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other:python:jyp_steps [2017/07/20 09:51] jypeter Added links to seaborn and colorbrewer |
other:python:jyp_steps [2023/12/15 14:37] jypeter Moved scikit-learn and scikit-image in front of lesser known libraries |
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You can start using python by reading the {{:other:python:python_intro_ipsl_oct2013_v2.pdf|Bien démarrer avec python}} tutorial that was used during a 2013 IPSL python class: | You can start using python by reading the {{:other:python:python_intro_ipsl_oct2013_v2.pdf|Bien démarrer avec python}} tutorial that was used during a 2013 IPSL python class: | ||
* this tutorial is in French (my apologies for the lack of translation, but it should be easy to understand) | * this tutorial is in French (my apologies for the lack of translation, but it should be easy to understand) | ||
- | * If you have too much trouble understanding this French Tutorial, you can read the first 6 chapters of the **Tutorial** in [[#the_official_python_documentation|the official Python documentation]] and chapters 1.2.1 to 1.2.5 in the [[#scipy_lecture_notes|Scipy Lecture Notes]]. Once you have read these, you can try to read the French tutorial again | + | * If you have too much trouble understanding this French Tutorial, you can read the first 6 chapters of the **Tutorial** in [[#the_official_python_documentation|the official Python documentation]] and chapters 1.2.1 to 1.2.5 in the [[#scientific_python_lectures|Scientific Python Lectures]]. Once you have read these, you can try to read the French tutorial again |
* it's an introduction to python (and programming) for the climate scientist: after reading this tutorial, you should be able to do most of the things you usually do in a shell script | * it's an introduction to python (and programming) for the climate scientist: after reading this tutorial, you should be able to do most of the things you usually do in a shell script | ||
* python types, tests, loops, reading a text file | * python types, tests, loops, reading a text file | ||
* 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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[[https://docs.python.org/3/|html]] - [[https://docs.python.org/3/download.html|pdf (in a zip file)]] | [[https://docs.python.org/3/|html]] - [[https://docs.python.org/3/download.html|pdf (in a zip file)]] | ||
+ | |||
+ | |||
+ | ===== Scientific Python Lectures ===== | ||
+ | |||
+ | Summary: //One document to learn numerics, science, and data with Python// | ||
+ | |||
+ | Note: this used to be called //Scipy Lecture Notes// | ||
+ | |||
+ | Where: [[https://lectures.scientific-python.org/_downloads/ScientificPythonLectures-simple.pdf|pdf]] - [[https://lectures.scientific-python.org/|html]] | ||
+ | |||
+ | This is **a really nice and useful document** that is regularly updated and used for the [[https://www.euroscipy.org/|EuroScipy]] tutorials. | ||
+ | |||
+ | This document will teach you lots of 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 [[https://lectures.scientific-python.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]] | ||
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- always remember that indices start at ''0'' and that the last element of an array is at index ''-1''!\\ First learn about //indexing// and //slicing// by manipulating strings, as shown in [[#part1|Part 1]] above (try '''This document by JY is awesome!'[::-1]'' and '''This document by JY is awesome!'[slice(None, None, -1)]'') 8-) | - always remember that indices start at ''0'' and that the last element of an array is at index ''-1''!\\ First learn about //indexing// and //slicing// by manipulating strings, as shown in [[#part1|Part 1]] above (try '''This document by JY is awesome!'[::-1]'' and '''This document by JY is awesome!'[slice(None, None, -1)]'') 8-) | ||
