DOC: Bash examples in PR #17353 now Python

And other suggestions by @eric-wieser.
Also standardizes quotes as '"'.

1 files changed, 31 insertions, 25 deletions

diff --git a/doc/source/user/how-to-io.rst b/doc/source/user/how-to-io.rst
index 64096bfc2..d962e1546 100644
--- a/doc/source/user/how-to-io.rst
+++ b/doc/source/user/how-to-io.rst
@@ -83,17 +83,19 @@ that have missing values if
 * **Each field has a fixed width**: Use the width as the `delimiter` argument.

   ::

 

-    # File with width=4. The data does not have to be justified (for example, the

-    # 2 in row 1), the last column can be less than width (for example, the 6 in

-    # row 2), and no delimiting character is required (for instance 8888 and 9 in row 3)

+    # File with width=4. The data does not have to be justified (for example,

+    # the 2 in row 1), the last column can be less than width (for example, the 6

+    # in row 2), and no delimiting character is required (for instance 8888 and 9

+    # in row 3)

 

-    $cat fixedwidth.txt  # doctest: +SKIP

+    >>> f = open("fixedwidth.txt").read()  # doctest: +SKIP

+    >>> print(f)  # doctest: +SKIP

     1   2      3

     44      6

     7   88889

 

-    # Showing spaces as '^'

-    $ tr ' ' '^' < fixedwidth.txt  # doctest: +SKIP

+    # Showing spaces as ^

+    >>> print(f.replace(" ","^"))  # doctest: +SKIP

     1^^^2^^^^^^3

     44^^^^^^6

     7^^^88889

@@ -107,12 +109,12 @@ that have missing values if
   `missing_values` argument.

   ::

 

-    $ cat nan.txt  # doctest: +SKIP

+    >>> print(open("nan.txt").read())  # doctest: +SKIP

     1 2 3

     44 x 6

     7  8888 9

 

-    >>> np.genfromtxt("nan.txt", missing_values='x')  # doctest: +SKIP

+    >>> np.genfromtxt("nan.txt", missing_values="x")  # doctest: +SKIP

     array([[1.000e+00, 2.000e+00, 3.000e+00],

            [4.400e+01,       nan, 6.000e+00],

            [7.000e+00, 8.888e+03, 9.000e+00]])

@@ -121,7 +123,7 @@ that have missing values if
   `invalid_raise=False`.

   ::

 

-    $ cat skip.txt  # doctest: +SKIP

+    >>> print(open("skip.txt").read())  # doctest: +SKIP

     1 2   3

     44    6

     7 888 9

@@ -139,21 +141,22 @@ that have missing values if
   or more spaces.

   ::

 

-      $ cat tabs.txt  # doctest: +SKIP

-      1 2 3

-      44    6

-      7 888 9

+    >>> f = open("tabs.txt").read()  # doctest: +SKIP

+    >>> print(f)  # doctest: +SKIP

+    1       2       3

+    44              6

+    7       888     9

 

-      # Showing the tabs (^I) and spaces

-      $ cat -T tabs.txt  # doctest: +SKIP

-      1^I2^I3

-      44^I ^I6

-      7^I888^I9

+    # Tabs vs. spaces

+    >>> print(f.replace("\t","^"))  # doctest: +SKIP

+    1^2^3

+    44^ ^6

+    7^888^9

 

-      >>> np.genfromtxt("tabs.txt", delimiter="\t", missing_values=" +")  # doctest: +SKIP

-      array([[  1.,   2.,   3.],

-             [ 44.,  nan,   6.],

-             [  7., 888.,   9.]])

+    >>> np.genfromtxt("tabs.txt", delimiter="\t", missing_values=" +")  # doctest: +SKIP

+    array([[  1.,   2.,   3.],

+           [ 44.,  nan,   6.],

+           [  7., 888.,   9.]])

 

 ******************************************************************************

 Read a file in .npy or .npz format

@@ -226,7 +229,7 @@ The ``.wav`` file header as a NumPy structured dtype::
     wav_header_dtype = np.dtype([

         ("chunk_id", (bytes, 4)), # flexible-sized scalar type, item size 4

         ("chunk_size", "<u4"),    # little-endian unsigned 32-bit integer

-        ("format", "S4"),         # 4-byte string

+        ("format", "S4"),         # 4-byte string, alternate spelling of (bytes, 4)

         ("fmt_id", "S4"),

         ("fmt_size", "<u4"),

         ("audio_fmt", "<u2"),     #

@@ -242,7 +245,10 @@ The ``.wav`` file header as a NumPy structured dtype::
         # it does not have a fixed size

     ])

 

-    data = np.fromfile(f,dtype=wave_header_dtype,count=1)[0]

+    header = np.fromfile(f, dtype=wave_header_dtype, count=1)[0]

+

+This ``.wav`` example is for illustration; to read a ``.wav`` file in real

+life, use Python's built-in module :mod:`wave`.

 

 Credit: Pauli Virtanen, :ref:`advanced_numpy`.

 

@@ -263,7 +269,7 @@ arrays using memory mapping.
 - Files output by :func:`numpy.save` (that is, using the numpy format) can be read

   using :func:`numpy.load` with the ``mmap_mode`` keyword argument::

 

-      large_array[some_slice] = np.load('path/to/small_array', mmap_mode='r')

+      large_array[some_slice] = np.load("path/to/small_array", mmap_mode="r")

 

 Memory mapping lacks features like data chunking and compression; more

 full-featured formats and libraries usable with NumPy include: