sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
9f466649bdd82b3009e6596f26f637bb185274a4c4534b29f3465c9ebb9a2ecb | Jupyter | 19,643 | 418 | # %% [markdown]
# # Patch extraction from Histology Images
#
# Click to open in: \[[GitHub](https://github.com/TissueImageAnalytics/tiatoolbox/blob/develop/examples/04-patch-extraction.ipynb)\]\[[Colab](https://colab.research.google.com/github/TissueImageAnalytics/tiatoolbox/blob/develop/examples/04-patch-extraction.i... |
837e061ffc59619ec67043593f103acc5317e72a63365aec95a294dc7037f710 | Jupyter | 19,727 | 373 | # %%
import pandas as pd
import numpy as np
import os
# from ComputeInputOutputDeltaSNRs import ComputeInputOutputDeltaSNRs
#-------------------------------------------------------------------------------------------------------
fs = 250
folder_name = '/../../../../ResultsComparison120s/'
repetition = 1 # can be... |
52bcdfb69177cd4ae45d5a33523b01f34531a450dc1614149e25c20b9856d8eb | Jupyter | 19,734 | 373 | # %%
import pandas as pd
import numpy as np
import os
# from ComputeInputOutputDeltaSNRs import ComputeInputOutputDeltaSNRs
#-------------------------------------------------------------------------------------------------------
fs = 250
folder_name = '/../../../../ResultsComparison120s/'
repetition = 1 # can be... |
5630e44907c642f124142b244bc9e5634b92a1bcb20be0f628a3508d98d394ee | Jupyter | 19,785 | 698 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/llm/gemini.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Google GenAI
# %% [markdown]
# In this notebook, we sho... |
70c42a063285ee0930a30450b3ade296d746134ebe68940a6064c157c7799291 | Jupyter | 19,787 | 479 | # %%
import pandas as pd
import numpy as np
import os
file_name = 'ResultsComparison120s'
file_path = os.getcwd()
target_csv_path = file_path + '/../../../../' + file_name + '/'
fs = 250
reps_range = [1,2,3,4,5]
for rep in reps_range:
print("starting the QT dataframe construction for rep =", rep)
... |
7fa086df077a8be800b5b393f45d547d3a36ba6d88e63cbcc2052e29f3cf560d | Jupyter | 19,819 | 328 | # %% [markdown]
# ### Allen function
#
# #### Arguments
# ##### The allen function allows for the selection of tracer experiments based upon primary injections sites and retrograde tracer activity. The functions accepts a number of arguments:
#
# - ROI_anterograde: ROI_anterograde has to be a ROI derived from 'http:/... |
808c2ec5bbc453e387ff32a2a45a1ac7273b331ccdee24bbd509f8578cae46f5 | Jupyter | 19,881 | 614 | # %%
import pandas as pd
import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
from scipy import stats
from scipy.optimize import curve_fit
# %% [markdown]
# # Import and process data
# %%
colors = {'LR':'#F57685', 'FCN':'#B51D49', 'CNN0':'#47A2F7','CNN1':'#47A2F7',
'CNN2':'#47A2F7', 'GCN... |
2c92b890307a64a0ff0e2518d86fbb98ec07056e33c49349b2a873bb35eae0c3 | Jupyter | 19,898 | 521 | # %%
############################################## 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... |
7b2cc23df5bcec6a5e6b91720d4ac1df52d3365dacd1106883c4d1942874542b | Jupyter | 19,899 | 521 | # %%
############################################## 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... |
0cc78bd5e859f47c2e624558625eab60da6d5609e06fc910316f16deb3a7d7f0 | Jupyter | 19,915 | 470 | # %% [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... |
52756ff6786b1f766be7297693868a4cea85cd1cb51e61be39c01f5af39c633e | Jupyter | 19,927 | 521 | # %%
############################################## 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... |
faaa0a3638a5e513f69b5c0dab097aa3a6b4ff443993a20d2f39e5d10c65b39a | Jupyter | 19,935 | 486 | # %%
import os
import sys
import numpy as np
import torch as th
import matplotlib.pyplot as plt
from IPython import get_ipython
motornet_in_cwd = os.path.exists("MotorNet") or os.path.exists("motornet")
colab_env = 'google.colab' in str(get_ipython()) if hasattr(__builtins__,'__IPYTHON__') else False
colab_initialized... |
ec2db20ef07e65eb90b87e2b256ededee16dbecb9f2aca7c9bd0571dd5b4428b | Jupyter | 19,945 | 522 | # %%
############################################## 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... |
