sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
530b7c293b910762bd97b0ba532aa0e788bb04d4cbad8a02c3bce53a15015d23 | Jupyter | 2,568 | 122 | # %% [markdown]
# # Load data
# %%
data_dir = '.';
load(sprintf('%s/data_simulation_11.mat',data_dir))
# %%
data_0 = permute(data_0, [3 2 1]);
data_1 = permute(data_1, [3 2 1]);
# %%
size(data_0), size(data_1)
# %%
num_trial = size(data_0, 1);
num_time = size(data_0, 3);
# %%
dat_0 = struct()
# %%
dat_0.seq = str... |
3f542ea67222da0eb9c6d39d438a34a706599322d73d06efb6fc7227af47902e | Jupyter | 2,573 | 96 | # %%
import logging
import os
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s: %(message)s',
level=logging.INFO,
)
logger = logging.getLogger('finetune.ipynb')
DATA_FOLDER = os.path.join(os.path.expanduser('~'), 'data', 'ds000030')
PREPROCESSED_IMAGES_FOLDER = os.path.join(DATA_FOLDER, 'pr... |
cf4e3456f6d977c661e04dead1c01bbc97b1de80efa87578ded522c71515597a | Jupyter | 2,574 | 126 | # %%
import os
import pickle
import pandas as pd
# %%
def get_nyt_data(year: int, data_path: str) -> list[str]:
"""Load New York Times data for a specific year.
Args:
year: The year to load data for
data_path: Path to the directory containing NYT data
Returns:
List of article tex... |
3486db08fd6fa50af7c2d0c8bfdb060351117aa1b5bfd3071b588ee73b2273ab | Jupyter | 2,579 | 87 | # %% [markdown]
# # Simulated EEG multiverse
#
# We first define the forking paths:
# %%
from comet.multiverse import Multiverse
forking_paths = {
"software": ["MNE", "FieldTrip", "EEGLab"],
"resampling": [250, 500, 512],
"stimulation": ["IAF", "9", "10", "11"],
"electrode": ["Pz", "O1", "O2", "P3", ... |
df1aaece480655e7a39897c720627943a816e7cb341ed7841eeef2191908ea72 | Jupyter | 2,580 | 82 | # %% [markdown]
# # Convert data to CLAHE
# The pre-process steps for HiP-CT 16bit data are as follows:
# * 3D CLAHE applied to the 16 bit data
# * Normalisation to 8 bit
# %%
import sys
sys.path.append('..')
import os
import glob
import natsort
import numpy as np
import skimage.io as skio
from segmentation.preproces... |
ea02481bb5fd410f9ba4b7bb2f0970dae53f00a3a36a7ed91ca6756c3ee43705 | Jupyter | 2,581 | 68 | # %%
library(glue)
library(ggplot2)
library(egg)
source('../utils/utils.r')
# %% [markdown]
# # ED Fig 7b: barplot of AUD p2g links (AUD peakSet)
# %%
corr_array <- readRDS("/gpfs/gibbs/pi/girgenti/JZhang/CL/multiome/data_AUD/fig5-GRN/motif_AUD_P2G/corr_array_full.RDS") # range: [-1, 1]
# %%
diff_array <- setNames(... |
f8f2d3c22bad2ada9b58883bd89978de4fe316f2ef2fd6c2e9bb1cda298d8cae | Jupyter | 2,587 | 74 | # %% [markdown]
# # Figure 6: Spirals 2D data
# %%
import matplotlib.pyplot as plt
import matplotlib.gridspec as gs
import h5py
import EIANN.utils as ut
import EIANN.plot as pt
from EIANN.generate_figures import *
pt.update_plot_defaults()
root_dir = ut.get_project_root()
%load_ext autoreload
%autoreload 2
# %%
fi... |
f719071529e6ea6962727145193a8cc89015a0b3c244343a491851fddfd98453 | Jupyter | 2,597 | 77 | # %%
# -*- coding: utf-8 -*-
import warnings, os
import pandas as pd
from sklearn.metrics import r2_score, mean_absolute_error
from catboost import CatBoostRegressor, Pool
warnings.filterwarnings("ignore")
# ======== Configuration ========
CSV_PATH = "9_makale_data_guncel.csv"
TARGET = "band_gap"
ID_COLS = ["material... |
596458667fddc474036baf4cd04f235174c660ef64648d6367c1fb76f0be9d1b | Jupyter | 2,598 | 81 | # %% [markdown]
# <a href="https://colab.research.google.com/github/agemagician/ProtTrans/blob/master/Embedding/TensorFlow/Advanced/ProtT5-XL-BFD.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# ##Important Notes:
# 1. ProtT5-XL-BFD... |
60b1e6c0bdf3d71a519884eeee4bda874999d274cc513d53a727453592ef234a | Jupyter | 2,612 | 79 | # %% [markdown]