- | - if you are a Matlab user (but the references are interesting for others as well), you can read the following: | + | - if you are a **Matlab user** (but the references are interesting for others as well), you can read the following: |
+ | - [[https://www.enthought.com/wp-content/uploads/2019/08/Enthought-MATLAB-to-Python-White-Paper-1.pdf|Migrating from MATLAB to Python]] on the [[https://www.enthought.com/software-development/|Enthought Software Development page]] | ||
- [[https://docs.scipy.org/doc/numpy-dev/user/numpy-for-matlab-users.html|Numpy for Matlab users]] | - [[https://docs.scipy.org/doc/numpy-dev/user/numpy-for-matlab-users.html|Numpy for Matlab users]] | ||
- [[http://mathesaurus.sourceforge.net/matlab-numpy.html|NumPy for MATLAB users]] (nice, but does not seem to be maintained any more) | - [[http://mathesaurus.sourceforge.net/matlab-numpy.html|NumPy for MATLAB users]] (nice, but does not seem to be maintained any more) | ||
- | - read the [[https://docs.scipy.org/doc/numpy-dev/user/quickstart.html|Quickstart tutorial]] | + | - read the really nice [[https://docs.scipy.org/doc/numpy/user/quickstart.html|numpy Quickstart tutorial]] |
- have a quick look at the full documentation to know where things are | - have a quick look at the full documentation to know where things are | ||
- Numpy User Guide | - Numpy User Guide | ||
- Numpy Reference Guide | - Numpy Reference Guide | ||
- Scipy Reference Guide | - Scipy Reference Guide | ||
+ | - read [[https://github.com/rougier/numpy-100/blob/master/100_Numpy_exercises.ipynb|100 numpy exercises]] | ||
==== Beware of the array view side effects ==== | ==== Beware of the array view side effects ==== | ||
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</code></note> | </code></note> | ||
- | ===== cdms2 and netCDF4 ===== | + | ==== Extra numpy information ==== |
- | 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]]. | + | <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> | ||
- | 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. | + | |
+ | * 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: | ||
+ | * {{ :other:python:indirect_indexing_2.py.txt |}}: Take a vertical slice in a 3D zyx array, along a varying y 'path' | ||
+ | * [[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://numpy.org/doc/stable/reference/arrays.indexing.html|Indexing routines (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]] | ||
+ | * More information about arrays: | ||
+ | * [[https://numpy.org/doc/stable/reference/routines.array-creation.html|Array creation routines]] | ||
+ | * [[https://numpy.org/doc/stable/reference/routines.array-manipulation.html|Array manipulation routines]] | ||
+ | * [[https://numpy.org/doc/stable/reference/routines.sort.html|Sorting, searching, and counting routines]] | ||
+ | * [[https://numpy.org/doc/stable/reference/maskedarray.html|Masked arrays]] | ||
+ | * [[https://numpy.org/doc/stable/reference/routines.ma.html|Masked array operations]] | ||
+ | * [[https://numpy.org/doc/stable/user/misc.html#ieee-754-floating-point-special-values|Dealing with special numerical values]] (//Nan//, //inf//) | ||
+ | * 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]]! | ||
+ | * 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]] | ||
+ | |||
+ | ===== 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 be stored in a [[other:newppl:starting#netcdf_and_related_conventions|NetCDF]] file. | ||
+ | |||
+ | * There may be different ways of dealing with NetCDF files, depending on which [[other:python:starting#some_python_distributions|python distribution]] you have access to | ||
==== cdms2 ==== | ==== cdms2 ==== | ||
- | Summary: cdms2 can read/write netCDF files (and read //grads// dat+ctl files) and provides a higher level interface than netCDF4. Unfortunately, cdms2 is only available in the [[other:python:starting#uv-cdat|UV-CDAT distribution]], and distributions where somebody has installed some version of //cdat-lite//. 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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- the tutorial is in French (soooorry!) | - the tutorial is in French (soooorry!) | ||
