387 lines
11 KiB
Text
387 lines
11 KiB
Text
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2021-01-12T16:11:12.111639Z",
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"start_time": "2021-01-12T16:11:11.914041Z"
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Populating the interactive namespace from numpy and matplotlib\n"
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]
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}
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],
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"source": [
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"%pylab inline"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Notebook magic"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2022-01-29T20:50:20.813162Z",
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"start_time": "2022-01-29T20:50:20.794562Z"
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}
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},
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"outputs": [],
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"source": [
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"from IPython.core.magic import Magics, magics_class, line_cell_magic\n",
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"from IPython.core.magic import cell_magic, register_cell_magic, register_line_magic\n",
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"from IPython.core.magic_arguments import argument, magic_arguments, parse_argstring\n",
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"import subprocess\n",
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"import os"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2022-01-29T20:50:21.613220Z",
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"start_time": "2022-01-29T20:50:21.557819Z"
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}
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},
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"outputs": [],
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"source": [
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"@magics_class\n",
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"class PyboardMagic(Magics):\n",
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" @cell_magic\n",
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" @magic_arguments()\n",
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" @argument('-skip')\n",
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" @argument('-unix')\n",
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" @argument('-pyboard')\n",
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" @argument('-file')\n",
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" @argument('-data')\n",
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" @argument('-time')\n",
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" @argument('-memory')\n",
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" def micropython(self, line='', cell=None):\n",
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" args = parse_argstring(self.micropython, line)\n",
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" if args.skip: # doesn't care about the cell's content\n",
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" print('skipped execution')\n",
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" return None # do not parse the rest\n",
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" if args.unix: # tests the code on the unix port. Note that this works on unix only\n",
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" with open('/dev/shm/micropython.py', 'w') as fout:\n",
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" fout.write(cell)\n",
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" proc = subprocess.Popen([\"../micropython/ports/unix/micropython-2\", \"/dev/shm/micropython.py\"], \n",
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" stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n",
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" print(proc.stdout.read().decode(\"utf-8\"))\n",
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" print(proc.stderr.read().decode(\"utf-8\"))\n",
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" return None\n",
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" if args.file: # can be used to copy the cell content onto the pyboard's flash\n",
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" spaces = \" \"\n",
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" try:\n",
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" with open(args.file, 'w') as fout:\n",
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" fout.write(cell.replace('\\t', spaces))\n",
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" printf('written cell to {}'.format(args.file))\n",
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" except:\n",
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" print('Failed to write to disc!')\n",
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" return None # do not parse the rest\n",
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" if args.data: # can be used to load data from the pyboard directly into kernel space\n",
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" message = pyb.exec(cell)\n",
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" if len(message) == 0:\n",
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" print('pyboard >>>')\n",
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" else:\n",
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" print(message.decode('utf-8'))\n",
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" # register new variable in user namespace\n",
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" self.shell.user_ns[args.data] = string_to_matrix(message.decode(\"utf-8\"))\n",
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" \n",
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" if args.time: # measures the time of executions\n",
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" pyb.exec('import utime')\n",
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" message = pyb.exec('t = utime.ticks_us()\\n' + cell + '\\ndelta = utime.ticks_diff(utime.ticks_us(), t)' + \n",
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" \"\\nprint('execution time: {:d} us'.format(delta))\")\n",
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" print(message.decode('utf-8'))\n",
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" \n",
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" if args.memory: # prints out memory information \n",
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" message = pyb.exec('from micropython import mem_info\\nprint(mem_info())\\n')\n",
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" print(\"memory before execution:\\n========================\\n\", message.decode('utf-8'))\n",
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" message = pyb.exec(cell)\n",
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" print(\">>> \", message.decode('utf-8'))\n",
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" message = pyb.exec('print(mem_info())')\n",
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" print(\"memory after execution:\\n========================\\n\", message.decode('utf-8'))\n",
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"\n",
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" if args.pyboard:\n",
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" message = pyb.exec(cell)\n",
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" print(message.decode('utf-8'))\n",
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"\n",
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"ip = get_ipython()\n",
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"ip.register_magics(PyboardMagic)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## pyboard"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 57,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2020-05-07T07:35:35.126401Z",
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"start_time": "2020-05-07T07:35:35.105824Z"
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}
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},
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"outputs": [],
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"source": [
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"import pyboard\n",
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"pyb = pyboard.Pyboard('/dev/ttyACM0')\n",
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"pyb.enter_raw_repl()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2020-05-19T19:11:18.145548Z",
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"start_time": "2020-05-19T19:11:18.137468Z"
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}
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},
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"outputs": [],
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"source": [
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"pyb.exit_raw_repl()\n",
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"pyb.close()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 58,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2020-05-07T07:35:38.725924Z",
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"start_time": "2020-05-07T07:35:38.645488Z"
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n"
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]
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}
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],
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"source": [
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"%%micropython -pyboard 1\n",
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"\n",
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"import utime\n",
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"import ulab as np\n",
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"\n",
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"def timeit(n=1000):\n",
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" def wrapper(f, *args, **kwargs):\n",
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" func_name = str(f).split(' ')[1]\n",
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" def new_func(*args, **kwargs):\n",