# ### Plotting RBP binding profiles
#
# Here, we show how to plot the RBP binding profiles.
#
# The RBP coordinates and PSI values can be downloaded from Zenodo.
# %%
import numpy as np
import pandas as pd
from tqdm.notebook import tqdm
import seaborn as sns
from matplotlib import pyplot as plt
i... |
f973ec9d0d04b32444f0aeb2631b409f200656a520198a4f95b6a7c357e66a0f | Jupyter | 2,627 | 67 | # %%
import pandas as pd
import numpy as np
from scipy import stats
import statsmodels.stats.multitest as sm
import seaborn as sns
import matplotlib.pyplot as plt
df = pd.read_csv('../../raw_data/Glycosphingolipid_analysis/Deep-surface_all_comparisons.csv')
df['patient_id'] = list(df['id'].str.split('_', expand = Tru... |
1b43861a34198a765bcf278a362e93d5d4e0c2b195caec75d3e296f83879ba9b | Jupyter | 2,637 | 76 | # %% [markdown]
# # Test and compare lineage tracing algorithms on a colony
# %%
from bread.data import SegmentationFile, Microscopy, Segmentation
from bread.algo.lineage import LineageGuesserNN, LineageGuesserNearestCell, LineageGuesserBudLum, accuracy
from bread.data import Lineage
# %%
import warnings
with warnin... |
7930a89ddb3590b7489bfd1a60bbe1f825b368b1c1c0765d9f8a928651140763 | Jupyter | 2,652 | 62 | # %%
import pandas as pd
import numpy as np
from scipy import stats
import statsmodels.stats.multitest as sm
from sklearn.experimental import enable_iterative_imputer
from sklearn.impute import IterativeImputer
# %%
# Load and tidy data
df = pd.read_csv('../../raw_data/Glycosphingolipid_analysis/Deep-surface_GSLs.c... |
2f83eda28f284bcda1ad346bb4373ff293f62578aa4a53dd736e61c1ed4b5410 | Jupyter | 2,656 | 125 | # %% [markdown]
# # Load data
# %%
data_dir = '.';
load(sprintf('%s/data_simulation_10.mat',data_dir))
# %%
data_0 = permute(data_0, [3 2 1]);
data_1 = permute(data_1, [3 2 1]);
# %%
size(data_0), size(data_1)
# %%
num_trial = size(data_0, 1);
num_time = size(data_0, 3);
# %%
dat_0 = struct()
# %%
dat_0.seq = str... |
f92a1b95ac18f79d12c47dc3f6d17650735452aadfd59af2e6489a6e1212a93f | Jupyter | 2,664 | 80 | # %% [markdown]
# # Generate Protein Embeddings
#
# As an example, here's how to proccess the frog reference proteome with ESM1b.
#
# You can find proteome fastas on ENSEMBL: https://uswest.ensembl.org/info/data/ftp/index.html
#
# We use a pretrained Transformer model from https://github.com/facebookresearch/esm. Th... |
521e2c5797ba4bd912e5812aead911920b43ff476c0acf4d065c5261446eaba3 | Jupyter | 2,673 | 84 | # %% [markdown]
# # 03 - NSD Stimulus Packaging
#
# Extract the 515 shared stimulus images from `nsd_stimuli.hdf5` and save as individual PNGs
# for Brain-Score stimulus set packaging.
# %%
import h5py
import numpy as np
import pandas as pd
from pathlib import Path
from PIL import Image
from tqdm.auto import tqdm
NS... |
3d19fd941eb6e3236300589eeb2edc6b13023a5655f208804c27271f9a45c5b4 | Jupyter | 2,681 | 44 | # %% [markdown]
# <a href="https://colab.research.google.com/github/agemagician/ProtTrans/blob/master/Prediction/ProtBert_BFD_Predict_MS.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# <h3>Predict Membrane/Soluble using ProtBert-BF... |
31d01f4966101657ad14149e5e8ee71af0cc119413d08896b16eb498d139e858 | Jupyter | 2,683 | 91 | # %% [markdown]
# ## Tutorial
#
# This tutorial exemplifies how to use the python module to list files and grab sessions from the google DATA folder.
#
# 1. list subjects
# 1. list sessions
# 1. list all files for a subject
# 1. retrieve a specific experiment
# 1. download all files of a type
#
# #### To install the... |
bbc56a9196051ec9e555e095520fa1e2fb9b15479aab3c7a88aeebe0dc5ee4a1 | Jupyter | 2,699 | 105 | # %%
import pandas as pd
import os
import numpy as np
import torch
import timm
import matplotlib.pyplot as plt
from dataset_SOFIA import DatasetSOFIA
# %%
folder_path = '' #path to top folder containing patient directories.
label_meta_data_path = '' #csv file with patient IDs and corresponding diagnostic labels.
o... |
88a9385d029c477422ec02454884d5653130b94721203c39c444525c92a1f530 | Jupyter | 2,702 | 83 | # %% [markdown]
# If running on google colab, uncomment and run the first cell
# Otherwise, build environment from Dockerfile with access to nvidia gpu
# %%
#@title Setup your linux environment on Google Colab
# !sudo apt install python3.8-full python3-pip
# !sudo update-alternatives --install /usr/bin/python3 pyth... |
1d3e090c8dea8966eace691a82b66466ef7e3809bddbcbc0cf170b0e3449c418 | Jupyter | 2,707 | 75 | # %%
import pandas as pd
import itertools
from matplotlib_venn import venn2, venn2_circles
import sys
sys.path.append('../utils/')
from utils import *
# %% [markdown]
# # Numbers: UP DEGs % overlap
# %%
degs_celltypes = []
for s in celltypes:
deg = pd.read_csv(f'/gpfs/gibbs/pi/girgenti/JZhang/CL/multiome/data_AU... |
1208269b63c869a6bd239681afa196cf82e3451c31709fe40d2b2b7155ad097d | Jupyter | 2,709 | 104 | # %% [markdown]
# ## This notebook demonstrates how to parse HAL logs
# %%
import os
import storm_control.sc_library.log_timing as logTiming
os.chdir("/home/hbabcock/Data/storm_control/logs/")
print(os.getcwd())
# %%
# Note that the no extensions are included in the log name.
all_msgs = logTiming.logTiming("hal400... |
af20402ebecda2f2e45ec0103a19948b1ec8377467ea480c9059719d336ffb08 | Jupyter | 2,715 | 84 | # %% [markdown]
# # Performance improvements for computationally expensive measures
#
# We can benchmark the performance of the local efficiency algorithm with the BCT implementation. Here, Comet offers a significant performance increase, especially for large networks.
