sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
702b726a35395a69f361504bc889641cf6ecf3da6a73b01088f3e12a712d66cd | Jupyter | 5,106 | 187 | # %%
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/'
# ── Output path ─────────────────────────... |
8ed5353fe0c6dabcee302b08136db2ae2c057016d5d41e4926eccc4475bdd2c4 | Jupyter | 5,108 | 157 | # %% [markdown]
# # 算符扩散
# %% [markdown]
# ## 概述
# %% [markdown]
# 在本教程中，我们将介绍作为混沌动力学和信息加扰诊断的算子扩散。我们将检查算子扩散作为电路深度 $L$ 的函数，可以将其视为离散量子系统中的时间 $t$。 此处考虑的算子扩散系数为：
# $$
# C_{y}(j,t)=\frac{1}{2} \text{Tr}([O(t), \sigma^{(j)}_{y}]^{\dagger}[O(t),\sigma^{(j)}_{y}]),
# $$
# 其中 $\sigma_{y}^{(j)}$ 是第 $j$ 个量子位的 Pauli-y 矩阵。 $O(0)$... |
0ff9499b397825eb097afa2939c2edec59a815f0f47e2c70e68d89fee925f3ea | Jupyter | 5,109 | 115 | # %%
import pickle as pkl
import matplotlib.pyplot as plt
import os
import numpy as np
# %%
path = "/srv/scratch/anusri/chrombpnet_paper/results/chrombpnet/ATAC/GM12878/"
#universal_transfer=path+"ATAC_10.08.2021_withuniversalbias/with_universal_bias_final_model/unplug/"
#invivo=path+"ATAC_10.09.2021_withinvivobias/f... |
b517b9c92db8beb9f4ba70b02923a335cabaae66d7c4d926add0c5ed17582250 | Jupyter | 5,114 | 122 | # %%
import os
import subprocess
import pandas as pd
os.environ["CUDA_VISIBLE_DEVICES"] ="0"
pythoner = "/home/sxh/anaconda3/envs/chemprop/bin/python"
optimizer = "/home/sxh/Research/chemprop/hyperparameter_optimization.py"
trainer = "/home/sxh/Research/chemprop/train.py"
predicter = "/home/sxh/Research/chemprop/p... |
9204f2602ec888c74d2b8e0fca8f81d2cde11f32dad289f1dad763a7f70dc3cc | Jupyter | 5,115 | 188 | # %%
import pandas as pd
import numpy as np
import glob
import os
from tqdm import tqdm
# %%
!pwd
# %% [markdown]
# # Databases Having Gene Phenotype relation of celegans
# %%
# gendr/Cele/Cele_GenDR_Gene_BioProcess.csv
# Monarch/Monarch_final/Celegans/Gene_Cele_BiologicalProcess.csv
# %%
BASE_DIR = '/storage/A... |
cb079c7c9b78b60567971c9d4a1ae126af971b9f0745ec3402dc2204adbda922 | Jupyter | 5,117 | 172 | # %%
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/'
!mkdir Celegans_chemical_gene
# ── Output ... |
7ac02f826a7ef300f1840a6e30520563e6ed2e9230758525acd14222c7d1670f | Jupyter | 5,163 | 186 | # %%
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"]="0"
np.random.seed(123)
tf.compat.v1.set_rand... |
eecf8c84ce7030b081859f355bb84383d9360c74dc1078c8ce1f1008f47687ab | Jupyter | 5,164 | 178 | # %% [markdown]
# # Phenotype ↔ ChemicalEntity Relation-Wise Merge
#
# Merges Phenotype–Chemical triples from PrimeKG; fills missing `tail_detail_name`
# via PubChem IUPAC lookup; deduplicates by `(head, relation, tail)`; and saves the result.
# %% [markdown]
# ## 0. Configuration
# %%
import os
import pandas as pd
... |
da076986985d0081693abe51f5c4fa04b9449751dc5cd7cd76bcc5bc5b05b1e6 | Jupyter | 5,174 | 107 | # %% [markdown]
# # Customized Contraction
# %% [markdown]
# ## Overview
#
# If the simulated circuit has large qubit counts, we recommend users try a customized contraction setup instead of the default one, which is greedy.
# %% [markdown]
# ## Setup
#
# Please refer to the [installation documentation](https://cot... |
3d131ed9b828a36fc7aa0d5877bde35dd0bbcdf086e2cf9f58c6d4ab3cf8e9ca | Jupyter | 5,176 | 173 | # %%
import os, sys, logging
import torch
from torch import nn, tensor
from functools import partial
sys.path.append(os.path.join(os.getcwd(), '..'))
from utils.DataLoaders_jupyter import Get_Dataset, Create_DataLoaders
from utils.DataLoaders_noSplit import Get_Dataset_noSplit, Create_DataLoaders_noSplit
from utils.L... |
e7c7495a020df41c5dfb954017cb5004e6b074d53990c98cfe158dfcc3795375 | Jupyter | 5,182 | 175 | # %% [markdown]
# # ChemicalEntity ↔ Tissue Relation-Wise Merge
#
# Merges Chemical–Tissue triples from EvoAGE; resolves tissue names via BTO;
# deduplicates by `(head, relation, tail)`; and saves the result.
# %% [markdown]
# ## 0. Configuration
# %%
import pandas as pd
# ── Base directories ──────────────────────... |
600907797b69a05839789b475cb1410dd6ba9669c693735cac2b7a628142d9ba | Jupyter | 5,190 | 170 | # %% [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 os
import pandas as pd
impor... |
b41aeeab9c54a2f5fa308d63fc076660b9c4b42d15806370d4f434437976dfbf | Jupyter | 5,200 | 185 | # %% [markdown]
# # Create Graph Dataset
# %%
import sys
import os
import pickle as pkl
import pandas as pd
import torch
path = os.path.join('..', '.')
