text stringlengths 67 1.03M | metadata dict |
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# Notebook from stefanpeidli/cellphonedb
Path: scanpy_cellphonedb.ipynb
<code>
from IPython.core.display import display, HTML
display(HTML("<style>.container { width:90% !important; }</style>"))
%matplotlib inline
import numpy as np
import pandas as pd
import matplotlib.pyplot as pl
import scanpy as sc
import cellph... | {
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# Notebook from innawendell/European_Comedy
Path: Analyses/The Evolution of The Russian Comedy_Verse_Features.ipynb
## The Analysis of The Evolution of The Russian Comedy. Part 3._____no_output_____In this analysis,we will explore evolution of the French five-act comedy in verse based on the following features:
- The... | {
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# Notebook from quantopian/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers
Path: Chapter4_TheGreatestTheoremNeverTold/Ch4_LawOfLargeNumbers_PyMC3.ipynb
# Chapter 4
`Original content created by Cam Davidson-Pilon`
`Ported to Python 3 and PyMC3 by Max Margenot (@clean_utensils) and Thomas Wiecki (@twiecki) a... | {
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# Notebook from ethiry99/HW16_Amazon_Vine_Analysis
Path: Vine_Review_Analysis.ipynb
<code>
# Dependencies and Setup
import pandas as pd_____no_output_____vine_review_df=pd.read_csv("Resources/vine_table.csv")
_____no_output_____vine_review_df.head()
_____no_output_____vine_review_df=vine_review_df.loc[(vine_review_df[... | {
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# Notebook from bbglab/adventofcode
Path: 2016/loris/day_1.ipynb
# Advent of Code 2016_____no_output_____
<code>
data = open('data/day_1-1.txt', 'r').readline().strip().split(', ')_____no_output_____class TaxiCab:
def __init__(self, data):
self.data = data
self.double_visit = []
self.... | {
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# Notebook from rabest265/GunViolence
Path: Code/demographics_Lat_Long.ipynb
<code>
#API calls to Google Maps for Lat & Long_____no_output_____# Dependencies
import requests
import json
from config import gkey
import os
import csv
import pandas as pd
import numpy as np
_____no_output_____# Load CSV file
csv_path = os.... | {
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# Notebook from debugevent90901/courseArchive
Path: ECE365/genomics/Genomics_Lab4/ECE365-Genomics-Lab4-Spring21.ipynb
# Lab 4: EM Algorithm and Single-Cell RNA-seq Data_____no_output_____### Name: Your Name Here (Your netid here)_____no_output_____### Due April 2, 2021 11:59 PM_____no_output_____#### Preamble (Don't c... | {
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"path": "ECE365/genomics/Genomics_Lab4/ECE365-Genomics-Lab4-Spring21.ipynb",
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# Notebook from justinshaffer/Extraction_kit_benchmarking
Path: code/Taxon profile analysis.ipynb
# Set-up notebook environment
## NOTE: Use a QIIME2 kernel_____no_output_____
<code>
import numpy as np
import pandas as pd
import seaborn as sns
import scipy
from scipy import stats
import matplotlib.pyplot as plt
impor... | {
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# Notebook from rpatil524/Community-Notebooks
Path: MachineLearning/How_to_build_an_RNAseq_logistic_regression_classifier_with_BigQuery_ML.ipynb
<a href="https://colab.research.google.com/github/isb-cgc/Community-Notebooks/blob/master/MachineLearning/How_to_build_an_RNAseq_logistic_regression_classifier_with_BigQuery_... | {
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# Notebook from jouterleys/BiomchBERT
Path: classify_papers.ipynb
Uses Fine-Tuned BERT network to classify biomechanics papers from PubMed_____no_output_____
<code>
# Check date
!rm /etc/localtime
!ln -s /usr/share/zoneinfo/America/Los_Angeles /etc/localtime
!date
# might need to restart runtime if timezone didn't ch... | {
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# Notebook from superkley/udacity-mlnd
Path: p2_sl_finding_donors/p2_sl_finding_donors.ipynb
# Supervised Learning: Finding Donors for *CharityML*
> Udacity Machine Learning Engineer Nanodegree: _Project 2_
>
> Author: _Ke Zhang_
>
> Submission Date: _2017-04-30_ (Revision 3)_____no_output_____## Content
- [Getting ... | {
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# Notebook from jfdahl/Advent-of-Code-2019
