![]() ![]() GitHub’s rendering of Gists is a bit buggy. (There is no private option, but a secret gist can only be seen by others if you give them the URL.) ![]() You have the choice of making you gist public or secret. ipynb notebook file) by dragging and dropping it into the gist website. (You can create one right now by going to. Gists are like mini repos you can easily share and embed. Instead, I recommend github’s “ gist” mechanism for saving and sharing such “one-off” notebooks and code snippets. If we find someting cool or useful, it is important to preserve these exploratory notebooks.Ī dedicated github repository can be overkill for a single file. Jupyter notebooks are an ideal format for open-ended exploratory analysis, since they are totally self-contained: they encapsulate text, code, and figures. When starting something new, we are often motivated to just start coding and get some results quick. This lecture outlines some suggested practices for each category. This is where “scripts” become “software.” Reusable software elements: In the course of our research computing, we often identify specialized routines that we want to package for reuse in other projects, or by other scientists. The code related to a single paper usually belongs together. Types of Projects #īased on my experience, I categorize three different types of “research code” scenarios commonly encountered in geosciences.Įxploratory analyses: When exploring a new idea, a single notebook or script is often all we need.Ī Single Paper: The “paper” is a standard unit of scientific output. Just putting all of your code into git repositories won’t magically turn a mess of scripts into a beautiful, well-organized project. It is also a key component of scientific reproducibility. Organization and Packaging of Python Projects #Ī complex research project often relies and many different programs and software packages to accomplish the research goals.Īn important part of scientific computing is deciding how to organize and structure the code you use for research.Ī well-structured project can make you a more efficient and effective researcher. Model validation: Comparing a state estimate to observationsĬalculating properties in the native model grid using xgcm Working with output from many different CMIP6 climate modelsĪssignment: Calculate wet bulb temperature for CMIP6 models Xarray Interpolation, Groupby, Resample, Rolling, and CoarsenĪssignment: More Xarray with El Niño-Southern Oscillation (ENSO) Data Organization and Packaging of Python ProjectsĪssignment: Pandas Fundamentals with Earthquake DataĪssignment: Pandas Groupby with Hurricane DataĪssignment: Xarray Fundamentals with Atmospheric Radiation Data An Introduction to Earth and Environmental Data Science
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