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|---|---|---|---|---|
4d681455ce84600ccbce55e7013560aa77fbe04a7f69753339d7c71a839e631e | Jupyter | 15,090 | 373 | # %% [markdown]
# Copyright (c) MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required... |
43dd8bd2f1cf1935673809582afdf80500875e99a15c28c2eede617e33cfb402 | Jupyter | 15,103 | 506 | # %%
import glob
import os
import pickle
import time
import matplotlib.pyplot as plt
import numpy as np
import zarr
from matplotlib.colors import hsv_to_rgb
from waveorder.io.reader import WaveorderReader
from waveorder.io.writer import WaveorderWriter
import waveorder as wo
%matplotlib inline
plt.style.use(["dark_b... |
d7f9b2c3eb84a77d9d212b3fd6f9db3700fc3ccd7267acd80b4b492aaa98fac4 | Jupyter | 15,103 | 326 | # %%
setwd("..")
# %%
source("Source/functions.R")
library("data.table")
library("circlize")
# %%
snv_info <- fread("Data/snv_loci_v2.csv")
snvIDs <- snv_info[, SNVID]
mito_barcodes <- read.csv("Data/mito_barcodes.csv", as.is = TRUE)
mitoIDs <- mito_barcodes[, "ID"]
chrmbases_properties <- fread("Report/artifact/ch... |
cef5a8c1a71de10ad41cdf48e77d79f84be2e4405b4dbe12facee0aab4de5f5c | Jupyter | 15,107 | 463 | # %%
import logging
import pandas as pd
import matplotlib.pyplot as plt
from IPython.display import Audio
import numpy as np
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import GridSearchCV
from sklearn.multioutput import MultiOu... |
f5e0b5377d72ba114b385b8a802369360c91b27f05973baeeb6d37a816683f2c | Jupyter | 15,127 | 504 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/low_level/response_synthesis.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Building Response Synthesis from Scrat... |
8bf6839ef8173b4f42804e719223493794eeb7021bcac1a19e7ae758d065ef72 | Jupyter | 15,151 | 383 | # %%
############################################## SETTINGS ##############################################
import os
from os import walk
from tqdm import tqdm
import pickle
import random
import csv
from datetime import datetime
import numpy as np
import pandas as pd
import plotly.graph_objects as go
import matplotl... |
d08fb7ab9bef924be27f5b12f1e7af70a9df3c8aab146a8f3a457fdd7c8a291b | Jupyter | 15,212 | 389 | # %%
############################################## SETTINGS ##############################################
import os
from os import walk
from tqdm import tqdm
import pickle
import random
import csv
from datetime import datetime
import numpy as np
import pandas as pd
import plotly.graph_objects as go
import matplotl... |
a5bb8fa4232c7e768b6081dd24c0eb69309eb7b227432a82b4810eabff7b0a9d | Jupyter | 15,231 | 538 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/llm/anthropic.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Anthropic
#
# Anthropic offers many state-of-the-art... |
dec43f4ddaa95352d255df10a27e7489b1a4f351d6b005a6f8de389d2570f206 | Jupyter | 15,294 | 391 | # %%
############################################## SETTINGS ##############################################
import os
from os import walk
from tqdm import tqdm
import pickle
import random
import csv
from datetime import datetime
import numpy as np
import pandas as pd
import plotly.graph_objects as go
import matplotl... |
8417a06e847ca8cca777bd130fe921a60313e5b1b375fc63222b1097df194dbe | Jupyter | 15,299 | 500 | # %%
from imagizer_cv import spatial_imagizer, get_spotdist, get_img_coord
import matplotlib.pyplot as plt
import scanpy as sc
import numpy as np
import pandas as pd
import cv2
import scanorama
import math
# %%
tissue_dirs= ['./Data/PSAPP_CO1/',
'./Data/PSAPP_CO2/',
'./Data/PSAPP_CO3/',
... |
246b3daf28cf28311bc87fa0cf4dc3b60f393396a0f9c5d8fcef3ca7c07783a6 | Jupyter | 15,390 | 467 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/low_level/evaluation.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Building Evaluation from Scratch
#
# We show ... |
3b2c5d88a5f66088aec4a5424e5f56851287035aeab10d94bd230dfa7131306f | Jupyter | 15,402 | 341 | # %% [markdown]
# Spectral Graph Model's Frequency Responses
# ---
# This notebook will use the following to reproduce Figure 2 of the manuscript:
# - `mean80_fibercount/fiberlength.csv`, the HCP connectome and distance matrix
# - `individual_subjects.mat`, individual subject's connectomes
# - `generate.py` module t... |
0c768520182de3142c01b6937015a36042090eea39e2a7cb811ed512cf802019 | Jupyter | 15,484 | 383 | # %% [markdown]
# # Import libraries and define functions + Initial setup
# %%
## Import Libraries
import pandas as pd
import numpy as np
from matplotlib_venn import venn2, venn2_circles, venn2_unweighted
from matplotlib_venn import venn3, venn3_circles
from matplotlib import pyplot as plt
%matplotlib inline
import se... |
4956f673387b3da14d115152f4c0ff58ee5445e53c93f0890bf4526f5bfdc488 | Jupyter | 15,518 | 384 | # %%
!pip install pingouin pycirclize
# %%
from google.colab import drive
drive.mount("/content/gdrive")
# %%
from pathlib import Path
import h5py
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import scipy.stats as sstats
import pingouin
# %%
matplotli... |
71845772fbcefcd718d33519e11f2d1b5d2cc5d2e3767d880141469af5800bfa | Jupyter | 15,647 | 382 | # %% [markdown]