1013a3cc085e30f3b911343ffded740dfc5eaf4d3fe6bf68818daa501199c79c | Jupyter | 19,946 | 524 | # %%
############################################## 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... |
9d72ce97ec8be0d257dc36a29184751e640cbd86098a7f70f306e5ae509837ab | Jupyter | 19,961 | 471 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/llm/portkey.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Portkey
#
# **Portkey** is a full-stack LLMOps platfor... |
70bb7883d8873cc2aa90f14af10fc2d85faeb79278bca8df0dfb4f9882c24489 | Jupyter | 19,962 | 590 | # %% [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... |
c8a1b6f7ff7bdafa49c17ef6912f6cec947472eec923492210f6b3337f57296d | Jupyter | 19,968 | 567 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/managed/GoogleDemo.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Google Generative Language Semantic Retriever
# ... |
167f24802c3d6fab5cfddb374cd10c870118013835c28260c5198e2f5e90acff | Jupyter | 19,978 | 548 | # %% [markdown]
# # IdpSAM notebook
#
# ## About
# Welcome to the idpSAM Colab notebook. IdpSAM is a latent [diffusion model](https://en.wikipedia.org/wiki/Diffusion_model) to generate ensembles of 3D conformations of [intrinsically disordered peptides and proteins](https://en.wikipedia.org/wiki/Intrinsically_disorder... |
e5905d4a71c84028b5ed2fe046341e8bfe1e6f733606d0a32a69c76e566e0164 | Jupyter | 19,980 | 523 | # %%
############################################## 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... |
2a1476122b0cf36aac3b312508c7c0927e85d9fed8ab9a429ba51a66222a0c3e | Jupyter | 19,981 | 523 | # %%
############################################## 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... |
6395d7c3828de860167a2dda41d61bd7ba5d69f643cc6cc572f980568b306b8d | Jupyter | 20,002 | 523 | # %%
############################################## 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... |
84aedde7d696b82293d431f174a62d442b0182d08c5dce813a22a8cbfc9a55a2 | Jupyter | 20,009 | 523 | # %%
############################################## 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... |
8b2ac093d8dfeda1379fa05a12f1bc0fe9f3d2e8115c34a86c73e3621b1f2088 | Jupyter | 20,020 | 526 | # %%
############################################## 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... |
faca3c1843347a98ab1352b873845e9e0b889e4b20da06dbb249c4ca56ce834f | Jupyter | 20,027 | 524 | # %%
############################################## 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... |
9431a871b1ca2e677721594a743326e8bd24d3b25273ec2194fc495becc0b31d | Jupyter | 20,029 | 631 | # %%
import numpy
import matplotlib
from matplotlib import pyplot
import pandas
import scipy
import seaborn
import tensorflow
import tensorflow.keras
from sklearn import preprocessing
# %%
%matplotlib inline
matplotlib.rcParams['figure.dpi'] = 120
matplotlib.rcParams['font.sans-serif'] = 'Helvetica'
pandas.set_option... |
91635e8d9b33f1ca010f5e2ec04b16e98bb8bd1755c1eee1285f8bf675e5cebd | Jupyter | 20,144 | 529 | # %%
############################################## 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... |
195fbb176f4f5179de66c6412e5886b489eca1ff24ba0718f9b3728390aed63b | Jupyter | 20,226 | 531 | # %%
############################################## 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... |
4bb8c965e781a0e8f5dd81bb5f7fb0f5c4a498349e4e6a98e4bec2d8532efde9 | Jupyter | 20,350 | 659 | # %% [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 steric_free_simulator import ReactionNetwork, VectorizedRxnNet, VecSim, O... |
073971bc59ea314c8b7bedda82865b459ea12a367d3da426267269024dea9edd | Jupyter | 20,401 | 652 | # %%
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
from matplotlib.colors import LogNorm, ListedColormap, LinearSegmentedColormap
import matplotlib.colors as mcolors
from matplotlib import ticker
from matplotlib.ticker import EngFormatter
from mpl_toolkits.axes_grid1 import make_axes_locat... |
534b78a55ea7e6e7c262599500313ff1de07d26d4ab658f56aa3e850fe1ede9e | Jupyter | 20,433 | 498 | # %% [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.