# This speed-up will affect all measures that dep... |
5c001c45e4b1b7de15fa31c2ebe60e89b422f0c24a422622d6da3ed79180ea97 | Jupyter | 2,716 | 69 | # %%
import pandas as pd
import numpy as np
from scipy import stats
import statsmodels.stats.multitest as sm
import seaborn as sns
# %%
df = pd.read_csv('../../raw_data/Glycosphingolipid_analysis/Deep-surface_GSLs_zscores.csv')
df['patient_id'] = list(df['id'].str.split('_', expand = True)[1].str.split('Deep', expand... |
212cd6dfe5ba973608cff687816a0e53b202ca3e8776d4b0c81ee7e03fbcccde | Jupyter | 2,725 | 98 | # %%
import anndata
import numpy as np
import sys
import torch
sys.path.insert(1, '../../../')
import velovae as vv
%load_ext autoreload
%autoreload 2
# %%
dataset = 'BMMC'
root = '/scratch/blaauw_root/blaauw1/gyichen'
adata = anndata.read_h5ad(f'{root}/data/BMMC_pp.h5ad')
adata.obs["clusters"] = adata.obs['celltype.l... |
51038badbf30f1ec0e0591caf7ff3a0ec31d41d9d07d53decb1992c03bea2878 | Jupyter | 2,727 | 109 | # %% [markdown]
# # Load packages
# * Load ImpedanceFitter and Matplotlib for the visualization
# * Adjust the figure size because otherwise the plots are relatively small
# %%
import matplotlib.pyplot as plt
import impedancefitter as ifit
plt.rcParams["figure.figsize"] = [15, 10]
# %% [markdown]
# # Import data
# ... |
387eb38559e7be16cc99703539f6389aadc3e9035254669e0f2fdb7207df630e | Jupyter | 2,728 | 121 | # %% [markdown]
# # Example code to apply the modular splicing model
# %% [markdown]
# ## Splicing delta PSI prediction
# %% [markdown]
# Example test variants come from ClinVar BRCA1 variants
# %%
from mmsplice.vcf_dataloader import SplicingVCFDataloader
from mmsplice import MMSplice, predict_all_table, predict_sav... |
88b68bf7fe3a3fbbe53082185d8d07a48309a63eea84b7897f3acf2f9477797f | Jupyter | 2,732 | 92 | # %% [markdown]
# # DataLoader Example
# %% [markdown]
# ## 1. Pytorch-geometric dataloader
# %%
import os
# import os.path as osp
# import sys
# sys.path.insert(0, 'source')
import torch
import numpy as np
import pandas as pd
import scipy
import argparse
import networkx as nx
import torch_geometric.transforms as T
f... |
0b1970e15546511c0b6b52610ff9d6192b556616d7b13985d1425ebc24bca802 | Jupyter | 2,764 | 57 | # %%
import numpy as np
def find_params(matrix_PSD_total, matrix_FC_total,
array_MCI_PSD, array_HC_PSD,
array_MCI_FC, array_HC_FC,
normstrat = 'standard', weight=1.0):
"""
Find model parameters that best reproduce empirical PSD and FC data
for MCI and HC grou... |
0058b8aaf0200ff5ee1f6b49e161b2c1f8f6b594ff774055a499c333848fe45d | Jupyter | 2,774 | 95 | # %%
import pandas as pd
import numpy as np
# %% [markdown]
# ## Get the Normalization
# %%
from nilearn.image import load_img
# %%
comps = [load_img(f"/Users/chris/projects/Nature2023MooneyScripts/results/prediction_error_components/components_{idx}.nii.gz").get_fdata() for idx in range(4)]
comps = [comp[comp != 0]... |
fc0da0880d6708f06a30dcb922480dd6b2443ce09abe0e9c42706dde30650e5f | Jupyter | 2,778 | 89 | # %% [markdown]
# <a href="https://colab.research.google.com/github/agemagician/ProtTrans/blob/master/Embedding/PyTorch/Advanced/ProtT5-XL-UniRef50.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# ## Important Notes:
# 1. ProtT5-XL-... |
7cf0b009265d8d439158c153fa47fbc8576c1df8f2245035cb92de10ea6820be | Jupyter | 2,782 | 88 | # %% [markdown]
# # Quickstart
#
# ***for Connectivity-based Hopfield Neural Networks (CHNNs)***
#
# - **Part 1**: The quickest way to plot and analyze your data on the CHNN projection
# - **Part 2**: Build your own CHNN projection
# %% [markdown]
# **Imports**
# %%
import sys
sys.path.append('..')
import pandas as... |
932a21ec50938c81e37b84b7bb8c99a1dbe0070908b3101f54d58c0d045ca246 | Jupyter | 2,796 | 76 | # %% [markdown]
# # Block-wise single value decomposition
#
# The blockwise implementation can take a while because data has to be read from the disk (all time samples for each block). This can be speed up by having more memory. An alternative approach is the ``svd_approximate`` function that works by averaging the mo... |
b23055ffe5cd2289e0049952132c62003e00c31df4a3b45993e47a2941b7feb1 | Jupyter | 2,799 | 144 | # %%
import numpy as np
# %%
accu_off = 68.6
accu_nat = 79.8
accu_pat = 70.2
accu_dat = 94.6
accu_pat_sam = 97.1
accu_sam = 92.3
time_off = 356.1
time_nat = 338.2
time_pat = 393.8
time_dat = 1272.7
time_pat_sam = 623.1
time_sam = 513.8
lambda_off = 93.5
lambda_nat = 96.8
lambda_pat = 114.7
lambda_dat = 252.4
lambda_... |
bc05083dc1c0d95a16d32278361ed5dd6133b3944fa1747227cd27edca9b0070 | Jupyter | 2,799 | 71 | # %% [markdown]
# # Instructions
# The following code was designed to score motion/freezing data from video multiple files in batch. This code should be run with carefully selected parameters using the more extensive visualization options provided in FreezeAnalysis.ipynb. In addition to saving frame-by-frame motion/fr... |
b8cbb1ada1fde981062a5ded9b6320ce796ba6f5a26b56ca3ae21efa13c679e0 | Jupyter | 2,803 | 77 | # %% [markdown]
# # Importing and Preparing the Data
# %%
import pickle
from utils.prepare_data import PrepareData
data = PrepareData()
full_dataset = data.prepare_all_data(vareas=[3], degrees=[1], event_related=False, exclude='online', print_mask=False,
anatomy=True, retinotopy=T... |
c0c756bef7eadc6cdf10e007b9ab2e937d733dcd62232214c0f97bd6f8087e59 | Jupyter | 2,803 | 77 | # %%
# Imports
from pathlib import Path
from deep_events.database.prepare_yaml import prepare_all_folder
from deep_events.csv_to_lines import csv_to_lines
from deep_events.csv_to_gaussian import csv_to_gaussian
from deep_events.gaussians_to_training import main as gaussian_to_training_events
from deep_events.database.c... |
ae829891c2c09360e3e02963a525c7503df92423cbc7edc73325fd66938b0820 | Jupyter | 2,804 | 112 | # %% [markdown]
# # Comparison between plugin and python results
# %%
from mmsplice.vcf_dataloader import SplicingVCFDataloader
from mmsplice import MMSplice, predict_all_table
from mmsplice.utils import max_varEff
# %% [markdown]
# ## mmsplice perl plugin results
# %% [markdown]
# Run MMSplice and obtain `variant_... |
820860ae4504dbdbe2b867d427ce3e5e7c6d681896fc91e313bc82b81f11e2e8 | Jupyter | 2,808 | 75 | # %% [markdown]
# Inputs
# - Control file: A CSV file that includes an 'Accession' column with a list of image accessions for radiomic feature extraction
# - Images folder - A folder containing subdirectories with images and segmentations in the format `<images_folder>/<accession>/<file.nii.gz>` where `file` is either... |
7a597686eea8090788010e7c60db6cb4637514d51f64560ca5f7efdc33eb01bf | Jupyter | 2,811 | 88 | # %% [markdown]
# # 1. Import CellRefiner and packages
# %%
import seaborn as sns
from matplotlib import pyplot as plt
import scanpy as sc
import cellrefiner as cr
# %% [markdown]
# # 2. Load Data
# %% [markdown]
# Both spatial transcriptomics data and the scRNA-seq data for the lymph node can be found at https://gi... |
1977afc542e0b7a49fea768e0a14014f66a72237f211b57c47d54cbe1feac2f6 | Jupyter | 2,825 | 78 | # %% [markdown]
