sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
5c401a3c032c27b86e0adacf1c80786193b6b8f3d7ef6eba22da180c10350760 | Jupyter | 1,299 | 47 | # %%
# Parameters
gene_familly = "G2-like"
# %%
import json
import pandas as pd
from sklearn.model_selection import train_test_split
# %%
# READ GENE_INFO JSON FILE
gene_info_path = "../data/gene_info.json"
dataset_path = "../data/one_vs_other/"
with open(gene_info_path, 'r') as json_file:
gene_info = json.loa... |
c0ad09a4198139fe68659104135dcfb1d995716ec7c1f4a574ac254686727b9f | Jupyter | 1,299 | 47 | # %%
# Parameters
gene_familly = "NZZ/SPL"
# %%
import json
import pandas as pd
from sklearn.model_selection import train_test_split
# %%
# READ GENE_INFO JSON FILE
gene_info_path = "../data/gene_info.json"
dataset_path = "../data/one_vs_other/"
with open(gene_info_path, 'r') as json_file:
gene_info = json.loa... |
15866a17af0f64354eb6baf914dc0913d80ae035e4998f52cc1a3785cb06ef50 | Jupyter | 1,300 | 47 | # %%
# Parameters
gene_familly = "Nin-like"
# %%
import json
import pandas as pd
from sklearn.model_selection import train_test_split
# %%
# READ GENE_INFO JSON FILE
gene_info_path = "../data/gene_info.json"
dataset_path = "../data/one_vs_other/"
with open(gene_info_path, 'r') as json_file:
gene_info = json.lo... |
337b4604833225bb93045a6ca405678f43b562dae7df54bebc436e35576a30a0 | Jupyter | 1,300 | 47 | # %%
# Parameters
gene_familly = "Trihelix"
# %%
import json
import pandas as pd
from sklearn.model_selection import train_test_split
# %%
# READ GENE_INFO JSON FILE
gene_info_path = "../data/gene_info.json"
dataset_path = "../data/one_vs_other/"
with open(gene_info_path, 'r') as json_file:
gene_info = json.lo... |
3cbfec76720ee269dffe4cf952baa8c6c13d06f93ffe1a336dce1b76804ca1fc | Jupyter | 1,300 | 47 | # %%
# Parameters
gene_familly = "HB-other"
# %%
import json
import pandas as pd
from sklearn.model_selection import train_test_split
# %%
# READ GENE_INFO JSON FILE
gene_info_path = "../data/gene_info.json"
dataset_path = "../data/one_vs_other/"
with open(gene_info_path, 'r') as json_file:
gene_info = json.lo... |
5ea90bfea9d84b75128c02e69da5789425bd6db53badbd736841555b51bd8a3d | Jupyter | 1,300 | 47 | # %%
# Parameters
gene_familly = "HRT-like"
# %%
import json
import pandas as pd
from sklearn.model_selection import train_test_split
# %%
# READ GENE_INFO JSON FILE
gene_info_path = "../data/gene_info.json"
dataset_path = "../data/one_vs_other/"
with open(gene_info_path, 'r') as json_file:
gene_info = json.lo... |
804ba8d430250d78649c7f5d2a2f9635a6a501069424be46dee1f990a016d598 | Jupyter | 1,301 | 47 | # %%
# Parameters
gene_familly = "MIKC_MADS"
# %%
import json
import pandas as pd
from sklearn.model_selection import train_test_split
# %%
# READ GENE_INFO JSON FILE
gene_info_path = "../data/gene_info.json"
dataset_path = "../data/one_vs_other/"
with open(gene_info_path, 'r') as json_file:
gene_info = json.l... |
c42c9b9cefd8944176966ba8a909ce5464fd6369ca7826a628a65e051878f356 | Jupyter | 1,301 | 47 | # %%
# Parameters
gene_familly = "S1Fa-like"
# %%
import json
import pandas as pd
from sklearn.model_selection import train_test_split
# %%
# READ GENE_INFO JSON FILE
gene_info_path = "../data/gene_info.json"
dataset_path = "../data/one_vs_other/"
with open(gene_info_path, 'r') as json_file:
gene_info = json.l... |
60c24ce554ce08f97f1a842ffde11e4242a034a9a10787addb4d39021bfe6bcc | Jupyter | 1,303 | 47 | # %%
# Parameters
gene_familly = "MYB_related"
# %%
import json
import pandas as pd
from sklearn.model_selection import train_test_split
# %%
# READ GENE_INFO JSON FILE
gene_info_path = "../data/gene_info.json"
dataset_path = "../data/one_vs_other/"
with open(gene_info_path, 'r') as json_file:
gene_info = json... |
d8663130d248495c038d4f940da852b95c2ea572d62aa7b4f5c899d909acbecf | Jupyter | 1,303 | 47 | # %%
# Parameters
gene_familly = "M-type_MADS"
# %%
import json
import pandas as pd
from sklearn.model_selection import train_test_split
# %%
# READ GENE_INFO JSON FILE
gene_info_path = "../data/gene_info.json"
dataset_path = "../data/one_vs_other/"
with open(gene_info_path, 'r') as json_file:
gene_info = json... |
b8052df24b2d5eef4814c478fd14cfa433eb57aaaf33446c33b707a73c453de7 | Jupyter | 1,330 | 34 | # %%
import warnings
import pandas as pd
import numpy as np
import scanpy as sc
import episcanpy.api as epi
warnings.filterwarnings("ignore")
sc.set_figure_params(dpi=80, figsize=(4,4), facecolor="white")
for path in ['../../datasets/Human_Lymph_Node_A1/','../../datasets/Human_Lymph_Node_D1/']:
adata_adt = sc.re... |
