| 1 |
{ |
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{ |
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"metadata": { |
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"metadata": { |
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"name": "" |
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"name": "" |
| 4 |
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| 5 |
"nbformat": 3, |
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"nbformat": 3, |
| 6 |
"nbformat_minor": 0, |
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"nbformat_minor": 0, |
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"worksheets": [ |
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"worksheets": [ |
| 8 |
{ |
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{ |
| 9 |
"cells": [ |
9 |
"cells": [ |
| 10 |
{ |
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{ |
| 11 |
"cell_type": "markdown", |
11 |
"cell_type": "markdown", |
| 12 |
"metadata": {}, |
12 |
"metadata": {}, |
| 13 |
"source": [ |
13 |
"source": [ |
| 14 |
"Uk\u00e1zka pou\u017eit\u00ed n\u00e1stroje IPython na manipulaci se senzorov\u00fdmi daty\n", |
14 |
"Uk\u00e1zka pou\u017eit\u00ed n\u00e1stroje IPython na manipulaci se senzorov\u00fdmi daty\n", |
| 15 |
"=======\n", |
15 |
"=======\n", |
| 16 |
"\n", |
16 |
"\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). 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", |
17 |
"P\u0159\u00edklad vyu\u017e\u00edv\u00e1 modulovou stavebnici MLAB a jej\u00ed knihovnu [pymlab](https://github.com/MLAB-project/MLAB-I2c-modules). Sn\u00edma\u010d je k po\u010d\u00edta\u010di p\u0159ipojen\u00fd p\u0159es rozhradn\u00ed USB a data jsou vy\u010d\u00edt\u00e1na p\u0159es [I\u00b2C](http://wiki.mlab.cz/doku.php?id=cs:i2c)\n", |
| 18 |
"\n", |
18 |
"\n", |
| 19 |
"Pou\u017eit\u00fd sn\u00edma\u010d [HMC5883L](http://www.magneticsensors.com/three-axis-digital-compass.php) m\u00e1 n\u00e1sleduj\u00edc\u00ed katalogov\u00e9 parametry: \n", |
19 |
"Pou\u017eit\u00fd sn\u00edma\u010d [HMC5883L](http://www.magneticsensors.com/three-axis-digital-compass.php) m\u00e1 n\u00e1sleduj\u00edc\u00ed katalogov\u00e9 parametry: \n", |
| 20 |
"\n", |
20 |
"\n", |
| 21 |
"* M\u011b\u0159\u00edc\u00ed rozsah +/- 800 uT\n", |
21 |
"* M\u011b\u0159\u00edc\u00ed rozsah +/- 800 uT\n", |
| 22 |
"* Rozli\u0161en\u00ed typicky 5 uT\n", |
22 |
"* Rozli\u0161en\u00ed typicky 5 uT\n", |
| 23 |
"* \u010cetnost m\u011b\u0159en\u00ed 75 Hz\n", |
23 |
"* \u010cetnost m\u011b\u0159en\u00ed 75 Hz\n", |
| 24 |
"\n", |
24 |
"\n", |
| 25 |
"Zprovozn\u011bn\u00ed demo k\u00f3du\n", |
25 |
"Zprovozn\u011bn\u00ed demo k\u00f3du\n", |
| 26 |
"---------------------\n", |
26 |
"---------------------\n", |
| 27 |
"\n", |
27 |
"\n", |
| 28 |
"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" |
28 |
"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 |
] |
29 |
] |
| 30 |
}, |
30 |
}, |
| 31 |
{ |
31 |
{ |
| 32 |
"cell_type": "code", |
32 |
"cell_type": "code", |
| 33 |
"collapsed": false, |
33 |
"collapsed": false, |
| 34 |
"input": [ |
34 |
"input": [ |
| 35 |
"!i2cdetect -l" |
35 |
"!i2cdetect -l" |
| 36 |
], |
36 |
], |
| 37 |
"language": "python", |
37 |
"language": "python", |
| 38 |
"metadata": {}, |
38 |
"metadata": {}, |
| 39 |
"outputs": [ |
39 |
"outputs": [ |
| 40 |
{ |
40 |
{ |
| 41 |
"output_type": "stream", |
41 |
"output_type": "stream", |
| 42 |
"stream": "stdout", |
42 |
"stream": "stdout", |
| 43 |
"text": [ |
43 |
"text": [ |
| 44 |
"i2c-0\ti2c \ti915 gmbus ssc \tI2C adapter\r\n", |
44 |
"i2c-0\ti2c \ti915 gmbus ssc \tI2C adapter\r\n", |
| 45 |
"i2c-1\ti2c \ti915 gmbus vga \tI2C adapter\r\n", |
45 |
"i2c-1\ti2c \ti915 gmbus vga \tI2C adapter\r\n", |
| 46 |
"i2c-2\ti2c \ti915 gmbus panel \tI2C adapter\r\n", |
46 |
"i2c-2\ti2c \ti915 gmbus panel \tI2C adapter\r\n", |
| 47 |
"i2c-3\ti2c \ti915 gmbus dpc \tI2C adapter\r\n", |
47 |
"i2c-3\ti2c \ti915 gmbus dpc \tI2C adapter\r\n", |
| 48 |
"i2c-4\ti2c \ti915 gmbus dpb \tI2C adapter\r\n", |
48 |
"i2c-4\ti2c \ti915 gmbus dpb \tI2C adapter\r\n", |
| 49 |
"i2c-5\ti2c \ti915 gmbus dpd \tI2C adapter\r\n", |
49 |
"i2c-5\ti2c \ti915 gmbus dpd \tI2C adapter\r\n", |
| 50 |
"i2c-6\ti2c \tDPDDC-B \tI2C adapter\r\n", |
50 |
"i2c-6\ti2c \tDPDDC-B \tI2C adapter\r\n", |
| 51 |
"i2c-7\ti2c \ti2c-tiny-usb at bus 001 device 070\tI2C adapter\r\n" |
51 |
"i2c-7\ti2c \ti2c-tiny-usb at bus 001 device 070\tI2C adapter\r\n" |
| 52 |
] |
52 |
] |
| 53 |
} |
53 |
} |
| 54 |
], |
54 |
], |
| 55 |
"prompt_number": 1 |
55 |
"prompt_number": 1 |
| 56 |
}, |
56 |
}, |
| 57 |
{ |
57 |
{ |
| 58 |
"cell_type": "markdown", |
58 |
"cell_type": "markdown", |
| 59 |
"metadata": {}, |
59 |
"metadata": {}, |
| 60 |
"source": [ |
60 |
"source": [ |
| 61 |
"Proto\u017ee pro p\u0159ipojen\u00ed \u010didel k po\u010d\u00edta\u010di pou\u017e\u00edv\u00e1me adapt\u00e9r i2c-tiny-usb. Vid\u00edme, \u017ee sb\u011brnice m\u00e1 aktu\u00e1ln\u011b ozna\u010den\u00ed nap\u0159\u00edklad i2c-8. \n", |
61 |
"Proto\u017ee pro p\u0159ipojen\u00ed \u010didel k po\u010d\u00edta\u010di pou\u017e\u00edv\u00e1me adapt\u00e9r i2c-tiny-usb. Vid\u00edme, \u017ee sb\u011brnice m\u00e1 aktu\u00e1ln\u011b ozna\u010den\u00ed nap\u0159\u00edklad i2c-8. \n", |
| 62 |
"\n", |
62 |
"\n", |
| 63 |
"V p\u0159\u00edpad\u011b, \u017ee v\u00fd\u0161e uveden\u00fd p\u0159\u00edklad vr\u00e1t\u00ed chybu, nebo pojmenov\u00e1n\u00ed \"unknown\" tak nem\u00e1me p\u0159\u00edstup k syst\u00e9mov\u00fdm rozhran\u00edm. Ten z\u00edsk\u00e1me vytvo\u0159en\u00edm souboru s n\u00e1sleduj\u00edc\u00edm obsahem ve slo\u017ece: /etc/udev/rules.d/i2c-devices.rules" |