# ## Running a simulator using existing data
# Consider the case when input data already exists, and that data already has a causal structure.
# We would like to simulate treatment assignment and outcomes based on this data.
#
# ### Initialize the data
# First we load the desired data into a pandas Dat... |
6e42b22ef7799a34de10a6e753c3bfeb62b7f6fc78958fea27d4559c63f46d4a | Jupyter | 2,827 | 91 | # %% [markdown]
# # Quickstart
#
# ***for Connectivity-based Hopfield Neural Networks (CHNNs)***
#
# - **Part 1**: The quickest way to plot and analyze your data on the CHNN projection
# - **Part 2**: Build your own CHNN projection
# %% [markdown]
# **Imports**
# %%
import sys
sys.path.append('..')
import pandas as... |
ef3081ab6e5abd406d67d90085d969a91c5fe2341432bb12748a1f72ee980459 | Jupyter | 2,830 | 84 | # %%
from pathlib import Path
import dxchange
import numpy as np
import tomopy
from ipywidgets import interact, interactive, fixed, interact_manual
import ipywidgets as widgets
import matplotlib.pyplot as plt
import sys
import os
sys.path.append(os.path.abspath(".."))
from utils import flat_correct, process_image, back... |
f6b8a550ff80fe55a5630118ada9a8e257315de1540ca6a6961cdff6a843f806 | Jupyter | 2,836 | 67 | # %% [markdown]
# # Figure 8: Learning top-down weights
# %%
import matplotlib.pyplot as plt
import matplotlib.gridspec as gs
import h5py
import EIANN as eiann
import EIANN.utils as ut
from EIANN.generate_figures import *
eiann.update_plot_defaults()
root_dir = ut.get_project_root()
# %%
figure_name = "Fig8_topdown... |
84a28b1e62662a1e582ea61f16b91ae7ad180c61e0ed2de7c6f07c5c104b7afa | Jupyter | 2,843 | 95 | # %%
# Run SwinSite prediction
import sys
!{sys.executable} predict.py \
-i ./example \
-o ./output/example \
-l ./logs/example.log
# %%
# List predicted pockets
import os
import re
import glob
OUTPUT_DIR = './output/example/example'
samples = sorted([d for d in os.listdir('./example') if os.path.isdir(... |
d05498163ea054d45c1fc764dc0afd56fd00d55f9cd1494ee1dfd99f929f403e | Jupyter | 2,847 | 107 | # %% [markdown]
# # Import
# %%
import numpy as np
import matplotlib.pyplot as plt
import sys
sys.path.append("..")
from src.preprocessing import (
convert_to_cnn2d_input, load_numpy_arrays
)
from src.model import build_cnn1d, build_cnn2d
from src.train import train_model, set_seed
# %% [markdown]
# # CNN 1D
#... |
cf96571628f6b144a435dea5341b271e54f7102fc050c44fb212873e93dd123e | Jupyter | 2,849 | 113 | # %% [markdown]
# # Instruction for DANST
# %% [markdown]
# ## 1.Prepare environment
# %%
!pip install scanpy
# %%
!pip install --user scikit-misc
# %%
!pip install igraph
# %%
!pip install leidenalg
# %%
!pip install pot
# %%
import warnings
warnings.filterwarnings("ignore")
# %%
import os
os.environ["CUDA_LAU... |
0b1c66d90bbfa6ffc92b42c7f097f32b1ac4d2df8fbb1a6de59507b4101e972a | Jupyter | 2,856 | 94 | # %%
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pickle as pkl
from mpl_toolkits.mplot3d import Axes3D # <--- This is important for 3d plotting
%matplotlib inline
# %%
# In simulation_res.pkl, we have
# a continuous prediction and label positions for multiple systems,
# and their co... |
a907e5e2fdfb3300061b949e70792477a1e2bfee3f45f8a44ae5ff2a0ca35f65 | Jupyter | 2,858 | 117 | # %%
import subprocess
import os
import pandas as pd
# %%
def list_files(directory):
paths = []
for root, dirs, files in os.walk(directory):
for file in files:
paths.append(os.path.join(root, file))
return paths
def read_file(file_path):
file_lines = []
with open(file_path, "... |
070dd7cd52bb9230482913493cc6962683e992cb9fe80d40ec0be2104435dfcc | Jupyter | 2,859 | 89 | # %% [markdown]
# <a href="https://colab.research.google.com/github/agemagician/ProtTrans/blob/master/Embedding/PyTorch/Advanced/ProtT5-XL-BFD.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# ## Important Notes:
# 1. ProtT5-XL-BFD h... |
07f72952aa7ab21d902ba14c0e1fb6640f81949d094fb889939b9d6411266315 | Jupyter | 2,862 | 93 | # %% [markdown]
# # Plot top k predictions, given query node and query relation
# %% [markdown]
# ## Load libraries
# %%
library(data.table)
library(ggplot2)
library(purrr)
library(patchwork)
library(dplyr)
library(ggbeeswarm)
# %% [markdown]
# ## Functions
# %%
get_pred_w_groundtruth <- function(x){
myindex... |
abcdaaece0832a57f2af78733fec98c08f9a20c9de8b4bf89a9e0c31c04a92aa | Jupyter | 2,865 | 86 | # %%
import os
import sys
sys.path.append(os.path.abspath(".."))