4e96852a9ccde809a06039535c19a7221d70308b21650a625bb130561c6dc5e2 | Jupyter | 1,340 | 50 | # %%
import warnings
import itertools
import pandas
import math
import sys
import os
import numpy as np
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
init_notebook_mode(connected=True)
# %%
# Add your data file to plot
data_file = "../data/realTweets/Twitter_volume_AAPL.csv"
# %%
# Plo... |
e4b8b9a7358b4f5bdeac8cf999e3b612858e626f6a19fea7dd548a79e5bd30b5 | Jupyter | 1,348 | 35 | # %%
# Be careful with running these examples; they can damage the whole data storage
from labdatatools import *
# list all files
files = rclone_list_files()
# %%
# Rename datatype: in this example: onephoton to one_photon
oldname = 'onephoton'
newname = 'one_photon'
torename = files[files.datatype == oldname]
torenam... |
e429973bc41002149dad828e864c7eb53078380c088504aae3e9abfc2c678f54 | Jupyter | 1,356 | 66 | # %% [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... |
9d654ea2c78bf41373f8c8805c078e2b7d4803886fb007a2efcc32b40457b614 | Jupyter | 1,368 | 52 | # %% [markdown]
# # Example usage of Multi-agent ChemGraph
# %% [markdown]
# ## Initiate the ChemGraph agent
# %%
from chemgraph.agent.llm_agent import ChemGraph
workflow_type = "multi_agent"
cg = ChemGraph(model_name='gpt-4o-mini', workflow_type = workflow_type, structured_output=False, return_option="state")
# %... |
baae594bd3fc891557bff79442fc169f9eb476419cb782aff13170331de2fe79 | Jupyter | 1,380 | 49 | # %%
import os
import time
import sys
import pandas as pd
# %%
def read_file(file_path):
file_lines = []
with open(file_path, "r") as file:
for line in file:
file_lines.append(line.strip())
return file_lines
def save_file(file_path, object):
with open(file_path, "w") as file:
... |
603b72d309fd7fa3b62614f23a3f7133c6f571329b64f07abbba156e262f19aa | Jupyter | 1,413 | 55 | # %%
import warnings
warnings.filterwarnings("ignore")
import pandas as pd
import numpy as np
import scanpy as sc
import matplotlib.pyplot as plt
import os
import sys
import torch
import multiprocessing as mp
from GAAEST import GAAEST
from lib_3d_OT.utils import *
# %%
device = torch.device('cuda:0' if torch.cuda.is_a... |
16bc06ddebe7fce48d4e1f5d7076c8b3646aa038a8806fe57a857538ee467af5 | Jupyter | 1,417 | 65 | # %%
##
#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 Parameter... |
d337309ca57f608b22fa6f62ac26ce2efb40dc0780ba9527d50a39465990c95c | Jupyter | 1,417 | 35 | # %%
#Visualize the agentic system
from chemgraph.agent.llm_agent import ChemGraph
system_prompt = """You are an expert in computational chemistry and molecular simulation. Your task is to generate and execute Python code to address the user's query accurately and efficiently.
Follow these steps:
1. **Generate Python... |
e4db226e190485b54f1f5e83780ed0d671be999ecf7ab6c8b73292b297ee7cae | Jupyter | 1,466 | 51 | # %%
import warnings
warnings.filterwarnings("ignore")
import pandas as pd
import numpy as np
import scanpy as sc
import matplotlib.pyplot as plt
import os
import sys
from sklearn.metrics.cluster import adjusted_rand_score
from SpaNCMG import Mix_SpaNCMG
from Train import train_SpaNCMG
from Mix_adj import Transfer_pyt... |
22b41b3870ec7fb91dcc24e329aabcdd381d19f299506ed5e8ce6a624472c316 | Jupyter | 1,471 | 45 | # %%
EXTRACTED_FASTQ = '/path/dataset_barcode_extracted.fastq' # path to extracted fasq files containing lineage barcodes
REFORMATTED_FASTA = "/path/dataset_barcode_reformat.fa" # path to reformatted fasta fileRE
LIB = "dataset," # string indicating which library the file came from including the comma
LIB_TXT = '/path/... |
1315decc7bb3a30404d162d87e87aa553f2c2009b20a7d68cc0b399cb71fbf14 | Jupyter | 1,485 | 56 | # %%
import numpy as np
import matplotlib.pyplot as plt
# %%
#load U and V at an example time point
data = np.load('../Data/Fig8_U_V.npz')
U_t = data['U_t']
V_t = data['V_t']
fig = plt.figure(figsize=(2, 1), dpi=300)
ax = fig.add_subplot(111)
#normalize the U and V bumps to the same height
U_t = U_t/np.max(U_t)
V_t ... |
817ed03897a84fc4ec0d0b8515ee39460883f1aa5d37b6a3c0384e7fe604dbc8 | Jupyter | 1,486 | 52 | # %%
import anndata
import pandas as pd
import scipy.io
def import_matrix_as_anndata(matrix_path, barcodes_path, genes_path):
df_barcodes = pd.read_csv(
barcodes_path, index_col=0, header=None, names=['barcode']
)
df_genes = pd.read_csv(
genes_path, header=None, index_col=0, names=['gene_id'... |
801e8c2cc9f43286528bc43ba74ca117b62075ade00dd58aa2868221cc1f0975 | Jupyter | 1,491 | 71 | # %% [markdown]
# # Install libraries
# %%
from google.colab import drive
drive.mount('/content/drive')
