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{ |
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"metadata": { |
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"name": "" |
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}, |
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"nbformat": 3, |
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"nbformat_minor": 0, |
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"worksheets": [ |
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{ |
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"cells": [ |
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{ |
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"cell_type": "markdown", |
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"metadata": {}, |
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"source": [ |
| 3656 |
kaklik |
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"Uk\u00e1zka pou\u017eit\u00ed n\u00e1stroje IPython na manipulaci se senzorov\u00fdmi daty modulu IMU01A\n", |
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kaklik |
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"=======\n", |
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"\n", |
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kaklik |
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"P\u0159\u00edklad vyu\u017e\u00edv\u00e1 modulovou stavebnici MLAB a jej\u00ed knihovnu [pymlab](https://github.com/MLAB-project/MLAB-I2c-modules).\n", |
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"Sn\u00edma\u010d je k po\u010d\u00edta\u010di p\u0159ipojen\u00fd p\u0159es rozhradn\u00ed USB a data jsou vy\u010d\u00edt\u00e1na p\u0159es [I\u00b2C](http://wiki.mlab.cz/doku.php?id=cs:i2c)\n", |
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kaklik |
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"\n", |
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kaklik |
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"Pou\u017eit\u00fd akcelerometr [MMA8451Q](http://www.freescale.com/webapp/sps/site/prod_summary.jsp?code=MMA8451Q) m\u00e1 n\u00e1sleduj\u00edc\u00ed katalogov\u00e9 parametry: \n", |
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"\n", |
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"* \u00b12g/\u00b14g/\u00b18g dynamically selectable full-scale\n", |
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"* Output data rates (ODR) from 1.56 Hz to 800 Hz\n", |
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"* 99 \u03bcg/\u221aHz noise\n", |
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"\n", |
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kaklik |
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"Zprovozn\u011bn\u00ed demo k\u00f3du\n", |
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"---------------------\n", |
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"\n", |
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kaklik |
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"Nejd\u0159\u00edve zjist\u00edme zda m\u00e1me p\u0159\u00edstup pro z\u00e1pis a \u010dten\u00ed do syst\u00e9mov\u00e9ho za\u0159\u00edzen\u00ed. A jak\u00e9 \u010d\u00edslo m\u00e1 I\u00b2C sb\u011brnice na kterou m\u00e1me p\u0159ipojen\u00e1 \u010didla. \n", |
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"P\u0159\u00edpadn\u011b je mo\u017en\u00e9 tuto \u010d\u00e1st p\u0159esko\u010dit a vyu\u017e\u00edt p\u0159\u00edmo predem ulo\u017een\u00fd datov\u00fd set.\n" |
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kaklik |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"collapsed": false, |
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"input": [ |
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"!i2cdetect -l" |
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], |
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"language": "python", |
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"metadata": {}, |
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"outputs": [ |
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{ |
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"output_type": "stream", |
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"stream": "stdout", |
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"text": [ |
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"i2c-0\ti2c \ti915 gmbus ssc \tI2C adapter\r\n", |
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"i2c-1\ti2c \ti915 gmbus vga \tI2C adapter\r\n", |
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"i2c-2\ti2c \ti915 gmbus panel \tI2C adapter\r\n", |
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"i2c-3\ti2c \ti915 gmbus dpc \tI2C adapter\r\n", |
