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# Notebook from Gabriel19-00477/ITBA-3207-Team-Typhoon-Analysts
Path: Data Sets Coding Analysis/Data Analysis and Coding for Both Data Sets.ipynb
EDA to Typhoon Mitigation and Response Framework (TMRF)_____no_output_____“Experience is a master teacher, even when it’s not our own.”
― Gina Greenlee_____no_output_____The... | {
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# Notebook from faisaladnanpeltops/spark-nlp-workshop
Path: jupyter/enterprise/healthcare/EntityResolution_ICDO_SNOMED.ipynb
[](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/jupyter/enterprise/healthcare/En... | {
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# Notebook from Hadryan/course-content
Path: tutorials/W1D4_GeneralizedLinearModels/W1D4_Tutorial2.ipynb
<a href="https://colab.research.google.com/github/NeuromatchAcademy/course-content/blob/master/tutorials/W1D4_GeneralizedLinearModels/W1D4_Tutorial2.ipynb" target="_parent"><img src="https://colab.research.google.c... | {
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# Notebook from michaelsilverstein/programming-workshops
Path: source/workshops/05_visualization/files/workshop.ipynb
# Introduction
## The Data Set
In today's workshop, we will revisit the data set you worked with in the Machine Learning workshop. As a refresher: this data set is from the GSE53987 dataset on Bipolar... | {
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# Notebook from luglilab/SP018-scRNAseq-Pelosi
Path: APE_POS.ipynb
# APE_POS analysis_____no_output_____### 0) Library upload_____no_output_____
<code>
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import sklearn as sk
import scipy as sp
import csv
import scanpy a... | {
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# Notebook from genepattern/TCGAImporter-notebooks
Path: TCGA_HTSeq_counts/OV/Ovarian Serous Cystadenocarcinoma (OV).ipynb
# Ovarian Serous Cystadenocarcinoma (OV)
[Jump to the urls to download the GCT and CLS files](#Downloads)_____no_output_____**Authors:** Alejandra Ramos, Marylu Villa and Edwin Juarez
**Is this w... | {
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# Notebook from chrispatsalis/bioinf575
Path: patsalis_hw3_refactoring.ipynb
# Homework 3: Functional file parsing_____no_output_____
<code>
local_files/MS_UMICH/bioinf_575/homework/homework3_refactoring/patsalis_hw3_refactoring.ipynb_____no_output_____
</code>
---
## Topic areas
* Functions
* I/O operations
* Strin... | {
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# Notebook from mariaeduardagimenes/Manual-Pratico-Deep-Learning
Path: Adaline.ipynb
No notebook anterior, nós aprendemos sobre o Perceptron. Vimos como ele aprende e como pode ser utilizado tanto para classificação binária quanto para regressão linear. Nesse notebook, nós veremos um algoritmo muito parecido com o Per... | {
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# Notebook from bollwyvl/nbpresent
Path: notebooks/proposal.ipynb
# nbpresent
nbslides is the evolution of the work by the Jupyter community to make notebooks into authorable, presentable, designed assets for communicating._____no_output_____> 1. The problem that this enhancement addresses. If possible include code or... | {
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# Notebook from PathwayMerger/PathMe-Resources
Path: notebooks/case_scenarios/evaluating_similarity_equivalent_pathways.ipynb
# Evaluating the degree of overlap between equivalent pathways in KEGG, Reactome, and WikiPathways
This notebook outlines the process of evaluating the overlap between the equivalent represent... | {
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# Notebook from czbiohub/BingWu_DarmanisGroup_TracheaDevTmem16a
Path: scrublet/scrublet_P4_Oct18_mut_green.ipynb
This example shows how to:
1. Load a counts matrix (10X Chromium data from human peripheral blood cells)
2. Run the default Scrublet pipeline
3. Check that doublet predictions make sense_____no_output___... | {
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# Notebook from guilhermealbm/TechSpaces
Path: graphs/python_graph.ipynb
<code>
import pandas as pd
import networkx as nx
import community
import operator_____no_output_____df = pd.read_csv('../tags_with_wiki_relationship.csv')
df_____no_output_____df_wiki = pd.read_csv('../tags_with_wiki_and_category.csv', linetermi... | {
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# Notebook from Coalemus/Python-Projects
Path: .incomplete/stockpredict/vantage/alphavantage.ipynb
<code>
from pandas_datareader import data
import matplotlib.pyplot as plt
import pandas as pd
import datetime as dt
import urllib.request, json
import os
import numpy as np
import tensorflow as tf # This code has been te... | {
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# Notebook from junyaogz/topic.recognition.td3-
Path: src/5_train_and_test_model1_in_tf/train_model1_and_test.ipynb
<code>
# [Author]: Jun Yao
# [Date]: 2021-12-10
# [Description]
# this file has the following functionalities
# (1) train model 1 in the paper and evaluate it against test data with golden labels.
# (2... | {
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# Notebook from bryansho/PCOS_WGS_16S_metabolome
Path: Revision/ANCOM/Metabolites/Metabolites_no_BAs.ipynb
# Metabolites w/o bile acids
Compare placebo v. letrozole and letrozole v. let-co-housed at time points 2 and 5.
