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--- other:python:misc_by_jyp [2022/03/08 16:40]
jypeter Added a string section
+++ other:python:misc_by_jyp [2022/12/12 12:11]
jypeter Added the "Storing objects and data in a file" section
@@ Line 41: / Line 41: @@
 ==== Playing with strings ====
-=== Filenames, etc ===
+=== Filenames, etc... ===
 Check [[other:python:misc_by_jyp#working_with_paths_and_filenames|Working with paths and filenames]] and [[other:python:misc_by_jyp#generating_file_names|Generating file names]]
@@ Line 228: / Line 228: @@
 </code>
+==== Storing objects and data in a file (shelve and friends) ====
+The built-in [[other:python:jyp_steps?s[]=shelve#the_shelve_package|shelve]] module can be **easily** used for storing temporary/intermediate data
+More options:
+  * Some [[other:python:jyp_steps#data_file_formats|non-NetCDF]] file formats
+  * Working with [[other:python:jyp_steps#netcdf_filesusing_cdms2_xarray_and_netcdf4|NetCDF]] files
 ==== Sorting ====
@@ Line 247: / Line 255: @@
 ==== numpy related stuff ====
+=== Using a numpy array to store arbitrary objects ===
+The numpy arrays are usually used to store [[https://numpy.org/doc/stable/reference/arrays.scalars.html|scalars]] of the same type (see also the [[https://numpy.org/doc/stable/reference/arrays.dtypes.html|Data type objects (dtype)]]), very often numerical values.
+It is also possible to store **arbitrary** Python objects in an array, rather than using nested lists or dictionaries!
+<code>>>> some_array = np.empty((2, 3), dtype=object)
+>>> some_array
+array([[None, None, None],
+       [None, None, None]], dtype=object)
+>>> some_array.shape
+(2, 3)
+>>> print(some_array[-1, -1])
+None
+>>> some_array[-1, 0] = filled_contour # e.g. save an existing cartopy filled contour object
+>>> some_array
+array([[None, None, None],
+       [<cartopy.mpl.contour.GeoContourSet object at 0x2ab679e8bf10>,
+        None, None]], dtype=object)</code>
+=== Dealing with a variable number of indices ===
+[[https://numpy.org/doc/stable/user/basics.indexing.html#dealing-with-variable-indices|Official reference]]
+<code>>>> i10 = np.identity(10)
+>>> i10
+array([[1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],
+       [0., 1., 0., 0., 0., 0., 0., 0., 0., 0.],
+...
+       [0., 0., 0., 0., 0., 0., 0., 0., 0., 1.]])
+>>> i10.shape
+(10, 10)
+>>> i10[3:7, 4:6]
+array([[0., 0.],
+       [1., 0.],
+       [0., 1.],
+       [0., 0.]])
+>>> s0 = slice(3, 7)
+>>> s1 = slice(4, 6)
+>>> i10[s0, s1]
+array([[0., 0.],
+       [1., 0.],
+       [0., 1.],
+       [0., 0.]])
+>>> my_slices = (s0, s1)
+>>> i10[my_slices]
+array([[0., 0.],
+       [1., 0.],
+       [0., 1.],
+       [0., 0.]])
+>>> my_fancy_slices = (s0, Ellipsis)
+>>> i10[my_fancy_slices]
+array([[0., 0., 0., 1., 0., 0., 0., 0., 0., 0.],
+       [0., 0., 0., 0., 1., 0., 0., 0., 0., 0.],
+       [0., 0., 0., 0., 0., 1., 0., 0., 0., 0.],
+       [0., 0., 0., 0., 0., 0., 1., 0., 0., 0.]])
+>>> i10[my_fancy_slices].shape
+(4, 10)
+>>> # WARNING! DANGERRRR! NEVER forget that a VIEW is NOT A COPY
+>>> # and that you can change the content of the original array by mistake
+>>> my_view = i10[my_slices]
+>>> my_view[:, :] = -1
+>>> my_view
+array([[-1., -1.],
+       [-1., -1.],
+       [-1., -1.],
+       [-1., -1.]])
+>>> i10
+array([[ 1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
+       [ 0.,  1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
+       [ 0.,  0.,  1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
+       [ 0.,  0.,  0.,  1., -1., -1.,  0.,  0.,  0.,  0.],
+       [ 0.,  0.,  0.,  0., -1., -1.,  0.,  0.,  0.,  0.],
+       [ 0.,  0.,  0.,  0., -1., -1.,  0.,  0.,  0.,  0.],
+       [ 0.,  0.,  0.,  0., -1., -1.,  1.,  0.,  0.,  0.],
+       [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  1.,  0.,  0.],
+       [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  1.,  0.],
+       [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  1.]])</code>
 === Finding and counting unique values ===

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