sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
a5d5ed2cc0ede53bf169c7448a4adab06a45cc89813dc0a30e8709f83c568bfa | Jupyter | 1,941 | 77 | # %%
import nibabel as nib
import cv2
import src.contextract as ce
import numpy as np
import matplotlib.pyplot as plt
from scipy import optimize
import os
import pickle
import src.datagenerator as dg
# %%
def histo(filename,bin=30):
#plots histogram of intensities of pixels of video/image found in filename
loa... |
1f4c4ea6a5ad4d09c450b3e88898f5478be7cb09e0988a5a6b7400958386d9a5 | Jupyter | 1,944 | 67 | # %%
import pandas as pd
import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
#load
rf = pd.read_csv('../outputs/RF_reg_TEST_pred.csv')
lasso = pd.read_csv('../outputs/lasso_reg_TEST_pred.csv')
# check if all rows are the same
if sum(rf['Measured_pH_TEST'] == lasso['Measured_pH_TEST']) == rf.shap... |
05bbf868f2e437992f8509230e911799594e8f84326c13e751d29a332261af92 | Jupyter | 1,948 | 70 | # %% [markdown]
# <a href="https://colab.research.google.com/github/agemagician/ProtTrans/blob/master/Embedding/TensorFlow/Advanced/ProtAlbert.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# <h3> Extracting protein sequences' featu... |
5ac2855390bbd65453a8689ea36f9ec7e5a9d7a1d3d1bd36e00ee5b0b7d5c793 | Jupyter | 1,950 | 72 | # %%
import scanpy as sc
import pandas as pd
import anndata as ad
import numpy as np
# %%
adata = sc.read_h5ad("/data/wuqinhua/phase/AD/AD_NatureNeuro_raw.h5ad")
adata
# %%
adata.var_names_make_unique()
# %%
adata.var.index = adata.var["feature_name"]
adata.var.index.name = None
# %%
sample_counts = adata.obs.grou... |
6ce477e0d8173b4242cef59ff6d1dc82900f584bf2787c72265011f2f716732d | Jupyter | 1,950 | 70 | # %% [markdown]
# <a href="https://colab.research.google.com/github/agemagician/ProtTrans/blob/master/Embedding/TensorFlow/Advanced/ProtBert-BFD.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# <h3> Extracting protein sequences' fea... |
049f6628f821ce92c25b3802a415cda4d9d2ed2b68375d37099bf1097c7e0511 | Jupyter | 1,952 | 45 | # %% [markdown]
# ## Demo: Connecting to a Locally Served vLLM Model (Minimal)
# %%
# Import the llm_graph
from chemgraph.agent.llm_agent import ChemGraph
print("Notebook created successfully!")
# %% [markdown]
# ### Initialize the Agent
#
# When initializing `llm_graph`:
# 1. Set `model_name` to the Hugging Face i... |
1ab0a484bd85c3ff5ecf8dc8d6810a81a69d51d25afa5ff76af5ecc542f19e32 | Jupyter | 1,959 | 72 | # %% [markdown]
# <a href="https://colab.research.google.com/github/agemagician/ProtTrans/blob/master/Embedding/PyTorch/Basic/ProtXLNet.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# <h3> Extracting protein sequences' features usi... |
6ec383978c5cf50210ba4f6aecf0210a6d9dc2f16ff70496142398afa42f19ca | Jupyter | 1,963 | 73 | # %%
library(CellChat)
count_net = read.csv("../../result/cci/MB/cci/MB_bin200_interaction_count_network.csv", row.names="X")
head(count_net)
# %%
quantile(count_net$counts, 0.9)
# %%
# subset count_net domain
# [5, 12, 10, 16, 7, 8, 14], #Cortex
# [4, 11, 17], # HPC
# [0, 15], # Thalamus
library(dplyr)
count_ne... |
c7d85b52b8dcb91ab863e8c7f1b1c55acbe2103822088320cb6b1ec746ab528f | Jupyter | 1,974 | 65 | # %%
'''
This is the data processing script for POP909:A Pop song Dataset for Music Arrangement Generation
============
It will allow you to quickly process the POP909 Files (Midi) into the Google Magenta's music representation
as like [Music Transformer](https://magenta.tensorflow.org/music-transformer)
... |
8031b1aa005fb76c9ca8b29b009394afcea01f3f05ca6c133fc89dc95f05dd8a | Jupyter | 1,976 | 53 | # %% [markdown]
# Input:
# - Accession CSV - A CSV file with a column called `Accession` with a list of study accessions to be brain extracted
# - Input directory - Path to a directory with the file structure `<input_images_folder>/<accession>/<file>.nii.gz` where `<file>` is `t1ce_img` and/or `flair_img`
# - Output d... |
09d02a3b69145f55a31f14b1e5dbe92df3b0d4cb38578d030e10d0f751762524 | Jupyter | 1,982 | 58 | # %% [markdown]
# # Doublet Detection on 8k PBMCs from 10x Genomics
# %%
import numpy as np
import doubletdetection
import tarfile
import matplotlib.pyplot as plt
# %% [markdown]
# ## Download Data from 10x
# %%
!curl -O http://cf.10xgenomics.com/samples/cell-exp/2.1.0/pbmc8k/pbmc8k_filtered_gene_bc_matrices.tar.gz
... |
07ee1484f8df30df3769b48a82504ee2556132c52f55a7214195780882531782 | Jupyter | 1,986 | 40 | # %% [markdown]
# # Run SATURN to integrate the AH atlas
# %% [markdown]
# ## Make the run csv file
# %%
# Make the csv
import pandas as pd
df = pd.DataFrame(columns=["path", "species", "embedding_path"])
df["species"] = ["human", "mouse", "pig", "macaF", "macaM"]
df["path"] = ["Vignettes/ah_atlas/data/human.h5ad", ... |
c8896bbbc451fddbe017f3ed9e8183c8a6b3780bfb5bd5a961936f6003c3c188 | Jupyter | 1,989 | 78 | # %% [markdown]
# # Interactive Example
#
# ## 1. Run PhaseNet in terminal or use QuakeFlow API
# %%
import os, sys
import numpy as np
import matplotlib.pyplot as plt
import obspy
import requests
sys.path.insert(0, os.path.abspath("../"))
# %%
# PHASENET_API_URL = "http://127.0.0.1:8000"
# PHASENET_API_URL = "http:/... |
556d607005e86155b9a6b69c7a3d2d2825e0791fbabf8bed2f0100deaa2789c8 | Jupyter | 1,994 | 69 | # %%
from google.cloud import storage
import os
os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "/Users/weiqiang/.dotbot/cloud/quakeflow_wayne.json"
def download_blob(bucket_name, source_blob_name, destination_file_name):
storage_client = storage.Client()
bucket = storage_client.bucket(bucket_name)
blob ... |
411643932e9405ed0349b4086b8186793ce004acb9d060a3ff5c38d54ec15ed0 | Jupyter | 2,004 | 69 | # %% [markdown]