# %%
# clone countnigra
parent_dir = '/content/drive/MyDrive' # install location
%cd $parent_dir
!git clone https://github.com/onnonuro/countnigra.git
# %%
# install required dependencies.
root = f'{parent_dir}/co... |
e953208b96672fbfe5269f4cd0900a881dc37b16fa58cb8557339f223f9b6c56 | Jupyter | 1,495 | 49 | # %%
from chemgraph.agent.llm_agent import ChemGraph
workflow_type = "rag_agent"
cg = ChemGraph(model_name='gpt4o', workflow_type = workflow_type, structured_output=False, return_option="state", base_url="https://apps-dev.inside.anl.gov/argoapi/api/v1/resource/chat/")
cg.visualize()
# %%
# Run geometry optimization ... |
1657f02040970bf69e64137ddaea7157693cb0a0da1ae0b24aa0b50495c69a64 | Jupyter | 1,501 | 37 | # %% [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
... |
8d01e7f8f14cb91872a8e59a49187a80c2046dd40bcdf2c898b4c7dfa4b9199a | Jupyter | 1,513 | 58 | # %%
import anndata as ad
import matplotlib.pyplot as plt
import mudata as md
import muon
import scanpy as sc
import time
t = time.time()
from scipy.sparse import issparse
import muon.prot as pt
for path in ['../../datasets/Human_Lymph_Node_A1/','../../datasets/Human_Lymph_Node_D1/']:
adata_omics1 = sc.read_... |
dc7d697b88c26f165559589ddf2a804e008b845d3a5ea479495f1a3f277147ec | Jupyter | 1,513 | 39 | # %%
import sys
sys.path.append("..")
from segmentation.preprocessing import helper
import os
save_path = '/hdd/yang/data/kidney_seg/high-res_training/90_10'
# %% [markdown]
# Seperate the training and testing dataset based on a ratio, train:test = 9:1 as default.
#
# The seperation was performed on the 512^3 cubes ... |
ff0eaf241f93537e4c25853914c602f871ecd39f0a407e3f33be06ad9f03aba8 | Jupyter | 1,536 | 77 | # %%
%load_ext autoreload
%autoreload 2
import scanpy as sc
from pprint import pprint
import os
from scale.config import load_config
from scale.utils import select_best_lambdas
from scale.training import train
from scale.clustering import calc_clusterings
from scale.search import calc_stability, calc_entropy
# chang... |
7d0d710a4af2f5de0c8e180b15da7fd258847a2a8b0da23b802debf9a31fef0f | Jupyter | 1,546 | 71 | # %% [markdown]
# [](https://colab.research.google.com/github/sagar87/scCCA/blob/main/docs/notebooks/pancreas.ipynb)
# %% [markdown]
# <div class="admonition note">
# <p class="admonition-title">Note</p>
# <p>In order to run this noteboo... |
d87546bf49e03c0f51d93c7732bc78763b19caa23e3830c4668a2ee191aeeafa | Jupyter | 1,548 | 57 | # %%
import json, os
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
# %%
# READ GENE_INFO JSON FILE
gene_info_path = "../data/gene_info.json"
dataset_path = "../data/one_vs_other/"
with open(gene_info_path, 'r') as json_file:
gene_info = json.load(json_fi... |
573f20ca45ccf7232ed36052ef5ca7489b26107cd8e8f609baffb4931100128b | Jupyter | 1,551 | 61 | # %%
%%bash
uv pip install /home/chlab/flync --upgrade
# %%
import os
import pandas as pd
import tempfile
from flync.features.feature_wrapper import FeatureWrapper as fw
import flync.features.feature_cleaning as ct
# Create a temporary directory and return its path
# Use mkdtemp() so tmp_dir is a string path usable a... |
c0e9aef598b7ecf000fac4269a241255ea85686a428cab8c35f2212d3a3c5d38 | Jupyter | 1,551 | 44 | # %% [markdown]
# # Approximate single value decomposition of a video dataset
#
# The aproximate implementation works by:
# * binned the movie in 1 s bins (per channel)
# * Estimating U from the binned movie.
# * Iterating over the raw data to estimate S.VT
#
# Details in wfield-tools/decomposition.py
#
# The firs... |
f0ce5c8ea57b6c40c89a3546b21e03e6b1effcbcf8bcbf39a3e277a481844a12 | Jupyter | 1,553 | 60 | # %%
import anndata as ad
import matplotlib.pyplot as plt
import mudata as md
import muon
import scanpy as sc
import time
# %%
from scipy.sparse import issparse
import muon.prot as pt
for path in ['../../datasets/Human_Lymph_Node_A1/','../../datasets/Human_Lymph_Node_D1/']:
adata_omics1 = sc.read_h5ad(path +... |
9ed821b5adbdab280ba87951862ee7447cda05e3136b8e612ca2f08c1d2479fa | Jupyter | 1,557 | 43 | # %%
from nilearn import datasets, plotting, surface
import numpy as np
# %%
import matplotlib.pyplot as plt
# %%
from nilearn.image import load_img
image = load_img("/Users/chris/projects/Nature2023MooneyScripts/results/encoding_significance/repetition-all/thresholded_pmap.nii")
# %%
fsaverage = datasets.fetch_surf... |
827a16ca2092b724666e5ac1ec4bb59d7240a04213dbbb69fa842874e6259419 | Jupyter | 1,565 | 56 | # %% [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... |
a3551d48f8c97e18a1bfed7792bd98bd6c47f981e58dabd8a7cbaeaaa0630bb5 | Jupyter | 1,578 | 55 | # %%
import numpy as np
import pandas as pd
import scanpy as sc
import muon as mu
from muon import MuData
from muon import prot as pt
import time
import psutil
import os
import memory_profiler
path='../../datasets/Mouse_Thymus5/'
adata_omics1 = sc.read_h5ad(path + 'adata_RNA.h5ad')
adata_omics2 = sc.read_h5ad(path... |
656f854a75f6eb8e67460ca74a6ef983012c290ad5a2e9789ec1fe42d370e73e | Jupyter | 1,582 | 54 | # %%
import numpy as np
import pandas as pd
import scanpy as sc
import muon as mu