... |
24a574548b947015a5dbe68c3db0f46e647ab255d5613d905de1ec6146cbdf38 | Jupyter | 20,479 | 671 | # %% [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... |
2986c10eeb2c6f9dcb0678049d42c9d9a2a716d16a64e99224f2d95c4e13419a | Jupyter | 20,491 | 628 | # %%
import itertools
import os
import pickle
import re
import time
import matplotlib
from matplotlib import pyplot
import numpy
import pandas
import scipy
import seaborn
# import tensorflow.keras
# %%
%matplotlib inline
matplotlib.rcParams['figure.dpi'] = 120
matplotlib.rcParams['font.sans-serif'] = 'Helvetica'
matp... |
86692f84ca18831382bfa27bc2da6b117de28e48753652013283b118b87567e1 | Jupyter | 20,627 | 550 | # %%
import os
import dynamics
from packaging import version
import tensorflow as tf
import pandas as pd
from matplotlib import pyplot as plt
from scipy import stats
import glob
import numpy as np
import matplotlib.pyplot as plt
import pytorch_lightning as ptl
from models import HigherOrderModel
from hypergraphs import... |
b0e9d4c5ac2803964130c0a5e7c86f437b5b960921206c8985bdd34aa73033cb | Jupyter | 20,643 | 577 | # %%
###########################################################################################################################
## The work in this notebook has been conducted in collaboration with Marijn Visser from the Vrije Univeriteit Amsterdam ##
###################################################################... |
de7a13c2dc1b434efe99eca8f6ec3f2034e2c803f87155dc2f638ba578aa798f | Jupyter | 20,669 | 412 | # %% [markdown]
# # Interactive Microscopy Control with ImJoy
#
# Author: [Wei OUYANG](https://oeway.github.io/)
#
# [ImJoy](https://imjoy.io) is a web framework for building interactive analysis tools. You can also use it to build easy-to-use and interactive data acquisition tool together with pycro-manager.
#
# In... |
5a3c36aebf9937ff0cbdf9573a1da6abc5514dd9e2ccfd9cf56c474b8c150630 | Jupyter | 20,683 | 692 | # %%
# Import basic packages
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
import math
from tabulate import tabulate
from difflib import SequenceMatcher
import warnings
warnings.filterwarnings('ignore')
from collections import defaultdict
from itertools import chain, combinations
from... |
23d94dcfa4350bfa71c497e42e6d9d104157f4a8fa93b1d5ce5612773f93ea1a | Jupyter | 20,709 | 612 | # %% [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... |
5a07d883e71ef06a4634e929690ccacc0a10ab5a1684a1b19f9051c0dce02014 | Jupyter | 20,854 | 321 | # %%
setwd("..")
# %%
source("Source/functions.R")
library("data.table")
library("reshape2")
library("pheatmap")
library("scales")
library("ggplot2")
library("ggrepel")
library("ggsignif")
library("parallel")
# %% [markdown]
# Load the metadata
# %%
snv_info <- fread("Data/snv_loci_v2.csv")
snvIDs <- snv_info[,SNVID... |
43adc632df5881a7d3b73d666ea56831cf1b474e6c6b07065ab1cee19e2b6e3e | Jupyter | 20,859 | 593 | # %%
## Generate enhancer pool
## 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 twobitreader import TwoBitFile
from sklearn.preprocessing import LabelEncoder
from tqdm import trange,... |
1f559ad89accac50680fe419b8e6164ff1b995b337dc7295ee69a7d4fd5bc8a9 | Jupyter | 20,912 | 497 | # %% [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... |
e291d7e016578326ee8e6574f5db868e59a19bff4d2c5979c07d5a6d44e9c573 | Jupyter | 20,943 | 673 | # %% [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 steric_free_simulator import ReactionNetwork, VectorizedRxnNet, VecSim, O... |
80c9ba05aac4238d3405db795e84331c77838f7c0614a550cb7aa313908ce672 | Jupyter | 20,956 | 565 | # %% [markdown]
# # Table of Contents
#
# ## - Import libraries and define functions + Initial setup
#
# ## - Pre-process ONT files
#
# ## - Make figures
# %% [markdown]
# # Import libraries and define functions + Initial setup
# %%
## Import Libraries
import pandas as pd
import numpy as np
from matplotlib_venn im... |
33ac6c49067a4c6f3d3113fc9e677a3a41b019d5248698916c7f2f218deaf7a0 | Jupyter | 20,967 | 555 | # %% [markdown]
# ## Notebook for Figure 7 and related content: Analysis of cryopreserved cells from T1D donors. Alpha cell dysfunction and beta-cell survival
# ### Author: J. Camunas-Soler
# ### Last Updated: Feb. 2020
# %%
import os
import matplotlib.pyplot as plt
import matplotlib as mpl
import pandas as pd
import ... |
c384c3014ba6831e1a629cef7db046d447f2707f8d4ffc93b217f10aa837bced | Jupyter | 20,986 | 369 | # %%
setwd("..")
