No changes between revisions
/Modules/Sensors/ALTIMET01A/SW/Python/ALTIMET_test.ipynb
14,7 → 14,7
"P\u0159esn\u00e9 m\u011b\u0159en\u00ed tlaku a jeho pou\u017eit\u00ed k ur\u010den\u00ed nadmo\u0159sk\u00e9 v\u00fd\u0161ky\n",
"=======\n",
"\n",
"P\u0159\u00edklad vyu\u017e\u00edv\u00e1 modulovou stavebnici MLAB a jej\u00ed Python knihovnu [pymlab](https://github.com/MLAB-project/MLAB-I2c-modules)\n",
"P\u0159\u00edklad vyu\u017e\u00edv\u00e1 modulovou stavebnici MLAB a jej\u00ed Python 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",
"\n",
"Pou\u017eit\u00fd sn\u00edma\u010d [MPL3115A2](http://www.freescale.com/webapp/sps/site/prod_summary.jsp?code=MPL3115A2) m\u00e1 n\u00e1sleduj\u00edc\u00ed katalogov\u00e9 parametry: \n",
"\n",
49,13 → 49,12
"i2c-3\ti2c \ti915 gmbus dpc \tI2C adapter\r\n",
"i2c-4\ti2c \ti915 gmbus dpb \tI2C adapter\r\n",
"i2c-5\ti2c \ti915 gmbus dpd \tI2C adapter\r\n",
"i2c-6\ti2c \tDPDDC-C \tI2C adapter\r\n",
"i2c-7\ti2c \tDPDDC-D \tI2C adapter\r\n",
"i2c-8\ti2c \ti2c-tiny-usb at bus 001 device 008\tI2C adapter\r\n"
"i2c-6\ti2c \tDPDDC-B \tI2C adapter\r\n",
"i2c-7\ti2c \ti2c-tiny-usb at bus 001 device 004\tI2C adapter\r\n"
]
}
],
"prompt_number": 1
"prompt_number": 111
},
{
"cell_type": "markdown",
84,12 → 83,12
"cell_type": "code",
"collapsed": false,
"input": [
"port = 8"
"port = 7"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
"prompt_number": 112
},
{
"cell_type": "markdown",
113,7 → 112,7
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
"prompt_number": 113
},
{
"cell_type": "markdown",
145,7 → 144,7
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 8
"prompt_number": 114
},
{
"cell_type": "markdown",
173,7 → 172,7
]
}
],
"prompt_number": 9
"prompt_number": 115
},
{
"cell_type": "markdown",
186,6 → 185,11
"cell_type": "code",
"collapsed": false,
"input": [
"import sys\n",
"import time\n",
"from IPython.display import clear_output\n",
"\n",
"\n",
"MEASUREMENTS = 100\n",
"t = np.zeros(MEASUREMENTS)\n",
"p = np.zeros(MEASUREMENTS)\n",
193,7 → 197,9
"# gauge.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",
"for n in range(MEASUREMENTS):\n",
" (t[n], p[n]) = gauge.get_tp()\n",
" print(n,t[n], p[n])"
" clear_output()\n",
" print(n,t[n], p[n])\n",
" sys.stdout.flush()"
],
"language": "python",
"metadata": {},
202,803 → 208,11
"output_type": "stream",
"stream": "stdout",
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"text": [
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"text": [
"\n",
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"text": [
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"text": [
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"text": [
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"text": [
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"text": [
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"text": [
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"text": [
"\n",
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{
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"text": [
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"(68, 22.625, 98290.5)"
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},
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"text": [
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{
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"text": [
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"text": [
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{
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"text": [
"\n",
"(74, 22.625, 98290.25)"
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"text": [
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"(75, 22.625, 98290.25)"
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"text": [
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"(76, 22.625, 98284.5)"
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"text": [
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"(82, 22.625, 98290.25)"
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"text": [
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"text": [
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"(84, 22.625, 98286.25)"
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"(86, 22.625, 98290.5)"
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"(87, 22.625, 98288.5)"
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{
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"stream": "stdout",
"text": [
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"(88, 22.625, 98288.5)"
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{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(89, 22.625, 98286.75)"
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"stream": "stdout",
"text": [
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"text": [
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{
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"stream": "stdout",
"text": [
"\n",
"(92, 22.625, 98290.75)"
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{
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"stream": "stdout",
"text": [
"\n",
"(93, 22.625, 98288.75)"
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{
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"stream": "stdout",
"text": [
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{
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"stream": "stdout",
"text": [
"\n",
"(95, 22.625, 98288.75)"
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{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(96, 22.625, 98290.5)"
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{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
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{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
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]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(99, 22.625, 98290.0)"
]
},
{
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"stream": "stdout",
"text": [
"\n"
]
}
],
"prompt_number": 34
"prompt_number": 117
},
{
"cell_type": "markdown",
1029,6 → 243,10
"cell_type": "code",
"collapsed": false,
"input": [
"import sys\n",
"import time\n",
"from IPython.display import clear_output\n",
"\n",
"MEASUREMENTS = 100\n",
"t = np.zeros(MEASUREMENTS)\n",
"p = np.zeros(MEASUREMENTS)\n",
1036,7 → 254,9
"# gauge.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",
"for n in range(MEASUREMENTS):\n",
" (t[n], p[n]) = gauge.get_tp()\n",
" print(n,t[n], p[n])"
" clear_output()\n",
" print(n,t[n], p[n])\n",
" sys.stdout.flush()"
],
"language": "python",
"metadata": {},
1045,803 → 265,11
"output_type": "stream",
"stream": "stdout",
"text": [
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"(1, 22.5625, 98296.25)"
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"text": [
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"text": [
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"text": [
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]
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"text": [
"\n",
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},
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"text": [
"\n",
"(22, 22.5625, 98298.5)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(23, 22.5625, 98298.5)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(24, 22.5625, 98298.5)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(25, 22.5625, 98300.5)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(26, 22.5625, 98300.5)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(27, 22.5625, 98296.0)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(28, 22.5625, 98300.0)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(29, 22.5625, 98306.75)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(30, 22.5625, 98302.75)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(31, 22.5625, 98302.75)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(32, 22.5625, 98302.75)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(33, 22.5625, 98302.75)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(34, 22.5625, 98302.75)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(35, 22.5625, 98302.75)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(36, 22.5625, 98300.25)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(37, 22.5625, 98300.25)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(38, 22.5625, 98296.5)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(39, 22.5625, 98296.5)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(40, 22.5625, 98296.5)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(41, 22.5625, 98296.5)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(42, 22.5625, 98300.0)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(43, 22.5625, 98300.0)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(44, 22.5625, 98300.0)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(45, 22.5625, 98300.5)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(46, 22.5625, 98300.5)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(47, 22.5625, 98300.5)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(48, 22.5625, 