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kaklik |
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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": [ |
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"Uk\u00e1zka pou\u017eit\u00ed n\u00e1stroje IPython na manipulaci se senzorov\u00fdmi daty\n", |
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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). 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 sn\u00edma\u010d [HMC5883L](http://www.magneticsensors.com/three-axis-digital-compass.php) m\u00e1 n\u00e1sleduj\u00edc\u00ed katalogov\u00e9 parametry: \n", |
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"\n", |
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"* M\u011b\u0159\u00edc\u00ed rozsah +/- 800 uT\n", |
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"* Rozli\u0161en\u00ed typicky 5 uT\n", |
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"* \u010cetnost m\u011b\u0159en\u00ed 75 Hz\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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"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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] |
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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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kaklik |
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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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"i2c-6\ti2c \tDPDDC-B \tI2C adapter\r\n", |
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"i2c-7\ti2c \ti2c-tiny-usb at bus 001 device 006\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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"prompt_number": 1 |
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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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}, |
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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": 29 |
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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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kaklik |
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" i2c = {\n", |
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" \"port\": port,\n", |
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" },\n", |
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kaklik |
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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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kaklik |
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" {\"name\": \"mag\", \"type\": \"mag01\", \"gauss\": 0.88, \"channel\": 1, }, \n", |
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kaklik |
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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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"mag_sensor = cfg.get_device(\"mag\")\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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kaklik |
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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": 31 |
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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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"Nyn\u00ed u\u017e m\u016f\u017eeme p\u0159\u00edmo komunikovat se za\u0159\u00edzen\u00edm pojmenovan\u00fdm jako mag_sensor. A vy\u010d\u00edst z n\u011bj sadu dat." |
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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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kaklik |
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"\n", |
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"MEASUREMENTS = 500\n", |
