb312484d68ef36b3184077d72f7324f1e23e77340f68df0b760a7f | Jupyter | 6,106 | 159 | # %% [markdown]
# # DeepExplainer Genomics Example
# %% [markdown]
# This runs DeepExplainer with the model trained on simulated genomic data from the DeepLIFT repo (https://github.com/kundajelab/deeplift/blob/master/examples/genomics/genomics_simulation.ipynb), using a dynamic reference (i.e. the reference varies dep... |
798641993e4a2231ae2b17fcf53e370044b9adfed19b8f4f3712e15f31cdb1ed | Jupyter | 6,107 | 176 | # %% [markdown]
# # 00 settings
# %%
import scipy.io as scio
import h5py
import numpy as np
import tifffile as tf
from PIL import Image
import hdf5storage
import pandas as pd
import numpy as np
import math
import random
import copy
from itertools import chain
import matplotlib.pyplot as plt
import matplotlib.cm as c... |
e238681bb992667c6f22b2d98b1bd88397cad1385404ca4c4c1aa7249d5443a5 | Jupyter | 6,137 | 199 | # %% [markdown]
# # 分类问题的量子机器学习技巧
#
# **一些常见设置和技巧的演示**
# %% [markdown]
# ## 概述
#
# 我们使用 fashion-MNIST 数据集来建立二进制分类任务，我们将尝试不同的输入编码方案，并对量子输出应用可能的经典后处理以提高分类精度。 在本教程中，我们使用 TensorFlow 后端，并尝试始终如一地使用 TensorCircuit 为量子函数提供的 **Keras 接口**，在这里我们可以神奇地将量子函数转换为 Keras 层。
# %%
from matplotlib import pyplot as plt
from sklearn.decom... |
28dd8879fd52ca61e0420eece8dd85dcb2e03f02828fa3585feeaa9fc5e728ea | Jupyter | 6,152 | 172 | # %% [markdown]
# # Code to generate RDMs for time averaged stimuli for review rebuttal
# %%
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from pathlib import Path
import matplotlib as mpl
mpl.rcParams['pdf.fonttype'] = 42
mpl.rcParams['ps.fonttype']... |
ab1fceeb15436c15ef50aecbdb3e9f7bab0a6b7aeb83b4c7b6560d8e236e8d88 | Jupyter | 6,159 | 196 | # %% [markdown]
# # Set up
# %%
#import necessary packages
import os
import glob
from nilearn import surface
import numpy as np
import nibabel as nib
import usefulFunctions as uf
# %%
# flag to save files
save_files = False
#set paths
base_dir = f'{os.path.dirname(os.getcwd())}/'
roi_dir = f'{base_dir}data/labels/a... |
8429c64a329c13aabbd11f98b627ca0385735a64efb3d68193cb48c9b91f8892 | Jupyter | 6,162 | 190 | # %% [markdown]
# # Gene → Anatomy Relation Pipeline
#
# Builds a unified, deduplicated edge table for the **Gene–Anatomy** 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`
#
# ---
#... |
7b71d1fc048bb0d7bd8f88263abb8e9a49307cdc22420cb8150075175ca341b2 | Jupyter | 6,165 | 212 | # %% [markdown]
# # 电路和门
# %% [markdown]
# ## 概述
#
# 在 TensorCircuit 中，$n$ 量子比特上的量子电路——通过蒙特卡洛轨迹方法支持无噪声和有噪声的模拟——由“tc.Circuit(n)”API 创建。
# 在这里，我们展示了如何创建基本电路，对它们应用门，并计算各种输出。
# %% [markdown]
# ## 设置
# %%
import inspect
import numpy as np
import tensorcircuit as tc
K = tc.set_backend("tensorflow")
# %% [markdown]
# 在 ... |
910147bfe839b555b3d289c14ad996338c294d61334a48fb63b172e1252b6a3a | Jupyter | 6,173 | 198 | # %% [markdown]
# # Gene → Tissue Relation Pipeline
#
# Builds a unified, deduplicated edge table for the strictly **Gene–Tissue** relation (excludes Anatomy), integrating aging-specific datasets.
#
# **Output schema:** `head | relation | tail | head_type | relation_type | tail_type | kg_source | kg_type | head_id_is... |
e5d4888e05ab9d6d76826fbd125d9bb00f7d902461333cf7941f1c70a64cf375 | Jupyter | 6,178 | 229 | # %% [markdown]
