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Tutorial Participant Instructions

Still have questions? Visit the individual tutorial channel on scipy2019.slack.com. (Contact SciPy@enthought.com if you need an invitation to Slack.)

Monday, July 8 8:00 am-Noon

Intro to Python Programming​

 

Matt Davis

Room 101

These are the setup instructions for the Introduction to Python tutorial at SciPy 2019. Please visit https://github.com/jiffyclub/scipy-2019-intro-to-python for up-to-date information on the tutorial.

 

If you don't already have Anaconda installed, download and install Anaconda for Python 3 (not Python 2): https://www.anaconda.com/distribution/.

 

If you're prompted to install VS Code we recommend you do install it unless you already have a code editor you prefer.

 

After installing Anaconda you can test your installation using these instructions: http://docs.anaconda.com/anaconda/user-guide/getting-started/#write-a-python-program-using-anaconda-prompt-or-terminal

Land on Vector Spaces: Practical Linear Algebra with Python​

 

Lorena Barba and Tingyu Wang

Room 201

 

Tutorial instructions may be viewed at https://github.com/engineersCode/EngComp4_landlinear

Xonsh - Bringing Python Data Science to your Shell

Gil Forsyth and Anthony Scopatz

Room 104

 

Tutorial materials may be viewed at https://github.com/xonsh/scipy2019_tutorial

Bayesian Statistics Made Simple

Allen Downey

Room 105

 

Tutorial materials may be viewed at https://allendowney.github.io/BayesMadeSimple/

Modern Time Series Analysis

Aileen Nielsen

Room 203

 

Python 3.x is required

 

the following packages should be installed and updated to the most recent version per the sample script:

import tensorflow as tf

import mxnet as mx

import xgboost as xgb

import sklearn

import statsmodels

import numpy as npy

import pandas as pd

Using SatPy to Process Earth-observing Satellite Data

 

David Hoese

Room 202

 

Tutorial materials may be viewed at https://github.com/pytroll/tutorial-satpy-half-day

Testing your Python Code with PyTest

 

John Leeman and Ryan May

Room 106

 

Tutorial materials may be viewed at https://leemangeophysicalllc.github.io/testing-with-python/

Monday, July 8 1:30 pm-5:30 pm

Bayesian Data Science: Simulation

 

Hugo Bowne-Anderson and Eric Ma

Room 203

 

Tutorial materials may be viewed at https://github.com/ericmjl/bayesian-stats-modelling-tutorial.

Complexity Science

 

Allen Downey

Room 105

 

Tutorial materials may be viewed at https://allendowney.github.io/ComplexityScience/

Intermediate Methods for Geospatial Data Analysis

 

Levi Wolf and Serge Rey

Room 202

 

Tutorial materials may be viewed at https://github.com/pysal/scipy2019-intermediate-gds

Introduction to Numerical Computing with NumPy

 

Alexandre Chabot-Leclerc

Room 201

 

Tutorial materials may be viewed at https://github.com/enthought/Numpy-Tutorial-SciPyConf-2019

Lights Camera Action! Scrape, Explore, and Model to Predict Oscar Winners & Box Office Hits

Sebastian Hanus, Deborah Hanus, Patricia Hanus, and Veronica Hanus

Room 104

 

Tutorial materials may be viewed at https://github.com/oscarpredictor/oscar-predictor

Reproducible Data Science in Python

 

Chandrasekhar Ramakrishnan and Xu Fei

Room 101

 

Tutorial materials may be viewed at https://github.com/SwissDataScienceCenter/r10e-ds-py

Visualize any Data Easily, from Notebooks to Dashboards

James Bednar, Jean-Luc Stevens, and Julia Signell

Room 106

 

Tutorial materials may be viewed at http://holoviz.org

Tuesday, July 9 8:00 am-Noon

Deep Learning Fundamentals: Forward Model, Differentiable Loss Function, and Optimization Routine

 

Eric Ma

Room 203

 

Tutorial materials will be available soon

Hands-on Satellite Imagery Analysis

 

Sara Safavi and Samapriya Roy

Room 101

 

This tutorial will be using a hosted Jupyter environment. Only a laptop with internet access and a web browser is needed.

Introduction to Bayesian Model Evaluation, Visualization, and Comparison Using Arviz

 

Ravin Kumar and Colin Carroll

Room 202

 

Tutorial materials may be viewed at https://github.com/canyon289/bayesian-model-evaluation/

Introduction to Matplotlib

Hannah Aizenman and Thomas Caswell

Room 201

 

Tutorial materials may be viewed here

RAPIDS: Open GPU Data Science

Anthony Scopatz, Nick Becker, Keith Kraus, Dante Gama Dessavre

Room 106

 

  • Pre-Tutorial Instructions for RAPIDS: Open GPU Data Science

    • This tutorial will be conducted via Jupyter Notebooks hosted on cloud service provider machines. Exact URLs for the Jupyter Notebooks will be provided on the day of the tutorial, as they are generated when the cloud machines are spun up.

    • Attendees should make sure to use a modern browser, such as a Google Chrome or Mozilla Firefox. Some laptops have security settings that block ports/websockets necessary for accessing cloud-hosted Jupyter Notebooks. To determine whether your laptop has a compatible configuration, please visit https://websocketstest.com . If your results say "WebSockets seem to work for you", you will be able to access the tutorial materials. If WebSockets do not work for you, please consider bringing an alternate laptop or temporarily changing your settings to allow for WebSockets, taking care to comply with any of your IT restrictions if you are using a corporate laptop.

    • Attendees will be required to conduct the tutorial on the cloud Notebooks, even if they have CUDA-compatible GPUs in their laptops.

Simulate and Generate: An Overview to Simulations and Generating Synthetic Data Sets in Python

Aileen Nielsen

Room 105

 

Python 3.x is required

 

the following packages should be installed and updated to the most recent version per the sample script:

import numpy as np

import pandas as pd

The Jupyter Interactive Widget Ecosystem

 

Matt Craig, Jason Grout, Martin Renou, Maarten Breddels, and Sylvain Corlay

Room 104

 

Tutorial materials may be viewed at https://github.com/jupyter-widgets/tutorial

Tuesday, July 9 1:30 pm-5:30 pm

Escape from Auto-manual Testing with Hypothesis!

 

Zac Hatfield-Dodds

Room 104

 

Tutorial materials may be viewed at https://github.com/Zac-HD/escape-from-automanual-testing

Getting Started with JupyterLab

Jason Grout, Matthias Bussonnier, and Stephanie Stattel

Room 202

 

Tutorial materials may be viewed at https://github.com/jupyterlab/scipy2019-jupyterlab-tutorial#installation

Getting Started with Tensorflow

Josh Gordon and Yufeng Guo 

Room 203

 

Attendees will need a laptop and an internet connection. We will use https://colab.research.google.com for all examples so there is nothing to install in advance.

Image Analysis in Python with SciPy and Scikit-image

Joshua Warner, Juan Nunez-Iglesias, and Stéfan van der Walt

Room 106

 

Tutorial materials may be viewed at https://github.com/scikit-image/skimage-tutorials/blob/master/preparation.md

Introduction to Data Processing in Python with Pandas

Daniel Chen

Room 201

 

Tutorial materials may be viewed at https://github.com/chendaniely/scipy-2019-pandas

Multi-dimensional Linked Data Exploration with Glue

Thomas Robitaille

Room 101

 

Tutorial materials may be viewed at https://github.com/glue-viz/glue/wiki/SciPy-2019-Tutorial-on-Multi-dimensional-Linked-Data-Exploration-with-Glue

Network Analysis Made Simple

Mridul Seth and Eric Ma  

Room 105

 

Tutorial materials may be viewed at https://github.com/ericmjl/Network-Analysis-Made-Simple

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