# #Simulation for knockout training:
# **Two-layer linear network, different bottleneck**
#
# Please see "script_linearnetwork_samebottleneck" for a simple case of knockout training with helpful documentation.
#
# Here, we are interested to see how knockout training performs when our KO network's bott... |
2052b37da59f34862bccd806b566cd9c3d8438a93ba4b939fb971388e4b14690 | Jupyter | 15,651 | 448 | # %%
# make sure jupyter path is correct for loading local moudules
import sys
# path to steric_simulator module relative to notebook
sys.path.append("../../../")
import copy
from steric_free_simulator import ReactionNetwork, VectorizedRxnNet, VecSim, Optimizer, EquilibriumSolver
import networkx as nx
import torch
from... |
e564a2fd29b0fdc0d442df38289a3afd0cfd049aecec24cf1d7817fd5303b9f4 | Jupyter | 15,757 | 570 | # %% [markdown]
# # `CleanSamples` model
# %%
# Sconda conda activate tensorflow2; cd /mnt/BioAdHoc/Groups/RaoLab/Edahi/Projects/09.TimeCourse5hmC/ForFerhatGit/FCDNN/results/deeplift_analysis/; python
# %%
%load_ext lab_black
# %%
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
impor... |
64a6c5e0e5097744e875752e789656bc3b0150be496e5e3454419d0d73a330cb | Jupyter | 15,780 | 540 | # %%
# import dependencies
import os
import numpy as np
import pandas as pd
import scanpy as sc
import loompy as lp
from MulticoreTSNE import MulticoreTSNE as TSNE
import seaborn as sns
import matplotlib.pyplot as plt
# %%
# set variables for file paths to read from and write to:
# set a working directory
wdir = "/mn... |
576403c9b8eed28ba41922c5d51ff5f8c6a005e3cc707ab20fa07504b49c3ee8 | Jupyter | 15,800 | 437 | # %% [markdown]
# ### Reproducing Fig4C with Matlab Code
# %%
import sys
sys.path.append("../../")
# spectrome imports:
from spectrome.forward import runforward
from spectrome.utils import functions, path, generate
from spectrome.brain import Brain
# Other modules:
import matplotlib.pyplot as plt
import numpy as np
... |
7b26871f2647d8c1411ce7d92c18af789576d647ef4951b3181f4918745ca522 | Jupyter | 15,938 | 299 | # %% [markdown]
# # Masking tissue region in whole slide images
#
# Click to open in: \[[GitHub](https://github.com/TissueImageAnalytics/tiatoolbox/blob/develop/examples/03-tissue-masking.ipynb)\]\[[Colab](https://colab.research.google.com/github/TissueImageAnalytics/tiatoolbox/blob/develop/examples/03-tissue-masking.... |
1b6688fed0131566a60a33ac133d16946262c50f58a99a5560060d68936a4ddd | Jupyter | 16,016 | 369 | # %% [markdown]
# Peakcalling Bam Stats and Filtering Report - Filtering Stats
# ============================================================
#
# This notebook is for the analysis of outputs from the peakcalling pipeline
#
# There are severals stats that you want collected and graphed (topics covered in this notebo... |
fb1db689e619381c40a9c6f3a9c16d010a44b4472da0a4c6bff71440070fef8a | Jupyter | 16,030 | 361 | # %% [markdown]
# <a href="https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/Boltz1.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # This is a work-in-progress notebook for [Boltz](https://github.com/jwohlwend... |
ecfd47430276bc4feaa0a1d3a618ca495e6f413d34b8153486a13dfcb948cd93 | Jupyter | 16,186 | 408 | # %% [markdown]