# # Mutation ↔ Gene Relation-Wise Merge
#
# Merges Mutation–Gene triples from EvoAGE;
# deduplicates by `(head, relation, tail)`; and saves the result.
# %% [markdown]
# ## 0. Configuration
# %%
import os
import pandas as pd
BASE_DIR = '/storage/Arushi/090526_EvoAge/kg_formation/'
PROC_DIR = BAS... |
ade19765adc2478d4d2ca50d3520881b2d0fa008d6feaceeaf4b912d8cfbd22e | Jupyter | 6,195 | 225 | # %%
import sys
sys.path.append('/Users/midani/OneDrive/proj/leap/manuscript')
import numpy as np
import pandas as pd
from tableone import TableOne
from scipy.stats import f_oneway, kruskal, mannwhitneyu, fisher_exact, chi2_contingency, ttest_ind
# %%
def assign_group(row):
bsid = row['BSID']
if bsid <= 85:... |
f8f54b7e7dd216257bb54530376f25d0f887b5177c6f2494ce81b8b9adada955 | Jupyter | 6,200 | 161 | # %% [markdown]
# ## ================================================================
# ## BRIAN2 SIMULATION OF A RECURRENT EXCITATORY NEURONAL NETWORK
# ## ================================================================
# This notebook simulates current-driven excitatory neurons without
# spike-frequency adaptation (... |
4cf9c23bae3a9d9ce8de779cd36289a10c5cda80c966ae73ef4561ba453a9c21 | Jupyter | 6,211 | 109 | # %% [markdown]
# ```{currentmodule} optimap
# ```
# %%
from optimap.utils import jupyter_render_animation as render
# %% [markdown]
# ```{tip}
# Download this tutorial as a {download}`Jupyter notebook <converted/phase.ipynb>`, or a {download}`python script <converted/phase.py>` with code cells. We highly recommend u... |
1d2fdb42abcb1701827774cc336d534348d289a7bb9e4d60aeae36c7b21b841c | Jupyter | 6,215 | 224 | # %% [markdown]
# # 线路优化与线路表达能力关系探究
# %% [markdown]
# ## 概述
# %% [markdown]
# 在本教程中，我们将展示电路参数优化和电路可表达性之间的关系。 利用变分量子本征求解器（VQE）算法，我们可以得到量子多体系统的基态能量。 随机参数化电路虽然层数越多，可表达性越大，但由于基态的纠缠熵满足“面积定律”，随机电路产生的纠缠无法得到准确的基态能量。
# 这里考虑的模型如下：
# $$
# H = \sum_{i=1}^{n}\sigma_{i}^{z}\sigma_{i+1}^{z}+\sum_{i=1}^{n}\sigma_{i}^{x},
# $$
# 其中 $... |
3fa33f4d60d23de45fe874264f7ea753ab35c8dcfced7eb8dafb0e9d35c06139 | Jupyter | 6,218 | 222 | # %% [markdown]
# # Mutation ↔ Protein Relation-Wise Merge
#
# Merges Mutation–Protein triples from CKG (×2); resolves protein tail names via UniProt;
# deduplicates by `(head, relation, tail)`; and saves the result.
# %% [markdown]
# ## 0. Configuration
# %%
import os
import pandas as pd
BASE_DIR = '/storage/Arus... |
d78e92a936775b64aba4b09a2a1d81304313d43a7187ade7b1e1480992cd8cdd | Jupyter | 6,236 | 169 | # %% [markdown]
# todo: all will be in the script (now trial stuff is in the script, and here I have full signal)
#
# Calculate 5 versions of ACF:
# * ACF on full signal
# * iSTTC on full signal
# * Pearsonr trial average
# * STTC trial average
# * STTC trial concat
#
# We do not have ground truth here but we have th... |
8f2f0c946243aaddcb3b2140deaf6392158cdd1affc6bcddb7c163eee2f48bab | Jupyter | 6,244 | 248 | # %%
import pandas as pd
import numpy as np
import glob
import os
from tqdm import tqdm
# %%
!pwd
# %% [markdown]
# %%
# Monarch/Monarch_final/Zebrafish/Gene_Zebra_Phenotype.csv
# zfin/zfin_Zebra_GENE_PHENOTYPE_GENE.csv
# %%
BASE_DIR = '/storage/Arushi/090526_EvoAge/kg_formation/'
MAPPING_DIR = BASE_DIR + 'da... |
8e1a9626fc7fee5805373f21b3c09447b911ce890851ff2c2a7b1a56542f3939 | Jupyter | 6,258 | 224 | # %% [markdown]
# # Gene ↔ Protein Relation-Wise Merge
#
# %% [markdown]
# ## 0. Configuration
# %%
import os
import pandas as pd
BASE_DIR = '/storage/Arushi/090526_EvoAge/kg_formation/'
DB_DIR = BASE_DIR + 'data_collection/databases_for_mapping/'
PROC_DIR = BASE_DIR + 'processed_data/'
OUT_PATH = BASE_DIR +... |
feb43ec571bb2390f44c3c20a53d1bde2724b11f90d51a3aa8eb8e73136487c9 | Jupyter | 6,268 | 214 | # %%
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
import seaborn as sns
from rdkit import Chem
from rdkit.Chem.Draw import IPythonConsole
#IPythonConsole.ipython_useSVG = True
import numpy as np
import pandas as pd
from tqdm import tqdm
from collections import defaultdict
from rdkit import Che... |
46e1ce081292f7599393819734e0a569e57ed236a67791b707574bdf99baa73b | Jupyter | 6,271 | 181 | # %%
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Feb 5 14:35:35 2025
Compare roi-averaged signal changes between Accept and Decline SH
@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 statsmodels.stats... |
a8425b714045c22961f954176047447f7b4b304e48d218f35aaaa042ab0b6c17 | Jupyter | 6,272 | 203 | # %% [markdown]
# # Registration
# %%
import xarray as xr
import numpy as np
import cedalion
import cedalion.io
import cedalion.dataclasses as cdc
import cedalion.geometry.registration
import cedalion.geometry.segmentation
import cedalion.plots
xr.set_options(display_expand_data=False);
# %% [markdown]
# ## Read op... |
62076d4b79d410cce6e86ab754609d138c19b7fc8ff06b4ac90d9c830d748a95 | Jupyter | 6,325 | 249 | # %%
from aaindex import Aaindex
import pandas as pd
from tqdm import tqdm
import seaborn as sns
import matplotlib.pyplot as plt
# %%
pwd
# %%
%config Completer.use_jedi = False
# %% [markdown]
# # process-aaindex-group
# %%
dfna = pd.read_csv('Appendix2.txt', sep='\t').set_index('accession_number')['subgroup']
wi... |
5a2490ce9b464ba732659e4d872c31ce611760a6effb64c17a9b47ac67c8d5e6 | Jupyter | 6,350 | 208 | # %% [markdown]
