other:python:jyp_steps
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other:python:jyp_steps [2019/09/05 14:22] jypeter Moved the bulk of the matplotlib section to its own page when it became too long |
other:python:jyp_steps [2020/02/04 08:33] jypeter [Cartopy + Iris] |
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| * the tutorial is very detailed about string handling, because strings offer an easy way to practice working with indices (indexing and slicing), before indexing numpy arrays. And our usual pre/post-processing scripts often need to do a lot of string handling in order to generate the file/variable/experiment names | * the tutorial is very detailed about string handling, because strings offer an easy way to practice working with indices (indexing and slicing), before indexing numpy arrays. And our usual pre/post-processing scripts often need to do a lot of string handling in order to generate the file/variable/experiment names | ||
| * after reading this tutorial, you should practice with the following: | * after reading this tutorial, you should practice with the following: | ||
| - | * [[https://files.lsce.ipsl.fr/public.php?service=files&t=9731fdad4521ac5fa6e84b392d3a2e44|Basic python training test (ipython notebook version)]] | + | * [[https://sharebox.lsce.ipsl.fr/index.php/s/S3EO8cLrhVDeQWA|Basic python training test (ipython notebook version)]] |
| * {{:other:python:tp_intro_python_oct2013_no_solutions.pdf|Basic python training test (pdf version)}} | * {{:other:python:tp_intro_python_oct2013_no_solutions.pdf|Basic python training test (pdf version)}} | ||
| * {{:other:python:tp_intro_python_oct2013_full.pdf|Basic python training test (pdf version, with answers)}} | * {{:other:python:tp_intro_python_oct2013_full.pdf|Basic python training test (pdf version, with answers)}} | ||
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| * See also: [[https://www.datacamp.com/community/tutorials/seaborn-python-tutorial| | * See also: [[https://www.datacamp.com/community/tutorials/seaborn-python-tutorial| | ||
| Python Seaborn Tutorial For Beginners]] | Python Seaborn Tutorial For Beginners]] | ||
| + | * Communicating/displaying/plotting your data (possibly for people not of your field): | ||
| + | * [[https://uxknowledgebase.com/introduction-to-designing-data-visualizations-part-1-31c056556133|Introduction to Designing Data Visualizations — Part 1]] | ||
| + | * [[https://uxknowledgebase.com/tables-other-charts-data-visualization-part-2-cfc582e4712c|Tables & Other Charts — Data Visualization Part 2]] | ||
| + | * [[https://uxknowledgebase.com/tables-other-charts-data-visualization-part-3-5bfab15ce525|Tables & Other Charts — Data Visualization Part 3]] | ||
| * Working with colors | * Working with colors | ||
| * [[https://matplotlib.org/users/colormaps.html|Choosing colormaps]] | * [[https://matplotlib.org/users/colormaps.html|Choosing colormaps]] | ||
| - | * [[https://matplotlib.org/cmocean/|Beautiful colormaps for oceanography: cmocean]] | + | * [[https://matplotlib.org/cmocean/|cmocean: Beautiful colormaps for oceanography]] |
| + | * [[https://jiffyclub.github.io/palettable/|Palettable: Color palettes for Python]] | ||
| * [[http://colorbrewer2.org|ColorBrewer 2.0]] is a tool that can help you understand, and experiment with //sequential//, //diverging// and //qualitative// colormaps | * [[http://colorbrewer2.org|ColorBrewer 2.0]] is a tool that can help you understand, and experiment with //sequential//, //diverging// and //qualitative// colormaps | ||
| Line 223: | Line 228: | ||
| Examples: | Examples: | ||
| + | * [[other:python:maps_by_jyp|Examples provided by JYP]] | ||
| * [[http://scitools.org.uk/cartopy/docs/latest/gallery.html|Gallery on the Cartopy web site]] | * [[http://scitools.org.uk/cartopy/docs/latest/gallery.html|Gallery on the Cartopy web site]] | ||
| * [[http://scitools.org.uk/iris/docs/latest/gallery.html|Gallery on the Iris web site]] | * [[http://scitools.org.uk/iris/docs/latest/gallery.html|Gallery on the Iris web site]] | ||
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| We list here some resources about non-NetCDF data formats that can be useful | We list here some resources about non-NetCDF data formats that can be useful | ||
| + | ==== The shelve package ==== | ||
| + | |||
| + | The [[https://docs.python.org/3/library/shelve.html|built-in shelve package]], can be easily used for storing data (python objects like lists, dictionaries, numpy arrays that are not too big, ...) on disk and retrieving them later | ||
| + | |||
| + | Use case: | ||
| + | - Use a script do to the heavy data pre-processing and store the (intermediate) results in a file using ''shelve'', or update the results | ||
| + | - Use another script for plotting the results stored with ''shelve''. This way you don't have to wait for the pre-processing step to finish each time you want to improve your plot(s) | ||
| + | |||
| + | Warning: | ||
| + | * read the [[https://docs.python.org/3/library/shelve.html|documentation]] and the example carefully (it's quite small) | ||
| + | * if you get the impression that the data is not saved correctly, re-read the parts about updating correctly the content of the shelve file | ||
| + | * you should be able to store most python objects in a shelve file, but it is safer to make tests | ||
| + | * do not forget to close the output file | ||
| + | * if you are dealing with big arrays and want to avoid performance issues, you should use netCDF files for storing the intermediate results | ||
| ==== json files ==== | ==== json files ==== | ||
other/python/jyp_steps.txt · Last modified: 2024/03/07 10:15 by jypeter
