sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
acdcf362b39a45abdd9971fe875870e34aaff12faf29bb0d2ee740d1a26af747 | Jupyter | 114 | 11 | # %% [markdown]
# # Quickstart
# %%
from connattractor import network, analysis
# %%
sys.path
# %% [markdown]
|
48932d004b34fd139adca2c5d6633d132799446f931cf97ddafd69cc2092d564 | Jupyter | 147 | 13 | # %% [markdown]
# # Quickstart
# %%
import sys
sys.path.append('..')
from connattractor import network, analysis
# %%
sys.path
# %% [markdown]
|
e954ed1584f6d914d27eecb4b6a9644b14cbf9d4483e3d7ed34411c72b0973b4 | Jupyter | 232 | 15 | # %% [markdown]
# # Quickstart
# %%
import pandas as pd
from connattractor import network, analysis
# %%
connfile = '../data_in/ex_connectome_partial_correlation.csv'
mtx = pd.read_csv(connfile).iloc[:, 1:]
mtx
# %% [markdown]
|
3ac8956c661c992d02a958d680ae26b4ab66c8db62f2997086ec967a679970db | Jupyter | 250 | 18 | # %%
import pandas as pd
import numpy as np
from scipy import stats
import statsmodels.stats.multitest as sm
# Load and tidy data
df = pd.read_csv('../../raw_data/Glycosphingolipid_analysis/Deep-surface_GSLs.csv')
# %%
df
# %%
df.columns
# %%
|
f3eb8634510a8f3636fae59a0cebe551d77505847333b253ffeb3c56c7299ec8 | Jupyter | 265 | 17 | # %% [markdown]
# # Quickstart
# %%
import sys
sys.path.append('..')
import pandas as pd
from connattractor import network, analysis
# %%
connfile = '../data_in/ex_connectome_partial_correlation.csv'
mtx = pd.read_csv(connfile).iloc[:, 1:]
mtx
# %% [markdown]
|
c1ad68414080593452a48eaefa0f65a0a17c9e7279afcad86a7a6f98ff28b722 | Jupyter | 332 | 23 | # %% [markdown]
# # Quickstart
# %% [markdown]
# ### Imports
# %%
import sys
sys.path.append('..')
import pandas as pd
from connattractor import network, analysis
# %%
## Obtain functional connecti
# %%
connfile = '../data_in/ex_connectome_partial_correlation.csv'
mtx = pd.read_csv(connfile).iloc[:, 1:]
mtx
# %% ... |
69c906a1f1dedafc7c42c8689a45e4e4383616da1ad1b859cd652e2d5f573b18 | Jupyter | 370 | 16 | # %% [markdown]
# # Figure 1 (diagram): Multi-layer networks with biological specialization
# %% [markdown]
# <img src="diagrams/Fig1_diagrams_dynamics.png" width="1200px">
# %% [markdown]
# ```{image} diagrams/Fig1_diagrams_dynamics.png
# :alt: Diagram of EIANN network architecture
# :class: bg-primary mb-1
# :width... |
3dba9abc1aaa1f05f87a0d516ab88242843d814deccfd8d954f5d2107682ae05 | Jupyter | 405 | 20 | # %%
import pandas as pd
# %%
all_edit_info_pd = pd.read_csv('only_5_cells_test/all_edit_info.tsv', sep='\t')
# %%
all_edit_info_pd = all_edit_info_pd[all_edit_info_pd['position'] == 3000527]
# %%
all_edit_info_pd.head()
# %%
filtered_site_info = pd.read_csv('only_5_cells_test/final_filtered_site_info_annotated.tsv... |
5c6e4906abe7fa27dac2c6fcc16011e2b06d1fae28f82e58c83d97b1172c6f49 | Jupyter | 412 | 20 | # %%
API_KEY = ""
# %%
import os
from mp_api.client import MPRester
os.makedirs("cifs", exist_ok=True)
with MPRester(API_KEY) as mpr:
docs = mpr.materials.summary.search(
band_gap=(0, 10),
fields=["material_id", "structure"]
)
for doc in docs:
cif_str = doc.structure.to(fmt="cif"... |
849ebb043f18cb6e632c5a84b42d33e6718685c69794350b8236046675f5158a | Jupyter | 457 | 20 | # %%
from chemgraph.agent.llm_agent import ChemGraph
workflow_type = "multi_agent"
cg = ChemGraph(
model_name='gpt-4o',
workflow_type = workflow_type,
structured_output=False,
return_option="state",
)
cg.visualize()
# %%
# Calculate the infrared absorption spectrum of water
query = "Calculate ... |
26745dfe0f62ef7aca6dbafd835f8a0bb953d4e72b4bf78410091b88132c3ad8 | Jupyter | 499 | 17 | # %%
import pandas as pd
import numpy as np
from scipy.stats import pearsonr, spearmanr
# %%
first_step = pd.read_csv('../results/Result_Stransitive_first_random_step.csv')
second_step = pd.read_csv('../results/Result_Stransitive_second_random_step.csv')
transitivity_DDG = first_step['DDG_JanusDDG'] + second_step['D... |
95ed025575fb935f53c32aed296bf228e105a1df14c6167055f5c97262875cf3 | Jupyter | 523 | 17 | # %%
import sys
sys.path.append('..')
