sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
736362f12a475c24c01a8971601c2961decdfe025a24312ce2779bfd5648468a | Jupyter | 4,392 | 192 | # %% [markdown]
# # List of indexing methods
#
# also refer to [the Xarray docs](https://docs.xarray.dev/en/stable/user-guide/indexing.html)
# %%
import cedalion
import cedalion.data
import cedalion.xrutils as xrutils
import xarray as xr
# %%
rec = cedalion.data.get_fingertapping()
ts = rec["amp"]
geo3d = rec.geo3d
... |
8c9bd0dcfd3387263f5413996e91c34f858a6c9b0137cf3c8a2be0b88e7eb6f6 | Jupyter | 4,398 | 134 | # %% [markdown]
# # MolecularFunction → Protein Relation Pipeline
#
# Builds a unified, deduplicated edge table for the strictly **MolecularFunction–Protein** relation (excludes Genes, oriented MolecularFunction → Protein, no swapping).
#
# **Output schema:** `head | relation | tail | head_type | relation_type | tail... |
70f4fd1b6184b43b4b2ab81f3ab1468f5213a6514716ae18fddf046ebab93a7d | Jupyter | 4,408 | 153 | # %%
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from joblib import load, dump
import seaborn as sns
# %%
# %%
colors = sns.color_palette(palette = 'rainbow',n_colors=5)
sns.palplot(colors)
# %%
# %%
task_name = 'ESOL'
batch_sizes, res = load('./%s.x1.res' % task_name)
sns.set(style =... |
41ead730244de3af7c9de9b6a6b79b1408dd3d082b64991d86d756e9e2530cfa | Jupyter | 4,413 | 178 | # %% [markdown]
# # Storing estimated HRFs in snirf files
#
# This notebook estimates the HRF in a finger-tapping experiment by blockaveraging and then stores the result in a snirf file.
# %%
# This cells setups the environment when executed in Google Colab.
try:
import google.colab
!curl -s https://raw.githu... |
8022dd86970b1b9ff58055393dc71bdffbe86e53180f833feeca8c6e5a5314e1 | Jupyter | 4,421 | 84 | # %% [markdown]
# # `Permutation` explainer
#
# This notebooks demonstrates how to use the Permutation explainer on some simple datasets. The Permutation explainer is model-agnostic, so it can compute Shapley values and Owen values for any model. It works by iterating over complete permutations of the features forward... |
93d566614081142b4b927c2c91d62e24c812d8cec9abb75e0ab3413f096aa5d1 | Jupyter | 4,422 | 148 | # %% [markdown]
# # MERA
# %% [markdown]
# ## 概述
#
# 在本教程中，我们将不涉及物理层面的探讨，而将演示如何使用TensorCircuit实现MERA (多尺度纠缠重整化假设，multi-scale entangled renormalization ansatz)。
# %% [markdown]
# ## 背景
#
# MERA是VQE的其中一种。它由一个量子比特的 $\ket{0}$ 态开始，逐层添加新的量子比特以扩张希尔伯特空间。
# 在将要训练的MERA（记作 $U(\theta)$ ）中，我们使用可变参量子门 $e^{i\theta XX}$ 、$e^{i\th... |
ec7bc05137fd2ac4b737908ccd4110d37138a9726c34386a14661112cebf1a63 | Jupyter | 4,422 | 166 | # %% [markdown]