98300.5)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(49, 22.5625, 98300.75)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(50, 22.5625, 98300.75)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(51, 22.5625, 98298.75)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(52, 22.5625, 98298.75)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(53, 22.5625, 98298.75)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(54, 22.5625, 98298.75)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(55, 22.5625, 98300.5)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(56, 22.5625, 98300.5)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(57, 22.5625, 98296.75)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(58, 22.5625, 98296.75)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(59, 22.5625, 98296.75)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(60, 22.5625, 98298.25)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(61, 22.5625, 98298.25)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(62, 22.5625, 98298.25)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(63, 22.5625, 98298.25)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(64, 22.5625, 98304.25)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(65, 22.5625, 98304.25)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(66, 22.5625, 98298.0)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(67, 22.5625, 98298.0)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(68, 22.5625, 98302.5)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(69, 22.5625, 98302.5)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(70, 22.5625, 98302.5)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(71, 22.5625, 98302.5)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(72, 22.5625, 98298.0)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(73, 22.5625, 98298.0)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(74, 22.5625, 98298.0)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(75, 22.5625, 98302.5)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(76, 22.5625, 98302.5)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(77, 22.5625, 98300.75)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(78, 22.5625, 98300.75)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(79, 22.5625, 98300.25)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(80, 22.5625, 98300.25)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(81, 22.5625, 98300.0)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(82, 22.5625, 98300.0)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(83, 22.5625, 98300.0)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(84, 22.5625, 98300.0)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(85, 22.5625, 98298.75)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(86, 22.5625, 98302.75)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(87, 22.5625, 98302.75)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(88, 22.5625, 98300.5)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(89, 22.5625, 98300.5)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(90, 22.5625, 98298.5)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(91, 22.5625, 98298.5)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(92, 22.5625, 98300.0)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(93, 22.5625, 98300.0)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(94, 22.5625, 98298.5)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(95, 22.5625, 98298.5)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(96, 22.5625, 98298.75)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(97, 22.5625, 98298.75)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(98, 22.5625, 98300.75)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(99, 22.5625, 98300.75)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
}
],
"prompt_number": 10
"prompt_number": 118
},
{
"cell_type": "code",
1872,7 → 300,7
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
"prompt_number": 82
},
{
"cell_type": "markdown",
1885,7 → 313,7
"cell_type": "markdown",
"metadata": {},
"source": [
"Minim\u00e1ln\u00ed nam\u011b\u0159en\u00e1 hodnota tlaku:"
"Minim\u00e1ln\u00ed, maxim\u00e1ln\u00ed hodnota tlaku a sm\u011brodatn\u00e1 odchylka pro m\u011b\u0159en\u00ed tlaku na stole:"
]
},
{
1892,27 → 320,31
"cell_type": "code",
"collapsed": false,
"input": [
"amin(ph)"
"[min(ph),max(ph),std(ph)]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"latex": [
"$$\\begin{bmatrix}98282.25, & 98290.75, & 2.28119897203\\end{bmatrix}$$"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 27,
"png": "iVBORw0KGgoAAAANSUhEUgAAAV4AAAAZBAMAAACLL1tJAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\ngg==\n",
"prompt_number": 95,
"text": [
"98346.0"
"[98282.25, 98290.75, 2.28119897203]"
]
}
],
"prompt_number": 27
"prompt_number": 95
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Minim\u00e1ln\u00ed nam\u011b\u0159en\u00e1 hodnota teploty:"
"Minim\u00e1ln\u00ed, maxim\u00e1ln\u00ed hodnota tlaku a sm\u011brodatn\u00e1 odchylka pro m\u011b\u0159en\u00ed teploty na stole:"
]
},
{
1919,27 → 351,31
"cell_type": "code",
"collapsed": false,
"input": [
"amin(th)"
"[min(th),max(th),std(th)]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"latex": [
"$$\\begin{bmatrix}22.625, & 22.625, & 0.0\\end{bmatrix}$$"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 28,
"png": "iVBORw0KGgoAAAANSUhEUgAAAMwAAAAZBAMAAACP/9xdAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nRU5ErkJggg==\n",
"prompt_number": 102,
"text": [
"22.5625"
"[22.625, 22.625, 0.0]"
]
}
],
"prompt_number": 28
"prompt_number": 102
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Maxim\u00e1ln\u00ed tlak"
"Otev\u0159eme soubor s m\u011b\u0159en\u00edm z podlahy a zpo\u010d\u00edt\u00e1me znovu statistick\u00e9 parametry. "
]
},
{
1946,27 → 382,20
"cell_type": "code",
"collapsed": false,
"input": [
"amax(ph)"
"data = np.load('./data_floor.npz')\n",
"tl=data['temp']\n",
"pl=data['preassure']"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 29,
"text": [
"98358.25"
]
}
],
"prompt_number": 29
"outputs": [],
"prompt_number": 98
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Maxim\u00e1ln\u00ed hodnota teploty"
"M\u011b\u0159en\u00ed tlaku"
]
},
{
1973,27 → 402,31
"cell_type": "code",
"collapsed": false,
"input": [
"amax(th)"
"[min(pl),max(pl),std(pl)]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"latex": [
"$$\\begin{bmatrix}98296.0, & 98306.75, & 2.24640824429\\end{bmatrix}$$"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 30,
"png": "iVBORw0KGgoAAAANSUhEUgAAAVQAAAAZBAMAAACcDcuAAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\n",
"prompt_number": 99,
"text": [
"22.625"
"[98296.0, 98306.75, 2.24640824429]"
]
}
],
"prompt_number": 30
"prompt_number": 99
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Sm\u011brodatn\u00e1 odchylka tlaku"
"M\u011b\u0159en\u00ed teploty"
]
},
{
2000,193 → 433,30
"cell_type": "code",
"collapsed": false,
"input": [
"std(ph)"
"[min(tl),max(tl),std(tl)]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"latex": [
"$$\\begin{bmatrix}22.5625, & 22.5625, & 0.0\\end{bmatrix}$$"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 31,
"png": "iVBORw0KGgoAAAANSUhEUgAAAOIAAAAZBAMAAADea+8+AAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\n/1en9wEoqgXKBBSl9QAAAABJRU5ErkJggg==\n",
"prompt_number": 101,
"text": [
"2.3781768226942255"
"[22.5625, 22.5625, 0.0]"
]
}
],
"prompt_number": 31
"prompt_number": 101
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Sm\u011brodatn\u00e1 odchylka teploty"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"std(th)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 32,
"text": [
"0.024518806557416305"
]
}
],
"prompt_number": 32
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Otev\u0159eme soubor s m\u011b\u0159en\u00edm z podlahy a zpo\u010d\u00edt\u00e1me znovu statistick\u00e9 parametry. "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data = np.load('./data_floor.npz')\n",
"tl=data['temp']\n",
"pl=data['preassure']"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"amin(pl)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 15,
"text": [
"98296.0"
]
}
],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"amin(tl)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 16,
"text": [
"22.5625"
]
}
],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"amax(pl)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 17,
"text": [
"98306.75"
]
}
],
"prompt_number": 17
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"amax(tl)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 18,
"text": [
"22.5625"
]
}
],
"prompt_number": 18
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"std(pl)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 19,
"text": [
"2.2464082442868674"
]
}
],
"prompt_number": 19
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"std(tl)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 14,
"text": [
"0.0"
]
}
],
"prompt_number": 14
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Pro porovn\u00e1n\u00ed je nyn\u00ed mo\u017en\u00e9 vykreslit graf z hodnot nam\u011b\u0159en\u00fdch na stole a na zemi. "
]