Description of data files:
1. mapping file = metadata
2. metabolites counts
3. metabolite index fi... | {
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# Notebook from ofou/course-content
Path: tutorials/W3D2_HiddenDynamics/student/W3D2_Tutorial4.ipynb
<a href="https://colab.research.google.com/github/NeuromatchAcademy/course-content/blob/master/tutorials/W3D2_HiddenDynamics/student/W3D2_Tutorial4.ipynb" target="_parent"><img src="https://colab.research.google.com/as... | {
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# Notebook from MatthiKrauss/qusco_school_2019_03_krotov_exercise
Path: exercise_03_three_level_system.ipynb
<img src="QuSCo_Logo_CMYK.jpg" alt="Here should be the qusco logo!" width="500">
---_____no_output_____
<code>
import numpy as np
import scipy
import matplotlib
import matplotlib.pylab as plt
import krotov
im... | {
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# Notebook from daekeun-ml/aws-deepcomposer-samples
Path: Lab 2/GAN.ipynb
## Introduction_____no_output_____This tutorial is a brief introduction to music generation using **Generative Adversarial Networks** (**GAN**s).
The goal of this tutorial is to train a machine learning model using a dataset of Bach compositio... | {
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# Notebook from ptpro3/ptpro3.github.io
Path: Projects/Challenges/Challenge09/challenge_set_9ii_prashant.ipynb
```
Topic: Challenge Set 9 Part II
Subject: SQL
Date: 02/20/2017
Name: Prashant Tatineni
```_____no_output_____
<code>
from sqlalchemy import create_engine
import pandas as pd
cnx = create_engine... | {
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# Notebook from Vixk2021/Foody
Path: projet_foody_analyse_VKO.ipynb
# PROJET FOODY_ Data Analyse_____no_output_____
<code>
# Import des dépendances
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import statsmodels.api as sm
import pymysql as sql
import seaborn as sns
sns.set()_____no_output___... | {
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# Notebook from kne42/starfish
Path: notebooks/BaristaSeq.ipynb
<code>
%matplotlib inline_____no_output_____
</code>
# BaristaSeq
BaristaSeq is an assay that sequences padlock-probe initiated rolling circle
amplified spots using a one-hot codebook. The publication for this assay can be
found [here](https://www.ncbi.... | {
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# Notebook from martin-fabbri/colab-notebooks
Path: 01_fitting_gaussian_process_model.ipynb
<a href="https://colab.research.google.com/github/martin-fabbri/colab-notebooks/blob/master/01_fitting_gaussian_process_model.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open... | {
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"path": "01_fitting_gaussian_process_model.ipynb",
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"Salmon"
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# Notebook from bmcs-group/bmcs_tutorial
Path: tour3_nonlinear_bond/3_1_nonlinear_bond.ipynb
<a id="top"></a>
# **3.1 Nonlinear bond - softening and hardening**
[](https://moodle.rwth-aachen.de/mod/page/view.php?id=551816) part 1_____no_output_____<div style="background-color:ligh... | {
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# Notebook from swarnabha13/ai-economist
Path: economic_simulation_basic.ipynb
# Foundation
Foundation is the name of the economic simulator built for the AI Economist ([paper here](https://arxiv.org/abs/2004.13332)). Foundation is specially designed for modeling economies in spatial, 2D grid worlds. The AI Economist... | {
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# Notebook from dongxulee/lifeCycle
Path: 20210416/simulationV_gamma4.ipynb
<code>
%pylab inline
from jax.scipy.ndimage import map_coordinates
from constant import *
import warnings
from jax import jit, partial, random, vmap
from tqdm import tqdm
warnings.filterwarnings("ignore")
np.printoptions(precision=2)Populatin... | {
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# Notebook from CactusPuppy/colab-notebooks
Path: notebooks/AlphaFold.ipynb
<a href="https://colab.research.google.com/github/CactusPuppy/colab-notebooks/blob/main/notebooks/AlphaFold.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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"path": "notebooks/AlphaFold.ipynb",
"matched_keywords": [
"bioinformatics"
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# Notebook from amygdala/terra-example-notebooks
Path: terra-notebooks-playground/R - How to save and load R objects from the workspace bucket.ipynb
# How to save and load R objects from the workspace bucket
Save intermediate work to R's native format for rapid loading.
<div class="alert alert-block alert-info">
<b>... | {
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# Notebook from ParasAlex/Big_Data_HW
Path: Copy_of_big_data_level_2.ipynb
<code>
import os
# Find the latest version of spark 3.0 from http://www.apache.org/dist/spark/ and enter as the spark version
# For example:
spark_version = 'spark-3.0.3'
#spark_version = 'spark-3.<enter version>'
os.environ['SPARK_VERSION']=s... | {
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# Notebook from smythi93/debuggingbook
Path: notebooks/Repairer.ipynb
# Repairing Code Automatically
So far, we have discussed how to track failures and how to locate defects in code. Let us now discuss how to _repair_ defects – that is, to correct the code such that the failure no longer occurs. We will discuss how ... | {
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# Notebook from stanzheng/advent-of-code
Path: 2015/Day1.ipynb
<code>
"""
--- Day 1: Not Quite Lisp ---
Santa was hoping for a white Christmas, but his weather machine's "snow" function is powered by stars, and he's fresh out! To save Christmas, he needs you to collect fifty stars by December 25th.
