path: root/circuitpython/extmod/ulab/docs/ulab-compare.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
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     "start_time": "2021-01-08T13:02:42.720862Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Notebook magic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-08T13:02:44.890094Z",
     "start_time": "2021-01-08T13:02:44.878787Z"
    }
   },
   "outputs": [],
   "source": [
    "from IPython.core.magic import Magics, magics_class, line_cell_magic\n",
    "from IPython.core.magic import cell_magic, register_cell_magic, register_line_magic\n",
    "from IPython.core.magic_arguments import argument, magic_arguments, parse_argstring\n",
    "import subprocess\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-08T13:06:20.583308Z",
     "start_time": "2021-01-08T13:06:20.525830Z"
    }
   },
   "outputs": [],
   "source": [
    "@magics_class\n",
    "class PyboardMagic(Magics):\n",
    "    @cell_magic\n",
    "    @magic_arguments()\n",
    "    @argument('-skip')\n",
    "    @argument('-unix')\n",
    "    @argument('-pyboard')\n",
    "    @argument('-file')\n",
    "    @argument('-data')\n",
    "    @argument('-time')\n",
    "    @argument('-memory')\n",
    "    def micropython(self, line='', cell=None):\n",
    "        args = parse_argstring(self.micropython, line)\n",
    "        if args.skip: # doesn't care about the cell's content\n",
    "            print('skipped execution')\n",
    "            return None # do not parse the rest\n",
    "        if args.unix: # tests the code on the unix port. Note that this works on unix only\n",
    "            with open('/dev/shm/micropython.py', 'w') as fout:\n",
    "                fout.write(cell)\n",
    "            proc = subprocess.Popen([\"../../micropython/ports/unix/micropython\", \"/dev/shm/micropython.py\"], \n",
    "                                    stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n",
    "            print(proc.stdout.read().decode(\"utf-8\"))\n",
    "            print(proc.stderr.read().decode(\"utf-8\"))\n",
    "            return None\n",
    "        if args.file: # can be used to copy the cell content onto the pyboard's flash\n",
    "            spaces = \"    \"\n",
    "            try:\n",
    "                with open(args.file, 'w') as fout:\n",
    "                    fout.write(cell.replace('\\t', spaces))\n",
    "                    printf('written cell to {}'.format(args.file))\n",
    "            except:\n",
    "                print('Failed to write to disc!')\n",
    "            return None # do not parse the rest\n",
    "        if args.data: # can be used to load data from the pyboard directly into kernel space\n",
    "            message = pyb.exec(cell)\n",
    "            if len(message) == 0:\n",
    "                print('pyboard >>>')\n",
    "            else:\n",
    "                print(message.decode('utf-8'))\n",
    "                # register new variable in user namespace\n",
    "                self.shell.user_ns[args.data] = string_to_matrix(message.decode(\"utf-8\"))\n",
    "        \n",
    "        if args.time: # measures the time of executions\n",
    "            pyb.exec('import utime')\n",
    "            message = pyb.exec('t = utime.ticks_us()\\n' + cell + '\\ndelta = utime.ticks_diff(utime.ticks_us(), t)' + \n",
    "                               \"\\nprint('execution time: {:d} us'.format(delta))\")\n",
    "            print(message.decode('utf-8'))\n",
    "        \n",
    "        if args.memory: # prints out memory information \n",
    "            message = pyb.exec('from micropython import mem_info\\nprint(mem_info())\\n')\n",
    "            print(\"memory before execution:\\n========================\\n\", message.decode('utf-8'))\n",
    "            message = pyb.exec(cell)\n",
    "            print(\">>> \", message.decode('utf-8'))\n",