},
2212,11 → 482,11
"output_type": "display_data",
"png": 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kSOuPxRinT3NxS2sLACCTCGzduhXBwcH47LPPUF5ejujoaMTExCAtLQ2JiYlY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\nrkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x7fd03e30b090>"
"<matplotlib.figure.Figure at 0x7fdc6f159590>"
]
}
],
"prompt_number": 4
"prompt_number": 104
},
{
"cell_type": "code",
2240,11 → 510,11
"output_type": "display_data",
"png": 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fP0737t257bbbeP/999m/fz9JSUmmt4gVEbkhQ8Iytfx7/Kr8er527dol5iQuWrRoUYl5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\ngg==\n",
"text": [
"<matplotlib.figure.Figure at 0x7fd03e275390>"
"<matplotlib.figure.Figure at 0x7fdc6f1eec10>"
]
}
],
"prompt_number": 5
"prompt_number": 103
},
{
"cell_type": "markdown",
2260,23 → 530,13
"from sympy import *\n",
"init_printing()\n",
"\n",
"#p0 = Symbol('p_0')\n",
"#l = Symbol('L')\n",
"#cp = Symbol('C_p')\n",
"#t0 = Symbol('T_0')\n",
"#g = Symbol('g')\n",
"#m = Symbol('M')\n",
"#r = Symbol('R')\n",
"#p = Symbol('p')\n",
"#h = Symbol('h')\n",
"\n",
"po = Symbol('po')\n",
"l = Symbol('l')\n",
"cp = Symbol('cp')\n",
"to = Symbol('to')\n",
"p0 = Symbol('p_0')\n",
"l = Symbol('L')\n",
"cp = Symbol('C_p')\n",
"t0 = Symbol('T_0')\n",
"g = Symbol('g')\n",
"m = Symbol('m')\n",
"r = Symbol('r')\n",
"m = Symbol('M')\n",
"r = Symbol('R')\n",
"p = Symbol('p')\n",
"h = Symbol('h')\n",
"\n",
2289,23 → 549,23
"outputs": [
{
"latex": [
"$$po \\left(- \\frac{h l}{to} + 1\\right)^{\\frac{g m}{l r}} = p$$"
"$$po \\left(- \\frac{L h}{to} + 1\\right)^{\\frac{M g}{L R}} = p$$"
],
"metadata": {},
"output_type": "pyout",
"png": "iVBORw0KGgoAAAANSUhEUgAAAMQAAAA5BAMAAACbhJmfAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMARIm7IjJ2qxDdVM1m\n75kH/PNjAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAEJ0lEQVRYCa1YTYgTSRj90slMOj35w4sXwRZE\nEVFz8OeiTtjjXpyT1wm4KChqLh7Eg31Yxj8k2VVQwZ+wB0FQzEU8BEQUPOyuZlQU0YPtxZFdwcyy\nLoPKanV1V6W+6kpXZUwdpt979epVprvrq0oARtB+uFfL//Xnm9+mRpA1IOK81XL2wUvHHdD//bJT\nT3twyG5are/PGpCQd08ANB0/5wwwjEB+Pwd2fbyc/WkEWeoIB/aqO0anpn93hw1bYTbgmJlN5bJ8\nlRrXUu1I+8pbL+5SKqsF1d7WJGz8inJR/cGNYxc5NAHOC9E14QesK0ocp10ON3FkAk6XRVepFbC/\nRYlj+yWHsxyZgO3ItNwL6HOkcXKDoUwFHj3YzZju6iwgx5qAZT4ijZOcG8G8C1f9H7muATkfGe5c\n31WFfA9pnDj/RjBbhqzyjeBWEXSrIrM/V7N1yFZETcCXI5w7CQVB1sAzqN/5COkKFGpI7JOuF2Jr\nJZgvxLH5fgBBxXaQPz3oJjRqyG1GirPIRx7ojinYjzSBTLQFYgoLdeRslGEdeFtsJPbJOHvefUmP\npu8iD1nWT/LeFwuJfZL5r4+N0cEqsh4BmLdgfgaJAnkqYFO4DRu3Aqx34YCL1T6T7P2OBGT6sSY8\nGrKhmpCl7rL/V+uympn0qNQtyz1antI8v5/DBHv7UY+i6VooDPHX+ZRsvsq6D3oUNXwmGF/HB9RU\nFiBPUcLLiNmk65KNO/d4YL19E+hFzVqKTVGR0jANs8E93LIqmV+gUV3EFIU2zgyY1aHNJ5BmQ6Z1\nm+wHBRfo/pLFG1IwAjX5vyg0UbdEwmyw4RNYCx88oLc126OuUxeCdgtK/PgSDZanyM1KqYiG2QCp\nBUjPkhW3wyXd0RTIyMnaTudSp3OH8uiNSp4izCa3rgmNd/8AbJgiY4tD3qic4kZZ9BZcqJO4INsH\nICelh94XUlGDT2cN+UYlP4swG6BUGXsGc5Ams5Fz35DrQvVGBTlRo9kAy5ccqIK1ii4LyON9lVn5\nlT3uudfn7gZiqcK7VIBmA9CTEut3DKdgfk0BibLDNyQaEyuD9lmWRq/LEAPQlMEw20aFL1bMnZ4U\niunNFuaYRdnXPtdE/ZVICCannKSWvCVJ2VHQpBRIy4qkCfSogE1htGqZfenklhqrwkwTr6b7sDhm\nuiUygJvkXB5VYdxB2aIOOdJRLfjywqqwYopFHdWK0hv0KwCrwoop0k2FqJNSeO3ZhLIqrBhaqilE\nrXQZOVI9OM6qMOoISddTiFoJj7LazhSrwoqhtxWaXir4oqdYn+FVWNRDnNKU/vgIquCvk5nNZFlE\nVTg+INwA4rpOQWUx2XzDS+4f1Eu+FRk2+7GhUbY5xu/6Yu8TkMdr2O4b+uK2bC2uqRT+Y5GqU6OF\n27jGBHDM01qo4RvdYgkTer/nUAAAAABJRU5ErkJggg==\n",
"prompt_number": 26,
"png": "iVBORw0KGgoAAAANSUhEUgAAAM0AAAA7BAMAAAAqWRNeAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nVFRM8z9e2xi+X5NrJwAAAABJRU5ErkJggg==\n",
"prompt_number": 73,
"text": [
" g\u22c5m \n",
" M\u22c5g \n",
" \u2500\u2500\u2500 \n",
" l\u22c5r \n",
" \u239b h\u22c5l \u239e \n",
" L\u22c5R \n",
" \u239b L\u22c5h \u239e \n",
"po\u22c5\u239c- \u2500\u2500\u2500 + 1\u239f = p\n",
" \u239d to \u23a0 "
]
}
],
"prompt_number": 26
"prompt_number": 73
},
{
"cell_type": "markdown",
2318,100 → 578,145
"cell_type": "code",
"collapsed": false,
"input": [
"#bf.subs(p0, 101325,simultaneous=True); # standardn\u00ed tlak v Pa na hladin\u011b mo\u0159e\n",
"#bf.subs(l, 0.0065,simultaneous=True); # Pokles teploty s v\u00fd\u0161kou [K/m]\n",
"#bf.subs(cp, 1007,simultaneous=True); # specifick\u00e9 teplo p\u0159i konstantn\u00edm tlaku J/(kg\u2022K)\n",
"#bf.subs(t0, 288.15,simultaneous=True); # standardn\u00ed teplota na hladin\u011b mo\u0159e K\n",
"#bf.subs(g, 9.80665,simultaneous=True); # standardn\u00ed gravita\u010dn\u00ed zrychlen\u00ed na povrchu zem\u011b\n",
"#bf.subs(m, 0.0289644,simultaneous=True); # mol\u00e1rn\u00ed hmotnost such\u00e9ho vzduchu\tkg/mol\n",
"#bf.subs(r, 8.31447,simultaneous=True); # univerz\u00e1ln\u00ed plynov\u00e1 konstanta J/(mol\u2022K)\n",
"solve(bf, h)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"latex": [
"$$\\begin{bmatrix}- \\frac{to}{L} \\left(\\left(\\frac{p}{po}\\right)^{\\frac{L R}{M g}} - 1\\right)\\end{bmatrix}$$"
],
"metadata": {},
"output_type": "pyout",
"png": "iVBORw0KGgoAAAANSUhEUgAAAMAAAAA0BAMAAAAu8So7AAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\n",
"prompt_number": 74,
"text": [
"\u23a1 \u239b L\u22c5R \u239e \u23a4\n",
"\u23a2 \u239c \u2500\u2500\u2500 \u239f \u23a5\n",
"\u23a2 \u239c M\u22c5g \u239f \u23a5\n",
"\u23a2 \u239c\u239bp \u239e \u239f \u23a5\n",
"\u23a2-to\u22c5\u239c\u239c\u2500\u2500\u239f - 1\u239f \u23a5\n",
"\u23a2 \u239d\u239dpo\u23a0 \u23a0 \u23a5\n",
"\u23a2\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u23a5\n",
"\u23a3 L \u23a6"
]
}
],
"prompt_number": 74
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def compute_altitude(pressure):\n",
" p0 = 101325 # standardn\u00ed tlak v Pa na hladin\u011b mo\u0159e\n",
" l = 0.0065 # Pokles teploty s v\u00fd\u0161kou [K/m]\n",
" cp = 1007 # specifick\u00e9 teplo p\u0159i konstantn\u00edm tlaku J/(kg\u2022K)\n",
" t0 = 288.15 # standardn\u00ed teplota na hladin\u011b mo\u0159e K\n",
" g = 9.80665 # standardn\u00ed gravita\u010dn\u00ed zrychlen\u00ed na povrchu zem\u011b\n",
" m = 0.0289644 # mol\u00e1rn\u00ed hmotnost such\u00e9ho vzduchu\tkg/mol\n",
" r = 8.31447 # univerz\u00e1ln\u00ed plynov\u00e1 konstanta J/(mol\u2022K)\n",
"\n",
"bfh=solve(bf, h)\n",
"\n",
"#bfh.subs([(p0,101325), (l,0.0065), (cp,1007), (t0,288.15), (g,9.80665), (m,0.0289644), (r, 8.31447), (p, 98296.0)],simultaneous=True).evalf()\n",
"\n",
"\n",
"\n",
"#bf.subs(p, 98296.0)\n",
"#N(bf,5)\n",
"#bf.evalf(n=3)"
" return ((-t0/l)*((pressure/p0)**((l*r)/(m*g)) -1))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 27
"prompt_number": 77
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Nyn\u00ed je mo\u017en\u00e9 spo\u010d\u00edtat nadmo\u0159skou v\u00fd\u0161ku pro pr\u016fm\u011brn\u00fd, minim\u00e1ln\u00ed a maxim\u00e1ln\u00ed tlak nam\u011b\u0159en\u00fd na podlaze."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"lambdify(bf,h)"
"[compute_altitude(average(pl)), compute_altitude(min(pl)), compute_altitude(max(pl))]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "SyntaxError",
"evalue": "invalid syntax (<string>, line 1)",
"output_type": "pyerr",
"traceback": [
"\u001b[0;36m File \u001b[0;32m\"<string>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m lambda po*(-h*l/to + 1)**(g*m/(l*r)) == p: (h)\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
"latex": [
"$$\\begin{bmatrix}254.884550761, & 255.253104054, & 254.336010772\\end{bmatrix}$$"
],
"metadata": {},
"output_type": "pyout",
"png": "iVBORw0KGgoAAAANSUhEUgAAAcYAAAAZBAMAAABZU5NUAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\noUX/AG/BhazV5YXLAAAAAElFTkSuQmCC\n",
"prompt_number": 105,
"text": [
"[254.884550761, 255.253104054, 254.336010772]"
]
}
],
"prompt_number": 28
"prompt_number": 105
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A pro pr\u016fm\u011brn\u00fd, minim\u00e1ln\u00ed a maxim\u00e1ln\u00ed tlak nam\u011b\u0159en\u00fd na stole."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"type(cp)"
"[compute_altitude(average(ph)), compute_altitude(min(ph)), compute_altitude(max(ph))]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"latex": [
"$$\\begin{bmatrix}255.976385359, & 256.426248725, & 255.701016336\\end{bmatrix}$$"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 60,
"png": "iVBORw0KGgoAAAANSUhEUgAAAcYAAAAZBAMAAABZU5NUAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\naFkqAAAAAElFTkSuQmCC\n",
"prompt_number": 106,
"text": [
"sympy.core.symbol.Symbol"
"[255.976385359, 256.426248725, 255.701016336]"
]
}
],
"prompt_number": 60
"prompt_number": 106
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Z nam\u011b\u0159en\u00fdch hodnot je vyd\u011bt, \u017ee tlakov\u00e1 v\u00fd\u0161ka stolu sed\u00ed p\u0159ibli\u017en\u011b s re\u00e1lnou v\u00fd\u0161kou 1 m. "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\n",
"\n",
"import sys\n",
"import time\n",
"\n",
"\n",
"\n",
"from IPython.display import clear_output\n",
"for i in range(10):\n",
" time.sleep(0.25)\n",
" clear_output()\n",
" print i\n",
" sys.stdout.flush()\n",
"\n"
" Out[106][0] - Out[105][0]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"latex": [
"$$1.09183459838$$"
],
"metadata": {},
"output_type": "pyout",
"png": "iVBORw0KGgoAAAANSUhEUgAAAH8AAAAPBAMAAAA/sQ3hAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nrr9tDt9BYd4cugAAAABJRU5ErkJggg==\n",
"prompt_number": 110,
