added any/all documentation

Author: v923z

docs/manual/source/conf.py

@@ -27,7 +27,7 @@
 author = 'Zoltán Vörös'
 
 # The full version, including alpha/beta/rc tags
-release = '2.2.0'
+release = '2.3.0'
 
 
 # -- General configuration ---------------------------------------------------

docs/manual/source/numpy-functions.rst

@@ -5,31 +5,141 @@ Numpy functions
 This section of the manual discusses those functions that were adapted
 from ``numpy``.
 
-1.  `numpy.argmax <#argmax>`__
-2.  `numpy.argmin <#argmin>`__
-3.  `numpy.argsort <#argsort>`__
-4.  `numpy.clip <#clip>`__
-5.  `numpy.convolve <#convolve>`__
-6.  `numpy.diff <#diff>`__
-7.  `numpy.equal <#equal>`__
-8.  `numpy.flip <#flip>`__
-9.  `numpy.interp <#interp>`__
-10. `numpy.isfinite <#isfinite>`__
-11. `numpy.isinf <#isinf>`__
-12. `numpy.max <#max>`__
-13. `numpy.maximum <#maximum>`__
-14. `numpy.mean <#mean>`__
-15. `numpy.median <#median>`__
-16. `numpy.min <#min>`__
-17. `numpy.minimum <#minimum>`__
-18. `numpy.not_equal <#equal>`__
-19. `numpy.polyfit <#polyfit>`__
-20. `numpy.polyval <#polyval>`__
-21. `numpy.roll <#roll>`__
-22. `numpy.sort <#sort>`__
-23. `numpy.std <#std>`__
-24. `numpy.sum <#sum>`__
-25. `numpy.trapz <#trapz>`__
+1.  `numpy.all <#all>`__
+2.  `numpy.any <#any>`__
+3.  `numpy.argmax <#argmax>`__
+4.  `numpy.argmin <#argmin>`__
+5.  `numpy.argsort <#argsort>`__
+6.  `numpy.clip <#clip>`__
+7.  `numpy.convolve <#convolve>`__
+8.  `numpy.diff <#diff>`__
+9.  `numpy.equal <#equal>`__
+10. `numpy.flip <#flip>`__
+11. `numpy.interp <#interp>`__
+12. `numpy.isfinite <#isfinite>`__
+13. `numpy.isinf <#isinf>`__
+14. `numpy.max <#max>`__
+15. `numpy.maximum <#maximum>`__
+16. `numpy.mean <#mean>`__
+17. `numpy.median <#median>`__
+18. `numpy.min <#min>`__
+19. `numpy.minimum <#minimum>`__
+20. `numpy.not_equal <#equal>`__
+21. `numpy.polyfit <#polyfit>`__
+22. `numpy.polyval <#polyval>`__
+23. `numpy.roll <#roll>`__
+24. `numpy.sort <#sort>`__
+25. `numpy.std <#std>`__
+26. `numpy.sum <#sum>`__
+27. `numpy.trapz <#trapz>`__
+
+all
+---
+
+``numpy``:
+https://numpy.org/doc/stable/reference/generated/numpy.all.html
+
+The function takes one positional, and one keyword argument, the
+``axis``, with a default value of ``None``, and tests, whether *all*
+array elements along the given axis evaluate to ``True``. If the keyword
+argument is ``None``, the flattened array is inspected.
+
+Elements of an array evaluate to ``True``, if they are not equal to
+zero, or the Boolean ``False``. The return value if a Boolean
+``ndarray``.
+
+.. code::
+        
+    # code to be run in micropython
+    
+    from ulab import numpy as np
+    
+    a = np.array(range(12)).reshape((3, 4))
+    
+    print('\na:\n', a)
+    
+    b = np.all(a)
+    print('\nall of the flattened array:\n', b)
+    
+    c = np.all(a, axis=0)
+    print('\nall of a along 0th axis:\n', c)
+    
+    d = np.all(a, axis=1)
+    print('\nall of a along 1st axis:\n', d)
+
+.. parsed-literal::
+
+    
+    a:
+     array([[0.0, 1.0, 2.0, 3.0],
+           [4.0, 5.0, 6.0, 7.0],
+           [8.0, 9.0, 10.0, 11.0]], dtype=float64)
+    
+    all of the flattened array:
+     False
+    
+    all of a along 0th axis:
+     array([False, True, True, True], dtype=bool)
+    
+    all of a along 1st axis:
+     array([False, True, True], dtype=bool)
+    
+    
+
+
+any
+---
+
+``numpy``:
+https://numpy.org/doc/stable/reference/generated/numpy.any.html
+
+The function takes one positional, and one keyword argument, the
+``axis``, with a default value of ``None``, and tests, whether *any*
+array element along the given axis evaluates to ``True``. If the keyword
+argument is ``None``, the flattened array is inspected.
+
+Elements of an array evaluate to ``True``, if they are not equal to
+zero, or the Boolean ``False``. The return value if a Boolean
+``ndarray``.
+
+.. code::
+        
+    # code to be run in micropython
+    
+    from ulab import numpy as np
+    
+    a = np.array(range(12)).reshape((3, 4))
+    
+    print('\na:\n', a)
+    
+    b = np.any(a)
+    print('\nany of the flattened array:\n', b)
+    
+    c = np.any(a, axis=0)
+    print('\nany of a along 0th axis:\n', c)
+    
+    d = np.any(a, axis=1)
+    print('\nany of a along 1st axis:\n', d)
+
+.. parsed-literal::
+
+    
+    a:
+     array([[0.0, 1.0, 2.0, 3.0],
+           [4.0, 5.0, 6.0, 7.0],
+           [8.0, 9.0, 10.0, 11.0]], dtype=float64)
+    
+    any of the flattened array:
+     True
+    
+    any of a along 0th axis:
+     array([True, True, True, True], dtype=bool)
+    
+    any of a along 1st axis:
+     array([True, True, True], dtype=bool)
+    
+    
+
 