Collect stars by ... | {
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# Notebook from gideonite/data-driven-pdes
Path: tutorial/Tutorial.ipynb
<code>
import os
import sys
from matplotlib import pyplot as plt
import numpy as np
from datadrivenpdes.core import equations
from datadrivenpdes.core import grids
import datadrivenpdes as pde
import tensorflow as tf
# tf.enable_eager_execution()... | {
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# Notebook from bruno-janota/DS-Unit-1-Sprint-2-Data-Wrangling-and-Storytelling
Path: module3-make-explanatory-visualizations/LS_DS_123_Make_Explanatory_Visualizations.ipynb
<a href="https://colab.research.google.com/github/bruno-janota/DS-Unit-1-Sprint-2-Data-Wrangling-and-Storytelling/blob/master/module3-make-explan... | {
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# Notebook from katakasioma/import
Path: Python/Jupyter_notebooks_solved/SwC_python_session-2-2.ipynb
# Python session - 2.2
## Functions and modules_____no_output_____## Functions
Functions are reusable blocks of code that you can name and execute any number of times from different parts of your script(s). This reu... | {
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"path": "Python/Jupyter_notebooks_solved/SwC_python_session-2-2.ipynb",
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# Notebook from MuhammadMiqdadKhan/Solution-of-IBM-s-Gloabal-Quantum-Challenge-2020
Path: Challenge4_CircuitDecomposition solution.ipynb
# Exercise 4: Circuit Decomposition
Wow! If you managed to solve the first three exercises, congratulations! The fourth problem is supposed to puzzle even the quantum experts among y... | {
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"path": "Challenge4_CircuitDecomposition solution.ipynb",
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# Notebook from UB-Mannheim/NFDI
Path: docs/docs/parsing/02_parsing_GEPRIS_search.ipynb
# Parsing GEPRIS for the list of funded NFDI projects with GEPRIS IDs and descriptions
Check out the the GEPRIS user interface for advanced search: https://gepris.dfg.de/gepris/OCTOPUS?task=doSearchExtended&context=projekt&keywords... | {
"repository": "UB-Mannheim/NFDI",
"path": "docs/docs/parsing/02_parsing_GEPRIS_search.ipynb",
"matched_keywords": [
"ecology"
],
"stars": 1,
"size": 25338,
"hexsha": "48239adae33706dc59b14f369ad625e31d920b1a",
"max_line_length": 2256,
"avg_line_length": 62.1029411765,
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# Notebook from weichen-yan/nrpytutorial
Path: in_progress/Tutorial-GiRaFFE_NRPy-Stilde-flux.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());
gta... | {
"repository": "weichen-yan/nrpytutorial",
"path": "in_progress/Tutorial-GiRaFFE_NRPy-Stilde-flux.ipynb",
"matched_keywords": [
"evolution"
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"stars": null,
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"max_line_length": 1001,
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# Notebook from shreyaraghavendra/BoltBio
Path: code/preprocessing/GE_getTable.ipynb
# Create a table with TCGA data_____no_output_____
<code>
# import libraries
import os
import sys
import pandas as pd
import numpy as np
import regex as re
from matplotlib import pyplot as plt
import time
PATH_TO_DATA = '/Users/kush... | {
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"path": "code/preprocessing/GE_getTable.ipynb",
"matched_keywords": [
"RNA-seq"
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# Notebook from philuttley/basic_linux_and_coding
Path: 6_astropy.ipynb
_____no_output_____## Sharing code is healthy for the community and the science it produces
- a community-developed core library for professional astronomical research
- combines many functionalities... | {
"repository": "philuttley/basic_linux_and_coding",
"path": "6_astropy.ipynb",
"matched_keywords": [
"STAR"
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# Notebook from mojito9542/gpt-2
Path: GPT-2.ipynb
<a href="https://colab.research.google.com/github/mojito9542/gpt-2/blob/master/GPT-2.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>_____no_output_____Initializing the notebook
_____no_output_____
... | {
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"path": "GPT-2.ipynb",
"matched_keywords": [
"STAR",
"evolution"
],
"stars": null,
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"max_line_length": 1354,
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# Notebook from jjc2718/generic-expression-patterns
Path: new_experiment/archive/debug.ipynb
# Debug
During a call with Casey and Jim, they noticed 2 unusual things in the generic_gene_summary table:
* Not all values in the `num_simulated` column were equal to 25, which should be the case
* There are some genes that ... | {
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"path": "new_experiment/archive/debug.ipynb",
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"limma",
"DESeq2"
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# Notebook from arm61/pylj
Path: examples/molecular_dynamics/intro_to_molecular_dynamics.ipynb
<code>
import warnings
import matplotlib.pyplot as plt
import numpy as np
from pylj import md, util, sample, forcefields
warnings.filterwarnings('ignore')_____no_output_____
</code>
# Atomistic simulation
The use of compu... | {
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"path": "examples/molecular_dynamics/intro_to_molecular_dynamics.ipynb",
"matched_keywords": [
"molecular dynamics"
],
"stars": 18,
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"max_line_length": 450,
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# Notebook from UPbook-innovations/nlu
Path: examples/colab/component_examples/classifiers/sentiment_classification_movies.ipynb
[](https://colab.research.google.com/github/Jo... | {
"repository": "UPbook-innovations/nlu",
"path": "examples/colab/component_examples/classifiers/sentiment_classification_movies.ipynb",
"matched_keywords": [
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"stars": 1,
"size": 18721,
"hexsha": "48290277edbc850b461c7271e338fa78aaa13868",
"max_line_length": 18721,
"avg_line_length": 187... |
# Notebook from BastianZim/openai-python
Path: examples/embeddings/Classification.ipynb
## Classification using the embeddings
In the classification task we predict one of the predefined categories given an input. We will predict the score based on the embedding of the review's text, where the algorithm is correct on... | {
"repository": "BastianZim/openai-python",
"path": "examples/embeddings/Classification.ipynb",
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# Notebook from beniza/learn-python
Path: experiments/.ipynb_checkpoints/InternetArchive-checkpoint.ipynb
# Manipulating the items on the archive.org website
The `archive.org` is arguably the largest collection of community contributed collection of `items` such as books, movies, audio, images and even code. The snipp... | {
"repository": "beniza/learn-python",
"path": "experiments/.ipynb_checkpoints/InternetArchive-checkpoint.ipynb",
"matched_keywords": [
"ecology"
],
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# Notebook from jamfeitosa/ia898
Path: src/isccsym.ipynb
# Function isccsym
## Description
Check if the input image is symmetric and return a boolean value.