# # Graph measures
#
# This script showcases how to use some graph measures included in the comet toolbox.
# %%
import numpy as np
from nilearn import datasets
from matplotlib import pyplot as plt
from comet import graph
# Get preprocessed time series data from the ABIDE dataset
subjects = [50008, 50... |
274ec4f49b54666840fcbbe84c8e7f02ab954c0ec73ff1507c45e1026c7b4e70 | Jupyter | 2,008 | 49 | # %% [markdown]
# # Preparing CIFTI data for connectivity analysis
#
# CIFTI data refers to surface-level data as e.g. used in the Human Connectome Project (HCP).
#
# For this tutorial, you will need to create a [BALSA account](https://balsa.wustl.edu/) and then download the data for a single subject first, e.g. by ... |
83c6b765075f023ff24412c18d57baff5e4c22a5246957f3c46acb4adc109d2c | Jupyter | 2,010 | 77 | # %%
import Makie
import CairoMakie
using ZebrafishHMM2023: normalize_all!, load_behaviour_free_swimming_trajs,
ZebrafishHMM_G4, markov_equilibrium, stubborness_factor
using HiddenMarkovModels: baum_welch, viterbi
using Statistics: mean, std
using Distributions: Normal, Gamma, fit_mle
using LinearAlgebra: eigen
# ... |
ab2390818ea5ff75e345a8cb37b3fa9992afa28f7b3bce86223167448d059102 | Jupyter | 2,021 | 63 | # %%
import json
import pandas as pd
from scipy.stats import mannwhitneyu
# %%
blast_path = "../test/results/BLAST/blast_family_metrics_id80.json"
model_path = "../test/results/2ndvoting/k3/roc_data_k3.json"
with open(blast_path, "r") as f:
blast_data = json.load(f)
with open(model_path, "r") as f:
model_data... |
23839a91cac1bf8ec4c336ee58d9a940014ac0ab042882588a8000ab8d272fe9 | Jupyter | 2,023 | 86 | # %%
import numpy as np
working_dir: str = './working_dir'
super_ivim_dc_filename: str = 'super_ivim_dc' # do not include .pt
ivimnet_filename: str = 'ivimnet' # do not include .pt
bvalues = np.array([0,15,30,45,60,75,90,105,120,135,150,175,200,400,600,800])
snr = 10
sample_size = 100
# %% [markdown]
# ## Simulate
... |
92709de372615db7c65e10c594db3e53fe1508c6212c56882218e25bbaf14636 | Jupyter | 2,033 | 60 | # %% [markdown]
# # Development of cell match across scans
# %%
import pandas as pd
import datajoint as dj
import matplotlib.pyplot as plt
%matplotlib inline
#from pipeline.utils import registration
import numpy as np
from neuro_data.static_images.data_schemas import stimulus, fuse, meso, experiment
stack = dj.create... |
2c542cb6f23dd3a2c856587ef957d5121eeafc41bca3f114a914dd68315318c5 | Jupyter | 2,036 | 60 | # %%
###--------------------------------------------LOAD LIBRARIES
import scanpy as sc
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import copy
# %%
adata = sc.read("../../../spatialDLPFC/nonIF_c2l_anndata_combined_30samples.h5ad")
# %%
# Prepare data
subset = adata[a... |
e0bd29957f1fd999e8c00e3efc7c3d11d80d5f2b4c4abd9d6863b862b261abed | Jupyter | 2,036 | 69 | # %%
import pandas as pd
import seaborn as sns
# %% [markdown]
# ## Plot
# 1. Plot the horizontal cutoff for training length
# 2. Test for post > repetition
# %%
path = "/Users/xujinliu/projects/cluster_results/long_native_trials/"
dfs = []
for synth_seed in range(1000):
for model_seed in range(4):
file... |
12e504b8a720c6787b53b58fc51dbcf9e43ed92516c6ba8ba5ac271faf1a16a2 | Jupyter | 2,041 | 90 | # %%
import scipy as sp
import pandas as pd
import numpy as np
from numpy import mean
from scipy import stats
from scipy.stats import ttest_ind
import warnings
warnings.filterwarnings('ignore')
# %%
#nhp donors
nhp= pd.read_csv('../data/Supplementary_Data5.csv')
nhp_feature= pd.read_csv('../data/Supplementary_Data2.cs... |
bd7d3a63760cf46b720219a3e7c9abd472325f9172e773bacbf9a39789e2fb53 | Jupyter | 2,068 | 63 | # %%
import warnings, json, joblib
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error
from sklearn.ensemble import RandomForestRegressor
warnings.filterwarnings("ignore")
# --- Configuration ---
CSV_PATH = "9_makale_da... |
6e8a50c54ff359323d6e3a0fee68e9d9fb97a09786579a06643f85685a06c8e2 | Jupyter | 2,082 | 73 | # %%
library(estimate)
# %% [markdown]
# # load the estimate dataset
# %%
OvarianCancerExpr <- system.file("extdata", "sample_input.txt", package="estimate")
read.table(OvarianCancerExpr)
# %% [markdown]
# # Convert the expression matrix to a gct format file
# %%
ESTIMATE_gct_path = "../../result/cci/IDC/Estimate_a... |
d93b4ac5696c9d53beef793e2101886ae6f18e878fb650aa67a56c3a7e18dab4 | Jupyter | 2,086 | 88 | # %% [markdown]
# # Import
# %%
import pandas as pd
import numpy as np
import pickle
import sys
sys.path.append("..")