# #Simulation for knockout training:
# **Two-layer linear network, same bottleneck**
#
#
# Idea:
# Treat a ground truth model (two-layer linear network) as the fruit fly that we'll perturb.
# We will generate control and silenced data from this ground truth model.
#
# We will then train two models on... |
a19078a909a2aca7da2f2c06990dca858889426cd4c15a602543d2edbeffa2fe | Jupyter | 16,231 | 517 | # %%
import os
import pickle
import re
import matplotlib
from matplotlib import pyplot
import numpy
import pandas
import scipy
import seaborn
import tensorflow.keras
# from sklearn import preprocessing
import tensorflow.keras
from tensorflow.keras.preprocessing import sequence
from tensorflow.keras.optimizers import ... |
716fb1d08869cec1d1ab39c8995851d271107e19ed98c76b3470c2c9a46d9250 | Jupyter | 16,331 | 414 | # %% [markdown]
# Copyright (c) MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required... |
f628810ac0c9b4443a4cd1eb940ceb73f1a02420927d1d13ced0ecc82970db3e | Jupyter | 16,362 | 376 | # %% [markdown]
# # Preprocessor code for the tFUS - mVEP BCI paradigm
#
# ### Written by Joshua Kosnoff except where otherwise noted. MNE Python / Autoreject code was copied liberally from their respective tutorials
#
#
# The mVEP BCI classifier is based on 12 stimuli per scan epoch - one for each row and column i... |
741c73e2d9a99e08e53ccd2bdff5fa17ec635027084e4df7dc9e662c7c7bf048 | Jupyter | 16,445 | 511 | # %% [markdown]
# Analyze merged snm3C-seq data for inhibitory (IN) and excitatory (EN) neurons from middle temporal gyrus obtained from Tian et al., 2023 paper
# %% [markdown]
# To make a .mcool files for snm3c-seq data:
# 1) Download ".3C.contact.tsv.gz" files from Supplementary data of GEO: GSE232495
# 2) Use Suppl... |
2896f5ffb60082d99ddf69e3d0fdb1b4dc8d06121622c3b259b81286738807b5 | Jupyter | 16,446 | 354 | # %% [markdown]
# # mVEP ESI N200 Analysis
#
# EEG Source Imaging, or ESI, is a technique to project EEG sensor data onto its corresponding cortical area. As a result, it allows us to get a better sense of the underlying brain activity than just performing sensor-level analysis (Especially due to the fact we had to ro... |
178e072b54c477657bd166d0a9148d7372b6a91069e2e459507df2effb1f4ddb | Jupyter | 16,520 | 436 | # %%
import os
import pandas as pd
import matplotlib.pyplot as plt
import loompy
import numpy as np
from chromograph.preprocessing.utils import *
from chromograph.plotting.motif_plot import motif_plot
from cytograph.visualization.scatter import *
from cytograph.visualization.colors import Colorizer
from cytograph.plot... |
be56c4bc8b404a6938599823db060f671e7f0fbdb334d0d0ddb62582b8f69f91 | Jupyter | 16,528 | 376 | # %%
import numpy as np
import pandas as pd
import random
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
from sklearn.preprocessing import MinMaxScaler
from scipy.stats import pearsonr
from json import dump
from sklearn.model_selection import RepeatedKFold
from collections import defaultd... |
240442be58e20b2e9033f46e1408c7c17571879b7a0aa583466b264be03af3b4 | Jupyter | 16,541 | 462 | # %% [markdown]
# #### GBM image post-processing
# ##### 1st May 2023
# ##### 2nd May ; plots as subplots (red dots), create typing class,
# ##### 3rd May : updated both the typing classes, generated one set of figures for 1st image
# ##### 8th May : cleaned the plots
# %%
import numpy as np
import pandas as pd
impor... |
e8a8c52e0924d7ea92b07affb5acdeb8b90bcb77037ab3e3d586fd9632c66a20 | Jupyter | 16,550 | 431 | # %% [markdown]
# Copyright (c) MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required... |
8b543bf3f23f91068c379eb86aaae03ec320c7e6237d551e15ec5f02b6260e60 | Jupyter | 16,553 | 388 | # %% [markdown]
# ## License
# This file is part of the project megFingerprinting. All of megFingerprinting code is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option)... |
454150b49721922f11b3211e8157890047e759de1b051db0e2769c896ededcee | Jupyter | 16,571 | 514 | # %% [markdown]
# Copyright (c) MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required... |
c01a016bfd300e771cd8dc23d90fe2e0f976909510599e08f72dd3fdf27cfbad | Jupyter | 16,748 | 430 | # %% [markdown]
# ## License
# This file is part of the project megFingerprinting. All of megFingerprinting code is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option)... |
3b723f0375a25c606446015d1a81825c1599c7cb9147129a4cf9a01886e6c1d3 | Jupyter | 16,782 | 408 | # %% [markdown]
# ## notebook 02p: position error precision plotting
#
# In this notebook we read in the experiment result dataframe and plot boxplots of individual performance variance separated by condition overlaid by scattered single participant data. We additonally calculate a quadratic regression and its local m... |
aa8842cf6dab62d0300c4b26011c9000410b1ca0787d6cf229a883461418208f | Jupyter | 16,816 | 421 | # %% [markdown]