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"list_meas = []\n", |
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"\n", |
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"for n in range(MEASUREMENTS):\n", |
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kaklik |
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"# mag_sensor.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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" clear_output()\n", |
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" (x, y, z) = mag_sensor.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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kaklik |
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"(499, [-111.69, -413.90999999999997, -388.36])\n" |
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] |
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} |
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], |
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"prompt_number": 6 |
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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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"V\u00fdstupn\u00ed jsou v jednotk\u00e1ch miliGauss a m\u011b\u0159\u00edc\u00ed rozsah je nastaven\u00fd na 0.88 Gauss. Viz konfigurace \u010didel naho\u0159e.\n", |
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"Nam\u011b\u0159en\u00e9 hodnoty n\u00e1sledn\u011b ulo\u017e\u00edme do souboru. " |
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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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"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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"prompt_number": 141 |
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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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"Zpracov\u00e1n\u00ed dat 2D\n", |
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kaklik |
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"-----------\n", |
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"\n", |
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"V dal\u0161\u00ed \u010d\u00e1sti budeme pracovat s daty v ulo\u017een\u00e9m souboru, kter\u00fd na\u010dteme do pol\u00ed x, y, z. " |
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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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"data = np.load('./calibration_data_2Dset.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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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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"prompt_number": 42 |
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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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"Nam\u011b\u0159en\u00e9 hodnoty vykresl\u00edme do 3D grafu." |
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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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"from mpl_toolkits.mplot3d.axes3d import Axes3D\n", |
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"#%pylab qt\n", |
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"%pylab inline\n", |
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"fig = plt.figure()\n", |
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"ax = Axes3D(fig)\n", |
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kaklik |
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"p = ax.scatter(x,y,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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"output_type": "stream", |
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"stream": "stdout", |