# # Explain PyTorch MobileNetV2 using the `Partition` explainer
#
# In this example we are explaining the output of MobileNetV2 for classifying images into 1000 ImageNet classes.
# %%
import json
import numpy as np
import torch
import torchvision
import shap
# %% [markdown]
# ### Loading Model and ... |
40a435e3e9a2f4869406ab18b1c08c623c649213007a3886179912b1b9b6efbe | Jupyter | 4,427 | 140 | # %%
import pandas as pd
import numpy as np
import statsmodels.formula.api as smf
from statsmodels.stats.multitest import multipletests
from scipy.stats import chi2
# ---------------------------
# Data
# ---------------------------
control_dist = [0.6484, 0.6310, 0.6835, 0.3532, 0.4503, 0.4678,
0.4226,... |
ad626ada86f7830906d14af18c2a9cdef6d1bffb5c08b4ff8334510c64d03b4d | Jupyter | 4,437 | 165 | # %%
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 ───────... |
898bb980ddcc414b5834167a9946fd4da5faef5ac7497ae668e340ef1b1d6c1b | Jupyter | 4,442 | 176 | # %%
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 ───────... |
569c25507df757c7a5f952019fcaa0fdb4c772a58f70b4215fb5a92a3f2e119a | Jupyter | 4,449 | 186 | # %%
# %%
# AgeAnno
# !cd data_collection
# !mkdir ageanno
# !wget https://github.com/vikkihuangkexin/AgeAnno/archive/refs/heads/main.zip
# !unizp main.zip
# %%
# AgeAnnoMO
# !cd data_collection
# ! mkdir ageannomo
# !wget https://github.com/vikkihuangkexin/AgeAnnoMO/archive/refs/heads/main.zip
# !unzip main.zip
... |
b763d59960aca2d64974a638e4b6a3a9dec8b84d31c189074e7ef39086c8103d | Jupyter | 4,465 | 152 | # %%
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from joblib import load, dump
import seaborn as sns
# %%
# %%
colors = sns.color_palette(palette = 'rainbow',n_colors=5)
sns.palplot(colors)
# %%
# %%
task_name = 'FreeSolv'
batch_sizes, res = load('./%s.res' % task_name)
sns.set(style =... |
da2c9eba617d65e7598dd405e03b9f2dd7ea873672cefa5eb085fd078bc52fb7 | Jupyter | 4,465 | 171 | # %%
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 ───────... |
cf130f4995ba4af11a382299f370ae824fc609a8b822f5dafac9a81853a7891c | Jupyter | 4,486 | 167 | # %%
import pandas as pd
import numpy as np
import glob
import os
from tqdm import tqdm
# %%
!pwd
# %% [markdown]
# %%
# Gene → CellularComponent
# Monarch/Monarch_final/Celegans/Gene_Cele_CellularComponent.csv
# %%
BASE_DIR = '/storage/Arushi/090526_EvoAge/kg_formation/'
MAPPING_DIR = BASE_DIR + 'data_collec... |
5803096bce42ec9ae1951b33f44b3e747cb335a8b004434b8d29e62cf2d5b83d | Jupyter | 4,493 | 118 | # %% [markdown]
# # Extended Figure 5 — Source Data Export
#
# **Extended Figure 5** investigates latent code linearity and the properties
# of RealNVP-based GAN priors. Panel 5C compares Adam and CMA-ES optimization
# performance as a function of RealNVP layer depth and latent dimensionality.
# Panels 5D/E show R² v... |
119be0708734db2df89d9927d61c2e99a51ce5ac95eef9b3d3ebb8a06b99b618 | Jupyter | 4,496 | 132 | # %%
import numpy as np
import matplotlib.pyplot as plt
import os
stim_dir = "/media/yuhui/LCT/subj01/stimAM/"
os.chdir(stim_dir)
# %%
def load_onsets_by_run(fname):
runs = []
with open(fname, "r") as f:
for line in f:
line = line.strip()
if not line:
runs.appen... |
c0ed73eeb473ec88a75ec3ee9bffe28117dcfffd6c5a8fed2e83394d8b20a159 | Jupyter | 4,508 | 104 | # %% [markdown]
# # Explain ResNet50 using the `Partition` explainer
#
# This notebook demonstrates how to use SHAP to explain image classification models. In this example we are explaining the output of ResNet50 model for classifying images into 1000 ImageNet classes.