from muon import MuData
from muon import prot as pt
#for path in ['../../datasets/Mouse_Thymus5/']:
for path in ['../datasets/10x_human_lymph_node_A1/','../datasets/10x_human_lymph_node_D1/']:
adata_omics1 = sc.read_h5ad(path + 'ad... |
b3d16f77fa39f855e4994d30f0667bca1512043bcdd5f2e22b73542f87f73c3d | Jupyter | 1,582 | 58 | # %%
import anndata
import scvelo as scv
import numpy as np
from deepvelo.utils import velocity, velocity_confidence, latent_time, update_dict
from deepvelo.utils.preprocess import autoset_coeff_s
from deepvelo.utils.plot import statplot, compare_plot
from deepvelo import train, Constants
import os
import time
# %%
da... |
085e2c768e87fd90061e99251c7ebb4419b7088c08706d865ebf2134e67d820e | Jupyter | 1,583 | 54 | # %%
import anndata
import scvelo as scv
import numpy as np
from deepvelo.utils import velocity, velocity_confidence, latent_time, update_dict
from deepvelo.utils.preprocess import autoset_coeff_s
from deepvelo.utils.plot import statplot, compare_plot
from deepvelo import train, Constants
import os
import time
import s... |
55d683a788bc2e72618b17df3a2ae93714cd5289cc2e9ce8e2ec232c8e77a958 | Jupyter | 1,583 | 58 | # %%
import anndata
import scvelo as scv
import numpy as np
from deepvelo.utils import velocity, velocity_confidence, latent_time, update_dict
from deepvelo.utils.preprocess import autoset_coeff_s
from deepvelo.utils.plot import statplot, compare_plot
from deepvelo import train, Constants
import os
import time
# %%
da... |
97f9a2137df35da67951cfa72f437a4624bd02ac8e7489a9e44b7dcaeb416b7c | Jupyter | 1,584 | 68 | # %%
##
#Load Packages
import numpy as np
import matplotlib.pyplot as plt
from numba import jit
import sys
import nibabel as nib
# %%
##
#Define Path to Code Database
DirPath = '/Your/Path/To/Code/'
##
#Define Output Path
OutputPath = '/Your/Path/To/Output/'
##
#Define Path to HCP1065 MD Map (in FSL this is equivale... |
08b4035c01fa72568ad21afcf8a8fa04cdd127790da2911405664a60e5559f1b | Jupyter | 1,586 | 57 | # %%
import anndata
import scvelo as scv
import numpy as np
from deepvelo.utils import velocity, velocity_confidence, latent_time, update_dict
from deepvelo.utils.preprocess import autoset_coeff_s
from deepvelo.utils.plot import statplot, compare_plot
from deepvelo import train, Constants
import os
import time
import s... |
287041efad262341bb6a5e7cefa071e7d3da03256bd13414f26375edb2df1c81 | Jupyter | 1,588 | 58 | # %%
import anndata
import scvelo as scv
import numpy as np
from deepvelo.utils import velocity, velocity_confidence, latent_time, update_dict
from deepvelo.utils.preprocess import autoset_coeff_s
from deepvelo.utils.plot import statplot, compare_plot
from deepvelo import train, Constants
import os
import time
# %%
da... |
45295eacb7c3543699babf0051b174eb31158ecbd6974e37387d667fb5aa2868 | Jupyter | 1,588 | 57 | # %%
import anndata
import scvelo as scv
import numpy as np
from deepvelo.utils import velocity, velocity_confidence, latent_time, update_dict
from deepvelo.utils.preprocess import autoset_coeff_s
from deepvelo.utils.plot import statplot, compare_plot
from deepvelo import train, Constants
import os
import time
import s... |
ff0d6dfaea7f424fd8137991a38f2553be7d98a8071ca17f3994ab73682e82d8 | Jupyter | 1,589 | 58 | # %%
import anndata
import scvelo as scv
import numpy as np
from deepvelo.utils import velocity, velocity_confidence, latent_time, update_dict
from deepvelo.utils.preprocess import autoset_coeff_s
from deepvelo.utils.plot import statplot, compare_plot
from deepvelo import train, Constants
import os
import time
# %%
da... |
a3460d0f3a95bae1f8b2b3a039ff877d60396c9f9d57ec126fbaaab55e33f9b9 | Jupyter | 1,591 | 58 | # %%
import anndata
import scvelo as scv
import numpy as np
from deepvelo.utils import velocity, velocity_confidence, latent_time, update_dict
from deepvelo.utils.preprocess import autoset_coeff_s
from deepvelo.utils.plot import statplot, compare_plot
from deepvelo import train, Constants
import os
import time
# %%
da... |
5a9ae4923149d616b64cada9c9c1ef1518fa5118ee5e448b5b8e1569902571f2 | Jupyter | 1,592 | 58 | # %%
import anndata
import scvelo as scv
import numpy as np
from deepvelo.utils import velocity, velocity_confidence, latent_time, update_dict
from deepvelo.utils.preprocess import autoset_coeff_s
from deepvelo.utils.plot import statplot, compare_plot
from deepvelo import train, Constants
import os
import time
# %%
da... |
1cad794ff2ec0f9ac6fa6e01c382b9b38a8b999a8bced9cd458cf6fd803f93a0 | Jupyter | 1,597 | 59 | # %%
import anndata
import scvelo as scv
import numpy as np
from deepvelo.utils import velocity, velocity_confidence, latent_time, update_dict
from deepvelo.utils.preprocess import autoset_coeff_s
from deepvelo.utils.plot import statplot, compare_plot
from deepvelo import train, Constants
import os
import time
import s... |
2e3254643ff56b7cef600891f74087b625d213e85e8353a13eecf641c4423edd | Jupyter | 1,600 | 32 | # %% [markdown]