Path: README.ipynb
# Advent-of-Code-2019
## About Advent of Code
Advent of Code is an Advent calendar of small programming puzzles for a variety of skill sets and skill levels that can be solved in any programming language you like. People use them as a speed contest, i... | {
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# Notebook from Akshat2127/Part-Of-Speech-Tagging
Path: HMM TaggerPart of Speech Tagging - HMM.ipynb
# Project: Part of Speech Tagging with Hidden Markov Models
---
### Introduction
Part of speech tagging is the process of determining the syntactic category of a word from the words in its surrounding context. It is ... | {
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# Notebook from feberhardt/stardist
Path: examples/2D/2_training.ipynb
<code>
from __future__ import print_function, unicode_literals, absolute_import, division
import sys
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
from glob import glob
from tq... | {
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# Notebook from fenago/Applied_Data_Analytics
Path: Chapter01/Exercise1.03/Exercise1.03.ipynb
# Understanding the data
In this first part, we load the data and perform some initial exploration on it. The main goal of this step is to acquire some basic knowledge about the data, how the various features are distributed... | {
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# Notebook from christianausb/vehicleControl
Path: path_following_lateral_dynamics.ipynb
<code>
import json
import math
import numpy as np
import openrtdynamics2.lang as dy
import openrtdynamics2.targets as tg
from vehicle_lib.vehicle_lib import *_____no_output_____# load track data
with open("track_data/simple_track... | {
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# Notebook from avkch/Python-for-beginners
Path: Python for beginners.ipynb
# Python programming for beginners
anton.kichev@clarivate.com_____no_output_____## Agenda
1. Background, why Python, [installation](#installation), IDE, setup
2. Variables, Boolean, None, numbers (integers, floating point), check type
3. List,... | {
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# Notebook from ads-ad-itcenter/qunomon.forked
Path: ait_repository/test/tests/eval_metamorphic_test_tf1.13_0.1.ipynb
# test note
* jupyterはコンテナ起動すること
* テストベッド一式起動済みであること
_____no_output_____
<code>
!pip install --upgrade pip
!pip install --force-reinstall ../lib/ait_sdk-0.1.7-py3-none-any.whlRequirement already sat... | {
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# Notebook from yelabucsf/scrna-parameter-estimation
Path: analysis/simulation/estimator_validation.ipynb
# Estimator validation
This notebook contains code to generate Figure 2 of the paper.
This notebook also serves to compare the estimates of the re-implemented scmemo with sceb package from Vasilis.
_____no_out... | {
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# Notebook from fatginger1024/NumericalMethods
Path: numerical5.ipynb
<center> <h1>Numerical Methods -- Assignment 5</h1> </center>_____no_output_____## Problem1 -- Energy density_____no_output_____The matter and radiation density of the universe at redshift $z$ is
$$\Omega_m(z) = \Omega_{m,0}(1+z)^3$$
$$\Omega_r(z)... | {
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# Notebook from daviesje/21cmFAST
Path: docs/tutorials/coeval_cubes.ipynb
# Running and Plotting Coeval Cubes_____no_output_____The aim of this tutorial is to introduce you to how `21cmFAST` does the most basic operations: producing single coeval cubes, and visually verifying them. It is a great place to get started w... | {
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# Notebook from rhaas80/nrpytutorial
Path: Tutorial-GRHD_Equations-Cartesian.ipynb
<script async src="https://www.googletagmanager.com/gtag/js?id=UA-59152712-8"></script>
<script>
window.dataLayer = window.dataLayer || [];
function gtag(){dataLayer.push(arguments);}
gtag('js', new Date());
gtag('config', 'UA-... | {
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"path": "Tutorial-GRHD_Equations-Cartesian.ipynb",
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# Notebook from DavidLeoni/iep
Path: jupman-tests.ipynb
<code>
import jupman;
jupman.init()_____no_output_____
</code>
# Jupman Tests
Tests and cornercases.
The page Title has one sharp, the Sections always have two sharps.