# # Run scDeepCluster on 10X PBMC dataset
# %% [markdown]
# This tutorial was implemented on Macbook pro CPU.
# %%
from time import time
import math, os
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.nn import Parameter
import torch.nn.functional as F
import torch.... |
ec3eb7245c6d3d0f416ace410a404311daf7bb1329deca2f744f671214adda00 | Jupyter | 6,353 | 238 | # %% [markdown]
# # Protein ↔ MolecularFunction Relation-Wise Merge
# %% [markdown]
# ## 0. Configuration
# %%
import os
import pandas as pd
import numpy as np
BASE_DIR = '/storage/Arushi/090526_EvoAge/kg_formation/'
PROC_DIR = BASE_DIR + 'processed_data/'
DB_DIR = BASE_DIR + 'data_collection/databases_for_map... |
da223bf2837085e91d48f225fffa1244af408f84876e6cd433ae6392aec5d6d1 | Jupyter | 6,358 | 198 | # %% [markdown]
# # 在量子电路模拟中使用 vmap
# %% [markdown]
# ## 概述
#
# 我们将现代机器学习库的高级特性 vmap 引入到量子电路模拟中。
# 通过对量子电路模拟的不同成分进行映射，我们可以高效地实现变分量子算法。
# 值得注意的是，在以下用例中，vmap 与 jit 和 AD 一起支持，可呈现高效的可微分模拟。
#
# 支持 vmap 范式的成分如下图所示。
#
# 
#
# 我们有两种不同类型的 vmap API，第一种是 ``vmap`` 而第二种是``vectorized_va... |
bb8f87206f680e68e02c89b8585d05f630ea31f8f2f901c2447ca41e84333f41 | Jupyter | 6,365 | 166 | # %% [markdown]
# ## ================================================================
# ## Recurrent Excitatory Network Simulation (Brian2)
# ## ================================================================
# This notebook reproduces **Figure S4** from the study, showing spiking activity
# in a recurrent network of... |
7b4663a850e58fccd5cfec0b8fa007ee470b917732d788e97ca22a0feaac4557 | Jupyter | 6,371 | 238 | # %%
import pandas as pd
import numpy as np
# %%
# %% [markdown]
# # MGI_DO
# %%
BASE_PATH = "/storage/Arushi/090526_EvoAge/kg_formation/"
# %%
NCBI_MOUSE_gene = pd.read_csv(f'{BASE_PATH}data_collection/databases_for_mapping/ncbi/Mus_musculus.gene_info',sep = '\t')
NCBI_MOUSE_gene
# Extract ENSMUSG pattern usi... |
f98c8e9dcb01a16d2b9073f7d776509af5d62a142aba8289495042c53ca9a514 | Jupyter | 6,375 | 199 | # %% [markdown]
# # Quantum Machine Learning for Classification Task
#
# **Demonstrations on some common setups and techniques**
# %% [markdown]
# ## Overview
#
# We use the fashion-MNIST dataset to set up a binary classification task, we will try different encoding schemes for the inputs and apply possible classica... |
6b1ad6fa60e6ecba4cafa6aea71183e35b5ccebd739dd90afb0d207d76314cb6 | Jupyter | 6,415 | 197 | # %%
from brian2 import *
import numpy as np
from time import time
set_device('cpp_standalone')
import time
import os
# %%
seed(1893)
#######################
## Neuron Parameters ##
#######################
area = 300*um**2 # surface area of the neuronal soma
Cm = (1*uF*cm**-2) * area # m... |
6cafef121f45bf59399bee487edee340c1c921d5bbaabb27e14aaecbdba10d69 | Jupyter | 6,431 | 231 | # %% [markdown]
# # 具有不同哈密顿量表示的一维 TFIM 上的 VQE
# %% [markdown]
# ## 概述
# %% [markdown]
# 对于 VQE 中哈密顿量 $H$ 的基态准备，我们需要计算哈密顿量 $H$ 的期望值，即 $\langle 0^N \vert U^{\dagger}(\theta) HU(\ theta) \vert 0^N \rangle$ 并根据梯度下降更新 $U(\theta)$ 中的参数 $\theta$。 在本教程中，我们将展示 TensorCircuit 支持的四种计算 $\langle H \rangle$ 的方法：
#
# 1, $\langle H ... |
ca5d0dcc9ac8a00f647e2e300d8ab989fd83de4f0d90145bb13c9970c3149a14 | Jupyter | 6,466 | 163 | # %%
import pickle as pkl
import viz_sequence
from scipy.stats import spearmanr, pearsonr
import matplotlib.pyplot as plt
from scipy.spatial.distance import jensenshannon
import numpy as np
from scipy.stats import entropy
import matplotlib.pyplot as plt
import scipy.ndimage
import random
random.seed(1)
# %%
main = "/s... |
9b966c0c0ccbda74a9a0141a27ee59b021cebb4c653fd61639bc08a895188cce | Jupyter | 6,472 | 213 | # %% [markdown]
# # Synthetic Artifacts
# %%
# 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")
... |
d2823397e65e183ab9c7e28006be8b781b3ec7fc18eb5282961f03d766a20da2 | Jupyter | 6,541 | 237 | # %%
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 os
os.environ["CUDA_VISIBLE_DEVICES"]="5"
np.random.seed(123)
tf.compat.v1.set_ran... |
cdb550faac8da2d89439940048148ce1eb09378cfc5357f31115aec4069d78cf | Jupyter | 6,551 | 173 | # %% [markdown]