- 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://uv-cdat.llnl.gov/documentation/cdms/cdms.html|official cdms documentation]] | + | - read the [[http://cdms.readthedocs.io/en/docstanya/index.html|official cdms documentation]] (link may change) |
- | - ask questions and get answers on the [[http://uvcdat.askbot.com/questions/|UV-CDAT askbot]] | + | |
+ | ==== xarray ==== | ||
+ | |||
+ | Summary: [[https://docs.xarray.dev/|xarray]] makes working with labelled multi-dimensional arrays in Python simple, efficient, and fun! [...] It is particularly tailored to working with netCDF files | ||
+ | |||
+ | === Some xarray related resources === | ||
+ | |||
+ | Note: more packages (than listed below) may be listed in the [[other:uvcdat:cdat_conda:cdat_8_2_1#extra_packages_list|Extra packages list]] page | ||
+ | |||
+ | * [[https://docs.xarray.dev/en/stable/generated/xarray.tutorial.load_dataset.html|xarray test datasets]] | ||
+ | |||
+ | * [[https://xcdat.readthedocs.io/|xcdat]]: xarray extended with Climate Data Analysis Tools | ||
+ | |||
+ | * [[https://xoa.readthedocs.io/en/latest/|xoa]]: xarray-based ocean analysis library | ||
+ | |||
+ | * [[https://uxarray.readthedocs.io/|uxarray]]: provide xarray styled functionality for unstructured grid datasets following [[https://ugrid-conventions.github.io/ugrid-conventions/|UGRID Conventions]] | ||
==== netCDF4 ==== | ==== netCDF4 ==== | ||
- | Summary: netCDF4 can read/write netCDF files and is available in most python distributions | + | Summary: //netCDF4 can read/write netCDF files and is available in most python distributions// |
Where: [[http://unidata.github.io/netcdf4-python/]] | Where: [[http://unidata.github.io/netcdf4-python/]] | ||
+ | ===== CDAT-related resources ===== | ||
+ | |||
+ | Some links, in case they can't be found easily on the [[https://cdat.llnl.gov|CDAT]] web site... | ||
+ | |||
+ | * [[https://cdat.llnl.gov/tutorials.html|Tutorials in ipython notebooks]] | ||
+ | * [[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/cdat-site/blob/master/eztemplate.md|EzTemplate Documentation]] | ||
===== Matplotlib ===== | ===== Matplotlib ===== | ||
+ | |||
+ | <note important> | ||
+ | The full content of this //matplotlib// section has been moved to\\ [[other:python:matplotlib_by_jyp|Working with matplotlib (JYP version)]]\\ after becoming too big to manage here | ||
+ | |||
+ | \\ Note: [[other:python:maps_by_jyp|Plotting maps with matplotlib+cartopy]] (examples provided by JYP) | ||
+ | </note> | ||
Summary: there are lots of python libraries that you can use for plotting, but Matplotlib has become a //de facto// standard | Summary: there are lots of python libraries that you can use for plotting, but Matplotlib has become a //de facto// standard | ||
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Help on //stack overflow//: [[https://stackoverflow.com/questions/tagged/matplotlib|matplotlib help]] | Help on //stack overflow//: [[https://stackoverflow.com/questions/tagged/matplotlib|matplotlib help]] | ||
- | |||
- | The documentation is good, but not always easy to use. <wrap hi>A good way to start with matplotlib</wrap> is to: | ||
- | - Look at the [[http://matplotlib.org/gallery.html|matplotlib gallery]] to get an idea of all you can do with matplotlib. Later, when you need to plot something, come back to the gallery to find some examples that are close to what you need and click on them to get the sources | ||
- | - Use the free hints provided by JY! | ||
- | - a Matplotlib //Figure// is a graphical window in which you make your plots... | ||
- | - a Matplotlib //Axis// is a plot inside a Figure... [[http://matplotlib.org/faq/usage_faq.html#parts-of-a-figure|More details]] | ||