from src.preprocessing import (
load_dataset, extract_gene_pathway,
cells_to_numpy, save_numpy_arrays
)
# %% [markdown]
# # Load data
# %%
dataset_path = "../data/dataset.csv"
X, y = load_... |
60e0c6c9cd6aec7012efb6d2fb18d4f5617e50c06619c3d9499b05388f85a18e | Jupyter | 2,102 | 59 | # %% [markdown]
# # Continuously varying dFC
#
# Dynamic functional connectivity can be estimated though the `connectivity` module. We start by getting some data from the ABIDE data set:
# %%
from matplotlib import pyplot as plt
from nilearn import datasets
# Preprocessed time series data from the ABIDE dataset
subj... |
f53965c42c4b040850f627b9a6d21d1451a0d6308b48f024fd87c03ae225bfcb | Jupyter | 2,108 | 67 | # %% [markdown]
# # Hemodynamic correction
#
# The hemodynamic correction is done by:
#
# - High pass filtering the signal above 0.1 Hz and subtracting the mean
# - Computing the regression coefficients between the 2 channels.
# - Subtracting a scaled version of the temporal components of one channel to the other to ... |
b4da6b52671f1971998b1918761e2b4da3c23aa71c983837718b61f166660f17 | Jupyter | 2,126 | 81 | # %%
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib.colors import LinearSegmentedColormap
import skopt
from sklearn.model_selection import RepeatedKFold, RepeatedStratifiedKFold
from sklearn.svm import SVR
from sklearn.model_selection import GridSearchCV... |
c95135b9f900b6bdd6d8c80f4f22110aa7574ba59aab925c202eaaa135504baa | Jupyter | 2,129 | 57 | # %% [markdown]
# # Spectrogram
#
# This notebook demonstrates how to use YASA to plot a multi-taper spectrogram of a full-night single-channel EEG recording.
#
# Please make sure to install the latest version of YASA first by typing the following line in your terminal or command prompt:
#
# `pip install --upgrade y... |
6437dbf4b27081cc63657ee8a513a8b83b8e454dd6671ad80a1142d113886b22 | Jupyter | 2,145 | 78 | # %%
import pandas as pd
import numpy as np
import scipy as sp
import statsmodels.stats.multitest as multi
# %%
phys_df = pd.read_csv('../data/Supplementary_Data2.csv')
# %%
blockers_list = ['DNQX, APV, Gabazine', 'DNQX, APV, gabazine',
'DNQX, APV, Picrotoxin']
phys_df['synapses'] = np.where(phys_df['Blocker... |
850c8814d91e0fa5783e892c09ff96c8c51e192032e0f699ae5a6239abd6a494 | Jupyter | 2,149 | 76 | # %% [markdown]
# <a href="https://colab.research.google.com/github/agemagician/ProtTrans/blob/master/Embedding/TensorFlow/Advanced/ProtXLNet.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# <h3> Extracting protein sequences' featur... |
2e01867f392eea8b97ca4492d67d2cd6148822dbe7a5c5ec9230bfe77c49d488 | Jupyter | 2,161 | 69 | # %%
import Makie
import CairoMakie
using ZebrafishHMM2023: ZebrafishHMM_G3_Sym_Full, ZebrafishHMM_G3_Sym,
load_full_obs, load_behaviour_free_swimming_trajs, ATol, normalize_all!
using HiddenMarkovModels: baum_welch, logdensityof
using Distributions: Gamma
using Statistics: mean, std
# %%
full_trajs = load_full_o... |
e8ca4b4dbca794ee3d9b294af2ae8ecadba1bdd281d8f4c4d6a06e772265f933 | Jupyter | 2,165 | 86 | # %%
import pandas as pd
import numpy as np
import torch
import timm
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.
model_path = '' # path to file containing tr... |
1c0891cf86dd87cdfe27e6f7aba7796efb49311d5291c4604ac4aebd4e69511c | Jupyter | 2,168 | 55 | # %% [markdown]
# ```{eval-rst}
# .. role:: nge-mint-green
# ```
# {nge-mint-green}`Train`
# ===================================
# %% [markdown]
# ### Data Preparation
# SKOOTS requires specific file naming conventions to locate training data. An annotated image `train_data.tif` should have associated ground truth ins... |
b14f5ed289ef18840006ac2df848b7133fb5bc08d1ac2ead97aa29238e01a8b8 | Jupyter | 2,168 | 87 | # %% [markdown]
# <a href="https://colab.research.google.com/github/agemagician/ProtTrans/blob/master/Generate/ProtXLNet.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# <h3> Generate protein sequences using ProtXLNet pretrained-mod... |
d4189c983f8ef2c404eda34904b7ae0d85c2767682bed2b8c46ad2d66416e546 | Jupyter | 2,181 | 74 | # %% [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... |
c31420c3febd5697fee8b9bbccd7d7a84fa71e3ac6710a4b12293510d37be3f3 | Jupyter | 2,189 | 79 | # %%
import numpy as np
import matplotlib.pyplot as plt
import os
os.environ["OPENCV_IO_ENABLE_OPENEXR"]="1"
import cv2
from os.path import join
import imageio.v2 as imageio
# %%
imgpath = '/home/alban/Dropbox/mlab/users/alban/works/InvRend/projects/pipeline_intrinsic_3datasets/test_outs/results_TwoCubesBlender128_Al... |
3ccefcef488f065e1a6abec428a0680f6ee306210e5776174cfbc989785b4826 | Jupyter | 2,190 | 68 | # %% [markdown]
# ### Assigning Clone IDs
# Assign clone IDs to each cell and identify multi-cell and single-cell clones.
# %%
import numpy as np, pandas as pd
from matplotlib import pyplot as plt
from matplotlib import colors
from matplotlib import figure
# %% [markdown]
# ### Read in files
# CLONE_MAT: Path to .cs... |
13900193fe9290134df6047ce9d3048ebc899371f6196aef998c050ad99fa398 | Jupyter | 2,196 | 86 | # %%
import pandas as pd
import numpy as np
import torch
import timm
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 diagnostic labels.