import gc
import numpy as np
import tomopy
import skimage
from scipy.ndimage import zoom
import matplotlib.pyplot as plt
from skimage.metrics import peak_signal_noise_ratio, structural_similarity
import torch.nn.functional as F
import random
import torch
... |
45a23daa9b2a4572f0e790c8bc70bd517ffb31190c5b40f40599d6897d278368 | Jupyter | 2,872 | 94 | # %%
import scanpy as sc
import pandas as pd
import numpy as np
from lib_3d_OT.utils import *
# %%
adata=sc.read_h5ad('//home/dbj/lib_3d_OT/revise/scGPT/673-embedding.h5ad')
# %%
adata
# %%
clustering(adata, n_clusters=7, radius=50, key='X', method='mclust', refinement=True,random=2024,n_comp=40)
# %%
sc.settings.f... |
18019f014503446f22767c03d8298e8b8139607236d5a0852652319bacd70177 | Jupyter | 2,878 | 89 | # %%
import scanpy as sc
import torch
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings("ignore")
# %%
# load and preprocess data
slice1 = sc.read_h5ad('E:/st_data/3D_rec/basic_usage_demo_1.h5ad')
slice2 = sc.read_h5ad('E:/st_data/3D_rec/basic_usage_demo_2.h5ad')
sc.pp.filter_cells(slice1, min_... |
326d560245cb0a306712549570a15b0e27b0c95a91e4c6344475241da5d97adc | Jupyter | 2,895 | 99 | # %%
import os
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
import seaborn as sns
from pylab import text
from scipy.stats import wilcoxon
df = pd.read_pickle("fig3Edata.pickle")
# %%
def stars(p):
if p < 0.0001:
return "****"
elif (p < 0.001):
return "***"
elif (p... |
f77b88477e8ad2d93bf4a62adf2f3dc2660b64cbf290b38db3f74823d17fab25 | Jupyter | 2,917 | 97 | # %% [markdown]
# # Reproducibility 4: figure 5
# - Comparison between different models
# - See folder: models_comp for detailed calculation
# %% [markdown]
# ## Import packages
# %%
import seaborn as sns
import matplotlib.pyplot as plt
import sys
sys.path.append('../')
from scipy.stats import pearsonr
from sklea... |
cd8daded8afb100af575b60f7d44b4ec49be14c9131fb1f957b932b946ff78a8 | Jupyter | 2,922 | 104 | # %%
import re, time, warnings, os, sys, copy
import gzip, pickle
import numpy as np
import pandas as pd
import gzip, pickle
import scipy, sklearn, mne
import auxiliary as aux
import data, rsa
sys.path.append('../../spaces/')
import embeddings as emb
import pubplot as pub
import matplotlib.pyplot as plt
pub.styles.s... |
fa4e16e9ce67d95d89a3baf283ddd67a60c17f4d56f5ef46f9870ff574e82d29 | Jupyter | 2,930 | 131 | # %% [markdown]
# # **kallisto | bustools** Report
# This Jupyter notebook was auto-generated by `kb`.
# %% [markdown]
# ### Install packages
# Uncomment and run this cell to install packages used in this notebook.
# %%
{{ packages }}
# %% [markdown]
# ### Import packages
# %%
import json
import numpy as np
import... |
901a3411ad473db8d68eeac00b967fcfeedef09153dcfaa12c02a15ee14e6cbd | Jupyter | 2,934 | 73 | # %%
import MultiTaskBattery.battery as bat
import Functional_Fusion.atlas_map as am
import numpy as np
from OptimalBattery.global_config import data_dir
# %% [markdown]
# # Cortical batteries
# %%
data_cortex,info = bat.fetch_task_library(version='V1',structures=['cortex_left','cortex_right'])
# %% [markdown]
# Mo... |
78407f63a8eda6948fbc4ccf0eb2e2150761fd3212c1d0ef0b322f9202a99116 | Jupyter | 2,943 | 89 | # %%
import anndata
import scvelo as scv
import scvi
import numpy as np
from velovi import preprocess_data, VELOVI
import torch
import os
import argparse
import time
# %%
root = '/scratch/blaauw_root/blaauw1/gyichen'
dataset = 'Erythroid'
adata = anndata.read_h5ad(f'{root}/data/{dataset}_pp.h5ad')
# %%
start = time.t... |
e371562840d53088c229a556391870478b6d2bd8a1496b9c13392f430dac4eed | Jupyter | 2,945 | 77 | # %%
import tifffile
import euler_gpu
import os, random
os.environ["CUDA_VISIBLE_DEVICES"] = "1"
import numpy as np
import torch
import h5py
from tqdm import tqdm
from math import pi as PI
device = torch.device("cuda:0")
BATCH_SIZE = 256
red_channel = 1
fixed_frame = 0
MAX_NUM_CENTROIDS = 100
# %%
tiff_folder = "/sto... |
4b9b5c48ed7dbaec91d7f7b4d57f9c19cce2e3565e8373f6f6815ed81933c6ac | Jupyter | 2,952 | 130 | # %% [markdown]
# # HumanBreastCancer (Xenium) Analysis Tutorial
# %% [markdown]
# <p style="color:#1E90FF;">
# <b>Data availability:</b> All processed spatial transcriptomics datasets are publicly available on Zenodo:
# <a href="https://doi.org/10.5281/zenodo.17522268" style="color:#1E90FF; text-decoration:underline;... |
0f1debd7d0cd3310f98dfd265c7a291d6b331d69e4c684972c9ffa99ed072a15 | Jupyter | 2,953 | 125 | # %%
#Copyright (c) 2019 Primoz Ravbar UCSB Licensed under BSD 2-Clause [see LICENSE for details] Written by Primoz Ravbar
# get ethograms from probabilities (prob outputs of NN)
import numpy as np
import scipy
from scipy import ndimage
from scipy import misc
import pickle
import pandas as pd
import time
import matpl... |
7bb5f7176f0188f62f9a8008f4333f2d22f3ae96423691d5312fe61239d97f69 | Jupyter | 2,956 | 97 | # %%
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import OptimalBattery.scripts._3experimental_design._1hcp_task_covariance as sh
import os
import seaborn as sb
from OptimalBattery.global_config import data_dir, save_dir
from matplotlib import gridspec
import OptimalBattery.design as ds
# %... |
071e4a762d155ebc27aed6b73176666971f7da3b70be86efccc22da294653683 | Jupyter | 2,972 | 111 | # %% [markdown]
# # Test null map generation functions
# %%
import sys
import os
import pathlib
import numpy as np
import pandas as pd
from IPython.display import display
from neuromaps.images import load_nifti, load_gifti
# current path
wd = pathlib.Path().resolve().parent
print(wd)
# import juspyce
sys.path.appen... |
cf6425ad7d9b21fbf895def081a034d10d82b82f0eeb549b0df460ce514fc3bd | Jupyter | 2,988 | 109 | # %%
import pandas as pd
import os
import numpy as np
import torch
import timm
import matplotlib.pyplot as plt
from dataset_EfficientNetV2 import Dataset_EfficientNetV2
# %%
folder_path = '' #path to top folder containing patient directories.