# %%
import json
from tensorflow.keras.applica... |
07ce3bdb6c95953404b528f413cfafd618280378969e7e421bee5a6e0da0dde2 | Jupyter | 4,516 | 167 | # %%
import pandas as pd
import numpy as np
import glob
import os
from tqdm import tqdm
# %%
!pwd
# %% [markdown]
# %%
# phenotype → CellularComponent
# Monarch/Monarch_final/Celegans/Cele_Phenotype_CellularComponent.csv
# %%
BASE_DIR = '/storage/Arushi/090526_EvoAge/kg_formation/'
MAPPING_DIR = BASE_DIR + 'd... |
de78a14da212918de69ff95681efec7a3f468dd41757900b3bd70012ae421c17 | Jupyter | 4,521 | 144 | # %% [markdown]
# # contractor 使用
#
# ## 概述
#
# 在本教程中，我们将演示如何利用不同类型的张量网络 contractor 进行电路仿真，以实现更好的时空消耗平衡。contractor 的定制是 TensorCircuit 库的主要亮点之一，因为更好的 contractor 可以更好地利用 TensorNetwok 仿真引擎的强大功能。
# %% [markdown]
# ## 设置
# %%
import tensorcircuit as tc
import numpy as np
import cotengra as ctg
import opt_einsum as oem
... |
97c8671f71d001f16af3e6b083458f2276e071bd4060ac35e6e7e42ddd5cf00a | Jupyter | 4,523 | 167 | # %%
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 ───────... |
b21fc9d41e5c227154979ae72a823c18764f3f2111b68756e053df38cd2d2a88 | Jupyter | 4,527 | 169 | # %%
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
# %%
# %%
BASE_DIR = '/storage/Arushi/090526_EvoAge/kg_formation/'
MAPPING_DIR = BASE_DIR + 'data_collection/databases_for_mapping/'
PROC_D... |
eee1158404a14f109204dc4bc3e277aa2591b09508e637a4ce1f280172048b49 | Jupyter | 4,528 | 172 | # %%
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 ───────... |
3b2cfcf0924b2b4b8ada91e2a85d8cc1af2e472f40fdcdd4ae078982e307dfc1 | Jupyter | 4,545 | 174 | # %%
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 ─────────────────────────... |
9b887a91dc730643417a19bb882666a8376e384fe6c00ce1a545a6e2c21c4831 | Jupyter | 4,553 | 167 | # %%
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 ───────... |
d0eebe3d81ec0a7389c91e86ce10a818d80b1be1cb19cad3c239248ec9a0f40f | Jupyter | 4,556 | 168 | # %%
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 ───────... |
e52fce090693b20dd7a958e035123eece398ce50513a877b1b20007d1ddc06ba | Jupyter | 4,556 | 168 | # %%
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 ─────────────────────────... |
c13f2772fac0070a7ae4541b4a848a4c1154c730972fcb4d0eb53924119c15dd | Jupyter | 4,563 | 116 | # %%
import numpy as np
# IP import warnings because of "module 'numpy' has no attribute 'warnings'"
import warnings
np.warnings = warnings
from scipy import stats
# add the path to the abcTau package
import sys
#sys.path.append('./abcTau')
sys.path.append('C:\\Users\\ipochino\\.conda\\envs\\isttc\\Lib\\site-packages\... |
eb42a155deb667099553db8cd600c1d1053ab6ff2a9200bb594526962cc852f6 | Jupyter | 4,567 | 173 | # %%
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 ... |
d1884bf0c7b2f279e05b517142027b53eeebd63a3f422a6f55d2a08c5ca07155 | Jupyter | 4,571 | 169 | # %%
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 ───────... |
8c763612e80d297e24dcb256e97aa9f0cdfaa4c09d41cb3c8d11e81aaee771fd | Jupyter | 4,583 | 174 | # %%
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 ───────... |
36092b690b156042957230a35ef36f3aa7fbc48425e6c62fbc0f100892a28361 | Jupyter | 4,592 | 169 | # %%
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 ───────... |
974f3d2e38dd6441a462c77677b226798bf895c9e455783a9de32e8f7764fad4 | Jupyter | 4,599 | 170 | # %%
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 ─────────────────────────... |
435fdad31d0470fa120f6f2170303a63fad333fd51084933b9217b6de6162af1 | Jupyter | 4,600 | 170 | # %%
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 ───────... |
2f1e4ae341a7fd29c3d6c39248cf8dcb1446f6453c18623a4858105c26eab97e | Jupyter | 4,608 | 170 | # %%
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 ───────... |
9a9d840ab2892b5c71129ebae47271fb452b237f7d2cfca20213b673a568542d | Jupyter | 4,616 | 170 | # %%
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 ───────... |
08f89fead6e14ac31ccb85b835a4a894414a572341fc95aaac1dba20da9d5f16 | Jupyter | 4,622 | 175 | # %%
from molmap import model as molmodel
import molmap
from molmap import dataset
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_a... |
28cf7b278604c9ea5b4675870af069327e40e8547d1d37f8cdf007accc8f16d8 | Jupyter | 4,626 | 171 | # %%
import pandas as pd
import numpy as np
import glob
import os
from tqdm import tqdm
# %%
!pwd
# %% [markdown]
# %%
# Gene → CellularComponent
# Monarch/Monarch_final/Celegans/Gene_Cele_CellularComponent.csv
# %%
BASE_DIR = '/storage/Arushi/090526_EvoAge/kg_formation/'
MAPPING_DIR = BASE_DIR + 'data_collec... |
5ab8782dcaa867a47b08f14161a1835f5c025ed16e8c70c36d6b234efa9251de | Jupyter | 4,628 | 163 | # %% [markdown]
# # General Benchmarking Debugging Tool
# %% [markdown]
# This notebook demonstrates the debugging mode used to differentiate the performance on different output results for text and image explanations. In the case of multiple output tokens / classes, it is usually useful to see the performance individ... |
62d28531d708a12e338a58592cd907001d1176d0b2444f820ee45d09ada065c7 | Jupyter | 4,629 | 179 | # %%
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 ───────... |
c7fb66dfee28845074c9a415a1501a4b8892e71e02118214637e916a359c429d | Jupyter | 4,636 | 162 | # %% [markdown]