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"text": [ |
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kaklik |
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"Populating the interactive namespace from numpy and matplotlib\n" |
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kaklik |
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] |
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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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"png": 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G2Q6PpyNYNg6W3YKKipGCZk8ETSAQhd1eLTKvViESievaD4JRo0Zg1KgRqq9r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CTXI0Q6BHCqCYQSaqINVt1F3lTU6xQRhEi7hdMWEUxPnjfrFGsVUZ6IQMJZU3OlnPQWrtbpKuVd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\ntWvXZvTo0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"text": [ |
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293 |
], |
3663 |
kaklik |
294 |
"prompt_number": 43 |
3662 |
kaklik |
295 |
}, |
|
|
296 |
{ |
|
|
297 |
"cell_type": "markdown", |
|
|
298 |
"metadata": {}, |
|
|
299 |
"source": [ |
|
|
300 |
"Pro lep\u0161\u00ed zobrazen\u00ed jednotliv\u00fdch zkreslen\u00ed vykresl\u00edme i 2D pr\u016fm\u011bt dat." |
|
|
301 |
] |
|
|
302 |
}, |
|
|
303 |
{ |
|
|
304 |
"cell_type": "code", |
|
|
305 |
"collapsed": false, |
|
|
306 |
"input": [ |
|
|
307 |
"plt.plot(x, z,'.')" |
|
|
308 |
], |
|
|
309 |
"language": "python", |
|
|
310 |
"metadata": {}, |
|
|
311 |
"outputs": [ |
3642 |
kaklik |
312 |
{ |
3662 |
kaklik |
313 |
"metadata": {}, |
|
|
314 |
"output_type": "pyout", |
3663 |
kaklik |
315 |
"prompt_number": 44, |
3642 |
kaklik |
316 |
"text": [ |
3663 |
kaklik |
317 |
"[<matplotlib.lines.Line2D at 0x7effdb72c550>]" |
3642 |
kaklik |
318 |
] |
|
|
319 |
}, |
|
|
320 |
{ |
3662 |
kaklik |
321 |
"metadata": {}, |
|
|
322 |
"output_type": "display_data", |
3663 |
kaklik |
323 |
"png": 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ZIYQQX331lUhLSxMLFy5UNK733nvPO0DEbreLhQsXKh6TEEK43W7x4IMPirvu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\nPxGRxjDxExFpDBM/EZHGMPETEWnM/wPulkrNjSVVUQAAAABJRU5ErkJggg==\n", |
3642 |
kaklik |
324 |
"text": [ |
3663 |
kaklik |
325 |
"<matplotlib.figure.Figure at 0x7effb80d0e90>" |
3642 |
kaklik |
326 |
] |
3662 |
kaklik |
327 |
} |
|
|
328 |
], |
3663 |
kaklik |
329 |
"prompt_number": 44 |
3662 |
kaklik |
330 |
}, |
|
|
331 |
{ |
|
|
332 |
"cell_type": "markdown", |
|
|
333 |
"metadata": {}, |
|
|
334 |
"source": [ |
|
|
335 |
"Z grafu je vid\u011bt, \u017ee se uplat\u0148uj\u00ed oba zn\u00e1m\u00e9 deforma\u010dn\u00ed jevy - soft-iron a hard-iron. Pokus\u00edme se je proto kompenzovat. Nejd\u0159\u00edve zkus\u00edme odstranit Hard-iron efekty zp\u016fsobuj\u00edc\u00ed offsety." |
|
|
336 |
] |
|
|
337 |
}, |
|
|
338 |
{ |
|
|
339 |
"cell_type": "code", |
|
|
340 |
"collapsed": false, |
|
|
341 |
"input": [ |
|
|
342 |
"xoffset = (min(x) + max(x))/2\n", |
|
|
343 |
"print xoffset" |
|
|
344 |
], |
|
|
345 |
"language": "python", |
|
|
346 |
"metadata": {}, |
|
|
347 |
"outputs": [ |
3642 |
kaklik |
348 |
{ |
3663 |
kaklik |
349 |
"output_type": "stream", |
|
|
350 |
"stream": "stdout", |
|
|
351 |
"text": [ |
|
|
352 |
"26.0\n" |
3642 |
kaklik |
353 |
] |
|
|
354 |
} |
|
|
355 |
], |
3663 |
kaklik |
356 |
"prompt_number": 45 |
3596 |
kaklik |
357 |
}, |
|
|
358 |
{ |
|
|
359 |
"cell_type": "code", |
|
|
360 |
"collapsed": false, |
|
|
361 |
"input": [ |
3662 |
kaklik |
362 |
"zoffset = (min(z) + max(z))/2\n", |
|
|
363 |
"print zoffset" |
3596 |
kaklik |
364 |
], |
|
|
365 |
"language": "python", |
|
|
366 |
"metadata": {}, |
3663 |
kaklik |
367 |
"outputs": [ |
|
|
368 |
{ |
|
|
369 |
"output_type": "stream", |
|
|
370 |
"stream": "stdout", |
|
|
371 |
"text": [ |
|
|
372 |
"-180.5\n" |
|
|
373 |
] |
|
|
374 |
} |
|
|
375 |
], |
|
|
376 |
"prompt_number": 46 |
3596 |
kaklik |
377 |
}, |
|
|
378 |
{ |
|
