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other:python:jyp_steps [2021/03/17 09:48] jypeter [Graphics related resources] updated Bart paper's link |
other:python:jyp_steps [2023/01/13 12:31] jypeter [NetCDF files: using cdms2, xarray and netCDF4] Added link to CliMAF and C-ESM-EP |
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==== Extra numpy information ==== | ==== Extra numpy information ==== | ||
- | * More information about array indexing: | + | <WRAP center round tip 60%> |
+ | You can also check the [[other:python:misc_by_jyp#numpy_related_stuff|numpy section]] of the //Useful python stuff// page | ||
+ | </WRAP> | ||
+ | |||
+ | |||
+ | * More information about **array indexing**:\\ <wrap em>Always check what you are doing on a simple test case, when you use advanced/fancy indexing!</wrap> | ||
* Examples: | * Examples: | ||
* {{ :other:python:indirect_indexing_2.py.txt |}}: Take a vertical slice in a 3D zyx array, along a varying y 'path' | * {{ :other:python:indirect_indexing_2.py.txt |}}: Take a vertical slice in a 3D zyx array, along a varying y 'path' | ||
- | * [[https://docs.scipy.org/doc/numpy/user/basics.indexing.html|Indexing]] (//index arrays//, //boolean index arrays//, //np.newaxis//, //Ellipsis//, //variable numbers of indices//, ...) | + | * [[https://numpy.org/doc/stable/user/basics.indexing.html|Array indexing basics (user guide)]] (//index arrays//, //boolean index arrays//, //np.newaxis//, //Ellipsis//, //variable numbers of indices//, ...) |
- | * [[https://docs.scipy.org/doc/numpy/user/quickstart.html#fancy-indexing-and-index-tricks|Fancy indexing]] and [[https://docs.scipy.org/doc/numpy/user/quickstart.html#the-ix-function|the ix_() function]] | + | * [[https://numpy.org/doc/stable/reference/arrays.indexing.html|Array indexing (reference manual)]] |
- | * [[https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html|Indexing (in the numpy reference manual)]] | + | * [[https://numpy.org/doc/stable/user/quickstart.html#advanced-indexing-and-index-tricks|Advanced indexing and index tricks]] and [[https://numpy.org/doc/stable/user/quickstart.html#the-ix-function|the ix_() function]] |
- | * [[https://docs.scipy.org/doc/numpy/reference/routines.indexing.html#routines-indexing|Indexing routines]] | + | * [[https://numpy.org/doc/stable/reference/routines.indexing.html#routines-indexing|Indexing routines]] |
* More information about arrays: | * More information about arrays: | ||
- | * [[https://docs.scipy.org/doc/numpy/reference/routines.array-creation.html#routines-array-creation|Array creation routines]] | + | * [[https://numpy.org/doc/stable/reference/routines.array-creation.html|Array creation routines]] |
- | * [[https://docs.scipy.org/doc/numpy/reference/routines.array-manipulation.html|Array manipulation routines]] | + | * [[https://numpy.org/doc/stable/reference/routines.array-manipulation.html|Array manipulation routines]] |
- | * [[https://docs.scipy.org/doc/numpy/reference/maskedarray.html|Masked arrays]] | + | * [[https://numpy.org/doc/stable/reference/routines.sort.html|Sorting, searching, and counting routines]] |
- | * [[https://docs.scipy.org/doc/numpy/reference/routines.ma.html|Masked array operations]] | + | * [[https://numpy.org/doc/stable/reference/maskedarray.html|Masked arrays]] |
- | * [[https://docs.scipy.org/doc/numpy/user/misc.html#ieee-754-floating-point-special-values|Dealing with special numerical values]] (//Nan//, //inf//) | + | * [[https://numpy.org/doc/stable/reference/routines.ma.html|Masked array operations]] |
- | * If you know that your data has missing values, it is cleaner and safer to handle them with [[https://docs.scipy.org/doc/numpy/reference/maskedarray.html|masked arrays]]! | + | * [[https://numpy.org/doc/stable/user/misc.html#ieee-754-floating-point-special-values|Dealing with special numerical values]] (//Nan//, //inf//) |
- | * [[https://docs.scipy.org/doc/numpy/user/misc.html#how-numpy-handles-numerical-exceptions|Handling numerical exceptions]] | + | * If you know that your data has missing values, it is cleaner and safer to handle them with [[https://numpy.org/doc/stable/reference/maskedarray.html|masked arrays]]! |