label_meta_data_path = '' #csv file with patient IDs and corresponding di... |
26cddeb1aaf37e2a36c6feb0f3964b2b2b6c869d1dce2cee96a9aaf0ce649060 | Jupyter | 3,010 | 83 | # %%
from miso.hist_features import get_features
from miso.utils import *
from miso import Miso
from PIL import Image
import pandas as pd
import numpy as np
import scanpy as sc
import os
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA
Image.MAX_IMAGE_PIXELS = None
Image.MAX_IMAGE_PIXELS = None
imp... |
b478632be4569aad07192b1ce6f7b16a80963dcf6dad7e355c719489182525c7 | Jupyter | 3,017 | 97 | # %%
import celldancer as cd
import anndata
import pandas as pd
import numpy as np
import scipy as sp
import os
# %%
dataset = "BMMC"
root = "/scratch/blaauw_root/blaauw1/gyichen"
adata = anndata.read_h5ad(f"{root}/data/{dataset}_pp.h5ad")
# %%
data_path = f"{root}/data/celldancer/{dataset}"
os.makedirs(data_path, ex... |
5dd23b3bd4b2f75f5a6c1adccdd3d50efccd808e5ad06df6055b8e72bdb3178a | Jupyter | 3,020 | 95 | # %% [markdown]
# ### Figure 1b: Editing Efficiency of Optimized CHyMErA System
#
# This section visualizes the editing efficiency for each Cas variant using boxplots. Summary statistics (sample size and mean percent edited) are also reported for each group.
# %%
library(dplyr)
library(tidyr)
library(ggplot2)
library... |
56566589f7da5043457e7e86ab7baaaa4a7abb9868380a623b673bfbc09b0b8f | Jupyter | 3,023 | 97 | # %%
import celldancer as cd
import anndata
import pandas as pd
import numpy as np
import scipy as sp
import os
# %%
dataset = "IO_EU"
root = "/scratch/blaauw_root/blaauw1/gyichen"
adata = anndata.read_h5ad(f"{root}/data/{dataset}_pp.h5ad")
# %%
data_path = f"{root}/data/celldancer/{dataset}"
os.makedirs(data_path, e... |
f7842663ac3973b4caa6c1cef9b7dcc875a2a507caedaf120dd34a05f03c6481 | Jupyter | 3,024 | 97 | # %%
import celldancer as cd
import anndata
import pandas as pd
import numpy as np
import scipy as sp
import os
# %%
dataset = "Pancreas"
root = "/scratch/blaauw_root/blaauw1/gyichen"
adata = anndata.read_h5ad(f"{root}/data/{dataset}_pp.h5ad")
# %%
data_path = f"{root}/data/celldancer/{dataset}"
os.makedirs(data_path... |
6bc50e95f72635d19e539a93a4539754a58ef53eb3a3ccf40de7db98ecd9c1e2 | Jupyter | 3,034 | 97 | # %%
import celldancer as cd
import anndata
import numpy as np
import scipy as sp
import pandas as pd
import os
# %%
dataset = 'Bonemarrow'
root = "/scratch/blaauw_root/blaauw1/gyichen"
adata = anndata.read_h5ad(f'{root}/data/{dataset}_pp.h5ad')
# %%
data_path = f"{root}/data/celldancer/{dataset}"
os.makedirs(data_pa... |
08c78750dd303011ad2d1fb192f4dffd3f3b7c33879f337b55adf0d22aef4fe7 | Jupyter | 3,035 | 97 | # %%
import celldancer as cd
import anndata
import numpy as np
import scipy as sp
import pandas as pd
import os
# %%
dataset = 'Neuron_scNT'
root = "/scratch/blaauw_root/blaauw1/gyichen"
adata = anndata.read_h5ad(f'{root}/data/{dataset}_pp.h5ad')
# %%
data_path = f"{root}/data/celldancer/{dataset}"
os.makedirs(data_p... |
4318c9b31d3c0b0326dd7d725395110e546b01982576c4f08c3c89e2caf38d75 | Jupyter | 3,036 | 94 | # %%
import celldancer as cd
import anndata
import pandas as pd
import numpy as np
import scipy as sp
import os
# %%
dataset = "Erythroid_Human"
root = "/scratch/blaauw_root/blaauw1/gyichen"
adata = anndata.read_h5ad(f"{root}/data/{dataset}_pp.h5ad")
# %%
data_path = f"{root}/data/celldancer/{dataset}"
os.makedirs(da... |
4caa240b6608ae4ecf60cfa61f85a69eab257bdb8951b1a7b9889034bd2f5760 | Jupyter | 3,036 | 97 | # %%
import celldancer as cd
import anndata
import pandas as pd
import numpy as np
import scipy as sp
import os
# %%
dataset = "Braindev"
root = "/scratch/blaauw_root/blaauw1/gyichen"
adata = anndata.read_h5ad(f"{root}/data/{dataset}_pp.h5ad")
# %%
data_path = f"{root}/data/celldancer/{dataset}"
os.makedirs(data_path... |
01f67fabae9c727d7b5a2ef11ac08fe18844e770de7afd99539351c4464c5c06 | Jupyter | 3,038 | 97 | # %%
import celldancer as cd
import anndata
import pandas as pd
import numpy as np
import scipy as sp
import os
# %%
dataset = "Dentategyrus"
root = "/scratch/blaauw_root/blaauw1/gyichen"
adata = anndata.read_h5ad(f"{root}/data/{dataset}_pp.h5ad")