## Synopse
Check for conjugate symmetry
- **b = isccsym(F)**
- **b**: Boolean.
- **F**: Image. Complex image._____no_output_____
<code>
import numpy ... | {
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"path": "src/isccsym.ipynb",
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"STAR"
],
"stars": 14,
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# Notebook from Zuyuf/Advanced-Machine-Learning-Specialization
Path: Introduction to Deep Learning/Week5/POS-task.ipynb
__This seminar:__ after you're done coding your own recurrent cells, it's time you learn how to train recurrent networks easily with Keras. We'll also learn some tricks on how to use keras layers and... | {
"repository": "Zuyuf/Advanced-Machine-Learning-Specialization",
"path": "Introduction to Deep Learning/Week5/POS-task.ipynb",
"matched_keywords": [
"bioinformatics"
],
"stars": 252,
"size": 30943,
"hexsha": "48604b7f6a574f0a80e291267099cbec457c3035",
"max_line_length": 416,
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# Notebook from davidevdt/datamining_jbi030
Path: 10a. neural_networks.ipynb
=================================================================================================================
# Lecture Notes: Neural Networks
##### D.Vidotto, Data Mining: JBI030 2019/2020
============================================... | {
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"path": "10a. neural_networks.ipynb",
"matched_keywords": [
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# Notebook from VCMason/PyGenToolbox
Path: notebooks/Therese/IRS_v2_Coil1.Coil2.Eed.Suz12.ipynb
<code>
%load_ext autoreload
%autoreload 2
import datetime
import os
import pandas as pd
print(datetime.datetime.now())
#dir(pygentoolbox.Tools)
%matplotlib inline
import matplotlib.pyplot as plt
from pygentoolbox.IRS_v2 im... | {
"repository": "VCMason/PyGenToolbox",
"path": "notebooks/Therese/IRS_v2_Coil1.Coil2.Eed.Suz12.ipynb",
"matched_keywords": [
"bwa"
],
"stars": null,
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# Notebook from alik604/ThinkBayes2
Path: soln/chap12.ipynb
# Classification_____no_output_____Think Bayes, Second Edition
Copyright 2020 Allen B. Downey
License: [Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/)_____no_output_____
<code>
... | {
"repository": "alik604/ThinkBayes2",
"path": "soln/chap12.ipynb",
"matched_keywords": [
"evolution"
],
"stars": null,
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# Notebook from WisePanda007/douban_sentiment
Path: RNN-LSTM.ipynb
### 待改进部分
1 过拟合 调参
2 数据不均衡：（1）使用下采样（2）使用auc分数_____no_output_____
<code>
import numpy as np
import pandas as pd
import pymongo
import tensorflow as tf
import os
import time
day=time.strftime("%Y-%m-%d", time.localtime())_____no_output_____#从数据库中读取数... | {
"repository": "WisePanda007/douban_sentiment",
"path": "RNN-LSTM.ipynb",
"matched_keywords": [
"STAR"
],
"stars": 5,
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# Notebook from manaminer/NLP-YELP
Path: NLP on Dataset From YELP.ipynb
# Importing Libraries & Dataset_____no_output_____
<code>
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('white')
%matplotlib inline_____no_output_____yelp = pd.read_csv('yelp.csv')____... | {
"repository": "manaminer/NLP-YELP",
"path": "NLP on Dataset From YELP.ipynb",
"matched_keywords": [
"STAR"
],
"stars": null,
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# Notebook from yaosichao0915/DeepImmuno
Path: reproduce/fig/.ipynb_checkpoints/supp4-checkpoint.ipynb
<code>
%matplotlib inline
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import matplotlib as mpl
import pickle
import itertools_____no_output_____import tensorflow as tf
import tensorflow.ker... | {
"repository": "yaosichao0915/DeepImmuno",
"path": "reproduce/fig/.ipynb_checkpoints/supp4-checkpoint.ipynb",
"matched_keywords": [
"immunology"
],
"stars": 20,
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"max_line_length": 10516,
"avg_line_length": 69.6719858156,
"alph... |
# Notebook from aman983/QCourse_Project-2021-2022
Path: Notebooks/Notebook-3.ipynb
<table width="100%"><tr><td style="color:#bbbbbb;background-color:#ffffff;font-size:11px;font-style:italic;text-align:right;">This cell contains some macros. If there is a problem with displaying mathematical formulas, please run this c... | {
"repository": "aman983/QCourse_Project-2021-2022",
"path": "Notebooks/Notebook-3.ipynb",
"matched_keywords": [
"evolution",
"biology"
],
"stars": null,
"size": 42025,
"hexsha": "486b3207933b8c09bdef041401f6669ec91c63cb",
"max_line_length": 1887,
"avg_line_length": 45.4816017316,
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# Notebook from michalk8/NeuralEE
Path: tests/notebooks/cortex_dataset.ipynb