- | * [[https://docs.scipy.org/doc/numpy/reference/routines.err.html|Floating point error handling]] | + | * If you know that some of your data //may// have masked values, play safe by explicitly using ''np.ma.some_function()'' rather than just ''np.some_function()'' |
+ | * More details in the [[https://github.com/numpy/numpy/issues/18675|Why/when does np.something remove the mask of a np.ma array ?]] discussion | ||
+ | * [[https://numpy.org/doc/stable/user/misc.html#how-numpy-handles-numerical-exceptions|Handling numerical exceptions]] | ||
+ | * [[https://numpy.org/doc/stable/reference/routines.err.html|Floating point error handling]] | ||
- | ===== cdms2 and netCDF4 ===== | + | ===== Using NetCDF files with Python ===== |
+ | |||
+ | <note tip>People using CMIPn and model data on the IPSL servers can easily search and process NetCDF files using: | ||
+ | * the [[https://climaf.readthedocs.io/|Climate Model Assessment Framework (CliMAF)]] environment | ||
+ | * and the [[https://github.com/jservonnat/C-ESM-EP/wiki|CliMAF Earth System Evaluation Platform (C-ESM-EP)]] | ||
+ | </note> | ||
There is a good chance that your input array data will come from a file in the [[other:newppl:starting#netcdf_and_file_formats|NetCDF format]]. | There is a good chance that your input array data will come from a file in the [[other:newppl:starting#netcdf_and_file_formats|NetCDF format]]. | ||
- | Depending on which [[other:python:starting#some_python_distributions|python distribution]] you are using, you can use the //cdms2// or or //netCDF4// modules to read the data. | + | Depending on which [[other:python:starting#some_python_distributions|python distribution]] you are using, you can use the //cdms2//, //xarray// or //netCDF4// modules to read the data. |
==== cdms2 ==== | ==== cdms2 ==== | ||
- | Summary: cdms2 can read/write netCDF files (and read //grads// dat+ctl files) and provides a higher level interface than netCDF4. cdms2 is available in the [[other:python:starting#uv-cdat|UV-CDAT distribution]], and can theoretically be installed independently of UV-CDAT (e.g. it will be installed when you install [[https://cmor.llnl.gov/mydoc_cmor3_conda/|CMOR in conda)]]. When you can use cdms2, you also have access to //cdtime//, that is very useful for handling time axis data. | + | Summary: cdms2 can read/write netCDF files (and read //grads// dat+ctl files) and provides a higher level interface than netCDF4. cdms2 is available in the [[other:python:starting#cdat|CDAT distribution]], and can theoretically be installed independently of CDAT (e.g. it will be installed when you install [[https://cmor.llnl.gov/mydoc_cmor3_conda/|CMOR in conda)]]. When you can use cdms2, you also have access to //cdtime//, that is very useful for handling time axis data. |
How to get started: | How to get started: | ||
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- you have to replace //cdms// with **cdms2**, and //MV// with **MV2** (sooorry about that, the tutorial was written when CDAT was based on //Numeric// instead of //numpy// to handle array data) | - you have to replace //cdms// with **cdms2**, and //MV// with **MV2** (sooorry about that, the tutorial was written when CDAT was based on //Numeric// instead of //numpy// to handle array data) | ||
- read the [[http://cdms.readthedocs.io/en/docstanya/index.html|official cdms documentation]] (link may change) | - read the [[http://cdms.readthedocs.io/en/docstanya/index.html|official cdms documentation]] (link may change) | ||
+ | |||
+ | ==== xarray ==== | ||
+ | |||
+ | Summary: [[http://xarray.pydata.org/en/stable/|xarray]] is an open source project and Python package that makes working with labelled multi-dimensional arrays simple, efficient, and fun! [...] It is particularly tailored to working with netCDF files | ||
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===== CDAT-related resources ===== | ===== CDAT-related resources ===== | ||
- | Some links, in case they can't be found easily on the [[https://uv-cdat.llnl.gov|UV-CDAT]] web site... | + | Some links, in case they can't be found easily on the [[https://cdat.llnl.gov|CDAT]] web site... |