63 |
"V p\u0159\u00edpad\u011b, \u017ee v\u00fd\u0161e uveden\u00fd p\u0159\u00edklad vr\u00e1t\u00ed chybu, nebo pojmenov\u00e1n\u00ed \"unknown\" tak nem\u00e1me p\u0159\u00edstup k syst\u00e9mov\u00fdm rozhran\u00edm. Ten z\u00edsk\u00e1me vytvo\u0159en\u00edm souboru s n\u00e1sleduj\u00edc\u00edm obsahem ve slo\u017ece: /etc/udev/rules.d/i2c-devices.rules" |
| 64 |
] |
64 |
] |
| 65 |
}, |
65 |
}, |
| 66 |
{ |
66 |
{ |
| 67 |
"cell_type": "raw", |
67 |
"cell_type": "raw", |
| 68 |
"metadata": {}, |
68 |
"metadata": {}, |
| 69 |
"source": [ |
69 |
"source": [ |
| 70 |
"KERNEL==\"i2c-[0-9]*\", GROUP=\"i2c\"" |
70 |
"KERNEL==\"i2c-[0-9]*\", GROUP=\"i2c\"" |
| 71 |
] |
71 |
] |
| 72 |
}, |
72 |
}, |
| 73 |
{ |
73 |
{ |
| 74 |
"cell_type": "markdown", |
74 |
"cell_type": "markdown", |
| 75 |
"metadata": {}, |
75 |
"metadata": {}, |
| 76 |
"source": [ |
76 |
"source": [ |
| 77 |
"Toto ozna\u010den\u00ed budeme je\u0161t\u011b d\u00e1le pot\u0159ebovat, proto si jej ulo\u017e\u00edme da prom\u011bnn\u00e9. " |
77 |
"Toto ozna\u010den\u00ed budeme je\u0161t\u011b d\u00e1le pot\u0159ebovat, proto si jej ulo\u017e\u00edme da prom\u011bnn\u00e9. " |
| 78 |
] |
78 |
] |
| 79 |
}, |
79 |
}, |
| 80 |
{ |
80 |
{ |
| 81 |
"cell_type": "code", |
81 |
"cell_type": "code", |
| 82 |
"collapsed": false, |
82 |
"collapsed": false, |
| 83 |
"input": [ |
83 |
"input": [ |
| 84 |
"port = 7" |
84 |
"port = 7" |
| 85 |
], |
85 |
], |
| 86 |
"language": "python", |
86 |
"language": "python", |
| 87 |
"metadata": {}, |
87 |
"metadata": {}, |
| 88 |
"outputs": [], |
88 |
"outputs": [], |
| 89 |
"prompt_number": 2 |
89 |
"prompt_number": 2 |
| 90 |
}, |
90 |
}, |
| 91 |
{ |
91 |
{ |
| 92 |
"cell_type": "markdown", |
92 |
"cell_type": "markdown", |
| 93 |
"metadata": {}, |
93 |
"metadata": {}, |
| 94 |
"source": [ |
94 |
"source": [ |
| 95 |
"Budeme pokra\u010dovat na\u010dten\u00edm pot\u0159ebn\u00fdch modul\u016f pro zach\u00e1zen\u00ed s I\u00b2C sn\u00edma\u010di." |
95 |
"Budeme pokra\u010dovat na\u010dten\u00edm pot\u0159ebn\u00fdch modul\u016f pro zach\u00e1zen\u00ed s I\u00b2C sn\u00edma\u010di." |
| 96 |
] |
96 |
] |
| 97 |
}, |
97 |
}, |
| 98 |
{ |
98 |
{ |
| 99 |
"cell_type": "code", |
99 |
"cell_type": "code", |
| 100 |
"collapsed": false, |
100 |
"collapsed": false, |
| 101 |
"input": [ |
101 |
"input": [ |
| 102 |
"import time\n", |
102 |
"import time\n", |
| 103 |
"import datetime\n", |
103 |
"import datetime\n", |
| 104 |
"import sys\n", |
104 |
"import sys\n", |
| 105 |
"\n", |
105 |
"\n", |
| 106 |
"from pymlab import config\n", |
106 |
"from pymlab import config\n", |
| 107 |
"import matplotlib.pyplot as plt\n", |
107 |
"import matplotlib.pyplot as plt\n", |
| 108 |
"import numpy as np" |
108 |
"import numpy as np" |
| 109 |
], |
109 |
], |
| 110 |
"language": "python", |
110 |
"language": "python", |
| 111 |
"metadata": {}, |
111 |
"metadata": {}, |
| 112 |
"outputs": [], |
112 |
"outputs": [], |
| 113 |
"prompt_number": 3 |
113 |
"prompt_number": 3 |
| 114 |
}, |
114 |
}, |
| 115 |
{ |
115 |
{ |
| 116 |
"cell_type": "markdown", |
116 |
"cell_type": "markdown", |
| 117 |
"metadata": {}, |
117 |
"metadata": {}, |
| 118 |
"source": [ |
118 |
"source": [ |
| 119 |
"Nyn\u00ed si nadefinujeme strukturu p\u0159ipojen\u00ed jednotliv\u00fdch \u010didel na I\u00b2C sb\u011brnici." |
119 |
"Nyn\u00ed si nadefinujeme strukturu p\u0159ipojen\u00ed jednotliv\u00fdch \u010didel na I\u00b2C sb\u011brnici." |
| 120 |
] |
120 |
] |
| 121 |
}, |
121 |
}, |
| 122 |
{ |
122 |
{ |
| 123 |
"cell_type": "code", |
123 |
"cell_type": "code", |
| 124 |
"collapsed": false, |
124 |
"collapsed": false, |
| 125 |
"input": [ |
125 |
"input": [ |
| 126 |
"cfg = config.Config(\n", |
126 |
"cfg = config.Config(\n", |
| 127 |
" i2c = {\n", |
127 |
" i2c = {\n", |
| 128 |
" \"port\": port,\n", |
128 |
" \"port\": port,\n", |
| 129 |
" },\n", |
129 |
" },\n", |
| 130 |
" bus = [\n", |
130 |
" bus = [\n", |
| 131 |
" {\n", |
131 |
" {\n", |
| 132 |
" \"type\": \"i2chub\",\n", |
132 |
" \"type\": \"i2chub\",\n", |
| 133 |
" \"address\": 0x71,\n", |
133 |
" \"address\": 0x71,\n", |
| 134 |
" \n", |
134 |
" \n", |
| 135 |
" \"children\": [\n", |
135 |
" \"children\": [\n", |
| 136 |
" {\"name\": \"mag\", \"type\": \"mag01\", \"gauss\": 0.88, \"channel\": 0, }, \n", |
136 |
" {\"name\": \"mag\", \"type\": \"mag01\", \"gauss\": 0.88, \"channel\": 0, }, \n", |
| 137 |
" ],\n", |
137 |
" ],\n", |
| 138 |
" },\n", |
138 |
" },\n", |
| 139 |
" ],\n", |
139 |
" ],\n", |
| 140 |
")" |
140 |
")" |
| 141 |
], |
141 |
], |
| 142 |
"language": "python", |
142 |
"language": "python", |
| 143 |
"metadata": {}, |
143 |
"metadata": {}, |
| 144 |
"outputs": [], |
144 |
"outputs": [], |
| 145 |
"prompt_number": 26 |
145 |
"prompt_number": 26 |
| 146 |
}, |
146 |
}, |
| 147 |
{ |
147 |
{ |
| 148 |
"cell_type": "markdown", |
148 |
"cell_type": "markdown", |
| 149 |
"metadata": {}, |
149 |
"metadata": {}, |
| 150 |
"source": [ |
150 |
"source": [ |
| 151 |
"Tuto strukturu inicializujeme, aby jsme dos\u00e1hli definovan\u00e9 konfigurace \u010didel." |
151 |
"Tuto strukturu inicializujeme, aby jsme dos\u00e1hli definovan\u00e9 konfigurace \u010didel." |
| 152 |
] |
152 |
] |
| 153 |
}, |
153 |
}, |
| 154 |
{ |
154 |
{ |
| 155 |
"cell_type": "code", |
155 |
"cell_type": "code", |
| 156 |
"collapsed": false, |
156 |
"collapsed": false, |
| 157 |
"input": [ |
157 |
"input": [ |
| 158 |
"cfg.initialize()\n", |
158 |
"cfg.initialize()\n", |
| 159 |
"mag_sensor = cfg.get_device(\"mag\")\n", |
159 |
"mag_sensor = cfg.get_device(\"mag\")\n", |
| 160 |
"time.sleep(0.5)" |
160 |
"time.sleep(0.5)" |
| 161 |
], |
161 |
], |
| 162 |
"language": "python", |
162 |
"language": "python", |
| 163 |
"metadata": {}, |
163 |
"metadata": {}, |
| 164 |
"outputs": [], |
164 |
"outputs": [], |
| 165 |
"prompt_number": 27 |
165 |
"prompt_number": 27 |
| 166 |
}, |
166 |
}, |
| 167 |
{ |
167 |
{ |
| 168 |
"cell_type": "markdown", |
168 |
"cell_type": "markdown", |
| 169 |
"metadata": {}, |
169 |
"metadata": {}, |
| 170 |
"source": [ |
170 |
"source": [ |
| 171 |
"Nyn\u00ed u\u017e m\u016f\u017eeme p\u0159\u00edmo komunikovat se za\u0159\u00edzen\u00edm pojmenovan\u00fdm jako mag_sensor. A vy\u010d\u00edst z n\u011bj sadu dat." |
171 |
"Nyn\u00ed u\u017e m\u016f\u017eeme p\u0159\u00edmo komunikovat se za\u0159\u00edzen\u00edm pojmenovan\u00fdm jako mag_sensor. A vy\u010d\u00edst z n\u011bj sadu dat." |
| 172 |
] |
172 |
] |
| 173 |
}, |
173 |
}, |
| 174 |
{ |
174 |
{ |
| 175 |
"cell_type": "code", |
175 |
"cell_type": "code", |
| 176 |
"collapsed": false, |
176 |
"collapsed": false, |
| 177 |
"input": [ |
177 |
"input": [ |
| 178 |
"import sys\n", |
178 |
"import sys\n", |
| 179 |
"import time\n", |
179 |
"import time\n", |
| 180 |
"from IPython.display import clear_output\n", |
180 |
"from IPython.display import clear_output\n", |
| 181 |
"\n", |
181 |
"\n", |
| 182 |
"MEASUREMENTS = 500\n", |
182 |
"MEASUREMENTS = 500\n", |
| 183 |
"list_meas = []\n", |
183 |
"list_meas = []\n", |
| 184 |
"\n", |
184 |
"\n", |
| 185 |
"for n in range(MEASUREMENTS):\n", |
185 |
"for n in range(MEASUREMENTS):\n", |
| 186 |
"# mag_sensor.route() #V p\u0159\u00edpad\u011b v\u00edce \u010didel je pot\u0159eba ke ka\u017ed\u00e9mu p\u0159ed jeho pou\u017eit\u00edm nechat vyroutovat cesutu na sb\u011brnici.\n", |
186 |
"# mag_sensor.route() #V p\u0159\u00edpad\u011b v\u00edce \u010didel je pot\u0159eba ke ka\u017ed\u00e9mu p\u0159ed jeho pou\u017eit\u00edm nechat vyroutovat cesutu na sb\u011brnici.\n", |
| 187 |
" clear_output()\n", |
187 |
" clear_output()\n", |
| 188 |
" (x, y, z) = mag_sensor.axes()\n", |
188 |
" (x, y, z) = mag_sensor.axes()\n", |
| 189 |
" list_meas.append([x, y, z])\n", |
189 |
" list_meas.append([x, y, z])\n", |
| 190 |
" print (n, list_meas[n])\n", |
190 |
" print (n, list_meas[n])\n", |
| 191 |
" time.sleep(0.2)\n", |
191 |
" time.sleep(0.2)\n", |
| 192 |
" sys.stdout.flush()" |
192 |
" sys.stdout.flush()" |
| 193 |
], |
193 |
], |
| 194 |
"language": "python", |
194 |
"language": "python", |
| 195 |
"metadata": {}, |
195 |
"metadata": {}, |
| 196 |
"outputs": [ |
196 |
"outputs": [ |
| 197 |
{ |
197 |
{ |
| 198 |
"output_type": "stream", |
198 |
"output_type": "stream", |
| 199 |
"stream": "stdout", |
199 |
"stream": "stdout", |
| 200 |
"text": [ |
200 |
"text": [ |
| 201 |
"(499, [5.109999999999999, 163.51999999999998, -689.85])\n" |
201 |
"(499, [5.109999999999999, 163.51999999999998, -689.85])\n" |
| 202 |
] |
202 |
] |
| 203 |
} |
203 |
} |
| 204 |
], |
204 |
], |
| 205 |
"prompt_number": 31 |
205 |
"prompt_number": 31 |
| 206 |
}, |
206 |
}, |
| 207 |
{ |
207 |
{ |
| 208 |
"cell_type": "markdown", |
208 |
"cell_type": "markdown", |
| 209 |
"metadata": {}, |
209 |
"metadata": {}, |
| 210 |
"source": [ |
210 |
"source": [ |
| 211 |
"V\u00fdstupn\u00ed jsou v jednotk\u00e1ch miliGauss a m\u011b\u0159\u00edc\u00ed rozsah je nastaven\u00fd na 0.88 Gauss. Viz konfigurace \u010didel naho\u0159e.\n", |
211 |
"V\u00fdstupn\u00ed jsou v jednotk\u00e1ch miliGauss a m\u011b\u0159\u00edc\u00ed rozsah je nastaven\u00fd na 0.88 Gauss. Viz konfigurace \u010didel naho\u0159e.\n", |
| 212 |
"Nam\u011b\u0159en\u00e9 hodnoty n\u00e1sledn\u011b ulo\u017e\u00edme do souboru. " |
212 |
"Nam\u011b\u0159en\u00e9 hodnoty n\u00e1sledn\u011b ulo\u017e\u00edme do souboru. " |
| 213 |
] |
213 |
] |
| 214 |
}, |
214 |
}, |
| 215 |
{ |
215 |
{ |
| 216 |
"cell_type": "code", |
216 |
"cell_type": "code", |
| 217 |
"collapsed": false, |
217 |
"collapsed": false, |
| 218 |
"input": [ |
218 |
"input": [ |
| 219 |
"np.savez(\"calibration_data_2Dset\", data=list_meas)" |
219 |
"np.savez(\"calibration_data_2Dset\", data=list_meas)" |
| 220 |
], |
220 |
], |
| 221 |
"language": "python", |
221 |
"language": "python", |
| 222 |
"metadata": {}, |
222 |
"metadata": {}, |
| 223 |
"outputs": [], |
223 |
"outputs": [], |
| 224 |
"prompt_number": 32 |
224 |
"prompt_number": 32 |
| 225 |
}, |
225 |
}, |
| 226 |
{ |
226 |
{ |
| 227 |
"cell_type": "markdown", |
227 |
"cell_type": "markdown", |
| 228 |
"metadata": {}, |
228 |
"metadata": {}, |
| 229 |
"source": [ |
229 |
"source": [ |
| 230 |
"Zpracov\u00e1n\u00ed dat 2D\n", |
230 |
"Zpracov\u00e1n\u00ed dat 2D\n", |
| 231 |
"-----------\n", |
231 |
"-----------\n", |
| 232 |
"\n", |
232 |
"\n", |
| 233 |
"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. " |
233 |
"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. " |
| 234 |
] |
234 |
] |
| 235 |
}, |
235 |
}, |
| 236 |
{ |
236 |
{ |
| 237 |
"cell_type": "code", |
237 |
"cell_type": "code", |
| 238 |
"collapsed": false, |
238 |
"collapsed": false, |
| 239 |
"input": [ |
239 |
"input": [ |
| 240 |
"data = np.load('./calibration_data_3Dset.npz')\n", |
240 |
"data = np.load('./calibration_data_3Dset.npz')\n", |
| 241 |
"list_meas = data['data']\n", |
241 |
"list_meas = data['data']\n", |
| 242 |
"x = list_meas[:, 0]\n", |
242 |
"x = list_meas[:, 0]\n", |
| 243 |
"y = list_meas[:, 1]\n", |
243 |
"y = list_meas[:, 1]\n", |
| 244 |
"z = list_meas[:, 2]" |
244 |
"z = list_meas[:, 2]" |
| 245 |
], |
245 |
], |
| 246 |
"language": "python", |
246 |
"language": "python", |
| 247 |
"metadata": {}, |
247 |
"metadata": {}, |
| 248 |
"outputs": [], |
248 |
"outputs": [], |
| 249 |
"prompt_number": 3 |