import os
import glob
import natsort
import numpy as np
import skimage.io as skio
from segmentation.postprocessing import postprocessor
# %%
inference_dir = '/hdd/yang/projects/glomeruli_segmentation/data/S-20-28/5.0um_voi01/inference/'
output_path = '/hdd/yang/projects/glomeruli_... |
da310698edf91a6768e72bdc0cef0d580e5664aea6e6d9ca5a36d66ec6c71dec | Jupyter | 571 | 28 | # %% [markdown]
# # Quickstart
# %% [markdown]
# ### Imports
# %%
import sys
sys.path.append('..')
import pandas as pd
from connattractor import network, analysis
# %% [markdown]
# ## Obtain functional connectivity matrix
#
# Should be a partial correlation matrix, square and symmetric.
#
# Below, we load in an ex... |
4c8130441b9efd02cc3a088d1571c84ae192f18106283fcecb413ca28aaa7bab | Jupyter | 578 | 18 | # %%
import numpy as np
import cv2
from sklearn.mixture import GaussianMixture as GMM
# %%
for i in range(1,39):
path="../dataset/Fused images to be used for segmentation/"+str(i)+".jpg"
img = cv2.imread(path)
img2 = img.reshape((-1,3))
gmm_model = GMM(n_components=4, covariance_type='tied').fit(img2)... |
c611633b5b29c8766a258be8825c5578317bd8f474114f7f3dcf781ea69361e5 | Jupyter | 609 | 48 | # %%
from dldata.stimulus_sets.hvm import HvMWithDiscfade
# %%
hvm = HvMWithDiscfade()
hvm
# %%
meta = hvm.meta
meta
# %%
meta[["id", "var"]]
# %%
meta.shape
# %%
import numpy as np
import pandas as pd
# %%
df_meta = pd.DataFrame(meta)
df_meta
# %%
all(meta["id"]==df_meta["id"])
# %%
df_meta["image_dldata_index... |
826dd96ad038795eca0ddbf8f654db06e1ce0ac8f572f9f72d3f5714b753b03a | Jupyter | 619 | 20 | # %%
from chemgraph.agent.llm_agent import ChemGraph
workflow_type = "single_agent"
cg = ChemGraph(model_name='gpt-4o-mini', workflow_type = workflow_type, structured_output=True, return_option="state")
cg.visualize()
# %%
# Run geometry optimization using MACE MP
query = "What are the vibrational frequencies of car... |
24747b9365b77ff61f5887d0b37ff9d704fda0dc99e17faa7d08ffce98fd6401 | Jupyter | 629 | 28 | # %% [markdown]
# # Test the landscape generated by sim_data.py
# %%
import sys
sys.path.append('../')
from src_simulate_data.sim_data import Simulated
import matplotlib.pyplot as plt
# %%
fig, axes = plt.subplots(3, 3, figsize=(9, 9))
cor_types= ['exp', 'tgaus', 'cascade', 'bio', 'add', 'quad', 'comp', 'saddle'... |
9a41aba8133ecf0af0d84f3e96f3e0044898489c6c871555ef5cce97ed379441 | Jupyter | 629 | 23 | # %%
from chemgraph.agent.llm_agent import ChemGraph
workflow_type = "single_agent"
cg = ChemGraph(model_name='claude-3-5-haiku-20241022', workflow_type = workflow_type, generate_report=True, return_option="state")
cg.visualize()
# %%
# Run geometry optimization using MACE MP
query = "What are the vibrational freque... |
e986f6d250929ff03fa5b7cdc9569cdd6002d6e983dd6a8e458bf0a6f1af6e63 | Jupyter | 641 | 22 | # %% [markdown]
# # Figure S4: H2E receptive fields
# %%
import EIANN as eiann