model_path = '' # path to... |
7d988f0d6ed431806d66a6aa1c4c308b7f396307bdebca29b233e583bb197154 | Jupyter | 2,197 | 80 | # %% [markdown]
# <a href="https://colab.research.google.com/github/agemagician/ProtTrans/blob/master/Embedding/PyTorch/Advanced/ProtBert.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# <h3> Extracting protein sequences' features u... |
5f8cd4ada34bd8ac533ffaba8620a95e4ba7e9d1eb76dc3341dd45bde2de4d3d | Jupyter | 2,203 | 67 | # %% [markdown]
# # Behavior Analysis: Get CSVs with Behavior Classification Across Files
# %% [markdown]
# Press SHIFT + ENTER to run code
# %% [markdown]
# ### USER INPUT!
# %%
## Define project
project_name = 'project_acc_MiniscopeCap'
# %% [markdown]
# ### Main Code: Create CSVs for Behavior Classification (per... |
56080bb6907f3966f7b0f6731e8f97be08172fe4e4dd80b87e9eb5507e3ad58d | Jupyter | 2,211 | 68 | # %% [markdown]
# # Behavior Analysis: Get CSVs with Behavior Classification Across Files
# %% [markdown]
# Press SHIFT + ENTER to run code
# %% [markdown]
# ### USER INPUT!
# %%
## Define project
project_name = 'project_ACC_MORPDreadd_Combined'
# %% [markdown]
# ### Main Code: Create CSVs for Behavior Classificat... |
9a653f0b1042a58170e17ee245365a7135a5c5f7262d168a071b1bc2c0796106 | Jupyter | 2,211 | 68 | # %% [markdown]
# # Behavior Analysis: Get CSVs with Behavior Classification Across Files
# %% [markdown]
# Press SHIFT + ENTER to run code
# %% [markdown]
# ### USER INPUT!
# %%
## Define project
project_name = 'project_ACC_MiniscopeSNI_Female'
# %% [markdown]
# ### Main Code: Create CSVs for Behavior Classificat... |
3e3870c0286dab322eff0c3bdf3a479627eb675345719846d84526311be31574 | Jupyter | 2,216 | 80 | # %% [markdown]
# <a href="https://colab.research.google.com/github/agemagician/ProtTrans/blob/master/Embedding/PyTorch/Advanced/ProtAlbert.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# <h3> Extracting protein sequences' features... |
06d017cba64ed11fd89b007eb6c9ac95414ec667ddd643b20f9275103c73bdc3 | Jupyter | 2,219 | 68 | # %% [markdown]
# # Behavior Analysis: Get CSVs with Behavior Classification Across Files
# %% [markdown]
# Press SHIFT + ENTER to run code
# %% [markdown]
# ### USER INPUT!
# %%
## Define project
project_name = 'project_ACC_Study4_NociFormalinResponse'
# %% [markdown]
# ### Main Code: Create CSVs for Behavior Cla... |
d4947b6fcb30ed2bede1d8db83e1e64447391b5f59cd907a80c3d7c42f5b61b4 | Jupyter | 2,219 | 69 | # %% [markdown]
# # Behavior Analysis: Get CSVs with Behavior Classification Across Files
# %% [markdown]
# Press SHIFT + ENTER to run code
# %% [markdown]
# ### USER INPUT!
# %%
## Define project
project_name = 'project_ACC_MiniscopeSNI_3WeekMorphine'
# %% [markdown]
# ### Main Code: Create CSVs for Behavior Cla... |
20527d4e230be940b5d97744e9a364656cd2e9d50e68df48e13f3527a3d0b914 | Jupyter | 2,223 | 79 | # %%
import matplotlib.pyplot as plt
from data_loading import load_dataset_graph
from deepcardio.utils import plot_tri
import torch
from deepcardio.losses import LpLoss, H1Loss
dltrain, dltest, data_processor = load_dataset_graph(
folder_path='../data', train_batch_sizes=[10], test_batch_sizes=[10])
device = 'cud... |
03696266450cd8a2b72d4e85ea42dcbfb98a463a72b31fe32a88c4eba2272f19 | Jupyter | 2,226 | 80 | # %% [markdown]
# <a href="https://colab.research.google.com/github/agemagician/ProtTrans/blob/master/Embedding/PyTorch/Advanced/ProtBert-BFD.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# <h3> Extracting protein sequences' featur... |
3a5c9e78ccdcf0c6df25b11268d5a18e3574bb472c6902204bfb5fd98da95190 | Jupyter | 2,232 | 103 | # %% [markdown]
# # CAMEX aligns various development stages of seven organs across seven different species
#
#
# This tutorial demonstrates that CAMEX aligns various development stages of seven organs across seven different species.