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"i2c-4\ti2c \ti915 gmbus dpb \tI2C adapter\r\n", |
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"i2c-5\ti2c \ti915 gmbus dpd \tI2C adapter\r\n", |
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kaklik |
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"i2c-6\ti2c \tDPDDC-B \tI2C adapter\r\n", |
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kaklik |
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"i2c-7\ti2c \ti2c-tiny-usb at bus 001 device 025\tI2C adapter\r\n" |
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kaklik |
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] |
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} |
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], |
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kaklik |
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"prompt_number": 1 |
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kaklik |
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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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"Proto\u017ee pro p\u0159ipojen\u00ed \u010didel k po\u010d\u00edta\u010di pou\u017e\u00edv\u00e1me adapt\u00e9r i2c-tiny-usb. Vid\u00edme, \u017ee sb\u011brnice m\u00e1 aktu\u00e1ln\u011b ozna\u010den\u00ed nap\u0159\u00edklad i2c-8. \n", |
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"\n", |
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"V p\u0159\u00edpad\u011b, \u017ee v\u00fd\u0161e uveden\u00fd p\u0159\u00edklad vr\u00e1t\u00ed chybu, nebo pojmenov\u00e1n\u00ed \"unknown\" tak nem\u00e1me p\u0159\u00edstup k syst\u00e9mov\u00fdm rozhran\u00edm. Ten z\u00edsk\u00e1me vytvo\u0159en\u00edm souboru s n\u00e1sleduj\u00edc\u00edm obsahem ve slo\u017ece: /etc/udev/rules.d/i2c-devices.rules" |
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] |
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}, |
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{ |
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"cell_type": "raw", |
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"metadata": {}, |
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"source": [ |
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"KERNEL==\"i2c-[0-9]*\", GROUP=\"i2c\"" |
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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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"Toto ozna\u010den\u00ed budeme je\u0161t\u011b d\u00e1le pot\u0159ebovat, proto si jej ulo\u017e\u00edme da prom\u011bnn\u00e9. " |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"collapsed": false, |
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"input": [ |
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kaklik |
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"port = 7" |
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kaklik |
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], |
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"language": "python", |
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"metadata": {}, |
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"outputs": [], |
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kaklik |
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"prompt_number": 2 |
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kaklik |
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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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"Budeme pokra\u010dovat na\u010dten\u00edm pot\u0159ebn\u00fdch modul\u016f pro zach\u00e1zen\u00ed s I\u00b2C sn\u00edma\u010di." |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"collapsed": false, |
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"input": [ |
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"import time\n", |
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"import datetime\n", |
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"import sys\n", |
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"\n", |
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"from pymlab import config\n", |
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"import matplotlib.pyplot as plt\n", |
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"import numpy as np" |
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], |
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"language": "python", |
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"metadata": {}, |
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"outputs": [], |
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kaklik |