# %%
source("Source/functions.R")
library("data.table")
library("ggplot2")
# %%
chrmbases_properties <- fread("Report/artifact/chrmbases_properties.csv.gz")
chrmgenes <- fread("Report/artifact/chrmgenes.csv")
chrmgenes <- rbind(chrmgenes,
chrmbases_properties[is_in_range == "Y" & is_in_primer ==... |
0e9d53d24331d93c1a7815df4bd5c948cc988208103f1563feecc9deb9cbf4c7 | Jupyter | 21,083 | 515 | # %%
import os
import dynamics
from packaging import version
import torch
import pandas as pd
from matplotlib import pyplot as plt
from scipy import stats
import glob
import numpy as np
import matplotlib.pyplot as plt
import pytorch_lightning as ptl
from models import HigherOrderModel
import seaborn as sns
#imports all... |
d4c4236834ee30e4d7bf97702010b76742d7a8465f497e8dced3320fdaadb9c3 | Jupyter | 21,240 | 673 | # %% [markdown]
# First import libraries and look for data
# %% [markdown]
# importing stuff, defining colors and styles
# %%
import rcp
%matplotlib inline
import os
import pandas as pd
import numpy as np
import scipy.stats as stats
pd.set_option('display.mpl_style', 'default')
pd.set_option('display.line_width', 50... |
3591a03a6021ec96363af94c21e600a7c0d72e1b3bfcc7cb1ce8374573b881b1 | Jupyter | 21,305 | 522 | # %%
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
from datetime import datetime
from tqdm import tqdm
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import anndata as ad
import scipy
import sklearn
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_se... |
26b221b19dbc0f5043a4090a0fe6554527c4d08bce24e34c633c5ce5c9e3a24d | Jupyter | 21,385 | 512 | # %% [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... |
c72089ab17db5946a084ca16a1c15672712dbdb6d9fd2d106e6d8543d26397a4 | Jupyter | 21,392 | 515 | # %% [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... |
1b6450b1d8927ad2a940cf78362e380ac6c5067c7c3e5243a58f1e7deb4d507c | Jupyter | 21,398 | 538 | # %% [markdown]
# # Cookbook: Every way to make a `Molecule`
# %% [markdown]
# Every pathway through the OpenFF Toolkit boils down to four steps:
#
# 1. Using other tools, assemble a graph of a molecule, including all of its atoms, bonds, bond orders, formal charges, and stereochemistry[^rs]
# 2. Use that information... |
522d6e1ed400020ed440337b0240fe08ba5d566211fdaf45ed4f44bb3e38af92 | Jupyter | 21,426 | 418 | # %% [markdown]
# # PENSA Tutorial Using GPCRmd Trajectories
# Here we show some common functions included in PENSA, using trajectories of a G protein-coupled receptor (GPCR). We retrieve the molecular dynamics trajectories for this tutorial from [GPCRmd](https://submission.gpcrmd.org/home/), an online platform for col... |
d149df97495ebffda44b4649c58f73e4bc42dc4aefab62dc627a3b3ba7f7314b | Jupyter | 21,467 | 702 | # %%
import functools
import logging
import os
import pickle
import numpy as np
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
import tensorflow as tf
import keras
from keras.models import Sequential, Model, load_model
from keras.layers import Dense, Dropout, Activation, ... |
d7eaf3f5ff2009941ce84accdacb062b90079921ef2adf3ca9f7123ec9c96a44 | Jupyter | 21,509 | 476 | # %% [markdown]
# # Using an external master clock for hardware control of a stage-scanning high NA oblique plane microscope
#
# Tutorial provided by [qi2lab](https://www.shepherdlaboratory.org).
#
# This tutorial uses Pycro-Manager to rapidly acquire terabyte-scale volumetric images using external hardware triggeri... |
0f9bfb204986ca4a4b197e672e28418839244158f7d604c092a255ba02079a4d | Jupyter | 21,593 | 724 | # %% [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 steric_free_simulator import ReactionNetwork, VectorizedRxnNet_Exp, VecSi... |
04a61e9757d27ca515e20d7a5e063ebaaf99765d1bd9bf95c44704a9457b5102 | Jupyter | 21,613 | 640 | # %%
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 logomaker
# %% [markdown]
# # Helper functions
# %%
%matplotlib inline
matplotlib.rcParams['figure.dpi'] = 12... |
dd6ddd95d1ad4146ad41dbc022ded53b48f0dcba3820a80c0ccf04b4d4e0a817 | Jupyter | 21,668 | 561 | # %%
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.lines as mlines
import seaborn as sns
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from moseq2_viz.util import read_yaml
from moseq2_viz.viz import _validate_and_order_syll_stats_params
# %%
moseq_df... |
ea6f6b2c48041a789922739b5ce1a1cda0b0c86cad96a26629b8089a7afd73b6 | Jupyter | 21,913 | 528 | # %% [markdown]
# Copyright (c) MONAI Consortium <br>
# Licensed under the Apache License, Version 2.0 (the "License"); <br>
# you may not use this file except in compliance with the License. <br>
# You may obtain a copy of the License at <br>
# http://www.apache.org/licenses/LICENSE-2.0 <br>
# Unless required by appli... |
9974ac92982d25637f27ec1df459a7e246d3565407c533356d781bf8987d7c07 | Jupyter | 21,923 | 350 | # %%
setwd("..")
