Authors: Lisa Tauxe, ltauxe@ucsd.edu & Hanna Asefaw, hasefaw@ucsd.edu
Computers are essential to all modern Earth Science research. We use them for compiling and analyzing data, preparing illustrations like maps or data plots, writing of manuscripts and so on. In this class you will learn computer programming with special applications useful to Earth Sciences. We will learn Python, an object-oriented programming language.
So why Python?
- Flexible, freely available, cross platform
- Easier to learn than many other languages
- It has many numerical, statistical and visualization packages
- It is well supported and has lots of online documentation
- The name 'Python' refers to 'Monty Python' - not the snake and many examples in Python documentation use jokes from the old Monty Python skits. If you have never heard of Monty Python, look it up on youtube; you are in for a treat.
Which Python?
- Python is undergoing a transition from 2.7 to 3. The notebooks in this course, apart from a few exceptions, are compatible with both.
- If you are not using the university computers for this class, we recommend that you install the most recent version of Anaconda python for your operating system : https://www.anaconda.com/download/
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| Number | Topic | Application |
|---|---|---|
| 1 | Intro to the course | |
| 2 | Variables and Operations | |
| 3 | Data structures | |
| 4 | Dictionaries, program loops (if, while and for) | |
| 5 | functions and modules | |
| 6 | NumPy and matplotlib | seismic records |
| 7 | NumPy arrays | |
| 8 | file systems and paths | |
| 9 | Pandas, file I/O | P-S wave arrival times |
| 10 | object oriented programming | objects and classes |
| 11 | recursions and exceptions | fibonacci spiral |
| 12 | lambda, map, filter reduce, list comprehension | |
| 13 | data wrangling with Pandas | seismic travel time plots |
| 14 | subplots, bar charts pie charts | elemental abundances |
| 15 | histograms and cumulative distribution functions | hypsometric curve |
| 16 | statistics 101 | Univariate data |
| 17 | hypothesis testing t, F | |
| 18 | application to grain sizes | grain sizes |
| 19 | line and curve fitting | Bivariate data & Hubble plot |
| 20 | maps | spatial data; earthquake locations/ depths |
| 21 | gridding and contouring | IGRF |
| 22 | rose diagrams and equal area projections | glacial striations |
| 23 | matrix math - dot and cross products | poles to planes and more |
| 24 | plotting great and small circles | |
| 25 | 3D plots of points and surfaces | benioff zone |
| 26 | Time series - periodograms | temporal data |
| 27 | Animations | Indian plate motion |
This class is entirely structured around a special programming environment called jupyter notebooks. A Jupyter notebook is a development environment where you can write, debug, and execute your programs. Put all the lectures and other directories associated with this package into a directory on your computer. To launch a notebook, follow the instructions on this website:
jupyter-notebook-beginner-guide.readthedocs.io/en/latest/execute.html
Launch Lecture_01_syllabus.ipynb
You should now be looking at the first notebook - read through it and do the practices. Then, you can try the other notebooks. They are meant to be read in order.