249 |
"prompt_number": 3 |
| 250 |
}, |
250 |
}, |
| 251 |
{ |
251 |
{ |
| 252 |
"cell_type": "markdown", |
252 |
"cell_type": "markdown", |
| 253 |
"metadata": {}, |
253 |
"metadata": {}, |
| 254 |
"source": [ |
254 |
"source": [ |
| 255 |
"Nam\u011b\u0159en\u00e9 hodnoty vykresl\u00edme do 3D grafu." |
255 |
"Nam\u011b\u0159en\u00e9 hodnoty vykresl\u00edme do 3D grafu." |
| 256 |
] |
256 |
] |
| 257 |
}, |
257 |
}, |
| 258 |
{ |
258 |
{ |
| 259 |
"cell_type": "code", |
259 |
"cell_type": "code", |
| 260 |
"collapsed": false, |
260 |
"collapsed": false, |
| 261 |
"input": [ |
261 |
"input": [ |
| 262 |
"from mpl_toolkits.mplot3d.axes3d import Axes3D\n", |
262 |
"from mpl_toolkits.mplot3d.axes3d import Axes3D\n", |
| 263 |
"#%pylab qt\n", |
263 |
"#%pylab qt\n", |
| 264 |
"%pylab inline\n", |
264 |
"%pylab inline\n", |
| 265 |
"fig = plt.figure()\n", |
265 |
"fig = plt.figure()\n", |
| 266 |
"ax = Axes3D(fig)\n", |
266 |
"ax = Axes3D(fig)\n", |
| 267 |
"p = ax.scatter(x,y,z)" |
267 |
"p = ax.scatter(x,y,z)" |
| 268 |
], |
268 |
], |
| 269 |
"language": "python", |
269 |
"language": "python", |
| 270 |
"metadata": {}, |
270 |
"metadata": {}, |
| 271 |
"outputs": [ |
271 |
"outputs": [ |
| 272 |
{ |
272 |
{ |
| 273 |
"output_type": "stream", |
273 |
"output_type": "stream", |
| 274 |
"stream": "stdout", |
274 |
"stream": "stdout", |
| 275 |
"text": [ |
275 |
"text": [ |
| 276 |
"Populating the interactive namespace from numpy and matplotlib\n" |
276 |
"Populating the interactive namespace from numpy and matplotlib\n" |
| 277 |
] |
277 |
] |
| 278 |
}, |
278 |
}, |
| 279 |
{ |
279 |
{ |
| 280 |
"metadata": {}, |
280 |
"metadata": {}, |
| 281 |
"output_type": "display_data", |
281 |
"output_type": "display_data", |
| 282 |
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cmzdv5dixas6e/QtnzzoZNiyFpKS/M2NG+/20wYMHM3jwYDweDz6fj29+8z4A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8nOXk5NTSccDqiq+g9XXTWqzmZMjpXL52hqw9mWkmFr4U/nWVvVHSrvQRRF+vXr1+hvsrKycDqd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\ndXVl8uTJAAwfPpw333yTJ554orYe575DCMG2bdsICwvDw8MDN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cmzdv5dixas6e/QtnzzoZNiyFpKS/M2NG+/20wYMHM3jwYDweDz6fj29+8z4A\njh8/zrPP7kOWfeh0Bq5ePcuoUXkYjcZ2hYZ9Ph+1tbXodDry8/PR6XQJrZYRvLANGTIEWf4Qv19G\nkoy43VdJSUlCo0kKNFjuz2i12ja/v2B3qdg7DOcuDRVd2lctvlC/i/5UoBr6ufApw6R7KtE8mGij\nNHtqvsGCpyzP1h2Emp8sf9rFoat9DkN9T1lZGW+++QGNjXamTy9m6dI5rF+/j8mTnyYr65+cPbuL\ny5eP8LOf3cu0aVOj/o7x48ezbNl53n//f9BoksnJsXPvvV9oZ3W0tLTw6qsbKS/XAX5Gj5ZZufJz\n6PX6gPiJ9kGJsg4nTpzIV74yiyeeeIGmph+QlDSP7GwfGRnnGDfu/rh/X28h3G+hs+5SIYhKV2ki\nXwi7w9WpHD9UOkNfpl8Ln/LGJdoy6ehBjDUtId4PeLjrIFyr0bgPoxkvHigDfWLtZC/GCUdtbS1P\nP70Bne4ukpJyKSnZTG3tn5GkNAyGZIYPX8rw4UupqHiOyZMndep7JUnijjtuYuHCepxOJzk5OSH3\nyrZs2cXFi6MYNmwBsixz+nQJhw8fZ/786wP3RUQjJ9IFt2LF7SxaNIfXX/8jZ8/uorAwk6985YeB\n3Cuv10tNTQ0Wi6Xb3/57S+WWSMn4IrI0OPdQlOzrK+7SUNdatfj6KIm0oCLtI8UqeIn6YQRfh87s\nl3UHoiCAELxwXRw6M57yv8+fP09lZSUWi4UxY8ZQVlaGyzWZQYNaE2gLC2/j8OH/ID3dQm3tEbKz\nJ1FXd5zUVGvUpcaCv7+jKi0XL9ah0RTjdDo/6fpRTHV1aWCRBSK2D4oUsdjZe5mbm8v3v/+Ndn9f\nVVXFt771BJWVPmS5ha985WYeeGBlu88pK34MJCSpfTFot9uNz+dDr9fH7C4NR3e7Ua1Wa6CuZn9A\nFb44Eco919XE83jl3IVCuV/WFcGL53UVVg20RuJGU9S6M+zYsYuXX96DRjMBj+cgS5eeZNKkkchy\nI263m1OnzlFTcxGz+RK/+tW3eOut97l8eT2FhemsWvVFTCZT3OYiqKioYP/+o5w6ZSMlxc7YsTnA\ncYqKBoX8fGdccCLHsqtiCPDYY7/i8uVbyM6+A6/XyksvfYcpU8Zy7bXXxnLavYZEW5IajSZkd4Ro\n3aU9YR2Gs/hUV2cfobtcncqx411pJRFzdrlc2O32uASICLqygIjyYsomuSkpKXH9wbtcLn73u60M\nGvQDjMZ6OnMbAAAgAElEQVQ0PB4XW7b8N/PmXceoUXW8++4vsdmGYzCcZejQG3jzzW08+uhXMJlM\nCbVg3njjfYYP/zqyfICKit3s23eaBx6YysyZyzs1TrT5bMqIRXFcNBbHyZPlZGT8DACdLg2/fxbl\n5eXdJnz9KdcunLtUKYaRoktF9ZrutKyF5dpf6NfCB22tpkTU1BTfISyoeLYHiqfwifm53W60Wm1c\nAkSg6y5ZZXkxs9mMTqejsbEx7nmWTqcTr9eA0dj61qrR6NBoMvF6vaxadQ8HDvwnaWnXkJl5D5mZ\nI6mo+D0VFRVtilHHG7fbTX29h6KiUWRnj8LptFJZuYX580fH7d6Esw5dLldAGIV1CLSzNoQgDh2a\nx/nze8nIWITf70SSDpGff0eX59jT9KbIy1DWuPLlRZQHFK5tse4kunZpf3rpEPR74ROIVhvxRjyY\nNpsNnU4X13548RA+ZUSkXq/HYDCEfNvsCrG4ZEOVF0ukVZ6amsrQoWYqKnaQm/sZGhpOYzJdZvDg\nwRgMBvLyhpCbOw+9PglZ9uPzWQP7aonCYDCQk2Ogrq6MrKwRSJIWvb4+UMopUQgxlGU54t6h0uL4\n8Y9X8d3v/pyGhnfw+Wq55ZaJzJ49O6HzVGn78qJcV5QBM0rXNoR/eYmWUL/l/iZ+/V74lItyIkqL\niYg7k8mE2WyO2/jK74n1OKXgCQtPWby5J+iovJj473j/0DQaDd/5zr/w0ksbOHWqhJycFB588K5A\nxOJtt13LW2+9hiRNwu+/wGc+k0JhYWHcvj8cX/rSjbz66ntcvrwLSbJx113XkpOTk/DvDaajvcNR\no0bx5pvPcu7cOZKSkigqKgoEHwkRFQ1oE7FA9sWgmUTPuaPcw3Du0lAdSDo6j/5Gvxc+QbyEL9Qe\nnih3FG9iWUCUKQA6na5dRGQiXL7RXNuulhcTP+pYrrOYW2ZmJv/2b18PLNJKD8DSpQsoKsqnoqKK\njIxRTJkypVsW2kGDBvH976+kqakJs9ncbXUxo32xUO5HZWVlBSJUlftR775bwtNPv4bb7WfcuCE8\n9dQPycvLi6v7LVH0J0sm0suL0l0aqRGwsCCVY4jC0v0JVfiiJFLQSm9Ijg+eX1dSAOJJLOkSwVb6\nm2++zbp1W5FluOWWWTz44MqQrlrxZhvKgoTWa9TU1MT69ZsoLa0iLy+Fe++9ifz8fCRJYuzYsYwd\nOzbmc431OdDr9VE1pu1NiEXy5MmTPPXUBiyWV0hLK6C09FV++tP/4de/frLXRSt2N71l/zCcuzQ4\nulTkiYpj/H4/GzZsIDc3N+qITpfLxfLlyzl//jxarZb777+fH/3oR9TV1bFy5UrKy8spLi7mzTff\nDKRHPPnkk7zxxhvo9XqeeeYZli1b1smr0Xl6fmVMMMrotVgWpWiiNHtS+EQKgHA7dbTHmIi5hhoz\nXukSW7Z8yO9+d4b8/F8iSVo2bHie7OwSbr/9c22+6+23/8Z77x0A4HOfu47Pf/7GNguDCAp48cW3\nqaiYTHb27dTUlPPccxv5yU/u69UdCHozpaWlyPJcjMZWt3BGxgqOHPlDm/3RcMndYjEOLgwdLh82\nUS7UvijA8Zh3uEhgsZa43W62bt3K8ePHOXPmDKNHj2bSpElMmjSJVatWha3d+ZOf/ISFCxficrmY\nOXMmN910E2vWrOGOO+7gq1/9Ki+++CKPP/44a9asYceOHWzatImTJ09SU1PD/PnzOX78eMJf2vuW\n07wLdHbBF3tkVqsVj8dDcnJyWCuqJ4RPCLLVag3kvEVj5XVHPqPD4cBqtSLLrW2VkpKSoha9ffv2\n8dJLb/D736+jsbGRAwfOYDYvRa9PQadLIjX1RvbtO93mmG3bPmLjxjpyc39MTs6/sWFDFR999I/A\nfJxOJy0tLTQ3N3P+vJOcnOlIkp7MzNFYrVlcvnwZn8/XL/cyEk2rpVqKLLfW87TbT5KT09Z6Fcnd\nBoPhkwT9JCwWSyCKV5nOYrPZsNvtOJ3OQMBGX70vfVFUxYuH8Bq9+uqrPPvss3zjG99g48aN3HXX\nXXg8nrDHG41GFi5cGPjvkSNHUlNTw9atW1mxYgUA99xzDyUlJQBs2bKFu+++G0mSyMvLY8KECezZ\