## Sezione 1
bla bla
## Sezione 2
Subsections always have three sharps
### Subsection ... | {
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# Notebook from llondon6/koalas
Path: factory/gmvrfit_reduce_to_gmvpfit_example.ipynb
# Dempnstration that GMVRFIT reduces to GMVPFIT (or equivalent) for polynomial cases
<center>Development for a fitting function (greedy+linear based on mvpolyfit and gmvpfit) that handles rational fucntions</center>_____no_output____... | {
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# Notebook from fernandascovino/pr-educacao
Path: notebooks/2_socioeconomic_data_validation.ipynb
<h1>Índice<span class="tocSkip"></span></h1>
<div class="toc"><ul class="toc-item"><li><span><a href="#Socioeconomic-data-validation" data-toc-modified-id="Socioeconomic-data-validation-1"><span class="toc-item-num">1&nbs... | {
"repository": "fernandascovino/pr-educacao",
"path": "notebooks/2_socioeconomic_data_validation.ipynb",
"matched_keywords": [
"evolution"
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# Notebook from ale-telefonica/market
Path: Scr/trainning/.ipynb_checkpoints/Untitled-checkpoint.ipynb
<code>
import MySQLdb
from sklearn.svm import LinearSVC
from tensorflow import keras
from keras.models import load_model
import tensorflow as tf
from random import seed
import pandas as pd
import numpy as np
import r... | {
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"matched_keywords": [
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# Notebook from SandyGuru/TeamFunFinalProject
Path: Run Project Models - Census Data.ipynb
<code>
from sklearn import *
from sklearn import datasets
from sklearn import linear_model
from sklearn import metrics
from sklearn import cross_validation
from sklearn import tree
from sklearn import neighbors
from sklearn impo... | {
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"path": "Run Project Models - Census Data.ipynb",
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# Notebook from drammock/mne-tools.github.io
Path: 0.15/_downloads/plot_brainstorm_phantom_ctf.ipynb
<code>
%matplotlib inline_____no_output_____
</code>
# Brainstorm CTF phantom tutorial dataset
Here we compute the evoked from raw for the Brainstorm CTF phantom
tutorial dataset. For comparison, see [1]_ and:
... | {
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# Notebook from freshskates/machine-learning
Path: Robert_Cacho_Proj2_stats_notebook.ipynb
## Instructions
Please make a copy and rename it with your name (ex: Proj6_Ilmi_Yoon). All grading points should be explored in the notebook but some can be done in a separate pdf file.
*Graded questions will be listed with "... | {
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# Notebook from gerhajdu/rrl_binaries_1
Path: 06498_oc.ipynb
# Example usage of the O-C tools
## This example shows how to construct and fit with MCMC the O-C diagram of the RR Lyrae star OGLE-BLG-RRLYR-02950_____no_output_____### We start with importing some libraries_____no_output_____
<code>
import numpy as np
im... | {
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# Notebook from hossainlab/dsnotes
Path: book/_build/jupyter_execute/pandas/23-Kaggle Submission.ipynb
<code>
import pandas as pd _____no_output_____train = pd.read_csv("http://bit.ly/kaggletrain")_____no_output_____train.head() _____no_output_____feature_cols = ['Pclass', 'Parch']
X = train.loc[:, feature_cols] ____... | {
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# Notebook from hashmat3525/Titanic
Path: Titanic.ipynb
# Import Necessary Libraries_____no_output_____
<code>
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn import preprocessing
from sklearn.ensemble import RandomForestClassifier
from sklearn import svm
from ... | {
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# Notebook from 1966hs/MujeresDigitales
Path: Repaso_algebra_LinealHeidy.ipynb
<a href="https://colab.research.google.com/github/1966hs/MujeresDigitales/blob/main/Repaso_algebra_LinealHeidy.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>_____no_outp... | {
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# Notebook from etattershall/trend-lifecycles
Path: Modelling trend life cycles in scientific research.ipynb
# Modelling trend life cycles in scientific research
**Authors:** E. Tattershall, G. Nenadic, and R.D. Stevens
**Abstract:** Scientific topics vary in popularity over time. In this paper, we model the life-cy... | {
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# Notebook from danikhani/CV1-2020
Path: Exercise3/Exercise3/local_feature_matching.ipynb
# Local Feature Matching
By the end of this exercise, you will be able to transform images of a flat (planar) object, or images taken from the same point into a common reference frame. This is at the core of applications such as... | {
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# Notebook from fcivardi/spark-nlp-workshop
Path: tutorials/old_generation_notebooks/colab/6- Sarcasm Classifiers (TF-IDF).ipynb
_____no_output_____https://www.kaggle.com/danofer/sarcasm
<div class="markdown-converter__text--rendered"><h3>Co... | {
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# Notebook from jpzhangvincent/MobileAppRecommendSys
Path: notebooks/Correlation between app size and app quality.ipynb
<code>
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline_____no_output_____app = pd.read_pickle('/Users/krystal/Desktop/app_cleaned.pickle')
app.head()_____no_... | {
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# Notebook from saudijack/unfpyboot
Path: Day_00/02_Strings_and_FileIO/00 Strings in Python.ipynb
# Strings in Python_____no_output_____## What is a string?_____no_output_____A "string" is a series of characters of arbitrary length.
Strings are immutable - they cannot be changed once created. When you modify a string,... | {
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# Notebook from oercompbiomed/CBM101
Path: C_Data_resources/2_Open_datasets.ipynb
# Acquiring Data from open repositories
A crucial step in the work of a computational biologist is not only to analyse data, but acquiring datasets to analyse as well as toy datasets to test out computational methods and algorithms. The... | {
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# Notebook from lmorri/personalize-movielens-20m
Path: getting_started/2.View_Campaign_And_Interactions.ipynb
# View Campaign and Interactions
In the first notebook `Personalize_BuildCampaign.ipynb` you successfully built and deployed a recommendation model using deep learning with Amazon Personalize.