# # Notes on oligopyrimidine regions:
#
# - Pyrimidines: C, T/U
# - NOT Pyrimidines: G, A
#
# ## Definitions of related motifs in different papers:
#
# [Lim et al 2021 (Hsieh)](https://www.nature.com/articles/s41467-021-24445-6):
# - 5'TOP: C at position 0, then at least 4 pyrimidines
# - PTRE: accor... |
c59f233845a1d8751c3e7b6543662e43ae6cd339159c624860d3cfed6822a60d | Jupyter | 16,822 | 539 | # %% [markdown]
# # Pre-Equilibrium Maximazation of Yield #
# %%
# make sure jupyter path is correct for loading local moudules
import sys
# path to steric_simulator module relative to notebook
sys.path.append("../../../")
import copy
# %%
from KineticAssembly_AD import ReactionNetwork, VectorizedRxnNet, VecSim, Opti... |
9b62d6544fe858aeb9dd7afc04de3ca47a487a7d0a7b95b58b9cdeaae2423f1a | Jupyter | 16,848 | 475 | # %% [markdown]
# Copyright (c) MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required... |
ac758d3ed4dd59654a143caecc4f20b25fa33022fa625509260c4e04d29d6c76 | Jupyter | 16,877 | 552 | # %%
# import dependencies
import os
import numpy as np
import pandas as pd
import scanpy as sc
import loompy as lp
from MulticoreTSNE import MulticoreTSNE as TSNE
import seaborn as sns
import matplotlib.pyplot as plt
# %%
# set variables for file paths to read from and write to:
# set a working directory
wdir = "/mn... |
004be2db5db038bfa661930af32c33a24ed8f3b1636bcfd108c4b89bf1e3482f | Jupyter | 16,894 | 374 | # %% [markdown]
# ## License
# This file is part of the project megFingerprinting. All of megFingerprinting code is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option)... |
c3267ce84be0f8d6adf1cdd3113dfd1469fee645066fb9aaa383596024d16f44 | Jupyter | 16,903 | 332 | # %% [markdown]
# # Modifying a SMIRNOFF force field
#
# In this example, we'll parameterize a ligand automatically, and then play with its parameters to demonstrate the Toolkit's ability to facilitate force field optimization. For each modification, we'll calculate the energy for the original conformation, and then m... |
ba9900ee1f85ee22637a12c4eacfa21f42ae383f213fcde1b567c26314aa4cd1 | Jupyter | 16,911 | 450 | # %% [markdown]
# # Import libraries and define functions + Initial setup
# %%
## Import Libraries
import pandas as pd
import numpy as np
from matplotlib_venn import venn2, venn2_circles, venn2_unweighted
from matplotlib_venn import venn3, venn3_circles
from matplotlib import pyplot as plt
%matplotlib inline
import se... |
3f718e71b2d24715cbbbf0f7863867005b763b72735e7f576616e55cb9d82abf | Jupyter | 16,914 | 374 | # %% [markdown]
# ## License
# This file is part of the project megFingerprinting. All of megFingerprinting code is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option)... |
073a8a28bbc7873d5f59a15cee74911e0ca14bf22fb06e23e23ba86543dad152 | Jupyter | 16,954 | 470 | # %%
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
c_vals = ['#e53e3e', '#3182ce', '#38a169', '#805ad5','#dd6b20', '#319795', '#718096', '#d53f8c', '#d69e2e',] #v6
c_vals_l = ['#feb2b2', '#90cdf4', '#9ae6b4', '#d6bcfa', '#f6ad55', '#81e6d9', '#e2e8f0', '#fbb6ce', '#faf089',] #v3
c_vals_d = ['... |
e1931a9c2fae9139c50189e8b76e4d4a22e2b55204fec37bff736d056b618d46 | Jupyter | 17,048 | 423 | # %% [markdown]
# Copyright (c) MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required... |
ee032f54e4661812e9bd32b7d344c2eed515e07191f4401acdfb381364b1d68a | Jupyter | 17,058 | 529 | # %%
import os
import numpy
import matplotlib
from matplotlib import pyplot
import pickle
import pandas
import scipy
import seaborn
import tensorflow
import tensorflow.keras
from tensorflow.keras import Model
from tensorflow.keras.preprocessing import sequence
from tensorflow.keras.optimizers import RMSprop
from tens... |
ecdd51a5541b528851e1d778bba94c9eb838587ac4259bad6559708c4ec219d0 | Jupyter | 17,086 | 525 | # %% [markdown]
# Copyright (c) MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required... |
63c51fa24deeee3b5d14273429b08e589ca97f9fedc2ef81d20af82bb0c3a9e9 | Jupyter | 17,115 | 523 | # %% [markdown]
# Copyright (c) MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required... |
e0743529babab626b691b86a22292c63dfb37b76202b47517a65cd6239abffda | Jupyter | 17,206 | 607 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/agent/openai_agent_query_cookbook.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # OpenAI Agent + Query Engine Exper... |
f65b9775a77c43cf92db54c2ff214f221820ffd101d7a271397ec06ef785101b | Jupyter | 17,256 | 482 | # %%
from utils_new import *
# %% [markdown]
# Load data from Zong et al. (2022), mouse 97045 session 20210307
# %% [markdown]
# Preprocess data, cluster neurons and visualize statistics
# %%
xy_all = {}
movetimes_all = {}
spk_all = {}
coords_all = {}
data_dir = '/Users/erihe/OneDrive - NTNU/TDA_review/weijan'
... |
246479fd73f9008d7ebb69f85dfc192d710d8a07355a4ad5989e8a2e0e44f9b8 | Jupyter | 17,272 | 522 | # %% [markdown]
# # Fully Local RAG Pipeline with Chroma + Ollama
#
# > **No API key required.** This notebook runs entirely on your local machine using [Ollama](https://ollama.com) for both the LLM and embeddings, and [ChromaDB](https://www.trychroma.com) as the vector store.
