Rev 3656 Rev 3663
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12 "metadata": {}, 12 "metadata": {},
13 "source": [ 13 "source": [
14 "Uk\u00e1zka pou\u017eit\u00ed n\u00e1stroje IPython na manipulaci se senzorov\u00fdmi daty modulu IMU01A\n", 14 "Uk\u00e1zka pou\u017eit\u00ed n\u00e1stroje IPython na manipulaci se senzorov\u00fdmi daty modulu IMU01A\n",
15 "=======\n", 15 "=======\n",
16 "\n", 16 "\n",
17 "P\u0159\u00edklad vyu\u017e\u00edv\u00e1 modulovou stavebnici MLAB a jej\u00ed knihovnu https://github.com/MLAB-project/MLAB-I2c-modules \n", 17 "P\u0159\u00edklad vyu\u017e\u00edv\u00e1 modulovou stavebnici MLAB a jej\u00ed knihovnu [pymlab](https://github.com/MLAB-project/MLAB-I2c-modules).\n",
-   18 "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",
-   19 "\n",
-   20 "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",
-   21 "\n",
-   22 "* \u00b12g/\u00b14g/\u00b18g dynamically selectable full-scale\n",
-   23 "* Output data rates (ODR) from 1.56 Hz to 800 Hz\n",
-   24 "* 99 \u03bcg/\u221aHz noise\n",
18 "\n", 25 "\n",
19 "Zprovozn\u011bn\u00ed demo k\u00f3du\n", 26 "Zprovozn\u011bn\u00ed demo k\u00f3du\n",
20 "---------------------\n", 27 "---------------------\n",
21 "\n", 28 "\n",
22 "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", 29 "Nejd\u0159\u00edve zjist\u00edme zda m\u00e1me p\u0159\u00edstup pro z\u00e1pis a \u010dten\u00ed do syst\u00e9mov\u00e9ho za\u0159\u00edzen\u00ed. A jak\u00e9 \u010d\u00edslo m\u00e1 I\u00b2C sb\u011brnice na kterou m\u00e1me p\u0159ipojen\u00e1 \u010didla. \n",
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40 "i2c-1\ti2c \ti915 gmbus vga \tI2C adapter\r\n", 47 "i2c-1\ti2c \ti915 gmbus vga \tI2C adapter\r\n",
41 "i2c-2\ti2c \ti915 gmbus panel \tI2C adapter\r\n", 48 "i2c-2\ti2c \ti915 gmbus panel \tI2C adapter\r\n",
42 "i2c-3\ti2c \ti915 gmbus dpc \tI2C adapter\r\n", 49 "i2c-3\ti2c \ti915 gmbus dpc \tI2C adapter\r\n",
43 "i2c-4\ti2c \ti915 gmbus dpb \tI2C adapter\r\n", 50 "i2c-4\ti2c \ti915 gmbus dpb \tI2C adapter\r\n",
44 "i2c-5\ti2c \ti915 gmbus dpd \tI2C adapter\r\n", 51 "i2c-5\ti2c \ti915 gmbus dpd \tI2C adapter\r\n",
45 "i2c-6\ti2c \tDPDDC-C \tI2C adapter\r\n", 52 "i2c-6\ti2c \tDPDDC-B \tI2C adapter\r\n",
46 "i2c-7\ti2c \tDPDDC-D \tI2C adapter\r\n", -  
47 "i2c-8\ti2c \ti2c-tiny-usb at bus 001 device 005\tI2C adapter\r\n" 53 "i2c-7\ti2c \ti2c-tiny-usb at bus 001 device 007\tI2C adapter\r\n"
48 ] 54 ]
49 } 55 }
50 ], 56 ],
51 "prompt_number": 1 57 "prompt_number": 1
52 }, 58 },
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75 }, 81 },
76 { 82 {
77 "cell_type": "code", 83 "cell_type": "code",
78 "collapsed": false, 84 "collapsed": false,
79 "input": [ 85 "input": [
80 "port = 8" 86 "port = 7"
81 ], 87 ],
82 "language": "python", 88 "language": "python",
83 "metadata": {}, 89 "metadata": {},
84 "outputs": [], 90 "outputs": [],
85 "prompt_number": 2 91 "prompt_number": 2
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136 ")" 142 ")"
137 ], 143 ],
138 "language": "python", 144 "language": "python",
139 "metadata": {}, 145 "metadata": {},
140 "outputs": [], 146 "outputs": [],
141 "prompt_number": 8 147 "prompt_number": 4
142 }, 148 },
143 { 149 {
144 "cell_type": "markdown", 150 "cell_type": "markdown",
145 "metadata": {}, 151 "metadata": {},
146 "source": [ 152 "source": [
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164 "text": [ 170 "text": [
165 "WARNING:pymlab.sensors.iic:HID device does not exist, we will try SMBus directly...\n" 171 "WARNING:pymlab.sensors.iic:HID device does not exist, we will try SMBus directly...\n"
166 ] 172 ]