# NeuralEE on CORTEX Dataset_____no_output_____`CORTEX` dataset contains 3005 mouse cortex cells and gold-standard labels for seven distinct cell types. Each cell type corresponds to a cluster to recover._____no_output_____
<code>
import ran... | {
"repository": "michalk8/NeuralEE",
"path": "tests/notebooks/cortex_dataset.ipynb",
"matched_keywords": [
"gene expression"
],
"stars": 6,
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"max_line_length": 148148,
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# Notebook from jerobado/lightkurve
Path: docs/source/tutorials/2-creating-light-curves/2-3-removing-scattered-light-using-regressioncorrector.ipynb
# Removing scattered light from *TESS* light curves using linear regression (`RegressionCorrector`)_____no_output_____## Learning Goals
By the end of this tutorial, you ... | {
"repository": "jerobado/lightkurve",
"path": "docs/source/tutorials/2-creating-light-curves/2-3-removing-scattered-light-using-regressioncorrector.ipynb",
"matched_keywords": [
"STAR"
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"stars": 235,
"size": 41340,
"hexsha": "486bb6f9dc61e4b004c702260df9fc55bdfc4ae4",
"max_line_length": 571,
"a... |
# Notebook from ecuriotto/training-data-analyst
Path: quests/rl/a2c/a2c_on_gcp.ipynb
# Policy Gradients and A2C
In the <a href="../dqn/dqns_on_gcp.ipynb">previous notebook</a>, we learned how to use hyperparameter tuning to help DQN agents balance a pole on a cart. In this notebook, we'll explore two other types of a... | {
"repository": "ecuriotto/training-data-analyst",
"path": "quests/rl/a2c/a2c_on_gcp.ipynb",
"matched_keywords": [
"evolution"
],
"stars": 4,
"size": 65695,
"hexsha": "486df6aab51df20493f9f4cbdcc3d6c2935865c1",
"max_line_length": 484,
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# Notebook from suyunu/AL-BNMF
Path: Aktif-Ogrenme-BNMF.ipynb
# Bayesci Negatif Olmayan Matris Ayrışımı için Aktif Eleman Seçimi
# Active Selection of Elements for Bayesian Nonnegative Matrix Factorization
<br>
<center> Burak Suyunu, Gönül Aycı, A.Taylan Cemgil </center>
<center> * Bilgisayar Mühendisliği Bölümü, Bo... | {
"repository": "suyunu/AL-BNMF",
"path": "Aktif-Ogrenme-BNMF.ipynb",
"matched_keywords": [
"neuroscience"
],
"stars": null,
"size": 259491,
"hexsha": "486fb291ae36ad81e29290d1976755b523758fa2",
"max_line_length": 129404,
"avg_line_length": 125.0559036145,
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# Notebook from ageller/IDEAS_FSS-Vis_2017
Path: FinalStudentProjects/2022spring/ErinCox/FinalProject.ipynb
<code>
# Import needed libraries.
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from bokeh.plotting import *
from bokeh.layouts import row, column
from bokeh.models import ColumnDataSour... | {
"repository": "ageller/IDEAS_FSS-Vis_2017",
"path": "FinalStudentProjects/2022spring/ErinCox/FinalProject.ipynb",
"matched_keywords": [
"STAR"
],
"stars": 1,
"size": 185287,
"hexsha": "48707d717a5a444805f074f931da5973cfb685a4",
"max_line_length": 150780,
"avg_line_length": 247.0493333333,
"alp... |
# Notebook from Madmaxcoder2612/Programming-Codes
Path: day19_recommenderSystem.ipynb
<code>
import pandas as pd_____no_output_____import matplotlib.pyplot as plt_____no_output_____import warnings
warnings.filterwarnings('ignore')_____no_output_____df = pd.read_csv('ml-100k/u.data',sep='\t', names=['user_id','item_id'... | {
"repository": "Madmaxcoder2612/Programming-Codes",
"path": "day19_recommenderSystem.ipynb",
"matched_keywords": [
"STAR"
],
"stars": null,
"size": 23844,
"hexsha": "4870d1cad8937f056a8169a9e31321cb325d0b1b",
"max_line_length": 1702,
"avg_line_length": 46.4795321637,
"alphanum_fraction": 0.6036... |
# Notebook from haenvely/deep_learning
Path: 16.1 Productionize Embeddings.ipynb
<code>
import requests
from bs4 import BeautifulSoup
import os
import time
try:
from urllib.request import urlretrieve
except ImportError:
from urllib import urlretrieve
import xml.sax
from sklearn import svm
import subprocess
imp... | {
"repository": "haenvely/deep_learning",
"path": "16.1 Productionize Embeddings.ipynb",
"matched_keywords": [
"STAR"
],
"stars": 668,
"size": 32501,
"hexsha": "4870d246b164de31407aa77c53e37c1c19bc1951",
"max_line_length": 240,
"avg_line_length": 31.0420248329,
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... |
# Notebook from cdrakesmith/CGATPipelines
Path: CGATPipelines/pipeline_docs/pipeline_peakcalling/notebooks/template_peakcalling_filtering_Report.ipynb
Peakcalling Bam Stats and Filtering Report - Filtering Stats
============================================================
This notebook is for the analysis of outputs ... | {
"repository": "cdrakesmith/CGATPipelines",
"path": "CGATPipelines/pipeline_docs/pipeline_peakcalling/notebooks/template_peakcalling_filtering_Report.ipynb",
