other:python:jyp_steps
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other:python:jyp_steps [2021/09/22 14:23] jypeter Added statsmodels, scikit-learn and scikit-image |
other:python:jyp_steps [2023/02/01 16:49] jypeter [xarray] Added some xarray resources |
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| ==== Extra numpy information ==== | ==== Extra numpy information ==== | ||
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| + | <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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| * [[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]] | ||
| - | ===== NetCDF files: using cdms2, xarray and netCDF4 ===== | + | ===== Using NetCDF files with Python ===== |
| - | 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]]. | + | <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> | ||
| - | 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. | + | * 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 ==== | ||
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| 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 | 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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| + | === 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 | ||
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| + | * [[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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other/python/jyp_steps.txt · Last modified: 2024/03/07 10:15 by jypeter