# # Phenotype ↔ Gene Relation-Wise Merge
#
# Merges Phenotype–Gene triples from PrimeKG; deduplicates by `(head, relation, tail)`;
# drops rows with unresolved `head_detail_name`; and saves the result.
# %% [markdown]
# ## 0. Configuration
# %%
import os
import pandas as pd
BASE_DIR = '/storage/Aru... |
d603dd585a48d1d465c4078ab9ab9997afd1822b67b4692c83c30fd00f581cb2 | Jupyter | 4,647 | 115 | # %% [markdown]
# # Gradient and Variational Optimization
# %% [markdown]
# ## Overview
#
# TensorCircuit is designed to make optimization of parameterized quantum gates easy, fast, and convenient. In this note, we review how to obtain circuit gradients and run variational optimization.
# %% [markdown]
# ## Setup
#... |
d4813c45efda55fe9d5e575ccd34c83969c6e18c5be40de6cc3737df76f7f2e2 | Jupyter | 4,651 | 132 | # %% [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... |
ac973c3a3173e206b4415588c6f9b5bd086c649502b527420515eba064c2d49c | Jupyter | 4,672 | 155 | # %%
import pandas as pd
import numpy as np
import glob
import os
from tqdm import tqdm
# %%
!pwd
# %%
# sgd/ALL_YEAST_GENE_PHENOTYPE.csv
# %% [markdown]
# # Databases Having Gene PHENOTYPE relation of yeast
# %%
#
# %%
BASE_DIR = '/storage/Arushi/090526_EvoAge/kg_formation/'
MAPPING_DIR = BASE_DIR + 'data_c... |
4efa18fc46904162ae74f7f85d625baead7890377c993f13a5e379ab94dfdcae | Jupyter | 4,692 | 158 | # %% [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... |
d34a5499746a7b9b1288f2046636f18a37e860736f41a7929036c20293cfa1d4 | Jupyter | 4,702 | 182 | # %%
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 ───────... |
0a3d8c71a15a8d9c8194132be3c7fb859e461eb5b5db4494d72c8e61d5d699d2 | Jupyter | 4,717 | 113 | # %% [markdown]
# # 定制收缩路径
# %% [markdown]
# ## 概述
#
# 如果模拟电路的量子比特数很大，我们建议用户尝试自定义收缩设置，而不是使用贪婪的默认设置。
# %% [markdown]
# ## 设置
#
# cotengra 安装请参考[安装文档](https://cotengra.readthedocs.io/en/latest/installation.html)，由于没有上传到PyPI，所以无法通过
# pip install 简单获取。最简单的安装方式是 ``pip install -U git+https://github.com/jcmgray/cotengra.g... |
9ae22c97191ecdeb71f0d26c803d2185b4fd016b7da619db34448c302bdeb9cc | Jupyter | 4,723 | 173 | # %%
import pandas as pd
import numpy as np
import glob
import os
from tqdm import tqdm
# %%
!pwd
# %% [markdown]
# # Databases Having Gene gene relation of yeast
# %%
# biogrid/biogrid_YEAST_GENE_GENE.csv
# biogrid/biogrid_YEAST_GENE_GENE_2.csv
# string/scer/string_YEAST_GENE_GENE.csv
# yeastnet/yeastnet_YEAST_GEN... |
ee7305ce8174f2b43d9f813ed8fcf77ec01ce846bae593122ffee1cd4c690e74 | Jupyter | 4,725 | 170 | # %%
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 ───────... |
319b922c615879ae64eed66d0c317d2f2045be2925d99efab737edb7fab60d2e | Jupyter | 4,729 | 183 | # %%
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... |
cc1bcaead5bc8a90191cc86b0c858d3e171c35915814580b012fddacc7e79029 | Jupyter | 4,730 | 183 | # %%
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... |
762b1ea1a5eaa6c8ed5e489c159303ab66e481c39fa03be52b926c47f38de94b | Jupyter | 4,733 | 183 | # %%
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... |
5b49ab89b73417cff81f7b429d33a7d881a2e625cf9e8009c224bbe34aa0a75f | Jupyter | 4,735 | 162 | # %%
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('C:\\Users\\ipochino\\AppData\\Local\\anaconda3\\envs\\isttc\\Lib\\site-packages\\abcTau') # IP: replaced previous line wi... |
03c152b3163a84f2199f460794f41812e88d657b8b5c4ab39249b797c96a9aa5 | Jupyter | 4,743 | 136 | # %%
import numpy as np
import pandas as pd
import pickle
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
from pathlib import Path
import re
import matplotlib
matplotlib.rcParams.update({'font.size': 10})
matplotlib.rcParams['pdf.fonttype'] = 42
matplotlib.rcParams['ps.fonttype'] = 42
matp... |
fe28bcbd98402825d1ccfb1c4cb18fafd6bbedabd9cb1d59b67ae64a9b128bf7 | Jupyter | 4,748 | 149 | # %% [markdown]
# # Set up
# %%
#import packages
import pandas as pd
from nilearn import surface
import numpy as np
import os
from scipy.stats import ttest_1samp
from nilearn import plotting
import matplotlib.pyplot as plt
from nilearn.datasets import fetch_surf_fsaverage
import nibabel as nib
import usefulFunctions ... |
b9677f3f89ef5083e47db9903066a679484d8ea8a0da704682deefecc76b45d4 | Jupyter | 4,749 | 160 | # %% [markdown]
# # Protein ↔ ChemicalEntity Relation-Wise Merge
#
# Merges Protein–Chemical triples from CrossBAR; resolves chemical tail names via
# PubChem (IUPAC + SMILES) with a DrugBank fallback; deduplicates by `(head, relation, tail)`;
# and saves the result.
# %% [markdown]
# ## 0. Configuration
# %%
import... |
5557d58e47a3f4f3f4c080942aff1c9b66d78f15434f53a3eec6b1d1ab7407f6 | Jupyter | 4,764 | 187 | # %%
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... |
7c19610b29820e4d4d3b756539f1fd65a1246025ba23f41d98bda42ab8cb402c | Jupyter | 4,767 | 187 | # %%
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... |
6a7f5f04f76554063dca5a4a4281548a03d43cbc50807c4ed6ec9ee302bca21e | Jupyter | 4,773 | 160 | # %%
Sys.setenv("OMP_NUM_THREADS" = 32)
Sys.setenv("OPENBLAS_NUM_THREADS" = 32)
Sys.setenv("MKL_NUM_THREADS" = 32)
Sys.setenv("VECLIB_MAXIMUM_THREADS" = 32)
Sys.setenv("NUMEXPR_NUM_THREADS" = 32)
library(Seurat)
library(SeuratObject)
library(DESeq2)
library(ggplot2)
library(scales)
library(qs)
library(dplyr)
# %% [m... |
13e456315dfbf20ae6d90afab82c3b9cd4f49cf019c68878b26ded98c51dc920 | Jupyter | 4,778 | 197 | # %%
!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... |
c798a1153496fddac5c939bbc5591510dc247b0a6b5f9253d70d29ee959d3af1 | Jupyter | 4,778 | 167 | # %% [markdown]
# ### Install arcos4py from pypi
# %%
! pip install arcos4py
# %% [markdown]
# ### Import packages
# %%
import pandas as pd
from arcos4py import ARCOS
TAB20 = [
"#1f77b4",
"#aec7e8",
"#ff7f0e",
"#ffbb78",
"#2ca02c",
"#98df8a",
"#d62728",
"#ff9896",
"#9467bd",
"... |
a9297fd071d14d415db784b05384568b787fb376b858db6ed7c7fbae5039e413 | Jupyter | 4,785 | 170 | # %% [markdown]
# # GLM Basis Functions
# %%
# 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")
... |
828b65a3792d834e4c5402369bf3bc981c68bade52cf7359620b50aaad7aec81 | Jupyter | 4,789 | 127 | # %%
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... |
c809607e85ce9c9e89bc12d98dd8c42d1daf14369d741b508e6fa6f8e0c2b13e | Jupyter | 4,797 | 185 | # %%
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... |
50c507f32a458a793b4238b65adda21c61d003aecbbc0825132c898918a5ff39 | Jupyter | 4,806 | 146 | # %% [markdown]
# # The usage of contractor
#
# ## Overview
#
# In this tutorial, we will demonstrate how to utilize different types of TensorNetwork contractors for the circuit simulation to achieve a better space-time tradeoff. The customization of the contractor is the main highlight for the TensorCircuit package ... |
59888f56c4d0de1eb5501c37cb73cd095911de38972b95c366a153603ac8c05f | Jupyter | 4,814 | 141 | # %%
import numpy as np
import pandas as pd
import pickle
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
from pathlib import Path