"matched_keywords": [
"ATAC-seq",
"ChIP-seq"
],
"stars": 49,
"size": 22855,
"hexsha": "48710ced4c69e6da27c4dec25ef7cbbacef1ca58",
"max_li... |
# Notebook from wikistat/AI-Frameworks
Path: IntroductionDeepReinforcementLearning/Deep_Q_Learning_CartPole.ipynb
<a href="https://colab.research.google.com/github/wikistat/AI-Frameworks/blob/master/IntroductionDeepReinforcementLearning/Deep_Q_Learning_CartPole.ipynb" target="_parent"><img src="https://colab.research.... | {
"repository": "wikistat/AI-Frameworks",
"path": "IntroductionDeepReinforcementLearning/Deep_Q_Learning_CartPole.ipynb",
"matched_keywords": [
"evolution"
],
"stars": 29,
"size": 37749,
"hexsha": "487256369f7b6eb5634d3e3d3c1e9cd79b8fd11b",
"max_line_length": 372,
"avg_line_length": 32.968558952,
... |
# Notebook from hatrungduc/spark-nlp-workshop
Path: tutorials/streamlit_notebooks/healthcare/NER_HUMAN_PHENOTYPE_GENE_CLINICAL.ipynb
[](https://colab.research.google.com/git... | {
"repository": "hatrungduc/spark-nlp-workshop",
"path": "tutorials/streamlit_notebooks/healthcare/NER_HUMAN_PHENOTYPE_GENE_CLINICAL.ipynb",
"matched_keywords": [
"biomarkers"
],
"stars": 687,
"size": 36313,
"hexsha": "4874918b9cd7def3e9a0f8a8c8845d40c7b6ee1d",
"max_line_length": 12334,
"avg_line_... |
# Notebook from markumreed/colab_sklearn
Path: recommender_systems_sklearn_movie_data.ipynb
<a href="https://colab.research.google.com/github/markumreed/colab_sklearn/blob/main/recommender_systems_sklearn_movie_data.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open I... | {
"repository": "markumreed/colab_sklearn",
"path": "recommender_systems_sklearn_movie_data.ipynb",
"matched_keywords": [
"STAR"
],
"stars": null,
"size": 116080,
"hexsha": "487570de2d1c79305a8ba2f79b3a51ff2af915fb",
"max_line_length": 58238,
"avg_line_length": 87.4755086662,
"alphanum_fraction"... |
# Notebook from ingolia/mcb200-2020
Path: 0904_statistics/04_exercise_dinucleotides-updated.ipynb
## Dinucleotides and dipeptides
We counted the occurrence of individual nucleotides in the genome and residues in the proteome.
In real biological sequences, adjacent positions are rarely independent. We now have most o... | {
"repository": "ingolia/mcb200-2020",
"path": "0904_statistics/04_exercise_dinucleotides-updated.ipynb",
"matched_keywords": [
"BioPython",
"bioinformatics"
],
"stars": null,
"size": 11279,
"hexsha": "4875f65dcf909a9d42edbf02e8af70b4915fad23",
"max_line_length": 437,
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# Notebook from czbiohub/scrnaseq-for-the-99-percent
Path: notebooks/346_bat_unaligned_kmers_in_human.ipynb
# Imports_____no_output_____
<code>
import glob
import os
import pandas as pd
import scanpy as sc
import seaborn as sns_____no_output_____
</code>
## Def describe_____no_output_____
<code>
def describe(df, r... | {
"repository": "czbiohub/scrnaseq-for-the-99-percent",
"path": "notebooks/346_bat_unaligned_kmers_in_human.ipynb",
"matched_keywords": [
"Scanpy",
"single-cell"
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"stars": 2,
"size": 267358,
"hexsha": "4876186d004d57930c159ae6fb7104672f1ce536",
"max_line_length": 19740,
"avg_line_length": 46... |
# Notebook from immersinn/rssfeed_link_collector
Path: notebooks/explore/Log Investigate 2017-04-24.ipynb
<code>
main_repo_dir = os.path.abspath(os.path.join('../..'))
sys.path.append(os.path.join(main_repo_dir, 'src'))_____no_output_____import datetime_____no_output_____import pandas_____no_output_____import utils___... | {
"repository": "immersinn/rssfeed_link_collector",
"path": "notebooks/explore/Log Investigate 2017-04-24.ipynb",
"matched_keywords": [
"evolution",
"biology",
"ecology"
],
"stars": null,
"size": 103686,
"hexsha": "4876b9933b979db59e466fd62797d46cad0145f6",
"max_line_length": 1296,
"avg_li... |
# Notebook from michael-swift/seqclone
Path: notebooks/SwitchTX_Figure.ipynb
<code>
import switchy.CloneStats as cs
import switchy.util as ut
import pandas as pd
import numpy as np
import sys
import os
import time
import random
import copy
import math
import scanpy as sc
%matplotlib inline
from matplotlib import pyplo... | {
"repository": "michael-swift/seqclone",
"path": "notebooks/SwitchTX_Figure.ipynb",
"matched_keywords": [
"Scanpy"
],
"stars": null,
"size": 996601,
"hexsha": "4876d8ec05a50f6a58b75070e3934fb5babc05d7",
"max_line_length": 165816,
"avg_line_length": 2244.5968468468,
"alphanum_fraction": 0.962443... |
# Notebook from ivirshup/scanpy-interactive
Path: notebooks/gene_selection.ipynb
# Gene selection widget prototype
This implemets a searchable list of genes, of which multiple can me selected (Cmd-click).
## Possible extensions
* Speed up updates to options in each selector. Takes a while when it's a long list.