# %%
source("Source/functions.R")
library("data.table")
library("ggplot2")
library("ggsignif")
library("ggrepel")
library("lme4")
library("car")
# %%
mito_barcodes <- read.csv("Data/mito_barcodes.csv", as.is = TRUE)
mitoIDs <- mito_barcodes[, "ID"]
snv_info <- read.csv("Data/snv_loci_v2.csv", as.is... |
95986edf1be3752b9cbf413d635235f8cee601363152c599de689b7f21d61c5f | Jupyter | 22,256 | 599 | # %% [markdown]
# # Table of Contents
#
# ## - Import libraries and define functions + Initial setup
#
# ## - Pre-process Illumina files
#
# ## - Make figures
# %% [markdown]
# # Import libraries and define functions + Initial setup
# %%
## Import Libraries
import pandas as pd
import numpy as np
from matplotlib_ve... |
07f5c57612de15b5a9d4d1d9ce945d9033347816d4aa863438cd29d5599afd8f | Jupyter | 22,273 | 692 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/data_connectors/WebPageDemo.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# # Web Page Reader
#
# Demonstrates our w... |
7635603eb56e6fc47023b909826670329d6c81f3c86e6de79de51989f22ed1b9 | Jupyter | 22,289 | 532 | # %% [markdown]
# Copyright (c) MONAI Consortium <br>
# Licensed under the Apache License, Version 2.0 (the "License"); <br>
# you may not use this file except in compliance with the License. <br>
# You may obtain a copy of the License at <br>
# http://www.apache.org/licenses/LICENSE-2.0 <br>
# Unless required by appli... |
602bb42e2b5a552153247fea0480d4a47d0923f4072c54822b9e647426cf73b9 | Jupyter | 22,338 | 597 | # %% [markdown]
# # Table of Contents
#
# ## - Import libraries and define functions + Initial setup
#
# ## - Pre-process Illumina files
#
# ## - Make figures
# %% [markdown]
# # Import libraries and define functions + Initial setup
# %%
## Import Libraries
import pandas as pd
import numpy as np
from matplotlib_ve... |
0c4001517234a4a2a21c607166159e95e6979d6c8c2ab59b4ba9b65c56d2ebad | Jupyter | 22,415 | 835 | # %%
import matplotlib.pyplot as plt
import numpy as np
from sklearn.metrics.pairwise import euclidean_distances
from typing import Any
np.random.seed(42)
IMAGE_SIZE = 32
NUM_TUNNELS = 6
MAX_RADIUS = 10
N = 1000
def key(x: Any):
if isinstance(x, tuple):
return f'{int(x[0][0])}-{int(x[1][0])}-{int(x[2][... |
493d65d11b2abdc629e8672ccad4e1bdbe0468d5667dda2b80d1feaeb86a6dff | Jupyter | 22,434 | 552 | # %% [markdown]
# # Table of Contents
# 1. Average coverage plots
# > 1.1 Experimental data
# > 1.2 Predictions
#
# 2. BigWig coverage plots
# > 2.1 Plot coverage of multiple models
# > 2.2 Plot coverage without axes
# > 2.3 Plot smoothed coverage without axes
# 3. H5 coverage plots
#
# ## 1. ... |
ebb009285c8fa4b1215f655537a7dd1e7cecb9ef6f86bb722e234f3df7639d1f | Jupyter | 22,481 | 650 | # %% [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... |
ad5fb6345446b6051cd141d3f59c7aad31fae621d700cf27e720a78ff1939cfa | Jupyter | 22,581 | 564 | # %% [markdown]
# # Time-aware Attention-based RNN (TA-RNN)
# %%
# Import necessary libraries
import numpy as np
from pandas import read_csv
import pandas as pd
import matplotlib.pyplot as plt
import random
from keras.models import Sequential, Model
from sklearn import metrics
from sklearn.metrics import confusion_mat... |
649948526d2d932c894b1e2071139121068e28043fa078fe35bab159513b1433 | Jupyter | 22,618 | 449 | # %% [markdown]
# # Signal resampling using a causal polyphase filter
#
# Polyphase decimation is an efficient approach to decimation filter implementation, which avoids excess calculations that are disregarded during decimation:
#
# - *Non-polyphase implementation of a decimator with anti-aliasing filter $H(z)$:*
# ... |
dbb0617e3c8f10abdf7293f1698553b6454ee5056908d799c988ece81d1817b8 | Jupyter | 22,708 | 456 | # %% [markdown]
# # Create a custom Environment
#
# In this tutorial, we will go over `Environment` objects, how they work and how to build a custom subclass to implement your own task design.