# # Figure 4 — Source Data Export
#
# **Figure 4** shows the hierarchical alignment between primate visual areas and
# GAN priors: per-area success rates (V1/V4/IT), population-level activation
# trajectories normalised to the maximum response, and per-experiment time
# constants of the evolution dynam... |
757a2e8367ddbf4140ec9d48b3a198a2e5444f7ed0259f683cadf80f8d1610fc | Jupyter | 6,568 | 241 | # %%
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 os
os.environ["CUDA_VISIBLE_DEVICES"]="6"
np.random.seed(123)
tf.compat.v1.set_rand... |
77b0b7d2131d5cb045ff471dbd85154294d94baf947f4a2814c3132eefb27fef | Jupyter | 6,596 | 186 | # %% [markdown]
# # TensorCircuit 中的 QuOperator
# %% [markdown]
# ## 概述
# %% [markdown]
# `tensorcircuit.quantum.QuOperator`, `tensorcircuit.quantum.QuVector` 和 `tensorcircuit.quantum.QuAdjointVector` 是从 TensorNetwork 包中采用的类。
# 当与其他组件交互时，它们的行为类似于矩阵/向量（列或行），而内部结构由张量网络维护以提高效率和紧凑性。
#
# QuOperator/QuVector 的典型张量网络结构对应于矩... |
911c0ea78f80e9285c501a98aa040e1f3ba27a10264636d93177cb4509dbda9a | Jupyter | 6,650 | 236 | # %% [markdown]
# # VQE on 1D TFIM with Different Hamiltonian Representation
# %% [markdown]
# ## Overview
# %% [markdown]
# For the ground state preparation of a Hamiltonian $H$ in VQE, we need to calculate the expectation value of Hamiltonian $H$, i.e., $\langle 0^N \vert U^{\dagger}(\theta) H U(\theta) \vert 0^N \... |
e631cdccd8ebd4b83383a87566aa06139b8adf16b316ff119f0be45e103befa9 | Jupyter | 6,697 | 216 | # %% [markdown]
# # ARCOS Pipeline in python
#
# This notebook ilustrates a example workflow from images to ARCOS analysis and visualization in napari. It uses the python package stardist for segmenting nuclei, skimage for image processing and trackpy for tracking cells over time.
# Subsequently ARCOS is used to analy... |
ca1a4e82ff4a3b112d7256e1541349f15e7f420c719cf1940e671ed60c1bea4b | Jupyter | 6,701 | 247 | # %%
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 os
os.environ["CUDA_VISIBLE_DEVICES"]="1"
np.random.seed(7)
tf.compat.v1.set_random... |
5cae4385fc0dd2129949adca023e951ec1335d080651dadeee443d2c7edae44c | Jupyter | 6,703 | 198 | # %%
import os
import sys
sys.path.append("/data/Lautaro/Documentos/BrainAgeCOVID/pyment-public-main/keras-explainability")
from explainability import LRP, LRPStrategy
from pyment.models import BinarySFCN, RegressionSFCN
from pyment.postprocessing import get_postprocessing
IMAGE_FOLDER = os.path.join('/data/Lautaro... |
c1fdb071fe02fd4920055dfdb891e41a9f0f38427035abe7fdfa19556f3567b2 | Jupyter | 6,728 | 200 | # %%
import numpy as np
import pandas as pd
import pickle
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
from pathlib import Path
import re
# get utils for thresholds
from src import util_analysis
from src import util_process_prolific as util_process
import importlib
from tqdm.auto import ... |
d4608e67db4545207ab43ebf78d80138b76f8b6f7923c0e017bc1883fdfcdc0b | Jupyter | 6,728 | 206 | # %% [markdown]
# # Circuits and Gates
# %% [markdown]
# ## Overview
#
# In TensorCircuit, a quantum circuit on $n$ qubits -- which supports both noiseless and noisy simulations via Monte Carlo trajectory methods -- is created by the ``tc.Circuit(n)`` API. Here we show how to create basic circuits, apply gates to th... |
cd9653d6d9713dabff889b02f3a208ab21c5aba313d553113c1a88e5031e4aef | Jupyter | 6,743 | 241 | # %%
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"]="5"
#tf.enable_eager_execution()
# %%
from molmap import loadmap
from tenso... |
56106fd98e6eafd69ada3dcc67fc43d450a095e5119f460a48763c53790def1d | Jupyter | 6,772 | 164 | # %% [markdown]
# Plots:
# 1. Heatmap for every unit (for Pearsonr trial average and STTC trial average)
# 2. ACF line plot average over units
# 3. ACF line plot for every unit
# %%
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from isttc.scripts.cfg_global import proj... |
430d6a2f2d2c4d6a08ec1b4bbe25579c83119c5b60780af0746152c28c272405 | Jupyter | 6,798 | 229 | # %% [markdown]
# # Optimization vs. expressibility of the circuit
# %% [markdown]
# ## Overview
# %% [markdown]
# In this tutorial, we will show the relationship between circuit parameter optimization and circuit expressibility. Utilizing the variational quantum eigensolver (VQE) algorithm, we can get the ground sta... |
2eaf0727ad4f7f54ec91bca89dde5250f163e090b2fe43be8af55cc03ba32c06 | Jupyter | 6,807 | 197 | # %% [markdown]