if path not in sys.path:
sys.path.append(os.path.abspath(path))
from src.protein_graph import pncaGraph
from tqdm import tqdm
import warnings
warnings.filterwa... |
10eab27f661aa405bfc5f6d4a8d95bce171ec0697738630a75e918586c755b3e | Jupyter | 5,208 | 170 | # %% [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 os
import pandas as pd
impor... |
b12843a8e2f016f683f8fbe72fe3c175e70bed3c019a179312779af3870660d6 | Jupyter | 5,220 | 197 | # %%
import pandas as pd
import numpy as np
import glob
import os
from tqdm import tqdm
# %%
!pwd
# %% [markdown]
# %%
! pwd
# %%
BASE_DIR = '/storage/Arushi/090526_EvoAge/kg_formation/'
MAPPING_DIR = BASE_DIR + 'data_collection/databases_for_mapping/'
PROC_DIR = BASE_DIR + 'processed_data/'
# ── Output ... |
2a01254e960aedb371d157b9db3c15a7c88ed1af0c8e8535b545d5d167f36f0a | Jupyter | 5,226 | 136 | # %% [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... |
6834a9aa13bbceac25fac99fe817c013cc599989157273b9d03c52ea31774bbb | Jupyter | 5,233 | 193 | # %%
import pandas as pd
import numpy as np
import glob
import os
from tqdm import tqdm
# %%
!pwd
# %% [markdown]
# # Databases Having Gene biologicalprocess relation of celegans
# %%
# gendr/Cele/Cele_GenDR_Gene_BioProcess.csv
# Monarch/Monarch_final/Celegans/Gene_Cele_BiologicalProcess.csv
# %%
BASE_DIR = '/s... |
19bd910e443509bac43b943bb4dcc768202ac593f3d7f2fe960d48ddb02f338c | Jupyter | 5,243 | 190 | # %%
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"]="0"
np.random.seed(123)
tf.compat.v1.set_rand... |
927759b85e744ca36d808a3b9b9333cf5ad166b26234c354936a86dfeb109f1e | Jupyter | 5,245 | 190 | # %%
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"]="0"
np.random.seed(123)
tf.compat.v1.set_rand... |
72923c0e9d4faa350c9ee583eff32023a0883f3f658cf8b6a9bdf308bc5c5d4e | Jupyter | 5,257 | 132 | # %% [markdown]
# # Figure 6 — Source Data Export
#
# **Figure 6** examines the Hessian geometry of the GAN loss landscape and
# its relationship to neural tuning curves. Panel 6C shows an example
# single-unit tuning curve sampled along Hessian eigenvectors. Panel 6D
# shows tuning heatmaps for preferred and non-pr... |
54381b661064ca94793733d60b3570b03e03fa9791695f17d7cfcaf6029c319d | Jupyter | 5,264 | 197 | # %%
import pandas as pd
import numpy as np
import glob
import os
from tqdm import tqdm
# %%
!pwd
# %% [markdown]
# %%
! pwd
# %%
BASE_DIR = '/storage/Arushi/090526_EvoAge/kg_formation/'
MAPPING_DIR = BASE_DIR + 'data_collection/databases_for_mapping/'
PROC_DIR = BASE_DIR + 'processed_data/'
# ── Output ... |
cbeff368b6437b01e5e1c97a345176ac1dd8075958b93338df823ea26d4116c7 | Jupyter | 5,267 | 210 | # %%
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"]="0"
np.random.seed(123)
tf.compat.v1.set_rand... |
f026e1cdc8cf24398b12380fcd86409ff430268d10ad79776d6583131ec82f94 | Jupyter | 5,274 | 150 | # %%
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'... |
8086772ddb39d202598df41f7e7fd7b75f0da41f34ccab6c8dd40da1495270e3 | Jupyter | 5,275 | 172 | # %% [markdown]
# # Advanced Automatic Differentiation
# %% [markdown]
# ## Overview
#
# In this section, we review some advanced AD tricks, especially their application to circuit simulations. With these advanced AD tricks, we can evaluate some quantum quantities more efficiently.