#
# [![Open In Colab](https://colab.rese... |
1748a75998ec658b4fc78b4c88aabae35c30f5e3b6f8201a798f383d6bebe865 | Jupyter | 17,275 | 372 | # %% [markdown]
# # Using `ChemicalEnvironments`
#
# Chemical Environments were created as a way to parse SMIRKS strings and make changes in chemical perception space.
# In this workbook, we will show you have chemical environments are initiated and used to make changes to a SMIRKS pattern.
#
# **Authors**
# * Cait... |
cb0a584e4457cd5671f4e5daf6df59f9297a6bab79ee5ea0340a4f41ddade210 | Jupyter | 17,395 | 450 | # %% [markdown]
# # Virtual Sites Showcase
# %% [markdown]
# The OpenFF Toolkit supports specifying virtual sites in a SMIRNOFF force field. This approach treats virtual sites as a feature of the potential energy function, rather than of the chemical system, cleanly seperating the system being modelled from the model ... |
4a81ee5ac6ddafed522042605f0d9eebb940faaf1e39f26d8d98302fefafe560 | Jupyter | 17,432 | 482 | # %%
# imports
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sn
from scipy import stats
import nibabel as nib
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_score
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import roc_auc_s... |
243bfc56679941d41a8424867db305794dc98cd217d7b18cbffae8b8502008ae | Jupyter | 17,462 | 579 | # %%
import os
import time
import matplotlib.pyplot as plt
import numpy as np
import zarr
from matplotlib.colors import hsv_to_rgb
from waveorder.io.reader import WaveorderReader
from waveorder.io.writer import WaveorderWriter
import waveorder as wo
%matplotlib inline
plt.style.use(["dark_background"]) # Plotting o... |
7e6f7c8010d573c9e41b4c47374f4f0eae82694f12ba0e78de46b9e291a15a2e | Jupyter | 17,462 | 579 | # %%
import os
import time
import matplotlib.pyplot as plt
import numpy as np
import zarr
from matplotlib.colors import hsv_to_rgb
from waveorder.io.reader import WaveorderReader
from waveorder.io.writer import WaveorderWriter
import waveorder as wo
%matplotlib inline
plt.style.use(["dark_background"]) # Plotting o... |
ca08a54fd0cf988c243f0d45deb4678107fc6ebab903429c3f147241814af25c | Jupyter | 17,488 | 462 | # %% [markdown]
# # parm@frosst-y to SMIRNOFF
#
# This notebook provides examples/utility functionality to assist with conversion of parm@frosst or relatives to SMIRNOFF format. Particularly, Christopher Bayly is generating modified AMBER `frcmod` files where the first entry for each parameter (i.e. `CT-CT-CT`) is rep... |
137182a0b45694672a79d50ae845c8746742d86c260acc090a7e848478f0f7fc | Jupyter | 17,547 | 410 | # %% [markdown]
#
# # notebook 00: process raw data
# In this notebook we read in the raw data for all valid participants as it is output by our experimental setup.
# First we read it in as a csv file, then we clean the data by deleting NaN entries, and finally we calculate the position error for all recorded trials.
... |
b3ba180538e1b2b5d7986156103c29276a31a65fe38515245e5cb10dbe72cae7 | Jupyter | 17,561 | 513 | # %%
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import glob
#import ngauge
#from ngauge import Neuron
from skimage.io import*
from skimage.morphology import binary_dilation, ball,cube
import mat73
import tifffile as tiff
from skimage.color import labe... |
b4a7f210ea89b873a0ab459b256740a867ce740e9bdfc83765dec9e8c9c1f9ac | Jupyter | 17,577 | 521 | # %%
import numpy as np
import xarray as xr
import pandas as pd
from pathta import Study
from frites import set_mpl_style
from frites.conn import conn_links
from research.study.pblt import get_model, get_anat_table
from visbrain.utils import normalize
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
f... |
6a8639dec668047026d105e61ef3952bd7af5f396484596370507ceb64030dc2 | Jupyter | 17,579 | 408 | # %% [markdown]
# # SSCD Training Protocol
#
# The Salmon Scale Circuli Detector (SSCD) system consists of two separate object detection models, working sequentially: the focus detector and the circulus detector. This notebook provides a general protocol for training each detector, based on the steps taken during the ... |
dd77a48ff879e205f36744ccdb735eb352222f8118e67b674da7142ec81fa772 | Jupyter | 17,652 | 491 | # %%
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import sklearn.metrics as skmts
import scipy.stats as scistat
import glob
plt.style.use('fivethirtyeight')
import gseapy as gp
import pathlib
# %%
"""
Parse CCLE datasets
download link: https://depmap.org/portal/download... |
5a11a1b3ccde49762c7e57c557227d00f38b1c00a2a2c21777548f44e581718e | Jupyter | 17,675 | 553 | # %%
# import dependencies
import os
import numpy as np
import pandas as pd
import scanpy as sc
import loompy as lp
from MulticoreTSNE import MulticoreTSNE as TSNE
import json
import base64
import zlib
from pyscenic.plotting import plot_binarization
from pyscenic.export import add_scenic_metadata
from pyscenic.cli.util... |
39e18dfed914355d2fca82034c240d5524cc9dee015b56ac1ba3937c71560971 | Jupyter | 17,734 | 410 | # %% [markdown]
#
# # notebook 00: process raw data
# In this notebook we read in the raw data for all valid participants as it is output by our experimental setup.
# First we read it in as a csv file, then we clean the data by deleting NaN entries, and finally we calculate the position error for all recorded trials.