- | - some examples are more //pythonic// (ie object oriented) than others, some example mix different styles of coding, all this can be confusing. Try to [[http://matplotlib.org/faq/usage_faq.html#coding-styles|use an object oriented way of doing things]]! | ||
- | - sometimes the results of the python/matplolib commands are displayed directly, sometimes not. It depends if you are in [[http://matplotlib.org/faq/usage_faq.html#what-is-interactive-mode|interactive or non-interactive]] mode | ||
- | - the documentation may mention [[http://matplotlib.org/faq/usage_faq.html#what-is-a-backend|backends]]. What?? Basically, you use python commands to create a plot, and the backend is the //thing// that will render your plot on the screen or in a file (png, pdf, etc...) | ||
- | - Read the [[http://www.labri.fr/perso/nrougier/teaching/matplotlib/|Matplotlib tutorial by Nicolas Rougier]] | ||
- | - Download the [[http://matplotlib.org/contents.html|pdf version of the manual]]. **Do not print** the 2800+ pages of the manual! Read the beginner's guide (Chapter //FIVE// of //Part II//) and have a super quick look at the table of contents of the whole document. | ||
===== Graphics related resources ===== | ===== Graphics related resources ===== | ||
+ | * [[http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003833|Ten Simple Rules for Better Figures]] | ||
+ | * [[https://www.machinelearningplus.com/plots/top-50-matplotlib-visualizations-the-master-plots-python/|Top 50 matplotlib Visualizations]] | ||
* [[http://seaborn.pydata.org/|Seaborn]] is a library for making attractive and informative statistical graphics in Python, built on top of matplotlib | * [[http://seaborn.pydata.org/|Seaborn]] is a library for making attractive and informative statistical graphics in Python, built on top of matplotlib | ||
- | * [[http://colorbrewer2.org|ColorBrewer 2.0]] is a tool that can help you understand, and experiment with //sequential//, //diverging// and //qualitative// colormaps | + | * See also: [[https://www.datacamp.com/community/tutorials/seaborn-python-tutorial| |
+ | 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]] | ||
+ | * **IPCC**-related //stuff//... | ||
+ | * [[https://www.ipcc.ch/site/assets/uploads/2019/04/IPCC-visual-style-guide.pdf|IPCC Visual Style Guide for Authors]] | ||
+ | * [[https://wg1.ipcc.ch/sites/default/files/documents/ipcc_visual-identity_guidelines.pdf|A new assessment cycle,A new visual identity]] | ||
+ | * [[https://link.springer.com/article/10.1007/s10584-019-02537-z|Communication of IPCC visuals: IPCC authors’ views and assessments of visual complexity]] | ||
+ | * [[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** | ||
+ | * 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) | ||
+ | * [[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/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 | ||
+ | * 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]] | ||
+ | * 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]] | ||
===== Basemap ===== | ===== Basemap ===== | ||
- | <note warning>It seems that basemap is going to be slowly phased out, in favor of [[#cartopy]]\\ More information in this [[https://github.com/matplotlib/basemap/issues/267|basemap github issue]] | + | <note warning>Basemap is going to be slowly phased out, in favor of [[#cartopy_iris|cartopy]]\\ More information in this: |
+ | * [[https://github.com/SciTools/cartopy/issues/920|cartopy github issue]] | ||
+ | * [[https://github.com/matplotlib/basemap/issues/267|basemap github issue]] | ||
</note> | </note> | ||
- | Summary: Basemap is an extension of Matplotlib that you can use for plotting maps, using different projections | + | Summary: //Basemap is an extension of Matplotlib that you can use for plotting maps, using different projections// |
Where: [[http://matplotlib.org/basemap/|Basemap web site]] | Where: [[http://matplotlib.org/basemap/|Basemap web site]] | ||