from EIANN.generate_figures import *
eiann.update_plot_defaults()
import nbimporter
import Figure_5 as fig5
# %%
figure_name = "FigS4_H2E_receptive_fields"
model_list = ["vanBP", "bpDale_fixed", "HebbWN_topsup", "BCM_topsup", "bpLike_WT_h... |
8cabb222597064713b81076be375add0b05d8e6026f6b2a36f10235620d53f4d | Jupyter | 647 | 35 | # %%
import scanpy as sc
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from scipy.io import mmread
import scipy.sparse as sp
import anndata
# %%
matrix = mmread("GSE166692_sciSpace_count_matrix.mtx.gz")
# %%
cell_data = pd.read_csv('GSE166692_sciSpace_cell_metadata.tsv.gz', compression='infer... |
59108c6cdc9d1860e26d72b8aab95f7726975454dbad6a5ca9b9df6ef25ed5cf | Jupyter | 666 | 10 | # %%
from chemgraph.agent.llm_agent import ChemGraph
workflow_type = "graspa"
system_prompt = "You are an expert in running grand canonical monte carlo simulation. You task is to use the tools given to answer the user's request"
cg = ChemGraph(model_name='gpt-4o-mini', workflow_type = workflow_type, system_prompt=syste... |
883ef1b604288117b7a8976da6655bf503ead30cd97f16dc7d866cf949da7a1d | Jupyter | 676 | 39 | # %% [markdown]
# ### Run all notebooks
#
# Run this notebook to run the whole simulation.
# %%
from general import *
%run prepare_data.py
# %%
parameters['run_optimization'] = True
parameters['save_data'] = True
%run 1_afferents.ipynb
# %%
parameters['run_optimization'] = True
parameters['save_data'] = True
%... |
f89845c8103ef227a1e40436a0edf2416a2b020f0d21773193570ae548b721e1 | Jupyter | 690 | 35 | # %%
from pathlib import Path
import numpy as np
import matplotlib.pyplot as plt
# %%
model_identifier = 'bi_lambda-30_lb-10'
compression_folder = Path(f'compression/{model_identifier}')
fnames = list(compression_folder.glob('wedge*npz'))
# %%
fname = fnames[2]
with np.load(fname) as handle:
data = handle['data'... |
2669e24eb0d29f2d2911a62dd3f985ef086e7f4322f546a7df7c77361509d332 | Jupyter | 700 | 22 | # %%
import scanpy as sc
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import glob, os
# %%
paths = glob.glob('../../processed_data/01-4layers_withendo/05-pathway_scoring/scores_4*.csv')
for pathway in paths:
df = pd.read_csv(pathway, index_col=0)
sns.boxplot(data=df, x="bayesSpace_... |
0b626169eac77574c6afaad03de8a8fe0518ce964e4fe39303e4fe31960533ce | Jupyter | 718 | 28 | # %% [markdown]
# # Database creation
#
# This notebook is used to make custom databeses for blasting. More details can be found [here](more details can be found here: https://biopython.org/docs/1.75/api/Bio.Blast.Applications.html)
# %%
from Bio.Blast.Applications import NcbimakeblastdbCommandline
# %%
#Specify you... |
c241670fbaef6d80f61d345090e1325c5738af4746f9d9b8ebd3fd37a2e73cb1 | Jupyter | 738 | 30 | # %% [markdown]
# # Quickstart
#
# ## for Connectivity-based Hopfield Neural Networks (CHNNs)
# %% [markdown]
# ### Imports
# %%
import sys
sys.path.append('..')