#
# Here, we use a RNA-seq data from seven organs across seven species at various d... |
ffd255ea735da6016879242ab12d8d29bb4064be3730678992a23d0cb1af56e3 | Jupyter | 2,234 | 70 | # %% [markdown]
# # Convert data to CLAHE
# This notebook shows how to prepare the data for inference
# * The data download from ESRF are originally jp2
# * Before inference, we will need to convert to .tif volume followed by a 3D CLAHE
# %%
import sys
sys.path.append('..')
import os
import glob
import natsort
import ... |
9469696f589db1ed73c9224971d43940d69288cdb8c8091a9fa0e297642798d0 | Jupyter | 2,243 | 63 | # %% [markdown]
# # Run many seeds of SATURN
# %% [markdown]
# To replicate SATURN results for frog and zebrafish embryogenesis you need to run SATURN 30 times with different seeds.
#
# To more easily do this analysis, we have provided a python script that will run SATURN a certain number of times.
#
#
# **NOTE: ru... |
30001a602a4b61b8ea54afe139573b43b134faa8138cb96809bc273ca33807b6 | Jupyter | 2,248 | 114 | # %% [markdown]
# ## Pipeline for model explainability
# %%
import os
# Check if we are in the correct directory
print("Current working directory:", os.getcwd())
path = os.path.abspath(os.path.join(os.getcwd(), '..', 'path.py'))
%run $path
import os
os.environ['KMP_DUPLICATE_LIB_OK'] = 'TRUE'
# %%
#Configure dataloa... |
f686ba3c274773172d2a9c3f38ecd374de930b8a19e4ce8d6b9e28d0376de2bd | Jupyter | 2,252 | 96 | # %% [markdown]
# # CAMEX facilitates the discovery of new populations and markers in Primate dlPFC
#
#
# This tutorial demonstrates that CAMEX could facilitates the discovery of new populations and markers in Primate dlPFC.
#
# Here, we use collected dlPFC scRNA-seq data from four species human, rhesus, marmoset, a... |
a317e66c24c39f873b6b8182749b68aaabdca9eb101c6e575fd789f4136eb02f | Jupyter | 2,273 | 97 | # %%
import warnings
import matplotlib.pyplot as plt
import pandas as pd
from scipy.stats import pearsonr
warnings.filterwarnings("ignore")
# %%
# Loading COHA time series data
ts_df = pd.read_csv("./data/SWOW_prediction/eval/time_series/ts_df.csv")
ts_df = (
ts_df.groupby(["words", "year", "property"])[["count"... |
82e777d4c05cd13ae5023c869b8bfcfe6b56dd2739205b1315781c5edaa280f5 | Jupyter | 2,279 | 67 | # %%
import numpy as np
import torch
import matplotlib.pyplot as plt
import EIANN.utils as ut
import EIANN.plot as pt
import EIANN.network as nt
from tqdm.autonotebook import tqdm
pt.update_plot_defaults()
# %%
epochs = 1
data_seed = 257
network_seed = 66049
analyze_receptive_fields = None # ['H2E']
train_dataloader... |
95087e118321e2b065be757d7ebdf2b98df44b750fe4058fc56be46fd38bd5cf | Jupyter | 2,281 | 66 | # %% [markdown]
# In this notebook, I briefly demonstrate the use of `ProximityCellMatch` and `BestProximityCellMatch` tables in `meso`
# %%
from pipeline import meso
# %% [markdown]
# Following demonstrates how to find matches between source scan: 25133-3-11 and target scan 25133-4-13.
# We also have a list of unit_... |
037e812e4d6f0df6c56d7662475fe2d65d4fecd862513ec3919c9f9cb64102c1 | Jupyter | 2,282 | 86 | # %%
##
#Load Packages
import numpy as np
import matplotlib.pyplot as plt
from numba import jit
import sys
# %%
##
#Define Path to Code Database
DirPath = '/Your/Path/To/Code/'
##
#Define Output Path
OutputPath = '/Your/Path/To/Output/'
# %%
##
#Load Functions
sys.path.append(''.join([DirPath,'bin']))
from EPGMotion... |
ab2fa78779e5510ba7645835f9c7137474c9893954c4806411de2ad512bb4e69 | Jupyter | 2,283 | 63 | # %% [markdown]
# # Advanced multiverse structures
#
# Sometimes a multiverse is more complex than a fully connected grid of options. To express dependencies and constraints, you can add rules in the `config` dictionary.
#
# Three rule types exist:
#
# - `"exclude"` — keep the universe but set specific keys to `NaN`... |
9d9c9c1535d6ac17a7b1d4ce5cf2a88466f839d8c1065fe99a09af9e75b032f7 | Jupyter | 2,285 | 80 | # %%
import numpy as np
import matplotlib.pyplot as plt
import tifffile
from import_augmentation_function import normalization_fun
base_dir = r'C:\Users\roumba\Documents\Software\deep-events'
data_path = base_dir+ r'\testing_data'
folder_name = r'\Modelsref_f8_c9_b16'
model_type= r'\MODELS_both_iSIM_fl'
MODELS, cel... |
3d7f7c03ef1d34570224bea10fc43212332921dbbfb17a86d1a08826f67c749d | Jupyter | 2,288 | 68 | # %%
from connattractor import analysis
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
# %%
connfile = '../data_in/ex_connectome_partial_correlation.csv'
mtx = pd.read_csv(connfile).iloc[:,1:]
# %% [markdown]
# ## Sweep beta with adjusted noise
# %%
simresults = dict()
h... |
cdc52d8c6fae29553eefcbd7db902e289f9d8b1cb813abf7d55f8a56df48a2b4 | Jupyter | 2,292 | 62 | # %% [markdown]
# # State-based dFC
#
# Currently, Comet includes five explicit state-based dFC methods as published in:
#
# > Mohammad Torabi, Georgios D Mitsis, Jean-Baptiste Poline, On the variability of dynamic functional connectivity<br>assessment methods, GigaScience, Volume 13, 2024, giae009, https://doi.org/1... |
91d190ffc12ff5c1366cb63f7ca004e4da4fef2cb45282ebfcc44907be7edc94 | Jupyter | 2,314 | 74 | # %%
import pickle
import pandas as pd
from sklearn.metrics import roc_curve, roc_auc_score, accuracy_score, precision_score, recall_score, f1_score
import matplotlib.pyplot as plt
import joblib
# Load the saved model file
model = joblib.load(r'H:\model\second_epilepsy\RF_model.pkl')
# Read the test dataset
data = pd... |
bfa4815df2b7947948935a4b193f058ce7c806d52655317a5b9c9f3646c15bd5 | Jupyter | 2,322 | 85 | # %%
import pandas as pd
# %%
# Load data from xlsx
df_1 = pd.read_excel("../../data/protein_inter/GSE102901_TableS1.xlsx", sheet_name=None)
# df_2 = pd.read_excel("./data/protein_inter/GSE102901_TableS4.xlsx", sheet_name=None)
# Select data from sheet 2
tmp = df_1["Sheet2"][["id1", "id2", "pos1", "mut1", "pos2", "... |
0502a8c2455dfe143c46ead6984213d94f7193373de9ccdf47f5e26456ac2087 | Jupyter | 2,334 | 43 | # %% [markdown]