# ```{eval-rst}
# .. role:: nge-mint-green
# ```
# {nge-mint-green}`Quickstart`
# =======================
#
# 1) Install [Anaconda](https://www.anaconda.com/)
# 2) Perform the installation by copying and pasting the following comands into your *Anaconda Prompt* (Windows) or *Terminal* (Max/Linux)
# 3) ... |
0d5f8714106a713c2f468708ec6ea538e61120a2d538a51dca3d6c6d29f11440 | Jupyter | 1,610 | 36 | # %%
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import matplotlib.ticker as ticker
plt.rcParams["figure.figsize"] = (10,8)
sns.set_context("paper", rc={"font.size":20,"axes.titlesize":20,"axes.labelsize":23, 'legend.fontsize': 17, 'lines.markersize':30})
arrays = {'v... |
6772ed01d41cfb708be42a0a06a78b93216c187d4ae20a640ee1557557b8057e | Jupyter | 1,615 | 55 | # %%
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import matplotlib.ticker as ticker
all_raw_cers = []
from matplotlib import rcParams
all_postLM_wers = []
data_frame = []
sns.set_context("paper", rc={"font.size":20,"axes.titlesize":20,"axes.labelsize":24, 'legend.fontsiz... |
3f7178a92bae609fb317844be841fe3ea32197228af52ddf9f197b4f902cfcca | Jupyter | 1,619 | 71 | # %% [markdown]
# # Detect cell boundaries based on ssDNA images
# %% [markdown]
# The following tutorial descibes a simple demo example for cell segmentation pipeline based on nuclei staining image. The core function was built on cellprofiler which you can refer to [CellProfiler as a Python package](https://github.c... |
d8f90ab4b2944f7cd74861fa4cc5872f7c5f2540e0de27ae4a198be2816413ed | Jupyter | 1,625 | 66 | # %%
import sys
sys.path.append('../semantic_spaces/')
import numpy as np
import pandas as pd
import embeddings as emb
# %%
'''
Load GloVe
'''
G = emb.glove.load_embedding('../semantic_spaces/glove-german/w2v_50D.txt')
# %%
'''
Retrieve starting point for our models
'''
X = np.zeros((len(G), len(G['kayak'])))
V = ... |
3c780d0883ad73d856195d35fde66c21b56e467396271758e88eb8e16a61c417 | Jupyter | 1,626 | 58 | # %% [markdown]
# # Quickstart
#
# ## for Connectivity-based Hopfield Neural Networks (CHNNs)
#
# ### Part 1: The quickeest way to
# %% [markdown]
# ### Imports
# %%
import sys
sys.path.append('..')
import pandas as pd
import seaborn as sns
from connattractor import network, analysis
# %% [markdown]
# ## Obtain f... |
5c87b7b2146797c3c4c6cf2dfdc957c08414d48e7b33f8a909d50a1e80003c67 | Jupyter | 1,646 | 84 | # %% [markdown]
# ### Installation
# %%
# if (!requireNamespace("BiocManager", quietly = TRUE))
# install.packages("BiocManager")
# BiocManager::install("cicero")
# %% [markdown]
# Vignette: https://www.bioconductor.org/packages/devel/bioc/vignettes/cicero/inst/doc/website.html
# %%
# %% [markdown]
# ### Im... |
b1f84bd8d92ad70dfdc40d8f6edc9207cf1d6266d0228d84b4e19466304a4545 | Jupyter | 1,646 | 72 | # %%
import tqdm
# %%
# %%
fileNameHapmap = "/home/ozdemiro/LAB/learningData/HapmapProject/HapMap/Data/hapmap3_r1_b36_fwd.CEU.qc.poly.recode.map"
with open(fileNameHapmap) as file:
hapmapData = file.read()
hapmapData = hapmapData.split("\n")
allDict = []
for x in range(22):
allDict.append([])
for x in ... |
38571d3effbfa874914dbcb4a9ddb806590b2f999d14afe6ec8e15c3b99368ec | Jupyter | 1,653 | 71 | # %% [markdown]
# # Detect cell boundaries based on ssDNA images
# %% [markdown]
# The following tutorial descibes a simple demo example for cell segmentation pipeline based on nuclei staining image. The core function was built on cellprofiler which you can refer to [CellProfiler as a Python package](https://github.c... |
d52cb48642c011ccc301fc3c57d99a28553c7849c8e1bd76552ae4c479ce5071 | Jupyter | 1,660 | 59 | # %%
import anndata
import scvelo as scv
import numpy as np
from deepvelo.utils import velocity, velocity_confidence, latent_time, update_dict
from deepvelo.utils.preprocess import autoset_coeff_s
from deepvelo.utils.plot import statplot, compare_plot
from deepvelo import train, Constants
import os
import time
# %%
da... |
3f7af60d8656906f9c2990255096ad19a88f6c083f4db5b1e52d9934e91a43cb | Jupyter | 1,667 | 66 | # %% [markdown]
# ## Spine Manual Classification Notebook
# %% [markdown]
# 1. Input file paths.
# %%
dataset_path = "example dataset"
metrics_path = f"{dataset_path}/metrics.csv"
scale = (1, 1, 1)
classification_save_path = "output/manual_classification.json"
classes = ["Stubby", "Mushroom", "Thin", "Filopodia",... |
e6f1f637f81e2b75efd0071c9518e322f935960fef9f8367d41b929a908cdbd9 | Jupyter | 1,667 | 57 | # %% [markdown]
# # Load data from a remote server
#
# Extracts trials recorded with the imager and converted into mj2 files.
#
# This can take a while depending on the proximity to data.
#
# **Note:** These functions are deprecated in favor of the ``load_stack`` function.
# %%
remotepath = '\\\\grid-hs.cshl.edu\\... |
10487cddfcf1b28a7e9f1fd5576186456171418e66e26e3bb62b878b4dd9f210 | Jupyter | 1,679 | 78 | # %%
import os
import numpy as np
import pandas as pd
import scanpy as sc
# %%
import sys
from pathlib import Path
import numpy as np
import pandas as pd
from scipy.stats import mode
import scanpy as sc
import sklearn
import warnings
sys.path.insert(0, "../")
import scgpt as scg
# extra dependency for similarity se... |
bb157e246221547be4a3cd2ea30e1c0af0494dc18406d48fc8d79fa173f5e151 | Jupyter | 1,686 | 72 | # %% [markdown]