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"prompt_number": 3 |
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kaklik |
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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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"Nyn\u00ed si nadefinujeme strukturu p\u0159ipojen\u00ed jednotliv\u00fdch \u010didel na I\u00b2C sb\u011brnici." |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"collapsed": false, |
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"input": [ |
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"cfg = config.Config(\n", |
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" i2c = {\n", |
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" \"port\": port,\n", |
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" },\n", |
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" bus = [\n", |
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" {\n", |
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" \"type\": \"i2chub\",\n", |
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" \"address\": 0x72,\n", |
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" \n", |
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" \"children\": [\n", |
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" {\"name\": \"acc\", \"type\": \"imu01_acc\", \"sensitivity\": 4.0, \"channel\": 0, }, \n", |
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" ],\n", |
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" },\n", |
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" ],\n", |
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")" |
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], |
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"language": "python", |
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"metadata": {}, |
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"outputs": [], |
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kaklik |
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"prompt_number": 30 |
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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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"Tuto strukturu inicializujeme, aby jsme dos\u00e1hli definovan\u00e9 konfigurace \u010didel." |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"collapsed": false, |
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"input": [ |
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"cfg.initialize()\n", |
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"acc = cfg.get_device(\"acc\")\n", |
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"time.sleep(0.5)" |
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], |
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"language": "python", |
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"metadata": {}, |
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"outputs": [ |
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{ |
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"output_type": "stream", |
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"stream": "stderr", |
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"text": [ |
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"WARNING:pymlab.sensors.iic:HID device does not exist, we will try SMBus directly...\n" |
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] |
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} |
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], |
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kaklik |
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"prompt_number": "*" |
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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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kaklik |
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"\u010cten\u00ed dat z akcelerometru\n", |
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"-------------------------\n", |
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"\n", |
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kaklik |
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"Nyn\u00ed u\u017e m\u016f\u017eeme p\u0159\u00edmo komunikovat se za\u0159\u00edzen\u00edm pojmenovan\u00fdm jako acc." |
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kaklik |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"collapsed": false, |
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"input": [ |
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kaklik |
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"import sys\n", |
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"import time\n", |