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- look at the [[http://matplotlib.org/basemap/api/basemap_api.html#module-mpl_toolkits.basemap|detailed official documentation]] | - look at the [[http://matplotlib.org/basemap/api/basemap_api.html#module-mpl_toolkits.basemap|detailed official documentation]] | ||
- | ===== Cartopy ===== | + | ===== Cartopy + Iris ===== |
- | Summary: //Cartopy makes use of the powerful PROJ.4, numpy and shapely libraries and has a simple and intuitive drawing interface to matplotlib for creating publication quality maps// | + | 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 web site]] | + | Where: [[http://scitools.org.uk/cartopy/docs/latest/|Cartopy]] and [[https://scitools-iris.readthedocs.io/en/stable/|Iris]] web sites |
- | Help on //stack overflow//: [[https://stackoverflow.com/questions/tagged/cartopy|cartopy help]] | + | Examples: |
+ | * [[other:python:maps_by_jyp|Examples provided by JYP]] | ||
+ | * Official gallery pages: [[https://scitools.org.uk/cartopy/docs/latest/gallery/index.html|Cartopy]] - [[https://scitools-iris.readthedocs.io/en/stable/generated/gallery/|Iris]] | ||
- | ===== Pandas ===== | + | Help on //stack overflow//: [[https://stackoverflow.com/questions/tagged/cartopy|Cartopy help]] - [[https://stackoverflow.com/questions/tagged/python-iris|Iris help]] |
- | Summary: //pandas is a library providing high-performance, easy-to-use data structures and data analysis tools// | + | ===== Maps and projections resources ===== |
+ | |||
+ | ==== About projections ==== | ||
+ | |||
+ | * [[https://egsc.usgs.gov/isb//pubs/MapProjections/projections.html|Map projections from USGS poster]] | ||
+ | * [[https://pubs.usgs.gov/pp/1395/report.pdf|Map projections - A working manual (USGS)]] | ||
+ | |||
+ | ==== Libraries ==== | ||
+ | |||
+ | * Projections in vcs | ||
+ | * [[http://matplotlib.org/basemap/users/mapsetup.html|Projections in basemap]] | ||
+ | * [[https://scitools.org.uk/cartopy/docs/latest/crs/projections.html|Projections in cartopy]] | ||
+ | |||
+ | |||
+ | ===== 3D plots resources ===== | ||
+ | |||
+ | * [[https://ipyvolume.readthedocs.io/en/latest/|Ipyvolume]] | ||
+ | * [[https://zulko.wordpress.com/2012/09/29/animate-your-3d-plots-with-pythons-matplotlib/|Animate your 3D plots with Python’s Matplotlib]] | ||
+ | * [[https://stackoverflow.com/questions/26796997/how-to-get-vertical-z-axis-in-3d-surface-plot-of-matplotlib|How to get vertical Z axis in 3D surface plot of Matplotlib?]] | ||
+ | |||
+ | ===== Data analysis ===== | ||
+ | |||
+ | ==== EDA (Exploratory Data Analysis) ? ==== | ||
+ | |||
+ | <note tip> | ||
+ | The //EDA concept// seems to apply to **time series** (and tabular data), which is not exactly the case of full climate model output data</note> | ||
+ | |||
+ | * [[https://www.geeksforgeeks.org/what-is-exploratory-data-analysis/|What is Exploratory Data Analysis ?]] | ||
+ | * //The method of studying and exploring record sets to apprehend their predominant traits, discover patterns, locate outliers, and identify relationships between variables. EDA is normally carried out as a preliminary step before undertaking extra formal statistical analyses or modeling.// | ||
+ | |||
+ | * [[https://medium.com/codex/automate-the-exploratory-data-analysis-eda-to-understand-the-data-faster-not-better-2ed6ff230eed|Automate the exploratory data analysis (EDA) to understand the data faster and easier]]: a nice comparison of some Python libraries listed below (''ydata-profiling'', ''D-Tale'', ''sweetviz'', ''autoviz'') | ||
+ | |||
+ | * [[https://www.geeksforgeeks.org/exploratory-data-analysis-in-python/|EDA in Python]] | ||
+ | |||
+ | |||
+ | ==== Easy to use datasets ==== | ||
+ | |||
+ | If you need standard datasets for testing, example, demos, ... | ||
+ | |||
+ | * [[https://docs.xarray.dev/en/stable/generated/xarray.tutorial.load_dataset.html|Tutorial datasets]] from [[#xarray|xarray]] (requires internet) | ||
+ | * Example: [[https://docs.xarray.dev/en/stable/examples/visualization_gallery.html|Using the 'air temperature' dataset]] | ||
