sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
0e8f981514911a30c9d76310b77bd44d0f6b0a6ebcfc420a06b2cc8157eec145 | Jupyter | 1,052 | 44 | # %%
# Parameters
gene_familly = "NZZ/SPL"
# %%
import json
import pandas as pd
from sklearn.model_selection import train_test_split
# %%
# READ GENE_INFO JSON FILE
gene_info_path = "../data/gene_info.json"
dataset_path = "../data/one_vs_other/"
with open(gene_info_path, 'r') as json_file:
gene_info = json.loa... |
385539f9f7d881ac35f6f98baec8301cc21f0c720bc7a2b32053a3d7e0df3ea3 | Jupyter | 1,052 | 44 | # %%
# Parameters
gene_familly = "G2-like"
# %%
import json
import pandas as pd
from sklearn.model_selection import train_test_split
# %%
# READ GENE_INFO JSON FILE
gene_info_path = "../data/gene_info.json"
dataset_path = "../data/one_vs_other/"
with open(gene_info_path, 'r') as json_file:
gene_info = json.loa... |
8f60aaf0abed200ea87a535697d1acd227e55fde0305098cf4a19097ed1bfceb | Jupyter | 1,052 | 44 | # %%
# Parameters
gene_familly = "BBR-BPC"
# %%
import json
import pandas as pd
from sklearn.model_selection import train_test_split
# %%
# READ GENE_INFO JSON FILE
gene_info_path = "../data/gene_info.json"
dataset_path = "../data/one_vs_other/"
with open(gene_info_path, 'r') as json_file:
gene_info = json.loa... |
95481d2d76dddf74cd85002467a675128d09cb63a7e0a5b03df057924908693d | Jupyter | 1,052 | 44 | # %%
# Parameters
gene_familly = "CO-like"
# %%
import json
import pandas as pd
from sklearn.model_selection import train_test_split
# %%
# READ GENE_INFO JSON FILE
gene_info_path = "../data/gene_info.json"
dataset_path = "../data/one_vs_other/"
with open(gene_info_path, 'r') as json_file:
gene_info = json.loa... |
1a6cc3a9e812c28f5b32100c3d3a824692bb99f67812bfbd608600a5e82e6222 | Jupyter | 1,053 | 44 | # %%
# Parameters
gene_familly = "Nin-like"
# %%
import json
import pandas as pd
from sklearn.model_selection import train_test_split
# %%
# READ GENE_INFO JSON FILE
gene_info_path = "../data/gene_info.json"
dataset_path = "../data/one_vs_other/"
with open(gene_info_path, 'r') as json_file:
gene_info = json.lo... |
41e381f975b289c4a7b83fe0103f6a17c933d899cf6df98110a48dfb2c61375a | Jupyter | 1,053 | 44 | # %%
# Parameters
gene_familly = "HRT-like"
# %%
import json
import pandas as pd
from sklearn.model_selection import train_test_split
# %%
# READ GENE_INFO JSON FILE
gene_info_path = "../data/gene_info.json"
dataset_path = "../data/one_vs_other/"
with open(gene_info_path, 'r') as json_file:
gene_info = json.lo... |
7948124988a64f44e4e0b82538303c4a8c3cb5480659837d8f3c54e3c3689622 | Jupyter | 1,053 | 44 | # %%
# Parameters
gene_familly = "HB-other"
# %%
import json
import pandas as pd
from sklearn.model_selection import train_test_split
# %%
# READ GENE_INFO JSON FILE
gene_info_path = "../data/gene_info.json"
dataset_path = "../data/one_vs_other/"
with open(gene_info_path, 'r') as json_file:
gene_info = json.lo... |
9d8add910c9b82f407b20a9732e133f5cdc61ab13edca9ba159fc0d9c3646b37 | Jupyter | 1,053 | 44 | # %%
# Parameters
gene_familly = "Trihelix"
# %%
import json
import pandas as pd
from sklearn.model_selection import train_test_split
# %%
# READ GENE_INFO JSON FILE
gene_info_path = "../data/gene_info.json"
dataset_path = "../data/one_vs_other/"
with open(gene_info_path, 'r') as json_file:
gene_info = json.lo... |
2472cdee5884bc5a47b6fcf7fd042cffcd1f586c1148768d8cb57766cd6527c6 | Jupyter | 1,054 | 44 | # %%
# Parameters
gene_familly = "MIKC_MADS"
# %%
import json
import pandas as pd
from sklearn.model_selection import train_test_split
# %%
# READ GENE_INFO JSON FILE
gene_info_path = "../data/gene_info.json"
dataset_path = "../data/one_vs_other/"
with open(gene_info_path, 'r') as json_file:
gene_info = json.l... |
f2f2c75f40ae590b5e95d5cbc64238acda6935bcf04a48159a748806d3a6e39f | Jupyter | 1,054 | 44 | # %%
# Parameters
gene_familly = "S1Fa-like"
# %%
import json
import pandas as pd
from sklearn.model_selection import train_test_split
# %%
# READ GENE_INFO JSON FILE
gene_info_path = "../data/gene_info.json"
dataset_path = "../data/one_vs_other/"
with open(gene_info_path, 'r') as json_file:
gene_info = json.l... |
1f711c8132a397055a8dee4d1c71dcaa2a9cc38b0ef89c6734b3b6c8a513b185 | Jupyter | 1,056 | 44 | # %%
# Parameters
gene_familly = "M-type_MADS"
# %%
import json
import pandas as pd
from sklearn.model_selection import train_test_split
# %%
# READ GENE_INFO JSON FILE
gene_info_path = "../data/gene_info.json"
dataset_path = "../data/one_vs_other/"
with open(gene_info_path, 'r') as json_file:
gene_info = json... |
485340572a6b601beac8a2de34c45280a3ac532ac58e4baab7f387a5ca8c3a40 | Jupyter | 1,056 | 44 | # %%
# Parameters
gene_familly = "MYB_related"
# %%
import json
import pandas as pd
from sklearn.model_selection import train_test_split
# %%
# READ GENE_INFO JSON FILE
gene_info_path = "../data/gene_info.json"
dataset_path = "../data/one_vs_other/"
with open(gene_info_path, 'r') as json_file:
gene_info = json... |
c0390ae6325acbf979acb8819ff932f3f0f7648f0311ce8440269cd53ab01291 | Jupyter | 1,082 | 37 | # %%
# %%
"""
Import libraries, model, and datasets
"""
import sys
import os
from pathlib import Path
import numpy as np
import matplotlib.pyplot as plt
import shutil
import torch
src_dir = os.getcwd()
src_dir = os.path.abspath(os.path.join(src_dir, '..')) + '/src' # Adjust the path as needed
print(src_dir)
sys.path.... |
c984b508d864118675eac2e8e333d61d9c92fb7332994331571f3422ba0e8854 | Jupyter | 1,113 | 16 | # %% [markdown]