import pandas as pd
from connattractor import network, analysis
# %% [markdown]
# ## Obtain functional connectivity matrix
#
# Should be a partial correl... |
2fb2d67e8f3fee5aea789e980897d3ae8e67507eb4af9494d133c869346c29c6 | Jupyter | 766 | 24 | # %% [markdown]
# <a href="https://colab.research.google.com/github/yiruchen4/Chen2025/blob/main/Code/ChenFigureS1.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %%
# @title Importe libraries and functions
from IPython.display import Image
import... |
f87baa6263c5fe6f6d0f5d0e0c830873158fd150186b8f0d21887b9c6bf8831e | Jupyter | 766 | 24 | # %%
from chemgraph.agent.llm_agent import ChemGraph
%load_ext autoreload
%autoreload 2
workflow_type = "multi_agent"
cg = ChemGraph(model_name='gpt-4o-mini', workflow_type = workflow_type, structured_output=False)
# %%
cg.visualize()
# %%
# Incorporate parameters from user's query
query = "Calculate the reaction e... |
edba8c6188362fc083899e32ce0e7bd7c3bd54a7d0f0398c7228580a12a4ac28 | Jupyter | 769 | 26 | # %%
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from matplotlib import rcParams
# %%
df = pd.read_csv("~/MinaRyten/Melissa/teamWood/ClusterProfiler/07-clusterProfiler_res_logfc1.csv")
df = df[df['p.adjust']<0.05]
df = df[df['annot_level'] == 2]
# figure size in inches
rcParams['figure.... |
bfc1eb55dd144d3760a14c5573ad5d13c4afa8127a44d85232d29ddab5d94941 | Jupyter | 782 | 26 | # %%
from chemgraph.agent.llm_agent import ChemGraph
workflow_type = "single_agent"
cg = ChemGraph(model_name='gpt-4o-mini', workflow_type = workflow_type, structured_output=True, return_option="state")
cg.visualize()
# %%
# Run geometry optimization using default parameters
query = "What is the smiles string caffei... |
6c48958166d2024c88a83eab052d17d3a0f1ee4dad3e1b4fbabc675fbe9ca772 | Jupyter | 796 | 35 | # %%
import math
from enum import Enum
from magicgui import magicgui, use_app
use_app("ipynb")
class Medium(Enum):
"""Enum for various media and their refractive indices."""
Glass = 1.520
Oil = 1.515
Water = 1.333
Air = 1.0003
@magicgui(
call_button="calculate", result_widget=True, layout... |
64a28753b96226edc748724d0003513d5aa689a1a771aeed60a5ddead0eda557 | Jupyter | 799 | 39 | # %%
# Import the cellseg package
import cellseg.plot
# Import plotting packages
import bokeh
from bokeh.io import output_file, show
bokeh.io.output_notebook()
# %% [markdown]
# ## Single image validation
# The purpose of this code is to validate individual images using the pipeline.
#
# After setting the round, pl... |
20d1d740d6aa4a2b27ecb204af6e8113a09e8b7a79355e0963576d4e28ae9ab2 | Jupyter | 818 | 37 | # %%
import numpy as np
import nibabel as nib
import cv2
import os
import src.animateStuff as ani
import src.contextract as ce
# %% [markdown]
# ## Set up input and output files
# %%
#Relevant files
inputfile = 'Datasets/subject_01.nii.gz' #Inputdata
basevid = 'subject_01.mp4' #base video output
contourvid = 'subje... |
ad1a748eea1f9ded1564599e0dc3b149c57a56e6f7d327c724d0a2e46c7c311c | Jupyter | 852 | 39 | # %%
import pandas as pd
import numpy as np