"text": [
"9\n"
"1.09183459838"
]
}
],
"prompt_number": 10
"prompt_number": 110
},
{
"cell_type": "markdown",
2425,7 → 730,7
"Absolutn\u00ed kalibrace tlaku\n",
"------------------\n",
"\n",
"V p\u0159\u00edpad\u011b, \u017ee by jsme pot\u0159ebovali tlakov\u00fd senzor pota\u017emo v\u00fd\u0161kom\u011br absolutn\u011b zkalibrovat, tak si k tomuto \u00fa\u010delu m\u016f\u017eeme p\u016fj\u010dit data z n\u011bkter\u00e9 kalibrovan\u00e9 stanice. V Praze pou\u017eijeme nap\u0159\u00edklad stanici CHMI pro Prahu 10. "
"V p\u0159\u00edpad\u011b, \u017ee by jsme pot\u0159ebovali tlakov\u00fd senzor pota\u017emo v\u00fd\u0161kom\u011br absolutn\u011b zkalibrovat, tak si k tomuto \u00fa\u010delu m\u016f\u017eeme p\u016fj\u010dit data z n\u011bkter\u00e9 kalibrovan\u00e9 stanice. V Praze pou\u017eijeme nap\u0159\u00edklad stanici CHMI pro Prahu 10. Ud\u00e1van\u00fd tlak je ale p\u0159epo\u010d\u00edtan\u00fd na hladinu mo\u0159e. Proto by bylo pot\u0159eba obsolutn\u00ed tlak op\u011bt spo\u010d\u00edtat z nadmo\u0159sk\u00e9 v\u00fd\u0161ky stanice. "
]
},
{
/Modules/Sensors/IMU01A/SW/Python/.ipynb_checkpoints/IMU_test-checkpoint.ipynb
11,15 → 11,23
"cell_type": "markdown",
"metadata": {},
"source": [
"Uk\u00e1zka pou\u017eit\u00ed n\u00e1stroje IPython na manipulaci se senzorov\u00fdmi daty\n",
"Uk\u00e1zka pou\u017eit\u00ed n\u00e1stroje IPython na manipulaci se senzorov\u00fdmi daty modulu IMU01A\n",
"=======\n",
"\n",
"P\u0159\u00edklad vyu\u017e\u00edv\u00e1 moduluvou stavebnici MLAB a jej\u00ed knihovnu https://github.com/MLAB-project/MLAB-I2c-modules \n",
"P\u0159\u00edklad vyu\u017e\u00edv\u00e1 modulovou stavebnici MLAB a jej\u00ed knihovnu [pymlab](https://github.com/MLAB-project/MLAB-I2c-modules).\n",
"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",
"\n",
"Pou\u017eit\u00fd sn\u00edma\u010d [MPL3115A2](http://www.freescale.com/webapp/sps/site/prod_summary.jsp?code=MPL3115A2) m\u00e1 n\u00e1sleduj\u00edc\u00ed katalogov\u00e9 parametry: \n",
"\n",
"* Tlakov\u00e9 rozli\u0161en\u00ed: 1,5 Pa\n",
"* Relativn\u00ed p\u0159esnost: 0,1 kPa\n",
"* Absolutn\u00ed tlakov\u00e1 p\u0159esnost 0,4 kPa\n",
"\n",
"Zprovozn\u011bn\u00ed demo k\u00f3du\n",
"---------------------\n",
"\n",
"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"
"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",
"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"
]
},
{
8209,7 → 8217,7
"cell_type": "markdown",
"metadata": {},
"source": [
"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. "
"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. "
]
},
{
8231,8 → 8239,8
"collapsed": false,
"input": [
"from mpl_toolkits.mplot3d.axes3d import Axes3D\n",
"%pylab qt\n",
"#%pylab inline\n",
"#%pylab qt\n",
"%pylab inline\n",
"fig = plt.figure()\n",
"ax = Axes3D(fig)\n",
"p = ax.scatter(x, y, z)\n",
8247,54 → 8255,22
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"amin(p)"
],
"language": "python",
"metadata": {},
"outputs": [
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 7,
"output_type": "display_data",
"png": 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VwsvMzGTLli3k5ubidKbi8+Uiij3R6yPxeo+h0XQlL29htUluL7poONu27WT7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3f7OlGbaWlp7N69m5de+hCNZjVKZQw63TuYTKNRq28DMujaVcfUqY+xYME35ORUEB8fypgxj+Pj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\nfNGGDRuYPXsVSuUPqFSnOHq0BQsXLsbtP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QkxMcOv2Jrr/yOu1q7sn4J/neG7Hvwehq/G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\nAElFTkSuQmCC\n",
"text": [
"96972.25"
"<matplotlib.figure.Figure at 0xb5c440c>"
]
}
],
"prompt_number": 7
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"amax(p)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 8,
"text": [
"96838.75"
]
}
],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"std(p)"
],
"language": "python",
8338,42 → 8314,6
}
],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.plot(t)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 11,
"text": [
"[<matplotlib.lines.Line2D at 0x9ee51ec>]"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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hISoqKlR5Lr4y3XM/c+ZMwhWT6eTprAQ/37QVc/78eaxfvx4tLS24+eabE+7T\naDSqOE9vvvkmFi5cCJvNNu37OtRyLsbHxzE4OIjNmzdjcHAQBQUFU6oMtZyLU6dO4Xe/+x38fj/O\nnDmD8+fP44033kgYo5ZzkUyq557qvMxK8Kfz/oDr3aVLl7B+/Xo8+eSTeOSRRwDEfoufPXsWAPDR\nRx9h4cKFsznFq+Lw4cPo7e3F4sWLsXHjRhw4cABPPvmkKs+FXq+HXq/HqlWrAAAbNmzA4OAgFi1a\npLpzceTIEaxevRrf/OY3kZeXh3Xr1uH9999X5bn4ynT/JpK9l0qn08kea1aCX+3X+EuShNraWlgs\nFjz//PPx251OJ/bs2QMA2LNnT/wXwvVs27ZtCAQCOH36NPbu3Yv77rsPf/zjH1V5LhYtWgSDwYCT\nJ08CAPr6+rBs2TKsXbtWdeeipKQEXq8XFy9ehCRJ6Ovrg8ViUeW5+Mp0/yacTif27t2LSCSC06dP\nw+fz4c4775Q/WLZfkEiXy+WSli5dKi1ZskTatm3bbE1jVrz77ruSRqORrFarVFpaKpWWlkr79u2T\nPvnkE6m8vPy6v1RtOh6PR1q7dq0kSZJqz8XRo0elsrIyacWKFdKjjz4qhcNh1Z6L5ubm+OWcTz31\nlBSJRFRzLqqrq6VvfetbUn5+vqTX66XXX39d9rm/9NJL0pIlS6Ti4mLJ7XanPP6sfvQiERFdffwE\nLiIilWHwExGpDIOfiEhlGPxERCrD4CciUhkGPxGRyvwf/46pxWPvViQAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x9d764cc>"
]
}
],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
/Modules/Sensors/IMU01A/SW/Python/IMU_test.ipynb
14,8 → 14,15
"Uk\u00e1zka pou\u017eit\u00ed n\u00e1stroje IPython na manipulaci se senzorov\u00fdmi daty modulu IMU01A\n",
"=======\n",
"\n",
"P\u0159\u00edklad vyu\u017e\u00edv\u00e1 modulovou stavebnici MLAB a jej\u00ed knihovnu https://github.com/MLAB-project/MLAB-I2c-modules \n",
"P\u0159\u00edklad vyu\u017e\u00edv\u00e1 modulovou stavebnici MLAB a jej\u00ed knihovnu [pymlab](https://github.com/MLAB-project/MLAB-I2c-modules).\n",
"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",
"\n",
"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",
"\n",
"* \u00b12g/\u00b14g/\u00b18g dynamically selectable full-scale\n",
"* Output data rates (ODR) from 1.56 Hz to 800 Hz\n",
"* 99 \u03bcg/\u221aHz noise\n",
"\n",
"Zprovozn\u011bn\u00ed demo k\u00f3du\n",
"---------------------\n",
"\n",
42,9 → 49,8
"i2c-3\ti2c \ti915 gmbus dpc \tI2C adapter\r\n",
"i2c-4\ti2c \ti915 gmbus dpb \tI2C adapter\r\n",
"i2c-5\ti2c \ti915 gmbus dpd \tI2C adapter\r\n",
"i2c-6\ti2c \tDPDDC-C \tI2C adapter\r\n",
"i2c-7\ti2c \tDPDDC-D \tI2C adapter\r\n",
"i2c-8\ti2c \ti2c-tiny-usb at bus 001 device 005\tI2C adapter\r\n"
"i2c-6\ti2c \tDPDDC-B \tI2C adapter\r\n",
"i2c-7\ti2c \ti2c-tiny-usb at bus 001 device 007\tI2C adapter\r\n"
]
}
],
77,7 → 83,7
"cell_type": "code",
"collapsed": false,
"input": [
"port = 8"
"port = 7"
],
"language": "python",
"metadata": {},
138,7 → 144,7
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 8
"prompt_number": 4
},
{
"cell_type": "markdown",
166,7 → 172,7
]
}
],
"prompt_number": 9
"prompt_number": 5
},
{
"cell_type": "markdown",
179,16 → 185,20
"cell_type": "code",
"collapsed": false,
"input": [
"import sys\n",
"import time\n",
"from IPython.display import clear_output\n",
"\n",
"MEASUREMENTS = 1000\n",
"x = np.zeros(MEASUREMENTS)\n",
"y = np.zeros(MEASUREMENTS)\n",
"z = np.zeros(MEASUREMENTS)\n",
"\n",
"list_meas = []\n",
"# 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",
"\n",
"for n in range(MEASUREMENTS):\n",
" (x[n], y[n], z[n]) = acc.axes()\n",
" print( n, x[n], y[n], z[n])"
" clear_output()\n",
" (x, y, z) = acc.axes()\n",
" list_meas.append([x, y, z])\n",
" print (n, list_meas[n])\n",
" sys.stdout.flush()"
],
"language": "python",
"metadata": {},
197,8014 → 207,22
"output_type": "stream",
"stream": "stdout",
"text": [
"(0, -0.81022499999999997, -0.54599999999999993, -0.26715)\n",
"(1, -0.83362499999999995, -0.52162500000000001, -0.26032499999999997)"
"(999, [0.038024999999999996, -0.00975, 0.966225])\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(2, -0.70297500000000002, -0.61327500000000001, -0.39487499999999998)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(3, -0.83996249999999995, -0.58694999999999997, -0.35489999999999999)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(4, -0.74246249999999991, -0.70199999999999996, -0.33052499999999996)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(5, -0.51285000000000003, -0.83947499999999997, -0.30614999999999998)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(6, -0.058987499999999998, -0.95647499999999996, -0.14235)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(7, 0.25447500000000001, -0.84142499999999998, 0.011699999999999999)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(8, 0.70199999999999996, -0.64203749999999993, -0.071175000000000002)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(9, 0.90284999999999993, -0.16965, -0.047774999999999998)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(10, 1.0125374999999999, 0.084824999999999998, -0.14624999999999999)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(11, 0.92917499999999997, 0.244725, -0.1028625)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(12, 0.80291249999999992, 0.59670000000000001, -0.44264999999999999)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(13, 0.57329999999999992, 0.77171249999999991, -0.4914)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(14, 0.35197499999999998, 0.84532499999999999, -0.48067499999999996)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(15, 0.10237499999999999, 