# # Navigating data ontologies
# %% [markdown]
# ```{tip}
# Open this tutorial in Google Colab for interactive viewing.
# ```
# %%
# @title Install AlphaGenome
# @markdown Run this cell to install AlphaGenome.
from IPython.display import clear_output
! pip install alphagenome
clear_output()
# %% [ma... |
92632356192881b0639eba5cf897cd7b2820141ede5506258500f282c8a027a2 | Jupyter | 1,687 | 69 | # %% [markdown]
# # Example code to apply the modular splicing model
# %% [markdown]
# Example test variants come from ClinVar BRCA1 variants
# %% [markdown]
# ## Splicing delta PSI prediction
# %%
from mmsplice.vcf_dataloader import SplicingVCFDataloader
from mmsplice import MMSplice, predict_all_table
from mmspli... |
15e006183c5bbcd10ab3d3a2f44f46e8478018100194daa9305d252228a658f1 | Jupyter | 1,690 | 85 | # %%
from ISAT.formats.coco import COCO
from ISAT.formats.yolo import YOLO
from ISAT.formats.labelme import LABELME
from ISAT.formats.voc import VOC
from ISAT.formats.voc_detection import VOCDetect
# %% [markdown]
# # 1. COCO
# %% [markdown]
# ## COCO to ISAT
# %%
coco = COCO()
coco.read_from_coco(annotation_file='... |
7e0a52d8bb96f6fae8dbedaab5d822676fc071532fc218b050f603453258ea58 | Jupyter | 1,707 | 91 | # %%
import yasa
edf_path = yasa.fetch_sample("night_young.edf")
hypno_path = yasa.fetch_sample("night_young_hypno.csv")
# %%
import mne
raw = mne.io.read_raw_edf(edf_path, preload=True)
raw
# %%
print(raw.ch_names)
# %%
raw.drop_channels(["ROC-A1", "LOC-A2", "EMG1-EMG2", "EKG-R-EKG-L"])
chan = raw.ch_names
print(ch... |
2785c385f3d5c19412b4fda67a67976d61fc816287b37005ec3d59bc9f28effc | Jupyter | 1,711 | 71 | # %%
from bce.antigen.antigen import AntigenChain
# %% [markdown]
# ## Load Antigen Information
# %%
pdb_id = "5i9q"
chain_id = "A"
antigen_chain = AntigenChain.from_pdb(id=pdb_id, chain_id = chain_id, token= "Your own token of ESM to get the ESM-C embeddings")
embeddings, backbone_atoms, rsa, coverage_dict= antige... |
54300a8bd4485ac406d8502e4215231d8602a50f6ced0327e0832ac69a6e40cf | Jupyter | 1,711 | 77 | # %%
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... |
2d281f18da5cfd9240205ca434a34053db91e0dc991bb1b96489dd3ee312f706 | Jupyter | 1,714 | 43 | # %%
library(tidyverse)
library(ggplot2)
library(ggthemes)
library(patchwork)
library(ggsci)
library(ggvenn)
library(ComplexHeatmap)
library(RColorBrewer)
library(ggbeeswarm)
library(tidyr)
library(circlize)
library(fastcluster)
library(gridExtra)
setwd("/data/wuqinhua/scPhase/Age")
# %% [markdown]
# ## 1. Attributio... |
bad92fcf900fa2393de31b81897313f7e214f2f0f6a3007ede7223a9b6c0045f | Jupyter | 1,714 | 69 | # %% [markdown]
# ## Figure 2A
# %%
import mido
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.markers import MarkerStyle
import pandas as pd
import os
import matplotlib as mpl
plt.style.use("article.mplstyle")
# %%
#plot params
figs_dir = './figures'
marker = MarkerStyle('s')
marker._transform.... |
f44c867ba0174f915501e267b54659c0432c94fb644d1baa117880b0d3c0c7d3 | Jupyter | 1,723 | 51 | # %%
import pandas as pd
import h5py
import numpy as np
import sys
sys.path.append('../code')
from utils import downsample, load_DeepSTARR_data
# %% [markdown]
# # Load DeepSTARR data
# Used to train ensembles
# %%
X_train, y_train, X_test, y_test, X_val, y_val = load_DeepSTARR_data('../data/DeepSTARR/Sequences_acti... |
cbfe2760e6d55a9fcb189b2c84ae907384d55bb4b798ce264ab5c79f9486532f | Jupyter | 1,725 | 65 | # %%
import pandas as pd
from matplotlib import pyplot as plt
import seaborn as sns
from scipy.stats import ttest_rel
# %%
th = pd.read_csv("./th_quantification.csv", sep=";", usecols=['Animal', 'Sample', 'ROI', 'Area', 'Mean', 'Min', 'Max', 'Notes'])
# %%
pda = ["NWE00130", "NWE00160", "NWE00161", "NWE00163", "NWE00... |
6f44efba7efea77ffcfdfd5685c7083cf0597b1307dbfe2449340d3c3b8c279e | Jupyter | 1,738 | 50 | # %%
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
import numpy as np
plt.rcParams.update({
'font.size': 8, # Default font size
'axes.titlesize': 10, # Title font size
'axes.labelsize': 9, # Axis labels font size
'xtick.labelsize': 8, # X-axis tick labels font ... |
64a440d7d9d7c26fa7e1bfd4a49814fb55d7cce6296af1e49520f94b06ca4acf | Jupyter | 1,743 | 67 | # %%
import numpy as np
import torch
import sys
sys.path.append('./code')
import matplotlib.pyplot as plt
from PIL import Image
from torchvision.models.vgg import vgg19
from utils import *
# %%
def imread(image, mode):
return np.array(Image.open(image).convert(mode))
if torch.cuda.is_available():
device = torc... |
b31e2414c490b33d362898b3e257d91c8e5a45d8755d3263473396aa7c6fd3e0 | Jupyter | 1,744 | 39 | # %% [markdown]
# # FreeSurfer vs FastSurfer
# FreeSurfer and/or FastSurfer are used for image segmentation and surface reconstructions in the Gonzo processing-pipeline.