* F... | {
"repository": "ivirshup/scanpy-interactive",
"path": "notebooks/gene_selection.ipynb",
"matched_keywords": [
"Scanpy"
],
"stars": null,
"size": 18339,
"hexsha": "4877d7dad5f5b73cce712e4961485072ac5918d2",
"max_line_length": 258,
"avg_line_length": 32.8655913978,
"alphanum_fraction": 0.53492556... |
# Notebook from cuttlefishh/papers
Path: palmyra-corals/notebooks/taxa_heatmaps.ipynb
## carter_taxa_heatmaps.ipynb_____no_output_____
<code>
from qiime2 import Artifact
from qiime2.plugins import feature_table
import pandas as pd
import numpy as np
import re
import matplotlib.pyplot as plt
import seaborn as sns
%mat... | {
"repository": "cuttlefishh/papers",
"path": "palmyra-corals/notebooks/taxa_heatmaps.ipynb",
"matched_keywords": [
"QIIME2"
],
"stars": 3,
"size": 389738,
"hexsha": "4878dc1bbc2f708189b734e8adaee787ec06583e",
"max_line_length": 42788,
"avg_line_length": 962.3160493827,
"alphanum_fraction": 0.95... |
# Notebook from janeite/course-content
Path: tutorials/W2D4_DynamicNetworks/student/W2D4_Tutorial2.ipynb
<a href="https://colab.research.google.com/github/NeuromatchAcademy/course-content/blob/master/tutorials/W2D4_DynamicNetworks/student/W2D4_Tutorial2.ipynb" target="_parent"><img src="https://colab.research.google.c... | {
"repository": "janeite/course-content",
"path": "tutorials/W2D4_DynamicNetworks/student/W2D4_Tutorial2.ipynb",
"matched_keywords": [
"evolution"
],
"stars": 2294,
"size": 47770,
"hexsha": "48790316b55789717495eb518d7c68efafde7ad3",
"max_line_length": 798,
"avg_line_length": 37.1173271173,
"alp... |
# Notebook from chandrabsingh/learnings
Path: cs221_ai/lec06-Search2-Astar.ipynb
>>> Work in Progress (Following are the lecture notes of Prof Percy Liang/Prof Dorsa Sadigh - CS221 - Stanford. This is my interpretation of his excellent teaching and I take full responsibility of any misinterpretation/misinformation pro... | {
"repository": "chandrabsingh/learnings",
"path": "cs221_ai/lec06-Search2-Astar.ipynb",
"matched_keywords": [
"STAR"
],
"stars": null,
"size": 10958,
"hexsha": "487d262e3c6bf33e3885e801089e3b3919467500",
"max_line_length": 258,
"avg_line_length": 27.3266832918,
"alphanum_fraction": 0.5485490053... |
# Notebook from jradavenport/EBHRD
Path: notebooks/metric_v2vis.ipynb
# Metric v2 vis
Based on Metric v1, but now exploring QuadTree binning
but now make the viz more "normal", aim for paper/proposals_____no_output_____
<code>
%matplotlib inline
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
... | {
"repository": "jradavenport/EBHRD",
"path": "notebooks/metric_v2vis.ipynb",
"matched_keywords": [
"STAR"
],
"stars": 3,
"size": 704620,
"hexsha": "487e25898ed7a184c48a76189abb554eab76e260",
"max_line_length": 157436,
"avg_line_length": 1149.4616639478,
"alphanum_fraction": 0.9564048707
} |
# Notebook from jorgemarpa/lightkurve
Path: docs/source/tutorials/2-creating-light-curves/2-1-combining-multiple-quarters.ipynb
# Combining multiple quarters of *Kepler* data_____no_output_____## Learning Goals
By the end of this tutorial, you will:
- Understand a *Kepler* Quarter.
- Understand how to download mult... | {
"repository": "jorgemarpa/lightkurve",
"path": "docs/source/tutorials/2-creating-light-curves/2-1-combining-multiple-quarters.ipynb",
"matched_keywords": [
"STAR"
],
"stars": null,
"size": 23643,
"hexsha": "487f3237653005709e20679298c565c1e00f1983",
"max_line_length": 684,
"avg_line_length": 34.... |
# Notebook from marcusvlc/PySyft
Path: examples/tutorials/advanced/websockets-example-MNIST-parallel/Asynchronous-federated-learning-on-MNIST.ipynb
# Tutorial: Asynchronous federated learning on MNIST
This notebook will go through the steps to run a federated learning via websocket workers in an asynchronous way usin... | {
"repository": "marcusvlc/PySyft",
"path": "examples/tutorials/advanced/websockets-example-MNIST-parallel/Asynchronous-federated-learning-on-MNIST.ipynb",
"matched_keywords": [
"STAR"
],
"stars": 2,
"size": 21121,
"hexsha": "487fc03becf572fa2040ac12e6e6eff4d0b728e2",
"max_line_length": 453,
"avg_... |
# Notebook from clij/clijpy
Path: python/benchmark_clijx_pull.ipynb
<code>
# init pyimage to get access to jar files
import imagej
ij = imagej.init('C:/programs/fiji-win64/Fiji.app/')
_____no_output_____# load some image data
from skimage import io
# sk_img = io.imread('https://samples.fiji.sc/blobs.png')
sk_img = io.... | {
"repository": "clij/clijpy",
"path": "python/benchmark_clijx_pull.ipynb",
"matched_keywords": [
"ImageJ"
],
"stars": 12,
"size": 25370,
"hexsha": "48805680e2ecedb07e838ee1c973d400740215d2",