#
# The advanced AD is possible in... |
0621572fdb437ac5a9e1779ec0c2a392cd14e500fac324bcfe325a97ea5cc3ef | Jupyter | 5,282 | 133 | # %% [markdown]
# ### MACSima proteins
# %%
## Call all functions
%run integrate_niches_functions.ipynb
# %%
save_folder="/data/Combined_Analysis/Integration/FetPed_SelectedGenes_8samples/"
# %%
### Maxima
P7_macsima = sc.read_h5ad("./P7w_macsima.h5ad")
# %%
sc.pp.neighbors(P7_macsima)
# %%
sc.tl.leiden(P7_macsi... |
36a7a3b1a837cc768cc4561e705ba3e285890ba9ee132f67c0b4ec87d6821f8b | Jupyter | 5,285 | 187 | # %%
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/'
DB_DIR = BASE_DIR + '... |
42f06b2234e73e52de096e6bcaf6781debc0031ed7aade1a18f75e86780427e1 | Jupyter | 5,286 | 193 | # %%
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(123)
tf.compat.v1.set_rand... |
fff1c9f3a4d534a05e285676aed168853d1b790129b689e0f01b48226bd6622f | Jupyter | 5,293 | 234 | # %% [markdown]
# # 电路基础
# %% [markdown]
# ## 概述
#
# 在这篇笔记中，我们将了解 TensorCircuit 中核心对象的基本操作-``tc.Circuit``，它支持无噪声仿真和基于蒙特卡洛轨迹的噪声仿真。更重要的是，几乎所有对 Circuit 对象的操作都是可微分的和可即时编译的，这是成功高效地进行变分量子算法模拟的关键。
# [WIP note]
# %% [markdown]
# ## 设置
# %%
from functools import partial
import inspect
import sys
import numpy as np
import te... |
15b0f4120e3f636aaad1ee204334fb2a8d93d060b22ce3a051449d5b45e0e747 | Jupyter | 5,301 | 189 | # %%
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/'
DB_DIR = BASE_DIR + '... |
56c45827513ff185e5f2a5c64509edadc35b96319ab7c7e67029f0317b8af55d | Jupyter | 5,311 | 168 | # %% [markdown]
# # Pathway ↔ Gene Relation-Wise Merge
#
# Merges Pathway–Gene triples from PrimeKG; drops rows with missing `tail_detail_name`;
# deduplicates by `(head, relation, tail)`; and saves the result.
# %% [markdown]
# ## 0. Configuration
# %%
import os
import pandas as pd
BASE_DIR = '/storage/Arushi/09... |
f4120b3adda873bafb01ecddcc50e8f09c9af7b217af9e938f96e2a95ad6babc | Jupyter | 5,311 | 152 | # %% [markdown]
# # Qudit Circuit Basics
#
# *A gentle intro to `tensorcircuit.quditcircuit.QuditCircuit`*
# %% [markdown]
#
# ## Overview
#
# This tutorial shows how to build and simulate **qudit** circuits (d‑level systems, where `d ≥ 3`) using `tensorcircuit`'s `QuditCircuit` API.
# **Highlights**
# - Create a `... |
1c4f2a67091e500c0bdb3bfbffd6a2669bf9a897fde9a0053e27a7d021b35410 | Jupyter | 5,312 | 195 | # %%
import pandas as pd
import numpy as np
import glob
import os
from tqdm import tqdm
# %%
!pwd
# %% [markdown]
# %%
# Monarch/Monarch_final/Drosophila/GENE_GENE_Droso_Droso.csv
# string/dmel/string_DROSO_GENE_GENE.csv
# %%
BASE_DIR = '/storage/Arushi/090526_EvoAge/kg_formation/'
MAPPING_DIR = BASE_DIR + 'd... |
695aa0b990c984cf7cb70a71aeeb13bfee4f97c21298e37f028a9c6a59df2bb8 | Jupyter | 5,313 | 155 | # %%
import os
import pandas as pd
import numpy as np
# %%
# ── Base directories ──────────────────────────────────────────────────────────
BASE_DIR = '/storage/Arushi/090526_EvoAge/kg_formation/'
PROC_DIR = BASE_DIR + 'processed_data/'
DB_DIR = BASE_DIR + 'data_collection/databases_for_mapping/'
# ── Require... |
3acd1600cf2113deb2cd29a7fa4f59f526bb4caf88e69ae7372620d741a13d6e | Jupyter | 5,314 | 193 | # %%
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"]="0"
np.random.seed(1)
tf.compat.v1.set_random... |
4d76640c832940d5491313c9a33f0f7a512b8dd98249bc61a79c7d05d6d96766 | Jupyter | 5,316 | 174 | # %% [markdown]
# # Set Up
# %%
# import necessary packages
import pandas as pd
import numpy as np
from nilearn import surface
import usefulFunctions as uf
import os
import glob
from scipy.stats import ttest_rel
# %%
# falg to save csvs
save_csv = True
# set directories
base_dir = f'{os.path.dirname(os.getcwd())}/'
... |
206861bad2afb2c52ddd027e96e474771f7f3fc3b75b4f32f6e24f60f8354a1c | Jupyter | 5,320 | 141 | # %% [markdown]
# # Figure 2 — Source Data Export
#
# **Figure 2** shows an example PIT (posterior inferotemporal) neuron evolution experiment (Exp 155),
# demonstrating the evolution trajectory and population PSTH dynamics across generations.
#
# ## Data requirements
#
# > **Raw neural recordings are required to ru... |
c058a632460837ec819500aa741795d38f084681df90878f2eb4b41c1e6bda5e | Jupyter | 5,327 | 146 | # %%
# using dataframes stored in /dfs, visualize connectivity as a heatmap
# %%
# import packages
import pandas as pd
import numpy as np
from matplotlib import pyplot
import matplotlib.pyplot as plt
import seaborn as sns
import cmocean
# %%
# open postsynaptic connectivity table of left admn sensory neurons
full_df... |
5f87cb50fa5710264acd2e3be346f7e4fd2c0617a6a948be787fe41ab10a8325 | Jupyter | 5,362 | 178 | # %% [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
# %%
# flag to save csv
save_csv = True
#set paths
base_dir = f'{os.path.dirname(os.getcwd())}/'
roi_dir = f'{base_dir}data/labels/'
psc_dir = f'{bas... |
6e334b35e8a86c160a9b7d291c1532fce5cf492c1cd030ad4db45862da711fb4 | Jupyter | 5,362 | 160 | # %% [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... |
61a1d783647cbaa22b1f59c5099c76e2a2f8ec0811ba6f50b695388116db546a | Jupyter | 5,376 | 174 | # %% [markdown]
# # Phenotype ↔ Phenotype Relation-Wise Merge
#
# Merges Phenotype–Phenotype triples from PrimeKG, GPKG, 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... |
11c5881919ca6254b350562643c25b04c1c394ed81f197e800bf4e9c7baed6c7 | Jupyter | 5,386 | 192 | # %% [markdown]
# This is an example of train and test pipeline for GemNet-OC model.
# Same task could be performed with pre-defined config from repository root:
# ```bash
# python run.py --config-name gemnet-oc.yaml
# python run.py --config-name gemnet-oc_test.yaml
# ```
# For detailed description please refer to [R... |
70d7ce7d196216eb300990d06cf236c5875f453d27fb458e5dfa769358f0a6b4 | Jupyter | 5,390 | 203 | # %%
import pandas as pd
import numpy as np
import glob
import os
from tqdm import tqdm
# %%
!pwd
# %%
# gendr/Droso/Droso_GenDR_Gene_BioProcess.csv
# Monarch/Monarch_final/Drosophila/Gene_Droso_BiologicalProcess.csv
# %%
BASE_DIR = '/storage/Arushi/090526_EvoAge/kg_formation/'
MAPPING_DIR = BASE_DIR + 'data_co... |
72a422472c5c28cda398f6cc7fd0277662b98dc3113bdfee427dbcea47d638b2 | Jupyter | 5,408 | 131 | # %% [markdown]
# # Generate tables for the empirical assessment on P1000 matched somatic +/- germline data at the patient-level
# Here, we analyze results of running models on empirical data: matched somatic +/- germline data from the P1000 dataset.
#
# Prerequisites:
# - you ran the empirical assessment experiment ... |
e5a0865254651dbf86aaa00b6aa9a193388c92c0b4d1b22c4a58d0244856ca6d | Jupyter | 5,410 | 153 | # %% [markdown]
# # 00 settings
# %%
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import seaborn as sns
from scipy.stats import pearsonr
from scipy import stats
from scipy import signal
import pymannkendall as mk
# settings
duration = 10
bs_start_idx = [2306,2338,... |
2a9797aa8865ab94af591a19d4d252e8ac1849474ddbb6cd833001ddfe45fd43 | Jupyter | 5,420 | 139 | # %%
import pandas as pd
import os
# %%
# ── Base directories ──────────────────────────────────────────────────────────
BASE_DIR = '/storage/Arushi/090526_EvoAge/kg_formation/'
PROC_DIR = BASE_DIR + 'processed_data/'
DB_DIR = BASE_DIR + 'data_collection/databases_for_mapping/'
# ── Output path ──────────────... |
4f0fcec670a54f3b2d2c18296a853d5b208289cc1a940820ab93d090779d567d | Jupyter | 5,429 | 178 | # %% [markdown]
# # 结合SKLearn实现的支持向量分类
#
# [_Mark (Zixuan) Song_](https://marksong.tech) 撰写
#
# 本示例结合了`sklearn`库中的`SVC`类，实现了支持向量分类。
# %% [markdown]
# ## 概述
#
# 本示例的目的是将量子机器学习（QML）转换器嵌入到SVC管道中并且介绍`tensorcircuit`与`scikit-learn`的一种连接方式。
# %% [markdown]
# ## 设置
#
# 安装 `scikit-learn` 和 `requests`. 本模型测试数据为 [德国信用]The d... |
f55a49d98bd05bb2b855794cff1e992d99253ddf21690a150622e8d525071a4c | Jupyter | 5,445 | 153 | # %%
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'
# %%
results_fold... |
4c9d09d3e5c245dca1fa2b235d68ae8410a2b50f2787fcb97266230907107a26 | Jupyter | 5,450 | 209 | # %%
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/'
# ── Output path ─────────────────────────... |
97decc5601d148fd86f1f313262950f168df2e18ec67f36d2e1ea8d259b2caf7 | Jupyter | 5,472 | 157 | # %% [markdown]
# # Operator spreading
# %% [markdown]
# ## Overview
# %% [markdown]
# In this tutorial, we will introduce the operator spreading that serves as a diagnostic of the chaotic dynamics and information scrambling. We will examine operator spreading as a function of the circuit depth $L$ that can be regard... |
c812d3100a91d9ceea536b0ce10e7dfd1edf8664fb6a32d20155d1dea56d00cc | Jupyter | 5,476 | 235 | # %% [markdown]