 argmax
 ------

docs/numpy-functions.ipynb

@@ -5,8 +5,8 @@
    "execution_count": 1,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2021-01-29T21:13:12.041212Z",
-     "start_time": "2021-01-29T21:13:10.138344Z"
+     "end_time": "2021-02-08T16:48:29.443204Z",
+     "start_time": "2021-02-08T16:48:29.246310Z"
     }
    },
    "outputs": [
@@ -31,11 +31,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2021-01-29T21:24:02.702618Z",
-     "start_time": "2021-01-29T21:24:02.696753Z"
+     "end_time": "2021-02-08T16:48:32.087734Z",
+     "start_time": "2021-02-08T16:48:32.079158Z"
     }
    },
    "outputs": [],
@@ -49,11 +49,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2021-01-29T21:24:05.849861Z",
-     "start_time": "2021-01-29T21:24:05.796272Z"
+     "end_time": "2021-02-08T16:48:34.384905Z",
+     "start_time": "2021-02-08T16:48:34.333699Z"
     }
    },
    "outputs": [],
@@ -232,6 +232,8 @@
    "source": [
     "This section of the manual discusses those functions that were adapted from `numpy`.\n",
     "\n",
+    "1. [numpy.all](#all)\n",
+    "1. [numpy.any](#any)\n",
     "1. [numpy.argmax](#argmax)\n",
     "1. [numpy.argmin](#argmin)\n",
     "1. [numpy.argsort](#argsort)\n",
@@ -259,6 +261,136 @@
     "1. [numpy.trapz](#trapz)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## all\n",
+    "\n",
+    "`numpy`: https://numpy.org/doc/stable/reference/generated/numpy.all.html\n",
+    "\n",
+    "The function takes one positional, and one keyword argument, the `axis`, with a default value of `None`, and tests, whether *all* array elements along the given axis evaluate to `True`. If the keyword argument is `None`, the flattened array is inspected. \n",
+    "\n",
+    "Elements of an array evaluate to `True`, if they are not equal to zero, or the Boolean `False`. The return value if a Boolean `ndarray`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-02-08T16:54:57.117630Z",
+     "start_time": "2021-02-08T16:54:57.105337Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "a:\n",
+      " array([[0.0, 1.0, 2.0, 3.0],\n",
+      "       [4.0, 5.0, 6.0, 7.0],\n",
+      "       [8.0, 9.0, 10.0, 11.0]], dtype=float64)\n",
+      "\n",
+      "all of the flattened array:\n",
+      " False\n",
+      "\n",
+      "all of a along 0th axis:\n",
+      " array([False, True, True, True], dtype=bool)\n",
+      "\n",
+      "all of a along 1st axis:\n",
+      " array([False, True, True], dtype=bool)\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%micropython -unix 1\n",
+    "\n",
+    "from ulab import numpy as np\n",
+    "\n",
+    "a = np.array(range(12)).reshape((3, 4))\n",
+    "\n",
+    "print('\\na:\\n', a)\n",
+    "\n",
+    "b = np.all(a)\n",
+    "print('\\nall of the flattened array:\\n', b)\n",
+    "\n",
+    "c = np.all(a, axis=0)\n",
+    "print('\\nall of a along 0th axis:\\n', c)\n",
+    "\n",
+    "d = np.all(a, axis=1)\n",
+    "print('\\nall of a along 1st axis:\\n', d)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## any\n",
+    "\n",
+    "`numpy`: https://numpy.org/doc/stable/reference/generated/numpy.any.html\n",
+    "\n",
+    "The function takes one positional, and one keyword argument, the `axis`, with a default value of `None`, and tests, whether *any* array element along the given axis evaluates to `True`. If the keyword argument is `None`, the flattened array is inspected. \n",
+    "\n",
+    "Elements of an array evaluate to `True`, if they are not equal to zero, or the Boolean `False`. The return value if a Boolean `ndarray`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-02-08T16:54:14.704132Z",
+     "start_time": "2021-02-08T16:54:14.693700Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "a:\n",
+      " array([[0.0, 1.0, 2.0, 3.0],\n",
+      "       [4.0, 5.0, 6.0, 7.0],\n",
+      "       [8.0, 9.0, 10.0, 11.0]], dtype=float64)\n",
+      "\n",
+      "any of the flattened array:\n",
+      " True\n",
+      "\n",
+      "any of a along 0th axis:\n",
+      " array([True, True, True, True], dtype=bool)\n",
+      "\n",
+      "any of a along 1st axis:\n",
+      " array([True, True, True], dtype=bool)\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%micropython -unix 1\n",
+    "\n",
+    "from ulab import numpy as np\n",
+    "\n",
+    "a = np.array(range(12)).reshape((3, 4))\n",
+    "\n",
+    "print('\\na:\\n', a)\n",
+    "\n",
+    "b = np.any(a)\n",
+    "print('\\nany of the flattened array:\\n', b)\n",
+    "\n",
+    "c = np.any(a, axis=0)\n",
+    "print('\\nany of a along 0th axis:\\n', c)\n",
+    "\n",
+    "d = np.any(a, axis=1)\n",
+    "print('\\nany of a along 1st axis:\\n', d)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},

docs/ulab-change-log.md

@@ -1,3 +1,9 @@
+Mon, 8 Feb 2021
+
+version 2.3.0
+
+    added any and all functions
+
 Fri, 29 Jan 2021
 
 version 2.2.0