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--- other:python:jyp_steps [2021/09/22 16:23] – Added statsmodels, scikit-learn and scikit-image jypeter
+++ other:python:jyp_steps [2023/03/29 15:38] – [Extra numpy information] Corrected links jypeter
@@ Line 123: / Line 123: @@
 ==== 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>
@@ Line 128: / Line 133: @@
       * {{ :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|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/reference/routines.indexing.html#routines-indexing|Indexing routines]]
   * 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]]
-===== 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 ====
@@ Line 160: / Line 172: @@
 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]]