path: root/circuitpython/extmod/ulab/docs/scipy-special.ipynb

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   "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": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-13T18:57:41.555892Z",
     "start_time": "2021-01-13T18:57:41.551121Z"
    }
   },
   "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": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-13T18:57:42.313231Z",
     "start_time": "2021-01-13T18:57:42.288402Z"
    }
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   "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",
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    }
   },
   "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": [
    "# scipy.special\n",
    "\n",
    "`scipy`'s `special` module defines several functions that behave as do the standard mathematical functions of the `numpy`, i.e., they can be called on any scalar, scalar-valued iterable (ranges, lists, tuples containing numbers), and on `ndarray`s without having to change the call signature. In all cases the functions return a new `ndarray` of typecode `float` (since these functions usually generate float values, anyway). \n",
    "\n",
    "At present, `ulab`'s `special` module contains the following functions:\n",
    "\n",
    "`erf`, `erfc`, `gamma`, and `gammaln`, and they can be called by prepending them by `scipy.special.`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-01-13T19:06:54.640444Z",
     "start_time": "2021-01-13T19:06:54.623467Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a:  range(0, 9)\n",
      "array([0.0, 0.8427007929497149, 0.9953222650189527, 0.9999779095030014, 0.9999999845827421, 1.0, 1.0, 1.0, 1.0], dtype=float64)\n",
      "\n",
      "b:  array([0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0], dtype=float64)\n",
      "array([1.0, 0.1572992070502851, 0.004677734981047265, 2.209049699858544e-05, 1.541725790028002e-08, 1.537459794428035e-12, 2.151973671249892e-17, 4.183825607779414e-23, 1.122429717298293e-29], dtype=float64)\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%micropython -unix 1\n",
    "\n",
    "from ulab import numpy as np\n",
    "from ulab import scipy as spy\n",
    "\n",
    "a = range(9)\n",
    "b = np.array(a)\n",
    "\n",
    "print('a: ', a)\n",
    "print(spy.special.erf(a))\n",
    "\n",
    "print('\\nb: ', b)\n",
    "print(spy.special.erfc(b))"
   ]
  }
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