167 } 173 }
168 ], 174 ],
169 "prompt_number": 9 175 "prompt_number": 5
170 }, 176 },
171 { 177 {
172 "cell_type": "markdown", 178 "cell_type": "markdown",
173 "metadata": {}, 179 "metadata": {},
174 "source": [ 180 "source": [
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177 }, 183 },
178 { 184 {
179 "cell_type": "code", 185 "cell_type": "code",
180 "collapsed": false, 186 "collapsed": false,
181 "input": [ 187 "input": [
182 "MEASUREMENTS = 1000\n", 188 "import sys\n",
183 "x = np.zeros(MEASUREMENTS)\n", 189 "import time\n",
184 "y = np.zeros(MEASUREMENTS)\n", -  
185 "z = np.zeros(MEASUREMENTS)\n", 190 "from IPython.display import clear_output\n",
186 "\n", 191 "\n",
-   192 "MEASUREMENTS = 1000\n",
-   193 "list_meas = []\n",
187 "# 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", 194 "# 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",
188 "\n", 195 "\n",
189 "for n in range(MEASUREMENTS):\n", 196 "for n in range(MEASUREMENTS):\n",
-   197 " clear_output()\n",
190 " (x[n], y[n], z[n]) = acc.axes()\n", 198 " (x, y, z) = acc.axes()\n",
-   199 " list_meas.append([x, y, z])\n",
191 " print( n, x[n], y[n], z[n])" 200 " print (n, list_meas[n])\n",
-   201 " sys.stdout.flush()"
192 ], 202 ],
193 "language": "python", 203 "language": "python",
194 "metadata": {}, 204 "metadata": {},
195 "outputs": [ 205 "outputs": [
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8194 } 212 }
8195 ], 213 ],
8196 "prompt_number": 72 214 "prompt_number": 7
8197 }, 215 },
8198 { 216 {
8199 "cell_type": "code", 217 "cell_type": "code",
8200 "collapsed": false, 218 "collapsed": false,
8201 "input": [ 219 "input": [
8202 "np.savez(\"calibration_data_set\", x=x, y=y, z=z)" 220 "np.savez(\"calibration_data_3Dset\", data=list_meas)"
8203 ], 221 ],
8204 "language": "python", 222 "language": "python",
8205 "metadata": {}, 223 "metadata": {},
8206 "outputs": [], 224 "outputs": [],
8207 "prompt_number": 73 225 "prompt_number": 8
8208 }, 226 },
8209 { 227 {
8210 "cell_type": "markdown", 228 "cell_type": "markdown",
8211 "metadata": {}, 229 "metadata": {},
8212 "source": [ 230 "source": [
Line 8262... Line 280...
8262 }, 280 },
8263 { 281 {
8264 "cell_type": "code", 282 "cell_type": "code",
8265 "collapsed": false, 283 "collapsed": false,
8266 "input": [ 284 "input": [
-   285 "import scipy\n",
-   286 "from scipy import optimize\n",
-   287 "import calibration_utils\n",
8267 "std(p)" 288 "\n",
-   289 "sensor_ref = 9.81\n",
-   290 "sensor_res = 10\n",
-   291 "noise_window = 20\n",
-   292 "noise_threshold = 40"
-   293 ],
-   294 "language": "python",
-   295 "metadata": {},
-   296 "outputs": [],
-   297 "prompt_number": 11
-   298 },
-   299 {
-   300 "cell_type": "code",
-   301 "collapsed": false,
-   302 "input": [
-   303 "measurements = np.array(list_meas)"
-   304 ],
-   305 "language": "python",
-   306 "metadata": {},
-   307 "outputs": [],
-   308 "prompt_number": 12
-   309 },
-   310 {
-   311 "cell_type": "code",
-   312 "collapsed": false,
-   313 "input": [
-   314 "meas_median=scipy.median(scipy.array([scipy.linalg.norm(v) for v in measurements]))\n",
-   315 "noise_threshold = meas_median * 0.1\n",
-   316 "print noise_threshold\n",
-   317 "flt_meas, flt_idx = calibration_utils.filter_meas(measurements, noise_window, noise_threshold)\n",
-   318 "print(\"remaining \"+str(len(flt_meas))+\" after filtering\")"
8268 ], 319 ],
8269 "language": "python", 320 "language": "python",
8270 "metadata": {}, 321 "metadata": {},
8271 "outputs": [ 322 "outputs": [
8272 { 323 {
-   324 "output_type": "stream",
-   325 "stream": "stdout",