+ | |||
+ | * [[https://scikit-learn.org/stable/datasets.html|Toy, real-world and generated datasets]] from [[#scikit-learn]] | ||
+ | * Example: [[https://lectures.scientific-python.org/packages/scikit-learn/index.html#a-simple-example-the-iris-dataset|using the 'iris' dataset]] | ||
+ | |||
+ | * [[https://scikit-image.org/docs/stable/api/skimage.data.html|Test images and datasets]] from [[#scikit-image]] | ||
+ | * Example: [[https://lectures.scientific-python.org/packages/scikit-image/index.html#data-types|Using the 'camera' dataset]] | ||
+ | |||
+ | * [[https://esgf-node.ipsl.upmc.fr/search/cmip6-ipsl/|CMIP6 data]] on ESGF | ||
+ | * Example : ''orog_fx_IPSL-CM6A-LR_piControl_r1i1p1f1_gr.nc'': | ||
+ | * [[http://vesg.ipsl.upmc.fr/thredds/fileServer/cmip6/CMIP/IPSL/IPSL-CM6A-LR/piControl/r1i1p1f1/fx/orog/gr/v20200326/orog_fx_IPSL-CM6A-LR_piControl_r1i1p1f1_gr.nc|HTTP]] download link | ||
+ | * [[http://vesg.ipsl.upmc.fr/thredds/dodsC/cmip6/CMIP/IPSL/IPSL-CM6A-LR/piControl/r1i1p1f1/fx/orog/gr/v20200326/orog_fx_IPSL-CM6A-LR_piControl_r1i1p1f1_gr.nc.dods|OpenDAP]] download link | ||
+ | |||
+ | * [[https://github.com/xCDAT/xcdat/issues/277|xCDAT test data GH discussion]] | ||
+ | |||
+ | |||
+ | ==== Pandas ==== | ||
+ | |||
+ | Summary: //pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool// | ||
Where: [[http://pandas.pydata.org|Pandas web site]] | Where: [[http://pandas.pydata.org|Pandas web site]] | ||
- | JYP's comment: pandas is supposed to be quite good for loading, processing and plotting time series, without writing custom code. You should at least have a quick look at: | + | JYP's comment: pandas is supposed to be quite good for loading, processing and plotting time series, without writing custom code. It is **very convenient for processing tables in xlsx files** (or csv, etc...). You should at least have a quick look at: |
- | * The [[http://www.scipy-lectures.org/packages/statistics/index.html|Statistics in Python]] tutorial that combines Pandas, [[http://statsmodels.sourceforge.net/|Statsmodels]] and [[http://seaborn.pydata.org/|Seaborn]] | + | |
- | * the cheat sheet on the [[https://www.enthought.com/services/training/pandas-mastery-workshop/|Enthought workshops advertising page]] | + | |
- | * the cheat sheet on the [[https://github.com/pandas-dev/pandas/tree/master/doc/cheatsheet|github Pandas doc page]] | + | |
- | ===== Scipy Lecture Notes ===== | + | * Some //Cheat Sheets//: |
+ | - Basics: [[https://github.com/fralfaro/DS-Cheat-Sheets/blob/main/docs/files/pandas_cs.pdf|Pandas Basics Cheat Sheet]] (associated with the [[https://www.datacamp.com/cheat-sheet/pandas-cheat-sheet-for-data-science-in-python#python-for-data-science-cheat-sheet:-pandas-basics-useth|Pandas basics]] //datacamp// introduction page) | ||
+ | - Intermediate: [[https://github.com/pandas-dev/pandas/blob/main/doc/cheatsheet/Pandas_Cheat_Sheet.pdf|Data Wrangling with pandas Cheat Sheet]] | ||
+ | * Some tutorials: | ||
+ | * [[http://pandas.pydata.org/docs/user_guide/10min.html|10 minutes to pandas]] | ||
+ | * The [[https://lectures.scientific-python.org/packages/statistics/index.html|Statistics in Python]] tutorial that combines Pandas, [[#statsmodels|statsmodels]] and [[http://seaborn.pydata.org/|Seaborn]] | ||
+ | * More [[http://pandas.pydata.org/docs/getting_started/tutorials.html|Community tutorials]]... | ||
- | Summary: //One document to learn numerics, science, and data with Python// | ||
- | Where: [[http://www.scipy-lectures.org/_downloads/ScipyLectures-simple.pdf|pdf]] - [[http://www.scipy-lectures.org/|html]] | + | ==== statsmodels ==== |
- | This is **a really nice and useful document** that is regularly updated and used for the [[https://www.euroscipy.org/|EuroScipy]] tutorials. You will learn more things about python, numpy and matplotlib, debugging and optimizing scripts, and also learn about using python for statistics, image processing, machine learning, washing dishes (this is just to check if you have read this page), etc... | + | [[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. |