# # Gonzo processing pipeline
#
# The Gonzo processing pipeline was built and tested on a Linux x86-64 platform.
# Although there might be workarounds for MacOS and Windows with virtualization, containers or other compatibility layers such as Rosetta, the instructions assumes you are on a Linux x86-64... |
e08c8169b60b2a7a0a20374d506fdd4628881a6df8109e4d76c63dcb6fe7f933 | Jupyter | 2,347 | 81 | # %%
import numpy as np
import cv2
from matplotlib import pyplot as plt
from skimage.morphology import extrema
from skimage.morphology import watershed as skwater
# %%
def ShowImage(title,img,ctype):
plt.figure(figsize=(10, 10))
if ctype=='bgr':
b,g,r = cv2.split(img) # get b,g,r
rgb_img ... |
45b5585c481a35a42b7d7022a674dfa1db96f3c17d7ae376b22b5e1c0e15d09d | Jupyter | 2,358 | 83 | # %% [markdown]
# ## Compute the azimuth and elevation phase maps
#
# This is an example for how to compute and plot phase maps and from repetetive stimulation
# In this examples there were 10 repeats.
#
# 1. Compute the fft of each stimulation
# 2. To correct for phase delays subtract the phase of stimuli moving in... |
24f339ff28db2123c2fa2294114a2a3c838eac97c5a3555e9ec35d4eb7215695 | Jupyter | 2,367 | 89 | # %%
import nibabel as nib
import cv2
import numpy as np
import src.contextract as ce
import matplotlib.pyplot as plt
from scipy import optimize
import os
import pickle
import src.contour as con
# %%
def printcont(cont,load,rgb):
#returns 2d np.array in the shape of load with all values equal to zero unless the va... |
60c17522d9498ebd242b9d3d6fe9a719a8d2c14968c8c66693de0fbd890847e7 | Jupyter | 2,367 | 72 | # %%
import json
import numpy as np
import matplotlib.pyplot as plt
from neuron_morphology.swc_io import morphology_from_swc
# %% [markdown]
# ### This notebook will walk through the expected format for surface_and_layers file. This is a required input for generating layer aligned swc files outside of the Allen Instit... |
c6b2bf6dae26e7a8465d45988e7b4e4e4812017a94f12ca64951136a5d95030d | Jupyter | 2,368 | 78 | # %%
import os
import json
import pandas as pd
import numpy as np
from utils.setup_paths_and_configs import setup_paths_and_config
# %%
# Config / paths
dataset = 'AIBL'
TL = True
save = False
sampling = "representative"
suffix = "_TL" if TL else ""
config_path = f"configs/config_{dataset}{suffix}_{sampling}.json"
if... |
6af4f2cb6adcb30a54796231f3f3bc30aed47300d10253c2ccf66fb6c3f7dc18 | Jupyter | 2,369 | 57 | # %% [markdown]
# # Title of paper
#
# Author 1<sup>1,2,\*</sup>, Author 2<sup>2,3</sup>, Author 3<sup>1,2</sup> (<sup>1</sup>Heidelberg University, Centre for Organismal Studies (COS), 69120 Heidelberg, Germany <br> <sup>2</sup>Living Systems Institute, University of Exeter, Exeter, EX4 4QD, United Kingdom <br> <sup>... |
486927359cc2e8adc0258b53231cfa843c1cbe1df8108e4855be60f39e0788ba | Jupyter | 2,389 | 81 | # %%
import pickle
import warnings
import numpy as np
import pandas as pd
from scipy.stats import pearsonr
from sklearn.metrics import r2_score
warnings.filterwarnings("ignore")
from functools import reduce
# %% [markdown]
# ### This notebook evalutes the moral relevance and moral polarity scores generated by the fr... |
50706c7afcc3f487f331f75cf029f7bf90b3c9f41c03b23871bab5b24079b48d | Jupyter | 2,396 | 63 | # %% [markdown]
# # STEP 2: 2_preprocess_get_features
# %% [markdown]
# Press SHIFT + ENTER to run code
# %% [markdown]
# ### USER INPUT!
# %%
## Define project
project_name = 'project_ACC_MORPDreadd_Combined'
# %% [markdown]
# ### Main Code: get features from pose
# USER INPUT NEEDED BELOW!!!
# WAIT UNTIL CODE FI... |
51c856096ffe5f5ff61e781f2be215f246ebd89fa6afeab6455f470ef0c35248 | Jupyter | 2,396 | 63 | # %% [markdown]
# # STEP 2: 2_preprocess_get_features
# %% [markdown]
# Press SHIFT + ENTER to run code
# %% [markdown]
# ### USER INPUT!
# %%
## Define project
project_name = 'project_ACC_MiniscopeSNI_Female'
