196 lines
8.5 KiB
Text
196 lines
8.5 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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"outputs": [],
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"source": [
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"import json\n",
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"\n",
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"import numpy as np\n",
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"from matplotlib import pyplot as plt\n",
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"import matplotlib\n",
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"\n",
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"import scipy.signal as sig"
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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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"outputs": [],
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"source": [
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"%matplotlib widget"
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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": 4,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "712481c28d9d4e1d874a66d31c3e8bff",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"(0, 64)"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"fig, ax = plt.subplots()\n",
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"a = np.array([-00.000732, -00.000352, -00.000666, -00.000202, -00.000706, -00.000006, -00.000597, -00.002039, 000.050663, -00.644566, 004.456614, -16.817095, 034.654587, -39.021217, 024.007816, -08.070650, 001.478795, -00.150260, 000.006110, -00.002328, -00.002322, -00.002426, -00.002177, -00.002452, -00.002333, -00.002438, -00.002342, -00.002396, -00.001979, -00.003049, -00.001720, -00.002686, -00.002168, -00.002507, -00.001868, -00.002899, -00.002017, -00.001952, -00.003255, -00.001080, -00.003335, -00.001575, -00.002704, -00.001872, -00.002735, -00.001983, -00.002191, -00.002478, -00.002155, -00.002203, -00.002328, -00.002206, -00.002443, -00.001770, -00.002718, -00.002004, -00.002378, -00.002112, -00.002122, -00.002691, -00.001679, -00.002690, -00.001946, -00.002232])\n",
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"b = np.array([-00.002734, -00.001325, -00.002220, -00.003693, -00.004907, -00.006454, -00.007737, 000.004823, -00.363143, 004.688968, -33.795303, 130.992630, -274.092651, 309.377991, -188.427826, 061.912941, -10.974002, 001.053608, -00.048927, 000.007710, 000.007010, 000.006493, 000.007234, 000.006725, 000.006938, 000.006694, 000.006356, 000.006173, 000.006333, 000.005684, 000.005697, 000.005575, 000.005101, 000.005693, 000.004319, 000.005344, 000.004673, 000.003566, 000.006213, 000.002719, 000.004850, 000.003755, 000.004243, 000.003419, 000.003960, 000.003498, 000.003297, 000.003877, 000.002836, 000.003487, 000.003144, 000.002824, 000.003355, 000.002528, 000.002975, 000.003012, 000.002137, 000.003112, 000.002416, 000.002512, 000.002084, 000.003008, 000.001837, 000.002351])\n",
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"ax.plot([3.906250*i for i in range(len(a))], np.sqrt(a**2 + b**2))\n",
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"a2 = ax.twiny()\n",
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"a2.set_xlim([0, len(a)])"
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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": 10,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "d4024377df494eac935fd487026edc8b",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"[<matplotlib.lines.Line2D at 0x7f2eb410f280>]"
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]
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},
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"execution_count": 10,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"fig, ax = plt.subplots()\n",
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"d = [50.000839,50.000839,50.000832,50.000824,50.000839,50.000832,50.000839,50.000824,50.000847,50.000824,50.000824,50.000839,50.000832,50.000839,50.000824,50.000824,50.000839,50.000824,50.000824,50.000835,50.000816,50.000832,50.000847,50.000832,50.000835,50.000824,50.000824,50.000832,50.000832,50.000843,50.000824,50.000832,50.000832,50.000832,50.000828,50.000832,50.000832,50.000824,50.000816,50.000835,50.000843,50.000824,50.000824,50.000832,50.000832,50.000847,50.000824,50.000824,50.000824,50.000835,50.000835,50.000851,50.000824,50.000824,50.000832,50.000828,50.000828,50.000824,50.000832,50.000835,50.000835,50.000832,50.000847,50.000824,50.000832,50.000839,50.000839,50.000824,50.000832,50.000832,50.000832,50.000835,50.000816,50.000820,50.000824,50.000832,50.000824,50.000832,50.000835,50.000832,50.000816,50.000820,50.000839,50.000839,50.000824,50.000839,50.000820,50.000820,50.000839,50.000832,50.000835,50.000828,50.000824,50.000839,50.000839,50.000839,50.000816,50.000832,50.