No changes between revisions
/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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\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": [
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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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"text": [
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},
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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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"text": [
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"stream": "stdout",
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"stream": "stdout",
"text": [
"\n",
"(56, 0.268125, -0.37829999999999997, 0.83362499999999995)"
]
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"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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"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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"text": [
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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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"\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)