#
# Let's start by importing what we need.
# %%
import os
import sys
import numpy as np
import torch as th
from typing impor... |
401ba8ff1304ba9f8b8181cee21718baec51a232e0a78f27b24632b71fb73d0e | Jupyter | 22,839 | 641 | # %%
import datetime
def printbar():
nowtime = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
print("\n"+"="*8 + "%s"%nowtime)
printbar()
# %%
import numpy as np
noesypr_nmr_120 = np.load(r'ac_120plasma_noesypr.npy')
cpmgpr_nmr_120 = np.load(r'ac_120plasma_cpmgpr.npy')
ledbp_nmr_120 = np.load(r'ac_1... |
7a6e78322d70129be4f140cdddad83f28c4fdaaf0e0ea3ff85ad0b59346565bf | Jupyter | 22,906 | 525 | # %% [markdown]
# <a href="https://colab.research.google.com/github/dauparas/ProteinMPNN/blob/main/colab_notebooks/quickdemo_wAF2.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# #ProteinMPNN w/AF2
# This notebook is intended as a q... |
e0804a64e6a13dec7315d8fbffd596819075fc2cd2ea91b48d686cb3771caaaf | Jupyter | 23,074 | 635 | # %% [markdown]
# # Knowledge Distillation For Fine-Tuning A GPT-3.5 Judge (Pairwise)
#
# There has been recent research that demonstrated GPT-4's ability to closely align to human judges when evaluating LLM generated texts (e.g., see [[1]](https://arxiv.org/abs/2306.05685), [[2]](https://arxiv.org/abs/2303.16634)). I... |
7d5125b34f041721491018dc26a10ce124f984d604443db83fe3849bba74c234 | Jupyter | 23,183 | 506 | # %% [markdown]
# # Table of Contents
# 1. Heatmaps
# > 1.1 Test metrics heatmaps
# > 1.2 TSS heatmaps
# > 1.3 F1 heatmaps
# 2. Precision-recall curves
# > 2.1 PRCs per model
# > 2.2 PRCs for smoothed predictions
#
# ## 1. Heatmaps
# %%
import numpy as np
import os
import matplotlib.pyplot as p... |
bfc23b2496f518f3d6fb460c15e2d91209b5b9a156984dd2a32a2cc250980dc6 | Jupyter | 23,223 | 634 | # %% [markdown]
# # 1 - Setup
# %%
%load_ext autoreload
%autoreload 2
from modules.model_selection import *
from modules.visualize import *
from modules.utils import *
from modules.feature_extraction import *
from modules.tracking import *
from scipy.stats import loguniform, uniform, randint
from sklearn.naive_bayes... |
29f95d4e2de27c5ff66ba49a7a887c4f3fdac462864becde95ace54ef055e14d | Jupyter | 23,318 | 641 | # %%
# Import necessary lib
import numpy as np
from pandas import read_csv
import pandas as pd
from pandas.api.types import is_string_dtype
from pandas.api.types import is_numeric_dtype
import pickle
from keras.preprocessing.sequence import pad_sequences
from sklearn.model_selection import train_test_split
from sklearn... |
e9505ca09f69a4e7c97103f126ea70a8357441d96cdbcfb329a98854dd696ab8 | Jupyter | 23,369 | 761 | # %%
import logging
import os
import pickle
import numpy as np
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
import tensorflow as tf
import keras
from keras.models import Sequential, Model, load_model
from keras.layers import Dense, Dropout, Activation, Flatten, Input, L... |
8e2762b50f6aeafdf6b658f037b615c22db02abb01e7d85e44ad5b218aca2f82 | Jupyter | 23,511 | 756 | # %%
# 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, Optimizer, EquilibriumSolver,TrapMetric
import networkx as nx
import to... |
007bead28610be403115f92696445b765044055fe403051a57decac4f2f94649 | Jupyter | 23,520 | 756 | # %%
# 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,TrapMetric
import networkx as nx
import... |
460f63afa0d404b7e6f34af84e1c7cdf8f7e59f0c5cbb5584541c53059bb8383 | Jupyter | 23,594 | 766 | # %%
import logging
import os
import pickle
import numpy as np
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
import tensorflow as tf
import keras
from keras.models import Sequential, Model, load_model
from keras.layers import Dense, Dropout, Activation, Flatten, Input, L... |
45f1c9cb00fdac0797db27bb93b48367ac542ed488a2c0fe69b793efd2d3df1a | Jupyter | 23,676 | 685 | # %% [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... |
b61608f834da5589e86aa638c2becceb7395869bb4d7eff9fd0f477458e330fe | Jupyter | 24,161 | 590 | # %%
import os
import pandas as pd
import numpy as np
import glob
import warnings
# %%
path = "/mnt/data/shared/jacob/GFNO/results"