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"from IPython.display import clear_output\n", |
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"\n", |
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"MEASUREMENTS = 1000\n", |
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"list_meas = []\n", |
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kaklik |
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"# acc.route() V p\u0159\u00edpad\u011b v\u00edce \u010didel je pot\u0159eba ke ka\u017ed\u00e9mu p\u0159ed jeho pou\u017eit\u00edm nechat vyroutovat cesutu na sb\u011brnici.\n", |
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"\n", |
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"for n in range(MEASUREMENTS):\n", |
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" clear_output()\n", |
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" (x, y, z) = acc.axes()\n", |
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" list_meas.append([x, y, z])\n", |
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" print (n, list_meas[n])\n", |
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" sys.stdout.flush()" |
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], |
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"language": "python", |
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"metadata": {}, |
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"outputs": [ |
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{ |
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"output_type": "stream", |
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"stream": "stdout", |
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"text": [ |
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"(999, [0.038024999999999996, -0.00975, 0.966225])\n" |
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] |
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} |
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], |
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"prompt_number": 7 |
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}, |
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{ |
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"cell_type": "code", |
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"collapsed": false, |
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"input": [ |
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"np.savez(\"calibration_data_3Dset\", data=list_meas)" |
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], |
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"language": "python", |
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"metadata": {}, |
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"outputs": [], |
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kaklik |
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"prompt_number": 8 |
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}, |
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{ |
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"cell_type": "markdown", |
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kaklik |
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"metadata": {}, |
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kaklik |
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"source": [ |
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kaklik |
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"Kalibrace akcelerometru\n", |
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"-----------------------\n", |
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"\n", |
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"Nam\u011b\u0159en\u00e1 data m\u016f\u017eeme tak\u00e9 z\u00edskat z p\u0159edem ulo\u017een\u00e9ho souboru. V n\u00e1sleduj\u00edc\u00edm bloku je otev\u0159en soubor s referen\u010dn\u00edmi daty, kter\u00fd se nach\u00e1z\u00ed v dokumenta\u010dn\u00ed slo\u017ece mudulu IMU01A. \n" |
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kaklik |
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] |
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kaklik |
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}, |
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{ |
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"cell_type": "code", |
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"collapsed": false, |
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"input": [ |
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"data = np.load('./calibration_data_3Dset.npz')\n", |
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"list_meas=data['data']" |
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], |
