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other:python:jyp_steps [2021/08/20 11:52] jypeter [json files] |
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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 | ||
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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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- 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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==== Extra numpy information ==== | ==== Extra numpy information ==== | ||
+ | |||
+ | <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> | * 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> | ||
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* {{ :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://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/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|Array indexing (reference manual)]] | + | * [[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]] | * [[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://numpy.org/doc/stable/reference/routines.indexing.html#routines-indexing|Indexing routines]] | ||
* More information about arrays: | * 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-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.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/maskedarray.html|Masked arrays]] | ||
* [[https://numpy.org/doc/stable/reference/routines.ma.html|Masked array operations]] | * [[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//) | * [[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 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/user/misc.html#how-numpy-handles-numerical-exceptions|Handling numerical exceptions]] | ||
* [[https://numpy.org/doc/stable/reference/routines.err.html|Floating point error handling]] | * [[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 be stored in a [[other:newppl:starting#netcdf_and_related_conventions|NetCDF]] file. |
- | 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. | + | * 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. 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 | ||
+ | |||
+ | === 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]] | ||
+ | |||
+ | * [[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]] | ||
+ | |||
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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://zulko.wordpress.com/2012/09/29/animate-your-3d-plots-with-pythons-matplotlib/|Animate your 3D plots with Python’s Matplotlib]] | * [[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?]] | * [[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 ===== | ||
+ | |||
===== Data file formats ===== | ===== Data file formats ===== | ||
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* Some tutorials: | * Some tutorials: | ||
* [[https://www.datacamp.com/community/blog/python-pandas-cheat-sheet|Pandas Cheat Sheet for Data Science in Python]] pandas introduction page | * [[https://www.datacamp.com/community/blog/python-pandas-cheat-sheet|Pandas Cheat Sheet for Data Science in Python]] pandas introduction page | ||
- | * 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 [[https://lectures.scientific-python.org/packages/statistics/index.html|Statistics in Python]] tutorial that combines Pandas, [[http://statsmodels.sourceforge.net/|Statsmodels]] and [[http://seaborn.pydata.org/|Seaborn]] |
- | ===== Scipy Lecture Notes ===== | ||
- | Summary: //One document to learn numerics, science, and data with Python// | + | ===== statsmodels ===== |
- | Where: [[http://www.scipy-lectures.org/_downloads/ScipyLectures-simple.pdf|pdf]] - [[http://www.scipy-lectures.org/|html]] | + | [[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. |
- | This is **a really nice and useful document** that is regularly updated and used for the [[https://www.euroscipy.org/|EuroScipy]] tutorials. | + | ===== 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 ===== | ||
- | 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... | + | [[https://scikit-image.org/|scikit-image]] is a collection of algorithms for image processing in Python |
- | * 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]] | + | |
===== Quick Reference and cheat sheets ===== | ===== Quick Reference and cheat sheets ===== |