# # Circuit Basics
# %% [markdown]
# ## Overview
#
# In this note, we will learn about basic operations of the core object in TensorCircuit - ``tc.Circuit`` which supports both noiseless simulation and noisy simulation with the Monte Carlo trajectory-based method. More importantly, near all the operat... |
95239aa620dbd740ee7a874c592213a51167ca4834d59b85d1bbb221626db16f | Jupyter | 5,491 | 207 | # %%
import pandas as pd
import numpy as np
import glob
import os
from tqdm import tqdm
# %%
!pwd
# %% [markdown]
# # Databases Having Gene gene relation of celegans
# %%
# fic/fic_CELE_GENE_GENE.csv
# wid/wid_CELE_GENE_GENE.csv
# string/celegans/string_CELE_GENE_GENE.csv
# Monarch/Monarch_final/Celegans/GENE_GENE_C... |
92e65b0c7adb18771ae8820a7df0b7b75a973b6a644d88f223cfc0df5b7befa6 | Jupyter | 5,495 | 207 | # %%
!pip install torch torchvision opencv-python tqdm
# %%
from google.colab import files
uploaded = files.upload()
# %%
!unzip mini_dataset.zip
# %%
import os, cv2, torch
from torch.utils.data import Dataset, DataLoader
class MiniWMH(Dataset):
def __init__(self, root="mini_dataset", size=224):
self... |
b74682bd00397adf967045e6c4a30c9589a6871b3f84e7f743d5980e6c94d6ac | Jupyter | 5,498 | 193 | # %%
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from tqdm import tqdm
from joblib import load, dump
import time
from molmap import dataset
from molmap import loadmap
from molmap import model as molmodel
import molmap
#use GPU, if negative value, CPUs will be used
import tensorflow as tf
#i... |
6ecd0f637f50e8058c58d7831c83dae0e2f0bb9da9c0f32b7f01d60090c1a4f3 | Jupyter | 5,500 | 146 | # %% [markdown]
# # Qudit（多能级量子比特） 电路基础
#
# *`tensorcircuit.quditcircuit.QuditCircuit` 的轻量入门*
# %% [markdown]
#
# ## 概述
#
# 这个教程展示了如何使用 `tensorcircuit` 的 `QuditCircuit` API 来构建和模拟 **qudit（多能级量子比特）** 电路（d 级系统，其中 `d ≥ 3`）。
#
# **要点**
# - 创建一个 `QuditCircuit(nqudits, dim)` with dimension `dim ∈ [3, 36]`；
# - 单比特门: `X`... |
97caccecce4486100fbcef4c8050cd68c06e3f6b788bcec90eb4752014d10643 | Jupyter | 5,511 | 164 | # %%
import numpy as np
import pandas as pd
from pathlib import Path
%matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
import src.util_analysis as util_analysis
# %%
# So that we can edit the text in illustrator
matplotlib.rcParams.update({'font.size': 10})
matplotlib.rcPar... |
6ead4192296720e9bf1d18a4df115fba53c71349c5cbea314cf05677a218599f | Jupyter | 5,518 | 192 | # %%
import torch
import numpy as np
import cv2
import os
import torch.nn.functional as F
import torchvision.transforms as transforms
from tqdm.auto import tqdm
from google.colab import drive
drive.mount('/content/drive')
import sys
sys.path.append('/content/drive/MyDrive/ResNetModel/src')
from model import build_m... |
798f4159de3d806aab4396000fa93f58742e8f00395943b8c95a67d2b5d25683 | Jupyter | 5,522 | 149 | # %%
import numpy as np
from itertools import islice
import pandas as pd
import pickle
import csv
import matplotlib.pyplot as plt
import seaborn as sns
from isttc.scripts.cfg_global import project_folder_path
from isttc.acfunc import acf_sttc, acf_pearsonr_trial_avg, acf_sttc_trial_avg, acf_sttc_trial_concat
from istt... |
84165ce568ba0a331a3793ee84cc75eda62485e505180c855e0b4e7c68908808 | Jupyter | 5,527 | 182 | # %%
from __future__ import print_function
from __future__ import division
from __future__ import unicode_literals
import numpy as np
import tensorflow as tf
from joblib import load,dump
from rdkit import Chem
import pandas as pd
from deepchem.models import GraphConvModel, MPNNModel
import deepchem as dc
from deepche... |
2e3f69c1443adb0a07030216e8adbaa43b9816b7da42b02e34c71646ec803df4 | Jupyter | 5,532 | 166 | # %%
import torch
import argparse
import torch.nn as nn
import torch.optim as optim
import time
from tqdm.auto import tqdm
from google.colab import drive
drive.mount('/content/drive')
import sys
sys.path.append('/content/drive/MyDrive/ResNetModel/src')
from model import build_model
from findmydatasets import get_dat... |
67c6499c2d6a30b8732c722d7bac46df03ae4a6f44197d8e49edeeeaae805eaa | Jupyter | 5,551 | 143 | # %% [markdown]
# # Figure 5 — Source Data Export
#
# **Figure 5** examines the temporal dynamics of the evolution process via
# time-binned PSTH analysis. Panel 5A shows the population-average PSTH at
# the first (block 0) and last (block 55) evolution blocks. Panel 5C shows
# how the activation trajectory changes ... |
6e524e6cb83717f8a4b86ccb39682b7b9ac62c4b21e3a124c059799a17246b48 | Jupyter | 5,575 | 212 | # %% [markdown]
# # mirTARbase
# %%
import pandas as pd
# %%
BASE_PATH = "/storage/Arushi/090526_EvoAge/kg_formation/data_collection/"
OUT_PATH = "/storage/Arushi/090526_EvoAge/kg_formation/processed_data/mirTARbase/"
# %%
drerio_ncbi = pd.read_csv(
f'{BASE_PATH}databases_for_mapping/ncbi/Danio_rerio.gene_info'... |
9b698ab7156e2fbbbd862bfb9d72d04de3a6d698c91f9fb196eae0d0ecc151e2 | Jupyter | 5,600 | 178 | # %% [markdown]