... |
3d86fe623ae05e5f63f5a4d54181e5d9fe66d4d17151465bba747be9bf0e24d6 | Jupyter | 17,775 | 408 | # %% [markdown]
# ## Multiple linear regression
# This code runs multiple linear regression within object-selective epoch defined based on ANOVA result (1. Defining selective epoch).
#
# Two regression models are applied to the selective epoch of each neuron
# 1) standard model (Boy-visual, Boy-auditory, Egg-visual, E... |
d2313a7f5596ab0295ccefc0511972622763dc5ec47d9bcbfdf8fa05ffd109d5 | Jupyter | 17,814 | 474 | # %%
from utils_new import *
# %% [markdown]
# Get data from Peyrache et al. (2015). Load and preprocess data.
# %%
data_dir = '/Users/erihe/OneDrive - NTNU/UMAPH/Data' # Data directory
session = 'Mouse28-140313'
data_path = data_dir + '/' + session + '/'
f = np.load(data_path + 'data.npz', allow_pickle = True)
data... |
d745063acd10fc2c2688d424456c6c1075a7b51d63bd7d5481635805713a6a91 | Jupyter | 17,847 | 466 | # %% [markdown]
# # Correlation Analysis
#
# Written by primarily by Joshua Kosnoff. MNE registration functions are heavily copied from MNE tutorials. Any additional credit is given in function headers
#
# To get a stable connectivity measurement for lower frequencies (i.e. theta; our main area of interest), we need... |
0e0a935d03d444de7eb5ef1fc244618236f75041456bd1ea303c46cb21368a0b | Jupyter | 17,882 | 737 | # %% [markdown]
# ## notebook 06: MLE heatmaps
#
# In this notebook we generate the overview panels for the MLE modelling and fit the models to the data for Figure 8 in the publication. To execute this script first run all cells in notebook 00: process_raw_data.
# %%
# importing python packages
import numpy as np
imp... |
21e47eca7607249ed0f2bdbce48629ef68519107e4189086d405d7f6cf4d622f | Jupyter | 17,904 | 477 | # %% [markdown]
# Copyright (c) MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required... |
2633fec2338b9bab49899fa520534c29ab4d17806e69763e64cd25c6920394e2 | Jupyter | 17,910 | 421 | # %% [markdown]
# # Check whether a SMIRNOFF-format force field is able to parametrize a dataset of interest
#
# This notebook runs a quick initial analysis of whether a molecule set can be simulated by a given SMIRNOFF-format force field. While some attempt has been made to improve the speed of this code, it is not p... |
8f4b04e01600592a1a2441d60a5284126e051237393e6f25bb8fe5438c11c099 | Jupyter | 17,979 | 522 | # %% [markdown]
# Copyright (c) MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required... |
5803d005c609ee143cd907153c7442a3652ff0ec28bfb23b40903f9b9725c58e | Jupyter | 18,009 | 447 | # %%
# 1st Cell
import polyfingerprints as pfp
import pandas as pd
import numpy as np
from wrapconfig import YAMLWrapConfig
import os
# %%
# 2nd Cell
RAW_CSV_PATH = "./cloud_points_data.csv"
OUTDIR="out"
if not os.path.exists(OUTDIR):
os.makedirs(OUTDIR)
# %%
# 3rd Cell
raw_df = pd.read_csv(RAW_CSV_PATH, sep=";"... |
b42dcc20d2d5159ec7672735a4d12b2864206aa9f4973421fe1bb8e3280e50df | Jupyter | 18,056 | 513 | # %% [markdown]
# # Library Import and Functions
# %%
## Import Libraries
import pandas as pd
import numpy as np
from matplotlib_venn import venn2, venn2_circles, venn2_unweighted
from matplotlib_venn import venn3, venn3_circles
from matplotlib import pyplot as plt
%matplotlib inline
import seaborn as sns
from wordclo... |
c9afaafe0c49379075c69d1d64e309718f39e7f7746f6202f67f64334ad07a8f | Jupyter | 18,062 | 499 | # %% [markdown]
# Copyright (c) MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required... |
893401edd7fa42c174dc95514e8ca1e1167e21165b0a34473fd7389e8e83c566 | Jupyter | 18,166 | 536 | # %% [markdown]
# # Table of contents
#
# ### - Import libraries and define functions + Initial setup
# %% [markdown]
# # Import libraries and define functions + Initial setup
# %%
## Import Libraries
import pandas as pd
import numpy as np
from matplotlib_venn import venn2, venn2_circles, venn2_unweighted
from matpl... |
953241356a77cc58e1b74253f6ba83a901345782a5093f8f7f635ae54e8e311a | Jupyter | 18,250 | 515 | # %% [markdown]
# # Library Import and Functions
# %%
## Import Libraries
import pandas as pd
import numpy as np
from matplotlib_venn import venn2, venn2_circles, venn2_unweighted
from matplotlib_venn import venn3, venn3_circles
from matplotlib import pyplot as plt
%matplotlib inline
import seaborn as sns
from wordclo... |
36bd5ab288828389089728c8599a66eec5be5bdbd8f2554d052cf8574d95ed80 | Jupyter | 18,251 | 525 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/managed/manage_retrieval_benchmark.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Semantic Retriever Benchmark
#
... |
0b25c2d425c9646f22f85e9a3a489e7951ff698e326b154b4c14dbb8da1457a9 | Jupyter | 18,256 | 547 | # %% [markdown]