os.chdir(path)
# %%
def to_numeric(x):
try:
x = float(x)
if x.is_integer():
x = int(x)
return x
except ValueError:
return x
def latex... |
8b8552a1e92d7f620d60d49c86583d1af7cbc6923329b816c09ea281f53e5127 | Jupyter | 24,268 | 584 | # %% [markdown]
# # Time-aware Attention-based RNN AutoEncoder (TA-RNN-AE)
# %%
# Import necessary libraries
import numpy as np
from pandas import read_csv
import pandas as pd
import matplotlib.pyplot as plt
import random
from keras.models import Sequential, Model
from sklearn import metrics
from sklearn.metrics impor... |
85e709844a878753785ac93f9c88eca373c49e6ec79ca7616c6b11893ef9acd0 | Jupyter | 24,560 | 586 | # %%
#!/usr/bin/Rscript
#############################################################
options(stringsAsFactors = FALSE)
##############################################################
suppressMessages(
{
library(tidyverse)
library(data.table)
library(dynamicTreeCut)
library(fastcluster)
library... |
a0fb84f8f96b790ff51ca61c07cf6ea1effe8bc5711c5c783b8c89b353c4514d | Jupyter | 24,641 | 912 | # %% [markdown]
# ## Simulation-based inference of spectral graph model frequency spectrum with `spectrome`: Usage example
# %% [markdown]
# ### Import modules with respective paths
# %%
# this path append is for binder only
import sys
sys.path.append("../../")
#generic modules
import matplotlib as mpl
import matplo... |
ecd32293d1b397e9c60a3888d316aa13048c9ffebadbc5b580ec53bc98785754 | Jupyter | 24,654 | 492 | # %% [markdown]
# ## notebook 04: quantifying condition and individuality effect
#
# We quantify the effect from zero to three objetcs, and separately from three to 99 objects, by plotting the differences in median individual position errors.
# %%
# importing python packages
import numpy as np
import pandas as pd
fro... |
0e2ad87468f73b6c6b1c40654e7c3aac18d39d847bd38605d4929f47d96225c8 | Jupyter | 24,720 | 592 | # %%
#!/usr/bin/Rscript
#############################################################
options(stringsAsFactors = FALSE)
##############################################################
suppressMessages(
{
library(tidyverse)
library(data.table)
library(dynamicTreeCut)
library(fastcluster)
library... |
1e01e87919ca882a42de3af54893ea8594c004fc8d4ee0d2a6edf4ec59c6cc7a | Jupyter | 24,969 | 729 | # %%
import keras
from keras.models import Sequential, Model, load_model
from keras.layers import Dense, Dropout, Activation, Flatten, Input, Lambda
from keras.layers import Conv2D, MaxPooling2D, Conv1D, MaxPooling1D, LSTM, ConvLSTM2D, GRU, BatchNormalization, LocallyConnected2D, Permute
from keras.layers import Conca... |
0a301d2d02f4ccf8388afe4d846edbcee955a3f1b49e8b3cc09e6db908ce6601 | Jupyter | 25,266 | 727 | # %% [markdown]
# # Surrogate-Based Null Hypothesis Testing for RQA-ML Classification & Clustering
#
# **Purpose:** Generate all data and publication-quality figures for the updated methods paper.
#
# This notebook demonstrates that the nonlinear dynamical structure captured by Recurrence Quantification Analysis (RQA... |
678c96d2c331e58b82d7e896da8e25d95300fa5ffd99895067a508887f77b92c | Jupyter | 25,425 | 562 | # %%
from utils_new import *
# %%
pd.set_option('display.max_rows', 10)
data_dir = '/Users/erihe/OneDrive - NTNU/TDA_review/functional'
AngularRate = pd.read_pickle(data_dir + '/AngularRate')
AngularRate_Stats = pd.read_pickle(data_dir + '/AngularRate_Stats')
BorderScore = pd.read_pickle(data_dir + '/BorderScore')
B... |
a03623c50860ebf5707a370a10010a3b3472e66857b8091bdbab970a59647815 | Jupyter | 25,428 | 786 | # %% [markdown]
# - This Jupyter notebook is dedicated to the reproducibility of the research article, "An automated system for Plasmodium falciparum detection and parasitemia estimation: evaluation on thin blood smear images." It serves as a valuable tool for manipulating datasets and replicating the results and evalu... |
9609b503d5cd5786722689ff76b3c96f23281243b710106f1c4240cb04827501 | Jupyter | 25,490 | 629 | # %% [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... |
9ba941f57e4482e654d036dc5bd4a7bd2a7dd1bf67ed567f6c004b6689ee5e56 | Jupyter | 25,566 | 696 | # %%