# # Utilizing vmap in Quantum Circuit Simulations
# %% [markdown]
# ## Overview
#
# We introduce vmap, the advanced feature of the modern machine learning library, to quantum circuit simulations.
# By vmapping different ingredients of quantum circuit simulation, we can implement variational quantum al... |
b3cf8006a4f5b3115ad02bcb8c9fc8e02dfb03cee241d445c87b41d35abbd4ef | Jupyter | 6,808 | 267 | # %% [markdown]
# # Head model fiducials and landmarks
#
# Cedalion ships with segmentations as well as brain and scalp surfaces for the Colin27 and ICBM-152 heads.
#
# This notebook documents the source of the fiducial landmarks and compares the outputs of the landmark builder
# which we distribute together with the... |
0b2fd5dbe86a3471e86ae68ed1d1bfe155c2626c56fcb35d2cd813127591a6da | Jupyter | 6,832 | 190 | # %%
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... |
3e54721458da01ff42bef28ad82b7245755750b1b1dc5a7888f1cd143273351b | Jupyter | 6,879 | 194 | # %%
#!/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... |
e68d9270e5c1b094263631a1775e5732515e224466bb367678519937db7b9471 | Jupyter | 6,887 | 166 | # %% [markdown]
# # PIMMS — phase separation in your browser 🧪
#
# [](https://colab.research.google.com/github/holehouse-lab/PIMMS/blob/master/colab/pimms_phase_separation_demo.ipynb)
#
# This notebook runs a small **[PIMMS](https://github.com... |
562cef7a4c716e1dd4de768a966ee3e3a707890fe7de514e3bf57c2b312018fc | Jupyter | 6,895 | 262 | # %%
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, spearmanr, pearsonr
from statsmodels.stats.multitest... |
ffbe8387be971cb4554604971ae4c9258d9d0a0eb8f1fdad6d10b87ad9d33daf | Jupyter | 6,915 | 298 | # %% [markdown]
# # QEM Applications
# %%
import tensorcircuit as tc
from tensorcircuit.results import qem
from tensorcircuit.results.qem import qem_methods, benchmark_circuits
from tensorcircuit.results.qem import (
zne_option,
apply_zne,
dd_option,
apply_dd,
apply_rc,
)
from tensorcircuit.cloud ... |
e0db5a7f71fc1588567b08ebce4cb359375aae7b71477e2690bb23081672bbd4 | Jupyter | 6,932 | 184 | # %% [markdown]
# ## ================================================================
# ## TIME STEP SENSITIVITY IN SINGLE-NEURON SIMULATIONS (BRIAN2)
# ## ================================================================
# This notebook investigates the impact of numerical integration step size (**dt**)
# on the preci... |
d72d36fdcf8dd3a35a62082abbe072ede28289f97658687ebbd4d9873d926969 | Jupyter | 6,937 | 186 | # %% [markdown]
# # C. elegans Gene–Phenotype (WormBase) — Relation-Wise KG Triple Construction
#
# ## Purpose
#
# This notebook implements the **complete end-to-end pipeline** for constructing Gene–Phenotype Knowledge Graph (KG) triples for *C. elegans*. It processes raw WormBase phenotype data, maps phenotype ontol... |
0bb699b0118b726089c2131581331d4dd182f71c71c2862585a50dfee463c815 | Jupyter | 6,980 | 254 | # %%
import pandas as pd
import numpy as np
import glob
import os
from tqdm import tqdm
# %%
!pwd
# %% [markdown]
# %%
# %%
BASE_DIR = '/storage/Arushi/090526_EvoAge/kg_formation/'
MAPPING_DIR = BASE_DIR + 'data_collection/databases_for_mapping/'
PROC_DIR = BASE_DIR + 'processed_data/'
# ── Required ou... |
4148f8c0f2f6d069f88c772341f1cf5f65ac58eef4c4936651847f0b37e4ef17 | Jupyter | 6,988 | 299 | # %% [markdown]
