diff options
Diffstat (limited to 'circuitpython/extmod/ulab/docs/ulab-compare.ipynb')
| -rw-r--r-- | circuitpython/extmod/ulab/docs/ulab-compare.ipynb | 467 |
1 files changed, 467 insertions, 0 deletions
diff --git a/circuitpython/extmod/ulab/docs/ulab-compare.ipynb b/circuitpython/extmod/ulab/docs/ulab-compare.ipynb new file mode 100644 index 0000000..69fa762 --- /dev/null +++ b/circuitpython/extmod/ulab/docs/ulab-compare.ipynb @@ -0,0 +1,467 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "ExecuteTime": { + "end_time": "2021-01-08T13:02:42.934528Z", + "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))" + ] + } + ], + "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 +} |