nsyfh56lafEHEkoeXqFSJUHNWWngajSbuSd6xoEzn6Ep5sZdffpU1a/6CRjMfi8VJScm/M2/eVNzu\ni8AsAByO8+TkpLQ57vDh86SmXo9O15pgnpw8m6NHP+baa6/B4/Gg1+tJSUn5pHarF3Cj1Zrxej3I\nchMGgwGXyxWwQkK55PraAtZdXH/99SxY8CHbt69CoxmCRrOfxx9/pMPjOgqm8fl87Up/Ccuxs8EZ\n/ZHudKM2NTWRmZnJ+PHjGT9+fEDAOqKmpobdu3fzyiuvUFdXFwgkS0tLo76+HmitCDRu3LjAMTk5\nOVRXV8fxbELT74VPIEQk3APTlcTzRFpRysR4ZZJ3UlJSTH0FExHd6vf7A+kcse4tbtr0AT/5yR+B\nO4ALZGV5aW7OZ/ToC2RnH6eyshrQkZV1hpUrv9/m2OzsZA4frgLGAjItLRdJSmot5mwwGNDpdEiS\nhNFo5M47Z7Bu3e+BkXg8F1iyJI9hw4a1OZfg6ifQ9RDxvkpVVRVlZRcwGrWMHz++XZCDVqvl6adX\nc/DgQaxWK8OG3cnQoUMpLS3lyJEjpKens3DhwqhfzjpyvwFhgzOUghgtvWUfrrPjJpJQwtfZqi1O\np5O77rqLn/3sZ6SlpbV7CRYVmoCI/5YoBpTwhco3i0ellUS7OoWFB13vHB/P6FYxr66mc/h8Pn7x\nizfQan+IXr8Qr9fPpUvfxmAws3mzxIgRGh56qIiMjAwmTVpBampqm+NvvnkRhw+/THn5Zfx+L/n5\nldx22yqSkpKw2WxtPjtnziwKCgZ9Uq9zUpumsOEW3eAQcWVtTJ/Px5UrV9BqtTH1K+vNlJeX89JL\nHyHLk/H77Qwa9Ce+/vXb24mfRqMJVHCx2+188MHfWb36d/j9i4CDTJ/+Ab/+9VNdrreq0+na3Rtx\nT5S1MLsSut+X6K7zsVqtnUqxcblc3HnnnSxfvpwvfelLQKuVJ6JDrVZrIFc1Ly+P2trawLG1tbXk\n5+fH9wRC0O+FLzjCT2lB9ebSYuJHLayOeHeO78q8lJanxWIJuF1j4cqVK1RVVWGzecjPH0VFxTk8\nniz8/ixMpoOMG/cijY1HuXjxMn6/np/97DVSUkzcc89iRo4ciSy3Vnz57ne/SHl5OWazmXHj7mwT\nrBJ8b4YPH86QIUPweDwdXs9ILrnm5mZ+//tN1NSk4fe7KSzcw513LgFIyKLrcrmorq5GkiTy8/PR\n6/UcO3aSo0cvYjTqmD17QlzFd/Pmj7FYlpCRMRSACxfgxIkTXHfddRGP+9nPXsJk+h9MppHIsp+P\nP/4Gu3btYv78+XGbG3T8ohIpdF+Zf9gXLb5ErgOhLL4RI0ZEdazdbue2225j8eLF/OAHPwj8/aJF\ni1i/fj0PPPAA69atY8mS1t/J4sWLefzxx1m1ahU1NTUcOHCAGTNmxPeEQtDvhU+J2IvyeDy9trQY\nfFrGy+fzodFoSE1NjduD3pW5BpcXE0LscrliGm/t2j/yv/+7EY1mEJWVl8jOfoshQ27n7NkS9Ppt\nzJr1cyyWAhyOavbsKWHnTolBg75IY2M9Tz21gUcf/UIgWnTQoEEUFBSEPN9EUFdXx5//vImysiIm\nTFiEJEmcP7+DI0dOMHfuZ8IuusERjNHS0tLCunVbqKvLArwMHnyEceMGs2nTFTIyrsXjcVJWtpMH\nHlgYt9QIh8ODwZAc+P8ajQWXyx7xGJ/PR0uLPSCWkqRBkoq4ePEiZ86coaioKKEVcaIJ3Relv5Sh\n+8qUi3i8qPTlgJxgOtOZYe/evWzfvp2LFy/y2muvAXD77bfzzDPPsHLlSp5++mmGDx/O2rVrAZg/\nfz6LFi1i/PjxaLVann/++W6JsO73wqesBCLLMi0tLb2ytBi0htzb7Xb8fj9msxlJknA6nXFdvGOZ\na7jyYl0Z8/Tp0/zv/75HWtqL6PUZwD+pqfkuhYX7mDTJgyxfQ1raKOz2Kmy2Evx+F4WFn6O29gRX\nrpynpcXF7t27ue2222K2hGO9Z2fOnGH9+kMcO9ZMc7MWr/ckU6aMw2TKo7HxDDqdLuSiKwI2jh07\nwbZtJ/B4/EydOoQ5c64LNJ0Nt+ju2XOExsbRFBa29gS8fHk/p0/voqBgJcnJ2Z/8XRPnzl2ISvii\nKQZw3XXF/OUvO8jNnYfHY0OjOcKIEUsiHqPRaJg1awq7dz9HevpXcTpPU1//Dr/4RRJJSf8kK8vB\nb37zU4YMGdLhHIPn25XfgdI6FPuNwusj/l1ZBzN4PzfWBsB90eID2ll80e7xLViwAKfTGfLfNm/e\nHPLvV69ezerVqzs/yS7Q74VPhLMnsrRYV4VPKSwmkwmj0Rj4Ifbkm2NH5cW6wuXLl5GkCZ+IHmRm\nfgaPZxAlJS/h9XrZunUH77zzLAAPPTSPbduMHD36Fyoq9BgMM7FaZd59dx+33HJLm+aubrcbu90e\niOIMRyzn4ff7qaqq4qWX3kWvX0529iWcTj+XLrkpKLiCzXaKIUPa74UoF92KigpKSi6QmnoDyckW\ndu/ejcFwiBkzrglYh6GsQqvVSVLSp/0ATaZs6uq8+HzewN/5fB602k/FzG63c/XqVYxGI4MGhW5z\nFInZs68D9rFv399ISdGzYsX1Ue2//Od/PsITTzzLnj23odVKmEyZ5OSsR6tN4sqVP/Poo8/y2mv/\n3en5xBtxbZViCOETu5V7h/01yCmUqPa3XnwwQITP6/UmvLRYLAIlBC8RwhKOaOba2fJisZx/UVER\nfv/ruN11GAxZWK17yM1tvUcbN27CbveyatWtzJgxHb/fT3KymT//+TnM5qfweiE/fyySpOHs2bNM\nmtRqBR0+fJQ33tiGz5dEZqaPr33tVlJTU+Nmjf/pTx/w8cctlJScx2SqoKAgD49nHx7PLqqqkrjp\npmsYP35cxHEOHTrOwYN+TKZq9HovY8YM59y5o8yf3/oyFtyaRkSV5uUlc+zYMczmTCQJWlpOccMN\nE9m7dydO52Q8Hgfp6WWMGnUD0BpK/oc/fITLNQifr4np01NZunROp54vjUbDnDkzmTNnZqeuVXp6\nOmvW/AcAa9eu5Ze/lNFqW91XKSkLOXPm5U6N193EEuQUqt9hX9s7DDd2f+vFBwNA+MQ+HnRP8nY0\nD2S0wpKoKivh5tqd3dhHjhzJd7/7Odas+TqSlIPF0sDjj3+Lxx77P2pqpmEy5bF9+4fcd18tc+Z8\nhmnTpjJt2jDAjcmURGHhNKqqzgTO4erVq/z2t7vIyroPszmD2tpSXn31Hb7znX8JfKfP56O5uTmw\nP9kZTp4s5eTJZFpaisnK0tPcLOF06jAYJjNyZD0/+ckXSU9Pj5jc6/V62bXrBH7/NNLTp+Fy2di/\n/wNuv/3T+xDcmkaWWzt/XHfdVGy23ezfvx5Z9jNzZhGzZk1j+PAaysouYzLpmDx5ceBZLynZg1Y7\nh4KCQvx+P/v2bWLMmIsMHTq0U+fdVYqKitBo/ojPtxKtNomWlu2MH1/U6XF6OlCko7zDUGW/oDUg\nKd7WYaKFLxi73R54rvoL/V74IHEdGpTjh0qVCCZY8CwWS9wtqVjoanmxWOe5YsUdLF26gMbGRvLz\n8/n444+5cmUMBQWfBcBmG8Rf/vICy5cvQ6PRsGLFfDZu3ENy8kyqqrZRWHiVkSNHAq3CJ8tDMJtb\nXTI5OWO5cGETTqcTo9FIfX09r732V2prtUiSg5tvnsTEiZGtMyVWqw2DYRBOp4ehQ5dQVbWdhobf\nkZlpYtKkwqjeiG02Gykpw8nPb6aubhsajRmf7whTp97S4bF6vZ6lS+eyeHHbgIyhQ4cyZMiQQEi/\nzWZDo9FQU9NETk4Ofr+MJGnQaLKx2yMHpsSD4N/AnDlzuPvuQ2zYsBKNJoPMzGaeeOI/2xyzY8dH\nvPvuRyQlGVi58taE9kCMN+GsQ7FfKElSh9Zhb0u1CDWXvtYZoyMGhPAJRFRnosYOt/j7fL5AE9ho\nBC+aMbuCclyx/xnPbuydIbjivyxL+P0+JEmD0ZiE260NzOnWW5eRnb2bEycOkZOTwtKlX8Zkaq3W\nkpaWhixX4/E40WoNXLlyFqOx9QXD7/fzxz9+QGPjDAoLx+F229m4cQPZ2RkUFxdHNc/Bg3Nwu0+S\nnT2Gs2evfBJ8lIok5VJePog//GETt9++MOK+Ymv3BS+TJ8/DZmvA5WrB7y/sVKCH8v5EskCKi7M4\nc+Y4eXkTcTpbcLvLSEqagdvtDoTxd8dLlSRJ/OAH3+SWW5by5pt/5coVH7/5zV+4//4bGDNmDO+/\n/wGPProBne7L+HxWtm17gt/+9j8DRQX6IuK+iAIKgnAFEjq7d9idrs7uek66mwEhfMpw5UQ0o1V+\nh5J4uQ4T8aALCy/W8mJKonmhuHz5Mlu2bEWWZZYsWdym151Ihi8qKsJi+Ts1NTlYLHk0N/+de+/9\nNGdMo9Ewd+5s5s5t3wA1Pz+fW28dzYYNL3LqVCN2ew0TJxbyzjsfcOONC7h4sZG8vDEAGAxJaDRF\nNDQ0hJ1vbW0tdXV1mM1mioqKKC4uZtmyBv7+9z1kZFRw+XIZFssNXH/9fPLz87h0aR/Hj59i0qTw\nVqTBYOCWW65l48Zt+P0Z6PUNLF9+XVRuJBGc1VFglrBAli+fxzvvbOf8+dPodF5uv30ygwcPbrPg\nirD+7uia8P77e2lomM/QodfT0lLF88+v49FHs3n99fcwm79HcnJrIYGammY2bdrCqlUPBo7tDdsT\n8Rg32r1DkQbTE4E04a5Hb7JI48GAED5BIl2HyrHjJXiR9uNiQQiMcMV0V+uic+fOce+9j9DUtAyQ\nePHFb/PGG/9FcXFxIDdQkiQKCwt56qmHWL++hKamo1x//QQWLYo+6XnRorlcuFCJz1fAqFH/il5v\nZufOtxk27CR5eSk0Nl4kM3MYXq8bn6+S5OTJIccpLT3Nn/50DFkuwu8/z4wZ57nhhnnMnHkt06df\ng8/n489/3k59/TRSU1ujJY3GTJqaLnU4x+HDh/G1r+XQ1NQUKHweCb/fz86d+zl1qhmQGDcunUWL\nZnV43ywWC1/4wk2BRsTBLzZOp5Pm5maOHTuD0+ll1KjBAWEMteB2xRPg9XopLa1lyJAHkSSJlJTB\nWK0jqKioCPF7DP8b7W+LL0S/d6jMCRXHeDyehAuieDHqb6jCF8ex/X4/drsdl8sVN9dhPAVPVFjR\naDQkJSV1Wx7jyy+vw2ZbSW7uPQDU1WXzwgtv8u///u12yfD5+fl89asr0Gq1ATemEq/Xy9WrV8P2\nsGts9DJ8+A0Yja1WlNFYTHV1NXffvYiXXy6hoiIDv9/K0qVDQ3ZYb22seoDs7M9jMqV8EhjyF6ZM\nqSEvLy/wxj5iRA5lZSewWDLx+bw4HKcoKhoW1fWyWCxRN/YsLT3DoUNQXHwLkqTh2LG9ZGYeZ/r0\nKR0fDCGvIUBzczPr1u3A45mMTmfiyJEj3HmnhuLi4VGF8ncm0bs1b1aHzXaF5ORB+P0+fL4rWCyj\n+eIXb+A//uNZvN4H8fmaMJv/zA