# # Overview of the examples
# Below is a flow chart outlining the Jupyter notebook examples that demonstrate the use of ERICA. The relevant inputs and outputs at each stage are given and are also colour coded in the notebooks. In these examples the outputs on the right-hand-side are saved to file and t... |
52037f48cd24103392b0082f2f09580d21a312dd49ef02b47f41635fd1442372 | Jupyter | 1,123 | 30 | # %%
import sys, os, torch
sys.path.append('../')
from utils.infer_funcs import do_mr_to_pct
# set device, use cuda if available
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# %%
# Change these to point to your data
# Set input and output data file paths
input_mr_file = '/Users/sitiyaakub/Doc... |
8efcdfa23922a4d1bfa944f79a72c4d2788af872c630bdeee5d7c64b62d2e5d1 | Jupyter | 1,127 | 45 | # %%
import unitvelo as utv
import anndata
import scvelo as scv
import numpy as np
import os
import argparse
import time
# %%
root = "/scratch/blaauw_root/blaauw1/gyichen"
dataset = "Hindbrain_pons"
adata = anndata.read_h5ad(f"{root}/data/{dataset}_pp.h5ad")
# %%
# Hyperparameters
velo_config = utv.config.Configurati... |
b0bce93c62112f31870b134fc86c7cca9d78da6e88159262f02336b001dc0304 | Jupyter | 1,151 | 49 | # %%
import brainpy as bp
import brainpy.math as bm
import numpy as np
import matplotlib.pyplot as plt
import scipy
from scipy.linalg import eig
#gridspec
import matplotlib.gridspec as gridspec
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
import sys
sys.path.append('..')
from cann import CANN1D
# %%
N... |
fe515a442b74f28cfce3a79c5954c9689258632ee95b179b6324c3232e094690 | Jupyter | 1,152 | 46 | # %%
import unitvelo as utv
import anndata
import scvelo as scv
import numpy as np
import os
import argparse
import time
# %%
root = "/scratch/blaauw_root/blaauw1/gyichen"
dataset = "Pancreas"
adata = anndata.read_h5ad(f"{root}/data/{dataset}_pp.h5ad")
# %%
# Hyperparameters
velo_config = utv.config.Configuration()
v... |
b6368caab05a6cacff2234490ebf8431e548f484775902e9d23c424e83169199 | Jupyter | 1,153 | 46 | # %%
import unitvelo as utv
import anndata
import scvelo as scv
import numpy as np
import os
import argparse
import time
# %%
root = "/scratch/blaauw_root/blaauw1/gyichen"
dataset = "Erythroid"
adata = anndata.read_h5ad(f"{root}/data/{dataset}_pp.h5ad")
# %%
# Hyperparameters
velo_config = utv.config.Configuration()
... |
24527c6859258a1da99004f69137749bd7a7748540672aaee35327a50d6017ee | Jupyter | 1,154 | 46 | # %%
import unitvelo as utv
import anndata
import scvelo as scv
import numpy as np
import os
import argparse
import time
# %%
root = "/scratch/blaauw_root/blaauw1/gyichen"
dataset = "Neuron_scNT"
adata = anndata.read_h5ad(f"{root}/data/{dataset}_pp.h5ad")
# %%
# Hyperparameters
velo_config = utv.config.Configuration(... |
731880917f2daf40277ab5706c3cb070e078b72dd7ca3fdbee4b2be2380dcf39 | Jupyter | 1,154 | 57 | # %%
import pandas as pd
from mmsplice.vcf_dataloader import SplicingVCFDataloader
from mmsplice import MMSplice, predict_save, predict_all_table
from mmsplice.utils import max_varEff
# example files
gtf = 'tests/data/test.gtf'
vcf = 'tests/data/test.vcf.gz'
fasta = 'tests/data/hg19.nochr.chr17.fa'
# dataloader to lo... |
aab8634177719f4a4b1f7c91c3a16a3ee7935468b2cc3e95ec12e06f73ea7515 | Jupyter | 1,155 | 30 | # %%
import math
# Calculate combinations of composition
def cal_num_comp(step_size, num_material):
k = int(1/step_size)
n = num_material - 1 + k
num_comp = math.comb(n, k)
return num_comp
# In this work, step_size=0.02, num_material=4
num_composition = cal_num_comp(step_size=0.02, num_materia... |
5f9b88697cfe62055c71726cb4df23200670c93004d206334d823c2cec6dad30 | Jupyter | 1,156 | 35 | # %%
import pandas as pd
import numpy as np
import scanpy as sc
import matplotlib
import matplotlib.pyplot as plt
from umap import UMAP
import sklearn
import seaborn as sns
from COSMOS import cosmos
from COSMOS.pyWNN import pyWNN
import warnings
warnings.filterwarnings('ignore')
random_seed = 20
# %%
path="../dataset... |
b553a8c77f2e59673c7540aca00e70f02624aec88fe8f1552a49ba22ca5d238c | Jupyter | 1,156 | 46 | # %%
import unitvelo as utv
import anndata
import scvelo as scv
import numpy as np
import os
import argparse
import time
# %%
root = "/scratch/blaauw_root/blaauw1/gyichen"
dataset = "Dentategyrus"
adata = anndata.read_h5ad(f"{root}/data/{dataset}_pp.h5ad")
# %%
# Hyperparameters
velo_config = utv.config.Configuration... |
db66a129f51c717c77c7c5cd99269c185d2fce5764eb6e2cb15ba9b5203a32d9 | Jupyter | 1,158 | 46 | # %%
import unitvelo as utv
import anndata
import scvelo as scv
import numpy as np
import os
import argparse
import time
# %%
root = "/scratch/blaauw_root/blaauw1/gyichen"
dataset = "IO_EU"
adata = anndata.read_h5ad(f"{root}/data/{dataset}_pp.h5ad")
# %%
# Hyperparameters
velo_config = utv.config.Configuration()
velo... |
7805e516868bb41ecca36695589391d82edd51d3fcd8840c0503825d2b6a540c | Jupyter | 1,162 | 46 | # %%
import os,csv,re
import pandas as pd
import numpy as np
import scanpy as sc
import math
from scipy.sparse import issparse
import random, torch
import warnings
warnings.filterwarnings("ignore")
import matplotlib.colors as clr
import matplotlib.pyplot as plt
import cv2
import anndata as ad
# %%
for i in range(9, 13... |
92df0f1dff85b6e5602011cca7ed404cae82d7c9400c7e21ae61dff5b570fc2d | Jupyter | 1,162 | 36 | # %%
import sys
sys.path.append('..')