"max_line_length": 3452,
"avg_line_length": 82.3701298701,
"alphanum_fraction": 0.6886874261
} |
# Notebook from bioexcel/biobb_wf_md_setup_api
Path: biobb_wf_md_setup_api/notebooks/biobb_MDsetupAPI_tutorial.ipynb
# Protein MD Setup tutorial using BioExcel Building Blocks (biobb) through REST API
**Based on the official GROMACS tutorial:** [http://www.mdtutorials.com/gmx/lysozyme/index.html](http://www.mdtutorial... | {
"repository": "bioexcel/biobb_wf_md_setup_api",
"path": "biobb_wf_md_setup_api/notebooks/biobb_MDsetupAPI_tutorial.ipynb",
"matched_keywords": [
"molecular dynamics"
],
"stars": null,
"size": 64162,
"hexsha": "48847355ca9b67305e25573ac5bad22857da14c7",
"max_line_length": 445,
"avg_line_length": ... |
# Notebook from morales-gregorio/elephant
Path: doc/tutorials/unitary_event_analysis.ipynb
# The Unitary Events Analysis_____no_output_____The executed version of this tutorial is at https://elephant.readthedocs.io/en/latest/tutorials/unitary_event_analysis.html
The Unitary Events (UE) analysis \[1\] tool allows us t... | {
"repository": "morales-gregorio/elephant",
"path": "doc/tutorials/unitary_event_analysis.ipynb",
"matched_keywords": [
"neuroscience"
],
"stars": 121,
"size": 24079,
"hexsha": "48855eda47c5746c36f6d8e65efeb4787e0eba72",
"max_line_length": 718,
"avg_line_length": 41.5872193437,
"alphanum_fracti... |
# Notebook from bmg-pcl/astronomy-python
Path: _extras/notebooks/07-plot.ipynb
# 7. Visualization
This is the seventh in a series of notebooks related to astronomy data.
As a continuing example, we will replicate part of the analysis in a recent paper, "[Off the beaten path: Gaia reveals GD-1 stars outside of the ma... | {
"repository": "bmg-pcl/astronomy-python",
"path": "_extras/notebooks/07-plot.ipynb",
"matched_keywords": [
"STAR"
],
"stars": 39,
"size": 738216,
"hexsha": "48862199c1a2cc4f46e020730d37acbe945bd0f7",
"max_line_length": 204684,
"avg_line_length": 578.9929411765,
"alphanum_fraction": 0.945896594... |
# Notebook from perseu912/insta_bot
Path: qbits/Untitled.ipynb
<code>
import qutip
from qutip import Bloch as b
import matplotlib.pyplot as plt
import numpy as np_____no_output_____from mpmath import limit
from mpmath import *
import numpy as np
#mp.dps = 20_____no_output_____
</code>
### q_exp
$$ exp_{q}(u) = \li... | {
"repository": "perseu912/insta_bot",
"path": "qbits/Untitled.ipynb",
"matched_keywords": [
"evolution"
],
"stars": null,
"size": 290957,
"hexsha": "488768a6178dd5f766bd9ebb7dd7872c9ed5c95c",
"max_line_length": 154664,
"avg_line_length": 636.6673960613,
"alphanum_fraction": 0.9385991744
} |
# Notebook from ShepherdCode/Soars2021
Path: Notebooks/GenCode_Explore_209.ipynb
# GenCode Explore
Explore the human RNA sequences from GenCode.
Assume user downloaded files from GenCode 38 [FTP](http://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_38/)
to a subdirectory called data.
Improve on GenCode_... | {
"repository": "ShepherdCode/Soars2021",
"path": "Notebooks/GenCode_Explore_209.ipynb",
"matched_keywords": [
"RNA"
],
"stars": 1,
"size": 404972,
"hexsha": "4887759b893ae07162c2b10ac900dc542106e497",
"max_line_length": 51694,
"avg_line_length": 433.1251336898,
"alphanum_fraction": 0.9292197979... |
# Notebook from Yu-Group/adaptive-wavelets
Path: notebooks/biology/init_nbs/08e_init_random_coif2.ipynb
<code>
%load_ext autoreload
%autoreload 2
%matplotlib inline
import os
import random
import numpy as np
import torch
import matplotlib.pyplot as plt
opj = os.path.join
import pickle as pkl
from ex_biology import p... | {
"repository": "Yu-Group/adaptive-wavelets",
"path": "notebooks/biology/init_nbs/08e_init_random_coif2.ipynb",
"matched_keywords": [
"biology"
],
"stars": 22,
"size": 134297,
"hexsha": "488a28eb78182dc11aafb06cf0f798cf80c49ed9",
"max_line_length": 34932,
"avg_line_length": 272.9613821138,
"alph... |
# Notebook from jazzcoffeestuff/blog
Path: _notebooks/2021-02-27-Sonny-Side-Up-Arnoldo-Perez-Hydro.ipynb
# "Sonny Side Up and Arnoldo Perez Hydro Natural"
> "Back with Plot Coffee Roasting we look at another coffe from Finca La Senda in Guatemala - this time their hydro-natural processed lot. Alongside we take a look ... | {
"repository": "jazzcoffeestuff/blog",
"path": "_notebooks/2021-02-27-Sonny-Side-Up-Arnoldo-Perez-Hydro.ipynb",
"matched_keywords": [
"evolution"
],
"stars": null,
"size": 10781,
"hexsha": "488a2c6047336ca038f9f1e26d1ac003b1ddacc3",
"max_line_length": 1277,
"avg_line_length": 115.9247311828,
"a... |
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