SmB6 Problem Solving:
Nick took you through the steps of reading in data and making useful plots. Everyone has their notebooks in their persistent directories, but I have also updated the class GitHub repository with a folder called “solutions” that contains the notebooks Nick first knocked out. You can now use “git pull” to update the cloned versions of that repository in your SciServer persistent directory and you’ll have those notebooks to play with! To get them, open a terminal and cd to your persistent directory. Once there, just type “git pull” and hit return. Voilà.
The publications that motivate our working with this dataset are found in the SmB6_data folder found in the class GitHub repository (and now cloned into your SciServer persistent directory).
There was a question about curve fitting if you have a specific form for the desired output equation. Nick was using polyfit which fits the data to a simple polynomial, but what if you had a reason to want a different, specific form for the equation? SciPy has an optimized general curve fitting method described here: https://lmfit.github.io/lmfit-py/model.html
That will even work with a module called models that has built in forms for equations that can be useful, like Gaussians, Lorentzians, exponentials, and more. Info on models can be found here: https://lmfit.github.io/lmfit-py/builtin_models.html#builtin-models-chapter