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"language": "python", |
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"metadata": {}, |
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"outputs": [], |
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"prompt_number": 16 |
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}, |
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{ |
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"cell_type": "code", |
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"collapsed": false, |
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"input": [ |
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kaklik |
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"data = np.load('./calibration_data_set.npz')\n", |
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"x=data['x']\n", |
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"y=data['y']\n", |
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"z=data['z']" |
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], |
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"language": "python", |
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"metadata": {}, |
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"outputs": [] |
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}, |
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{ |
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"cell_type": "code", |
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"collapsed": false, |
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"input": [ |
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kaklik |
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"from mpl_toolkits.mplot3d.axes3d import Axes3D\n", |
|
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"#%pylab qt\n", |
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"%pylab inline\n", |
| 3655 |
kaklik |
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"fig = plt.figure()\n", |
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"ax = Axes3D(fig)\n", |
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"p = ax.scatter(x, y, z)\n", |
|
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"#pyplot.show()\n" |
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], |
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"language": "python", |
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"metadata": {}, |
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"outputs": [ |
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{ |
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"output_type": "stream", |
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"stream": "stdout", |
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"text": [ |
|
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284 |
"Populating the interactive namespace from numpy and matplotlib\n" |
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] |
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kaklik |
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}, |
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kaklik |
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{ |
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"metadata": {}, |
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kaklik |
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"output_type": "display_data", |
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"png": 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p2HE6W7fuwu0+g0WLHuabb84G0gkEVmGzpeDzPYdONwRJegePRwYGY7UGWLmy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\nnUoM6Czcwc8AAAAASUVORK5CYII=\n", |
| 3655 |
kaklik |
291 |
"text": [ |
| 3668 |
kaklik |
292 |
"<matplotlib.figure.Figure at 0x7fc77d537c90>" |
| 3655 |
kaklik |
293 |
] |
|
|
294 |
} |
|
|
295 |
], |
| 3668 |
kaklik |
296 |
"prompt_number": 17 |
| 3655 |
kaklik |
297 |
}, |
|
|
298 |
{ |
|
|
299 |
"cell_type": "code", |
|
|
300 |
"collapsed": false, |
|
|
301 |
"input": [ |
| 3663 |
kaklik |
302 |
"import scipy\n", |
|
|
303 |
"from scipy import optimize\n", |
|
|
304 |
"import calibration_utils\n", |
|
|
305 |
"\n", |
|
|
306 |
"sensor_ref = 9.81\n", |
|
|
307 |
"sensor_res = 10\n", |
|
|
308 |
"noise_window = 20\n", |
|
|
309 |
"noise_threshold = 40" |
| 3655 |
kaklik |
310 |
], |
|
|
311 |
"language": "python", |
|
|
312 |
"metadata": {}, |
| 3663 |
kaklik |
313 |
"outputs": [], |
| 3668 |
kaklik |
314 |
"prompt_number": 18 |
| 3663 |
kaklik |
315 |
}, |
|
|
316 |
{ |
|
|
317 |
"cell_type": "code", |
|
|
318 |
"collapsed": false, |
|
|
319 |
"input": [ |
|
|
320 |
"measurements = np.array(list_meas)" |
|
|
321 |
], |
|
|
322 |
"language": "python", |
|
|
323 |
"metadata": {}, |