- | ===== Quick Reference ===== | + | Note: check the example in the [[https://lectures.scientific-python.org/packages/statistics/index.html|Statistics in Python]] tutorial |
+ | |||
+ | |||
+ | ==== scikit-learn ==== | ||
+ | |||
+ | [[http://scikit-learn.org/|scikit-learn]] is a Python library for machine learning, and is one of the most widely used tools for supervised and unsupervised machine learning. Scikit–learn provides an easy-to-use, consistent interface to a large collection of machine learning models, as well as tools for model evaluation and data preparation | ||
+ | |||
+ | Note: check the example in [[https://lectures.scientific-python.org/packages/scikit-learn/index.html|scikit-learn: machine learning in Python]] | ||
+ | |||
+ | |||
+ | ==== scikit-image ==== | ||
+ | |||
+ | [[https://scikit-image.org/|scikit-image]] is a collection of algorithms for image processing in Python | ||
+ | |||
+ | Note: check the example in [[https://lectures.scientific-python.org/packages/scikit-image/index.html|scikit-image: image processing]] | ||
+ | |||
+ | |||
+ | ==== YData Profiling ==== | ||
+ | |||
+ | [[https://docs.profiling.ydata.ai/|YData Profiling]]: a leading package for data profiling, that automates and standardizes the generation of detailed reports, complete with statistics and visualizations. | ||
+ | |||
+ | |||
+ | ==== D-Tale ==== | ||
+ | |||
+ | [[https://github.com/man-group/dtale|D-Tale]] brings you an easy way to view & analyze Pandas data structures. It integrates seamlessly with ipython notebooks & python/ipython terminals. | ||
+ | |||
+ | |||
+ | ==== Sweetviz ==== | ||
+ | |||
+ | [[https://github.com/fbdesignpro/sweetviz|Sweetviz]] is pandas based Python library that generates beautiful, high-density visualizations to kickstart EDA (Exploratory Data Analysis) with just two lines of code. | ||
+ | |||
+ | |||
+ | ==== AutoViz ==== | ||
+ | |||
+ | [[https://github.com/AutoViML/AutoViz|AutoViz]]: the One-Line Automatic Data Visualization Library. Automatically Visualize any dataset, any size with a single line of code | ||
+ | |||
+ | |||
+ | ===== Data file formats ===== | ||
+ | |||
+ | 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 ==== | ||
+ | |||
+ | More and more applications use //json files// as configuration files or as a mean to use text files to exchange data (through serialization/deserialization ). | ||
+ | |||
+ | //json// files look basically like a **list of (nested) python dictionaries** that would have been dumped to a text file | ||
+ | |||
+ | * [[https://docs.python.org/3/library/json.html|json module]] documentation | ||
+ | * [[https://realpython.com/python-json/|Working With JSON Data in Python]] tutorial | ||
+ | * example script: ''/home/users/jypeter/CDAT/Progs/Devel/beaugendre/nc2json.py'' | ||
+ | * A compact (not easy to read...) //json// file can be pretty-printed with\\ ''cat file.json | python -m json.tool | less'' | ||
+ | |||
+ | ==== LiPD files ==== | ||
+ | |||
+ | Resources for //Linked PaleoData//: | ||
+ | * [[http://linked.earth/projects/lipd/|LiPD]] | ||
+ | * [[https://doi.org/10.5194/cp-12-1093-2016|Technical note: The Linked Paleo Data framework – | ||
+ | a common tongue for paleoclimatology]] @ GMD | ||
+ | * [[https://github.com/nickmckay/LiPD-utilities|LiPD-utilities]] @ github | ||
+ | |||
+ | ==== BagIt files ==== | ||
+ | |||
+ | //BagIt//, a set of hierarchical file layout conventions for storage and transfer of arbitrary digital content. | ||
+ | |||
+ | * [[https://tools.ietf.org/html/draft-kunze-bagit-16|The BagIt File Packaging Format]] | ||
+ | * [[https://github.com/LibraryOfCongress/bagger|Bagger]] (BagIt GUI) | ||
+ | * [[https://github.com/LibraryOfCongress/bagit-python|bagit-python]] | ||
+ | |||
+ | ===== Quick Reference and cheat sheets ===== | ||