This notebook ... | {
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# Notebook from MusabNaik/LinMLTBS
Path: LinMLTBS.ipynb
<code>
%load_ext Cython
import numpy as np
np.set_printoptions(precision=2,suppress=True,linewidth=250,threshold=2000)_____no_output_____import numpy as np
import pandas as pd
import pyBigWig
import math
import csv
import multiprocessing_____no_output_____bw = py... | {
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# Notebook from detrout/encode4-curation
Path: encode-mirna-2018-01.ipynb
Submitting various things for end of grant._____no_output_____
<code>
import os
import sys
import requests
import pandas
import paramiko
import json
from IPython import display_____no_output_____from curation_common import *
from htsworkflow.su... | {
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# Notebook from schwaaweb/aimlds1_11-NLP
Path: M11_A_DJ_NLP_Assignment.ipynb
[View in Colaboratory](https://colab.research.google.com/github/schwaaweb/aimlds1_11-NLP/blob/master/M11_A_DJ_NLP_Assignment.ipynb)_____no_output_____### Assignment: Natural Language Processing_____no_output_____In this assignment, you will w... | {
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# Notebook from bgalbraith/course-content
Path: tutorials/W3D1_BayesianDecisions/W3D1_Tutorial1.ipynb
<a href="https://colab.research.google.com/github/NeuromatchAcademy/course-content/blob/master/tutorials/W3D1_BayesianDecisions/W3D1_Tutorial1.ipynb" target="_parent"><img src="https://colab.research.google.com/assets... | {
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# Notebook from kcbhamu/kaldo
Path: docs/docsource/theory.ipynb
<!--  -->
## Introduction
Understanding heat transport in semiconductors and insulators is of fundamental importance because of its technological impact in elec... | {
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# Notebook from dauparas/tensorflow_examples
Path: VAE_cell_cycle.ipynb
<a href="https://colab.research.google.com/github/dauparas/tensorflow_examples/blob/master/VAE_cell_cycle.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>_____no_output_____https... | {
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# Notebook from carpenterlab/2021_Haghighi_submitted
Path: 0-preprocess_datasets.ipynb
### Cell Painting morphological (CP) and L1000 gene expression (GE) profiles for the following datasets:
- **CDRP**-BBBC047-Bray-CP-GE (Cell line: U2OS) :
* $\bf{CP}$ There are 30,430 unique compounds for CP dataset, median ... | {
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# Notebook from EdTonatto/UFFS-2020.2-Inteligencia_Artificial
Path: T-RNA/Tarefa-1/Solucao-Tarefa1-RNA-simples.ipynb
# Rede Neural Simples
### Implementando uma RNA Simples
O diagrama abaixo mostra uma rede simples. A combinação linear dos pesos, inputs e viés formam o input h, que então é passado pela função de ati... | {
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# Notebook from scw-ss/-2018-06-27-cfmehu-python-ecology-lesson
Path: _episodes_pynb/04-merging-data_clean.ipynb
# Combining DataFrames with pandas
In many "real world" situations, the data that we want to use come in multiple
files. We often need to combine these files into a single DataFrame to analyze
the data. Th... | {
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# Notebook from mukamel-lab/ALLCools
Path: docs/allcools/cell_level/step_by_step/100kb/04a-PreclusteringAndClusterEnrichedFeatures-mCH.ipynb
# Preclustering and Cluster Enriched Features
## Purpose
The purpose of this step is to perform a simple pre-clustering using the highly variable features to get a pre-clusters ... | {
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# Notebook from majkelx/astwro
Path: examples/deriving_psf_stenson.ipynb
# Deriving a Point-Spread Function in a Crowded Field
### following Appendix III of Peter Stetson's *User's Manual for DAOPHOT II*
### Using `pydaophot` form `astwro` python package_____no_output_____All *italic* text here have been taken from St... | {
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# Notebook from lukassnoek/NI-edu
Path: NI-edu/fMRI-introduction/week_4/fmriprep.ipynb
# Fmriprep
Today, many excellent general-purpose, open-source neuroimaging software packages exist: [SPM](https://www.fil.ion.ucl.ac.uk/spm/) (Matlab-based), [FSL](https://fsl.fmrib.ox.ac.uk/fsl/fslwiki), [AFNI](https://afni.nimh.ni... | {
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# Notebook from nishadalal120/NEU-365P-385L-Spring-2021
Path: homework/key-random_walks.ipynb
# Homework - Random Walks (18 pts)_____no_output_____## Continuous random walk in three dimensions
Write a program simulating a three-dimensional random walk in a continuous space. Let 1000 independent particles all start at... | {
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# Notebook from aniket371/tapas
Path: notebooks/sqa_predictions.ipynb
<a href="https://colab.research.google.com/github/google-research/tapas/blob/master/notebooks/sqa_predictions.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>_____no_output_____###... | {
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"virology",
"immunology"
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# Notebook from patrickphatnguyen/deepchem
Path: examples/tutorials/06_Going_Deeper_on_Molecular_Featurizations.ipynb
# Tutorial Part 6: Going Deeper On Molecular Featurizations
One of the most important steps of doing machine learning on molecular data is transforming this data into a form amenable to the applicatio... | {
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# Notebook from neoaksa/IMDB_Spider
Path: Movie_Analysis.ipynb
[View in Colaboratory](https://colab.research.google.com/github/neoaksa/IMDB_Spider/blob/master/Movie_Analysis.ipynb)_____no_output_____