    "            message = pyb.exec('print(mem_info())')\n",
    "            print(\"memory after execution:\\n========================\\n\", message.decode('utf-8'))\n",
    "\n",
    "        if args.pyboard:\n",
    "            message = pyb.exec(cell)\n",
    "            print(message.decode('utf-8'))\n",
    "\n",
    "ip = get_ipython()\n",
    "ip.register_magics(PyboardMagic)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## pyboard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-07T07:35:35.126401Z",
     "start_time": "2020-05-07T07:35:35.105824Z"
    }
   },
   "outputs": [],
   "source": [
    "import pyboard\n",
    "pyb = pyboard.Pyboard('/dev/ttyACM0')\n",
    "pyb.enter_raw_repl()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-19T19:11:18.145548Z",
     "start_time": "2020-05-19T19:11:18.137468Z"
    }
   },
   "outputs": [],
   "source": [
    "pyb.exit_raw_repl()\n",
    "pyb.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-05-07T07:35:38.725924Z",
     "start_time": "2020-05-07T07:35:38.645488Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "%%micropython -pyboard 1\n",
    "\n",
    "import utime\n",
    "import ulab as np\n",
    "\n",
    "def timeit(n=1000):\n",
    "    def wrapper(f, *args, **kwargs):\n",
    "        func_name = str(f).split(' ')[1]\n",
    "        def new_func(*args, **kwargs):\n",
    "            run_times = np.zeros(n, dtype=np.uint16)\n",
    "            for i in range(n):\n",
    "                t = utime.ticks_us()\n",
    "                result = f(*args, **kwargs)\n",
    "                run_times[i] = utime.ticks_diff(utime.ticks_us(), t)\n",
    "            print('{}() execution times based on {} cycles'.format(func_name, n, (delta2-delta1)/n))\n",
    "            print('\\tbest: %d us'%np.min(run_times))\n",
    "            print('\\tworst: %d us'%np.max(run_times))\n",
    "            print('\\taverage: %d us'%np.mean(run_times))\n",
    "            print('\\tdeviation: +/-%.3f us'%np.std(run_times))            \n",
    "            return result\n",
    "        return new_func\n",
    "    return wrapper\n",
    "\n",
    "def timeit(f, *args, **kwargs):\n",
    "    func_name = str(f).split(' ')[1]\n",
    "    def new_func(*args, **kwargs):\n",
    "        t = utime.ticks_us()\n",
    "        result = f(*args, **kwargs)\n",
    "        print('execution time: ', utime.ticks_diff(utime.ticks_us(), t), ' us')\n",
    "        return result\n",
    "    return new_func"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__END_OF_DEFS__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparison of arrays"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## equal, not_equal\n",
    "\n",
    "`numpy`: https://numpy.org/doc/stable/reference/generated/numpy.equal.html\n",
    "\n",
    "`numpy`: https://numpy.org/doc/stable/reference/generated/numpy.not_equal.html\n",
    "\n",
    "In `micropython`, equality of arrays or scalars can be established by utilising the `==`, `!=`, `<`, `>`, `<=`, or `=>` binary operators. In `circuitpython`, `==` and `!=` will produce unexpected results. In order to avoid this discrepancy, and to maintain compatibility with `numpy`, `ulab` implements the `equal` and `not_equal` operators that return the same results, irrespective of the `python` implementation.\n",
    "\n",
    "These two functions take two `ndarray`s, or scalars as their arguments. No keyword arguments are implemented."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-08T14:22:13.990898Z",
     "start_time": "2021-01-08T14:22:13.941896Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a:  array([0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0], dtype=float64)\n",
      "b:  array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], dtype=float64)\n",
      "\n",
      "a == b:  array([True, False, False, False, False, False, False, False, False], dtype=bool)\n",