# %%
extended_p_me_t = pd.read_csv("../probabilistic_mapping_extention/extended_p_me_t.csv", index_col=0)
np.sum(extended_p_me_t, axis=1)
# %%
common_t_types = extended_p_me_t.index
msk_t_types = np.asarray([("IMN" not in x) & ("NN" not in x) for x in common_t_types])
comm... |
eeed1e13be8b5bbea264ca48e8682bdf559ab7a98f50a6c6aacd7de8a8f233f6 | Jupyter | 858 | 23 | # %% [markdown]
# ```{eval-rst}
# .. role:: nge-mint-green
# ```
# {nge-mint-green}`Inference`
# ===================================
# %% [markdown]
# ### Model File
# SKOOTS needs a pretrained model file to run inference on a new image. This should be generated at the conclusion of training, and has the extension `*.... |
8d24842d8670f8f2f4e5f02a34e51384ed8ac6a458a2642e54c94df0401b80fd | Jupyter | 860 | 36 | # %%
import pandas as pd
from scipy.stats import fisher_exact
import re
import sys
sys.path.append('../utils/')
from utils import *
from plot_utils import *
# %%
# Calculate the values for the contingency table
# `expressed_yes`, `intersected`, and `gwas_yes` provided above
expressed_no = total_genes_len - expressed_... |
39619f5c2ec06765f9216c593ccea0d631d234afcce4e8ee4a28ab107b250f56 | Jupyter | 891 | 35 | # %%
import numpy as np
import pandas as pd
import pegasus as pg
import json
import os
import pegasusio
from pegasusio import UnimodalData, MultimodalData
import matplotlib.pyplot as plt
import itertools
from matplotlib import cm
import scanpy as sc
from matplotlib.pyplot import rc_context
import matplotlib.gridspec a... |
a9d6c5b4a0a95fb491bc1f48dbdd0124ca24965fd136a6e5c5edd5a9eacf6aa3 | Jupyter | 903 | 44 | # %%
import sys
sys.path.append('../../')
# %%
from downstream.cci import cal_spot_lr_score
# %%
import numpy as np
import pandas as pd
import scanpy as sc
# %%
adata = sc.read_h5ad('./result/cci/MB/adata_mouse_brain.h5ad')
adata
# %%
CT_dataframe = pd.read_csv('./result/cci/MB/Mouse_Brain_bin200_celltypes_datafram... |
0eee0992b475a7287c3ae8234bc0aba10fb048cdb6630f6f5456c052e1ba75d2 | Jupyter | 911 | 38 | # %%
import numpy as np
import pylab as plt
from wfield import atlas_from_landmarks_file
from wfield import *
localdisk = '/home/data/JC111/20230520_164209/wfield/'
lmarks = glob(pjoin(localdisk,'*landmarks*.json'))
if len(lmarks):
atlas, areanames, brain_mask = atlas_from_landmarks_file(lmarks[0],do_transform=T... |
500877b7a59b3c7f34a378c86510b49f02cde77f471888e063548894ff7ed5b3 | Jupyter | 918 | 43 | # %%
import nibabel as nib
import numpy as np
import os
import glob
# %%
npys = glob.glob('*.npy')
# %%
npys
# %%
np.load(npys[0]).shape
# %%
ref_bold_nii = nib.load('sub-NeuroMET00401_task-rest_space-MNI152NLin2009cAsym_desc-brain_mask.nii.gz')
# %%
template = 'tpl-MNI152NLin2009cAsym_res-01_T1w.nii.gz'
# %%
te... |
a85bb19f0ed8ef922f906443f15a48fdf7328d6bd58b751f7558adc08d553c20 | Jupyter | 939 | 30 | # %% [markdown]
# ### Explore model
# This scripts runs some data exploration methods to grasp the underlying data structure. To run the script, a pandas dataframe has to be read that contains a table with all samples and features.
