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+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2021-01-13T18:54:58.722373Z",
+ "start_time": "2021-01-13T18:54:57.178438Z"
+ }
+ },
+ "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"
+ }
+ },
+ "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": [
+ "# 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))"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.5"
+ },
+ "toc": {
+ "base_numbering": 1,
+ "nav_menu": {},
+ "number_sections": true,
+ "sideBar": true,
+ "skip_h1_title": false,
+ "title_cell": "Table of Contents",
+ "title_sidebar": "Contents",
+ "toc_cell": false,
+ "toc_position": {
+ "height": "calc(100% - 180px)",
+ "left": "10px",
+ "top": "150px",
+ "width": "382.797px"
+ },
+ "toc_section_display": true,
+ "toc_window_display": true
+ },
+ "varInspector": {
+ "cols": {
+ "lenName": 16,
+ "lenType": 16,
+ "lenVar": 40
+ },
+ "kernels_config": {
+ "python": {
+ "delete_cmd_postfix": "",
+ "delete_cmd_prefix": "del ",
+ "library": "var_list.py",
+ "varRefreshCmd": "print(var_dic_list())"
+ },
+ "r": {
+ "delete_cmd_postfix": ") ",
+ "delete_cmd_prefix": "rm(",
+ "library": "var_list.r",
+ "varRefreshCmd": "cat(var_dic_list()) "
+ }
+ },
+ "types_to_exclude": [
+ "module",
+ "function",
+ "builtin_function_or_method",
+ "instance",
+ "_Feature"
+ ],
+ "window_display": false
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}