|
|
324 |
"outputs": [], |
| 3668 |
kaklik |
325 |
"prompt_number": 19 |
| 3663 |
kaklik |
326 |
}, |
|
|
327 |
{ |
|
|
328 |
"cell_type": "code", |
|
|
329 |
"collapsed": false, |
|
|
330 |
"input": [ |
|
|
331 |
"meas_median=scipy.median(scipy.array([scipy.linalg.norm(v) for v in measurements]))\n", |
|
|
332 |
"noise_threshold = meas_median * 0.1\n", |
|
|
333 |
"print noise_threshold\n", |
|
|
334 |
"flt_meas, flt_idx = calibration_utils.filter_meas(measurements, noise_window, noise_threshold)\n", |
|
|
335 |
"print(\"remaining \"+str(len(flt_meas))+\" after filtering\")" |
|
|
336 |
], |
|
|
337 |
"language": "python", |
|
|
338 |
"metadata": {}, |
| 3655 |
kaklik |
339 |
"outputs": [ |
|
|
340 |
{ |
| 3663 |
kaklik |
341 |
"output_type": "stream", |
|
|
342 |
"stream": "stdout", |
| 3655 |
kaklik |
343 |
"text": [ |
| 3663 |
kaklik |
344 |
"0.0973014827316\n", |
| 3668 |
kaklik |
345 |
"remaining 703 after filtering\n" |
| 3655 |
kaklik |
346 |
] |
|
|
347 |
} |
|
|
348 |
], |
| 3668 |
kaklik |
349 |
"prompt_number": 20 |
| 3655 |
kaklik |
350 |
}, |
|
|
351 |
{ |
|
|
352 |
"cell_type": "code", |
|
|
353 |
"collapsed": false, |
|
|
354 |
"input": [ |
| 3663 |
kaklik |
355 |
" p0 = calibration_utils.get_min_max_guess(flt_meas, sensor_ref)\n", |
|
|
356 |
" cp0, np0 = calibration_utils.scale_measurements(flt_meas, p0)\n", |
|
|
357 |
" print(\"initial guess : avg \"+str(np0.mean())+\" std \"+str(np0.std()))\n", |
|
|
358 |
"\n", |
|
|
359 |
" def err_func(p, meas, y):\n", |
|
|
360 |
" cp, np = calibration_utils.scale_measurements(meas, p)\n", |
|
|
361 |
" err = y*scipy.ones(len(meas)) - np\n", |
|
|
362 |
" return err" |
| 3655 |
kaklik |
363 |
], |
|
|
364 |
"language": "python", |
|
|
365 |
"metadata": {}, |
|
|
366 |
"outputs": [ |
|
|
367 |
{ |
| 3663 |
kaklik |
368 |
"output_type": "stream", |
|
|
369 |
"stream": "stdout", |
| 3655 |
kaklik |
370 |
"text": [ |
| 3663 |
kaklik |
371 |
"initial guess : avg 9.55977014083 std 0.17596103611\n" |
| 3655 |
kaklik |
372 |
] |
| 3663 |
kaklik |
373 |
} |
|
|
374 |
], |
| 3668 |
kaklik |
375 |
"prompt_number": 21 |
| 3663 |
kaklik |
376 |
}, |
|
|
377 |
{ |
|
|
378 |
"cell_type": "code", |
|
|
379 |
"collapsed": false, |
|
|
380 |
"input": [ |
|
|
381 |
" p1, cov, info, msg, success = optimize.leastsq(err_func, p0[:], args=(flt_meas, sensor_ref), full_output=1)\n", |
|
|
382 |
" if not success in [1, 2, 3, 4]:\n", |
|
|
383 |
" print(\"Optimization error: \", msg)\n", |
|
|
384 |
" print(\"Please try to provide a clean logfile.\")\n", |
|
|
385 |
" sys.exit(1)\n", |
|
|
386 |
"\n", |
|
|
387 |
" cp1, np1 = calibration_utils.scale_measurements(flt_meas, p1)\n", |
|
|
388 |
" print(\"optimized guess : avg \"+str(np1.mean())+\" std \"+str(np1.std()))" |
|
|
389 |
], |
|
|
390 |
"language": "python", |
|
|
391 |
"metadata": {}, |
|
|
392 |
"outputs": [ |
| 3655 |
kaklik |
393 |
{ |
| 3663 |
kaklik |
394 |
"output_type": "stream", |
|
|
395 |
"stream": "stdout", |
| 3655 |
kaklik |
396 |
"text": [ |
| 3663 |
kaklik |
397 |
"optimized guess : avg 9.80797237599 std 0.141020849641\n" |
| 3655 |
kaklik |
398 |
] |
|
|
399 |
} |
|
|
400 |
], |
| 3668 |
kaklik |
401 |
"prompt_number": 22 |
| 3663 |
kaklik |
402 |
}, |
|
|
403 |
{ |
|
|
404 |
"cell_type": "code", |
|
|
405 |
"collapsed": false, |
|
|
406 |
"input": [ |
| 3668 |
kaklik |
407 |
"%pylab qt\n", |
|
|
408 |
"#%pylab inline\n", |
| 3663 |
kaklik |
409 |
"calibration_utils.plot_results(True, measurements, flt_idx, flt_meas, cp0, np0, cp1, np1, sensor_ref)" |
|
|
410 |
], |
|
|
411 |
"language": "python", |
|
|
412 |
"metadata": {}, |
|
|
413 |
"outputs": [ |
|
|
414 |
{ |
|
|
415 |
"output_type": "stream", |
|
|
416 |
"stream": "stdout", |
|
|
417 |
"text": [ |
|
|
418 |
"Populating the interactive namespace from numpy and matplotlib\n" |
|
|
419 |
] |
|
|
420 |
} |
|
|
421 |
], |
| 3668 |
kaklik |
422 |
"prompt_number": 24 |
| 3663 |
kaklik |
423 |
}, |
|
|
424 |
{ |
| 3667 |
kaklik |
425 |
"cell_type": "markdown", |
|
|
426 |
"metadata": {}, |
|
|
427 |
"source": [ |
|
|
428 |
"\u010cten\u00ed dat z gyroskopu\n", |
|
|
429 |
"---------------------\n", |
|
|
430 |
"\n" |
|
|
431 |
] |
|
|
432 |
}, |
|
|
433 |
{ |
| 3663 |
kaklik |
434 |
"cell_type": "code", |
|
|
435 |
"collapsed": false, |
| 3667 |
kaklik |
436 |
"input": [ |
|
|
437 |
"cfg = config.Config(\n", |
|
|
438 |
" i2c = {\n", |
|
|
439 |
" \"port\": port,\n", |
|
|
440 |
" },\n", |
|
|
441 |
"\n", |
|
|
442 |
"\tbus = [\n", |
|
|
443 |
"\t\t{\n", |
|
|
444 |
" \"type\": \"i2chub\",\n", |
|
|
445 |
" \"address\": 0x72,\n", |
|
|
446 |
" \n", |
|
|
447 |
" \"children\": [\n", |
|
|
448 |
" {\"name\": \"gyro\", \"type\": \"imu01_gyro\", \"channel\": 0, }\n", |
|
|
449 |
" ],\n", |
|
|
450 |
"\t\t},\n", |
|
|
451 |
"\t],\n", |