# %%
from fmri_decoder.model import MVPA
# %%
# load dataframe from disk that contains... |
d945055365be05865f99daf739842fd732ddd1d45803a4e76510855b239f874e | Jupyter | 939 | 55 | # %%
from brainscore_core.supported_data_standards.brainio import get_stimulus_set, get_assembly
# %%
list_stimulus_sets()
# %%
list_assemblies()
# %%
stim_pub = get_stimulus_set("movshon.FreemanZiemba2013.aperture-public")
stim_pub
# %%
assy_pub = get_assembly("movshon.FreemanZiemba2013.aperture.public")
assy_pub
... |
0108ce6b5879f6aabda56850aacf4ca394ad639486f9e57bf9cec2f11e5cbac6 | Jupyter | 968 | 23 | # %% [markdown]
# ```{eval-rst}
# .. role:: nge-green
# ```
# {nge-green}`Inference with SKOOTS`
# ===================================
# %% [markdown]
# ## Inference in the CLI
# SKOOTS can be used to segment on a new image using the command line interface. With an image in mind, and a pretrained model file, simply wr... |
9c3aeb564be2ea179c4149ad662596c4011d4a5aabe969cc87939ddd70df7604 | Jupyter | 968 | 61 | # %%
import os
import mne
import mne_nirs
import matplotlib.pyplot as plt
# %%
plt.rcParams['axes.labelsize'] = 15
plt.rcParams['axes.titlesize'] = 15
plt.rcParams['xtick.labelsize'] = 15
plt.rcParams['ytick.labelsize'] = 15
plt.rcParams['font.size'] = 15
plt.rcParams['font.weight'] = 'normal'
# %% [markdown]
# # upl... |
dbb417c65656566627c0be62a66c4792defa650a0a20726dba26f4b194c912cc | Jupyter | 969 | 51 | # %%
from biomart import BiomartServer
import csv
import datatable
import pandas as pd
def save_file(file_path, object):
with open(file_path, "w") as file:
for line in object:
file.write(line + "\n")
# %%
server = BiomartServer("http://ensembl.org/biomart") # grch37.ensembl.org/biomart
mart = ... |
614530dd0876fc709ef3073567ef9f2fa1b545434c73a7ba23ba0b0f06ae2deb | Jupyter | 997 | 24 | # %%
library(ggplot2)
library(ggrepel)
library(ggpubr)
library(data.table)
# %%
df <- fread('database/zrfus_binding_peaksig.closetgene_zrfus.tsv', data.table=F)
zr_gene <- readLines('database/ZRFUS_signature_genes.93.mouse.gene')
# %%
ggplot(df, aes(x=peaksig_x, y=peaksig_y) ) +
geom_point(size=3, color =... |
18341f2d26fe271b122d5d06c231d96e2786c62b4bd2d8974190756f048034c9 | Jupyter | 1,005 | 35 | # %% [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... |
4f07a8c42e585dde910ff6e6ff5ffb852595da7b6b82b4adb8db51096d098a54 | Jupyter | 1,005 | 32 | # %% [markdown]
# # Figure S7: Receptive fields with biological plasticity
# %%
from EIANN.generate_figures import *
import EIANN as eiann
eiann.update_plot_defaults()
import nbimporter
import Figure_5 as fig5
# %%
figure_name = "FigS7_biological_receptive_fields"
model_list = ["BTSP_WT_hebbdend", "Supervised_HebbWN_... |
86a52273b166d019698cec03642322335f65c7644cf42ae9186ef6d141fcf003 | Jupyter | 1,007 | 34 | # %%
import pickle
import matplotlib.pyplot as plt
import pandas
import numpy as np
from utils.classification import load_all_train, load_iter0, load_iterX
from utils.visuals import show_classifier_results
from utils.meta import *
import configparser as cfg
from pathlib import Path
import seaborn as sns
# %%
config ... |
0a7af9b04b935609e74abbd590bb5ab4dedcfa59c493e052128d523667de1a05 | Jupyter | 1,012 | 32 | # %%
import pandas as pd
import random
# %%
S_t = pd.read_csv('../Stransitive_to_process.csv')
S_t['W']= S_t.apply(lambda x: x['MTS'][0], axis=1)
S_t['POS']= S_t.apply(lambda x: x['MTS'][1:-1], axis=1)
S_t['M']= S_t.apply(lambda x: x['MTS'][-1], axis=1)
S_t['RANDOM'] = S_t.apply(lambda x: random_AA(), axis=1)
def ra... |
2ba470a4f4aa7427c64a9864fa20a0d2269e610f48ef6aeaa8389fe208df3823 | Jupyter | 1,027 | 48 | # %%
import pandas as pd
import numpy as np
# %%
extended_p_me_t = pd.read_csv("../probabilistic_mapping_extention/extended_p_me_t.csv", index_col=0)
np.sum(extended_p_me_t, axis=1)
# %%
common_t_types = extended_p_me_t.index
msk_t_types = np.asarray([("IMN" not in x) & ("NN" not in x) for x in common_t_types])
comm... |
8eced6b0024d05414575b153b26cca3ed6ec2a481e0adff897dc2abadc5c97cb | Jupyter | 1,032 | 35 | # %% [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... |
028bcc0e55e1a8b793a1e8293bb92d32ffa46122668869a4a1f1ebe68f78f3f3 | Jupyter | 1,036 | 35 | # %% [markdown]
# # Creating Design Matrix
#
# This script creates two instruction files for design matrices, one for the presentation of of objects and one fore scrambled. In the files the columns correspond to stimulus onset, stimulus duration and .
