Line 48... |
Line 48... |
48 |
"i2c-2\ti2c \ti915 gmbus panel \tI2C adapter\r\n", |
48 |
"i2c-2\ti2c \ti915 gmbus panel \tI2C adapter\r\n", |
49 |
"i2c-3\ti2c \ti915 gmbus dpc \tI2C adapter\r\n", |
49 |
"i2c-3\ti2c \ti915 gmbus dpc \tI2C adapter\r\n", |
50 |
"i2c-4\ti2c \ti915 gmbus dpb \tI2C adapter\r\n", |
50 |
"i2c-4\ti2c \ti915 gmbus dpb \tI2C adapter\r\n", |
51 |
"i2c-5\ti2c \ti915 gmbus dpd \tI2C adapter\r\n", |
51 |
"i2c-5\ti2c \ti915 gmbus dpd \tI2C adapter\r\n", |
52 |
"i2c-6\ti2c \tDPDDC-B \tI2C adapter\r\n", |
52 |
"i2c-6\ti2c \tDPDDC-B \tI2C adapter\r\n", |
53 |
"i2c-7\ti2c \ti2c-tiny-usb at bus 001 device 007\tI2C adapter\r\n" |
53 |
"i2c-7\ti2c \ti2c-tiny-usb at bus 001 device 008\tI2C adapter\r\n" |
54 |
] |
54 |
] |
55 |
} |
55 |
} |
56 |
], |
56 |
], |
57 |
"prompt_number": 1 |
57 |
"prompt_number": 27 |
58 |
}, |
58 |
}, |
59 |
{ |
59 |
{ |
60 |
"cell_type": "markdown", |
60 |
"cell_type": "markdown", |
61 |
"metadata": {}, |
61 |
"metadata": {}, |
62 |
"source": [ |
62 |
"source": [ |
Line 86... |
Line 86... |
86 |
"port = 7" |
86 |
"port = 7" |
87 |
], |
87 |
], |
88 |
"language": "python", |
88 |
"language": "python", |
89 |
"metadata": {}, |
89 |
"metadata": {}, |
90 |
"outputs": [], |
90 |
"outputs": [], |
91 |
"prompt_number": 2 |
91 |
"prompt_number": 28 |
92 |
}, |
92 |
}, |
93 |
{ |
93 |
{ |
94 |
"cell_type": "markdown", |
94 |
"cell_type": "markdown", |
95 |
"metadata": {}, |
95 |
"metadata": {}, |
96 |
"source": [ |
96 |
"source": [ |
Line 110... |
Line 110... |
110 |
"import numpy as np" |
110 |
"import numpy as np" |
111 |
], |
111 |
], |
112 |
"language": "python", |
112 |
"language": "python", |
113 |
"metadata": {}, |
113 |
"metadata": {}, |
114 |
"outputs": [], |
114 |
"outputs": [], |
115 |
"prompt_number": 3 |
115 |
"prompt_number": 29 |
116 |
}, |
116 |
}, |
117 |
{ |
117 |
{ |
118 |
"cell_type": "markdown", |
118 |
"cell_type": "markdown", |
119 |
"metadata": {}, |
119 |
"metadata": {}, |
120 |
"source": [ |
120 |
"source": [ |
Line 142... |
Line 142... |
142 |
")" |
142 |
")" |
143 |
], |
143 |
], |
144 |
"language": "python", |
144 |
"language": "python", |
145 |
"metadata": {}, |
145 |
"metadata": {}, |
146 |
"outputs": [], |
146 |
"outputs": [], |
147 |
"prompt_number": 4 |
147 |
"prompt_number": 30 |
148 |
}, |
148 |
}, |
149 |
{ |
149 |
{ |
150 |
"cell_type": "markdown", |
150 |
"cell_type": "markdown", |
151 |
"metadata": {}, |
151 |
"metadata": {}, |
152 |
"source": [ |
152 |
"source": [ |
Line 170... |
Line 170... |
170 |
"text": [ |
170 |
"text": [ |
171 |
"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" |
172 |
] |
172 |
] |
173 |
} |
173 |
} |
174 |
], |
174 |
], |
175 |
"prompt_number": 5 |
175 |
"prompt_number": "*" |
176 |
}, |
176 |
}, |
177 |
{ |
177 |
{ |
178 |
"cell_type": "markdown", |
178 |
"cell_type": "markdown", |
179 |
"metadata": {}, |
179 |
"metadata": {}, |
180 |
"source": [ |
180 |
"source": [ |
Line 292... |
Line 292... |
292 |
"noise_threshold = 40" |
292 |
"noise_threshold = 40" |
293 |
], |
293 |
], |
294 |
"language": "python", |
294 |
"language": "python", |
295 |
"metadata": {}, |
295 |
"metadata": {}, |
296 |
"outputs": [], |
296 |
"outputs": [], |
297 |
"prompt_number": 11 |
297 |
"prompt_number": 19 |
298 |
}, |
298 |
}, |
299 |
{ |
299 |
{ |
300 |
"cell_type": "code", |
300 |
"cell_type": "code", |
301 |
"collapsed": false, |
301 |
"collapsed": false, |
302 |
"input": [ |
302 |
"input": [ |
303 |
"measurements = np.array(list_meas)" |
303 |
"measurements = np.array(list_meas)" |
304 |
], |
304 |
], |
305 |
"language": "python", |
305 |