<code>
# I've already uploaded three files onto googledrive, you can use uploaded function blew to upload the files.
... | {
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# Notebook from SvetozarMateev/Data-Science
Path: DataScienceExam/Exam.ipynb
<code>
import pandas as pd
import scipy.stats as st
import matplotlib.pyplot as plt
import numpy as np
import operator_____no_output_____
</code>
# Crimes
### Svetozar Mateev_____no_output_____## Putting Crime in the US in Context
_____no_ou... | {
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# Notebook from ProteinsWebTeam/ebi-metagenomics-examples
Path: mgnify/src/notebooks/American_Gut_filter_based_in_location.ipynb
# American Gut Project example
This notebook was created from a question we recieved from a user of MGnify.
The question was:
```
I am attempting to retrieve some of the MGnify results fr... | {
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# Notebook from JeanFraga/DS-Unit-1-Sprint-1-Dealing-With-Data
Path: module2-loadingdata/JeanFraga_LS_DS8_112_Loading_Data.ipynb
# Lambda School Data Science - Loading, Cleaning and Visualizing Data
Objectives for today:
- Load data from multiple sources into a Python notebook
- From a URL (github or otherwise)
- ... | {
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# Notebook from MarineLasbleis/GrowYourIC
Path: notebooks/sandbox-grow.ipynb
# Let's Grow your Own Inner Core!_____no_output_____### Choose a model in the list:
- geodyn_trg.TranslationGrowthRotation()
- geodyn_static.Hemispheres()
### Choose a proxy type:
- age
- position
- phi
- theta
-... | {
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# Notebook from googlegenomics/datalab-examples
Path: datalab/genomics/Getting started with the Genomics API.ipynb
<!-- Copyright 2015 Google Inc. All rights reserved. -->
<!-- Licensed under the Apache License, Version 2.0 (the "License"); -->
<!-- you may not use this file except in compliance with the License. -->... | {
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# Notebook from ventolab/HGDA
Path: immune_CD45enriched_load_detect_doublets.ipynb
<code>
import scrublet as scr
import numpy as np
import pandas as pd
import scanpy as sc
import matplotlib.pyplot as plt
import os
import sys
import scipy
def MovePlots(plotpattern, subplotdir):
os.system('mkdir -p '+str(sc.settin... | {
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# Notebook from schabertrobbinger/jupyter-notebook-slides
Path: Presentation.ipynb
**Fact: Amazon.com is rife with deceptive product marketing.**_____no_output_____<img src="reviews.png">
If you squint hard enough, you can see that Warren Buffett is **not** actually the author of this book..._____no_output_____It is ... | {
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# Notebook from alex-w/lightkurve
Path: docs/source/tutorials/1-getting-started/plotting-target-pixel-files.ipynb
# Plotting Target Pixel Files with Lightkurve_____no_output_____## Learning Goals
By the end of this tutorial, you will:
- Learn how to download and plot target pixel files from the data archive using [L... | {
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# Notebook from eunicenjuguna/Python4Bioinformatics2020
Path: Notebooks/00.ipynb
# Python For Bioinformatics
Introduction to Python for Bioinformatics - available at https://github.com/kipkurui/Python4Bioinformatics.