000824,50.000832,50.000832,50.000839,50.000824,50.000832,50.000828,50.000832,50.000828,50.000835,50.000832,50.000843,50.000839,50.000820,50.000832,50.000835,50.000824,50.000824,50.000828,50.000820,50.000820,50.000828,50.000832,50.000832,50.000828,50.000835,50.000839,50.000820,50.000832,50.000832,50.000824,50.000832,50.000832,50.000839,50.000839,50.000816,50.000828,50.000832,50.000839,50.000824,50.000824,50.000824,50.000835,50.000824,50.000832,50.000839,50.000835,50.000832,50.000828,50.000835,50.000828,50.000828,50.000824,50.000824,50.000839,50.000832,50.000824,50.000832,50.000832,50.000820,50.000851,50.000824,50.000824,50.000839,50.000824,50.000839,50.000832,50.000835,50.000820,50.000832,50.000839,50.000832,50.000832,50.000824,50.000832,50.000824,50.000832,50.000839,50.000839,50.000832,50.000816,50.000835,50.000854,50.000824,50.000816,50.000832,50.000832,50.000835,50.000816,50.000832,50.000824,50.000832,50.000832,50.000832,50.000824,50.000832,50.000824,50.000835,50.000832,50.000835,50.000832,50.000832,50.000828,50.000839,50.000824,50.000839,50.000824,50.000824,50.000839,50.000816,50.000839,50.000816,50.000832,50.000839,50.000839,50.000832,50.000824,50.000832,50.000820,50.000824,50.000835,50.000824,50.000835,50.000832,50.000824,50.000824,50.000820,50.000839,50.000816,50.000832,50.000832,50.000832,50.000824,50.000847,50.000824,50.000839]\n",
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"\n",
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"ax.plot(d)"
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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": 26,
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"metadata": {},
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"outputs": [],
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"source": [
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"with open('impl_test_out.json') as f:\n",
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" impl_measurements = json.load(f)"
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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": 27,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "dd23cf23221e4e14aaafdd58bb9416d9",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"fig, axs = plt.subplots(len(impl_measurements), figsize=(8, 20), sharex=True)\n",
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"fig.tight_layout()\n",
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"axs = axs.flatten()\n",
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"\n",
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"for (label, data), ax in zip(impl_measurements.items(), axs):\n",
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" ax.set_title(label)\n",
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" ax.plot(data[1:-1])\n",
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" mean = np.mean(data[1:-1])\n",
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" rms = np.sqrt(np.mean(np.square(data[1:-1] - mean)))\n",
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" ax.text(0.2, 0.2, f'mean={mean:.3}Hz, rms={rms*1e3:.3}mHz', ha='center', va='center', transform=ax.transAxes,\n",
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" bbox=dict(boxstyle=\"square\", ec=(0,0,0,0), fc=(1,1,1,0.8)))"
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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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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([[ 2.35232554e-18, 4.70465108e-18, 2.35232554e-18,\n",
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" 1.00000000e+00, -1.97549493e+00, 9.75650918e-01],\n",
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" [ 1.00000000e+00, 2.00000000e+00, 1.00000000e+00,\n",
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" 1.00000000e+00, -1.97916324e+00, 9.79319515e-01],\n",
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" [ 1.00000000e+00, 2.00000000e+00, 1.00000000e+00,\n",
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" 1.00000000e+00, -1.98597735e+00, 9.86134166e-01],\n",
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" [ 1.00000000e+00, 2.00000000e+00, 1.00000000e+00,\n",
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" 1.00000000e+00, -1.99495144e+00, 9.95108965e-01]])"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"sig.butter(8, 20e-3, output='sos', fs=10.0)"
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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": "winlabenv",
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"language": "python",
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"name": "winlabenv"
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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": "3.7.6"
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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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