from AIF_selection_automatic import AutoAifSel
from os import getcwd
from os.path import join
from IPython.display import Image
base = join(getcwd(),'data')
brain_mask_fn = join(base, 'brain_mask_3d_anon.nii.gz') #MANDATORY filepath to brain_mask file
conc_fn = join(base, '4d_con... |
281cc1423ae039cb50c60afaef914e965d6bd95fd60c0f5d38cee4e9fef4e6f1 | Jupyter | 1,163 | 35 | # %% [markdown]
# # Computes the baseline for each trial in the dataset
# %%
from wfield import *
localdisk='/mnt/dual/temp_folder/CSP23_20200226/'
# load dat
dat_path = glob(pjoin(localdisk,'*.dat'))[0]
dat = mmap_dat(dat_path)
# load trial onsets
trial_onsets = np.load(pjoin(localdisk,'trial_onsets.npy')).astype(in... |
3bd5b36e861a71964e6fab25c2d0ea7cdf9191063623300c7c7d93ba5e1f737d | Jupyter | 1,168 | 43 | # %%
import numpy as np
import matplotlib.pyplot as plt
REORDER = ['P','B','M','F','V',
'TH','DH',
'T','D','N','S','Z',
'CH','JH','SH','ZH',
'NG','K','G',
'Y','L','R','W',
'HH','OY','OW',
'AO','AA','AW','AY','AE','EH',
... |
b779ca877913daccb78aae4420872f562fa0c639153489002e768afe7fe24e85 | Jupyter | 1,176 | 46 | # %%
import os
import torch
import pandas as pd
import scanpy as sc
from sklearn import metrics
import multiprocessing as mp
import warnings
warnings.filterwarnings("ignore")
from GraphST import GraphST
from lib_3d_OT.utils import *
# %%
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
# %%
ada... |
8facf88a69f1d0c30e782a120f8c391528d1dde6c50fd75396f1a4a86be5668f | Jupyter | 1,187 | 44 | # %% [markdown]
# # Perform image registration to correct motion
#
# Memory maps a file and performs motion correction. Use ``apply_shifts=False`` to run without changing the data.
#
# The file needs to be mapped in ``r+`` mode
# %%
localdisk = '/mnt/dual/temp_folder/CSP23_20200226' # this should be an SSD or a fas... |
c5ef1f37df1abc80919ea775c523d80b1b5fedbe2dc8cb0e4824cd44bd9a8c60 | Jupyter | 1,192 | 51 | # %% [markdown]
# # Example usage of Single-agent ChemGraph
# %% [markdown]
# ## Initiate the ChemGraph agent
# %%
from chemgraph.agent.llm_agent import ChemGraph
workflow_type = "single_agent"
# Adjust model_name with your preferred model.
cg = ChemGraph(model_name='gpt-4o-mini', workflow_type = workflow_type, str... |
7019702e10991a8872b22018ae7843b1e1a3036834b21e16cea6df9717d4bf94 | Jupyter | 1,194 | 46 | # %%
import unitvelo as utv
import anndata
import scvelo as scv
import numpy as np
import os
import argparse
import time
# %%
root = "/scratch/blaauw_root/blaauw1/gyichen"
dataset = "Erythroid_Human"
adata = anndata.read_h5ad(f"{root}/data/{dataset}_pp.h5ad")
# %%
# Hyperparameters
velo_config = utv.config.Configurat... |
9dc4ab3856c81840cf6a2d55fa5d0d248bc45e4b8d256ef599f3f91ae59eca16 | Jupyter | 1,195 | 45 | # %%
import os
import re
from scipy import stats
import tempfile
from pathlib import Path
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
import matplotlib
from matplotlib.colors import rgb2hex
import pickle as pkl
from sklearn.model_selection import train_test_split
from sklearn.discriminan... |
47375023d45ff1b9f49e7d1c2dc82c634e39c01a387dc653b0a10fe9ce1b0d0f | Jupyter | 1,198 | 53 | # %%
import numpy as np
import matplotlib.pyplot as plt
from sklearn.externals import joblib
from pkg_resources import resource_filename
from mmsplice.utils import LINEAR_MODEL, LOGISTIC_MODEL, EFFICIENCY_MODEL
# %%
sklearn_LINEAR_MODEL = joblib.load(resource_filename(
'mmsplice', 'models/linear_model.pkl'))
sklea... |
ccd51f38025657d75e976a7e389086c06dcbd3721152bd769ff77e77030e26e9 | Jupyter | 1,206 | 32 | # %% [markdown]
# ## Extract the stimulus times from imager data
# %%
from wfield import *
fnames = natsorted(glob('Frames_*.dat'))
im = ImagerStack(fnames)
# There is an onset per trial
stimonsets = np.zeros((len(im.frames_offset)-1),dtype=int)
stimoffsets = np.zeros((len(im.frames_offset)-1),dtype=int)
for i in ra... |
15598ccc2fa9d701be32a17fe5bb3079999767dd0dbc7029dbbf035c4884f3b6 | Jupyter | 1,211 | 47 | # %%
import unitvelo as utv
import anndata
import scvelo as scv
import numpy as np
import os
import argparse
import time
# %%
root = "/scratch/blaauw_root/blaauw1/gyichen"
dataset = "IPSC"
adata = anndata.read_h5ad(f"{root}/data/{dataset}_pp.h5ad")
# %%
# Hyperparameters
velo_config = utv.config.Configuration()
velo_... |
81ea256ce3c7be6a2500b1b3da75b512400e03f27a7d52085a70c4375e63b0f6 | Jupyter | 1,213 | 47 | # %%
import unitvelo as utv
import anndata
import scvelo as scv
import numpy as np
import os
import argparse
import time
# %%
root = "/scratch/blaauw_root/blaauw1/gyichen"
dataset = "BMMC"
adata = anndata.read_h5ad(f"{root}/data/{dataset}_pp.h5ad")
# %%
# Hyperparameters
velo_config = utv.config.Configuration()
velo_... |
6a1bc8bfe31bea939ff3712788240ccc5f133593e2b2211b6f8e45c51bc15b12 | Jupyter | 1,216 | 37 | # %% [markdown]
# # Loading an existing multiverse
#
# An existing multiverse can be loaded in two different ways:
#
# 1. As creating a multiverse (without running it) is cheap, one can simply run the same script/functions again, and comment out the `.run()` method.
# 2. The multiverse can also be loaded from disk by... |
9f8843e825f01e15ba58c574ebad134e138bc156bf67607f8a3e444f2f4b076a | Jupyter | 1,216 | 45 | # %% [markdown]
# # Quickstart
#
# ## for Connectivity-based Hopfield Neural Networks (CHNNs)
# %% [markdown]
# ### Imports
# %%
import sys
sys.path.append('..')
import pandas as pd
import seaborn as sns
from connattractor import network, analysis
# %% [markdown]
# ## Obtain functional connectivity matrix
#
# Shou... |
61f3e6438c115365ebaf59b793b7117894fdeb319d11213d670217d6ff907c36 | Jupyter | 1,217 | 47 | # %%
import unitvelo as utv
import anndata
import scvelo as scv
import numpy as np
import os
import argparse
import time
# %%
root = "/scratch/blaauw_root/blaauw1/gyichen"
dataset = "Braindev"
adata = anndata.read_h5ad(f"{root}/data/{dataset}_pp.h5ad")
# %%
# Hyperparameters
velo_config = utv.config.Configuration()
v... |
f284b105e91f0340e6441cea30a1c6d041c62dee46ac94bb8216f17acfd2f008 | Jupyter | 1,222 | 31 | # %% [markdown]
# # Figure S6: Neural representations with biological plasticity rules
# %%
from EIANN.generate_figures import *
import EIANN as eiann
eiann.update_plot_defaults()
import nbimporter
import Figure_2 as fig2
# %%
figure_name = "FigS6_biological_representations"
model_list = ["BTSP_WT_hebbdend", "Supervi... |
36435783b11b1435a5b60e83b580ea4ff224a1dc2cceb03b6e684ca881045119 | Jupyter | 1,223 | 47 | # %%
import unitvelo as utv
import anndata
import scvelo as scv
import numpy as np
import os
import argparse
import time
# %%
root = "/scratch/blaauw_root/blaauw1/gyichen"
dataset = "Bonemarrow"
adata = anndata.read_h5ad(f"{root}/data/{dataset}_pp.h5ad")
# %%
# Hyperparameters
velo_config = utv.config.Configuration()... |
c1fb0665722ec24cbe7a34c0d6a35e649f1a97fc92407f2e3efe3ea2e396e992 | Jupyter | 1,223 | 47 | # %%
import unitvelo as utv
import anndata
import scvelo as scv
import numpy as np
import os
import argparse
import time
# %%
root = "/scratch/blaauw_root/blaauw1/gyichen"
dataset = "Retina"
adata = anndata.read_h5ad(f"{root}/data/{dataset}_pp.h5ad")
# %%
# Hyperparameters
velo_config = utv.config.Configuration()
vel... |
5ca52a68e7c502c9393d698802c46cb01bbc7f02976173c21f41824c6afa555b | Jupyter | 1,229 | 38 | # %% [markdown]
# ### Plot raw fMRI signal
# This script plots the raw time series signal (samples) from all selected features across all folds. This illustration is very similar to Figure 2 in Haynes and Rees, Current Biology, 15, 2005. To run the script, a pandas dataframe has to be read that contains a table with al... |
aaa9fd9512a0a0e74e7666946944a4b0fc2a152bab4b0552850cb31b1e64a653 | Jupyter | 1,235 | 43 | # %%
import anndata
import scvelo as scv
import numpy as np
from deepvelo.utils import velocity, velocity_confidence, latent_time, update_dict
from deepvelo.utils.preprocess import autoset_coeff_s
from deepvelo.utils.plot import statplot, compare_plot
from deepvelo import train, Constants
import os
import time
# %%
da... |
217c86650dff9f95b069d4103b7dfecf9f7c52305f8378159ab92ef0ad0028a1 | Jupyter | 1,241 | 46 | # %%
import warnings
warnings.filterwarnings("ignore")
import pandas as pd
import numpy as np
import scanpy as sc
import matplotlib.pyplot as plt
import os
import sys
import STAGATE_pyG
from lib_3d_OT.utils import *
# %%
adata=sc.read_visium('/home/dbj/mouse/DLPFC/DLPFC/151674/')
adata.var_names_make_unique()
truth = ... |
9146d2e3066c443e6a00aff3bb55539cdafcda82b49ce6fa52abb43fce54c85e | Jupyter | 1,256 | 48 | # %%
import numpy as np
import nibabel as nib
import pandas as pd
deg1 = [[0, 12, 1],
[30, 12, 1],
[60, 12, 1],
[113, 12, 1],
[143, 12, 1],
[173, 12, 1]]
deg2 = [[6, 12, 1],
[36, 12, 1],
[66, 12, 1],
[107, 12, 1],
[137, 12, 1],
... |
555ba3ced9ca9cca53639c69958a765652f79839326d13a12f71db583ca1e154 | Jupyter | 1,262 | 44 | # %%
library(ArchR)
library(stringr)
library(ggplot2)
library(dplyr)
library(glue)
library(parallel)
source('../utils/utils.r')
addArchRThreads(threads = 12)
# %%
projAUD <- loadArchRProject(path = "/gpfs/gibbs/pi/girgenti/JZhang/CL/multiome/data_AUD/fig1-UMAP/CNA-ATAC/",
force = FALSE, showL... |
ef4d0318807bcad32981d54f173941c72dfa5cb2f7d48fdc77743df4dde2ef2c | Jupyter | 1,280 | 45 | # %%
gene_familly = None
# %%
import json
import pandas as pd
from sklearn.model_selection import train_test_split
# %%
# READ GENE_INFO JSON FILE
gene_info_path = "../data/gene_info.json"
dataset_path = "../data/one_vs_other/"
with open(gene_info_path, 'r') as json_file:
gene_info = json.load(json_file)
# FOC... |
e9d60c8f70b42ea864aa4bbffe1ee960a76f6f309dfbf408db47df63e9dc2cdb | Jupyter | 1,281 | 43 | # %%
import warnings
import pandas as pd
warnings.filterwarnings("ignore")
# %%
for data, d_path in zip(
["COHA", "NYT"],
[
"./data/SWOW_prediction/eval/time_series/ts_df.csv",
"./data/SWOW_prediction/eval/time_series/nyt_ts_df.csv",
], strict=False,
):
print(data)
ts_df = pd.read... |
2928ac3ae81e1cd0a0d77fa84fa46712b942ed9e3bb32bc4a82d90ff0544b727 | Jupyter | 1,294 | 54 | # %%
import os
import obspy
from obspy import UTCDateTime
from obspy.clients.fdsn import Client
# %% [markdown]
# ## 1. Download data using obspy
# %%
client = Client("SCEDC")
data_dir = "mseed"
if not os.path.exists(data_dir):
os.makedirs(data_dir)
# %%
starttime = UTCDateTime("2019-07-04T17:00:00")
endtime = UTC... |
74b9f15b95464940c5a438c1ee778c0c5216f290ec89370ec286111df2f13c30 | Jupyter | 1,294 | 47 | # %%
# Parameters
gene_familly = "B3"
# %%
import json
import pandas as pd
from sklearn.model_selection import train_test_split
# %%
# READ GENE_INFO JSON FILE
gene_info_path = "../data/gene_info.json"
dataset_path = "../data/one_vs_other/"
with open(gene_info_path, 'r') as json_file:
gene_info = json.load(jso... |
00a245bcbe3d3c8cb21c408a32bb8e2b36f5c425a58b333c80d56f3b2c0d2551 | Jupyter | 1,295 | 47 | # %%
# Parameters
gene_familly = "SAP"
# %%
import json
import pandas as pd
from sklearn.model_selection import train_test_split
# %%
# READ GENE_INFO JSON FILE
gene_info_path = "../data/gene_info.json"
dataset_path = "../data/one_vs_other/"
with open(gene_info_path, 'r') as json_file:
gene_info = json.load(js... |
1e8dad44d1142859889557a9a52c84e62c00905979fc91523b30c2ca1c91a871 | Jupyter | 1,295 | 47 | # %%
# Parameters
gene_familly = "CPP"
# %%
import json
import pandas as pd
from sklearn.model_selection import train_test_split
# %%
# READ GENE_INFO JSON FILE
gene_info_path = "../data/gene_info.json"
dataset_path = "../data/one_vs_other/"
with open(gene_info_path, 'r') as json_file:
gene_info = json.load(js... |
2118258222a48bfd3c3ea0c5882bafdd46f7acad501b1ea18b9d583183029066 | Jupyter | 1,295 | 47 | # %%
# Parameters
gene_familly = "GRF"
# %%
import json
import pandas as pd
from sklearn.model_selection import train_test_split
# %%
# READ GENE_INFO JSON FILE
gene_info_path = "../data/gene_info.json"
dataset_path = "../data/one_vs_other/"
with open(gene_info_path, 'r') as json_file:
gene_info = json.load(js... |
244ec7d7ff26557c622f678a4787179a355bab150d8e43464ddbbe4882da3e67 | Jupyter | 1,295 | 47 | # %%
# Parameters
gene_familly = "C3H"
# %%
import json
import pandas as pd
from sklearn.model_selection import train_test_split
# %%
# READ GENE_INFO JSON FILE
gene_info_path = "../data/gene_info.json"
dataset_path = "../data/one_vs_other/"
with open(gene_info_path, 'r') as json_file:
gene_info = json.load(js... |
32c7f5f919d3a3fc070c12e89ac86bea9148cbb37d8a82dd664cfbc482d69cf7 | Jupyter | 1,295 | 47 | # %%
# Parameters
gene_familly = "DBB"
# %%
import json
import pandas as pd
from sklearn.model_selection import train_test_split
# %%
# READ GENE_INFO JSON FILE
gene_info_path = "../data/gene_info.json"
dataset_path = "../data/one_vs_other/"
with open(gene_info_path, 'r') as json_file:
gene_info = json.load(js... |
3d39420d69cd744bb60d6e68ce75a16744021c13f91f94b5b9a3a11c3eb3a273 | Jupyter | 1,295 | 47 | # %%
# Parameters
gene_familly = "LBD"
# %%
import json
import pandas as pd
from sklearn.model_selection import train_test_split
# %%
# READ GENE_INFO JSON FILE
gene_info_path = "../data/gene_info.json"
dataset_path = "../data/one_vs_other/"
with open(gene_info_path, 'r') as json_file:
gene_info = json.load(js... |
66245ed3c68f16f56c9de3e96d5f19ad6b05cebc2faf7a75f9b74dd05416cf92 | Jupyter | 1,295 | 47 | # %%
# Parameters
gene_familly = "SRS"
# %%
import json
import pandas as pd
from sklearn.model_selection import train_test_split
# %%
# READ GENE_INFO JSON FILE
gene_info_path = "../data/gene_info.json"
dataset_path = "../data/one_vs_other/"
with open(gene_info_path, 'r') as json_file:
gene_info = json.load(js... |
707721dd5495d7811e4d475d7159ed57ae21fcd8ebd1046a2d01b0c82c7ce5ed | Jupyter | 1,295 | 47 | # %%
# Parameters
gene_familly = "LSD"
# %%
import json
import pandas as pd
from sklearn.model_selection import train_test_split
# %%
# READ GENE_INFO JSON FILE
gene_info_path = "../data/gene_info.json"
dataset_path = "../data/one_vs_other/"
with open(gene_info_path, 'r') as json_file:
gene_info = json.load(js... |
76bab5db55a17061c5db7b20bfaea9a108d7e1c4a2281a510ee2c4131959c4c4 | Jupyter | 1,295 | 47 | # %%
# Parameters
gene_familly = "ARF"
# %%
import json
import pandas as pd
from sklearn.model_selection import train_test_split
# %%
# READ GENE_INFO JSON FILE
gene_info_path = "../data/gene_info.json"
dataset_path = "../data/one_vs_other/"
with open(gene_info_path, 'r') as json_file:
gene_info = json.load(js... |
7ffd3a0157adacd72c3cdfe9250307650db307594831abc80a3f3c840bd9b126 | Jupyter | 1,295 | 47 | # %%
# Parameters
gene_familly = "AP2"
# %%
import json
import pandas as pd
from sklearn.model_selection import train_test_split
# %%
# READ GENE_INFO JSON FILE
gene_info_path = "../data/gene_info.json"
dataset_path = "../data/one_vs_other/"
with open(gene_info_path, 'r') as json_file:
gene_info = json.load(js... |
83079dbd95a8c448a3b42b075e009022944c87a4bb0a892dfc9fec735ae19668 | Jupyter | 1,295 | 47 | # %%
# Parameters
gene_familly = "MYB"
# %%
import json
import pandas as pd