# %% [markdown]
# ### Main Code: get features from pose
# USER INPUT NEEDED BELOW!!!
# WAIT UNTIL CODE FI... |
6c8feb7c6daf0bfa9e818c56aecff4e38cd4ba9d62f52fb0dc394fc922b4f29a | Jupyter | 2,408 | 97 | # %%
import numpy as np
import matplotlib.pyplot as plt
import os
os.environ["OPENCV_IO_ENABLE_OPENEXR"]="1"
import cv2
from os.path import join
# %%
def draw_shape(img_base, type = 'sq'):
sizex = int(np.random.random(1)*200)
sizey = int(np.random.random(1) * 200)
xstart = np.random.choice(range(1000-si... |
8c37c91b57c4edc6674cdc5722a65b5ce8085747f755522562b2bc8b057fb177 | Jupyter | 2,425 | 86 | # %% [markdown]
# <a href="https://colab.research.google.com/github/agemagician/ProtTrans/blob/master/Embedding/PyTorch/Advanced/ProtXLNet.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# <h3> Extracting protein sequences' features ... |
511e849925aadd16041cb87bc1a52b3854cc6f9bff1ab40d93fd6beeb6642eb6 | Jupyter | 2,431 | 63 | # %% [markdown]
# # STEP 2: 2_preprocess_get_features
# %% [markdown]
# Press SHIFT + ENTER to run code
# %% [markdown]
# ### USER INPUT!
# %%
## Define project
project_name = 'project_ACC_MiniscopeSNI_3WeekMorphine'
# %% [markdown]
# ### Main Code: get features from pose
# USER INPUT NEEDED BELOW!!!
# WAIT UNTIL ... |
b1040cd3ed9a92230516a533781b5aea24edb0f8f6b0f6980d4ac6040df082af | Jupyter | 2,434 | 89 | # %% [markdown]
# <a href="https://colab.research.google.com/github/google-deepmind/hybrid_rnns_reward_learning/blob/main/hybrid_rnns_reward_learning/train_models.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Installs, imports,... |
ca366f54a063b51b4d592905fd2c1e7de55da68e4c4f2dce2aa5c5f1d4e8bdb5 | Jupyter | 2,435 | 116 | # %%
from pathlib import Path
# %%
# brainio_contrib functionality now in brainscore_core.supported_data_standards.brainio
# %%
from mkgu_packaging.movshon import aperture_correct
# %%
from brainscore_core.supported_data_standards.brainio import get_stimulus_set, get_assembly
# %%
from brainscore_core.supported_dat... |
2fffd08be417c4c523e1a097f4479987adffb0ff95e15cd6595e39546f32196c | Jupyter | 2,436 | 65 | # %% [markdown]
# # State-based dFC for voxelwise analysis
#
# Voxelwise state-based dFC analysis is supported naturally by reshaping data to 2D `(unit, time)` and feeding it into
# the same algorithms used for ROI-level analyses.
#
# - *Units* are voxels (or surface vertices).
# - *Time* is the number of volumes / T... |
a289c5521e984483e2936d0fad29c54ba6cc3b22b98ad3891e112bcd791e62ec | Jupyter | 2,444 | 82 | # %% [markdown]
# <a href="https://colab.research.google.com/github/agemagician/ProtTrans/blob/master/Visualization/ProtBert_attention_head_view.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# <h3> Attention head view for protein s... |
aeff8ee9babbce0fd1260e67b5fcd41a0ec48e4e918e8265af2abd4ea6634999 | Jupyter | 2,446 | 49 | # %%
import pandas as pd
# %%
# Export ST1
df = pd.read_csv('../../stats/02-4layers_qMRIvsST_withendo/Stats_expanded_pathway_analysis_pval_no_SWM.csv')
min5 = pd.read_csv('../../processed_data/01-4layers_withendo/paths_to_keep_all.csv', index_col = 0)
df = df[df['Unnamed: 0'].isin(min5['pathways_with_min_5_genes'])]
... |
c2a6ba3efd84b3c0ddf06fc0bf30e4347ac314ea2c9b111bde0b5c3c814ab85b | Jupyter | 2,449 | 90 | # %% [markdown]
# # Loading package
# [https://docs.scvi-tools.org/en/stable/tutorials/notebooks/multimodal/totalVI.html]
# %%
import anndata as ad
import matplotlib.pyplot as plt
import mudata as md
import muon
import scanpy as sc
import scvi
import time
t = time.time()
scvi.settings.seed = 1234
sc.set_figure_param... |
cbf3ce89f64a94a0aa0021b71665684de4a925e3de2939d494600dcb90795378 | Jupyter | 2,453 | 82 | # %% [markdown]
# <a href="https://colab.research.google.com/github/agemagician/ProtTrans/blob/master/Visualization/ProtXLNet_attention_head_view.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# <h3> Attention head view for protein ... |
1c61c36688d5ed6e37452d012e88402fd4c0596ccd98ab0994aa6a9d0c8f39ce | Jupyter | 2,454 | 84 | # %% [markdown]
# # Correction of tissue dielectric properties
#
# This notebook shows how to perform the correction described in our paper: https://doi.org/10.1016/j.bioelechem.2021.107773