- | * [[https://uv-cdat.llnl.gov/tutorials.html|Tutorials in ipython notebooks]] | + | * [[https://cdat.llnl.gov/tutorials.html|Tutorials in ipython notebooks]] |
* [[http://cdat-vcs.readthedocs.io/en/latest/|VCS: Visualization Control System]] | * [[http://cdat-vcs.readthedocs.io/en/latest/|VCS: Visualization Control System]] | ||
* [[https://github.com/CDAT/vcs/issues/238|Colormaps in vcs examples]] | * [[https://github.com/CDAT/vcs/issues/238|Colormaps in vcs examples]] | ||
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* [[https://www.carbonbrief.org/guest-post-the-perils-of-counter-intuitive-design-in-ipcc-graphics|The perils of counter-intuitive design in IPCC graphics]] | * [[https://www.carbonbrief.org/guest-post-the-perils-of-counter-intuitive-design-in-ipcc-graphics|The perils of counter-intuitive design in IPCC graphics]] | ||
* Working with **colors** | * Working with **colors** | ||
- | * **Do not use the outdated //rainbow// colormap!** | + | * Choosing specific colors: use [[https://www.w3schools.com/colors/colors_names.asp|HTML color names]], the [[https://www.w3schools.com/colors/colors_picker.asp|HTML color picker]], etc... |
+ | * **Do not use the outdated //rainbow// and //jet// colormaps!** | ||
* [[https://pjbartlein.github.io/datagraphics/index.html|The End of the Rainbow? Color Schemes for Improved Data Graphics]] (Light and Bartlein, EOS 2004, including replies and comments) | * [[https://pjbartlein.github.io/datagraphics/index.html|The End of the Rainbow? Color Schemes for Improved Data Graphics]] (Light and Bartlein, EOS 2004, including replies and comments) | ||
* [[http://colorspace.r-forge.r-project.org/articles/endrainbow.html|Somewhere over the Rainbow]] | * [[http://colorspace.r-forge.r-project.org/articles/endrainbow.html|Somewhere over the Rainbow]] | ||
+ | * [[https://www.nature.com/articles/s41467-020-19160-7|The misuse of colour in science communication]] | ||
* [[https://matplotlib.org/users/colormaps.html|Choosing colormaps]] | * [[https://matplotlib.org/users/colormaps.html|Choosing colormaps]] | ||
* [[https://matplotlib.org/cmocean/|cmocean: Beautiful colormaps for oceanography]] | * [[https://matplotlib.org/cmocean/|cmocean: Beautiful colormaps for oceanography]] | ||
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* The [[http://hclwizard.org/|hclwizard]] provides tools for manipulating and assessing colors and palettes based on the underlying ''colorspace'' software | * The [[http://hclwizard.org/|hclwizard]] provides tools for manipulating and assessing colors and palettes based on the underlying ''colorspace'' software | ||
* NCL (NCAR Command Language) [[https://www.ncl.ucar.edu/Document/Graphics/color_table_gallery.shtml|Color table Gallery]] | * NCL (NCAR Command Language) [[https://www.ncl.ucar.edu/Document/Graphics/color_table_gallery.shtml|Color table Gallery]] | ||
+ | * JYP's favorite title: [[https://www.researchgate.net/publication/220943662_The_Which_Blair_Project_A_Quick_Visual_Method_for_Evaluating_Perceptual_Color_Maps|The "Which Blair Project": A Quick Visual Method for Evaluating Perceptual Color Maps]] | ||
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===== Cartopy + Iris ===== | ===== Cartopy + Iris ===== | ||
- | Summary: //Cartopy is a Python package for advanced map generation with a simple matplotlib interface// and //Iris is a Python package for analysing and visualising meteorological and oceanographic data sets// | + | Summary: |
+ | * **Cartopy** is //a matplolib-based Python package designed for geospatial data processing in order to produce maps and other geospatial data analyses// | ||
+ | * **Iris** is //a powerful, format-agnostic, community-driven Python package for analysing and visualising Earth science data.// | ||
- | Where: [[http://scitools.org.uk/cartopy/docs/latest/|Cartopy]] and [[http://scitools.org.uk/iris/index.html|Iris]] web sites | + | Where: [[http://scitools.org.uk/cartopy/docs/latest/|Cartopy]] and [[https://scitools-iris.readthedocs.io/en/stable/|Iris]] web sites |
Examples: | Examples: | ||
* [[other:python:maps_by_jyp|Examples provided by JYP]] | * [[other:python:maps_by_jyp|Examples provided by JYP]] | ||