03PBHVucWDng6aCUco6xA+3Y8Xe4fKFk/xKK4ejzqdfYEBIXzi\nRiZK+ISLwmazxT0asitzDlVeTKfT0dzcHNfr0NEcm5sd6HQij0xGo8mhvr4Jo9HYLhn+1KlTbNmy\nD71ey8yZE9ix4zAXLtQzfHg2y5fPYd26v1NZqUeWncybV8Add9zU5vjCwkz27z+DxZKN3+/D7S4n\nJyefvLw8vvWtO2loaMBkMpGenh64Lkq8Xi8uF5hMrZZY6yLf/rPTpk3GZtvL/v1vodFILFs2mpEj\nR8RcxSYc1dWNJCUNQ6NpXQDT0oZRWXm8y+OWlZVjt4+mqKjVNdvUZGHPnn2MGFHcoTsuUqI3hH5Z\nu//+G/jNb9bS2DgKn6+auXPTKC4uZsSIEZhMRv761xKSkozcd9/qqMtjDUSULx7KRPxw1mFwtaCO\nrMPgwgZWq7Vdbdz+wIAQPkG8hU+ZHC9JUqDXWDyJdc7hyot1ZcxYWbZsNtu3v0pLSx4g4/G8yq23\n3t6udNWJEyf5xS82o9XOx+fz8NJLa5gw4U6GDr2T48ePsXXrf2MwTKe5Wcbr1XLp0iFGjSpsU2j6\nxhvnUl39Fy5ePIssu5g9O4uJEycArdaP6LYebk/SYDAwbFgyly4dJi9vIk1NNej1VeTkXNPmcxqN\nhvnzZzFvXmuOm3gDjzeZmUk0N1/E5xuGVqvDZqth7Ni2z1gk66Kuro7t2w/T2OiguDib2bOnYjAY\nPon2/FTcWve/Q1+TaNxxYr8QCESWKhfccePG8uij2Z8UBx/BiBEjAnNevHgRixcvCnsNuqNSSbzp\nzgCUSNZhqL1DcS+jaQDc1NREenp6Qs6jJxkQwhfvBV8peCI4xOVyJeRB7+ycg8ueBVtUiSDSHP1+\nPwsWzOPb377C+vWPI0kS3/72zdx8803tPvv++/tISrqJtLSRtLS0YLcvxun0YTAkU1Awi3/84wV0\nuqtkZq5Eo9Fz/vwb7Nixu43wJScn89BDK6ivr0eSJOrr6ykvL2fkyJGBhbsja/zzn19ISclOysoO\nkpGRxJ13Xh82ACWR19bhcHDuXDXl5cc4cuRjRozI4Zprcrn22nkAnD1bxubNB2lqsjFxYgHLls1v\nk59os9nYsOEfwHVYLBkcPHgSl2svn/3sHIYPL2Lv3n9y5YoFvd5EU9Mhbr01ughXgXLB1ev1gZxD\ns9kcSK9QWh8Wi4WxY8ei0WgCe4mhrp9wySeypqegr4pqNITam+3IOhRji3JuVqtVdXX2dcQCHevD\nHkrwxFtWd6QeRCK4vJgoe9aVMbuC8loZDAa+/OV7efDB+0J+1uv1cvDgQc6eLaOpaQLp6RI6nRZZ\n9uL3tyaE+3xu/P4W/P6xmEytQRg63Thqao7h8/mora1Fp9ORlZWFVqslIyODNWteo6TkKi6XlsLC\nP7B69b0MHjy4zY88VICAxWLhrrs+G9N5u1wuLl26hNvtZfDgvIh1M2W5tbFquL3W7ds/prp6OIsW\nLcBub6G2dgdz507CZDJ9UpllL5cupSBJQzh8+DhW6zv8y7/cETi+trYWhyOPgoLWvczBg6dRWrqB\npUv9pKens2LFbA4dOovb7WP8+BGBnMiuEm7BFWIYLnLR4/Hw1FPP8eGHh5EkP/fcs5iHH34wzLf0\nbhItqLGOHc46FPfH5XIhyzKlpaUsWbKE3Nxc0tLSMBqNTJ48mcmTJ1NcXNzp7y8pKeGHP/whHo+H\n++67jx/96EcxzT9eDDjhiwXx9uNwOMI2W01kjmAkOlteDOIvfMrxlNcqmlQJn8/Hc8+9zsGDJhob\nh1Na+iJlZYtoanJjt2/k6tUxXLiQi99/mhtvnMDu3VVYrfuQZS+FhR6GDMnjN79Zz+XLOvx+N9dd\nl8ntt3+WXbt28ec/Oxg06BvodGZqanbwm9+8zS9/+cM2P/JwycWxNDV1uVy8885OTp2Sqa5uQqu9\nwoMPXs+sWe1Lfp05c5atW0/g9WoYPjyVxYtntrNwLl1qJCdnFj4fZGXl4nSOobm5mby8PHbv3sf2\n7RXk5NzF4MFDSUqawt///gK33NIU2JPR6/X4/Z8mrXs8DvT6T88pOzubZcvatzHy+/1cuXIFr9dL\nTk5OVJbXyZOneO+9A9TWNpCRoWH8+NFMmDCc4cOHA63PSPBvJtj6ePnl3/PBB3pycv4AuFm79jGG\nDSth2bLYXkI6oq/mpyVCVMX9EUUsJk+eTEVFBS+++CJlZWW43W5eeeUVSktLKS0t7VRgnM1m46GH\nHmLv3r1kZWWxcOFCli1bxtSpU+N6Dp1hQAif8iERbpZoQnTFIi5aGEUK/+9ui687y4tFg1LwNBpN\n1KkSZ860Ri0WFNyD1XoWj8fG8eP/y+TJy1m48D+prNzI9OmlzJkzG5PJRHn581y54iU7O5Pi4qto\ntamcPz+aIUNm4vf72b37r4wYcYRTp84CxRiNycgy5OZO49y5dwECguZyuQJRj6H2qzq7H3LhwgVO\nnPBSW5tJauoS7PYGXnxxI0VFQxg8eHDgc7W1tbz3Xjm5ucswGpMoLz/Gzp0HWLz4M23Gy8pKoqHh\n6ieBOn58vquYTEPZunUHW7ZcoqXFhNE4CLu9ksGDLZjNWdjt9oDw5efnM2rUaU6f/gitNhO//zzL\nl0+IuGj6fD7+9rdt7N9fhyxrycvzs3Ll0oj7PJWVlfzxj0exWJZSVlZGfX0lp0/XsW9fI/fc42L8\n+LEhjwu2Pg4fLic19Wvo9UZk2Yhe/1kOHNjPokULkWUZu93epReTcCQqqrOvVzsRcQFLlizhnnvu\niXmcvXv3Mm3aNHJzcwG48847KSkpUYWvO4lGoILD/y0WS4e1HbtL+LpaXizUmF1FuLCcTmdU10qJ\n3W6nutrJrl1baGqSkOUpyPJhysrOk5NzkIKCWzAYzlBYWMizz64nL+9WLJartLQcY+7cazl3zkp6\nevEn5wVabSHl5ZcZMmQwcAS/fzFgoKFhN8OGhc+ZC96vgk+jGZX7VcGVNhwOB7t2HaayshmHo47q\nahepqQswGjPQaMxYraMpK6toI3z19fVoNEMwGluDVHJyRlFevqndnBYsuIaNG/dQVWVBp/MwebKZ\nQ4fKeOutY+j1C5Ckt2hpOQBYSEo6yowZ5jZ5gRqNhptumse4ceU4HE5ycqZ02Kj2zJkzvPPOWVyu\nUWi1yZw+fQzYyN13LycrKyvks1ZZWYUkjcFm8+J05jN48DQcjnfIylrEtm1bwgpfMAUFWZw5c4Lk\n5AmAjM9XytCheZhMpkDpuXAvJsFi2Nf27TpDdwbONDY2djm4pbKyMiB6ADk5OZw5c6ZLY3aVASF8\nwWRmtd0AACAASURBVBFQ4Rb9WAQv+Ph4o9yXjFd5sXgJnzKQRpKksA1z/X4/77yziS1bjmAy6Rg7\nNpvS0npaa00auXTpAA7HRAyGSdTXv4FWa0Kj+TwnT/4Th+MqS5aMpaysjMbGYoqLWxPanc6l7N+/\nlqlTh/GPf5zCaEzH5XLicJxlxIhRTJ48iYMHz7J58+P4fCYKCz1tClZHe52CoxmVof0+n4+Skn9w\n5cowMjKm09BQTmXly/j9l9FqU2lqukBurgGjse3PzGQy4fNVBBaZlpY6MjLaV2TJzMzkC19YQFVV\nFWlpadTU1HLsmJPMzDEYDGMZN+5erl59H0nyMn68hbvuWtYuP1Cr1XZq7+706TKuXs1l2LBl+Hwe\nLl5sZv3693G7ixg+XGb58nntfhNmswmv9yqSlI0kaXG760lONqHR6MNGigZz+PBhRo/O45///B11\ndUeRZQdjxrRw550/bROWH+rFJFy39Y7C+LtTQOI5bqIINXZzc3OXg1vES6USt9vdpTG7yoAQPiWh\nFv1w+W6deXATGTAiOsbHo7xYPFDuK4pUiaamprDXq6TkA9544wK5uQ9y5swe3n77XebN+x4ZGRls\n2PBThgyZh8ezFat1KyZTDpI0CI/nAlCA3b6TBQu+zLlz59pcX7GwzJ9/HRcuvMOFC6VoNH6WLBnC\n5MmT0Gg0/Nu/Pcxtt52ioaGBcePGYbFYAgvlrl372bPnLKmpFhYuHB91YWSlGDqdTurrdRQVTf4k\nbWQi06bNorLyT1y9eoacHDPDhjkpLp4QqJGp1WopKipi4sQKTpzYikZjwWi8woIFoVv/iF56FouF\ns2cvYjRmM3KkhaNHd6PXDyE/fxizZvm4++7FUSfFi+sXCovFhN/f2uevtvYSNls2eXljKCy8gXPn\n9nL8eCnXXDOpzTFjxoxh9Ogyjh79GIejEq/Xx7hxc6mp2cby5cNoamqKWDDh//7vNV56aTuSNBWf\nz8hNN7m59dabmTJlCkajMZAmEeleBDf+DRVIE5zk3d1pPfGkuwJn4pHOkJeXR21tbeD/X7lyJaq+\njolkQAtfsOAF57t1dtx4IvbMRJpEPMuLxRqIo9xXVBbbDh6rsbGRuro6cnJySE1NZfv2Y2RnfwGL\nJQ+7vQWd7m6sVh/NzbuprvbgdG5l0aJ/Zf/+Rqqrq7FYyigoSGL48BmMHeskNTUVi8WC3/8ep07p\nyMwsxGrdw803D0ej0bBq1QpsNhtarbbN26lGo2HkyJE4HA5SU1MDb5n79h1ixw4nWVm3ADJvv72N\nL33J0sYdGQ2tz4obr9eNTmcAZHJzU3nooftpampBkmDIkEKSkpLaVUH5zGeuYfToOnw+Hzk546LK\n/8zLy2TPnnIGD56DTmegtHQX06bpufXWpZ1uswShn9mJEycycmQJtbU7qa21YjbLjB/fajGazXnU\n119sd4xOp2PFipuYObOcixfNVFU1AxfJyTGyY8cpNm0qJynJwxe+MI8hQ4YEGuSazWa8Xi8vv1xC\nSsqb6HRpeL0NbN78Rb773YdjTmeIJpBGuK2BNhWN4lUCrLdWhOns2FartcvCN2PGDB588EFqa2vJ\nyMjg7bff5sknn+zSmF1lQAhfsKtTLELxEDzluPHKEVS6W0V193jW1OzsXDvaV1SO99FHu/jFL97G\n4TBx5cpxZs6ciM3WglZbT0rKEAwGIz5fLdXVZ2lpycRg+AYazX727v0NY8eOx2DYQX7+NPx+mdLS\nVzAYhvHtb/+Ys2d1eDxpuFxrmTAhhy984SZmzJgeyFMMt0gq5yb+++TJKnJy5uH3y3i9TrzeAi5e\nrOy08BmNRubMGc62bR+i0RTg9dYwZUpqxJ53Svdcbm5uYCEOlfQd7MoeOnQoCxa08I9/lCDLcOut\nw9HrtaxfvweACRMymT372i4FVQwaNIivfe16Nm8+wrlzldhsZiZOvB+/34fNVk5+fnbI43Q6HcXF\nxRQUFJCUlITL5eJXv9qAXr+MwsJ8mpqqWbt2EytWXM8bb2zDbs/G729k9GgZrTYPnS4Nn89GQ8OH\nNDX5efXVt/j6178YtxyyUGH8rVV6XJ9Ev0ZuLitKtPVnQglfPCy+5ORknnvuORYuXIjH4+Hee+9l\n7ty5XRqzqwwI4YNPFz3xtgfENcG7q8IXzt3qdrsjNjdNJJHyFkNRX