|
379 |
"cell_type": "code", |
|
|
380 |
"collapsed": false, |
|
|
381 |
"input": [ |
3662 |
kaklik |
382 |
"plt.plot(x-xoffset, z-zoffset,'.')" |
3596 |
kaklik |
383 |
], |
|
|
384 |
"language": "python", |
|
|
385 |
"metadata": {}, |
3663 |
kaklik |
386 |
"outputs": [ |
|
|
387 |
{ |
|
|
388 |
"metadata": {}, |
|
|
389 |
"output_type": "pyout", |
|
|
390 |
"prompt_number": 47, |
|
|
391 |
"text": [ |
|
|
392 |
"[<matplotlib.lines.Line2D at 0x7effb8413050>]" |
|
|
393 |
] |
|
|
394 |
}, |
|
|
395 |
{ |
|
|
396 |
"metadata": {}, |
|
|
397 |
"output_type": "display_data", |
|
|
398 |
"png": 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Gr1yNXZ+3+K1WK7RarfvrsLAwOBwOX5+WiCjo+apP0+ctfq1WC5vN5v7a4XBgwoThnzcV\nFRXuxyaTCSaTydehEREp2pN9mmazGWaz2evj+nx1zuPHj+P06dOoqalBY2Mjdu/ejTNnzgwNgqtz\nEhENM9YNqBR7IxYhBDZu3Ii2tjYAQE1NDX7yk58MDYKJn4jIY4pN/OMKgomfiMhjXI+fiIjGhYmf\niEhlmPiJiFSGiZ+ISGWY+ImIFMpXqxErNvFz+WUiUjtfzdxVbOLn8stEpHa+Wo1YsYmfyy8TkVq5\nKh79/cCKFfKvRqzYxM/ll4lIrVwVj4YGIDxc/hzo80XapOLyy0SkVr6ueHDJBiIihRlrcTYXrtVD\nRKQyXKuHiCjI+WsYOxM/EZFC+GsYOxM/EZFC+GsYO2v8REQKMd5OXRd27hIRqQw7d4mIaFyCLvFz\n8TYiIu8EXeLn4m1ERN6RnPhPnDiBgoIC99eNjY1ITU1Feno6du3a5d6/c+dOpKSkwGAwoLm52bto\nwcXbiIi8JWmtnvLyctTX12Pu3LnufaWlpTh+/Dji4+OxZMkSXL9+HQ6HA5cuXUJTUxO6u7vx05/+\nFJ9//rlXAdfVedbrTUSkdCUlzmrG5MnOHOfr3CapxW8wGPC73/3O3ZtstVrR19eH+Ph4AEB2djYa\nGhpw5coVZGVlAQDi4uLw6NEj3L9/36uAXYu3MekTUajwdwl71MRfXV2NpKSkIVtLSwtef/31Ia+z\nWq3QarXur6Ojo2GxWGC1WhETEzNsPxERPebvEvaopZ6ioiIUFRWNeRCtVgubzeb+2mq1QqfTISIi\nYsh+m80G3VOa6hUVFe7HJpMJJpNpzPMSEYWC8ZawzWYzzGaz1+eTPIHLbDbjo48+wuHDhwEAc+fO\nxbFjxxAfH4+lS5eioqICYWFh2Lp1K86fP4/u7m4sX74c169fHx4EJ3ARkUrIWc+Xmjsl34hFo9FA\no9G4vz5w4AAKCgowMDCA7Oxs6PV6AEBGRgbS0tLgcDjw4YcfSj0dEVFIcNXzAeeHQCBuOMUlG4iI\n/Cg319mJm5zs/a1luVYPEVEQGGkhNqnlHyZ+IqIgZTI9Lv/k54+//MNF2oiIgpS/h3My8RMRBVBJ\nCXDunPNxaytw547vzxlUiZ8rcxLRaIIxR3R0AA6H8/HAAJCS4vtzBlXi58qcRDSaYMwRrjKPS1OT\n788ZVImfK3MS0WiCMUfU1QGLFgGRkcAXXwAvv+z7cwbVqB5P70dJROqithzB4ZxERCrD4ZxERDQu\nTPxERCrDxE9EJDOlDytl4icikpnSh5Uy8RMRyUzpw0pDPvGPdcmVmOgc9jV9OnD7tt/DI6IQVFfn\nXGzN22WXfSXkh3OOteqdTge4bgP8/PNAd7dPwiAikh2Hcz6F65Lrv/4LuHQJmDbNOUvO1foPD3/8\nusuXAxMjEZE/hXyL3zWT75tvgCtXHu9/9lngq6+crf30dGfSnzHDJyEQEfkEZ+6OwXW7s8E8ueEB\nEZHSMPGP4eFDoLAQ+Owz4N49ee53SUQUSH6r8VssFixbtgwmkwnz589HY2MjAKCxsRGpqalIT0/H\nrl273K/fuXMnUlJSYDAY0Nzc7HGActHpgJMngfZ2Zfe2E5GyKH0yliTCQzt27BD79+8XQgjR3t4u\nXnnlFSGEELNnzxZdXV1CCCFyc3NFa2uraGlpEa+99poQQog7d+4IvV4/4jElhEFE5BeZmUIAzi0/\nP9DRDCU1d0709INi8+bNiIyMBAD09/cjKioKNpsNdrsd8fHxAIDs7Gw0NDQgMjISWVlZAIC4uDg8\nevQI9+/fxzPPPCPbBxcRkS8pfTKWFKOWeqqrq5GUlDRk6+zsxKRJk9DT04O1a9diz549sFgs0Gq1\n7u+Ljo6GxWKB1WpFTEzMsP1ERMFC6ZOxpBi1xV9UVISioqJh+7/88kusWbMGe/fuRUZGBqxWK2w2\nm/t5q9UKnU6HiIiIIfttNht0T/nNVVRUuB+bTCaYTCYPfxQiIvnpdMoZ/Wc2m2E2m70+jsejem7c\nuIFVq1bh6NGjSEpKcu+fO3cujh07hvj4eCxduhQVFRUICwvD1q1bcf78eXR3d2P58uW4fv368CB4\nIxYiIo9JzZ0e1/jffvtt2O12lJWVAQB0Oh1OnDiBAwcOoKCgAAMDA8jOzoZerwcAZGRkIC0tDQ6H\nAx9++KHHARIRkbxUM46fiCjUcK0eIiIaFyZ+IiKVUWXiD8mZeERE46TKxK/026IREfmSKhN/KM7E\nIyIaL1WO6nGt0X/wYOjMxCMiz5SUOK/+J092zs4NxlzAZZmJiDww1m1ZgwGHcxIReUDNJV+2+IlI\nlUKh5MtSDxGRyrDUQ0RE48LET0SkMkz8REQqw8RPRCGDy7GMDxM/EYUMLscyPkz8HmKLgki51Dw2\n3xNM/B5ii4JIuULxxui+4PGtF9WOLQoi5VLSjdGVjBO4PBQKs/2IKDT4bQLX999/j7y8PGRmZmLR\nokX45ptvAACNjY1ITU1Feno6du3a5X79zp07kZKSAoPBgObmZo8DVBpXi4JJn0hZRup/KykBnnsO\nmDYNWLSI/XJuwkP79u0Tu3fvFkIIUVtbK8rLy4UQQsyePVt0dXUJIYTIzc0Vra2toqWlRbz22mtC\nCCHu3Lkj9Hr9iMeUEAYRhZjiYiEyM4XIyRHiwQPPv//ZZ4UAnNuKFc59mZmP9wFC5OfLGXHgSc2d\nHtf4y8vL4XA4AAC3b99GbGwsbDYb7HY74uPjAQDZ2dloaGhAZGQksrKyAABxcXF49OgR7t+/j2ee\neUa2Dy4iCg2ugROAs6Xuaa3+u+8eP7bbnf+6+uQAYO5c9su5jJr4q6ursW/fviH7amtr8eqrr2Lh\nwoX429/+hvr6elgsFmi1WvdroqOj0dXVhUmTJg1J8tHR0bBYLEz8RDSMtwMnoqIeJ/yICOe/dXVA\nYSGg0QA1NSzRuoya+IuKilBUVDTic5988gna29uxZMkStLa2wmazuZ+zWq3Q6XSIiIgYst9ms0HH\n3zwRjaCuzruBE3o90NDgbNnX1Dj36XTAyZPyxhkKPC717NmzB88//zzWrl2LKVOmYOLEiYiOjkZE\nRAS6uroQHx+P+vp6VFRUICwsDFu3bsWvfvUrdHd3w+FwYNq0aSMet6Kiwv3YZDLBZDJJ/ZmIKAh5\nOxTz6NHQH3FnNpthNpu9Po7Hwznv3buHdevWobe3FwMDA3j33XeRlpaGpqYm/PKXv8TAwACys7Ox\ne/duAM5RPWfPnoXD4cC+ffswf/784UEE0XBOIiKl4I1YiIhUhjdiISJF4HpWysfET0Sy4npWysfE\nrwBsIVEo4XpWysfErwBsIVEo4QqZysfVORWALSQKJVwhU/k4qkcBuOInBYOSEufV6eTJzlY9/1YD\nj8M5icinTKbHa+nk57NVrwQczklEPsWSZOhgi59IZaSWbFiSVB6WekgS1m3VhyWb0MFSD0nCoaTq\nw5INMfGrHJNA8JI68Y/j7ImlHpVj3TZ4sWRDUnMnJ3CpnK8m27DvwPd4tUZSsdRDPsG+A89IKduw\nZENSscVPPqHW1qjUKx0pNxrn0ggkFVv85BO+ao3KvZKp3MeTeqWj1g9KCgwmfvIJV2tU7hKE3CUk\nuY8nNYGzbEP+xMRPQUXulrHcx5OawH31QUk0Eg7npKAi9/BTDmelYOb3mbtff/01dDod7HY7AKCx\nsRGpqalIT0/Hrl273K/buXMnUlJSYDAY0NzcLPV0RADkbxmzpU1qJCnxW61WbNmyBZMmTXLvKy0t\nxeHDh3H58mU0NTXh+vXruHbtGi5duoSmpib88Y9/xKZNm2QLPBDMZnOgQxiXYIgzGGIEGKfcGKcy\neJz4hRBYv3499uzZg6ioKADOD4K+vj7Ex8cDALKzs9HQ0IArV64gKysLABAXF4dHjx7h/v37Mobv\nX8HyxxAMcQZDjADjlBvjVIZRx/FXV1dj3759Q/bNmDEDP/vZz/Dyyy8DcH4QWK1WaLVa92uio6PR\n1dWFSZMm4Zlnnhmy32KxDNlHRET+NWqLv6ioCF9++eWQrb29HdXV1ViwYAF6enqQnZ2NmJgY2Gw2\n9/dZrVbodDpotdoh+202G3QsphIRBZbwwo9//GPR19cnhBBizpw54ubNm8LhcIjc3Fzx+eefi5aW\nFrFw4ULhcDjE7du3xezZs0c8zsyZMwUAbty4cePmwTZz5kxJudurJRs0Go378YEDB1BQUICBgQFk\nZ2dDr9cDADIyMpCWlgaHw4EPP/xwxON0dnZ6EwYREXlAEeP4iYjIfzhzl4hIZQKS+JU++ev7779H\nXl4eMjMzsWjRInzzzTeKjNNisWDZsmUwmUyYP38+GhsbFRmny4kTJ1BQUOD+WqlxAoDD4cCGDRsw\nf/58LFiwADdv3gxIHIM1NTVhwYIFAJzl0fT0dBiNRmzcuNE9e7Oqqgp6vR5paWk4c+aM32Ps7+/H\n2rVrYTQakZKSgtOnTysy1oGBAfziF79Aeno6MjIy8Pe//12Rcbrcu3cPcXFx6OjokCdOST0DXrBY\nLCI3N1f86Ec/GtIx3NXVJYQQIjc3V7S2toqWlhbx2muvCSGEuHPnjtDr9X6Lcd++fWL37t1CCCFq\na2tFeXm5EEKI2bNnKyrOHTt2iP379wshhGhvbxevvPKKIuMUQoiysjKRmJgo1qxZ496ntPd9sGPH\njomf//znQgghGhsbRV5eXkDicHn33XdFUlKSSEtLE0IIsWzZMnHx4kUhhBAbNmwQJ06cEP/6179E\nUlKSsNvtwmKxiKSkJPf/MX+pqakRmzdvFkII8d1334m4uDixfPlyxcV68uRJUVRUJIQQwmw2i+XL\nlysyTiGEsNvtYsWKFeLFF18UX3/9tSzvvV/X4xeDJn/l5eUBePrkr8jIyBEnf/ljDkB5eTkcDgcA\n4Pbt24iNjYXNZoPdbldUnJs3b0ZkZCQAZ0srKipKkXECgMFgwMqVK/HRRx8BUOb7PtiVK1ewePFi\nAEBKSgquXr3q1/M/KSEhAcePH8fatWsBANeuXYPRaAQA5OTkoL6+HmFhYTAYDAgPD0d4eDgSEhLQ\n1taG5ORkv8WZn5+P1atXA3BeNYWHhysy1ry8PCxduhQAcOvWLcTGxqKhoUFxcQLAW2+9hdLSUuzZ\nsweAPO+9z0o91dXVSEpKGrItW7YMS5YsGXPyl8VigdVqRUxMzLD9/oizpaUFEyZMwMKFC/HBBx9g\nxYoVsFgsiouzs7MTkyZNQk9PD9auXYs9e/YoMs6Wlha8/vrrQ14X6Pd9LE/GFxYW5m4MBMKqVasw\nceLjdpoYNCZDSb+7KVOmYOrUqbDZbMjPz8c777wz5PempFjDwsJQWFiI8vJyFBQUKPJ3Wltbi+nT\np7sbQ0IIWeL0WYu/qKgIRUVFQ/a98MILqK6uRnV1tXvy1+nTp0ec/BUREeGXyV8jxenyySefoL29\nHUuWLEFra6si4/zyyy+xZs0a7N27FxkZGbBarYqM80lPTu7zd5yexudwODBhgnLGQgyOZbQJk7Gx\nsX6Prbu7G6tWrcKmTZuwZs0abN26VbGx1tbW4u7du5g3bx56e3sVF2dNTQ00Gg0aGhpw/fp1rFu3\nDt9++63Xcfr1L/kf//gHLly4gAsXLuDZZ59FfX09oqOjERERga6uLgghUF9fD6PRCIPBgHPnzkEI\ngTt37sDhcGDatGl+iXPPnj04dOgQAGcLZuLEiYqM88aNG8jPz8fhw4eRnZ0NwJmwlBbnSJQep8Fg\nwJ///GcAzk5o11WqUsydOxcX/3OvxrNnz8JoNGLevHn461//ir6+PlgsFnz11Vd46aWX/BrX3bt3\nkZWVhffeew+FhYWKjfXQoUPu0klUVBTCwsKQnJysuDgvXrwIs9mMCxcuYM6cOfjDH/6AxYsXex1n\nwO65K9fkL18oKirCunXr8Pvf/x4DAwOoqalRZJxvv/027HY7ysrKAAA6nQ4nTpxQXJwuGo1G0e/7\nYCtXrsT58+dhMBgAwP03EGiu39/evXtRXFwMu92OWbNmYfXq1dBoNCgrK0NGRgYcDgcqKysRERHh\n1/gqKythsViwa9cu90it/fv3o6ysTFGxrl69GoWFhcjMzER/fz/279+PxMRERf5OB9NoNLK895zA\nRUSkMsoT1bBWAAAAM0lEQVQpWhIRkV8w8RMRqQwTPxGRyjDxExGpDBM/EZHKMPETEakMEz8Rkcow\n8RMRqcz/A1D+DQmdcGLaAAAAAElFTkSuQmCC\n", |
|
|
399 |
"text": [ |
|
|
400 |
"<matplotlib.figure.Figure at 0x7effb83053d0>" |
|
|
401 |
] |
|
|
402 |
} |
|
|
403 |
], |
|
|
404 |
"prompt_number": 47 |
3662 |
kaklik |
405 |
}, |
|
|
406 |
{ |
|
|
407 |
"cell_type": "markdown", |
|
|
408 |
"metadata": {}, |
|
|
409 |
"source": [ |
3663 |
kaklik |
410 |
"Interaktivn\u00ed test, kter\u00fd vypisuje aktua\u00e1ln\u00ed azimut. Lze si na n\u011bm vyzkou\u0161et funkci kompasu. Je vid\u011bt, \u017ee p\u0159i vzniku v\u011bt\u0161\u00edho n\u00e1klonu, dvouos\u00fd kompas za\u010dne ukazovat p\u0159esn\u011b opa\u010dn\u00fd sm\u011br." |
3662 |
kaklik |
411 |
] |
|
|
412 |
}, |
|
|
413 |
{ |
|
|
414 |
"cell_type": "code", |
|
|
415 |
"collapsed": false, |
|
|
416 |
"input": [ |
3663 |
kaklik |
417 |
"try:\n", |
|
|
418 |
"\n", |
|