<small><small><i>
## Attribution
These tutorials are an adaptation of the Introduction to Python fo... | {
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# Notebook from sreramk1/sentiment-analysis
Path: sentiment_analysis_experiment/Sentiment_analysis_experiment_1.ipynb
<code>
import numpy as np
import tensorflow_datasets as tfds
import tensorflow as tf
tf.config.run_functions_eagerly(False)
#tfds.disable_progress_bar()_____no_output_____tf.version.VERSION_____no_ou... | {
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# Notebook from pritishyuvraj/profit-from-stock
Path: ranking_stocks_by_category.ipynb
<code>
import yfinance as yf
import pandas as pd
import csv_____no_output_____# Address to folders
stock_info_directory = "/Users/pyuvraj/CCPP/data_for_profit_from_stock/all_stocks_historical_prices/stocks"
ranked_growth_stocks = s... | {
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# Notebook from Jaume-JCI/hpsearch
Path: nbs/examples/complex_dummy_experiment_manager.ipynb
<code>
#hide
#default_exp examples.complex_dummy_experiment_manager
from nbdev.showdoc import *
from block_types.utils.nbdev_utils import nbdev_setup, TestRunner
nbdev_setup ()
tst = TestRunner (targets=['dummy'])_____no_outp... | {
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# Notebook from ValRCS/RCS_Data_Analysis_Python_2019_July
Path: Irises_ML_Intro/Irises Data Analysis Workflow_06_2019.ipynb
<h1><center>Introductory Data Analysis Workflow</center></h1>
_____no_output_____
https://xkcd.com/2054_____no_output_____# An example m... | {
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# Notebook from siddsrivastava/Image-captionin
Path: 2_Training.ipynb
# Computer Vision Nanodegree
## Project: Image Captioning
---
In this notebook, you will train your CNN-RNN model.
You are welcome and encouraged to try out many different architectures and hyperparameters when searching for a good model.
Thi... | {
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# Notebook from pyladiesams/graphdatabases-gqlalchemy-beginner-mar2022
Path: solutions/gqlalchemy-solutions.ipynb
# 💡 Solutions
Before trying out these solutions, please start the [gqlalchemy-workshop notebook](../workshop/gqlalchemy-workshop.ipynb) to import all data. Also, this solutions manual is here to help you... | {
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# Notebook from MichielStock/SelectedTopicsOptimization
Path: Chapters/06.MinimumSpanningTrees/Chapter6.ipynb
# Minimum spanning trees
*Selected Topics in Mathematical Optimization*
**Michiel Stock** ([email](michiel.stock@ugent.be))
_____no_output_____
<code>
import matplotlib.pyplot as plt
%... | {
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# Notebook from emilynomura1/1030MidtermProject
Path: src/data-cleaning-final.ipynb
<code>
# Import packages
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# Read in data. If data is zipped, unzip the file and change file path accordingly
yelp = pd.read_csv("../yelp_academic_dataset_business.c... | {
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# Notebook from HypoChloremic/python_learning
Path: learning/matplot/animation/basic_animation.ipynb
<code>
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import matplotlib
from IPython.display import HTML
_____no_output_____def update_line(num, data, line):
print(num)... | {
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# Notebook from Switham1/PromoterArchitecture
Path: src/plotting/OpenChromatin_plotsold.ipynb
<code>
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from scipy import stats
from statsmodels.formula.api import ols
import researchpy as rp
from pingouin import kruskal
from pyb... | {
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# Notebook from CCADynamicsGroup/SummerSchoolWorkshops
Path: 4-Science-case-studies/1-Computing-orbits-for-Gaia-stars.ipynb
<code>
%run ../setup/nb_setup
%matplotlib inline_____no_output_____
</code>
# Compute a Galactic orbit for a star using Gaia data
Author(s): Adrian Price-Whelan
## Learning goals
In this tut... | {
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# Notebook from quantumjot/segment-classify-track
Path: stardist_segmentation.ipynb
# Segmentation
This notebook shows how to use Stardist (Object Detection with Star-convex Shapes) as a part of a segmentation-classification-tracking analysis pipeline.
The sections of this notebook are as follows:
1. Load images
... | {
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# Notebook from hongyehu/Sim-Clifford
Path: circuit.ipynb
<code>
import numpy
from context import vaeqst_____no_output_____import numpy
from context import base_____no_output_____base.RandomCliffordGate(0,1)_____no_output_____
</code>
# Random Clifford Circuit_____no_output_____## RandomCliffordGate_____no_output____... | {
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"path": "circuit.ipynb",
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# Notebook from andelpe/curso-intro-python
Path: tema_9.ipynb
<font size=6>
<b>Curso de Programación en Python</b>
</font>
<font size=4>
Curso de formación interna, CIEMAT. <br/>
Madrid, Octubre de 2021
Antonio Delgado Peris
</font>
https://github.com/andelpe/curso-intro-python/
<br/>_____no_output_____# Tem... | {
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"scikit-bio"
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# Notebook from jamesbut/evojax
Path: examples/notebooks/TutorialTaskImplementation.ipynb
<a href="https://colab.research.google.com/github/google/evojax/blob/main/examples/notebooks/TutorialTaskImplementation.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Cola... | {
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"path": "examples/notebooks/TutorialTaskImplementation.ipynb",
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"stars": 365,