# %%
data_path = f"{root}/data/celldancer/{dataset}"
os.makedirs(data_... |
6172dbe07d759c378b897b06697ea02fa253756671c430750113d3c6b8111929 | Jupyter | 3,043 | 74 | # %% [markdown]
# <div style="hwidth: 100%; background-color: #ddd; overflow:hidden; ">
# <div style="display: flex; justify-content: center; align-items: center; border-bottom: 10px solid #80c4e7; padding: 3px;">
# <h2 style="position: relative; top: 3px; left: 8px;">S2 Project: DNA Classification - (part0... |
c34452e56c1b2485dc2d5848c0ed601d4aa26d051655cd37621e9e3709e3f079 | Jupyter | 3,043 | 137 | # %%
from nipype import Node, JoinNode, Workflow
from nipype.pipeline import engine as pe
from nipype.interfaces.utility import IdentityInterface, Function
import os
# %%
def square(x):
return x**2
def combine(x):
print(x)
import numpy as np
return np.sum(x)
# %%
inputnode = pe.Node(IdentityInterface... |
d2f6f8dcf412da37d31c47472e5109bfaa64e205507582f77ef5f292e3083b13 | Jupyter | 3,050 | 82 | # %%
#This section has been modified based on the open-source code provided by PeSTO. For specific usage details, please refer to https://github.com/SonyCSLParis/pesto
import os
import sys
import h5py
import json
import numpy as np
import torch as pt
import pandas as pd
import matplotlib.pyplot as plt
from tqdm import ... |
66c76b45005559e20f48245efd5089e1213b3841b5d7f938d44d392c51646b9c | Jupyter | 3,054 | 93 | # %%
import anndata
import scvelo as scv
import scvi
import numpy as np
from velovi import preprocess_data, VELOVI
import torch
import os
import argparse
import time
# %%
root = '/scratch/blaauw_root/blaauw1/gyichen'
dataset = 'BMMC'
adata = anndata.read_h5ad(f'{root}/data/{dataset}_pp.h5ad')
# %%
start = time.time()... |
72c74354f7480a95e0373405e183646fd19ceb4a1be952f99d53746ceaa4dcde | Jupyter | 3,056 | 93 | # %%
import anndata
import scvelo as scv
import scvi
import numpy as np
from velovi import preprocess_data, VELOVI
import torch
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')
# %%
start = time.time(... |
6793070ff34ff4b410530263dfbee91e5f2d861a5149afcbbaf2877cdf7decff | Jupyter | 3,058 | 93 | # %%
import anndata
import scvelo as scv
import scvi
import numpy as np
from velovi import preprocess_data, VELOVI
import torch
import os
import argparse
import time
# %%
root = '/scratch/blaauw_root/blaauw1/gyichen'
dataset = 'Retina'
adata = anndata.read_h5ad(f'{root}/data/{dataset}_pp.h5ad')
# %%
start = time.time... |
179c09d649d01b532820691d11ea3ca00f328e6b2721e70770068c8a3a7df34c | Jupyter | 3,062 | 93 | # %%
import anndata
import scvelo as scv
import scvi
import numpy as np
from velovi import preprocess_data, VELOVI
import torch
import os
import argparse
import time
# %%
root = '/scratch/blaauw_root/blaauw1/gyichen'
dataset = 'Braindev'
adata = anndata.read_h5ad(f'{root}/data/{dataset}_pp.h5ad')
# %%
start = time.ti... |
68603bbf1b0fac78f517d70851615f5ace0c596b0ced39772c3294386d38431f | Jupyter | 3,062 | 93 | # %%
import anndata
import scvelo as scv
import scvi
import numpy as np
from velovi import preprocess_data, VELOVI
import torch
import os
import argparse
import time
# %%
root = '/scratch/blaauw_root/blaauw1/gyichen'
dataset = 'Pancreas'
adata = anndata.read_h5ad(f'{root}/data/{dataset}_pp.h5ad')
# %%
start = time.ti... |
82f67cdf543a493b578f66c1d7269c451352aa8e9a2b83ef451e13c41bd15361 | Jupyter | 3,062 | 128 | # %% [markdown]
# # JuSpyce API test: data transformations
# %%
import sys
import os
from glob import glob
import pathlib
import numpy as np
import pandas as pd
from IPython.display import display
import seaborn as sns
# current path
wd = pathlib.Path().resolve().parent
print(wd)
# import juspyce
sys.path.append(os.... |
9c9487b99e8ddeb806b66b12e283cddfa7e55afd237eac9df7d3c96caa02bcac | Jupyter | 3,063 | 90 | # %%
import anndata
import scvelo as scv
import scvi
import numpy as np
from velovi import preprocess_data, VELOVI
import torch
import os
import argparse
import time
# %%
root = '/scratch/blaauw_root/blaauw1/gyichen'
dataset = 'Dentategyrus'
adata = anndata.read_h5ad(f'{root}/data/{dataset}_pp.h5ad')
# %%
start = tim... |
c255bc30fa153e919c7d5aa68129453c5be97b5a5815fd08333317c838db6ca7 | Jupyter | 3,063 | 101 | # %%
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pickle as pkl
from mpl_toolkits.mplot3d import Axes3D
%matplotlib inline
# %%
# data.pkl can be collected using the evaluation script, simply by mannually saving data after testing.
# Important things for visualization: input pos (x_in)... |
376e33e5b1a6c5818b5f9de9ddee7596a0dac6f35dc6a6b29c6a11c920bce74e | Jupyter | 3,066 | 93 | # %%
import anndata
import scvelo as scv
import scvi
import numpy as np
from velovi import preprocess_data, VELOVI
import torch
import os
import argparse
import time
# %%
root = '/scratch/blaauw_root/blaauw1/gyichen'
dataset = 'Bonemarrow'
adata = anndata.read_h5ad(f'{root}/data/{dataset}_pp.h5ad')
# %%
start = time.... |
d8b683b80ae016ebec9cce36eda74a6c53743a753003b8475c612e21ce757495 | Jupyter | 3,068 | 93 | # %%
import anndata
import scvelo as scv
import scvi
import numpy as np
from velovi import preprocess_data, VELOVI
import torch
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')
# %%
start = time... |
8cc8e2188dfc1cc5d62ed38aef6acc2129a88780c734a1211d7f46f3d008cc31 | Jupyter | 3,070 | 98 | # %% [markdown]
# # Loading packages
# - tutorial[https://docs.scvi-tools.org/en/stable/tutorials/notebooks/multimodal/MultiVI_tutorial.html]
# %%
import numpy as np
import scanpy as sc
import scvi
import pandas as pd
import anndata as ad
import time
from scipy.sparse import issparse
def combine(adata_RNA, adata_ADT... |
fc5692df7f819ea1a023326f4a4d352bfdf5d3ac5d4eefb7252746c98c7eaffd | Jupyter | 3,072 | 115 | # %%
import matplotlib.pyplot as plt
from data_loading import load_dataset_gino
from deepcardio.utils import plot_tri, create_gif_tri
import torch
from deepcardio.losses import LpLoss, H1Loss
dltrain, dltest, data_processor = load_dataset_gino(
folder_path='../data', train_batch_sizes=[10], test_batch_sizes=[10])
... |
623ced32fee40fe529faf32c3a75ac9189bf729231fef0538b16245d8b6d5b8c | Jupyter | 3,074 | 93 | # %%
import anndata
import scvelo as scv
import scvi
import numpy as np
from velovi import preprocess_data, VELOVI
import torch
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')
# %%
start = t... |
144c664410d628207619cf2747481e7f60ed3838cba5498fe68ae4483e8f8d24 | Jupyter | 3,076 | 93 | # %%
import anndata
import scvelo as scv
import scvi
import numpy as np
from velovi import preprocess_data, VELOVI
import torch
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')
# %%
start = ... |
788fbfa750092f235a643ea946bf1bbc8dcc45f023cba25ce4f2b738cd716458 | Jupyter | 3,084 | 93 | # %%
import anndata
import scvelo as scv
import scvi
import numpy as np
from velovi import preprocess_data, VELOVI
import torch
import os
import argparse
import time
# %%
root = '/scratch/blaauw_root/blaauw1/gyichen'
dataset = 'IPSC'
adata = anndata.read_h5ad(f'{root}/data/{dataset}_pp.h5ad')
# %%
start = time.time()... |
cacadefe16623d0c4b513b9a9bebb4f8ae3e0d8d6e0ec3486ae280fabb36bea6 | Jupyter | 3,093 | 126 | # %% [markdown]
# # Load data
# %%
data_dir = '.';
# %%
load(sprintf('%s/data_simulation_10.mat',data_dir))
# %%
data_0 = permute(data_0, [2 1 3]);
data_1 = permute(data_1, [2 1 3]);
# %%
size(data_0), size(data_1)
# %% [markdown]
# # Load external package
# %%
addpath('../../../_external/DKCCA/simulationFiles', ... |
d1778d74cf11fc0830b9c7a067589609d440f4dd7b3cd2a0cc51f72ea3db308c | Jupyter | 3,094 | 80 | # %%
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/archr_original/Proj_8-integrativeAnalysis_sub_cell_types/... |
a022f14aeff69e98a66d375d47c42ccfbba644b92a1421dca67d3ad280b40c8b | Jupyter | 3,098 | 60 | # %% [markdown]
# # Meta-Niche
# %% [markdown]
# ## Calculate surrounding cell component
# %% [markdown]
# When considering *niches*, we always say surrounding cellular or molecular component of a target cell. However, focusing on only a single cell would never make sense in reference of any biology process, which pr... |
4bea76f8e0609e3b341811f349d05c018639528fa356203b02b5a138bc4c7a78 | Jupyter | 3,103 | 101 | # %%
import celldancer as cd
import anndata
import numpy as np
import scipy as sp
import pandas as pd
import os
# %%
dataset = 'Retina'
root = "/scratch/blaauw_root/blaauw1/gyichen"
adata = anndata.read_h5ad(f'{root}/data/{dataset}_pp.h5ad')
adata.obs['clusters'] = adata.obs['Annotation'].to_numpy()
# %%
data_path = ... |
0a3ea73388b9d50ce11ea838e92570f465013f9c5b48ba028f8330ec02c49d5e | Jupyter | 3,129 | 76 | # %% [markdown]
# # STEP 3: preprocess_get_behaviors
# %% [markdown]
# Press SHIFT + ENTER to run code
# %% [markdown]
# ### USER INPUT!
# %%
## Define project
project_name = 'project_ACC_MORPDreadd_Combined'
# %% [markdown]
# ### MAIN CODE: predict behaviors based on features
# USER INPUT NEEDED BELOW!!!
# WAIT U... |
fc969374be850aaa3da6054a78b5fe736a8704c30d9fe6d33868f2658caee749 | Jupyter | 3,129 | 76 | # %% [markdown]
# # STEP 3: preprocess_get_behaviors
# %% [markdown]
# Press SHIFT + ENTER to run code
# %% [markdown]
# ### USER INPUT!
# %%
## Define project
project_name = 'project_ACC_MiniscopeSNI_Female'
# %% [markdown]
# ### MAIN CODE: predict behaviors based on features
# USER INPUT NEEDED BELOW!!!
# WAIT U... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.