import re
import matplotlib
matplotlib.rcParams.update({'font.size': 10})
matplotlib.rcParams['pdf.fonttype'] = 42
matplotlib.rcParams['ps.fonttype'] = 42
matp... |
9fd4cd85df5f0d212662675c88ecac01dc668bdbe6cd4111ddaaa01d19552f0c | Jupyter | 4,814 | 177 | # %%
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 p... |
adf2b17f64f01b07120c7b98af2049d9d20c81d0ee1e55e28f9f002f5d199bf5 | Jupyter | 4,822 | 184 | # %%
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 ─────────────────────────... |
46eca93d97780b768412989b8c033542b889a031c02760fa5bab7c6ec7981f6b | Jupyter | 4,837 | 177 | # %%
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 p... |
434bb182b4dae6f99ef7557271030fbd2213f1b3ad26dc45ae28d9ca7c360833 | Jupyter | 4,838 | 185 | # %%
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 ... |
0c4029157a857cc6bee5ad71f9fdc371f8a8b72ed2841ca0aec2c5555f2962db | Jupyter | 4,852 | 170 | # %%
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 ─────────────────────────... |
d3e92748120e36d040134ca2d7dbf7788f25fe9c660c5edabae4dda98f33c06a | Jupyter | 4,858 | 168 | # %%
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 ─────────────────────────... |
ea96cbf687ed8562bddf27a4b421e0c1548dd0b56340838998992663327bc58a | Jupyter | 4,860 | 163 | # %%
import numpy as np
import pandas as pd
import pickle
from isttc.scripts.cfg_global import project_folder_path
from isttc.spike_utils import get_lv
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
from matplotlib.colors import LinearSegmentedColormap
mpl.rcParams['pdf.fonttype'] = 4... |
2f61548d8650c4b5e5d985af057d02d9a0c0d5333fb429bdadf783a71b8267f2 | Jupyter | 4,862 | 167 | # %% [markdown]
# # Mutation ↔ Chemical Relation-Wise Merge
#
# > **Note:** The original notebook was an unfinished template — every data source was
# > commented out and the merge referenced undefined `*_GENE_GENE` variables (it would have
# > raised `NameError`), and its output path pointed at the wrong `GENE_GENE` ... |
04b86a51d28eee4202d9d3fef25e598ad84d19cec05ad91ecac681552f59f4eb | Jupyter | 4,871 | 165 | # %%
from molmap import model as molmodel
import molmap
from molmap import dataset
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_a... |
9446fb279eb7c57ca6e0d91c8d4d9f9005cc172138560c65263f707ff6933688 | Jupyter | 4,874 | 182 | # %%
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... |
17acace2584e8baff95ba8b2a5857219ad8e25768f20f017fc8613a125bcaa1f | Jupyter | 4,885 | 169 | # %%
from molmap import model as molmodel
import molmap
from molmap import dataset
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_a... |
b08b8d7272067c0c090ec5b8ae5ef45a57b6bdd4a3e9f4f661db5ec44f401e40 | Jupyter | 4,892 | 171 | # %%
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 ─────────────────────────... |
3fe736e3953c3bf8bd3afc5f9e938239b79a613b18ecc3cb709849d74392d4ca | Jupyter | 4,895 | 171 | # %%
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_inhibits_biologic... |
b12f7123d3c6b3555c9d997ba868d7239205392430a2b5809af68e2734c25d3f | Jupyter | 4,896 | 166 | # %%
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_random_... |
7ed4ae213d0ac2a3334d6f095527ca9bb55460cd11eb833da5d39d5463069552 | Jupyter | 4,904 | 110 | # %%
# Importing libraries
import os
import cv2
from PIL import Image
import sys # Import sys for sys.exit()
# Set path to the Google Drive folder with images
from google.colab import drive
drive.mount('/content/drive')