# # Drosophila Heart OCM Image Segmentation (FlyNet 3.0) training code
# Author: Xiangping Ouyang
#
# Date: April 29th, 2024
#
# Description: This notebook contains training code to build the FlyNet3.0 model. A complete description of the model can be found within the "An Attention LSTM U-Net model fo... |
5f57f50cba791f80dbb0f64d8353e1af63a7361c9ab08575fda26b7953023f5d | Jupyter | 18,307 | 326 | # %% [markdown]
# Jeff's initial writeup -- 2019_01_21
# ## Comparing molecule loading using RDKit and OpenEye
#
# It's really important that both RDKitToolkitWrapper and OpenEyeToolkitWrapper load the same file to equivalent OFFMol objects. If the ToolkitWrappers don't create the same OFFMol from a single file/extern... |
3cc3b46dce1b4095eb35c2f0fdb8843bc95ca39b2f146e41faab38c953819372 | Jupyter | 18,343 | 464 | # %% [markdown]
# ## Notebook for Figure 2 and related content: Correlations genes-ephys in beta-cells and pathway analysis
# ### Author: J. Camunas-Soler
# ### Last Updated: Feb. 2020
# %%
import os
import matplotlib.pyplot as plt
import matplotlib as mpl
import pandas as pd
import seaborn as sns
import pickle
import... |
8a60647641d0e65bbcf50b9fbc11a0b55056c6718ec0bcc8e1fa2a6256af1f28 | Jupyter | 18,392 | 490 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/cookbooks/prometheus2_cookbook.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Prometheus-2 Cookbook
#
# In this n... |
964d7c5c474d4eed16c1f5b7a021da4cc3b4db8bfa9796e4b915d5fc8f7f9cfa | Jupyter | 18,649 | 516 | # %%
import numpy as np
import random as rd
import matplotlib.pyplot as plt
from matplotlib import lines
import seaborn as sns
import time
from numba import njit
# %% [markdown]
# ## Support Functions
# %%
@njit
def sig(x: float, k: float) -> float: return (x > 0)*(x**2/(k**2 + x**2))
@njit
def IsPositive(x: floa... |
79097a670b39c60df46ac5ab06b877035e9039bbdc944e9bc0f4c9d953f2e6fc | Jupyter | 18,753 | 444 | # %% [markdown]
# # Host-guest complex setup and simulation using SMIRNOFF
#
# This notebook takes a SMILES string for a guest and a 3D structure for a host, and generates an initial structure of the complex using docking. It then proceeds to solvate, parameterize the system, and then minimize and do a short simulatio... |
0439a78a77f57a8ee1e39a5bd1b2771a48b4f5f6431df86a2cd89b05b66ab678 | Jupyter | 18,929 | 522 | # %%
import os, glob
import matplotlib.pyplot as plt
import loompy
import numpy as np
import pandas as pd
from cytograph.visualization.scatter import *
from cytograph.plotting.dendrogram import dendrogram
from pybedtools import BedTool
from bs4 import BeautifulSoup
from matplotlib.lines import Line2D
# %%
## Replac... |
83351fdd43f38f7e32c6c459a6a816c7bcc1d3527995934c050f45edc056d8a5 | Jupyter | 18,942 | 485 | # %% [markdown]
# <hr style="height:2px;">
#
# # Targeted multi-contrast microscopy using attention-based multi-instance learning for tissue sections
#
# In this tutorial, we will perform a selective acquisition on a tissue micro-array (TMA) core containing pancreatic cancer with a brightfield camera (QCamera) and Se... |
78cc1d4a40ea251818e5d0f0dd0592809e3187fd821853d5594c0755d9ccd312 | Jupyter | 18,999 | 449 | # %% [markdown]
# Copyright (c) MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required... |
3b7dce4cc1659228fbe452e8715b05e2ed639f024c38baa9049e5c558efce593 | Jupyter | 19,013 | 489 | # %%
## DeepGBM Development
%config InlineBackend.figure_format = 'retina'
%matplotlib inline
import matplotlib.pyplot as plt
from typing import List
import sys, os
import numpy as np
from deeplift.dinuc_shuffle import dinuc_shuffle
from deeplift.visualization import viz_sequence
import shap
from twobitreader import ... |
ef1091683da098ed1322ada0e05979b0f7722cbe121883e803479eefcaf49734 | Jupyter | 19,154 | 625 | # %% [markdown]
# # Pre-Equilibrium Maximazation of Yield #
# %%
# make sure jupyter path is correct for loading local moudules
import sys
# path to steric_simulator module relative to notebook
# sys.path.append("../../../")
import copy
# %%
# make sure jupyter path is correct for loading local moudules
import sys
# ... |
4004edf9fe4923bdce5cc6cdf2381f6a3c685135d562445addfce20b26431a7b | Jupyter | 19,170 | 625 | # %% [markdown]
# # Pre-Equilibrium Maximazation of Yield #
# %%
# make sure jupyter path is correct for loading local moudules
import sys
# path to steric_simulator module relative to notebook
# sys.path.append("../../../")
import copy
# %%
# make sure jupyter path is correct for loading local moudules
import sys
# ... |
deec1b54e6c5eb984d0a21ffab8a5533a0bf0a65e3786cdb9fbb1c40a0b9ce0a | Jupyter | 19,221 | 550 | # %%
import os, sys, glob
import pandas as pd
import matplotlib.pyplot as plt
import loompy
import numpy as np
from sklearn.neighbors import NearestNeighbors
from chromograph.pipeline import config
from chromograph.preprocessing.utils import *
from cytograph.plotting.colors import colors75
from cytograph.visualization... |
cab50bc5ae117872bfedef576fff5b2310c3af12dbb84a783619b5fac00289b6 | Jupyter | 19,325 | 364 | # %%
setwd("..")
