add documentation

Author: v923z

docs/numpy-functions.ipynb

@@ -238,6 +238,7 @@
     "1. [numpy.argmin](#argmin)\n",
     "1. [numpy.argsort](#argsort)\n",
     "1. [numpy.asarray*](#asarray)\n",
+    "1. [numpy.bincount](#bincount)\n",
     "1. [numpy.bitwise_and](#bitwise_and)\n",
     "1. [numpy.bitwise_or](#bitwise_and)\n",
     "1. [numpy.bitwise_xor](#bitwise_and)\n",
@@ -612,6 +613,68 @@
     "print('a == c: {}'.format(a is c))"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## bincount\n",
+    "\n",
+    "`numpy`: https://numpy.org/doc/stable/reference/generated/numpy.bincount.htm\n",
+    "\n",
+    "This method counts number of occurrences of each value in array of non-negative integers. A method accepts a single positional, and two keyword arguments, `weights`, and `minlength`. The positional arguments is assumed to be a one-dimensional array of unsigned integers that are either 8 or 16 bits wide. For other types, a `TypeError` is raised.\n",
+    "\n",
+    "The `weights` keyword argument can be supplied to apply weigths to the counts. In this case, the return value is a `float` array, otherwise, it is a `uint16`. The `weights` are assumed to be a one-dimensional `ndarray`, otherwise, a `TypeError` is raised. \n",
+    "\n",
+    "`minlength` can be supplied to give a lower bound to the length of the `ndarray` returned. In this case, the unused slots are filled with zeros."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "a: array([0, 1, 2, 3, 4, 5, 6, 7], dtype=uint8)\n",
+      "bincount(a): array([1, 1, 1, 1, 1, 1, 1, 1], dtype=uint16)\n",
+      "\n",
+      "a: array([0, 0, 1, 1, 1, 2, 2, 2, 2], dtype=uint8)\n",
+      "bincount(a): array([2, 3, 4], dtype=uint16)\n",
+      "\n",
+      "a: array([0, 1, 2, 3, 4, 5, 6, 7], dtype=uint8)\n",
+      "w: array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.8], dtype=float64)\n",
+      "bincount(a, weights=w): array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.8], dtype=float64)\n",
+      "\n",
+      "a: array([0, 1, 2, 3], dtype=uint8)\n",
+      "bincount(a, minlength=8): array([1, 1, 1, 1, 0, 0, 0, 0], dtype=uint16)\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%micropython -unix 1\n",
+    "\n",
+    "from ulab import numpy as np\n",
+    "\n",
+    "a = np.array(range(8), dtype=np.uint8)\n",
+    "\n",
+    "print(f'a: {a}\\nbincount(a): {np.bincount(a)}')\n",
+    "\n",
+    "a = np.array([0, 0, 1, 1, 1, 2, 2, 2, 2], dtype=np.uint8)\n",
+    "print(f'\\na: {a}\\nbincount(a): {np.bincount(a)}')\n",
+    "\n",
+    "a = np.array(range(8), dtype=np.uint8)\n",
+    "w = np.array([0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.8])\n",
+    "\n",
+    "print(f'\\na: {a}\\nw: {w}\\nbincount(a, weights=w): {np.bincount(a, weights=w)}')\n",
+    "\n",
+    "a = np.array(range(4), dtype=np.uint8)\n",
+    "print(f'\\na: {a}\\nbincount(a, minlength=8): {np.bincount(a, minlength=8)}')\n"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -2944,7 +3007,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.8.5 ('base')",
+   "display_name": "base",
    "language": "python",
    "name": "python3"
   },
@@ -3006,11 +3069,6 @@
     "_Feature"
    ],
    "window_display": false
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "9e4ec6f642f986afcc9e252c165e44859a62defc5c697cae6f82c2943465ec10"
-   }
   }
  },
  "nbformat": 4,

docs/ulab-change-log.md

@@ -1,3 +1,9 @@
+Wed, 3 Sep 2025
+
+version 6.9.0
+
+    add bincount
+    
 Fri, 06 Jun 2025
 
 version 6.8.0