# # Disease ↔ Mutation Relation-Wise Merge
#
# Merges Disease–Mutation triples from hald;
# deduplicates by `(head, relation, tail)`; and saves the result.
# %% [markdown]
# ## 0. Configuration
# %%
import pandas as pd
BASE_DIR = '/storage/Arushi/090526_EvoAge/kg_formation/'
PROC_DIR = BASE_DIR ... |
1de06d14748e7f4d3871e03ef102eeecd5fbb8e467f9162ce0ba05272b9337c1 | Jupyter | 6,996 | 266 | # %%
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, spearmanr, pearsonr
from statsmodels.stats.multitest... |
c8c6c73beb2f9e34342c761e3ba6872064671c7d0d59471de9756be329ac3c1b | Jupyter | 7,023 | 205 | # %% [markdown]
# # 00 settings
# %%
import scipy.io as scio
import h5py
import numpy as np
import tifffile as tf
from PIL import Image
import hdf5storage
import pandas as pd
import numpy as np
import math
import random
import copy
from itertools import chain
import matplotlib.pyplot as plt
import matplotlib.cm as c... |
24f9ea9fcde0540f9781e9c4e9a2278872f7f9ed8f62dc162e8b253bfb2ffa63 | Jupyter | 7,028 | 200 | # %%
import numpy as np
import pandas as pd
import pickle
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
from pathlib import Path
import re
# get utils for thresholds
from src import util_analysis
from src import util_process_prolific as util_process
import importlib
from tqdm.auto import ... |
2009de291eacbb0a9e5275ad5687bb0588ebf3a1186d3559c2f8ec9f1e982450 | Jupyter | 7,048 | 193 | # %%
# Example code using PyTorch and torchvision
import torch
from torchvision import models, transforms
from PIL import Image
import os
import numpy as np
import cv2
import time # Import time for a small delay
# Define the denormalize function
def denormalize(
x,
mean=[0.485, 0.456, 0.406],
std=[0.229, 0... |
2ea1be6b7f3253aa6e1b932d07b3d0be727998f7170d882a3e057decd8db4dbe | Jupyter | 7,072 | 225 | # %% [markdown]
# # 一维横场 Ising 模型 VQE
# %% [markdown]
# ## 概述
#
# 本教程的主要目的不是关于 VQE 物理层面的讨论，而是我们通过演示
# 这个简单的 VQE 玩具模型来了解张量电路的主要技术组件和用法。
# %% [markdown]
# ## 背景
#
# 基本上，我们训练一个参数化的量子电路，其线路结构为重复的 $e^{i\theta} ZZ$ 和 $e^{i\theta X}$ 层的 $U(\rm{\theta})$。 而要最小化的目标是这个任务 $\mathcal{L}(\rm{\theta})=\langle 0^n\vert U(\theta)^\... |
abe30d3f113a79ac229c12fce07fa1024e832de5a7e986006fae6f42152f6663 | Jupyter | 7,073 | 241 | # %% [markdown]
# # Protein ↔ CellularComponent Relation-Wise Merge
#
# Merges Protein–CellularComponent triples from CKG (×2), CrossBAR, and TARKG;
# fills missing protein head names from UniProt; deduplicates by `(head, relation, tail)`;
# and saves the result.
# %% [markdown]
# ## 0. Configuration
# %%
import os
... |
79f94ad08f1f3199600430021297cfc4f30c2042800a99c2c2bad6aba03b80cc | Jupyter | 7,087 | 210 | # %%
import pandas as pd
from scipy.stats.stats import pearsonr
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(style="white")
# %%
dff = pd.read_csv('./results/results.csv')
# %%
dff = dff.set_index('model')
# %%
dff.index
# %%
col = 'etc_rmse'
v = dff[col].apply(lambda x:float(x.... |
ff66a1a0277b3bdab34c4fdc4387c95a068b4be5ebe31669c3809adf56cadd2a | Jupyter | 7,087 | 211 | # %% [markdown]
# # Calculate relationship between AP and dendritic spikes as a function of relative phase between excitation and inhibition
#
# The simulations had either:
# 1. Sinusoidal inhibition at 64 Hz to the soma, or 16 Hz to the distal dendrites.
# 2. Sinusoidal excitation to either apical or basal dendrites... |
67a4fe6fdaaa981534d00090e08f5b74ea8616946222f62c2cb5b63d226f1d80 | Jupyter | 7,127 | 193 | # %%
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.bonemarrow()
# %... |
f8421db756b14418f854b4feddf252e481e07ec29f9c285c5452b3d03bf84cec | Jupyter | 7,129 | 190 | # %%
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... |
5c6932e152a7f95165868d0c9b10ade0ed67378fbd75c52546819d2eaa2f673d | Jupyter | 7,130 | 225 | # %% [markdown]
# # Determine the threshold current for action potentials
#
# The simulations had:
# 1. Poisson inhibition at the soma and dendrites
# 2. Poisson excitation at the soma and dendrites
#
# Here we calculate the firing rate histogram in response to a current ramp at the soma, depending on:
# 1. strengt... |
d4414e4fa8fb29a555728d41e5e8dc33930c503887c21c59f0ad14023643aa14 | Jupyter | 7,137 | 192 | # %%
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... |
b4277c60eaebf5b296cb20e1f258d11562d8ba4a19a9a31bdcea788f26a23a55 | Jupyter | 7,139 | 244 | # %% [markdown]
# # Protein ↔ BiologicalProcess Relation-Wise Merge
#
# Merges Protein–BiologicalProcess triples from CKG, CrossBAR, Monarch and TARKG;
# fills missing protein head names from UniProt; deduplicates by `(head, relation, tail)`;
# and saves the result.
# %% [markdown]
# ## 0. Configuration
# %%
import ... |
2e2b4c14ce297b991a3eda7f705dad52bf592db8957349f8c5c17b2259159e27 | Jupyter | 7,141 | 170 | # %% [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... |
e118f4a5ca985c45c3996e7012d2ff7e28d1fc73dc0c52d9318d9b6a63925e33 | Jupyter | 7,154 | 245 | # %% [markdown]
# Generate plots based on parametric dataset (compare 4 methods - two full signal and two trial-based):
# * % of failed estimates (failed estimation and negative R-squared)
# * ACF decline in specific range
# * CI: 0 in the interval, width?
# * R-squared (on unit level and as %)
#
# Inclusion criteria:... |
2d1a5d6f029aa4a315206830a4e22f34f336b03ac69dcf566a71c06568d8a315 | Jupyter | 7,158 | 180 | # %% [markdown]
# # QuOperator in TensorCircuit
# %% [markdown]
# ## Overview
# %% [markdown]
# `tensorcircuit.quantum.QuOperator`, `tensorcircuit.quantum.QuVector` and `tensorcircuit.quantum.QuAdjointVector` are classes adopted from the TensorNetwork package.