|
419 |
" for n in range(MEASUREMENTS):\n", |
|
|
420 |
" (x, y, z) = mag_sensor.axes()\n", |
|
|
421 |
" phi = np.arctan2(x-xoffset, z-zoffset)\n", |
|
|
422 |
" clear_output()\n", |
|
|
423 |
" print ((phi*180)/pi+180)\n", |
|
|
424 |
" sys.stdout.flush()\n", |
|
|
425 |
"except KeyboardInterrupt:\n", |
|
|
426 |
" sys.exit(0)" |
|
|
427 |
], |
|
|
428 |
"language": "python", |
|
|
429 |
"metadata": {}, |
|
|
430 |
"outputs": [ |
|
|
431 |
{ |
|
|
432 |
"output_type": "stream", |
|
|
433 |
"stream": "stdout", |
|
|
434 |
"text": [ |
|
|
435 |
"308.027821813\n" |
|
|
436 |
] |
|
|
437 |
} |
|
|
438 |
], |
|
|
439 |
"prompt_number": 36 |
|
|
440 |
}, |
|
|
441 |
{ |
|
|
442 |
"cell_type": "markdown", |
|
|
443 |
"metadata": {}, |
|
|
444 |
"source": [ |
|
|
445 |
"Vid\u00edme, \u017ee po tomto zp\u016fsobu korekce m\u00e1 vykreslen\u00fd \u00fatvar od kru\u017enice je\u0161t\u011b v\u00fdznamn\u00e9 odchylky. Nyn\u00ed pot\u0159ebujeme kompenzovat soft-iron efekty. K tomu mus\u00edme pracovat s nam\u011b\u0159en\u00fdmi daty, jako s elipsou a naj\u00edt jej\u00ed hlavn\u00ed a vedlej\u0161\u00ed poloosu. Postup je trochu komplikovan\u011bj\u0161\u00ed, proto adoptujeme k\u00f3d z [PaparazziUAV](http://wiki.paparazziuav.org/wiki/ImuCalibration). Ten pomoc\u00ed fitov\u00e1n\u00ed elipsoindu najde korek\u010dn\u00ed parametry citlivosti pro jednotliv\u00e9 osy. Zanedb\u00e1v\u00e1 ale p\u0159\u00edpadnou rotaci elipsoidu popsanou v \u010dl\u00e1nku [Compensating for Tilt, Hard-Iron, and Soft-Iron Effects](http://www.sensorsmag.com/sensors/motion-velocity-displacement/compensating-tilt-hard-iron-and-soft-iron-effects-6475)\n", |
|
|
446 |
"Tento p\u0159\u00edstup je tak\u00e9 v\u00fdhodn\u00fd v tom, \u017ee vyu\u017e\u00edv\u00e1 v\u0161echny 3 osy magnetometru. Proto nen\u00ed pot\u0159eba pro kompenzaci n\u00e1klonu pou\u017e\u00edvat akcelerometr. Kompenzace akcererometrem by ale pozd\u011bji m\u011bla b\u00fdt vyu\u017eita ke korekci rotace os magnetometru v\u016f\u010di vn\u011bj\u0161\u00edmu pouzdru.\n", |
|
|
447 |
"\n", |
|
|
448 |
"Zpracov\u00e1n\u00ed dat 3D\n", |
|
|
449 |
"-----------\n", |
|
|
450 |
"\n", |
|
|
451 |
"K tomu pou\u017eijeme datov\u00fd set, kde jsou body rozprost\u0159en\u00e9 po cel\u00e9m povrchu koule. " |
|
|
452 |
] |
|
|
453 |
}, |
|
|
454 |
{ |
|
|
455 |
"cell_type": "code", |
|
|
456 |
"collapsed": false, |
|
|
457 |
"input": [ |
|
|
458 |
"data = np.load('./calibration_data_3Dset.npz')\n", |
|
|
459 |
"list_meas = data['data']\n", |
|
|
460 |
"x = list_meas[:, 0]\n", |
|
|
461 |
"y = list_meas[:, 1]\n", |
|
|
462 |
"z = list_meas[:, 2]" |
|
|
463 |
], |
|
|
464 |
"language": "python", |
|
|
465 |
"metadata": {}, |
|
|
466 |
"outputs": [], |
|
|
467 |
"prompt_number": 1 |
|
|
468 |
}, |
|
|
469 |
{ |
|
|
470 |
"cell_type": "code", |
|
|
471 |
"collapsed": false, |
|
|
472 |
"input": [ |
|
|
473 |
"from mpl_toolkits.mplot3d.axes3d import Axes3D\n", |
|
|
474 |
"#%pylab qt ## po odkomentovani a zakomentovani nasledujiciho radku udela vystup do QT okna. \n", |
|
|
475 |
"%pylab inline\n", |
|
|
476 |
"fig = plt.figure()\n", |
|
|
477 |
"ax = Axes3D(fig)\n", |
|
|
478 |
"p = ax.scatter(x,y,z)" |
|
|
479 |
], |
|
|
480 |
"language": "python", |
|
|
481 |
"metadata": {}, |
|
|
482 |
"outputs": [ |
|
|
483 |
{ |
|
|
484 |
"output_type": "stream", |
|
|
485 |
"stream": "stdout", |
|
|
486 |
"text": [ |
|
|
487 |
"Populating the interactive namespace from numpy and matplotlib\n" |
|
|
488 |
] |
|
|
489 |
}, |
|
|
490 |
{ |
|
|
491 |
"metadata": {}, |
|
|
492 |
"output_type": "display_data", |
|
|
493 |
"png": 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X6yksLESv1ye1WkbowjZ48GBk+QMkSUYQTHi9F0hLS0EUU4INlvszOp2uze8v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ZfbMZkS2LLlE3bu3ABci16/mm+++Ykvvnjf/buekWFtadJNmzbhcMxAFCf9bhj+JGbzaAoKJrNr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\n", |
|
|
494 |
"text": [ |
|
|
495 |