1/PL37xNkbjQ5w+/Tuczod5\n//0Ghg49i8/3FA7H/UhSE2bzRs6eTUKSHkarPcPChXPwei3cdpuHG254kU2bNvHWW5dYtuy/qK+/\nyltvrSEt7fM4nQbgGpqb/8q8ebUxWwTJyQZOnjzDqVPnkaRsGhuPMWbMYGbNmt7psaZMmcCgQVlY\nrVZMpmER8/YgNveceBnSarVMmjSeiRPHIcsyp0+XsXVrE4WFNwJw+PA+0tNPM3FidMEk4Rg7djRj\nxozC5XKxY8d+TpzYBMjMmDGIMWOicwk3NzfjcCSTldXq0kpNzePy5RRef70EjebzFBYOxefzcPz4\nWjSaC1itW7HZymhqMmE230Z5+Uh+/es/8sgj9weuWyLoyDqMNZCmr1p8wbjd7jatlWJl+fLlLF++\nvMvjxIsBI3wejwe73R5IGI4m360zdCU5vrvLi3U0ptLq7CiNQ4wHohlsHk5nDW73RFJSlmO376Ww\ncAE225Pcd58Pg6GAP/1pIh9+eAW93kxy8gJOnjzE6NEtjBkzmaysLJKSMikqGkdm5iD27DmE0XgN\njY0OcnIW4Xbb0OvL+OijKubOraCgoCDq825paeHixYvk5ZlZt24jPt8dmM1pFBRYKCu7SENDAxkZ\nGdFfyE/Iy8sjLy8v4AqOhXALsLBKRI1M5QJ8+fJVLJYRgeNTU4dSVVXGxIkxTaHdfEwmEzfcMId5\n85wRrepQWCwWtNpmnM4mTKZUnM5mtForjY0ehg1r7T+o1eoxGofy4x9/leee+xWVlS5SU+9nwoRr\n8fszuHixlJqaGgYNGtTBt8VOqN9qpAjfcIE0wdZ6okj0nqTyeig9Jf2NASN8wk0nHtxEJIB2lmjK\ni3Wn8CmtTkmSOhXV2lpe6xxW60G02iJk2YfH04xW68Zg0CFJFpYvX4bT6eQPf9jH3Lmr2LlzHefP\ne/F4mjGbm8jJWQxAWloSLlcVXq8Hm81Dc/NxvF4bbrcTj6cco9GG2TwCh8PB2bNnsVqtZGSEbyor\nyzINDQ389rebaW4eiscDfr+Oa65JITXVQk7OMKqrHbS0tMQkfIlCLMBChKBtikVqqonS0moslixA\nprGxkpEjtXFvVxOuw0MkzGYzd955HW+99WdkORtJusodd0xn586jVFScYNCgibhczfj957nuuiW8\n9dZ8vvrV/+by5SFcuZJPVZUdn+8oBw5ko9ebyMvLYerUqVRWVrJ9+0EcDi/TphW3C7RJFJ211KF1\nzYkm/zOWuSSCcC/tqvD1YVJSUgJ7e+GixLqKEJSOHpTeUF4seMxIVmdHNDY28sgj/0VtbTFO5yQu\nXXoW0OF2n2fixNlUVKzhuuuSeOedEsaOHcn586VUV9fR0HCJlJRb0OtzGDMmjVdeeYfHHnuIqVMn\ns2fPeo4ceYUrVy4iSbVI0nkqKo6TlZXNtGm3odMd4KWXjnP8uJvMzGEMHpzE8uVXmDt3Vpu5iXP4\n5z8P43Zfw5Aho5FlmWPHjlFfX8Xo0aNxOJrQaK6Slha6P19vQrkAT506kZqaf1BZuRdZliksdDFx\n4nUh96qCF+BEEPzsjxs3hu98Jz9Q4T8tLY3Bg/N4/fUSLl/eh0bjZMWK6wK5hRqNFY/nKFqtHlk+\ni91+lf/P3nnHR1Vm//9979TMpBdSCSH0QEINJSCgKCCoiAqKDcS2uLrF/cm6uq5r19V13V3Lrn3X\n5YvKLoooIEWK9F5DQktIL6TMZGYy7d77+yPccYiTniCCn9eLfyaXe5/73Huf85xzPudzPvywhNjY\n4Xi9B7jyyjyOHLEiiuPR64NYtGgLsiwxbNiQpobUpvG2B4E8dbU9mVar/Z4eZuMcblufx/kmt1yM\nRg8uIcOnoispzC2duz3yYl0B/2t6vV7q6+uRJKndOc8lS76gpGQ0BkMSp0+/jN1uIDIylqSkQ6Sl\nOdm1K5+VK8ewdm0J8B9iYq5Fr49FUTzU1W1iwoTr6N07i/z8byksLGTNmq0UFdWQnb0NtzsMvX48\nen13YCt6fS4xMYewWu3s2NGTmJhJVFUdwWx2smbNMYYPzwjIkLTZPBiNob77z8jIoqJiFcXFNej1\nbq6/fuiPrv2KXq9n+vTLqK6uBhrq/hq3E1I9kUB5Q4/Hc45x7AqEhoaeM68RERE89NAcbDYbBoPh\nnPBpSkpvwsP74nSeAUzs3ZtGXNz1JCYm4vUOZsmS35KaehspKf0A0GgMbNmytt2Gr6ugfj/+0RL/\nUOmPhUjjdDrb5e3/GHDJGL6uzJn5X6Oz5cW6KtQpyzI2m81Xi9cRI1xebsXlMrJ//zO43Q8gCOHU\n1LxMr14z2LBhGfn5VyCKl6MoLrzeDYSF9WbixL5s2JCDIGQRHi5TXX0avd7N6tVbWLGimLy8SoqL\nu1NfX0By8hUoSgImU3+6dfuSrKzefPFFNaGh/TGZ4gkKiqWo6H0iInS43e6Ahq9//wSysw9gMIQg\nyxKCUMzdd19Njx7JmEymdpUDXAjQaDTExMQE/FsgJqO6+DqdToCAajT+i29XLMCiKAbcZIwf35/P\nPy8iIeFyyssPotM5iY6OOnufBkCH1/td4bMse9Bq22+w1e9KzZ92BomjKQQSQ1DH0FLjX38izflW\nbbkYOzPAJWT4VJxPw3chyoupY1J3mh3V+Dxx4gSFhSc5dmwPsvwLRHEyYEAQXJw6tZSamkr0+iGY\nTIMBhTNnIsnPP0xW1mgGDYplx47/cfiwl/BwmV/84mpefHEJBQXp2GyjUZQSZDmF8vJ3CA+/F43m\nBMnJMWi1WoxGLSaTk4qKfNxuhdra41xxRZ/vLajq/KWnp2Gz2dmx4ytEUeDaa/uRmtqDoKCgH72m\nYlugGkNo8Eg0Gs33vJHGjWbb0si0I5gy5XL0+s3s3r2cmBgNwcFBWCw5hIQkUVKymdRUMxrNXoqK\nTGi1JtzuXdxwQ2aHrvnVV6v4298W43JJZGb255ln/l+H6fttMU5NbU6aItKoBrSz87jqdf2/hYu1\n+zpcQobvfHp8F6K8WOPSBKDDTXOPHz/O/Pl/5MyZZLzeetzuSkTxDDpdLF6vh9raciTJjcezHq+3\n39l8iANR/Jxly0qxWouRpGwiI2cRHz+Av/1tGSdPhlBfn4HROAKnMx+j8Uu0Wi863Tf06RNMdHQ5\nhYWh1NcfxmCwUFS0i/r6EpKSJOx26Zy6I/VZQMM8jhw5jOHDB/veBbvd3qH7/7HizJkz7N59FK1W\nx8CBPXzNedvTSqgzQ3OiKDJp0nguv3wcOTk5FBUVsWfPRgoKKsjPr8DlGo3Xe5pBg75h2LAhpKVl\n+bo/tAeHDh3i5Ze/JCzsn4SGxrJr1z958cU3efHFxzp8Lx1Bc0Qa1VNvb2PZtuAnj+8iQkfKDloD\nt9uN3W7vdHmx9o5XVYDxbxMkimKnaOW9//7HHDvmRhRHoNPF4Ha/jV6vwe0GUXyP7t3HIMtJFBSc\nRpY/QlFchIZWk5ISSVhYGocPx1FRkcWRI8dRlFGcODEAQShElmuxWg8jCMfRak8SFuYiI+MA/fsn\nU1ERzcmTaZjN3cnO/ieDB4+ib99biY8fQHHxPg4dOsq4caPZsWMvmzblIkkKGRnduPrqSU3ehyzL\nZ3vouYmJibkoO06rqKqqYunSfUhSHwwGA8ePZzNjhhKweL+lGrfGoTn12/J6ve1efBVFYfHiL9i2\nzY0oJuFweMnLyyEh4WGSksYjSU5yc19l7tzeJCcnd2gucnJykOUrMBgaag0jIm5h1657OnRO9R66\nYm1R57NxmUVTjWXbSqRpPO6fPL6LCF1VVOpyufB4PK2qe2sL/BeUtrK//BVg/MfkL9nWkfnYvz8X\nr3cKJlMaOt0kFEWDyfRPzOYYBg/+MzExY6ioKKSiYh5u92mCgw3ccUcWFRVpOBwZuN1lhIT0R5Yr\nyctzIkmhGI02JKmM6uptgIAs1yIIbgoLe5GTc5qoqOFERgaRnNyHgwfHExkZQUJCGgCiqMfjsXP0\naC6rV1eSmDgLQRDYvHkVCQmHSUvr973O37Iss3r1FnJztWg0oYjiVmbOHNym+sAfCzweD/v3ZyPL\nqURFJaHX67FYNBw+fKrVqjXNhebcbnfAGjdo8FBMJlOLi29hYSGbNpWj0w2lqiqfY8dqKCrqSU1N\nNnV15QwYMAtRTKCsrIzu3bt36P0NDw9HEHaiKDKCIOJw5BIff+GUs7QGgZ4HcI4xbK233ng96Ay5\nsgsVl4zh83+gnZUkDiQvptFoOlVerD1Qi/WBgLV4nWX8NRoFp/NLXK4ioACdrgcTJowlISGJ7Gwr\n2dk7KC09gSybGTfuLmJielNa+glQRU1NHZGRPSgtPYyi1CDLdpzOLxkwoA8HDmwiKOhaNJoYjMa7\nqK5+j1697qC29j8oSjf27y8gPj6O+PgIbLZDWCxDz3ZoP0D//hPYt+84wcED0OkaGIMN3uURX+G0\nuiArikJ+fj45ORqSk8cjCAI2WzLr1m3lzjsvLsN35swZVq7cx9GjZygtLSIzUyQhoVennNvfE4Hv\n6v5kWebrr9fy4YdrcbsVMjISeOCBWwkJCQlI3FDHeeTIMUQxjeLiOgQhHJ2uG4JwFcXFK5Hl9zl1\najl//nNfBgzYwUMP3dqiWk5TGD9+PJmZW9i791cIQjx6/R4ef3xhh+fjfJccBEIgpm5LRBp/pRqN\nRoPFYvnJ47uYoLIa25t3CyQvptPpfHVwnY3W5vn8C+JVpmJL9YHt/UCrqqooK/NiNP4CSeqOLFuB\nXzFnzu8YOTKTa655AJstCkXREx29kBMnNpCSMg6LJYXMTC3Ll3+BxRKNTncUnc6CzXYQszmJggIP\nXq9CRMSIs+17wqmvT0CW7URFDcZm2wD0oahoP8nJFVx++Thyc3eg1YpMmJBFQkICeXlF1NdXAz3P\nbkyqCQ9vYK6qDEaV5NNA9DH7xMA1miBqaxvKO7qSyHE+0WCA9qHRZJKebqCm5jDbtm1n9Ggvonia\njIxOkHoJgNzcXN59dw+xsU+h14eRnf0ZH3/8FQ8+OLdJObC8vCI8njRCQoYgCCKSFEJ4+DeYzQWU\nlR2ntvYYo0e/QHLyAAoKNvGPf3zK448vaNf4dDodL7/8BAcPHsRutzNw4JwuVYnpDHRkfWkNkUaS\nJNasWcN9991Hz549SUhIoL6+nsGDB5ORkXFOv8dAmD17Nnv27EGr1TJlyhT+9re/AQ0CF/Pnz2fv\n3r1ER0ezaNEiUlJSAHj//fd55ZVXAFi4cCHz5s1r9z22FpeM4fNfwNTdfnvQkrxYe7oetIS21Ae2\nVBDf2nO2hDNnzmA0JmA0/o+6OhdgJyoqlAED+qMoCgkJvZCkG6irE7FauyFJX7N37z9xOvMYO3YS\nDz00lKef/g92+xjs9gL0+mHMnHkHVVVn+OqrFzAY9hIWdjlFRfsxGErwesFohORkMxrNNkaOHM9l\nl11NTEwMY8eeO7ahQwdx8OCXHDtWjkajIzS0hAkTZpzznNQQT0JCAgbDdjyenhiNoZSVHWHAgEif\nTJh