path = "/content/drive/MyDrive/ResNetModel/outputs/prediction_results"
# Check if the directory ex... |
2d6a39e48d588babd219df3946068ccc72aea7f11c2ae611be1c87d1d47dd60e | Jupyter | 4,906 | 157 | # %% [markdown]
# #
# %% [markdown]
# ## 0 · Imports & Base Paths
# %%
import pandas as pd
import os
# ── Base directories ──────────────────────────────────────────────────────────
BASE_DIR = '/storage/Arushi/090526_EvoAge/kg_formation/'
PROC_DIR = BASE_DIR + 'processed_data/'
# ── Output path ───────────... |
4a4c044c6ba78c3121e7e3f8000acd497cbbb01b9cbf0b52d0c081bf5d9d5f18 | Jupyter | 4,908 | 181 | # %%
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 ... |
c2704c05edb292784dcbe62840ef27c3a3a7f7ca9dec19243c37ee83ac83ca40 | Jupyter | 4,915 | 156 | # %%
import pandas as pd
import numpy as np
import glob
import os
from tqdm import tqdm
# %%
!pwd
# %%
# %% [markdown]
# # Databases Having Gene gene relation of yeast
# %%
# eSLDB/esld_YEAST_GENE_cellularComponent.csv
# Monarch/Monarch_final/Yeast/Gene_Yeast_CellularComponent.csv
# %%
BASE_DIR = '/storage/A... |
4aa870cb32734b1e4c17b3f88302f435e6a153f786c41699fe808d4a8a0e5e29 | Jupyter | 4,957 | 180 | # %% [markdown]
# # 高级自动微分
# %% [markdown]
# ## 概述
#
# 在本节中，我们将回顾一些高级 AD 技巧，尤其是它们在电路模拟中的应用。借助这些高级的 AD 技巧，我们可以更高效地评估一些量子量。
#
# 高级 AD 在 TensorCircuit 中是可能的，因为我们已经以后端不可知的方式实现了几个与 AD 相关的 API，它们的实现紧密遵循
# [JAX AD 实现](https://jax.readthedocs.io/en/latest/notebooks/autodiff_cookbook.html)的设计理念。
# %% [markdown]
# ## 设置
# %... |
84030e48e51c4a8cd1cb73bf24267f84da51c280fcd2ec3f927a8385a7c3122b | Jupyter | 4,959 | 177 | # %%
from molmap import model as molmodel
import molmap
from molmap import dataset
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_... |
f2b955a2fa3c955ddd953ef12b74bd7abdb150f624c8dfd51058fe0b9497c031 | Jupyter | 4,960 | 157 | # %% [markdown]
# #
# %% [markdown]
# ## 0 · Imports & Base Paths
# %%
import pandas as pd
import os
# ── Base directories ──────────────────────────────────────────────────────────
BASE_DIR = '/storage/Arushi/090526_EvoAge/kg_formation/'
PROC_DIR = BASE_DIR + 'processed_data/'
# ── Output path ───────────... |
48785c43d296b3d6243c313b90d457bda4e7e90bf28dfc927c426112c69fd810 | Jupyter | 4,966 | 175 | # %% [markdown]
# # Pathway ↔ Pathway Relation-Wise Merge
#
# Merges Pathway–Pathway triples from PrimeKG and TARKG;
# 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 = ... |
a5e531eda030d21cca9bfc10da1abb66570492e7891aea00819bcc1f194ff599 | Jupyter | 4,971 | 184 | # %%
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... |
dad4a01a34e45581122dd4fc7c43b5cc2d980af35cd87ee582dceb726aa60030 | Jupyter | 4,980 | 186 | # %%
import pandas as pd
import numpy as np
import glob
import os
from tqdm import tqdm
# %%
!pwd
# %% [markdown]
# %%
# flybase/Flybase_Droso_Gene_Phenotype.csv
# Monarch/Drosophila/Gene_Droso_Phenotype_MONARCH.csv
# %%
BASE_DIR = '/storage/Arushi/090526_EvoAge/kg_formation/'
MAPPING_DIR = BASE_DIR + 'data... |
7a953afaa6997cb911b2a899ef2d8282693ede3c42fe53ae7dbc6a2c46ddf90f | Jupyter | 4,982 | 134 | # %%
import pandas as pd
import numpy as np
# %%
# =============================================================================
# BASE PATHS — Update these to match your local directory structure
# =============================================================================
your_path_here = '/storage/Arushi/090526_E... |
ba84bec696056a387c1530fa0d6410e90e257931729a9c1e2087445b7b6eca66 | Jupyter | 4,986 | 173 | # %%
from molmap import loadmap