* The nice and convenient Python 2.7 Quick Reference: [[http://rgruet.free.fr/PQR27/PQR2.7_printing_a4.pdf|pdf]] - [[http://rgruet.free.fr/PQR27/PQR2.7.html|html]] | * The nice and convenient Python 2.7 Quick Reference: [[http://rgruet.free.fr/PQR27/PQR2.7_printing_a4.pdf|pdf]] - [[http://rgruet.free.fr/PQR27/PQR2.7.html|html]] | ||
+ | * A possibly more [[http://iysik.com/PQR2.7/PQR2.7.html|up-date-version]] | ||
+ | |||
+ | * Python 3 [[https://perso.limsi.fr/pointal/python:abrege|Quick reference]] and [[https://perso.limsi.fr/pointal/python:memento|Cheat sheet]] | ||
+ | |||
+ | * [[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 ===== | ||
+ | * [[https://pyformat.info/|PyFormat]]: //With this site we try to show you the most common use-cases covered by the old and new style string formatting API with practical examples// | ||
===== Some good coding tips ===== | ===== Some good coding tips ===== | ||
Line 247: | Line 498: | ||
Depending on the distribution, the editor and the programming environment you use, you may have access to a graphical version of the debugger. UV-CDAT users can use ''pydebug my_script.py'' | Depending on the distribution, the editor and the programming environment you use, you may have access to a graphical version of the debugger. UV-CDAT users can use ''pydebug my_script.py'' | ||
+ | |||
+ | ===== jupyter and notebook stuff ===== | ||
+ | |||
+ | FIXME Misc notes, resources and links to organize later | ||
+ | |||
+ | * [[https://beta.jupyterbook.org/|jupyter {book}]]: Jupyter Book is an open source project for building beautiful, publication-quality books and documents from computational material. | ||
+ | |||
+ | ===== Using a Python IDE ===== | ||
+ | |||
+ | **IDE** = //Integrated Development Environment// | ||
+ | |||
+ | There are lots of ways to use Python and develop scripts, from using a lightweight approach (your favorite text editor with builtin python syntax highlighting, e.g. **emacs** and ''python -i myscript.py'') to a full-fledged IDE. You'll find below some IDE related links | ||
+ | |||
+ | * [[https://www.datacamp.com/community/tutorials/data-science-python-ide|Top 5 Python IDEs For Data Science]] | ||
+ | * [[http://noeticforce.com/best-python-ide-for-programmers-windows-and-mac|Python IDE: The10 Best IDEs for Python Programmers]] | ||
+ | * [[https://www.techbeamers.com/best-python-ide-python-programming/|Get the Best Python IDE]] | ||
+ | * [[https://wiki.python.org/moin/IntegratedDevelopmentEnvironments]] | ||
+ | |||
+ | ==== Spyder ==== | ||
+ | |||
+ | * [[https://github.com/spyder-ide/spyder|Home page]] | ||
+ | * [[http://pythonhosted.org/spyder/|Documentation]] | ||
+ | |||
===== Improving the performance of your code ===== | ===== Improving the performance of your code ===== | ||
Line 272: | Line 546: | ||
===== Python 2.7 vs Python 3 ===== | ===== Python 2.7 vs Python 3 ===== | ||
- | The official [[https://docs.python.org/2.7/howto/pyporting.html|Porting Python 2 Code to Python 3]] page gives the required information to make the transition from python 2 to python 3. It is still safe to use Python 2.7, so there is no rush to change to Python 3. | + | It is still safe to use Python 2.7, but **you should consider upgrading to Python 3**, unless some key modules you need are not compatible (yet) with Python 3 |
+ | |||
+ | You should start writing code that will, when possible, work both in Python 2 and Python 3 | ||
+ | |||
+ | Some interesting reading: | ||
+ | |||
+ | * [[https://docs.python.org/3/whatsnew/3.0.html|What’s New In Python 3.0]].\\ Examples: | ||
+ | * ''print'' is now a function. Use ''print('Hello')'' | ||
+ | * You cannot test a difference with ''<>'' any longer! Use ''!='' | ||
+ | |||
+ | * The official [[https://docs.python.org/2.7/howto/pyporting.html|Porting Python 2 Code to Python 3]] page gives the required information to make the transition from python 2 to python 3. | ||
===== What now? ===== | ===== What now? ===== |