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--- other:python:misc_by_jyp [2022/12/12 15:24] – Added the global variables section jypeter
+++ other:python:misc_by_jyp [2023/04/27 17:52] – [Data representation] Improved jypeter
@@ Line 32: / Line 32: @@
 <code>sys.exit('Some optional message about why we are stopping')</code>
+===== Data representation =====
+A few notes for a future section or page about about //data representation// (bits and bytes) on disk and in memory, vs //data format//
+  * Binary data representation of some numbers:
+    * [[https://en.wikipedia.org/wiki/Integer_(computer_science)|Integers]]
+      * Range:
+        * 4-byte integers (''numpy.int32''): −2,147,483,648 to 2,147,483,647
+        * 8-byte integers (''numpy.int64''): −9,223,372,036,854,775,808 to 9,223,372,036,854,775,807
+      * Tech note: signed integers use [[https://en.wikipedia.org/wiki/Two%27s_complement|two's complement]] for coding negative integers
+    * [[https://en.wikipedia.org/wiki/IEEE_754|Floating point numbers]] (//IEEE 754// standard aka //IEEE Standard for Binary Floating-Point for Arithmetic//)
+      * Range:
+        * 4-byte float (''numpy.float32''): ~8 significant digits * 10E±38
+          * See also [[https://en.wikipedia.org/wiki/Single-precision_floating-point_format|Single-precision floating-point format]]
+        * 8-byte float (''numpy.float64''): ~15 significant digits * 10E±308
+      * Special values:
+        * [[https://en.wikipedia.org/wiki/NaN|NaN]] (''numpy.nan''): //Not a Number//
+        * Infinity (''-numpy.inf'' and ''numpy.inf'')
+        * Note: it is cleaner to use masks (and [[https://numpy.org/doc/stable/reference/maskedarray.generic.html|Numpy masked arrays]]) than NaNs, when you have to deal with missing values !
+    * [[https://en.wikipedia.org/wiki/Bit_numbering|Bit numbering]]
+    * [[https://en.wikipedia.org/wiki/Endianness|Endianness]]
+    * A rather technical example: we //play// with a numpy 4-byte integer scalar
+      * <code>>>> one_int32 = np.int32(1)
+>>> one_int32
+>>> type(one_int32)
+<class 'numpy.int32'>
+>>> one_int32.dtype
+dtype('int32')
+>>> one_int32.shape # A numpy SCALAR, is an ARRAY WITH NO SHAPE !
+()
+>>> one_int32[0]
+Traceback (most recent call last):
+  File "<stdin>", line 1, in <module>
+IndexError: invalid index to scalar variable.
+>>> one_int32[()] # Note how to access the single element, when there is NO SHAPE
+>>> one_int32.ndim # NO SHAPE means no dimensions, but there is ONE element
+>>> one_int32.size
+>>> one_int32.nbytes # The element requires 4 bytes of storage
+>>> hex(one_int32) # We can print the hexadecimal representation for INTEGERS scalars and arrays
+'0x1'
+>>> hex(one_int32 * 15)
+'0xf'
+>>> hex(one_int32 * 16)
+'0x10'
+# 'Serialize' the data (i.e. change the data to a series of bytes)
+# Note: the serialized data seems to be printed in the reverse order of 'hex(one_int32)'
+>>> one_int32_serialized = one_int32.tobytes()
+>>> type(one_int32_serialized)
+<class 'bytes'>
+>>> len(one_int32_serialized)
+>>> one_int32_serialized
+b'\x01\x00\x00\x00'
+>>> one_int32_serialized.hex(' ') # Another way to print the hexadecimal values
+'01 00 00 00'
+# Use the following in the unlikely case where you need to change the endianness (bytes ordering)
+>>> one_int32_reversed_endian = one_int32.byteswap()
+>>> one_int32_reversed_endian # Same bytes in a different order represent a different number (of course)
+16777216
+>>> hex(one_int32_reversed_endian) # Compare to the output of hex(one_int32) above
+'0x1000000'
+>>> one_int32_reversed_endian.tobytes()
+b'\x00\x00\x00\x01'</code>
+    * Another technical example: we use an array of 2 integers\\ When using ''byteswap()'', notice how bytes are swapped by groups of 4 bytes, because int32 use 4 bytes
+      * <code>>>> array_example = np.asarray((3, 17), dtype=np.int32)
+>>> array_example
+array([ 3, 17], dtype=int32)
+>>> array_example.shape, array_example.ndim, array_example.size, array_example.nbytes
+((2,), 1, 2, 8)
+>>> array_example.tobytes().hex(' ', 4)
+'03000000 11000000'
+>>> array_example.byteswap().tobytes().hex(' ', 4)
+'00000003 00000011'
+</code>
+  * Array addressing
+    * python/C vs Fortran...
+  * disk and ram usage: how to check the usage (available ram and disk), best practice on multi-user systems (how much allowed?)
+    * ''du'', ''df'', ''cat /proc/meminfo'', ''top''
+  * understanding and reverse-engineering //binary// format
+    * ''od'', ''strings''
+  * binary vs text format: ascii, utf, raw
+    * text related functions in python: ''str'', ''int'', ''float'', ''ord'', ...
+      * lists conversion with ''map'' and ''join''
+  * Misc : ''md5sum''
 ===== Checking if a file/directory is writable by the current user =====
@@ Line 453: / Line 548: @@
 >>> np.add.reduceat(np.sort(vals), slices_indices)
 array([3. , 4.5, 8. ])</code>
+===== matplotlib related stuff =====
+==== Working with time axes (and ticks) ====
+If you have problems setting the limits of a time axis, choosing the ticks' locations, or specifying the style of the labels, you should check the:
+  * [[https://matplotlib.org/stable/gallery/index.html#ticks|Ticks examples' gallery]]
+  * [[https://matplotlib.org/stable/gallery/text_labels_and_annotations/date.html|Date tick labels example]]