0.92527499999999996, -0.40121249999999997)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(16, -0.086774999999999991, 0.90479999999999994, -0.312975)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(17, -0.47872499999999996, 0.81461249999999996, -0.27689999999999998)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(18, -0.77999999999999992, 0.53722499999999995, -0.31004999999999999)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(19, -0.90674999999999994, 0.085800000000000001, -0.17354999999999998)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(20, -0.98377499999999996, -0.36854999999999999, -0.24862499999999998)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(21, -0.83460000000000001, -0.638625, -0.2457)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(22, -0.55379999999999996, -0.81363750000000001, -0.2145)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(23, -0.35148750000000001, -0.818025, -0.69712499999999999)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(24, -0.33344999999999997, -0.30419999999999997, -0.96914999999999996)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(25, -0.39097499999999996, 0.075075000000000003, -0.82289999999999996)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(26, -0.80145, 0.252525, -0.74002499999999993)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(27, -0.53576250000000003, 0.69029999999999991, -0.54794999999999994)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(28, -0.16574999999999998, 0.9204, -0.48701249999999996)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(29, 0.2588625, 0.83752499999999996, -0.40462499999999996)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(30, 0.65617499999999995, 0.61522500000000002, -0.34709999999999996)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(31, 0.69419999999999993, 0.18817499999999998, -0.61181249999999998)"
]
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{
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"text": [
"\n",
"(32, 0.75562499999999999, -0.10237499999999999, -0.81509999999999994)"
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"stream": "stdout",
"text": [
"\n",
"(33, 0.43972499999999998, -0.27397499999999997, -0.88432499999999992)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(34, 0.56940000000000002, -0.41827500000000001, -0.56940000000000002)"
]
},
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"stream": "stdout",
"text": [
"\n",
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"stream": "stdout",
"text": [
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"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(37, -0.27299999999999996, -0.54599999999999993, -0.82192500000000002)"
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(38, -0.47969999999999996, -0.46507499999999996, -0.70784999999999998)"
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},
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"stream": "stdout",
"text": [
"\n",
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"text": [
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},
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"text": [
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"text": [
"\n",
"(45, -0.063375000000000001, 0.110175, -1.1456249999999999)"
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},
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"stream": "stdout",
"text": [
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},
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"text": [
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"stream": "stdout",
"text": [
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"stream": "stdout",
"text": [
"\n",
"(56, 0.268125, -0.37829999999999997, 0.83362499999999995)"
]
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"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(57, 0.02145, -0.66592499999999999, 0.66592499999999999)"
]
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"stream": "stdout",
"text": [
"\n",
"(58, -0.48067499999999996, -0.699075, 0.27689999999999998)"
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"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"(59, -0.79169999999999996, -0.62497499999999995, -0.0014624999999999998)"
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"output_type": "stream",
"stream": "stdout",
"text": [
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"stream": "stdout",
"text": [
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},
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"text": [
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"text": [
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"(70, -0.54648750000000001, 0.79803749999999996, -0.5884125)"
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"text": [
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},
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"text": [
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},
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"text": [
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},
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},
{
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},
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"\n",
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"stream": "stdout",
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"\n",
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"output_type": "stream",
"stream": "stdout",
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"\n",
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"\n",
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},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
}
],
"prompt_number": 72
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"np.savez(\"calibration_data_set\", x=x, y=y, z=z)"
"np.savez(\"calibration_data_3Dset\", data=list_meas)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 73
"prompt_number": 8
},
{
"cell_type": "markdown",
8264,49 → 282,142
"cell_type": "code",
"collapsed": false,
"input": [
"std(p)"
"import scipy\n",
"from scipy import optimize\n",
"import calibration_utils\n",
"\n",
"sensor_ref = 9.81\n",
"sensor_res = 10\n",
"noise_window = 20\n",
"noise_threshold = 40"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"measurements = np.array(list_meas)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"meas_median=scipy.median(scipy.array([scipy.linalg.norm(v) for v in measurements]))\n",
"noise_threshold = meas_median * 0.1\n",
"print noise_threshold\n",
"flt_meas, flt_idx = calibration_utils.filter_meas(measurements, noise_window, noise_threshold)\n",
"print(\"remaining \"+str(len(flt_meas))+\" after filtering\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 9,
"output_type": "stream",
"stream": "stdout",
"text": [
"2.3585270827361722"
"0.0973014827316\n",
"remaining 703 after filtering"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
}
],
"prompt_number": 9
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.plot(p)"
" p0 = calibration_utils.get_min_max_guess(flt_meas, sensor_ref)\n",
" cp0, np0 = calibration_utils.scale_measurements(flt_meas, p0)\n",
" print(\"initial guess : avg \"+str(np0.mean())+\" std \"+str(np0.std()))\n",
"\n",
" def err_func(p, meas, y):\n",
" cp, np = calibration_utils.scale_measurements(meas, p)\n",
" err = y*scipy.ones(len(meas)) - np\n",
" return err"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 9,
"output_type": "stream",
"stream": "stdout",
"text": [
"[<matplotlib.lines.Line2D at 0xa00bb2c>]"
"initial guess : avg 9.55977014083 std 0.17596103611\n"
]
},
}
],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": [
" p1, cov, info, msg, success = optimize.leastsq(err_func, p0[:], args=(flt_meas, sensor_ref), full_output=1)\n",
" if not success in [1, 2, 3, 4]:\n",
" print(\"Optimization error: \", msg)\n",
" print(\"Please try to provide a clean logfile.\")\n",
" sys.exit(1)\n",
"\n",
" cp1, np1 = calibration_utils.scale_measurements(flt_meas, p1)\n",
" print(\"optimized guess : avg \"+str(np1.mean())+\" std \"+str(np1.std()))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
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lM5Md3A0Nxt5nV+TiJkE3WuEC2OPQ4+OZiFlxL1gRtchFa0yC0w5dPvhK3Ffk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\nHr14Uf2G0lLIoXMIL4Lety/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"output_type": "stream",
"stream": "stdout",
"text": [
"<matplotlib.figure.Figure at 0x9eb46cc>"
"optimized guess : avg 9.80797237599 std 0.141020849641\n"
]
}
],
"prompt_number": 9
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%pylab qt\n",
"#%pylab inline\n",
"calibration_utils.plot_results(True, measurements, flt_idx, flt_meas, cp0, np0, cp1, np1, sensor_ref)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"prompt_number": 18
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
/Modules/Sensors/IMU01A/SW/Python/calibration_data_3Dset.npz
Cannot display: file marked as a binary type.
svn:mime-type = application/octet-stream
Property changes:
Added: svn:mime-type
+application/octet-stream
\ No newline at end of property
/Modules/Sensors/IMU01A/SW/Python/calibration_utils.py
0,0 → 1,270
 