# %%
source("Source/functions.R")
library("data.table")
# %%
snv_info <- fread("Data/snv_loci_v2.csv")
snvIDs <- snv_info[, SNVID]
chrmproperties <- fread("Report/artifact/chrmbases_properties.csv.gz")
setkey(chrmproperties, "pos")
# %%
MitoInfo <- fread(file = "Report/metadata/MitoInfo.csv")
dim(M... |
df445756b53489f8570e7ce92a0437c86c96e034ababb128dc0a4c4aa6ce27f2 | Jupyter | 19,392 | 487 | # %% [markdown]
# ## Notebook for Figure 6 and related content: Alpha cells heterogeneity and ML for cell type classification from ephys
# ### Author: J. Camunas-Soler
# ### Last Updated: Feb. 2020
# %%
import os
import matplotlib.pyplot as plt
import matplotlib as mpl
import pandas as pd
import seaborn as sns
import... |
9a06d2b818ccb9fa8e3a37e34edb7e3b3c38357cc814b44e792993b2efe70afc | Jupyter | 19,409 | 548 | # %%
import pandas as pd
import numpy as np
import nibabel as nib
import nbimporter
from functions import *
from numpy import *
import matplotlib.pyplot as plt
%matplotlib inline
import shutil
import os
import subprocess
# %%
D7_dir='/home/mahdi/Desktop/data_selection_D7'
n,name=count(D7_dir)
out_dir='/home/mahdi/Des... |
174b040b4d0ed4338e1f33580f7726bd7ea34939194dd4914e04ff47e9ccf714 | Jupyter | 19,415 | 414 | # %% [markdown]
# Detailed description
# =====
#
# Most likely you have run the examples of TINA, and saw that it can be really easy to work with a dataset, when all the parameters are suited to the specific dataset. The functions of tinaui have some predefined values which are defined in the source code by
#
#
# `... |
b5be30c373bd2914a8cdcda73813470f9b88d3ee6717b93bc27161cf27b4a758 | Jupyter | 19,432 | 541 | # %% [markdown]
# # Multi-Agent Workflow with Weaviate QueryAgent
# %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/agent/multi_agent_workflow_with_weaviate_queryagent.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.... |
58610d7aa71497615b014fa105f7634e47af1a96165e2788e86107757b76a2e2 | Jupyter | 19,478 | 629 | # %%
import pandas as pd
import numpy as np
import nibabel as nib
import nbimporter
from functions import *
from numpy import *
import matplotlib.pyplot as plt
%matplotlib inline
import os
import subprocess
# %% [markdown]
# # 1- make structure for files
# %%
if not os.path.exists('/home/mahdi/Desktop/valid'):
... |
22700a899038df988891ff5efb8a2fc801b9fdc5d9bc7a93030fd14cf49ffc28 | Jupyter | 19,502 | 623 | # %%
import glob
import os
import pickle
import time
import matplotlib.pyplot as plt
import numpy as np
import zarr
from matplotlib.colors import hsv_to_rgb
from waveorder.io.reader import WaveorderReader
from waveorder.io.writer import WaveorderWriter
import waveorder as wo
%matplotlib inline
plt.style.use(["dark_b... |
8b27e5531d09b887077fd2cf26fa398fc68455ae465c89a69a3d37adba095d61 | Jupyter | 19,550 | 572 | # %% [markdown]
# Copyright (c) MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required... |
7795f9ac2812a0a4f34ca07228b6748cc22b31f98ec742792b23f04de5e0deff | Jupyter | 19,629 | 478 | # %%
import os
import sys
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from IPython import get_ipython
colab_env = 'google.colab' in str(get_ipython()) if hasattr(__builtins__,'__IPYTHON__') else False
colab_initialized = True if os.path.exists("MotorNet") else False
if colab_env and not... |
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