454 lines
13 KiB
Text
454 lines
13 KiB
Text
{
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"cells": [
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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": "2020-05-01T09:27:13.438054Z",
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"start_time": "2020-05-01T09:27:13.191491Z"
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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": 2,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2020-08-03T18:32:45.342280Z",
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"start_time": "2020-08-03T18:32:45.338442Z"
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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": 3,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2020-07-23T20:31:25.296014Z",
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"start_time": "2020-07-23T20:31:25.265937Z"
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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\", \"/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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"# Polynomials\n",
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"\n",
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"Functions in the polynomial sub-module can be invoked by importing the module first."
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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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"## polyval\n",
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"\n",
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"`numpy`: https://docs.scipy.org/doc/numpy/reference/generated/numpy.polyval.html\n",
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"\n",
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"`polyval` takes two arguments, both arrays or other iterables."
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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": 187,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2019-11-01T12:53:22.448303Z",
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"start_time": "2019-11-01T12:53:22.435176Z"
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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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"coefficients: [1, 1, 1, 0]\n",
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"independent values: [0, 1, 2, 3, 4]\n",
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"\n",
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"values of p(x): array([0.0, 3.0, 14.0, 39.0, 84.0], dtype=float)\n",
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"\n",
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"ndarray (a): array([0.0, 1.0, 2.0, 3.0, 4.0], dtype=float)\n",
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"value of p(a): array([0.0, 3.0, 14.0, 39.0, 84.0], 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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"import ulab as np\n",
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"from ulab import poly\n",
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"\n",
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"p = [1, 1, 1, 0]\n",
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"x = [0, 1, 2, 3, 4]\n",
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"print('coefficients: ', p)\n",
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"print('independent values: ', x)\n",
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"print('\\nvalues of p(x): ', poly.polyval(p, x))\n",
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"\n",
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"# the same works with one-dimensional ndarrays\n",
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"a = np.array(x)\n",
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"print('\\nndarray (a): ', a)\n",
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"print('value of p(a): ', poly.polyval(p, a))"
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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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"## polyfit\n",
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"\n",
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"`numpy`: https://docs.scipy.org/doc/numpy/reference/generated/numpy.polyfit.html\n",
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"\n",
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"polyfit takes two, or three arguments. The last one is the degree of the polynomial that will be fitted, the last but one is an array or iterable with the `y` (dependent) values, and the first one, an array or iterable with the `x` (independent) values, can be dropped. If that is the case, `x` will be generated in the function, assuming uniform sampling. \n",
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"\n",
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"If the length of `x`, and `y` are not the same, the function raises a `ValueError`."
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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": 189,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2019-11-01T12:54:08.326802Z",
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"start_time": "2019-11-01T12:54:08.311182Z"
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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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"independent values:\t array([0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0], dtype=float)\n",
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"dependent values:\t array([9.0, 4.0, 1.0, 0.0, 1.0, 4.0, 9.0], dtype=float)\n",
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"fitted values:\t\t array([1.0, -6.0, 9.000000000000004], dtype=float)\n",
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"\n",
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"dependent values:\t array([9.0, 4.0, 1.0, 0.0, 1.0, 4.0, 9.0], dtype=float)\n",
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"fitted values:\t\t array([1.0, -6.0, 9.000000000000004], 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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"import ulab as np\n",
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"from ulab import poly\n",
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"\n",
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"x = np.array([0, 1, 2, 3, 4, 5, 6])\n",
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"y = np.array([9, 4, 1, 0, 1, 4, 9])\n",
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"print('independent values:\\t', x)\n",
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"print('dependent values:\\t', y)\n",
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"print('fitted values:\\t\\t', poly.polyfit(x, y, 2))\n",
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"\n",
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"# the same with missing x\n",
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"print('\\ndependent values:\\t', y)\n",
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"print('fitted values:\\t\\t', poly.polyfit(y, 2))"
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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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"### Execution time\n",
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"\n",
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"`polyfit` is based on the inversion of a matrix (there is more on the background in https://en.wikipedia.org/wiki/Polynomial_regression), and it requires the intermediate storage of `2*N*(deg+1)` floats, where `N` is the number of entries in the input array, and `deg` is the fit's degree. The additional computation costs of the matrix inversion discussed in [inv](#inv) also apply. The example from above needs around 150 microseconds to return:"
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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": 560,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2019-10-20T07:24:39.002243Z",
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"start_time": "2019-10-20T07:24:38.978687Z"
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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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"execution time: 153 us\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 -pyboard 1\n",
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"\n",
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"import ulab as np\n",
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"from ulab import poly\n",
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"\n",
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"@timeit\n",
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"def time_polyfit(x, y, n):\n",
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" return poly.polyfit(x, y, n)\n",
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"\n",
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"x = np.array([0, 1, 2, 3, 4, 5, 6])\n",
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"y = np.array([9, 4, 1, 0, 1, 4, 9])\n",
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"\n",
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"time_polyfit(x, y, 2)"
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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",
|
|
"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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