# Copyright (C) 2010 Antoine Drouin
#
# This file is part of Paparazzi.
#
# Paparazzi is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 2, or (at your option)
# any later version.
#
# Paparazzi is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with Paparazzi; see the file COPYING. If not, write to
# the Free Software Foundation, 59 Temple Place - Suite 330,
# Boston, MA 02111-1307, USA.
#
 
from __future__ import print_function, division
 
import re
import numpy as np
from numpy import sin, cos
from scipy import linalg, stats
 
import matplotlib
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
 
 
def get_ids_in_log(filename):
"""Returns available ac_id from a log."""
f = open(filename, 'r')
ids = []
pattern = re.compile("\S+ (\S+)")
while True:
line = f.readline().strip()
if line == '':
break
m = re.match(pattern, line)
if m:
ac_id = m.group(1)
if not ac_id in ids:
ids.append(ac_id)
return ids
 
 
def read_log(ac_id, filename, sensor):
"""Extracts raw sensor measurements from a log."""
f = open(filename, 'r')
pattern = re.compile("(\S+) "+ac_id+" IMU_"+sensor+"_RAW (\S+) (\S+) (\S+)")
list_meas = []
while True:
line = f.readline().strip()
if line == '':
break
m = re.match(pattern, line)
if m:
list_meas.append([float(m.group(2)), float(m.group(3)), float(m.group(4))])
return np.array(list_meas)
 
 
def read_log_mag_current(ac_id, filename):
"""Extracts raw magnetometer and current measurements from a log."""
f = open(filename, 'r')
pattern = re.compile("(\S+) "+ac_id+" IMU_MAG_CURRENT_CALIBRATION (\S+) (\S+) (\S+) (\S+)")
list_meas = []
while True:
line = f.readline().strip()
if line == '':
break
m = re.match(pattern, line)
if m:
list_meas.append([float(m.group(2)), float(m.group(3)), float(m.group(4)), float(m.group(5))])
return np.array(list_meas)
 
 
def filter_meas(meas, window_size, noise_threshold):
"""Select only non-noisy data."""
filtered_meas = []
filtered_idx = []
for i in range(window_size, len(meas)-window_size):
noise = meas[i-window_size:i+window_size, :].std(axis=0)
if linalg.norm(noise) < noise_threshold:
filtered_meas.append(meas[i, :])
filtered_idx.append(i)
return np.array(filtered_meas), filtered_idx
 
 
def get_min_max_guess(meas, scale):
"""Initial boundary based calibration."""
max_meas = meas[:, :].max(axis=0)
min_meas = meas[:, :].min(axis=0)
n = (max_meas + min_meas) / 2
sf = 2*scale/(max_meas - min_meas)
return np.array([n[0], n[1], n[2], sf[0], sf[1], sf[2]])
 
 
def scale_measurements(meas, p):
"""Scale the set of measurements."""
l_comp = []
l_norm = []
for m in meas[:, ]:
sm = (m - p[0:3])*p[3:6]
l_comp.append(sm)
l_norm.append(linalg.norm(sm))
return np.array(l_comp), np.array(l_norm)
 
 
def estimate_mag_current_relation(meas):
"""Calculate linear coefficient of magnetometer-current relation."""
coefficient = []
for i in range(0, 3):
gradient, intercept, r_value, p_value, std_err = stats.linregress(meas[:, 3], meas[:, i])
coefficient.append(gradient)
return coefficient
 
 
def print_xml(p, sensor, res):
"""Print xml for airframe file."""
print("")
print("<define name=\""+sensor+"_X_NEUTRAL\" value=\""+str(int(round(p[0])))+"\"/>")
print("<define name=\""+sensor+"_Y_NEUTRAL\" value=\""+str(int(round(p[1])))+"\"/>")
print("<define name=\""+sensor+"_Z_NEUTRAL\" value=\""+str(int(round(p[2])))+"\"/>")
print("<define name=\""+sensor+"_X_SENS\" value=\""+str(p[3]*2**res)+"\" integer=\"16\"/>")
print("<define name=\""+sensor+"_Y_SENS\" value=\""+str(p[4]*2**res)+"\" integer=\"16\"/>")
print("<define name=\""+sensor+"_Z_SENS\" value=\""+str(p[5]*2**res)+"\" integer=\"16\"/>")
 
 
def plot_results(block, measurements, flt_idx, flt_meas, cp0, np0, cp1, np1, sensor_ref):
"""Plot calibration results."""
plt.subplot(3, 1, 1)
plt.plot(measurements[:, 0])
plt.plot(measurements[:, 1])
plt.plot(measurements[:, 2])
plt.plot(flt_idx, flt_meas[:, 0], 'ro')
plt.plot(flt_idx, flt_meas[:, 1], 'ro')
plt.plot(flt_idx, flt_meas[:, 2], 'ro')
plt.xlabel('time (s)')
plt.ylabel('ADC')
plt.title('Raw sensors')
 
plt.subplot(3, 2, 3)
plt.plot(cp0[:, 0])
plt.plot(cp0[:, 1])
plt.plot(cp0[:, 2])
plt.plot(-sensor_ref*np.ones(len(flt_meas)))
plt.plot(sensor_ref*np.ones(len(flt_meas)))
 
plt.subplot(3, 2, 4)
plt.plot(np0)
plt.plot(sensor_ref*np.ones(len(flt_meas)))
 
plt.subplot(3, 2, 5)
plt.plot(cp1[:, 0])
plt.plot(cp1[:, 1])
plt.plot(cp1[:, 2])
plt.plot(-sensor_ref*np.ones(len(flt_meas)))
plt.plot(sensor_ref*np.ones(len(flt_meas)))
 
plt.subplot(3, 2, 6)
plt.plot(np1)
plt.plot(sensor_ref*np.ones(len(flt_meas)))
 
# if we want to have another plot we only draw the figure (non-blocking)
# also in matplotlib before 1.0.0 there is only one call to show possible
if block:
plt.show()
else:
plt.draw()
 
 
def plot_mag_3d(measured, calibrated, p):
"""Plot magnetometer measurements on 3D sphere."""
# set up points for sphere and ellipsoid wireframes
u = np.r_[0:2 * np.pi:20j]
v = np.r_[0:np.pi:20j]
wx = np.outer(cos(u), sin(v))
wy = np.outer(sin(u), sin(v))
wz = np.outer(np.ones(np.size(u)), cos(v))
ex = p[0] * np.ones(np.size(u)) + np.outer(cos(u), sin(v)) / p[3]
ey = p[1] * np.ones(np.size(u)) + np.outer(sin(u), sin(v)) / p[4]
ez = p[2] * np.ones(np.size(u)) + np.outer(np.ones(np.size(u)), cos(v)) / p[5]
 
