the first line of CPython examples was cut off

docs/manual/source/ulab.rst

@@ -469,10 +469,10 @@ in ``numpy``.
 numpy:
 https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.flatten.htm
 
-``.flatten`` returns the flattened array. The array can be flatten in
+``.flatten`` returns the flattened array. The array can be flattened in
 ``C`` style (i.e., moving horizontally in the matrix), or in ``fortran``
 style (i.e., moving vertically in the matrix). The ``C``-style
-flattening is the default, and it is also fast, because this is just
+flattening is the default, and it is also fast, because this is just a
 verbatim copy of the contents.
 
 .. code::
@@ -508,8 +508,8 @@ verbatim copy of the contents.
 ~~~~~~~~~~~~
 
 The contents of an ``ndarray`` can be accessed directly by calling the
-``.asbytearray`` method. This will return a pointer to the underlying
-``array`` object, which can then be manipulated directly.
+``.asbytearray`` method. This will simply return a pointer to the
+underlying ``array`` object, which can then be manipulated directly.
 
 **WARNING:** ``asbytearray`` is a ``ulab``-only method; it has no
 equivalent in ``numpy``.
@@ -559,7 +559,9 @@ applied to the results of timed ADC conversions.
     print("mean of results:\t", np.mean(a))
     print("std of results:\t", np.std(a))
 Likewise, data can be read directly into ``ndarray``\ s from other
-interfaces, e.g., SPI, I2C etc.
+interfaces, e.g., SPI, I2C etc, and also, by laying bare the
+``ndarray``, we can pass results of ``ulab`` computations to anything
+that can read from a buffer.
 
 .transpose
 ~~~~~~~~~~
@@ -781,6 +783,7 @@ columns the matrix has. This feature will be added in future versions of
 .. code::
 
     # code to be run in CPython
+    a = array([[1, 2, 3], [4, 5, 6], [7, 8, 6]])
     b = array([10, 20, 30])
     a+b
 
@@ -1018,6 +1021,7 @@ use cases this fact should not make a difference.
 .. code::
 
     # code to be run in CPython
+    a = array([1, 2, 3, 4, 5, 6, 7, 8])
     a < 5
 
 
@@ -1978,6 +1982,7 @@ The same matrix diagonalised with ``numpy`` yields:
 .. code::
 
     # code to be run in CPython
+    a = array([[1, 2, 1, 4], [2, 5, 3, 5], [1, 3, 6, 1], [4, 5, 1, 7]], dtype=np.uint8)
     x, y = eig(a)
     print('eigenvectors of a:\n', x)
     print('\neigenvalues of a:\n', y)
@@ -2111,6 +2116,7 @@ it will be treated as a complex array:
 .. code::
 
     # code to be run in CPython
+    fft.fft([1, 2, 3, 4, 1, 2, 3, 4])
 
 
 
@@ -2610,4 +2616,4 @@ for the ``pyboard``.
 
 .. code::
 
-    # code to be run in CPython
+    # code to be run in CPython

docs/templates/manual.tpl

@@ -18,7 +18,7 @@
 {%- else -%}
 .. code::
 
-{{ '\n'.join(['# code to be run in CPython'] + cell.source.strip().split('\n')[1:]) | indent}}
+{{ '\n'.join(['# code to be run in CPython'] + cell.source.strip().split('\n')) | indent}}
 {%- endif -%}
 {% endblock input %}
 

docs/ulab-manual.ipynb

@@ -31,11 +31,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 512,
+   "execution_count": 515,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2019-10-19T17:49:57.781782Z",
-     "start_time": "2019-10-19T17:49:54.892112Z"
+     "end_time": "2019-10-19T19:43:24.387588Z",
+     "start_time": "2019-10-19T19:43:20.965252Z"
     }
    },
    "outputs": [],
@@ -69,11 +69,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 502,
+   "execution_count": 514,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2019-10-19T13:38:27.980040Z",
-     "start_time": "2019-10-19T13:38:27.972434Z"
+     "end_time": "2019-10-19T19:43:16.101055Z",
+     "start_time": "2019-10-19T19:43:16.034165Z"
     }
    },
    "outputs": [
@@ -107,7 +107,7 @@
     "{%- else -%}\n",
     ".. code::\n",
     "\n",
-    "{{ '\\n'.join(['# code to be run in CPython'] + cell.source.strip().split('\\n')[1:]) | indent}}\n",
+    "{{ '\\n'.join(['# code to be run in CPython'] + cell.source.strip().split('\\n')) | indent}}\n",
     "{%- endif -%}\n",
     "{% endblock input %}\n",
     "\n",
@@ -889,7 +889,7 @@
     "\n",
     "numpy: https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.flatten.htm\n",
     "\n",
-    "`.flatten` returns the flattened array. The array can be flatten in `C` style (i.e., moving horizontally in the matrix), or in `fortran` style (i.e., moving vertically in the matrix). The `C`-style flattening is the default, and it is also fast, because this is just verbatim copy of the contents."
+    "`.flatten` returns the flattened array. The array can be flattened in `C` style (i.e., moving horizontally in the matrix), or in `fortran` style (i.e., moving vertically in the matrix). The `C`-style flattening is the default, and it is also fast, because this is just a verbatim copy of the contents."
    ]
   },
   {
@@ -940,7 +940,7 @@
    "source": [
     "### .asbytearray\n",
     "\n",
-    "The contents of an `ndarray` can be accessed directly by calling the `.asbytearray` method. This will return a pointer to the underlying `array` object, which can then be manipulated directly.\n",
+    "The contents of an `ndarray` can be accessed directly by calling the `.asbytearray` method. This will simply return a pointer to the underlying `array` object, which can then be manipulated directly.\n",
     "\n",
     "**WARNING:** `asbytearray` is a `ulab`-only method; it has no equivalent in `numpy`."
    ]
@@ -1014,7 +1014,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Likewise, data can be read directly into `ndarray`s from other interfaces, e.g., SPI, I2C etc. "
+    "Likewise, data can be read directly into `ndarray`s from other interfaces, e.g., SPI, I2C etc, and also, by laying bare the `ndarray`, we can pass results of `ulab` computations to anything that can read from a buffer."
    ]
   },
   {