Xarray Tips and Tricks¶

Build a multi-file dataset from an OpenDAP server¶

One thing we love about xarray is the open_mfdataset function, which combines many netCDF files into a single xarray Dataset.

But what if the files are stored on a remote server and accessed over OpenDAP. An example can be found in NOAA's NCEP Reanalysis catalog.

Binder for Reproducible Research¶

This final lesson is concerned with the topic of reproducibility. Nearly everyone agrees that reproducibility is an important principle for science: if results are not reproducible, they are not valid.

But how do we achieve reproducibility in practice? In computational / data science, a particular analysis, calculation, or notebook may depend on hundreds of different software packages, each with many different versions. Reproducibility of our results depends on having the correct version.

Homework 10: Cartopy¶

1) Plot data from NARR¶

NARR is NCEP's North American Regional Reanalysis, a widely used product for studying the weather and climate of the continental US. The data is available from NOAA's Earth System Research Laboratory via OPeNDAP, meaing that xarray can opent the data "remotely" without downloading a file.

Maps in Scientific Python¶

Making maps is a fundamental part of geoscience research. Maps differ from regular figures in the following principle ways:

• Maps require a projection of geographic coordinates on the 3D Earth to the 2D space of your figure.
• Maps often include extra decorations besides just our data (e.g. continents, country borders, etc.)
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Speed, Performance, and Profiling in Python¶

1) Write a function to find the maximum of a list of numbers using pure core python (no numpy)¶

Dask for Parallel Computing in Python¶

In past lectures, we learned how to use numpy, pandas, and xarray to analyze various types of geoscience data. In this lecture, we address an incresingly common problem: what happens if the data we wish to analyze is "big data"

Aside: What is "Big Data"?¶

There is a lot of hype around the buzzword "big data" today. Some people may associate "big data" with specific sortware platforms (e.g. "Hadoop", "spark"), while, for others, "big data" means specific machine learning techniques. But I think wikipedia's definition more ...

Assignment 8 : Xarray Groupby¶

Here will will calculate the NINO 3.4 index of El Nino variabillity and use it to analyze datasets.

First read this page from NOAA. It tells you the following.

• The Nino 3.4 region is defined as the region between +/- 5 deg. lat, 170 W - 120 W lon.
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Assignment 7 : Xarray¶

In this assignment, we will use Xarray to analyze top-of-atmosphere radiation data from NASA's CERES project.

Public domain, by NASA, from Wikimedia Commons

I have pre-downloaded and subsetted a portion of this dataset for use in our class. You can download it here: http://ldeo.columbia.edu/~rpa/CERES_EBAF-TOA_Edition4.0_200003-201701.condensed.nc. The size of the data file is 702.53 MB. It will take a minute or two to download.

Xarray for multidimensional gridded data¶

In last week's lecture, we saw how Pandas provided a way to keep track of additional "metadata" surrounding tabular datasets, including "indexes" for each row and labels for each column. These features, together with Pandas' many useful routines for all kinds of data munging and analysis, have made Pandas one of the most popular python packages in the world.

Assignment 6: Pandas Groupby with Hurricane Data¶

Import pandas and matplotlib

Pandas: Groupby¶

groupby is an amazingly powerful function in pandas. But it is also complicated to use and understand. The point of this lesson is to make you feel confident in using groupby and its cousins, resample and rolling.

These notes are loosely based on the Pandas GroupBy Documentation.

Imports:

Pandas¶

Pandas is a an open source library providing high-performance, easy-to-use data structures and data analysis tools. Pandas is particularly suited to the analysis of tabular data, i.e. data that can can go into a table. In other words, if you can imagine the data in an Excel spreadsheet, then Pandas is the tool for the job.

Assignment 4: More Matplotlib¶

The goal here is to replicate the figures you see as closely as possible.

In order to get some data, you will have to run the code in the cells below. Don't worry about how this code works. In the end, it will give you some numpy arrays, which you will use in your plots. You are not allowed to use any packages other than numpy and matplotlib to complete your assignment.

More Matplotlib¶

Matplotlib is the dominant plotting / visualization package in python. It is important to learn to use it well. In the last lecture, we saw some basic examples in the context of learning numpy. This week, we dive much deeper. The goal is to understand how matplotlib represents figures internally.

Assignment 3 - Numpy and Matplotlib¶

Due Thursday September 27¶

1 Creating and Manipulating Arrays¶

First import numpy and matplotlib

Numpy and Matplotlib¶

These are two of the most fundamental parts of the scientific python "ecosystem". Most everything else is built on top of them.

Assignment 2: Python Functions and Classes¶

Part I: Exploring the Python Standard Library¶

Skim the documentation for the datetime module

1. Import the datetime module¶

Python Functions, Classes, and Modules¶

For longer and more complex tasks, it is important to organize your code into reuseable elements. For example, if you find yourself cutting and pasting the same or similar lines of code over and over, you probably need to define a function to encapsulate that code and make it reusable. An important principle in programming in DRY more ...

Assignment 1¶

Part 1: Files, Git, GitHub¶

Use JupyterLab to launch a terminal and use the terminal to do the following tasks:

1. Create a new directory called resume within your home directory
2. Create an empty file within this directory called Readme.md

Now use JupyterLab to edit the file:

1. Navigate to the directory in the file browser
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Core Python Language¶

Mostly copied from the official python tutorial

Invoking Python¶

There are three main ways to use python.

1. By running a python file, e.g. python myscript.py
2. Through an interactive console (python interpreter or ipython shell)
3. In an interactive iPython notebook

We will be using the iPython notebook.

Python Versions¶

There are two versions of the python language out there: python 2 and python 3. Python 2 is more common in the wild but is depracated. The community is moving to python 3. As new python learners, you should learn python 3. But it is important to be aware that python 2 exists. It is possible that a package you want to use is only supported in python 2. In general, it is pretty easy to switch between then.