from sklearn.model_selection import train_test_split
# %%
# READ GENE_INFO JSON FILE
gene_info_path = "../data/gene_info.json"
dataset_path = "../data/one_vs_other/"
with open(gene_info_path, 'r') as json_file:
gene_info = json.load(js... |
8b7491f5f2a86491ef867c3809856fe4d2b4cf1fb98a625d64ec572123e7fb7b | Jupyter | 1,295 | 47 | # %%
# Parameters
gene_familly = "RAV"
# %%
import json
import pandas as pd
from sklearn.model_selection import train_test_split
# %%
# READ GENE_INFO JSON FILE
gene_info_path = "../data/gene_info.json"
dataset_path = "../data/one_vs_other/"
with open(gene_info_path, 'r') as json_file:
gene_info = json.load(js... |
913abc003af9aca4163f837ea301fb2e4d62d8cb36d0dcf4ea0ae3e2d695c4aa | Jupyter | 1,295 | 47 | # %%
# Parameters
gene_familly = "Dof"
# %%
import json
import pandas as pd
from sklearn.model_selection import train_test_split
# %%
# READ GENE_INFO JSON FILE
gene_info_path = "../data/gene_info.json"
dataset_path = "../data/one_vs_other/"
with open(gene_info_path, 'r') as json_file:
gene_info = json.load(js... |
93273e35864dc55aa318e57dbe18f5c118d1226385e66c78ffbed4033be9c3e7 | Jupyter | 1,295 | 47 | # %%
# Parameters
gene_familly = "SBP"
# %%
import json
import pandas as pd
from sklearn.model_selection import train_test_split
# %%
# READ GENE_INFO JSON FILE
gene_info_path = "../data/gene_info.json"
dataset_path = "../data/one_vs_other/"
with open(gene_info_path, 'r') as json_file:
gene_info = json.load(js... |
99c1a31e34fab0557a327bb8ced86a3240f3252d849b65d386fd137b49c4167e | Jupyter | 1,295 | 47 | # %%
# Parameters
gene_familly = "EIL"
# %%
import json
import pandas as pd
from sklearn.model_selection import train_test_split
# %%
# READ GENE_INFO JSON FILE
gene_info_path = "../data/gene_info.json"
dataset_path = "../data/one_vs_other/"
with open(gene_info_path, 'r') as json_file:
gene_info = json.load(js... |
b00cd96ddd3a75ac664020be40d948e4a85a1010ce8b7a96d9ecb0a62f5f6fa9 | Jupyter | 1,295 | 47 | # %%
# Parameters
gene_familly = "HSF"
# %%
import json
import pandas as pd
from sklearn.model_selection import train_test_split
# %%
# READ GENE_INFO JSON FILE
gene_info_path = "../data/gene_info.json"
dataset_path = "../data/one_vs_other/"
with open(gene_info_path, 'r') as json_file:
gene_info = json.load(js... |
db59e30cc389c1e350606c2dc1ca9df92103fad0d4c58f38f2277d09397ed047 | Jupyter | 1,295 | 47 | # %%
# Parameters
gene_familly = "NAC"
# %%
import json
import pandas as pd
from sklearn.model_selection import train_test_split
# %%
# READ GENE_INFO JSON FILE
gene_info_path = "../data/gene_info.json"
dataset_path = "../data/one_vs_other/"
with open(gene_info_path, 'r') as json_file:
gene_info = json.load(js... |
dd69003a69a8560339a6287f988703d45d41db9b9b59d31c54c5ca72c7273cda | Jupyter | 1,295 | 47 | # %%
# Parameters
gene_familly = "TCP"
# %%
import json
import pandas as pd
from sklearn.model_selection import train_test_split
# %%
# READ GENE_INFO JSON FILE
gene_info_path = "../data/gene_info.json"
dataset_path = "../data/one_vs_other/"
with open(gene_info_path, 'r') as json_file:
gene_info = json.load(js... |
e40f931b2083b3fffd16f0e2843965b0c9d646dbcd2fd8c6ea6b2fe1248fd574 | Jupyter | 1,295 | 47 | # %%
# Parameters
gene_familly = "WOX"
# %%
import json
import pandas as pd
from sklearn.model_selection import train_test_split
# %%
# READ GENE_INFO JSON FILE
gene_info_path = "../data/gene_info.json"
dataset_path = "../data/one_vs_other/"
with open(gene_info_path, 'r') as json_file:
gene_info = json.load(js... |
eb6a63ca820bb8c9d222935b04fba03df138bfd2ee16fedf6bf5c3bd17b46cfb | Jupyter | 1,295 | 47 | # %%
# Parameters
gene_familly = "LFY"
# %%
import json
import pandas as pd
from sklearn.model_selection import train_test_split
# %%
# READ GENE_INFO JSON FILE
gene_info_path = "../data/gene_info.json"
dataset_path = "../data/one_vs_other/"
with open(gene_info_path, 'r') as json_file:
gene_info = json.load(js... |
f0b66131227a46c26daded679563afdd5a949622d966f3898bebe3dfeb3ea622 | Jupyter | 1,295 | 47 | # %%
# Parameters
gene_familly = "VOZ"
# %%
import json
import pandas as pd
from sklearn.model_selection import train_test_split
# %%
# READ GENE_INFO JSON FILE
gene_info_path = "../data/gene_info.json"
dataset_path = "../data/one_vs_other/"
with open(gene_info_path, 'r') as json_file:
gene_info = json.load(js... |
fd86e1817c04d5817d1e15294690b18fb200dee4d00a7594fa7f9f190a8fbe5c | Jupyter | 1,295 | 47 | # %%
# Parameters
gene_familly = "ERF"
# %%
import json
import pandas as pd
from sklearn.model_selection import train_test_split
# %%
# READ GENE_INFO JSON FILE
gene_info_path = "../data/gene_info.json"
dataset_path = "../data/one_vs_other/"
with open(gene_info_path, 'r') as json_file:
gene_info = json.load(js... |
032067fa1c15713a2e189c6fae0e10169f754b2dbb0aef15345f747a8a7a3cbb | Jupyter | 1,296 | 47 | # %%
# Parameters
gene_familly = "TALE"
# %%
import json
import pandas as pd
from sklearn.model_selection import train_test_split
# %%
# READ GENE_INFO JSON FILE
gene_info_path = "../data/gene_info.json"