# They behave like a matrix/vector (column or row) when i... |
894a599e09de7704d27eceeeb373894b53a49cca968a95b6bfec0211e6f19b2b | Jupyter | 7,178 | 210 | # %%
import scipy.io as scio
import h5py
import numpy as np
import tifffile as tf
from PIL import Image
import hdf5storage
import pandas as pd
import numpy as np
import math
import random
import copy
from itertools import chain
import matplotlib.pyplot as plt
import matplotlib.cm as cm
from matplotlib.gridspec import... |
ffcc8514b660d94471bcd47be1e8f994d86b5a6dea7e1221d746b74c31aada07 | Jupyter | 7,187 | 246 | # %%
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.metrics import classification_report, confusion_matrix
from sklearn.metrics import roc_curve, auc
import seaborn as sns
sns.set(style='white', palette='deep', font='sans-serif', font_scale=1.5)
# %%
s = sns.color_palette("Set1", n_colors=5)
sns.pal... |
88a5a9165c2b1a3bca7986db66f66d22488d04e512bd58616f024b0a54696467 | Jupyter | 7,219 | 235 | # %% [markdown]
# # Data Structures and I/O
# %%
# 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"... |
dafde2331769b01c671e9e9c5a80a46a445e79351897401caa36f2ac727ae277 | Jupyter | 7,229 | 236 | # %% [markdown]
# # 对泡利字符串和的评估
# %% [markdown]
# ## 概述
# %% [markdown]
# 我们需要在各种量子算法中评估电路上许多泡利字符串项的总和，VQE 中哈密顿量 $H$ 的基态制备就是一个典型的例子。
# 我们需要计算哈密顿量$H$的期望值，即$\langle 0^N \vert U^{\dagger}(\theta) H U(\theta) \vert 0^N \rangle$并更新参数 $U(\theta)$
# 中的 $\theta$ 基于 VQE 工作流程中的梯度下降。在本教程中，我们将演示 ``TensorCircuit`` 支持的五种方法来计算 $\lan... |
42c99cbd9a692a969aff374f617655c990d771abe4cce1b68d3d1a69d1c0b517 | Jupyter | 7,232 | 180 | # %% [markdown]
# # TensorCircuit SDK 对接天衍量子云
#
# 本教程沿用 TensorCircuit 云 SDK 的 provider-agnostic 工作流，通过统一的 `tc.cloud.apis` 入口连接中电信天衍量子计算云平台。内容覆盖设备查询、任务生命周期、云模拟器、批量提交，以及真实量子机的拓扑校验。
#
# 天衍平台提供 `tianyan_sw` 等模拟器和 `tianyan176` 等真实量子设备，原生任务格式为 QCIS。设备名称和在线状态以平台实时返回为准。平台访问与服务信息请见[天衍量子云](https://qc.zdxlz.com/)。
#
# ## 环境准备
... |
642e3038a80a40a5f968d01a98592ed16e6ce19c5c7bc5381d4165e797afeec5 | Jupyter | 7,237 | 229 | # %% [markdown]
# # VQE on 1D TFIM
# %% [markdown]
# ## Overview
#
# The main aim of this tutorial is not about the physics perspective of VQE, instead, we demonstrate
# the main ingredients of TensorCircuit by this simple VQE toy model.
# %% [markdown]
# ## Background
#
# Basically, we train a parameterized quantu... |
fb743b7c1e4be2ed412da7db5993ab2b752ac627f3c96d65b0abf753c9e7d814 | Jupyter | 7,238 | 271 | # %%
!pip install torch torchvision opencv-python tqdm
# %%
from google.colab import files
uploaded = files.upload()
# %%
!unzip mini_dataset.zip
# %%
import os, cv2, torch, torch.nn as nn
import matplotlib.pyplot as plt
from torch.utils.data import Dataset, DataLoader
import random
import numpy as np
device = "c... |
90bb780036b77b91b9499e4cca8664e8386ddb5ba99e174f4147590a2f992586 | Jupyter | 7,251 | 246 | # %% [markdown]
# Generate plots based on parametric dataset (compare 4 methods - two full signal and two trial-based):
# * % of failed estimates (failed estimation and negative R-squared)
# * ACF decline in specific range
# * CI: 0 in the interval, width?
# * R-squared (on unit level and as %)
#
# Inclusion criteria:... |
46b54e558455c103dc79e53e9a4382190d3847cd5763d4789187a84f81775735 | Jupyter | 7,264 | 249 | # %% [markdown]
# Generate plots based on parametric dataset (compare 4 methods - two full signal and two trial-based):
# * % of failed estimates (failed estimation and negative R-squared)
# * ACF decline in specific range
# * CI: 0 in the interval, width?
# * R-squared (on unit level and as %)
#
# Inclusion criteria:... |
72caef44d535e02686c2348c3de7d437e777768cf2239fb8b6c225813742fa83 | Jupyter | 7,273 | 213 | # %%
import sys
import os
# Add the parent directory of `notebook/` to sys.path
sys.path.append(os.path.abspath(".."))