# FastSurfer is a deep-learning based drop-in alternative for FreeSurfer.
# Brain segmentations are used in several parts of the processing pipeline f... |
b1471b1fd5061a48e6d38bfab106d9be50d0863cfc8259c23ec3b3a560bd03c5 | Jupyter | 1,747 | 34 | # %% [markdown]
# # Visualising a multiverse
#
# As an example, we inspect the famous *“Female hurricanes are deadlier than male hurricanes”* multiverse from [Simonsohn et al. (2020)](https://www.nature.com/articles/s41562-020-0912-z). The implementation of this multiverse is implemented in the [Hurricane multiverse (... |
d1aee6e4a8f9aa94e6165dcee2e723f4dee5303633dcd3d223e28e0e0b7c6ee7 | Jupyter | 1,757 | 80 | # %%
library(GSVA)
library(GSEABase)
library(org.Hs.eg.db)
library(AnnotationDbi)
library(Biobase)
library(GSEABase)
library(GSVAdata)
library(genefilter)
library(limma)
library(RColorBrewer)
library(tidyverse)
library(msigdbr)
library(patchwork)
rm(list=ls())
library(stringr)
# %% [markdown]
# # Load HALLMARK dat... |
c9a40a06c2b43fcadd34aee7f99ef6cd555c30370ee2e4ca2575f0ec962d2756 | Jupyter | 1,771 | 40 | # %%
import os, joblib, pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.neural_network import MLPRegressor
from sklearn.metrics import r2_score, mean_absolute_error
CSV_PATH="9_makale_data_guncel.csv"; TARGET="band_gap"; ID_COLS=["material... |
9f6a1aac45b19a06a4a785dd1338fe1fcd45e8e6c548691ab0993a7481c179b7 | Jupyter | 1,776 | 49 | # %% [markdown]
# # Compute the stimulus average for imager data
#
# Computes the stimulus onset from the stimulus information (analog data) and uses it to compute the stimulus triggered average.
#
# %%
# Load trial information, like the stimulus onset and the time of each frame
# this is done better by the parser... |
0f1ee71e47e22b3cdadb9db5715ca8a20613a9bd83c7aefe6303c251ebfc81f7 | Jupyter | 1,795 | 52 | # %% [markdown]
# # Figure S3: H2E confusion
# %%
import matplotlib.pyplot as plt
import matplotlib.gridspec as gs
import h5py
import EIANN.utils as ut
from EIANN.generate_figures import *
import EIANN as eiann
eiann.update_plot_defaults()
# %%
figure_name = "FigS3_confusion_plots"
model_list = ["vanBP", "bpDale_lea... |
3154564f80f29f53a25af6225303c1a90e19776ae8a73b257a5b7eafdd310b19 | Jupyter | 1,818 | 110 | # %%
# %%
import scipy.io as sio
# %%
import os
# %%
import h5py
# %%
from brainscore.knownfile import KnownFile as kf
# %%
data_dir = "/braintree/home/jjpr/dev/mkgu_packaging/mkgu_packaging/movshon/FreemanZiemba2013"
# %%
movshon_mat_path = os.path.join(data_dir, "data/FreemanZiemba2013_V1V2data.mat")
# %%
mov... |
446583711c8613536c068f0f1d63e0ce7381ec10e35021b522b043ffcc2f540b | Jupyter | 1,835 | 56 | # %% [markdown]
# # Multiverse analysis on a high performance cluster
#
# Multiverse analyses can be computationally expensive and thus may require a high performance cluster.
# A simple example for running a multiverse within a SLURM environment is provided in this tutorial.
#
# First, create a job script (e.g., `... |
f35856af0292a93dd69d04af9fd5ee9bbb3c5ee817ed412f08ff10ac86c57960 | Jupyter | 1,843 | 73 | # %%
import anndata
import numpy as np
import sys
import torch
sys.path.insert(1, '../../../')
import velovae as vv
%load_ext autoreload
%autoreload 2
# %%
dataset = 'Braindev'
root = '/scratch/blaauw_root/blaauw1/gyichen'
adata = anndata.read_h5ad(f'{root}/data/velovae/continuous/{dataset}/{dataset}.h5ad')
# %%
#Unc... |
74cdce556111629a0842361662263b35e5af85968229b2bcc0879fc19e2b12a9 | Jupyter | 1,852 | 72 | # %% [markdown]
# <a href="https://colab.research.google.com/github/agemagician/ProtTrans/blob/master/Embedding/PyTorch/Basic/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 usin... |
93b18c93b8b41a1900ff5021a9a8ace994cc7bd5ae1f2a7194c4f0aa8be17660 | Jupyter | 1,856 | 70 | # %% [markdown]
# # Calculate word and phrasal level characteristics for reporting in paper
# %%
import pandas as pd
# %%
stim = pd.read_excel('3_phrases_2023-07-11.xlsx', index_col=0)
stim = stim[stim.denotation == 'baseline']
# %% [markdown]
# Look at word-level charateristics
# %%
columns_to_summarize = ['Length... |
084917f8de4768901c5e6d3a1f1d28c9a7a1467666fb7002993c2effc633a8c2 | Jupyter | 1,866 | 50 | # %%
import ZebrafishHMM2023
import Makie
import CairoMakie
using Statistics: mean
using HiddenMarkovModels: logdensityof, baum_welch, transition_matrix, initial_distribution, viterbi
using Test: @test, @testset
using ZebrafishHMM2023: load_artr_wolf_2023, HMM_ARTR, normalize_transition_matrix, ATol
# %%
fig = Makie.... |
417141add3741e405c6ef01017092caa6d063a2bc637b1da1272d3e471d1de6b | Jupyter | 1,867 | 59 | # %%
import os
import torch
import pandas as pd
import scanpy as sc