"<matplotlib.figure.Figure at 0x7f10f2b9d950>" |
|
|
496 |
] |
|
|
497 |
} |
|
|
498 |
], |
|
|
499 |
"prompt_number": 2 |
|
|
500 |
}, |
|
|
501 |
{ |
|
|
502 |
"cell_type": "code", |
|
|
503 |
"collapsed": false, |
|
|
504 |
"input": [ |
3662 |
kaklik |
505 |
"import scipy\n", |
|
|
506 |
"from scipy import optimize\n", |
|
|
507 |
"import calibration_utils\n", |
|
|
508 |
"\n", |
|
|
509 |
"sensor_ref = 1.\n", |
|
|
510 |
"sensor_res = 0.73\n", |
|
|
511 |
"noise_window = 10\n", |
|
|
512 |
"noise_threshold = 1000" |
3642 |
kaklik |
513 |
], |
3662 |
kaklik |
514 |
"language": "python", |
|
|
515 |
"metadata": {}, |
|
|
516 |
"outputs": [], |
3663 |
kaklik |
517 |
"prompt_number": 3 |
3596 |
kaklik |
518 |
}, |
|
|
519 |
{ |
3662 |
kaklik |
520 |
"cell_type": "markdown", |
|
|
521 |
"metadata": {}, |
|
|
522 |
"source": [ |
|
|
523 |
"Uprav\u00edme strukuturu pole m\u011b\u0159en\u00fdch hodnot do sn\u00e1ze indexovateln\u00e9ho form\u00e1tu." |
|
|
524 |
] |
|
|
525 |
}, |
|
|
526 |
{ |
3596 |
kaklik |
527 |
"cell_type": "code", |
|
|
528 |
"collapsed": false, |
|
|
529 |
"input": [ |
3662 |
kaklik |
530 |
"measurements = np.array(list_meas)" |
3596 |
kaklik |
531 |
], |
|
|
532 |
"language": "python", |
|
|
533 |
"metadata": {}, |
|
|
534 |
"outputs": [], |
3663 |
kaklik |
535 |
"prompt_number": 4 |
3596 |
kaklik |
536 |
}, |
|
|
537 |
{ |
3662 |
kaklik |
538 |
"cell_type": "markdown", |
|
|
539 |
"metadata": {}, |
|
|
540 |
"source": [ |
|
|
541 |
"Spo\u010d\u00edt\u00e1me medi\u00e1n magnitudy zm\u011b\u0159en\u00fdch vektor\u016f. A nastav\u00edme podle n\u011bj rozhodovac\u00ed \u00farove\u0148 pro filtraci a ofiltrujeme nam\u011b\u0159en\u00e1 kalibra\u010dn\u00ed data. " |
|
|
542 |
] |
|
|
543 |
}, |
|
|
544 |
{ |
3596 |
kaklik |
545 |
"cell_type": "code", |
|
|
546 |
"collapsed": false, |
|
|
547 |
"input": [ |
3662 |
kaklik |
548 |
"meas_median=scipy.median(scipy.array([scipy.linalg.norm(v) for v in measurements]))\n", |
|
|
549 |
"noise_threshold = meas_median * 0.8\n", |
|
|
550 |
"print noise_threshold\n", |
|
|
551 |
"flt_meas, flt_idx = calibration_utils.filter_meas(measurements, noise_window, noise_threshold)\n", |
|
|
552 |
"print(\"remaining \"+str(len(flt_meas))+\" after filtering\")" |
3596 |
kaklik |
553 |
], |
|
|
554 |
"language": "python", |
|
|
555 |
"metadata": {}, |
3663 |
kaklik |
556 |
"outputs": [ |
|
|
557 |
{ |
|
|
558 |
"output_type": "stream", |
|
|
559 |
"stream": "stdout", |
|
|
560 |
"text": [ |
|
|
561 |
"460.895326306\n", |
|
|
562 |
"remaining 346 after filtering\n" |
|
|
563 |
] |
|
|
564 |
} |
3596 |
kaklik |
565 |
], |
3663 |
kaklik |
566 |
"prompt_number": 5 |
3596 |
kaklik |
567 |
}, |
|
|
568 |
{ |
3662 |
kaklik |
569 |
"cell_type": "markdown", |
|
|
570 |
"metadata": {}, |
|
|
571 |
"source": [ |
|
|
572 |
"Spo\u010d\u00edt\u00e1me odhad elipsoidu z nam\u011b\u0159en\u00fdch minim\u00e1ln\u00edch a maxim\u00e1ln\u00edch hodnot." |
|
|
573 |
] |
|
|
574 |
}, |
|
|
575 |
{ |
3596 |
kaklik |
576 |
"cell_type": "code", |
|
|
577 |
"collapsed": false, |
|
|
578 |
"input": [ |
3662 |
kaklik |
579 |
" p0 = calibration_utils.get_min_max_guess(flt_meas, sensor_ref)\n", |
|
|
580 |
" cp0, np0 = calibration_utils.scale_measurements(flt_meas, p0)\n", |
|
|
581 |
" print(\"initial guess : avg \"+str(np0.mean())+\" std \"+str(np0.std()))\n", |
|
|
582 |
"\n", |
|
|
583 |
" def err_func(p, meas, y):\n", |
|
|
584 |
" cp, np = calibration_utils.scale_measurements(meas, p)\n", |
|
|
585 |
" err = y*scipy.ones(len(meas)) - np\n", |
|
|
586 |
" return err" |
3596 |
kaklik |
587 |
], |
|
|
588 |
"language": "python", |
|
|
589 |
"metadata": {}, |
3662 |
kaklik |
590 |
"outputs": [ |
|
|
591 |
{ |
|
|
592 |
"output_type": "stream", |
|
|
593 |
"stream": "stdout", |
|
|
594 |
"text": [ |
|
|
595 |
"initial guess : avg 0.999920050427 std 0.0671243703038\n" |
|
|
596 |
] |
|
|
597 |
} |
|
|
598 |
], |
3663 |
kaklik |
599 |
"prompt_number": 6 |