# %%
import numpy as np
import nibabel as nib
import pandas as pd
... |
a3da7782c66cb6c6d761c3f6bc4b0967c44a0cb3c2622e5991fec12bb7bc756c | Jupyter | 1,036 | 38 | # %% [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... |
3177594e17192c796f016839b1dca240454030e1824708fbd647649a3d30b3be | Jupyter | 1,047 | 44 | # %%
# 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... |
015a89e7ca3b5deb5823f71a211a15b0ea905203f37b839288c6054e6411b07f | Jupyter | 1,048 | 44 | # %%
# 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... |
0a65dc2ba8c10fc9ef2db8fd6b9b839876c92d8065c047b97abb723b64b5779a | Jupyter | 1,048 | 44 | # %%
# 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... |
0cff127e962f9c834f164e25eb28d3f7db59240701e857d7414b58a76e2ee11b | Jupyter | 1,048 | 44 | # %%
# 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... |
100c458ad951af343cc983ba86fb2bace0fe93b957f0cfc8a8eb87d572928041 | Jupyter | 1,048 | 44 | # %%
# 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... |
1b9c3ea118b803e7f34a77a26704c00b1195b7d9e01368b766dcab94ad3ed09a | Jupyter | 1,048 | 44 | # %%
# 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... |
366afe503224e988621572f3bb91c48e78589aed116a9dad468072ace6f8754b | Jupyter | 1,048 | 44 | # %%
# 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... |
4af85532711378ea0e4fffe6fbf3579c24ed6919834cd5025ee2c7ca65ca3099 | Jupyter | 1,048 | 44 | # %%
# 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... |
4d7b46f9893aa7b68bf594b434e626157e34ba5084ee016cf2ae32acee7f638d | Jupyter | 1,048 | 44 | # %%
# 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... |
4f4cad35d07acaa7b44ea4af445ec975330e19d9e0aab195e579198c818f50d5 | Jupyter | 1,048 | 44 | # %%
# 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... |
65d74ab0fd4cf45204daec75cd866b82e66225d63a5d4b6378c260bcffeae188 | Jupyter | 1,048 | 44 | # %%
# 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... |
6ab6130aca097a0fa4bcfe8f7731ed3253fb3d8e4a287d478effc6b6ff11d7d3 | Jupyter | 1,048 | 44 | # %%
# 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... |
7cdb7aa0f41a14c493bd7415951a6262cd8926b0fb315ad4fc420b79f702484f | Jupyter | 1,048 | 44 | # %%
# 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... |
90079cbc6e375178db9610ba755c72321c20490bb3c7ea15f279fad9a763f989 | Jupyter | 1,048 | 44 | # %%
# 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... |
b0ef3aa1edfcd574b9d1ead1da84cd38389093acd7a136bf798da5f684765967 | Jupyter | 1,048 | 44 | # %%
# 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... |
bb70c961672bce5e37eb606892f9e888fff9150b03e732c0cf965eb975c7c728 | Jupyter | 1,048 | 44 | # %%
# 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... |
bedceb8bb98debbeb8948e9b32fea697d3e97e6b540b28cb4190474e45e6d2eb | Jupyter | 1,048 | 44 | # %%
# 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... |
cf5380830aa2b099a7b6b1d281097f6d69e9ff81df44e5736e983b5c493a5ee4 | Jupyter | 1,048 | 44 | # %%
# 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... |
dc8a61fa0337f04629b1b3cc6fbb09ee41457482c0889b08d76b365ba5e7eaa1 | Jupyter | 1,048 | 44 | # %%
# 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... |
df48f8d9e5146db96ac3716840ecebce980b8197eecf3d23297be211f4c9ddb6 | Jupyter | 1,048 | 44 | # %%
# 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... |
f1223ee080f04fd6e7968bd6af95100f98d9df16d15a26d1cb14774dc16ddf46 | Jupyter | 1,048 | 44 | # %%
# 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... |
f2ac8fb45b1f18a77ab8fc5a2a7e1f11f08fed44ca1f19b4d16957f441f81702 | Jupyter | 1,048 | 44 | # %%
# 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... |
f803924fec4d450005d2f803dcfb1de6d5e4613530233cdcba52fd94e520714b | Jupyter | 1,048 | 44 | # %%