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# Notebook from wangyendt/deeplearning_models
Path: sklearn/sklearn learning/demonstration/auto_examples_jupyter/applications/plot_species_distribution_modeling.ipynb
<code>
%matplotlib inline_____no_output_____
</code>
# Species distribution modeling
Modeling species' geographic distributions is an important
prob... | {
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"path": "sklearn/sklearn learning/demonstration/auto_examples_jupyter/applications/plot_species_distribution_modeling.ipynb",
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"stars": 1,
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# Notebook from bastivkl/nh2020-curriculum
Path: we-geometry-benson/class-notebook.ipynb
# The Structure and Geometry of the Human Brain
[Noah C. Benson](https://nben.net/) <[nben@uw.edu](mailto:nben@uw.edu)>
[eScience Institute](https://escience.washingtonn.edu/)
[University of Washington](https://www.wash... | {
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# Notebook from TechLabs-Dortmund/nutritional-value-determination
Path: webgrabber_wikilisten.ipynb
# webgrabber für Listen von Wikipedia
_____no_output_____
<code>
# Gebäckliste
import requests
from bs4 import BeautifulSoup
# man muss der liste einen letzten eintrag geben, weil sonst weitere listen unter der eigent... | {
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"path": "webgrabber_wikilisten.ipynb",
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# Notebook from biosustain/p-thermo
Path: notebooks/28. Resolve issue 37-Reaction reversibility.ipynb
# Introduction
Now that I have removed the RNA/DNA node and we have fixed many pathways, I will re-visit the things that were raised in issue #37: 'Reaction reversibility'. There were reactions that we couldn't revers... | {
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# Notebook from goldford/Ecosystem-Model-Data-Framework
Path: notebooks/Analysis - Visualize Monte Carlo Results (R 3.6) v2.ipynb
<code>
# G Oldford Feb 19 2022
# visualize monte carlo results from ecosim Monte Carlo
# uses ggplot2
#
# https://erdavenport.github.io/R-ecology-lesson/05-visualization-ggplot2.html_____n... | {
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# Notebook from jcjveraa/docs-2
Path: site/en/tutorials/structured_data/time_series.ipynb
##### Copyright 2019 The TensorFlow Authors._____no_output_____
<code>
#@title 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... | {
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# Notebook from haltakov/course-content-dl
Path: tutorials/W1D2_LinearDeepLearning/student/W1D2_Tutorial1.ipynb
<a href="https://colab.research.google.com/github/NeuromatchAcademy/course-content-dl/blob/main/tutorials/W1D2_LinearDeepLearning/student/W1D2_Tutorial1.ipynb" target="_parent"><img src="https://colab.resear... | {
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"path": "tutorials/W1D2_LinearDeepLearning/student/W1D2_Tutorial1.ipynb",
"matched_keywords": [
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"stars": null,
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"max_line_length": 603,
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# Notebook from Steve-Hawk/nrpytutorial
Path: Tutorial-ScalarWaveCurvilinear.ipynb
<script async src="https://www.googletagmanager.com/gtag/js?id=UA-59152712-8"></script>
<script>
window.dataLayer = window.dataLayer || [];
function gtag(){dataLayer.push(arguments);}
gtag('js', new Date());
gtag('config', 'UA-... | {
"repository": "Steve-Hawk/nrpytutorial",
"path": "Tutorial-ScalarWaveCurvilinear.ipynb",
"matched_keywords": [
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"stars": null,
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# Notebook from junelsolis/ZeroCostDL4Mic
Path: Colab_notebooks/Deep-STORM_2D_ZeroCostDL4Mic.ipynb
# **Deep-STORM (2D)**
---
<font size = 4>Deep-STORM is a neural network capable of image reconstruction from high-density single-molecule localization microscopy (SMLM), first published in 2018 by [Nehme *et al.* in Op... | {
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"path": "Colab_notebooks/Deep-STORM_2D_ZeroCostDL4Mic.ipynb",
"matched_keywords": [
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# Notebook from mathemage/TheMulQuaBio
Path: notebooks/17-MulExplInter.ipynb
<code>
library(repr) ; options(repr.plot.res = 100, repr.plot.width=5, repr.plot.height= 5) # Change plot sizes (in cm) - this bit of code is only relevant if you are using a jupyter notebook - ignore otherwise_____no_output_____
</code>
<!-... | {
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# Notebook from bigginlab/OxCompBio
Path: tutorials/MD/02_Protein_Visualization.ipynb
# <span style='color:darkred'> 2 Protein Visualization </span>
***
For the purposes of this tutorial, we will use the HIV-1 protease structure (PDB ID: 1HSG). It is a homodimer with two chains of 99 residues each. Before starting to... | {
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# Notebook from FangmingXie/scf_enhancer_paper
Path: eran/.ipynb_checkpoints/Regress_June25_mc-checkpoint.ipynb
# Stage 1: Correlation for individual enhancers_____no_output_____
<code>
import pandas as pd
import numpy as np
import time, re, datetime
import matplotlib.pyplot as plt
from matplotlib.colors import Liste... | {
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"path": "eran/.ipynb_checkpoints/Regress_June25_mc-checkpoint.ipynb",
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# Notebook from ceb8/lightkurve
Path: docs/source/tutorials/2.02-recover-a-planet.ipynb
# How to recover a known planet in Kepler data_____no_output_____This tutorial demonstrates the basic steps required to recover a transiting planet candidate in the Kepler data.