# # Support Vector Classification with SKLearn
#
# Authored by [_Mark (Zixuan) Song_](https://marksong.tech)
#
# We use the `SKLearn` library to implement `SVC` in the following tutorial.
# %% [markdown]
# ## Overview
#
# The aim of this tutorial is to implant a quantum machine learning (QML) transf... |
6b5e9c3b517fb44ef39da25200ddd9f4f6d02010a5c1fd1f015c1336cffee68c | Jupyter | 5,606 | 141 | # %%
import pandas as pd
import os
# %%
# ── Base directories ──────────────────────────────────────────────────────────
BASE_DIR = '/storage/Arushi/090526_EvoAge/kg_formation/'
PROC_DIR = BASE_DIR + 'processed_data/'
DB_DIR = BASE_DIR + 'data_collection/databases_for_mapping/'
# ── Output path ──────────────... |
8fc0b5dd5726dae7cd2e6327a391696c07ae2e34f28e5e61627a317a73915a39 | Jupyter | 5,610 | 101 | # %% [markdown]
# # `bar` plot
#
# This notebook is designed to demonstrate (and so document) how to use the `shap.plots.bar` function. It uses an XGBoost model trained on the classic UCI adult income dataset (which is classification task to predict if people made over 50k in the 90s).
# %%
import xgboost
import sha... |
7b482ed80448bd9423216782180e73ec54b9a841915d1182cec1606628ac52ec | Jupyter | 5,615 | 234 | # %%
import pandas as pd
import numpy as np
import glob
import os
from tqdm import tqdm
# %%
!pwd
# %% [markdown]
# %%
# Monarch/Monarch_final/Zebrafish/GENE_GENE_Zebrafish_Zebrafish.csv
# string/drer/string_Zebra_GENE_GENE.csv
# %%
BASE_DIR = '/storage/Arushi/090526_EvoAge/kg_formation/'
MAPPING_DIR = BASE_D... |
50facbc9aa1b592d041d3e4056d520b85f74f17166aa98383baaa23cb17cdc5a | Jupyter | 5,624 | 157 | # %%
import os
import re
import pandas as pd
import numpy as np
# %%
# ── Base directories ──────────────────────────────────────────────────────────
BASE_DIR = '/storage/Arushi/090526_EvoAge/kg_formation/'
PROC_DIR = BASE_DIR + 'processed_data/'
DB_DIR = BASE_DIR + 'data_collection/databases_for_mapping/'
#... |
f2b6e857efd62f6d6d99ed69fd96a5b2b64e6322ac97cf93cdebd31527786f31 | Jupyter | 5,642 | 173 | # %% [markdown]
# # Experiment 7 figure generation and statistics
# %%
import pickle as pkl
import pandas as pd
import numpy as np
from pathlib import Path
%matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
# So that we can edit the text in illustrator
matplotlib.... |
d6f8ce906080a9fee940a2045859d98ef8921635d1b6b6049229c158a1ca3b7d | Jupyter | 5,662 | 182 | # %% [markdown]
# This is an example of train and test pipeline for PaiNN model from schnetpack library.
# Same task could be performed with pre-defined config from repository root:
# ```bash
# python run.py --config-name painn.yaml
# ```
# For detailed description please refer to [README](../nablaDFT/README.md).
# ... |
0e6c6a6c242aade40e914437f57b6bacb14a31884e8994b6c66b81d6854c633e | Jupyter | 5,671 | 150 | # %% [markdown]
# # AMPD - Automatic Multiscale Peak Detection
#
# This notebook provides an end-to-end pipeline for processing and analyzing fNIRS data collected during a finger-tapping task. The primary goal is to identify peaks in the time series data using an **Optimized AMPD** algorithm.