# measurements
mx = measured[:, 0]
my = measured[:, 1]
mz = measured[:, 2]
 
# calibrated values
cx = calibrated[:, 0]
cy = calibrated[:, 1]
cz = calibrated[:, 2]
 
# axes size
left = 0.02
bottom = 0.05
width = 0.46
height = 0.9
rect_l = [left, bottom, width, height]
rect_r = [left/2+0.5, bottom, width, height]
 
fig = plt.figure(figsize=plt.figaspect(0.5))
if matplotlib.__version__.startswith('0'):
ax = Axes3D(fig, rect=rect_l)
else:
ax = fig.add_subplot(1, 2, 1, position=rect_l, projection='3d')
# plot measurements
ax.scatter(mx, my, mz)
plt.hold(True)
# plot line from center to ellipsoid center
ax.plot([0.0, p[0]], [0.0, p[1]], [0.0, p[2]], color='black', marker='+', markersize=10)
# plot ellipsoid
ax.plot_wireframe(ex, ey, ez, color='grey', alpha=0.5)
 
# Create cubic bounding box to simulate equal aspect ratio
max_range = np.array([mx.max() - mx.min(), my.max() - my.min(), mz.max() - mz.min()]).max()
Xb = 0.5 * max_range * np.mgrid[-1:2:2, -1:2:2, -1:2:2][0].flatten() + 0.5 * (mx.max() + mx.min())
Yb = 0.5 * max_range * np.mgrid[-1:2:2, -1:2:2, -1:2:2][1].flatten() + 0.5 * (my.max() + my.min())
Zb = 0.5 * max_range * np.mgrid[-1:2:2, -1:2:2, -1:2:2][2].flatten() + 0.5 * (mz.max() + mz.min())
# add the fake bounding box:
for xb, yb, zb in zip(Xb, Yb, Zb):
ax.plot([xb], [yb], [zb], 'w')
 
ax.set_title('MAG raw with fitted ellipsoid and center offset')
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_zlabel('z')
 
if matplotlib.__version__.startswith('0'):
ax = Axes3D(fig, rect=rect_r)
else:
ax = fig.add_subplot(1, 2, 2, position=rect_r, projection='3d')
ax.plot_wireframe(wx, wy, wz, color='grey', alpha=0.5)
plt.hold(True)
ax.scatter(cx, cy, cz)
 
ax.set_title('MAG calibrated on unit sphere')
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_zlabel('z')
ax.set_xlim3d(-1, 1)
ax.set_ylim3d(-1, 1)
ax.set_zlim3d(-1, 1)
plt.show()
 
