This is the start of a CDAT tutorial series. We will be installing a "lite" version of the CDAT in Linux via conda.
The Ramer-Douglas-Peucker (RDP) algorithm is a curve simplification method. To apply it on coordinates defined by latitudes/longitudes, we need to replace the Cartesian geometry with a spherical one.
It is a common practice to use `git` to back-up and sync ones "dotfiles". I also create Python script to help automate this process.
A Python script that "scans through" a collection of local git repositories and generates a report for me.
A toy Python script that converts an image into a spiral curve art.
The convolution functions in `scipy` do not work well with missing data. We create a 2D convolution function that allows a controllable tolerance to missing values. It is first implemented in Fortran, then using `scipy` in an FFT approach.
Peak prominence can be used to identify relatively organized regional maxima while filtering out local disturbances.
In this post we create a *DiskCacher* class that can be used as a *with* context manager, a function wrapper and as the pie-syntax decorator. It modifies a given computation function, and tries to use the cached data if possible, and only do the re-computation if the cache is not found or the user forces an overwrite.
On top of netcdf4, there are some more advanced packages in Python that could make the manipulation of NetCDF data, and your life a lot easier.