Python API to new modules at https://argovis.colorado.edu¶
The Argo program has provided temperature, salinity and pressure data (T/S/P) on a global scale.
The web app Argovis provides data access and visualization.
The following code is an interface to some of the new features at Argovis. Recently we have been including other data sets and data derived gridded products. API access is available for these datasets so that those inclined can retrieve Argovis data in a python environment.
R API to www.argovis.com¶
Mentioned in this blog post, Argovis has an API that can send JSON data of Argo profiles, platforms and selections, and their metadata. This post will again retrieve Argodata, but this time in an R environment.
This script will guide an R user to:
Python API to www.argovis.com¶
This post is the first part of a three-part series on argovis. Supplementary tutorial videos are found here.
My Summer on the volcano¶
I'm going to write about my 2018 Summer. This post is all about my time at NOAA's Mauna Loa Station. I began measuring ozone for the world's standard Dobson Spectrophotometer (083) in June 2018 and stayed until October 31, 2018. I measured through the Kilauea volcanic eruption and was there when we were hit by Hurricane Lane. It was quite an honor performing this calibration and was an experience working at 11000 feet above sea level. Here are some pictures of where I worked
Argovis Data Visualization and Extraction Tutorials¶
Tutorials describe several website use cases step by step and offers the user a break from reading. The first tutorial narrates a typical use case on how a user can make a custom selection around the Labrador sea at 500-1000 meters during Summer 2017, and download the data and its metadata table locally. The second tutorial
Ocean Modeling with the Navier-Stokes Equations: or as I think of it, 'my missed oppurtunity.'¶
By Tyler Tucker
note to reader: I wrote this project for Peter Blomgrens MATH 793A course in numerical partial differential equations. This is the presentation.¶
I wrote this project for Peter Blomgrens MATH 793A course in numerical partial differential equations. This is the presentation.
Bayesian Predictive Posterior for a Normal Distribution using SIR Prior¶
My Bayesian statistics class had me compute the predictive posterior distribution of a normal distribution. You can do this by hand if you use the standard improper reference (SIR) prior. This was painfull to do on my own, and I thought it would be helpful to include this on my blog, in case anyone wants to compute this, and wants to see how I did it.
Recursive Taylor Series Using Python's Sympy Library¶
Greetings everyone, I ran into some homework problems involving taking a multivariate Taylor expansion and noticed that Sympy's doesn't have this feature. They do have a series expansion at one point. However, things go sour for additional terms. I managed to write a multivariate Taylor series expanded about the origin up to the second order and provided the code below for those who are interested. If you see something that can be improved, let me know at firstname.lastname@example.org. Thanks!
Nonlinear Least squares fitting time series in Python¶
This post is the third part of a three-part series into argovis.
Nonlinear least squares fitting of time series¶
This post is the 2nd part of a three-part series into argovis.