- | * [[http://scitools.org.uk/cartopy/docs/latest/gallery.html|Gallery on the Cartopy web site]] | + | * Official gallery pages: [[https://scitools.org.uk/cartopy/docs/latest/gallery/index.html|Cartopy]] - [[https://scitools-iris.readthedocs.io/en/stable/generated/gallery/|Iris]] |
- | * [[http://scitools.org.uk/iris/docs/latest/gallery.html|Gallery on the Iris web site]] | + | |
- | * [[http://scitools.org.uk/iris/docs/latest/examples/index.html|Examples on the Iris web site]] | + | |
- | Help on //stack overflow//: [[https://stackoverflow.com/questions/tagged/cartopy|cartopy help]] | + | Help on //stack overflow//: [[https://stackoverflow.com/questions/tagged/cartopy|Cartopy help]] - [[https://stackoverflow.com/questions/tagged/python-iris|Iris help]] |
===== Maps and projections resources ===== | ===== Maps and projections resources ===== | ||
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//json// files look basically like a **list of (nested) python dictionaries** that would have been dumped to a text file | //json// files look basically like a **list of (nested) python dictionaries** that would have been dumped to a text file | ||
- | * [[https://docs.python.org/2/library/json.html|json module]] documentation | + | * [[https://docs.python.org/3/library/json.html|json module]] documentation |
* [[https://realpython.com/python-json/|Working With JSON Data in Python]] tutorial | * [[https://realpython.com/python-json/|Working With JSON Data in Python]] tutorial | ||
* example script: ''/home/users/jypeter/CDAT/Progs/Devel/beaugendre/nc2json.py'' | * example script: ''/home/users/jypeter/CDAT/Progs/Devel/beaugendre/nc2json.py'' | ||
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This document will teach you even more things about python, numpy and matplotlib, debugging and optimizing scripts, and about using python for statistics, image processing, machine learning, washing dishes (this is just to check if you have read this page), etc... | This document will teach you even more things about python, numpy and matplotlib, debugging and optimizing scripts, and about using python for statistics, image processing, machine learning, washing dishes (this is just to check if you have read this page), etc... | ||
* Example: the [[http://www.scipy-lectures.org/packages/statistics/index.html|Statistics in Python]] tutorial that combines [[other:python:jyp_steps#pandas|Pandas]], [[http://statsmodels.sourceforge.net/|Statsmodels]] and [[http://seaborn.pydata.org/|Seaborn]] | * Example: the [[http://www.scipy-lectures.org/packages/statistics/index.html|Statistics in Python]] tutorial that combines [[other:python:jyp_steps#pandas|Pandas]], [[http://statsmodels.sourceforge.net/|Statsmodels]] and [[http://seaborn.pydata.org/|Seaborn]] | ||
+ | |||
+ | ===== statsmodels ===== | ||
+ | |||
+ | [[https://www.statsmodels.org/|statsmodels ]] is a Python module that provides classes and functions for the estimation of many different statistical models, as well as for conducting statistical tests, and statistical data exploration. | ||
+ | |||
+ | ===== scikit-learn ===== | ||
+ | |||
+ | [[http://scikit-learn.org/|scikit-learn]] is an open source machine learning library that supports supervised and unsupervised learning. It also provides various tools for model fitting, data preprocessing, model selection and evaluation, and many other utilities. | ||
+ | |||
+ | ===== scikit-image ===== | ||
+ | |||
+ | [[https://scikit-image.org/|scikit-image]] is a collection of algorithms for image processing in Python | ||
===== Quick Reference and cheat sheets ===== | ===== Quick Reference and cheat sheets ===== | ||
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* [[https://www.cheatography.com/weidadeyue/cheat-sheets/jupyter-notebook/pdf_bw/|Jupyter Notebook Keyboard Shortcuts]] | * [[https://www.cheatography.com/weidadeyue/cheat-sheets/jupyter-notebook/pdf_bw/|Jupyter Notebook Keyboard Shortcuts]] | ||
+ | |||
+ | ===== Miscellaneous Python stuff ===== | ||
+ | |||
+ | Check the page about [[other:python:misc_by_jyp|useful python stuff that has not been sorted yet]] | ||
===== Misc tutorials ===== | ===== Misc tutorials ===== |