"language": "python", |
306 |
"metadata": {}, |
306 |
"metadata": {}, |
307 |
"outputs": [], |
307 |
"outputs": [], |
308 |
"prompt_number": 12 |
308 |
"prompt_number": 20 |
309 |
}, |
309 |
}, |
310 |
{ |
310 |
{ |
311 |
"cell_type": "code", |
311 |
"cell_type": "code", |
312 |
"collapsed": false, |
312 |
"collapsed": false, |
313 |
"input": [ |
313 |
"input": [ |
Line 334... |
Line 334... |
334 |
"text": [ |
334 |
"text": [ |
335 |
"\n" |
335 |
"\n" |
336 |
] |
336 |
] |
337 |
} |
337 |
} |
338 |
], |
338 |
], |
339 |
"prompt_number": 14 |
339 |
"prompt_number": 21 |
340 |
}, |
340 |
}, |
341 |
{ |
341 |
{ |
342 |
"cell_type": "code", |
342 |
"cell_type": "code", |
343 |
"collapsed": false, |
343 |
"collapsed": false, |
344 |
"input": [ |
344 |
"input": [ |
Line 360... |
Line 360... |
360 |
"text": [ |
360 |
"text": [ |
361 |
"initial guess : avg 9.55977014083 std 0.17596103611\n" |
361 |
"initial guess : avg 9.55977014083 std 0.17596103611\n" |
362 |
] |
362 |
] |
363 |
} |
363 |
} |
364 |
], |
364 |
], |
365 |
"prompt_number": 15 |
365 |
"prompt_number": 22 |
366 |
}, |
366 |
}, |
367 |
{ |
367 |
{ |
368 |
"cell_type": "code", |
368 |
"cell_type": "code", |
369 |
"collapsed": false, |
369 |
"collapsed": false, |
370 |
"input": [ |
370 |
"input": [ |
Line 386... |
Line 386... |
386 |
"text": [ |
386 |
"text": [ |
387 |
"optimized guess : avg 9.80797237599 std 0.141020849641\n" |
387 |
"optimized guess : avg 9.80797237599 std 0.141020849641\n" |
388 |
] |
388 |
] |
389 |
} |
389 |
} |
390 |
], |
390 |
], |
391 |
"prompt_number": 16 |
391 |
"prompt_number": 23 |
392 |
}, |
392 |
}, |
393 |
{ |
393 |
{ |
394 |
"cell_type": "code", |
394 |
"cell_type": "code", |
395 |
"collapsed": false, |
395 |
"collapsed": false, |
396 |
"input": [ |
396 |
"input": [ |
397 |
"%pylab qt\n", |
397 |
"#%pylab qt\n", |
398 |
"#%pylab inline\n", |
398 |
"%pylab inline\n", |
399 |
"calibration_utils.plot_results(True, measurements, flt_idx, flt_meas, cp0, np0, cp1, np1, sensor_ref)" |
399 |
"calibration_utils.plot_results(True, measurements, flt_idx, flt_meas, cp0, np0, cp1, np1, sensor_ref)" |
400 |
], |
400 |
], |
401 |
"language": "python", |
401 |
"language": "python", |
402 |
"metadata": {}, |
402 |
"metadata": {}, |
403 |
"outputs": [ |
403 |
"outputs": [ |
Line 405... |
Line 405... |
405 |
"output_type": "stream", |
405 |
"output_type": "stream", |
406 |
"stream": "stdout", |
406 |
"stream": "stdout", |
407 |
"text": [ |
407 |
"text": [ |
408 |
"Populating the interactive namespace from numpy and matplotlib\n" |
408 |
"Populating the interactive namespace from numpy and matplotlib\n" |
409 |
] |
409 |
] |
- |
|
410 |
}, |
- |
|
411 |
{ |
- |
|
412 |
"metadata": {}, |
- |
|
413 |
"output_type": "display_data", |
- |
|
414 |
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lekIX3tLCkq3DtnI7/TZHrh9h5QcjySRd93LZU1hzOIJUoNnppU2NVOswLNQ/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415 |
"text": [ |
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416 |
"<matplotlib.figure.Figure at 0x7f7113c306d0>" |
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417 |
] |
410 |
} |
418 |
} |
411 |
], |
419 |
], |
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"prompt_number": 18 |
420 |
"prompt_number": 26 |
413 |
}, |
421 |
}, |
414 |
{ |
422 |
{ |
415 |
"cell_type": "code", |
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"cell_type": "code", |
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"collapsed": false, |
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"collapsed": false, |
417 |
"input": [], |
425 |
"input": [], |