dataset_path = "../data/one_vs_other/"
with open(gene_info_path, 'r') as json_file:
gene_info = json.load(j... |
1449d275131b3a59901ccb14bf8df68376732673389227ae1d4ea9b520af8fc0 | Jupyter | 1,296 | 47 | # %%
# Parameters
gene_familly = "GeBP"
# %%
import json
import pandas as pd
from sklearn.model_selection import train_test_split
# %%
# READ GENE_INFO JSON FILE
gene_info_path = "../data/gene_info.json"
dataset_path = "../data/one_vs_other/"
with open(gene_info_path, 'r') as json_file:
gene_info = json.load(j... |
2023be1cba5d57ef2e10de1bbd70b9a224f7255aa99d14689dbf4ec242f13063 | Jupyter | 1,296 | 47 | # %%
# Parameters
gene_familly = "GATA"
# %%
import json
import pandas as pd
from sklearn.model_selection import train_test_split
# %%
# READ GENE_INFO JSON FILE
gene_info_path = "../data/gene_info.json"
dataset_path = "../data/one_vs_other/"
with open(gene_info_path, 'r') as json_file:
gene_info = json.load(j... |
55bd0272e6f8517d9334bdfc3aea012056a0f9132b58b3e66fddfe491509627d | Jupyter | 1,296 | 47 | # %%
# Parameters
gene_familly = "BES1"
# %%
import json
import pandas as pd
from sklearn.model_selection import train_test_split
# %%
# READ GENE_INFO JSON FILE
gene_info_path = "../data/gene_info.json"
dataset_path = "../data/one_vs_other/"
with open(gene_info_path, 'r') as json_file:
gene_info = json.load(j... |
5cd6fe1cbd6c41dee29bbe71952f8ba73864405ed96802046f7f7bdb8c5191bb | Jupyter | 1,296 | 47 | # %%
# Parameters
gene_familly = "GRAS"
# %%
import json
import pandas as pd
from sklearn.model_selection import train_test_split
# %%
# READ GENE_INFO JSON FILE
gene_info_path = "../data/gene_info.json"
dataset_path = "../data/one_vs_other/"
with open(gene_info_path, 'r') as json_file:
gene_info = json.load(j... |
5d34577c003cadf2b7502ef24e4c7c92cc35524afaa30aa6b5a9e129402dc119 | Jupyter | 1,296 | 47 | # %%
# Parameters
gene_familly = "bHLH"
# %%
import json
import pandas as pd
from sklearn.model_selection import train_test_split
# %%
# READ GENE_INFO JSON FILE
gene_info_path = "../data/gene_info.json"
dataset_path = "../data/one_vs_other/"
with open(gene_info_path, 'r') as json_file:
gene_info = json.load(j... |
685346da5c6df946ca86216b2323253b12929c2309c6eb73138ff8871a7df108 | Jupyter | 1,296 | 47 | # %%
# Parameters
gene_familly = "C2H2"
# %%
import json
import pandas as pd
from sklearn.model_selection import train_test_split
# %%
# READ GENE_INFO JSON FILE
gene_info_path = "../data/gene_info.json"
dataset_path = "../data/one_vs_other/"
with open(gene_info_path, 'r') as json_file:
gene_info = json.load(j... |
79a5721a898add6d0223a0f10e2595ab0ebbdfc9d3b196f57ad6d9c368d5dec0 | Jupyter | 1,296 | 47 | # %%
# Parameters
gene_familly = "STAT"
# %%
import json
import pandas as pd
from sklearn.model_selection import train_test_split
# %%
# READ GENE_INFO JSON FILE
gene_info_path = "../data/gene_info.json"
dataset_path = "../data/one_vs_other/"
with open(gene_info_path, 'r') as json_file:
gene_info = json.load(j... |
859feedd11bd2f386a30a9f98e0ea8e16b83611ae9d7097ad61a266c0b2ca2ec | Jupyter | 1,296 | 47 | # %%
# Parameters
gene_familly = "WRKY"
# %%
import json
import pandas as pd
from sklearn.model_selection import train_test_split
# %%
# READ GENE_INFO JSON FILE
gene_info_path = "../data/gene_info.json"
dataset_path = "../data/one_vs_other/"
with open(gene_info_path, 'r') as json_file:
gene_info = json.load(j... |
9402e7ee937288f2d8c741ebb1431217904585d4b4bced957f6950b96b586f52 | Jupyter | 1,296 | 47 | # %%
# Parameters
gene_familly = "FAR1"
# %%
import json
import pandas as pd
from sklearn.model_selection import train_test_split
# %%
# READ GENE_INFO JSON FILE
gene_info_path = "../data/gene_info.json"
dataset_path = "../data/one_vs_other/"
with open(gene_info_path, 'r') as json_file:
gene_info = json.load(j... |
f3313da197375380180ce3b145c60e588bcf95d092591b33bd07a12ef5b387ef | Jupyter | 1,296 | 47 | # %%
# Parameters
gene_familly = "bZIP"
# %%
import json
import pandas as pd
from sklearn.model_selection import train_test_split
# %%
# READ GENE_INFO JSON FILE
gene_info_path = "../data/gene_info.json"
dataset_path = "../data/one_vs_other/"
with open(gene_info_path, 'r') as json_file:
gene_info = json.load(j... |
32a3f85028d41dc8211bc54f43d158c11b7f9d5e97227db35e0c0fc80fe90b01 | Jupyter | 1,297 | 47 | # %%
# Parameters
gene_familly = "NF-YC"
# %%
import json
import pandas as pd
from sklearn.model_selection import train_test_split
# %%
# READ GENE_INFO JSON FILE
gene_info_path = "../data/gene_info.json"
dataset_path = "../data/one_vs_other/"
with open(gene_info_path, 'r') as json_file:
gene_info = json.load(... |
4d95150769d5ece7f01742d2fffa51fae76a83d5a19ba95ad4e5b6c27688864f | Jupyter | 1,297 | 47 | # %%
# Parameters
gene_familly = "CAMTA"
# %%
import json
import pandas as pd
from sklearn.model_selection import train_test_split
# %%
# READ GENE_INFO JSON FILE
gene_info_path = "../data/gene_info.json"
dataset_path = "../data/one_vs_other/"
with open(gene_info_path, 'r') as json_file:
gene_info = json.load(... |
4eecbeff746ca2b85bb569447d5e800e46ae3d7a4663d5d72d3be50a39988c9f | Jupyter | 1,297 | 47 | # %%
# Parameters
gene_familly = "NF-YA"
# %%
import json
import pandas as pd
from sklearn.model_selection import train_test_split
# %%
# READ GENE_INFO JSON FILE
gene_info_path = "../data/gene_info.json"
dataset_path = "../data/one_vs_other/"
with open(gene_info_path, 'r') as json_file:
gene_info = json.load(... |
71ebff3156da702678a65bf91c29ad59539cf1cbe71bab5e000a2ded1ef03402 | Jupyter | 1,297 | 47 | # %%
# Parameters
gene_familly = "ZF-HD"
# %%
import json
import pandas as pd
from sklearn.model_selection import train_test_split
# %%
# READ GENE_INFO JSON FILE
gene_info_path = "../data/gene_info.json"
dataset_path = "../data/one_vs_other/"
with open(gene_info_path, 'r') as json_file:
gene_info = json.load(... |
7a3ca3a1b27b34e51719763dad0a249cf59cddcc559d081527d987a413dc363f | Jupyter | 1,297 | 47 | # %%
# Parameters
gene_familly = "ARR-B"
# %%
import json
import pandas as pd
from sklearn.model_selection import train_test_split
# %%
# READ GENE_INFO JSON FILE
gene_info_path = "../data/gene_info.json"
dataset_path = "../data/one_vs_other/"
with open(gene_info_path, 'r') as json_file:
gene_info = json.load(... |
940e6b8f6c5413a63a6c22610fe380dff29db4ce0cc85bd45c2f09b3c090ab93 | Jupyter | 1,297 | 47 | # %%
# Parameters
gene_familly = "YABBY"
# %%
import json
import pandas as pd
from sklearn.model_selection import train_test_split
# %%
# READ GENE_INFO JSON FILE
gene_info_path = "../data/gene_info.json"
dataset_path = "../data/one_vs_other/"
with open(gene_info_path, 'r') as json_file:
gene_info = json.load(... |
c76fc960436fa3c25445f3b308e78657b58dc7800863115c22d7dc58c3ae1628 | Jupyter | 1,297 | 47 | # %%
# Parameters
gene_familly = "NF-YB"
# %%
import json
import pandas as pd
from sklearn.model_selection import train_test_split
# %%
# READ GENE_INFO JSON FILE
gene_info_path = "../data/gene_info.json"
dataset_path = "../data/one_vs_other/"
with open(gene_info_path, 'r') as json_file:
gene_info = json.load(... |
ebb21c524aafb28fcda71f68b415438bd46eb9a5ab46ee13c01bc2c5b69601d3 | Jupyter | 1,297 | 47 | # %%
# Parameters
gene_familly = "NF-X1"
# %%
import json
import pandas as pd
from sklearn.model_selection import train_test_split
# %%
# READ GENE_INFO JSON FILE
gene_info_path = "../data/gene_info.json"
dataset_path = "../data/one_vs_other/"
with open(gene_info_path, 'r') as json_file:
gene_info = json.load(... |
803bed25995872a7dc531780029136c0707fdfb93fdacf960be76305c9682328 | Jupyter | 1,298 | 47 | # %%
# Parameters
gene_familly = "HD-ZIP"
# %%
import json
import pandas as pd
from sklearn.model_selection import train_test_split
# %%
# READ GENE_INFO JSON FILE
gene_info_path = "../data/gene_info.json"
dataset_path = "../data/one_vs_other/"
with open(gene_info_path, 'r') as json_file:
gene_info = json.load... |
a179c9f825d97d6fd019d9d2771fdc30c1c834c2cf5229503228d800d013184c | Jupyter | 1,298 | 47 | # %%
# Parameters
gene_familly = "HB-PHD"
# %%
import json
import pandas as pd
from sklearn.model_selection import train_test_split
# %%
# READ GENE_INFO JSON FILE
gene_info_path = "../data/gene_info.json"
dataset_path = "../data/one_vs_other/"
with open(gene_info_path, 'r') as json_file:
gene_info = json.load... |
ae32118a8f39f9e0f3fa5e571419850304bd9a19187426a767dd25d572ebf8f7 | Jupyter | 1,298 | 47 | # %%
# Parameters
gene_familly = "E2F/DP"
# %%
import json
import pandas as pd
from sklearn.model_selection import train_test_split
# %%
# READ GENE_INFO JSON FILE
gene_info_path = "../data/gene_info.json"
dataset_path = "../data/one_vs_other/"
with open(gene_info_path, 'r') as json_file:
gene_info = json.load... |
fbb0931cfdde484355fd4b14f02ce93e2e5760f059be38a75d000bbedbe01929 | Jupyter | 1,298 | 47 | # %%
# Parameters
gene_familly = "Whirly"
# %%
import json
import pandas as pd
from sklearn.model_selection import train_test_split
# %%
# READ GENE_INFO JSON FILE
gene_info_path = "../data/gene_info.json"
dataset_path = "../data/one_vs_other/"
with open(gene_info_path, 'r') as json_file:
gene_info = json.load... |
07633f1f7b67393bcd76938f6145bf4f1ac3b45ea7f6ed94f5f70598b39ae33e | Jupyter | 1,299 | 47 | # %%
# Parameters
gene_familly = "CO-like"
# %%
import json
import pandas as pd
from sklearn.model_selection import train_test_split
# %%
# READ GENE_INFO JSON FILE
gene_info_path = "../data/gene_info.json"
dataset_path = "../data/one_vs_other/"
with open(gene_info_path, 'r') as json_file:
gene_info = json.loa... |
1d8cd4eba970ad6d0887e820849eb57f9e0b438b48a91988fa51abd53de0f7aa | Jupyter | 1,299 | 47 | # %%
# Parameters
gene_familly = "BBR-BPC"
# %%
import json
import pandas as pd
from sklearn.model_selection import train_test_split
# %%
# READ GENE_INFO JSON FILE
gene_info_path = "../data/gene_info.json"
dataset_path = "../data/one_vs_other/"
with open(gene_info_path, 'r') as json_file:
gene_info = json.loa... |
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