3662 |
kaklik |
600 |
}, |
|
|
601 |
{ |
|
|
602 |
"cell_type": "markdown", |
|
|
603 |
"metadata": {}, |
|
|
604 |
"source": [ |
|
|
605 |
"Optimalizujeme odhad fitov\u00e1n\u00edm elipsoidu." |
|
|
606 |
] |
|
|
607 |
}, |
|
|
608 |
{ |
|
|
609 |
"cell_type": "code", |
|
|
610 |
"collapsed": false, |
|
|
611 |
"input": [ |
|
|
612 |
" p1, cov, info, msg, success = optimize.leastsq(err_func, p0[:], args=(flt_meas, sensor_ref), full_output=1)\n", |
|
|
613 |
" if not success in [1, 2, 3, 4]:\n", |
|
|
614 |
" print(\"Optimization error: \", msg)\n", |
|
|
615 |
" print(\"Please try to provide a clean logfile.\")\n", |
|
|
616 |
" sys.exit(1)\n", |
|
|
617 |
"\n", |
|
|
618 |
" cp1, np1 = calibration_utils.scale_measurements(flt_meas, p1)\n", |
|
|
619 |
" print(\"optimized guess : avg \"+str(np1.mean())+\" std \"+str(np1.std()))" |
|
|
620 |
], |
|
|
621 |
"language": "python", |
|
|
622 |
"metadata": {}, |
|
|
623 |
"outputs": [ |
|
|
624 |
{ |
|
|
625 |
"output_type": "stream", |
|
|
626 |
"stream": "stdout", |
|
|
627 |
"text": [ |
|
|
628 |
"optimized guess : avg 0.999137645687 std 0.0293532050592\n" |
|
|
629 |
] |
|
|
630 |
} |
|
|
631 |
], |
3663 |
kaklik |
632 |
"prompt_number": 7 |
3662 |
kaklik |
633 |
}, |
|
|
634 |
{ |
|
|
635 |
"cell_type": "markdown", |
|
|
636 |
"metadata": {}, |
|
|
637 |
"source": [ |
|
|
638 |
"Vykresl\u00edme v\u00fdsledek filtrace a fitov\u00e1n\u00ed. " |
|
|
639 |
] |
|
|
640 |
}, |
|
|
641 |
{ |
|
|
642 |
"cell_type": "code", |
|
|
643 |
"collapsed": false, |
|
|
644 |
"input": [ |
|
|
645 |
"%pylab qt\n", |
3663 |
kaklik |
646 |
"#%pylab inline\n", |
3662 |
kaklik |
647 |
"calibration_utils.plot_results(False, measurements, flt_idx, flt_meas, cp0, np0, cp1, np1, sensor_ref)\n", |
|
|
648 |
"calibration_utils.plot_mag_3d(flt_meas, cp1, p1)" |
|
|
649 |
], |
|
|
650 |
"language": "python", |
|
|
651 |
"metadata": {}, |
|
|
652 |
"outputs": [ |
|
|
653 |
{ |
|
|
654 |
"output_type": "stream", |
|
|
655 |
"stream": "stdout", |
|
|
656 |
"text": [ |
|
|
657 |
"Populating the interactive namespace from numpy and matplotlib\n" |
|
|
658 |
] |
|
|
659 |
}, |
|
|
660 |
{ |
|
|
661 |
"output_type": "stream", |
|
|
662 |
"stream": "stderr", |
|
|
663 |
"text": [ |
|
|
664 |
"WARNING: pylab import has clobbered these variables: ['cov', 'info']\n", |
|
|
665 |
"`%pylab --no-import-all` prevents importing * from pylab and numpy\n" |
|
|
666 |
] |
|
|
667 |
} |
|
|
668 |
], |
3663 |
kaklik |
669 |
"prompt_number": 8 |
3662 |
kaklik |
670 |
}, |
|
|
671 |
{ |
|
|
672 |
"cell_type": "markdown", |
|
|
673 |
"metadata": {}, |
|
|
674 |
"source": [ |
|
|
675 |
"Nyn\u00ed m\u016f\u017eeme z\u00edskan\u00e9 scale faktory a offsety pou\u017e\u00edt na kompenzaci libovoln\u00e9ho m\u011b\u0159en\u00ed a n\u00e1sledn\u011b vypo\u010d\u00edtat polohov\u00e9 \u00fahly platformy ve sf\u00e9rick\u00fdch sou\u0159adnic\u00edch." |
|
|
676 |
] |
|
|
677 |
}, |
|
|
678 |
{ |
|
|
679 |
"cell_type": "code", |
|
|
680 |
"collapsed": false, |
|
|
681 |
"input": [ |
|
|
682 |
"for n in range(MEASUREMENTS):\n", |
|
|
683 |
" m = mag_sensor.axes()\n", |
|
|
684 |
" sm = (m - p1[0:3])*p1[3:6]\n", |
|
|
685 |
" r = norm(sm)\n", |
|
|
686 |
" theta = np.arccos(sm[2]/r)\n", |
|
|
687 |
" phi = np.arctan2(sm[1],sm[0])\n", |
|
|
688 |
" clear_output()\n", |
|
|
689 |
" print (r,(theta*180)/pi,(phi*180)/pi)\n", |
|
|
690 |
" sys.stdout.flush()" |
|
|
691 |
], |
|
|
692 |
"language": "python", |
|
|
693 |
"metadata": {}, |
|
|
694 |
"outputs": [ |
|
|
695 |
{ |
3663 |
kaklik |
696 |
"output_type": "stream", |
|
|
697 |
"stream": "stdout", |
3662 |
kaklik |
698 |
"text": [ |
3663 |
kaklik |
699 |
"(1.0033330291141624, 159.16410600293204, 61.66914855382452)\n" |
3662 |
kaklik |
700 |
] |
|
|
701 |
} |
|
|
702 |
], |
3663 |
kaklik |
703 |
"prompt_number": 35 |
3596 |
kaklik |
704 |
} |
|
|
705 |
], |
|
|
706 |
"metadata": {} |
|
|
707 |
} |
|
|
708 |
] |
|
|
709 |
} |