 
def read_turntable_log(ac_id, tt_id, filename, _min, _max):
""" Read a turntable log.
return an array which first column is turnatble and next 3 are gyro
"""
f = open(filename, 'r')
pattern_g = re.compile("(\S+) "+str(ac_id)+" IMU_GYRO_RAW (\S+) (\S+) (\S+)")
pattern_t = re.compile("(\S+) "+str(tt_id)+" IMU_TURNTABLE (\S+)")
last_tt = None
list_tt = []
while True:
line = f.readline().strip()
if line == '':
break
m = re.match(pattern_t, line)
if m:
last_tt = float(m.group(2))
m = re.match(pattern_g, line)
if m and last_tt and _min < last_tt < _max:
list_tt.append([last_tt, float(m.group(2)), float(m.group(3)), float(m.group(4))])
return np.array(list_tt)
/Modules/Sensors/MAG01A/SW/Python/MAG_test.ipynb
110,7 → 110,7
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
"prompt_number": 29
},
{
"cell_type": "markdown",
142,7 → 142,7
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
"prompt_number": 30
},
{
"cell_type": "markdown",
170,7 → 170,7
]
}
],
"prompt_number": 5
"prompt_number": 31
},
{
"cell_type": "markdown",
188,9 → 188,6
"from IPython.display import clear_output\n",
"\n",
"MEASUREMENTS = 500\n",
"#x = np.zeros(MEASUREMENTS)\n",
"#y = np.zeros(MEASUREMENTS)\n",
"#z = np.zeros(MEASUREMENTS)\n",
"list_meas = []\n",
"\n",
"for n in range(MEASUREMENTS):\n",
237,7 → 234,7
"cell_type": "markdown",
"metadata": {},
"source": [
"Zpracov\u00e1n\u00ed dat\n",
"Zpracov\u00e1n\u00ed dat 2D\n",
"-----------\n",
"\n",
"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. "
247,13 → 244,15
"cell_type": "code",
"collapsed": false,
"input": [
"data = np.load('./calibration_data_3Dset.npz')\n",
"list_meas = data['data']"
"data = np.load('./calibration_data_2Dset.npz')\n",
"x = data['x']\n",
"y = data['y']\n",
"z = data['z']"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 142
"prompt_number": 42
},
{
"cell_type": "markdown",
271,11 → 270,7
"%pylab inline\n",
"fig = plt.figure()\n",
"ax = Axes3D(fig)\n",
"#plt.subplot(121)\n",
"p = ax.scatter((list_meas)[1],(list_meas)[5],(list_meas)[3])\n",
"#p = ax.plot3D(list_meas)\n",
"#plt.subplot(122)\n",
"#p = ax.scatter(x, y, z)\n"
"p = ax.scatter(x,y,z)"
],
"language": "python",
"metadata": {},
290,13 → 285,13
{
"metadata": {},
"output_type": "display_data",
"png": 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FtWkXG/b47DzYRB5gNHkkKiuPxicEsTCbkTSsxOG8dchX+6d/i1vP10rwKrXo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\n3JIhLfhhZEu6gZ1bMcxMWUJcyTPA1thXuVw2v49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B5hA2KIlI13Wz9BrgvTh3ELD3IQzX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\nmYkzu+yyC4CtiTNOlTu8gF1co7auyM1JVm61WkUqlWogft5aYi0I2WT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A5ezCniii6YgP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\n",
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G2Q6PpyNYNg6W3YKKipGCZk8ETSAQhd1eLTKvViESievaD4JRo0Zg1KgRqq9r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CTXI0Q6BHCqCYQSaqINVt1F3lTU6xQRhEi7hdMWEUxPnjfrFGsVUZ6IQMJZU3OlnPQWrtbpKuVd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\nUWQAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x7f3f8205d590>"
"<matplotlib.figure.Figure at 0x7effb8981ad0>"
]
}
],
"prompt_number": 143
"prompt_number": 43
},
{
"cell_type": "markdown",
317,21 → 312,21
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 144,
"prompt_number": 44,
"text": [
"[<matplotlib.lines.Line2D at 0x7f3f820aa790>]"
"[<matplotlib.lines.Line2D at 0x7effdb72c550>]"
]
},
{
"metadata": {},
"output_type": "display_data",
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ZIYQQX331lUhLSxMLFy5UNK733nvPO0DEbreLhQsXKh6TEEK43W7x4IMPirvu\nukt8+umnqvj8rl69KjIzM/ttC0ZcYT31ohACTz75JDZt2oRFixYBkId+dnd3D5j8NXbs2EEnfwV7\nDsCaNWsgSRIA4MKFCxg/fjxcLtegE9LCFdMzzzyDsWPHApBbbImJiYrH5GEymfDQQw/h97//PQDl\nP7++mpqa8MADDwAAcnJycOzYsZA912DS09NRV1eHZcuWAQCOHz+O/Px8AMC8efNQX1+P2NhYmEwm\nxMfHIz4+Hunp6Th16hSysrJCElNJSQmWLl0KQD4iio+PVzyuRYsWYf78+QCAzz//HOPHj0dDQ4Pi\n79W6deuwatUqbNq0CYA6Pr+TJ0/iv//9L6xWK3p6evDSSy8FJa6QlXpqamqQkZHR77JgwQIUFxdj\n6tSpAOQfAqfTOWDyl8PhgNPpRHJy8oDtwY6pra0NY8aMwdy5c/HKK6/gwQcfhMPhUDSmc+fOISEh\nAV1dXVi2bBk2bdoU1piGiqutrQ0PP/xwv/uF8/MbyY2xxMbGen/Uw2Hx4sWIi7velhJ9xk0o9b7c\ndNNNuPnmm+FyuVBSUoIXX3yx33uiVFyxsbFYvnw51qxZg7KyMsXfq127dmHChAnexooQQvGYAPnz\nW7duHQ4cOOAdOdlXoHGFrMU/2OSvO++8EzU1NaipqUFXVxesViv27t0btslfg8Xk8cEHH+DMmTMo\nLi7GiRMnFI/p448/RmlpKV5++WXk5eXB6XSGdZLccO9VX2qavHdjLJIkYcwY5cYv9H1uz/tyY4wu\nlwvjx48PaRydnZ1YvHgxnnrqKZSWlmL9+vWqiGvXrl348ssvMXPmTHzzzTeKxlRbW4uYmBg0NDSg\nvb0djz32GP79738rGhMATJ48Genp6QDk/JmamooTJ06MOq6wfis+++wzHDp0CIcOHcLEiRNRX1+P\npKQk6HQ6dHR0QAiB+vp65Ofnw2Qy4cCBAxBC4IsvvoAkSUhJSQl6TJs2bcIbb7wBQP51jYuLUzym\n06dPo6SkBG+++SasVisAOakpGdNQ1BSXyWTC+++/D0DucPYcWSolMzMThw8fBgDs378f+fn5mDlz\nJv7yl7+gu7sbDocDn3zyCe65556QxfDll1+isLAQmzdvxvLly1UR1xtvvOEtpyQmJiI2NhZZWVmK\nxnT48GHY7XYcOnQI06dPx+uvv44HHnhA8c+vtrYWa9euBQBcvHgRLpcLhYWFo44rrDX+vmJiYrzX\nRzP5a7Q9EC2eAAAA7klEQVRsNhsee+wxvPbaa+jt7UVtba3iMW3YsAFutxsVFRUAAIPBgD179iga\nU18xMTGq+fz6euihh3Dw4EGYTCYA8H6W4eZ5b15++WWsWLECbrcbd999N5YuXYqYmBhUVFQgLy8P\nkiRh48aN0Ol0IYtl48aNcDgcqK6u9o642rp1KyoqKhSLa+nSpVi+fDkKCgpw7do1bN26FVOmTFH8\nveorJiZGFZ+fzWbD448/7q3p19bWIjU1ddRxcQIXEZHGcAIXEZHGMPETEWkMEz8RkcYw8RMRaQwT\nPxGRxjDxExFpDBM/EZHGMPETEWnM/wPulkrNjSVVUQAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x7f3f81d6bad0>"
"<matplotlib.figure.Figure at 0x7effb80d0e90>"
]
}
],
"prompt_number": 144
"prompt_number": 44
},
{
"cell_type": "markdown",
351,17 → 346,14
"metadata": {},
"outputs": [
{
"ename": "TypeError",
"evalue": "'float' object is not iterable",
"output_type": "pyerr",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-145-8671977becc2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mxoffset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0mxoffset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mTypeError\u001b[0m: 'float' object is not iterable"
"output_type": "stream",
"stream": "stdout",
"text": [
"26.0\n"
]
}
],
"prompt_number": 145
"prompt_number": 45
},
{
"cell_type": "code",
372,7 → 364,16
],
"language": "python",
"metadata": {},
"outputs": []
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"-180.5\n"
]
}
],
"prompt_number": 46
},
{
"cell_type": "code",
382,13 → 383,31
],
"language": "python",
"metadata": {},
"outputs": []
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 47,
"text": [
"[<matplotlib.lines.Line2D at 0x7effb8413050>]"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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KFeLFF18UX3/9tSzvvV/X4xeDJn/l5eUBePrkr8jIyBEnf/ljDkB5eTkcDgcA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\n8RMRqcz/A1D+DQmdcGLaAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x7effb83053d0>"
]
}
],
"prompt_number": 47
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"D\u00e1le 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)"
"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."
]
},
{
395,9 → 414,96
"cell_type": "code",
"collapsed": false,
"input": [
"try:\n",
"\n",
" for n in range(MEASUREMENTS):\n",
" (x, y, z) = mag_sensor.axes()\n",
" phi = np.arctan2(x-xoffset, z-zoffset)\n",
" clear_output()\n",
" print ((phi*180)/pi+180)\n",
" sys.stdout.flush()\n",
"except KeyboardInterrupt:\n",
" sys.exit(0)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"308.027821813\n"
]
}
],
"prompt_number": 36
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"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",
"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",
"\n",
"Zpracov\u00e1n\u00ed dat 3D\n",
"-----------\n",
"\n",
"K tomu pou\u017eijeme datov\u00fd set, kde jsou body rozprost\u0159en\u00e9 po cel\u00e9m povrchu koule. "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data = np.load('./calibration_data_3Dset.npz')\n",
"list_meas = data['data']\n",
"x = list_meas[:, 0]\n",
"y = list_meas[:, 1]\n",
"z = list_meas[:, 2]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from mpl_toolkits.mplot3d.axes3d import Axes3D\n",
"#%pylab qt ## po odkomentovani a zakomentovani nasledujiciho radku udela vystup do QT okna. \n",
"%pylab inline\n",
"fig = plt.figure()\n",
"ax = Axes3D(fig)\n",
"p = ax.scatter(x,y,z)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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X6yksLESv1ye1WkbowjZ48GBk+QMkSUYQTHi9F0hLS0EUU4INlvszOp2uze8v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ZfbMZkS2LLlE3bu3ABci16/mm+++Ykvvnjf/buekWFtadJNmzbhcMxAFCf9bhj+JGbzaAoKJrNr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\n",
"text": [
"<matplotlib.figure.Figure at 0x7f10f2b9d950>"
]
}
],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import scipy\n",
"from scipy import optimize\n",
"\n",
"import calibration_utils\n",
"\n",
"sensor_ref = 1.\n",
408,7 → 514,7
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 146
"prompt_number": 3
},
{
"cell_type": "markdown",
426,7 → 532,7
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 147
"prompt_number": 4
},
{
"cell_type": "markdown",
447,19 → 553,17
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": "*"
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"flt_meas = measurements"
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"460.895326306\n",
"remaining 346 after filtering\n"
]
}
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": "*"
"prompt_number": 5
},
{
"cell_type": "markdown",
492,7 → 596,7
]
}
],
"prompt_number": 150
"prompt_number": 6
},
{
"cell_type": "markdown",
525,7 → 629,7
]
}
],
"prompt_number": 151
"prompt_number": 7
},
{
"cell_type": "markdown",
539,6 → 643,7
"collapsed": false,
"input": [
"%pylab qt\n",
"#%pylab inline\n",
"calibration_utils.plot_results(False, measurements, flt_idx, flt_meas, cp0, np0, cp1, np1, sensor_ref)\n",
"calibration_utils.plot_mag_3d(flt_meas, cp1, p1)"
],
561,7 → 666,7
]
}
],
"prompt_number": 152
"prompt_number": 8
},
{
"cell_type": "markdown",
586,34 → 691,16
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": "*"
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 92,
"output_type": "stream",
"stream": "stdout",
"text": [
"583.14932841582277"
"(1.0033330291141624, 159.16410600293204, 61.66914855382452)\n"
]
}
],
"prompt_number": 92
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
"prompt_number": 35
}
],
"metadata": {}
/Modules/Sensors/MAG01A/SW/Python/calibration_utils.py
131,7 → 131,7
 
 
def plot_results(block, measurements, flt_idx, flt_meas, cp0, np0, cp1, np1, sensor_ref):
"""Plot calibration results."""
"""Plot calibration results in 2D graphs."""
plt.subplot(3, 1, 1)
plt.plot(measurements[:, 0])
plt.plot(measurements[:, 1])
139,8 → 139,8
plt.plot(flt_idx, flt_meas[:, 0], 'ro')
plt.plot(flt_idx, flt_meas[:, 1], 'ro')
plt.plot(flt_idx, flt_meas[:, 2], 'ro')
plt.xlabel('time (s)')
plt.ylabel('ADC')
plt.xlabel('sample number')
plt.ylabel('miligauss')
plt.title('Raw sensors')
 
plt.subplot(3, 2, 3)
149,10 → 149,16
plt.plot(cp0[:, 2])
plt.plot(-sensor_ref*np.ones(len(flt_meas)))
plt.plot(sensor_ref*np.ones(len(flt_meas)))
plt.xlabel('sample number')
plt.ylabel('-')
plt.title('First approximation')
 
plt.subplot(3, 2, 4)
plt.plot(np0)
plt.plot(sensor_ref*np.ones(len(flt_meas)))
plt.xlabel('sample number')
plt.ylabel('-')
plt.title('magnitude')
 
plt.subplot(3, 2, 5)
plt.plot(cp1[:, 0])
160,10 → 166,16
plt.plot(cp1[:, 2])
plt.plot(-sensor_ref*np.ones(len(flt_meas)))
plt.plot(sensor_ref*np.ones(len(flt_meas)))
plt.xlabel('sample number')
plt.ylabel('-')
plt.title('separate axes')
 
plt.subplot(3, 2, 6)
plt.plot(np1)
plt.plot(sensor_ref*np.ones(len(flt_meas)))
plt.xlabel('sample number')
plt.ylabel('-')
plt.title('magnitude')
 
# if we want to have another plot we only draw the figure (non-blocking)
# also in matplotlib before 1.0.0 there is only one call to show possible