      "a != b:  array([False, True, True, True, True, True, True, True, True], dtype=bool)\n",
      "a == 2:  array([False, False, True, False, False, False, False, False, False], dtype=bool)\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%micropython -unix 1\n",
    "\n",
    "from ulab import numpy as np\n",
    "\n",
    "a = np.array(range(9))\n",
    "b = np.zeros(9)\n",
    "\n",
    "print('a: ', a)\n",
    "print('b: ', b)\n",
    "print('\\na == b: ', np.equal(a, b))\n",
    "print('a != b: ', np.not_equal(a, b))\n",
    "\n",
    "# comparison with scalars\n",
    "print('a == 2: ', np.equal(a, 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## minimum\n",
    "\n",
    "`numpy`: https://docs.scipy.org/doc/numpy/reference/generated/numpy.minimum.html\n",
    "\n",
    "Returns the minimum of two arrays, or two scalars, or an array, and a scalar. If the arrays are of different `dtype`, the output is upcast as in [Binary operators](#Binary-operators). If both inputs are scalars, a scalar is returned. Only positional arguments are implemented.\n",
    "\n",
    "## maximum\n",
    "\n",
    "`numpy`: https://docs.scipy.org/doc/numpy/reference/generated/numpy.maximum.html\n",
    "\n",
    "Returns the maximum of two arrays, or two scalars, or an array, and a scalar. If the arrays are of different `dtype`, the output is upcast as in [Binary operators](#Binary-operators). If both inputs are scalars, a scalar is returned. Only positional arguments are implemented."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-08T13:21:17.151280Z",
     "start_time": "2021-01-08T13:21:17.123768Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "minimum of a, and b:\n",
      "array([1.0, 2.0, 3.0, 2.0, 1.0], dtype=float64)\n",
      "\n",
      "maximum of a, and b:\n",
      "array([5.0, 4.0, 3.0, 4.0, 5.0], dtype=float64)\n",
      "\n",
      "maximum of 1, and 5.5:\n",
      "5.5\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%micropython -unix 1\n",
    "\n",
    "from ulab import numpy as np\n",
    "\n",
    "a = np.array([1, 2, 3, 4, 5], dtype=np.uint8)\n",
    "b = np.array([5, 4, 3, 2, 1], dtype=np.float)\n",
    "print('minimum of a, and b:')\n",
    "print(np.minimum(a, b))\n",
    "\n",
    "print('\\nmaximum of a, and b:')\n",
    "print(np.maximum(a, b))\n",
    "\n",
    "print('\\nmaximum of 1, and 5.5:')\n",
    "print(np.maximum(1, 5.5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## clip\n",
    "\n",
    "`numpy`: https://docs.scipy.org/doc/numpy/reference/generated/numpy.clip.html\n",
    "\n",
    "Clips an array, i.e., values that are outside of an interval are clipped to the interval edges. The function is equivalent to `maximum(a_min, minimum(a, a_max))` broadcasting takes place exactly as in [minimum](#minimum). If the arrays are of different `dtype`, the output is upcast as in [Binary operators](#Binary-operators)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-08T13:22:14.147310Z",
     "start_time": "2021-01-08T13:22:14.123961Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a:\t\t array([0, 1, 2, 3, 4, 5, 6, 7, 8], dtype=uint8)\n",
      "clipped:\t array([3, 3, 3, 3, 4, 5, 6, 7, 7], dtype=uint8)\n",
      "\n",
      "a:\t\t array([0, 1, 2, 3, 4, 5, 6, 7, 8], dtype=uint8)\n",
      "b:\t\t array([3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0], dtype=float64)\n",
      "clipped:\t array([3.0, 3.0, 3.0, 3.0, 4.0, 5.0, 6.0, 7.0, 7.0], dtype=float64)\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%micropython -unix 1\n",
    "\n",
    "from ulab import numpy as np\n",
    "\n",
    "a = np.array(range(9), dtype=np.uint8)\n",
    "print('a:\\t\\t', a)\n",
    "print('clipped:\\t', np.clip(a, 3, 7))\n",
    "\n",
    "b = 3 * np.ones(len(a), dtype=np.float)\n",
    "print('\\na:\\t\\t', a)\n",
    "print('b:\\t\\t', b)\n",
    "print('clipped:\\t', np.clip(a, b, 7))"
   ]
  }
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