# Here, only grey matter as an exemplary type of tissue is considered.
# %% [markdown]
# # Load packages
# * Load ImpedanceFitte... |
a379b8ca2ef9548b653deefaa3f82a266c7197d78912c95f7dbdfb53d7dcaf39 | Jupyter | 2,462 | 82 | # %% [markdown]
# <a href="https://colab.research.google.com/github/agemagician/ProtTrans/blob/master/Visualization/ProtAlbert_attention_head_view.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# <h3> Attention head view for protein... |
88228b9c69711fcb0e2e17e5c67125bb2670cecfcc8da80e40d3a1c3eb890268 | Jupyter | 2,467 | 91 | # %%
options(stringsAsFactors=FALSE)
# %%
library(data.table)
library(cowplot)
library(Matrix)
library(BuenColors)
# %%
metrics_pctcells = read.csv('./peaks_frequency_results/metrics/clustering_scores.csv',
row.names=1)
metrics_pctcells[['cutoff']] = as.numeric(substring(rownames(metrics_pctc... |
320da44cc7191c665a1791c004e9182ac4db89bc8861025d1d44eeeb06fa3203 | Jupyter | 2,473 | 91 | # %%
options(stringsAsFactors=FALSE)
# %%
library(data.table)
library(cowplot)
library(Matrix)
library(BuenColors)
# %%
metrics_pctcells = read.csv('./peaks_frequency_results/metrics/clustering_scores.csv',
row.names=1)
metrics_pctcells[['cutoff']] = as.numeric(substring(rownames(metrics_pctc... |
a683c660f3ca542621e0b7ec01ecc9391440b540e76d6ab6a2191b0a1716c9a3 | Jupyter | 2,476 | 91 | # %%
options(stringsAsFactors=FALSE)
# %%
library(data.table)
library(cowplot)
library(Matrix)
library(BuenColors)
# %%
metrics_pctcells = read.csv('./peaks_frequency_results/metrics/clustering_scores.csv',
row.names=1)
metrics_pctcells[['cutoff']] = as.numeric(substring(rownames(metrics_pctc... |
5840a6e894091f4c43aad978eb28952cee89146aa6f1516b04230d2b67d54318 | Jupyter | 2,479 | 80 | # %%
import pandas as pd
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import copy
import pickle
import tensorflow as tf
import keras
from sklearn.utils import Bunch
from scikeras.wrappers import KerasRegressor
from sklearn.model_selection import cross_validate, KFold
from utils.ml import... |
fb740f1ad74785c9b8dc09a85a5e2869d389eb98d8e7d7e3b3f569d16a5d6e06 | Jupyter | 2,482 | 91 | # %%
options(stringsAsFactors=FALSE)
# %%
library(data.table)
library(cowplot)
library(Matrix)
library(BuenColors)
# %%
metrics_pctcells = read.csv('./peaks_frequency_results/metrics/clustering_scores.csv',
row.names=1)
metrics_pctcells[['cutoff']] = as.numeric(substring(rownames(metrics_pctc... |
0f912ff4cf432cc46bd80ca05418b2031c9ea7def61fde514fbbd6a47a47a3ad | Jupyter | 2,483 | 76 | # %%
# -*- coding: utf-8 -*-
import warnings, joblib, pandas as pd
from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error
import lightgbm as lgb
warnings.filterwarnings("ignore")
# --- Data Loading and Feature Selection ---
df = pd.read_csv(CSV_PATH)
# Remove non-feature columns
df = df.drop(co... |
f5f988e6361b826742d7274fdbf603084eb2baee1ad7c0af7cd7c1cda4540352 | Jupyter | 2,503 | 66 | # %% [markdown]
# <a href="https://colab.research.google.com/github/beatLaboratory/TIMBRE/blob/main/extract_data.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# #Importing and downsampling data
#
# Here we show how the behavioral,... |
a1440539b25e0ba3cf2569749693e5eeeea49209be448416ecd249f6e4460273 | Jupyter | 2,514 | 118 | # %%
import os, importlib, sys, time, h5py, itertools
# %%
import numpy as np
from scipy import io, linalg, stats, ndimage, signal
from scipy.sparse import diags
# %%
import matplotlib.pyplot as plt
import matplotlib.colorbar as cb
# %%
import matplotlib.pyplot as plt
from matplotlib import rc
rc('font',**{'family':... |
83d4ce6f2f3b47545b0854b98d9450ec6bd71fa137f92da127e623d8bbb16592 | Jupyter | 2,517 | 81 | # %% [markdown]
# <a href="https://colab.research.google.com/github/agemagician/ProtTrans/blob/master/Embedding/TensorFlow/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-X... |
dd1ef869e207e8e397bdc1c92588048b74cddcc22f2dd7768b48965b3e439e4e | Jupyter | 2,549 | 78 | # %%
import os
os.chdir('/app/NBT-Classifier/')
import glob
import random
import pandas as pd
import numpy as np
import openslide
from PIL import Image
import matplotlib.pyplot as plt
import cv2
import json
# %%
def generate_tissueClsmask(wsi_path=None, anno_pt=None):
slide=openslide.OpenSlide(wsi_path)
with o... |
c602902e5c77375192ed018016eb59f3e0da675b8fb03c06cac1cb91d6d541db | Jupyter | 2,557 | 119 | # %%
cd '~/hydra/_external/NeuralTraj/'
# %%
run("startup.m")
# %% [markdown]
# ## Load data
# %%
data_dir = '~/hydra/_research/ADA/simulation_SSM';
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)
# %%
n... |
7a38639918cfedf1e8aa6e1a401556cf0e8109b2f992e602c629a2eef8bd50ce | Jupyter | 2,558 | 81 | # %% [markdown]
# # Topoplot
#
# The [yasa.topoplot](https://yasa-sleep.org/generated/yasa.topoplot.html) function is a wrapper around [mne.viz.plot_topomap](https://mne.tools/stable/generated/mne.viz.plot_topomap.html) function.
# %%
import yasa
import pandas as pd
# %% [markdown]
# ## Simple example
#
# Let's cre... |
59889769fc7a8c7a1d05c353d10184a1e54b2f5dc8c2944e7828884e3f082316 | Jupyter | 2,559 | 92 | # %%
%%capture --no-stderr
%pip install -U langgraph langchain-openai
# %%
from inference_auth_token import get_access_token
from langchain_openai import ChatOpenAI
access_token = get_access_token()
base_url="https://inference-api.alcf.anl.gov/resource_server/sophia/vllm/v1"
model="Qwen/Qwen3-32B"
try:
llm = Chat... |
8ac2711a2c0331d05b0a7031505d892d3bd8d3d9eb24502c2b11887825fafaed | Jupyter | 2,567 | 97 | # %%
import matplotlib.pyplot as plt
from data_loading import load_dataset_gino
from deepcardio.utils import plot_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])
device = 'cuda'... |
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