#
# The **AMPD** algorit... |
32a674cdc117674db5bfcad278ba37bb6c153bcb4613974c52c7491f7742d024 | Jupyter | 5,685 | 198 | # %%
# using dataframes stored in /dfs, compute cosine similarity between each left ADMN sensory neuron
# %%
# import packages
import pandas as pd
import numpy as np
from matplotlib import pyplot,patches
import matplotlib.pyplot as plt
import seaborn as sns
import cmocean
from sklearn.metrics.pairwise import cosine_... |
a7dc798c7f960c310705f611fb627a515031ad3398b2333d12e44b46fd9c7b11 | Jupyter | 5,698 | 204 | # %%
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/'
# ── Output path ... |
5c14eb018a8b5ff5b7b0f30d179d8ac3278308a275a655f3b956f83c3607066c | Jupyter | 5,700 | 208 | # %%
import sys
sys.path.append('/Users/midani/OneDrive/proj/leap/manuscript')
import os
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 = lib_aux... |
dcf29e5fa1e69a75bbf8a7addaedb74bf344d0ab767e0e04904d9c7408304d65 | Jupyter | 5,702 | 155 | # %% [markdown]
# # 00 settings
# %%
import pandas as pd
import h5py
import hdf5storage
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import seaborn as sns
from scipy.stats import pearsonr
from scipy import stats
from scipy import signal
import pymannkendall as mk
# settings
duration =... |
3714d6289d185c1568b3017a74ae3a78d7040aa9e5ec1a879cd773da0feaf16d | Jupyter | 5,712 | 210 | # %%
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/'
# ── Output path ... |
e4724e5eba1ffcf3c240690801a40b306634af1f41137aaf569e84f8fcc59041 | Jupyter | 5,728 | 193 | # %%
import sys
import os
# Add the parent directory of `notebook/` to sys.path
sys.path.append(os.path.abspath(".."))
# model
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch_geometric as tg
import torch_scatter
import e3nn
from e3nn import o3
from typing import Dict, Union
# crystal ... |
d21922db48bd374669d4a7f4d898e2c23e35d953242c09ac371e4bc323f9c794 | Jupyter | 5,744 | 159 | # %% [markdown]
# todo here some parametric runs:
# %%
import pandas as pd
import numpy as np
import csv
import random
import matplotlib.pyplot as plt
import seaborn as sns
from isttc.scripts.cfg_global import project_folder_path
from isttc.acfunc import acf_pearsonr_trial_avg, acf_sttc_trial_avg
# %% [markdown]
# #... |
f889e91a09750a8a654b51adf7fd008cd718a3f15ca33b19a0fffe15d55c8d09 | Jupyter | 5,748 | 200 | # %%
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"
# %%
def get_attentiveFP_idx(df):
... |
cde9c6fab6915edfb48ac204e5aa5ee129a29f5c4e9f96cfac71c9235be429a0 | Jupyter | 5,755 | 214 | # %%
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/'
# ── Output path ... |
64681a6b4ef8313a649784a38c9f2a6d24fb83132922c3b137e66799aac93d18 | Jupyter | 5,760 | 156 | # %%
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
from pathlib import Path
%matplotlib inline
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
import matpl... |
c3df3c8cda8b07ce36b35371d6ca9499c27cd2edbbf99d4ee85e706a8d166bfb | Jupyter | 5,781 | 194 | # %%
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"]="0"
def get_attentiveFP_idx(df, file = './sp... |
d1586e4d2013ecc1c9ccce237675cbc78d506b7dbd581e8ab66df8712f423be5 | Jupyter | 5,796 | 200 | # %%
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"]="0"
def get_attentiveFP_idx(df, file = './sp... |
36bd9d4b35637dd0b03bffe4e8feff0dfa418187d3dda93912c375636179e8dd | Jupyter | 5,818 | 185 | # %% [markdown]
# # Display 3D neuron with crystals
# %% [markdown]
# Load img
# %%
import nrrd
lab_n, _ = nrrd.read(r"D:\Zuohan\neuron\neuron.nrrd")
lab_c, _ = nrrd.read(r"D:\Zuohan\neuron\crystal.nrrd")
import yaml
from CrystalTracer3D.io import CrystalReader
with open('config.yml', 'r') as ymlfile:
cfg = yaml... |
7ac57a2c874b3d23c863567f782322a404e92578b2422727ce8e4fc50d599c4c | Jupyter | 5,828 | 157 | # %% [markdown]
# Loading dataset from Allen repository:
#
# * we use stimulus set "Functional Connectivity", spontaneous 30 minute block (animals are shown gray screen), 26 mice (4 genotypes)
# * data is loaded from warehouse once (60Gb) and then used locally
# * spikes from spontaneous session are loaded and stored ... |
3f6996aeae96375d4693036654a57ec944298e7cf657d619ef98eb30fc060daf | Jupyter | 5,839 | 150 | # %% [markdown]