# 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... |
0590e3bb980f2bebe1977683ca0aef9dd9044018d353a728987d2dde1a43e283 | Jupyter | 1,049 | 44 | # %%
# 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... |
07d006ab799118bb0a420370445dfe2c4da2fbee31738cc78529aa9fe39cf747 | Jupyter | 1,049 | 44 | # %%
# 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... |
207695bd95664ac0f39b836d1d5e22904753dfebd995526ec3d2a420b6f4a0e7 | Jupyter | 1,049 | 44 | # %%
# 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... |
2a30ab67c1222ce439f303f343922506239186deeb29c040fa808cc2b5c199d0 | Jupyter | 1,049 | 44 | # %%
# 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... |
61fd6e7aeeedfe847eb3d04f7a4e489ff980836f92473bc1133b5a30156939f2 | Jupyter | 1,049 | 44 | # %%
# 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... |
74fe8d93a9f8506cc9bd9a1bc0d7906d649470b8e887749f35f7f0fdff9c9d44 | Jupyter | 1,049 | 44 | # %%
# 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... |
7576aee56310d5b63ab0ba2693c36aa51c5000bd7c2973bf45a991a56dd01a2a | Jupyter | 1,049 | 44 | # %%
# 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... |
7974614e7d1f325dec6811d0cd8db696e10cbb85301480269bb767f33439610e | Jupyter | 1,049 | 44 | # %%
# 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... |
8c7d37f6673182edb1eb22cb378d39ad76f43b9623bed90ab0e371b6c817afed | Jupyter | 1,049 | 44 | # %%
# 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... |
953be38c037eee927e5c661bef6f6f4ab4993571aa59e04538b46a56e9831fec | Jupyter | 1,049 | 44 | # %%
# 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... |
c2a46c84471aa74ccfbb4bde79d724a7e5554fcf25165cfe40024d0a5511030f | Jupyter | 1,049 | 44 | # %%
# 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... |
27cb0df4101abf56bbe83037fbcbb06da065a731d732e84420d5e61adf6e4eed | Jupyter | 1,050 | 44 | # %%
# 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(... |
67b8e9be533df90909c4efa9e13dd0b1ffe04373d8c1ab8a22dba00d966c56c2 | Jupyter | 1,050 | 44 | # %%
# 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(... |
7e6dadaaa0e1f1b2f6c6ccbbfd928dd8a85776fff1e7fa36d5989e517ca0b6b3 | Jupyter | 1,050 | 44 | # %%
# 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(... |
9f7e226b97a74c7a9d3014adc759abf9d3cd55b9ed2e9e5d15674a9e357a601c | Jupyter | 1,050 | 44 | # %%
# 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(... |
a845e0e917b697c5662c158bfa009a33326a6cf096ad2c9ad061a06dcce6e4a9 | Jupyter | 1,050 | 44 | # %%
# 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(... |
b60870f0fe5423b906f2bce9847f80dbc81716137563861657a4a8a04f074a6f | Jupyter | 1,050 | 44 | # %%
# 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(... |
c767cb38cf3de8ebb2594653fa7ffa067763a1cb21ff001431c4e4bffe771b6b | Jupyter | 1,050 | 44 | # %%
# 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(... |
cf2013631c82972d779029113868a75985396c574b4ae65886b02e8025f45132 | Jupyter | 1,050 | 44 | # %%
# 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(... |
0672470e3a572cff8f51ef95c5314797b4d752ce5d3a4c73377d9954cb8a202e | Jupyter | 1,051 | 44 | # %%
# 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... |
54e3dd2a8f9e947e93bbcebab904bcc2c6e1153752e2ba061bd828c5d57a4396 | Jupyter | 1,051 | 44 | # %%
# 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... |
6d9e1fc704d0dc60b66be5e70a4870102b0d93f61ab7b0b9554f48a9dd444885 | Jupyter | 1,051 | 44 | # %%
# 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... |
f99aeb5c0788e33f90ab1a2fea8478fb071872df0325951ce5447240ada997ab | Jupyter | 1,051 | 44 | # %%
# 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... |
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