import SpatialGlue
for path in ['../../datasets/Human_Lymph_Node_A1/','../../datasets/Human_Lymph_Node_D1/']:
adata_omics1 = sc.read_h5ad(path + 'adata_RNA.h5ad')
adata_omics2 = sc.read_h5ad(path + 'adata_ADT.h5ad')
adata_omics1.var_... |
430234f300ee380b9696c9ca98b2434f023343eed8a98931b79e53554f17a688 | Jupyter | 1,871 | 100 | # %%
import os
import numpyro.distributions as dist
import pandas as pd
from jax import random
from PIL import Image
from splotch import get_input_data, run_nuts
from splotch.models import get_default_priors
from splotch.visualization import (
plot_variable_on_slides,
)
os.environ["XLA_FLAGS"] = "--xla_force_hos... |
98014e491d1e54fc0e56806bddfd91e7ca57ad96ab7e5e1c3d39f8b7d7c3d72e | Jupyter | 1,872 | 72 | # %% [markdown]
# # Interactive Example
# %%
from gradio_client import Client
import obspy
import numpy as np
import json
import pandas as pd
import matplotlib.pyplot as plt
import json
# %% [markdown]
# ## 2. Prepare seismic waveforms
#
# Find more details in obspy's tutorials:
#
# [FDSN web service client for Ob... |
52a995de1d90b20368aea10804bab494e30ca7241a6cc5acc8f598110deb0629 | Jupyter | 1,880 | 65 | # %% [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 pd
i... |
663c7ebe2cf0fb0174d09b201a7749ecba16eb2ad267632cc482aaa0900d0f60 | Jupyter | 1,882 | 65 | # %%
from model import run_exp
from model import default_params as params
import utils as utl
from brian2 import Hz
from pathlib import Path
import pandas as pd
config = {
'path_comp' : './input/Completeness_princeton.csv', # completness data
'path_con' : './input/Connectivity_princeton.parquet', #... |
fd04d0a871304c67f40f25c67f5a5cd936fa857e6697a512fa82690315fc53de | Jupyter | 1,882 | 72 | # %% [markdown]
# <a href="https://colab.research.google.com/github/agemagician/ProtTrans/blob/master/Embedding/PyTorch/Basic/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' features ... |
3bdc2bac48eba047ff67b0bce98368885048e2d97aea2cac3364b2f2ff357f1d | Jupyter | 1,896 | 70 | # %%
import scanpy as sc
import pandas as pd
import anndata as ad
import numpy as np
# %%
adata = sc.read_h5ad("/data/wuqinhua/phase/LungCancerCell/Lung_CancerCell_raw.h5ad")
adata
# %%
adata.obs
# %%
adata.var.index = adata.var["feature_name"]
# %%
adata.obs["sample"].unique()
# %%
adata.var.index
# %%
# filter... |
9dbcfab3ffcb75a804f1e9c6f74851e676e422848cd06707e76929584656a09f | Jupyter | 1,896 | 65 | # %% [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... |
6991a3a7189513d992dbac7ca2683aa979804964f17185a5d04d6e0c958c2887 | Jupyter | 1,900 | 90 | # %%
import nilearn
import os
import numpy as np
#plotting.view_img_on_surf(intersect_img, threshold=.1, surf_mesh='fsaverage',colorbar=False)
# %%
#!pip install matplotlib==3.3.0
from nilearn import plotting
# %%
import nibabel as nib
# %%
# /Users/isaacchristian/Desktop/Princeton/RESEARCH/META/results/n28_p3_beta... |
2f54c45c1150cdd915dc1889c33016944d1aae0e0badd69bc3835d5dad7a5dd7 | Jupyter | 1,901 | 97 | # %%
from Models.SCILD_main import *
from Models.Help_func import create_LRDatabase_D
import scanpy as sc
import squidpy as sq
import warnings
from scipy.sparse import csr_matrix
warnings.filterwarnings('ignore')
np.random.seed(42)
# %% [markdown]
# # import data
# %%
adata = sc.read_h5ad('./Example_data/PDAC_process... |
f9724433418eadafadc2b456fae45990b790058990d1f84d3df9eb98da352661 | Jupyter | 1,901 | 75 | # %% [markdown]
# ### training the BrainAlignNet
# %%
from train import set_GPU, fit_deepreg
# %%
base = "/home/alicia/notebook/alicia/BrainAlignNet"
config_path = f"{base}/demo_notebook/demo_network_config.yaml"
log_dir = f"{base}/demo_notebook"
experiment_name = "test_full_network"
max_epochs = 2
initial_epoch = 1
... |
c94dfbb6c9b2dd16d5ed05ccaeb4fc6ee55922dbccf0b55c1dc8a7de1bd31d6c | Jupyter | 1,910 | 44 | # %% [markdown]
# <a href="https://colab.research.google.com/github/agemagician/ProtTrans/blob/master/Prediction/ProtBert-BFD-Predict-SS3.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# <h3>Predict secondary structure 3 states usin... |
c2bb850b1ee99b9f88df473ddda1fba706333c37611fa00280911429a93950d3 | Jupyter | 1,916 | 72 | # %% [markdown]
# <a href="https://colab.research.google.com/github/agemagician/ProtTrans/blob/master/Embedding/PyTorch/Basic/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 us... |
582d8e092e8de9a29d0bbd711b07c90e7b674ecb8c6a5dc79e8260458e2f3dc1 | Jupyter | 1,926 | 70 | # %% [markdown]
# <a href="https://colab.research.google.com/github/agemagician/ProtTrans/blob/master/Embedding/TensorFlow/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' feature... |
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