|
|
452 |
")\n", |
|
|
453 |
"\n", |
|
|
454 |
"cfg.initialize()\n", |
|
|
455 |
"#acc = cfg.get_device(\"acc\")\n", |
|
|
456 |
"gyro = cfg.get_device(\"gyro\")\n", |
|
|
457 |
"sys.stdout.write(\" MLAB accelerometer sensor IMU01A module example \\r\\n\")\n", |
|
|
458 |
"time.sleep(0.5)\n", |
|
|
459 |
"gyro.route()" |
|
|
460 |
], |
|
|
461 |
"language": "python", |
|
|
462 |
"metadata": {}, |
|
|
463 |
"outputs": [ |
|
|
464 |
{ |
|
|
465 |
"output_type": "stream", |
|
|
466 |
"stream": "stderr", |
|
|
467 |
"text": [ |
|
|
468 |
"WARNING:pymlab.sensors.iic:HID device does not exist, we will try SMBus directly...\n" |
|
|
469 |
] |
|
|
470 |
}, |
|
|
471 |
{ |
|
|
472 |
"output_type": "stream", |
|
|
473 |
"stream": "stdout", |
|
|
474 |
"text": [ |
|
|
475 |
" MLAB accelerometer sensor IMU01A module example \r\n" |
|
|
476 |
] |
|
|
477 |
}, |
|
|
478 |
{ |
|
|
479 |
"metadata": {}, |
|
|
480 |
"output_type": "pyout", |
|
|
481 |
"prompt_number": 4, |
|
|
482 |
"text": [ |
|
|
483 |
"True" |
|
|
484 |
] |
|
|
485 |
} |
|
|
486 |
], |
|
|
487 |
"prompt_number": 4 |
|
|
488 |
}, |
|
|
489 |
{ |
|
|
490 |
"cell_type": "code", |
|
|
491 |
"collapsed": false, |
|
|
492 |
"input": [ |
|
|
493 |
"import sys\n", |
|
|
494 |
"import time\n", |
|
|
495 |
"from IPython.display import clear_output\n", |
|
|
496 |
"\n", |
|
|
497 |
"MEASUREMENTS = 100\n", |
|
|
498 |
"list_meas = []\n", |
|
|
499 |
"# acc.route() V p\u0159\u00edpad\u011b v\u00edce \u010didel je pot\u0159eba ke ka\u017ed\u00e9mu p\u0159ed jeho pou\u017eit\u00edm nechat vyroutovat cesutu na sb\u011brnici.\n", |
|
|
500 |
"\n", |
|
|
501 |
"for n in range(MEASUREMENTS):\n", |
|
|
502 |
" clear_output()\n", |
|
|
503 |
" (x, y, z) = gyro.axes()\n", |
|
|
504 |
" list_meas.append([x, y, z])\n", |
|
|
505 |
" print (n, list_meas[n])\n", |
|
|
506 |
" sys.stdout.flush()" |
|
|
507 |
], |
|
|
508 |
"language": "python", |
|
|
509 |
"metadata": {}, |
|
|
510 |
"outputs": [ |
|
|
511 |
{ |
|
|
512 |
"output_type": "stream", |
|
|
513 |
"stream": "stdout", |
|
|
514 |
"text": [ |
|
|
515 |
"(99, [195, -118, 7])\n" |
|
|
516 |
] |
|
|
517 |
} |
|
|
518 |
], |
|
|
519 |
"prompt_number": 5 |
|
|
520 |
}, |
|
|
521 |
{ |
|
|
522 |
"cell_type": "code", |
|
|
523 |
"collapsed": false, |
|
|
524 |
"input": [ |
|
|
525 |
"measurements = np.array(list_meas)\n", |
|
|
526 |
"\n", |
|
|
527 |
"%pylab qt\n", |
|
|
528 |
"plt.subplot(1, 1, 1)\n", |
|
|
529 |
"plt.plot(measurements[:, 0])\n", |
|
|
530 |
"plt.plot(measurements[:, 1])\n", |
|
|
531 |
"plt.plot(measurements[:, 2])\n", |
|
|
532 |
"plt.xlabel('sample number')\n", |
|
|
533 |
"plt.ylabel('ADC')\n", |
|
|
534 |
"plt.title('Raw sensors')" |
|
|
535 |
], |
|
|
536 |
"language": "python", |
|
|
537 |
"metadata": {}, |
|
|
538 |
"outputs": [ |
|
|
539 |
{ |
|
|
540 |
"output_type": "stream", |
|
|
541 |
"stream": "stdout", |
|
|
542 |
"text": [ |
|
|
543 |
"Populating the interactive namespace from numpy and matplotlib\n" |
|
|
544 |
] |
|
|
545 |
}, |
|
|
546 |
{ |
|
|
547 |
"metadata": {}, |
|
|
548 |
"output_type": "pyout", |
|
|
549 |
"prompt_number": 6, |
|
|
550 |
"text": [ |
|
|
551 |
"<matplotlib.text.Text at 0x7f5b38053450>" |
|
|
552 |
] |
|
|
553 |
} |
|
|
554 |
], |
|
|
555 |
"prompt_number": 6 |
|
|
556 |
}, |
|
|
557 |
{ |
|
|
558 |
"cell_type": "code", |
|
|
559 |
"collapsed": false, |
|
|
560 |
"input": [ |
|
|
561 |
"help(map)" |
|
|
562 |
], |
|
|
563 |
"language": "python", |
|
|
564 |
"metadata": {}, |
|
|
565 |
"outputs": [ |
|
|
566 |
{ |
|
|
567 |
"output_type": "stream", |
|
|
568 |
"stream": "stdout", |
|
|
569 |
"text": [ |
|
|
570 |
"Help on built-in function map in module __builtin__:\n", |
|
|
571 |
"\n", |
|
|
572 |
"map(...)\n", |
|
|
573 |
" map(function, sequence[, sequence, ...]) -> list\n", |
|
|
574 |
" \n", |
|
|
575 |
" Return a list of the results of applying the function to the items of\n", |
|
|
576 |
" the argument sequence(s). If more than one sequence is given, the\n", |
|
|
577 |
" function is called with an argument list consisting of the corresponding\n", |
|
|
578 |
" item of each sequence, substituting None for missing values when not all\n", |
|
|
579 |
" sequences have the same length. If the function is None, return a list of\n", |
|
|
580 |
" the items of the sequence (or a list of tuples if more than one sequence).\n", |
|
|
581 |
"\n" |
|
|
582 |
] |
|
|
583 |
} |
|
|
584 |
], |
|
|
585 |
"prompt_number": 10 |
|
|
586 |
}, |
|
|
587 |
{ |
|
|
588 |
"cell_type": "code", |
|
|
589 |
"collapsed": false, |
| 3663 |
kaklik |
590 |
"input": [], |
|
|
591 |
"language": "python", |
|
|
592 |
"metadata": {}, |
|
|
593 |
"outputs": [] |
| 3655 |
kaklik |
594 |
} |
|
|
595 |
], |
|
|
596 |
"metadata": {} |
|
|
597 |
} |
|
|
598 |
] |
|
|
599 |
} |