import numpy as np
import pandas as pd
from pymatgen.symmetry.analyzer import SpacegroupAnalyzer
from pymatgen.io.ase import AseAtomsAdaptor
from multiprocessing import Pool, cpu_count
from ase import... |
3eb3c653e44c5f7aa35763174ece95efb90b71b40c04907ce23818840c7b77a6 | Jupyter | 7,283 | 221 | # %% [markdown]
# The notebooks shows how to calculate intrinsic timescales on the continious and epoched data. Four methods are used.
# %%
import numpy as np
import pandas as pd
import pickle
from itertools import islice
from statsmodels.tsa.stattools import acf
from datetime import datetime
import matplotlib.pyplot ... |
c32ff8fdafb8d022d122c8c0811f076f782ccb0001df199acad11f75627e46eb | Jupyter | 7,295 | 187 | # %% [markdown]
# ## ================================================================
# ## BRIAN2 SIMULATION OF A RECURRENT EXCITATORY NEURONAL NETWORK
# ## ================================================================
# This notebook simulates a population of excitatory neurons connected through **AMPA** and **NMDA... |
33f2690af6fca57b275dd215c1c83af828b256afc9537408724004978ffe3d61 | Jupyter | 7,318 | 250 | # %% [markdown]
# Generate plots based on parametric dataset (compare 4 methods - two full signal and two trial-based):
# * % of failed estimates (failed estimation and negative R-squared)
# * ACF decline in specific range
# * CI: 0 in the interval, width?
# * R-squared (on unit level and as %)
#
# Inclusion criteria:... |
ba18f2c7481218a69676243911f35134f47d5cfde31d767793d1c61129dd4a0c | Jupyter | 7,334 | 206 | # %%
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Feb 5 14:35:35 2025
Compare signal changes in roi between two RTs
fig 3 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 statsmodels.s... |
aee16b4c91651b212f0dc7dbf35c2c0b3e10283ed382208209d4040e93a5ab36 | Jupyter | 7,343 | 246 | # %% [markdown]
# # 通过激发态 VQE 探测多体局域化
# %% [markdown]
# ## 概述
# %% [markdown]
# 本教程介绍了一种通过激发态 VQE 探测多体局域化 (MBL) 的新方法。 这里考虑的托管有 MBL 相变的模型是有相互作用的 Aubry-Andr$\acute{e}$ 模型，其内容如下：
# $$
# H=\sum_{i}(\sigma^{x}_{i}\sigma^{x}_{i+1}+\sigma^{y}_{i}\sigma^{y}_{i+1} +V_{0}\sigma_{i}^{z} \sigma_{i+1}^{z} ) \\
# +W_{0} \sum_{i=1}... |
5c27e229890f2287f15bbd07d9ff682098abb8b5c8143911c171b6048a3b1850 | Jupyter | 7,348 | 279 | # %%
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... |
0f17c5bdde72698ced46c52368e57de4649f0cd1192b7189d60abff240b09c3f | Jupyter | 7,358 | 265 | # %% [markdown]
# # 使用 PyTorch 进行量子机器学习
# %% [markdown]
# ## 概述
#
# 在本教程中，我们展示了具有与 [mnist_qml](mnist_qml.ipynb) 相同设置的 MNIST 二元分类 QML 示例。
# 这一次，我们使用 PyTorch 机器学习管道来构建 QML 模型。
# 同样，本教程不是关于最佳 QML 实践或最佳 PyTorch 管道实践，而是关于 PyTorch 和 TensorCircuit 之间集成的演示。
# %% [markdown]
# ## 设置
# %%
import time
import numpy as np
import... |
bf9614342bef0a5bad9236b52829e565ca4ade4346b2c63e3b4b9108be5fa0ec | Jupyter | 7,379 | 183 | # %%
!pip uninstall typing_extensions -y
!pip install --upgrade typing_extensions
!pip install torch torchvision
# %%
import torch
import nibabel as nib
import numpy as np
import matplotlib.pyplot as plt
import os
from torchvision.utils import make_grid
import torchvision.transforms as T
from scipy.ndimage import z... |
9fabeff362d28285559f5c43c3a73108cad42e429330c3822e66a100052166f9 | Jupyter | 7,411 | 210 | # %% [markdown]
# ## ================================================================
# ## TIME STEP SENSITIVITY IN SINGLE-NEURON SIMULATIONS (BRIAN2)
# ## ================================================================
# This notebook investigates the impact of numerical integration step size (**dt**)
# on the preci... |
3a8673217b0513ea78a2efa92f2c98854b6f436939c74d8bc232df6d12d05c43 | Jupyter | 7,415 | 197 | # %%
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.pancreas()
# %% ... |
761fa1a8d2598511cc80ad2202016e161017a954fd9466586cf143b282d8db03 | Jupyter | 7,424 | 213 | # %%
## Necessary imports
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from statsmodels.stats.multitest import multipletests
# %%
"""
Non-parametric comparison of AUC between stress and stim conditions across multiple timepoints
with repeated measurements per animal.
Workflow (per timepoin... |
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