from voxcell import VoxelData, RegionMap
import numpy
import h5py
import pandas
from matplotlib import pyplot as plt
from matplotlib.colors import LinearSegmentedColormap
from joblib import Parallel, delayed, parallel_config
# Paths to data.
# Make sure that all voxel datasets have the same resolution.
# Part... |
1be54f89f365a04f40738c64c50f74b1decd261f9500e470edbf9dcf178cf723 | Jupyter | 25,609 | 695 | # %% [markdown]
# <a href="https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/llama_dataset/ragdataset_submission_template.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# %% [markdown]
# <a id='top'></a>
# # `Ll... |
ed089581246014e8060214ebfb927cab3b52c92990dfd6ab262ae04373824f47 | Jupyter | 25,675 | 667 | # %% [markdown]
# ## Notebook for Figure 3 and related content: Beta Cells Heterogeneity, Predictive Set of genes for Electrophysiology
# ### Author: J. Camunas-Soler
# ### Last Updated: Feb. 2020
# %%
import matplotlib.pyplot as plt
import matplotlib as mpl
import pandas as pd
import seaborn as sns
import numpy as np... |
7cb6887208b6d56dc677f9cf1dffffbe8044c33ab873fc5ccaddac02934cb288 | Jupyter | 25,831 | 639 | # %% [markdown]
# # Table of contents
#
# ### - Import libraries and define functions + Initial setup
#
# ### - Data pre-processing (Our Data)
#
# ### - Data pre-processing (GTEX Data)
#
# ### - Data pre-processing (Merge Data)
#
# ### - Make figures
# %% [markdown]
# # Import libraries and define functions + Ini... |
ed8fc09b62bf0beb3212746f6a60563dfb027036d6733e408ee27eb0d4e703bf | Jupyter | 25,906 | 603 | # %%
import pandas as pd
import numpy as np
from itertools import chain
from scipy.stats import chi2_contingency
import cooler
import bioframe
import cooltools
from cooltools.lib.numutils import fill_diag
import matplotlib.pyplot as plt
import seaborn as sns
from matplotlib_venn import venn2
from pybedtools import BedT... |
21480a9f02819e081720a81ae2a5db4cae38f4447d883138688a4c2d9260d612 | Jupyter | 26,042 | 564 | # %% [markdown]
# # Oracle AI Vector Search with Document Processing
# Oracle AI Vector Search is designed for Artificial Intelligence (AI) workloads that allows you to query data based on semantics, rather than keywords.
# One of the biggest benefits of Oracle AI Vector Search is that semantic search on unstructured d... |
8dc14c4809553cf27dbe05363aae50d5daeca0c579ab641bbc88bd549ba160ad | Jupyter | 26,120 | 741 | # %%
## Supplemental 4 cCRE descriptors
import os, sys, glob
import subprocess
import pandas as pd
import matplotlib.pyplot as plt
import loompy
import logging
import numpy as np
import pyBigWig
from pybedtools import BedTool
import pybedtools
from chromograph.preprocessing.utils import *
# %%
f_peaks = '/proj/DEV_A... |
8ce15a1e8caed65efe6373eaf867f7b36ad3a01c548d0e449f97894f7a9ed03d | Jupyter | 26,222 | 659 | # %% [markdown]
# # Imports and functions
# %% [markdown]
# For questions email: vv86@sussex.ac.uk
# %%
from google.colab import drive
drive.mount('/content/drive')
# %%
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.patches as patches
%matplotlib inline
from matplotlib... |
d9957e96325d78be445cef64906475be58e97b3d88375f65bfa8969cd4669ba2 | Jupyter | 26,257 | 797 | # %% [markdown]
# # Optimization Framework using auto-diff to predict binding rates
# %% [markdown]
# ## Import the necessary modules
# %%
# 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
# %%
fro... |
57db93555b396efb5229c8d860c07b5ed9ef4c7f4d8bd553e9273f5e2930834f | Jupyter | 26,321 | 818 | # %%
import random as rnd
import math
import time
# %%
class Problem:
subnetworks = 35
type = 2
costs = [100, 150, 150, 150, 100, 150, 100, 100, 100, 100,
100, 100, 150, 100, 100, 100, 150, 100, 100, 100,
100, 100, 150, 150, 100, 150, 100, 100, 150, 100,
150, 100, 100, 100, 100
]
directs = [1, 20, 1... |
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