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" run_times = np.zeros(n, dtype=np.uint16)\n",
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" for i in range(n):\n",
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" t = utime.ticks_us()\n",
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" result = f(*args, **kwargs)\n",
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" run_times[i] = utime.ticks_diff(utime.ticks_us(), t)\n",
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" print('{}() execution times based on {} cycles'.format(func_name, n, (delta2-delta1)/n))\n",
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" print('\\tbest: %d us'%np.min(run_times))\n",
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" print('\\tworst: %d us'%np.max(run_times))\n",
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" print('\\taverage: %d us'%np.mean(run_times))\n",
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" print('\\tdeviation: +/-%.3f us'%np.std(run_times)) \n",
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" return result\n",
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" return new_func\n",
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" return wrapper\n",
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"\n",
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"def timeit(f, *args, **kwargs):\n",
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" func_name = str(f).split(' ')[1]\n",
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" def new_func(*args, **kwargs):\n",
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" t = utime.ticks_us()\n",
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" result = f(*args, **kwargs)\n",
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" print('execution time: ', utime.ticks_diff(utime.ticks_us(), t), ' us')\n",
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" return result\n",
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" return new_func"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"__END_OF_DEFS__"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# scipy.signal\n",
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"\n",
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"This module defines the single function:\n",
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"\n",
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"1. [scipy.signal.sosfilt](#sosfilt)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## sosfilt\n",
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"\n",
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"`scipy`: https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.sosfilt.html \n",
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"\n",
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"Filter data along one dimension using cascaded second-order sections.\n",
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"\n",
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"The function takes two positional arguments, `sos`, the filter segments of length 6, and the one-dimensional, uniformly sampled data set to be filtered. Returns the filtered data, or the filtered data and the final filter delays, if the `zi` keyword arguments is supplied. The keyword argument must be a float `ndarray` of shape `(n_sections, 2)`. If `zi` is not passed to the function, the initial values are assumed to be 0."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2020-06-19T20:24:10.529668Z",
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"start_time": "2020-06-19T20:24:10.520389Z"
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"y: array([0.0, 1.0, -4.0, 24.0, -104.0, 440.0, -1728.0, 6532.000000000001, -23848.0, 84864.0], dtype=float)\n",
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"\n",
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"\n"
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]
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}
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],
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"source": [
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"%%micropython -unix 1\n",
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"\n",
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"from ulab import numpy as np\n",
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"from ulab import scipy as spy\n",
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"\n",
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"x = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\n",
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"sos = [[1, 2, 3, 1, 5, 6], [1, 2, 3, 1, 5, 6]]\n",
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"y = spy.signal.sosfilt(sos, x)\n",
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"print('y: ', y)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2020-06-19T20:27:39.508508Z",
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"start_time": "2020-06-19T20:27:39.498256Z"
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"y: array([4.0, -16.0, 63.00000000000001, -227.0, 802.9999999999999, -2751.0, 9271.000000000001, -30775.0, 101067.0, -328991.0000000001], dtype=float)\n",
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"\n",
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"========================================\n",
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"zf: array([[37242.0, 74835.0],\n",
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"\t [1026187.0, 1936542.0]], dtype=float)\n",
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"\n",
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"\n"
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]
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}
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],
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"source": [
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"%%micropython -unix 1\n",
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"\n",
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"from ulab import numpy as np\n",
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"from ulab import scipy as spy\n",
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"\n",
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"x = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\n",
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"sos = [[1, 2, 3, 1, 5, 6], [1, 2, 3, 1, 5, 6]]\n",
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"# initial conditions of the filter\n",
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"zi = np.array([[1, 2], [3, 4]])\n",
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"\n",
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"y, zf = spy.signal.sosfilt(sos, x, zi=zi)\n",
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"print('y: ', y)\n",
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"print('\\n' + '='*40 + '\\nzf: ', zf)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.5"
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},
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"toc": {
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"base_numbering": 1,
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"nav_menu": {},
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"number_sections": true,
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"sideBar": true,
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"skip_h1_title": false,
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"title_cell": "Table of Contents",
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"title_sidebar": "Contents",
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"toc_cell": false,
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"toc_position": {
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"height": "calc(100% - 180px)",
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"left": "10px",
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"top": "150px",
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"width": "382.797px"
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},
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"toc_section_display": true,
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"toc_window_display": true
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},
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"varInspector": {
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"cols": {
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"lenName": 16,
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"lenType": 16,
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"lenVar": 40
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},
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"kernels_config": {
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"python": {
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"delete_cmd_postfix": "",
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"delete_cmd_prefix": "del ",
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"library": "var_list.py",
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"varRefreshCmd": "print(var_dic_list())"
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},
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"r": {
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"delete_cmd_postfix": ") ",
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"delete_cmd_prefix": "rm(",
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"library": "var_list.r",
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"varRefreshCmd": "cat(var_dic_list()) "
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}
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},
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"types_to_exclude": [
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"module",
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"function",
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"builtin_function_or_method",
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"instance",
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"_Feature"
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],
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"window_display": false
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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