# # Extended Figure 4 — Source Data Export
#
# **Extended Figure 4** provides additional temporal detail for the attribution
# and trajectory analyses in Figure 5. Panel 4B shows time-binned
# differential attribution scores for five bin sizes (5, 10, 20, 25, 50 ms).
# Panel 4C replicates the time-bin... |
dff566a1bf857906b032e2d6c5efb80280e74a9d5811ef30164be1d781b1c2fd | Jupyter | 5,872 | 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
from tqdm.auto import tqdm
from pathlib import Path
%matplotlib inline
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
import matpl... |
7f42e8b15446d20c8926e453fe90a998f9e74afa5b9f4228b01b42cfbfbd9879 | Jupyter | 5,874 | 199 | # %%
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"]="0"
np.random.seed(123)
tf.compat.v1.set_rand... |
9fa13fc48611b6bb4cf71acdbc60f3264337a57eb47868e98367252c3de32aeb | Jupyter | 5,879 | 158 | # %%
import os
import pandas as pd
import numpy as np
# %%
# ── Base directories ──────────────────────────────────────────────────────────
BASE_DIR = '/storage/Arushi/090526_EvoAge/kg_formation/'
PROC_DIR = BASE_DIR + 'processed_data/'
DB_DIR = BASE_DIR + 'data_collection/databases_for_mapping/'
# ── Output ... |
40447b75248070dbf96e5809eacd302d9fbe1b74d4064452fcd7820fa4a749d6 | Jupyter | 5,883 | 216 | # %%
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(123)
tf.compat.v1.set_rand... |
e9837a2897b5b0dbad86e97268fc0351251c087380c6ede9a5985aaf512aca12 | Jupyter | 5,883 | 251 | # %% [markdown]
# # Analyse user response data
# %%
import pandas as pd
import matplotlib.pyplot as pl
import numpy as np
import json
import shap
# %%
responses = []
with open("trial_data2_3_3_2019.csv") as f:
lines = f.readlines()
for line in lines:
parts = line.replace('""', "'").replace('"', "").sp... |
14f57b2908459675544b45b6cc6f0ac850dbf6108c797e19fa7d2d6cdd941474 | Jupyter | 5,895 | 202 | # %%
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"]="0"
np.random.seed(123)
tf.compat.v1.set_rand... |
80d8302511ea678c0b5fff725a1a7aed1f3a9f14bcd312c472a64aed63453fd6 | Jupyter | 5,898 | 166 | # %% [markdown]
# # MolecularFunction → ChemicalEntity Relation Pipeline
#
# Builds a unified, deduplicated edge table for the strictly **MolecularFunction–ChemicalEntity** relation (no swapping, only native MolecularFunction → ChemicalEntity).
#
# **Output schema:** `head | relation | tail | head_type | relation_typ... |
15f24bdeffa7c4bbec1b93a08ff998674e0e6301aa042c7f79f65feb76cf6a04 | Jupyter | 5,915 | 171 | # %% [markdown]
# Plot info about Allen dataset subset that we use.
#
# We use 8 areas: 6 visual cortical areas (V1, LM, AL, RL, AM, PM) and 2 thalamic (LGN, LP).
#
# below mapping from Rudelt at el (found in github code):
# structures = ["VISp", "VISl", "VISrl", "VISal", "VISpm", "VISam", "LGd", "LP"]
# stru... |
76c99f6cf7ac3c713d3623960a3d07bfd5bc8e41ed5ffb4fc9a591c68737c59f | Jupyter | 5,916 | 173 | # %%
# OS interface for interacting with the file system
import os
# Core data manipulation and visualisation libraries
import numpy as np # Numerical computing (arrays, math functions)
from scipy.linalg import logm, sqrtm # Matrix operations for Riemannian geometry
# ✅ Confirmati... |
0d13564a86daf530a91e085ca7d6d155c0c836d2b9d73c40361f06cfc489c105 | Jupyter | 5,968 | 212 | # %% [markdown]
# # Biomarker search
# Predictors of neurofeedback success?
# %%
import os
import pandas as pd
import src.my_settings as settings
sett = settings.settings()
# %%
# Read participants.tsv
df_p = pd.read_csv(os.path.join(sett["git_path"], "data", "participants.tsv"), sep="\t")
# remove sub-07 and sub-1... |
d89b29a198cebb1b32c6bb1e7fe349e11520e5d7a77e9be9b2ec66dadfc58503 | Jupyter | 5,976 | 215 | # %%
import sys
sys.path.append('/Users/midani/OneDrive/proj/leap/manuscript')
import os
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 = lib_aux... |
6551336f4b8dc27bf8b01a5710954f48f423b7846df25350a79c797e1c8c2a12 | Jupyter | 5,992 | 175 | # %%
# %%
# %%
import os
import pandas as pd
# ── Change only these two lines to relocate all I/O ──────────────────────────
BASE_PATH = "/storage/Arushi/090526_EvoAge/kg_formation/data_collection/" # all raw input files live here
OUT_PATH = "/storage/Arushi/090526_EvoAge/kg_formation/processed_data/" # all ... |
2c2642a41c51cbd1e58774118dc212254a0d1d478a5f3d034bb7279a801769d3 | Jupyter | 5,994 | 145 | # %%
import os
import pandas as pd
import numpy as np
# %%
# ── Base directories ──────────────────────────────────────────────────────────
BASE_DIR = '/storage/Arushi/090526_EvoAge/kg_formation/'
PROC_DIR = BASE_DIR + 'processed_data/'
DB_DIR = BASE_DIR + 'data_collection/databases_for_mapping/'
# ── Requir... |
aca528041207a7a76bf2fbf3e29643cd6e1897a27f6e72f9724e9d1df4224b93 | Jupyter | 6,000 | 188 | # %% [markdown]
# # Experiment 7 figure generation and statistics
# %%
import pickle as pkl
import pandas as pd
import numpy as np
from pathlib import Path
%matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
# So that we can edit the text in illustrator
matplotlib.... |
6a5fba642fd5a5cc5468b73cc3d3681929e56c29afe3742d3c6c41467a66eb8e | Jupyter | 6,004 | 162 | # %%
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 ... |
d2ab8da741e233d4433c1db38e3ca719e627c17f7d17a763dc4c2fb1a0cded9e | Jupyter | 6,011 | 151 | # %%
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... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.