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
import seaborn as sns
from rdkit import Chem
from rdkit.Chem import Draw
from rdkit.Chem.Draw import IPythonConsole
from rdkit.Chem.MolStandardize import rdMolStandardize
from rdkit import RDLogger
#IPythonConsole.ip... |
261359c178b48c084703017d23b79eb2045639f79b316bbfe0b718dea944f29b | Jupyter | 5,000 | 185 | # %%
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... |
27cf2e0567a327930663f79579b5744ca3a69c8fe937667dcf0e071d407f9c21 | Jupyter | 5,019 | 106 | # %% [markdown]
# # Demonstration of DCBC evaluation using GPU acceleration
# This notebook shows an quick example of a Distance controlled boundary coefficient (DCBC) evaluation between numpy cpu vs. pytorch gpu version.
#
# ## Installation and Dependencies
# Ensure Python version >= 3.6 and pip installable on your s... |
0ae17c531cf711058f8963db52bf47c92bf632f4dd265b9f7c5f62719dcd8606 | Jupyter | 5,020 | 171 | # %% [markdown]
# #
# %% [markdown]
# ## 0 · Imports & Base Paths
# %%
import pandas as pd
import os
# ── Base directories ──────────────────────────────────────────────────────────
BASE_DIR = '/storage/Arushi/090526_EvoAge/kg_formation/'
PROC_DIR = BASE_DIR + 'processed_data/'
# ── Output path ───────────... |
8f0e2f8cc864e05e9d51c2d53f4f9a927df73b9f669dd5c9b200028d5cb10c45 | Jupyter | 5,030 | 157 | # %%
# %%
import os
import re
import glob
import numpy as np
import pandas as pd
# ─────────────────────────────────────────────────────────────────────────────
# USER CONFIGURATION
# BASE_PATH : root folder containing all raw input data
# OUT_PATH : folder where all processed CSVs will be saved
# ─────────────────... |
86bdc087feca9106c233103363305e9ffe8242c20d2a7f9da8379dca40329112 | Jupyter | 5,052 | 166 | # %%
from molmap.feature.fingerprint import Extraction as fext
from molmap.feature.descriptor import Extraction as dext
# %%
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
# %% [markdown]
# # Descriptors variance distribution
# %%
#load summary info.
i... |
7b77c666b0dfb638f278e8388ff559a53d6bd697d317847a8c9148d86b651359 | Jupyter | 5,062 | 113 | # %% [markdown]
# # `violin` summary plot
#
# This notebook is designed to demonstrate (and so document) how to use the `shap.plots.violin` function.
#
# It uses an XGBoost model trained on the toy diabetes dataset provided by the scikit-learn library (source URL: https://www4.stat.ncsu.edu/~boos/var.select/diabetes... |
1df751d0cad8f0be029755eb06ed1de428c00ce07e2f082974e4bb6fc2698b94 | Jupyter | 5,077 | 168 | # %%
## Necessary imports
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from statsmodels.stats.multitest import multipletests
# %%
hier_clustering_cell_perc_fname = '../cell_percentage_hier_corr 10Hz.csv'
# Load hierarchical clustering cell percentage data
# - Each row = one trial
# - Each c... |
aeb05d56ab2310865fc08b864f3d326b95cb54b897ada9a4dabdd410d6041f20 | Jupyter | 5,100 | 132 | # %% [markdown]
# # Access to data
#
# NablaDFT includes three types of databases:
#
# 1. **Energy database.** There are molecule structure, energy, and forces. Data are available via the atomic simulation environment (ASE) interface.
# 2. **Hamiltonian database.** There are molecule structure, energy, forces, hamilt... |
09e310203fda0054f830ce0729dad0b5648e58bdcee5cf1f789398abb53d6f34 | Jupyter | 5,101 | 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"]="1"
np.random.seed(123)
tf.compat.v1.set_rand... |
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