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other:python:jyp_steps [2023/12/15 10:25] jypeter [YData Profiling] Added D-Tale |
other:python:jyp_steps [2023/12/15 14:53] jypeter Moved cdms2 below xarray |
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* 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 | * 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 ==== | ||
- | |||
- | 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: | ||
- | - read [[http://www.lsce.ipsl.fr/Phocea/file.php?class=page&file=5/pythonCDAT_jyp_2sur2_070306.pdf|JYP's cdms tutorial]], starting at page 54 | ||
- | - 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) | ||
- | - read the [[http://cdms.readthedocs.io/en/docstanya/index.html|official cdms documentation]] (link may change) | ||
==== xarray ==== | ==== xarray ==== | ||
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* [[https://docs.xarray.dev/en/stable/generated/xarray.tutorial.load_dataset.html|xarray test datasets]] | * [[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://xcdat.readthedocs.io/|xCDAT]]: ''xarray'' extended with Climate Data Analysis Tools** |
* [[https://xoa.readthedocs.io/en/latest/|xoa]]: xarray-based ocean analysis library | * [[https://xoa.readthedocs.io/en/latest/|xoa]]: xarray-based ocean analysis library | ||
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* [[https://uxarray.readthedocs.io/|uxarray]]: provide xarray styled functionality for unstructured grid datasets following [[https://ugrid-conventions.github.io/ugrid-conventions/|UGRID Conventions]] | * [[https://uxarray.readthedocs.io/|uxarray]]: provide xarray styled functionality for unstructured grid datasets following [[https://ugrid-conventions.github.io/ugrid-conventions/|UGRID Conventions]] | ||
+ | |||
+ | ==== cdms2 ==== | ||
+ | |||
+ | <note important>''cdms2'' is unfortunately not maintained anymore and is slowly being **phased out in favor of a combination of [[#xarray|xarray]] and [[https://xcdat.readthedocs.io/|xCDAT]]**</note> | ||
+ | |||
+ | 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: | ||
+ | - read [[http://www.lsce.ipsl.fr/Phocea/file.php?class=page&file=5/pythonCDAT_jyp_2sur2_070306.pdf|JYP's cdms tutorial]], starting at page 54 | ||
+ | - 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) | ||
+ | - read the [[http://cdms.readthedocs.io/en/docstanya/index.html|official cdms documentation]] (link may change) | ||
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===== Data analysis ===== | ===== 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|YData Profiling]], [[#d-tale|D-Tale]], [[#sweetviz|sweetviz]], [[#autoviz|AutoViz]]) | ||
+ | |||
+ | * [[https://www.geeksforgeeks.org/exploratory-data-analysis-in-python/|EDA in Python]] | ||
+ | |||
==== Easy to use datasets ==== | ==== Easy to use datasets ==== | ||
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* [[https://github.com/xCDAT/xcdat/issues/277|xCDAT test data GH discussion]] | * [[https://github.com/xCDAT/xcdat/issues/277|xCDAT test data GH discussion]] | ||
+ | |||
+ | |||
==== Pandas ==== | ==== Pandas ==== | ||
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Note: check the example in the [[https://lectures.scientific-python.org/packages/statistics/index.html|Statistics in Python]] tutorial | 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]] | ||
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[[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. | [[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. | ||
- | ==== 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]] | + | ==== Sweetviz ==== |
- | ==== scikit-image ==== | + | |
- | [[https://scikit-image.org/|scikit-image]] is a collection of algorithms for image processing in Python | + | [[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. |
- | Note: check the example in [[https://lectures.scientific-python.org/packages/scikit-image/index.html|scikit-image: image processing]] | + | |
+ | ==== 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 ===== | ===== Data file formats ===== | ||
- | We list here some resources about non-NetCDF data formats that can be useful | + | * We list below some resources about **non-NetCDF data formats** that can be useful |
+ | |||
+ | * Check the [[#using_netcdf_files_with_python|Using NetCDF files with Python]] section otherwise | ||
==== The shelve package ==== | ==== The shelve package ==== |