8273 "metadata": {}, 326 "text": [
-   327 "0.0973014827316\n",
-   328 "remaining 703 after filtering"
-   329 ]
-   330 },
-   331 {
8274 "output_type": "pyout", 332 "output_type": "stream",
8275 "prompt_number": 9, 333 "stream": "stdout",
8276 "text": [ 334 "text": [
8277 "2.3585270827361722" 335 "\n"
8278 ] 336 ]
8279 } 337 }
8280 ], 338 ],
8281 "prompt_number": 9 339 "prompt_number": 14
8282 }, 340 },
8283 { 341 {
8284 "cell_type": "code", 342 "cell_type": "code",
8285 "collapsed": false, 343 "collapsed": false,
8286 "input": [ 344 "input": [
-   345 " p0 = calibration_utils.get_min_max_guess(flt_meas, sensor_ref)\n",
-   346 " cp0, np0 = calibration_utils.scale_measurements(flt_meas, p0)\n",
-   347 " print(\"initial guess : avg \"+str(np0.mean())+\" std \"+str(np0.std()))\n",
-   348 "\n",
-   349 " def err_func(p, meas, y):\n",
-   350 " cp, np = calibration_utils.scale_measurements(meas, p)\n",
-   351 " err = y*scipy.ones(len(meas)) - np\n",
8287 "plt.plot(p)" 352 " return err"
8288 ], 353 ],
8289 "language": "python", 354 "language": "python",
8290 "metadata": {}, 355 "metadata": {},
8291 "outputs": [ 356 "outputs": [
8292 { 357 {
8293 "metadata": {}, -  
8294 "output_type": "pyout", 358 "output_type": "stream",
8295 "prompt_number": 9, 359 "stream": "stdout",
8296 "text": [ 360 "text": [
8297 "[<matplotlib.lines.Line2D at 0xa00bb2c>]" 361 "initial guess : avg 9.55977014083 std 0.17596103611\n"
8298 ] 362 ]
8299 }, 363 }
-   364 ],
-   365 "prompt_number": 15
-   366 },
-   367 {
-   368 "cell_type": "code",
-   369 "collapsed": false,
-   370 "input": [
-   371 " p1, cov, info, msg, success = optimize.leastsq(err_func, p0[:], args=(flt_meas, sensor_ref), full_output=1)\n",
-   372 " if not success in [1, 2, 3, 4]:\n",
-   373 " print(\"Optimization error: \", msg)\n",
-   374 " print(\"Please try to provide a clean logfile.\")\n",
-   375 " sys.exit(1)\n",
-   376 "\n",
-   377 " cp1, np1 = calibration_utils.scale_measurements(flt_meas, p1)\n",
-   378 " print(\"optimized guess : avg \"+str(np1.mean())+\" std \"+str(np1.std()))"
-   379 ],
-   380 "language": "python",
-   381 "metadata": {},
-   382 "outputs": [
8300 { 383 {
8301 "metadata": {}, -  
8302 "output_type": "display_data", 384 "output_type": "stream",
8303 "png": 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lM5Md3A0Nxt5nV+TiJkE3WuEC2OPQ4+OZiFlxL1gRtchFa0yC0w5dPvhK3Ffk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\nHr14Uf2G0lLIoXMIL4Lety/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385 "stream": "stdout",
8304 "text": [ 386 "text": [
8305 "<matplotlib.figure.Figure at 0x9eb46cc>" 387 "optimized guess : avg 9.80797237599 std 0.141020849641\n"
8306 ] 388 ]
8307 } 389 }
8308 ], 390 ],
8309 "prompt_number": 9 391 "prompt_number": 16
-   392 },
-   393 {
-   394 "cell_type": "code",
-   395 "collapsed": false,
-   396 "input": [
-   397 "%pylab qt\n",
-   398 "#%pylab inline\n",
-   399 "calibration_utils.plot_results(True, measurements, flt_idx, flt_meas, cp0, np0, cp1, np1, sensor_ref)"
-   400 ],
-   401 "language": "python",
-   402 "metadata": {},
-   403 "outputs": [
-   404 {
-   405 "output_type": "stream",
-   406 "stream": "stdout",
-   407 "text": [
-   408 "Populating the interactive namespace from numpy and matplotlib\n"
-   409 ]
-   410 }
-   411 ],
-   412 "prompt_number": 18
-   413 },
-   414 {
-   415 "cell_type": "code",
-   416 "collapsed": false,
-   417 "input": [],
-   418 "language": "python",
-   419 "metadata": {},
-   420 "outputs": []
8310 } 421 }
8311 ], 422 ],
8312 "metadata": {} 423 "metadata": {}
8313 } 424 }
8314 ] 425 ]