We will show how you can recover the signal of [Kepl... | {
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"path": "docs/source/tutorials/2.02-recover-a-planet.ipynb",
"matched_keywords": [
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# Notebook from jai-singhal/data_science
Path: pandas/01-pandas_introduction.ipynb
<!--<img width=700px; src="../img/logoUPSayPlusCDS_990.png"> -->
<p style="margin-top: 3em; margin-bottom: 2em;"><b><big><big><big><big>Introduction to Pandas</big></big></big></big></b></p>_____no_output_____
<code>
%matplotlib inlin... | {
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"path": "pandas/01-pandas_introduction.ipynb",
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# Notebook from jacsonrbinf/minicurso-mineracao-interativa
Path: resultados/4.Proxy.ipynb
Para entrar no modo apresentação, execute a seguinte célula e pressione `-`_____no_output_____
<code>
%reload_ext slide_____no_output_____
</code>
<span class="notebook-slide-start"/>
# Proxy
Este notebook apresenta os seguin... | {
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# Notebook from shubham3121/PySyft-TensorFlow
Path: examples/Part 02 - Intro to Private Training with Remote Execution.ipynb
# Part 2: Intro to Private Training with Remote Execution
In the last section, we learned about PointerTensors, which create the underlying infrastructure we need for privacy preserving Deep Le... | {
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# Notebook from DavidStirling/profiling-resistance-mechanisms
Path: 3.feature-differences/1.apply-signatures.ipynb
# Apply Signature Analysis to Cell Morphology Features
Gregory Way, 2020
Here, I apply [`singscore`](https://bioconductor.org/packages/devel/bioc/vignettes/singscore/inst/doc/singscore.html) ([Foroutan ... | {
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"matched_keywords": [
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"stars": null,
"size": 701404,
"hexsha": "d07ab040c7419cee18e41e5fbd1187b380f64381",
"max_line_length": 189448,
"avg_line_length": 445.... |
# Notebook from nextstrain/seasonal-cov
Path: data-wrangling/.ipynb_checkpoints/make_s1_s2_rdrp_reference-checkpoint.ipynb
<code>
import re
from Bio import SeqIO
from Bio.Seq import Seq
from Bio.SeqRecord import SeqRecord
from Bio.Alphabet import IUPAC
from Bio.SeqFeature import SeqFeature, FeatureLocation_____no_outp... | {
"repository": "nextstrain/seasonal-cov",
"path": "data-wrangling/.ipynb_checkpoints/make_s1_s2_rdrp_reference-checkpoint.ipynb",
"matched_keywords": [
"RNA"
],
"stars": 4,
"size": 5469,
"hexsha": "d07b3afc6a6e0d394dd4a6e6e8d0425c1904dc97",
"max_line_length": 133,
"avg_line_length": 38.2447552448... |
# Notebook from vitutorial/exercises
Path: LatentFactorModel/LatentFactorModel.ipynb
<a href="https://colab.research.google.com/github/vitutorial/exercises/blob/master/LatentFactorModel/LatentFactorModel.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></... | {
"repository": "vitutorial/exercises",
"path": "LatentFactorModel/LatentFactorModel.ipynb",
"matched_keywords": [
"STAR"
],
"stars": 1,
"size": 82041,
"hexsha": "d080d0956c0f4475db66015747f22df1dfb03649",
"max_line_length": 796,
"avg_line_length": 42.4862765407,
"alphanum_fraction": 0.544410721... |
# Notebook from anti-destiny/Kalman-and-Bayesian-Filters-in-Python
Path: 02-Discrete-Bayes.ipynb
[Table of Contents](./table_of_contents.ipynb)_____no_output_____# Discrete Bayes Filter_____no_output_____# 离散贝叶斯滤波_____no_output_____
<code>
%matplotlib inline_____no_output_____#format the book
import book_format
book_... | {
"repository": "anti-destiny/Kalman-and-Bayesian-Filters-in-Python",
"path": "02-Discrete-Bayes.ipynb",
"matched_keywords": [
"evolution"
],
"stars": null,
"size": 487180,
"hexsha": "d081d31e81b68e8b63a02c7a554a1a3b16110e55",
"max_line_length": 44944,
"avg_line_length": 131.7414818821,
"alphanu... |
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