6bOMQ0Y8JTqcTu11DQkKDwPT48YPYvfskCQmnyMoa0mWLfV5ePoKQicHQEC6LibmCgwdfbLbh\nb0VFFXFx0Vgs+9DpLEiSBa22gpCQAxiNxYSF3UhERBK5uSeAOPbvP9WhTaxWq2XUqMAtodqLrvT4\noHPTNY3LLIxGI9dddx0TJkzgueeeQ6/Xs2/fPj788ENEUWT79u3Nnm/evHl8+umnyLLMNddcwxdf\nfMF1113Hyy+/TGpqKosXL2b16tX88pe/ZNmyZeTn5/OnP/2Jffv2IcsymZmZTJ8+vckSnc7CJWP4\n/NGeRf+Hkhdr7rxNtQk6H4iLi6OwcDeS9ATR0dNxuU5itd5LWVkZGRkZKIoFq7WO2NgxVFQU4HQe\nJzc3iTFj7mbHDjd2+3LGjFlIREQa27cvxmodRXFxJUOGpFFffxM5OR8C24mLqyE5OZba2n8RHS0y\nevRQbr314YDhLdUTd7vdzJp1BTU1NYiiSFDQAEJCQnxtaPwRFhbGzJmDWbduG7W1HgYPjuGyy7LQ\n6/XnLMhNdWBvLFbc1VqK7YHRaCQoyIvNVk1wcCR6vYbU1BAmTRrTqUX7jRf8iIhwZPmwL4dWV3eK\n7t2/HzpT527Xrr3s3l1NSUkZijIGs9lBbe0GioqCqKhwEhxcQ3T0Ho4dSwFS8XgK0GrzKC4upnv3\n7h0e748BXSl+7Y+wsDAkSWLevHkMHTo04DGBMG3aNKDBuUhLS6O8vByAb775hr///e8ATJ48mblz\n5wKwfv16pk+fTlBQQyPmqVOnsnr1am677bbOubEm8JPhawGqsskPKS92IdYHmkwmoqOjKSuLxmLZ\njqJYiIkZid1uJzg4mKuuGk5u7mdYLEuRpFIEwUZExEiGDWvooL5x4wbCwuowmczExydTWXkYj2cg\nHo+TsDALf/nLL0hPT8doNOLxeNBqtRgMhibvM1CYNzq6wcNRlU2aQmJiYsCcXqCi45bEigEfyamr\nCsDbClEUmTJlCCtXbsdqNaPROJg4MZXg4OAuvW5mZiajRx9g166XEcVITKYT3Hff/IDHyrLMZ5/t\npE+fn1NdvYYtW5bjdmcjy30wGKYRFBSGLCeSl/cKBoOWkJAhGAynSEy8ieXL1zF37qxzPPMfUv3k\nx2hQVTQmt/jn+NpyTw6Hg2XLlrFixQoASkpK6Natm+/voaGhVFVVUVpaeo53FxMTQ1lZWUduoVW4\nZAxfIHJLc2iPvFhX7vgvtPpAjUZD9+6xVFQcxO3ehqI4KCoq5M0387nyyiuZNGkcixZtRZbHERd3\nLfn5u5GkHEpLdxMfP4LoaDMu1w6qq3sQHd2dyMhlGAy5lJWFMmNGBgMHDiQsLAxBEJqVTVJ1EdsS\n5u0ImqqxUg2hy+X6wQrAm0O3bt2YM2ciNpuNoKCgLhFqbwytVsv/+3/3kZubi9PppGfP65pkCTZI\neCnk5BSybVsVgvAoirIAQZiBJI3FYrEhSdmIYjx6fTA9eyaTlDQdq/UEgnAYo9F4DqXf6XQ265lD\n13c66Ap0pcfX+LxN9eK76qqrOHPmzPd+X7FiBfHx8SiKwvz587nzzjvp0+e7NlaNmadqSVVTv3cl\nLhnDB+cSOpp66RvLizVQnluvwtBVH5OaW9RoNJ1aH9geeL1eSkpKqKoqpLJyI3A9EIkoOlm37iT/\n+c8i7rjjdvr1i+fw4UQEIY/u3Q2Ul4ezefNn9Omzn0mT4rjiikzWr98GwPz5C0hNTUUURXQ6HTU1\nNQE/xrq6OvR6PVarlaKiIrRaLb169Wp2E9BZLN7moC6qLpfLF7ZpTQF4Y1ZjV0Kv1xMZGQk0tGg6\nHxBFkQEDBrR4nE6nIz5ey6ZNG1EUAVEsBPTATiQpBkHQIgiHCAsLQaNxUFBQQ2RkGW73V4wfP6NN\n6ifqvKvHdPa7cb7CkZ197sZjttlsAUPha9asafY8999/P5GRkTzxxBO+3+Pi4qioqPBFYWpra+nW\nrRtxcXHk5OT4jquoqGDw4MEdvZ0WcUkZPhWBDNSFED5sDDVn5fV6EYS2tQlqCe0Zq78X/MEH/+X0\n6W4IQgKKogFqkOVf4/Ec4sknX+fqq6eSmTkAQQglJGQQmzcfQa8vx2yux+U6zMiRcxg4cCADBw5s\n1bUPHjzIXXc9yPHjxwCF9PTLuOyyB9Bqaxk3roqpUyeelzlo6/nb0uT0QgnV/VC4/PLhbNy4lsrK\nbVitqxHFECTJjCD8BwhHry8nM/NeBEEgL++fpKRkce210wK+Q0155v6bEbUcyeVyXbBC0YFwvoyq\nWgTfWkiSxPz58wkNDfXl81RMmjSJjz/+mKeffpqvv/6aQYMGodFomDhxIq+88gpPPfUUkiSxatUq\nfvWrX3X4flrCJWX4/Hf+KvvyQgof+sOfParVas9hwXUG2jLWQCSaAwdOYrUKKEoc8DXwDWBCFHsj\nSafYvHkzN998NadPf8ju3euw22XS0rozcuRvsVgK2LhxHaNGNa2z6D++devWcf318/B4rgbWAbUc\nPnwj6elnGDZsFlu3LmXw4FLi4+M7Oi1dDn9KeVPqG43rqzqqhnK+0BFPJycnl8OHT6HT1RIfn4ko\nDsBiOYFWe4To6BAk6RCxsb3Q64NwuXL49a9nM3v2dW2+jv9mRA2Pq+tBU/PfVimwxpqXnYXzyRZt\nzzpWWFjIf/7zH/r16+fz8keNGsWHH37oK1Po168f0dHRfPTRRwCkpqby8MMPM3z4cBRF4ZFHHqFH\njx6dc0PN4JIyfCr867j8pbw6Gj7sDMMXqE2QKip9vtHUpqCqqooDBw4iSf2BAqAWOAlE0RDl82C3\n24mKiuKpp37Ohx/+Hxs2hDNgwI0IgoiiSGg0rV8Y7rvv/+HxRAO/Acw09Pq7jRMndjNixC1oNBHt\n7nx+IaC5+iqv1/u9UF0gz/BCNYatwd69+/noo8MEBY0kKMhCZeUekpKuYsiQfgwYEMvWrU8jinGc\nOhXE8eOv0K1bBbfd9kinXb+5+fdn9HZWT70LEU0Z1bbcV0pKSkDmNDQ0Jv7kk08C/u2ee+7hnns6\nLhXXFlxyhk9dTDweD0Cnyov5X6OtH0LjWjx/Mk1X1Ac2Z6T9FWkC5RSPHDlCaOg4amoO4PX2QVEu\nA55EEK7H46nE6dzNV18JTJo0ibi4OK6/fhpHjiyitHQnGo0Rp3M9U6ZM8l3r3XffY8mSlcTEhPHS\nS0+TlJSEIAhIkoTT6aSqqhRRzEKWtwPpgIAobiYycigWSylabSkxMa3zHn8sUI2ZVqv1vRP+/X9j\nNwAAIABJREFUeasfsotCZ+Prr/cTE3M9ZnMM4eE9OHnyNAMHhtO7dzqVlQXU1Djo1u1nGAyhREQ8\nisPxPH/96zqSkxM7lA9q7jsNxOiF70uBBeqpJ8tyl+Tgz6ciTEdqI38MuKQMn9vtxmaz+V7ollQI\n2gr/Wq7WvqAdIdN0BE0ZA9XgNZdTNJlMCEINWm0K4eHPoChgs72GRvMRqakjGDHic6qrD/DZZ6tZ\nsOBO4uLi+N3v5rBx4y7cbokxY6bSt29fAH7zm0d56621KMrDCEIuK1ZcRnb2VoxGI2VlZeTk5NCn\nTxrHjqXicr0ErAJOEx1dR69eWWi133DnneM7/VleiGhqMVY3Kg6Hw9dTzn8xPh/d1zuCmhoLJSV5\naLVn6N69G/369cdmW0xBwT4OHfoGi6WeysoSwsNT0WhC0GiikKR+ZGfnnhcihD9akgJTPUSXy4Xb\n7e5U8YPzuXmzWq1dXu7yQ+KSMnzqYi6KInV1dV12jda8oG3JLZ4Pj6UtcmdDhw4lNraao0cLgJ1o\nNBAVNRmv18bAgfMJCuqG0RiLxXLM938SEhKYM2fG98719tv/h6JsRBD6A+B0FvPEE0+wYMECbr75\nHurrk/B4rBgMyxAEG7COe++dx7PPPtOubvHnE06nk127DlNSUkdERBCjR6d1SZf36upq1qw5gN2u\nwWDwcOWVg4iNjQ3Yfd1/8ZUk6QcncZSWllJaWsnJk3sIChrLsWNrGDashIUL5/Pee4uw2bohCEE4\nnUcpKztNdPRwgoMlNJpKIiLarzXamd9T47ytugHxzx12VsPf8+XxWa3WLnlXLxRcUobPYDD4WHRd\nZUhaMlKK8v02QS2FRbrC8KnnbI/cWUFBAVVVOmJirqGubhcwEIdjFWFhxzAYwqmvr6SubmWz5BVF\nUdizZx+S5AFC/X4PxWYr5be/fZaqqgfRahegKB5EcTZPPjmae+65p83enXqvjTcWXb2h2LBhDyUl\ncURFDaa8/Axff72H668f16kkJUmSWL36ABrNMBISonA4LKxevYObb47AYDAE7L7uHyZtisTRHs+k\nPaSOvXuziY6eSXS0QHl5Dh6PndTUaJKTk9m2LRet9kaczmUIgozHswGr9RMSE6eQng6XX97xvFBX\nGZLm8oZNhapb8s67OtTp/+wu5s4MABdvELcZqAteVxabB/rN5XJhsVjweDyEhIQQHBzcqlxAVy3Q\nHo8Hq9WKKIqEh4djNBpb9WGdOHECQRjGwIF/oHfvEcTHlxMSspXHHpuForyO1/sq99yTxsSJlzV5\njq+//oZ33z1KcHAccBeKsgVFeR9BWMKNN95Ifn4Ronjl2aMFbLZannjieZKT+3PbbfficrnOOZ//\n/LRmrtavX88ddyzgjjt+xrZt21o8vq2or6+nuNhDfHxf9Hoj0dFJ1NWFYLFYOvU6DocDh0NHSEiD\nhJvJFIbHExywTk9djNWctslkwmw2ExQUhFar9ZXPOBwO7HY79fX1voL8rtgsut1uNm3ayc6dJ8jO\nhtDQoaSlXU5YWENrIJ1O5PTpxej1fyEs7DnM5lcJC0vkwQcH8tJLj2IwGDp1PJ2F5ogianmFXq8n\nKCgIs9mM2Wz2qRKpIgh2ux2Hw4HT6cTtduP1es9rqPNi7swAl5jH508W6cprNF6EA3Vy6Mg5OwI1\nxKq2UmpP+Ua3bt2Q5VycThtOZwZOpw6328OXX+6ib98kfvGL25vt7ybLMitWHCAu7n7mzh3Hv/89\nk7q6mxBFmV/+cj6zZs3i//7vCzZu/DeK8jRu95+RZRmt9jSCYGD16jt5/vlXeOqpxyksLOTttz+j\nstJO797RREdHUlJST0iInhkzRgdsVrp27Vpuu20B9fXPABLr19/OZ599RFZWVluns0lotVoEwYvX\n60ar1Z/d6dd3OunBaDSi07lwOm0YjcG43fWIoh2TydSq/x8ob9ia4u/OINFs2LCNurq+hIRYABOH\nDh3D4TjE7bdfh8fjYcyYPuzY8Q2yLKHR5GMyScTHD6VHjx6+XGZ7cSHJijXlHQYqsQB8324gNZr2\nIlCo82L2+C4pw+ePrlLz8D9fW/JmLZ2zo4avMVNTla1qT0grIyODa67pzWuvTcfl6oEsZ6PVuqmp\nGcOxYz34/e9f5803/xBQakxRFBwOBydPHuHbb3+DTmfgllvexeHI5qGH0n1khVde+SO33no/+fkD\nkKRaRPFv6HQNH6LHcz+bNv0Fq9XKwoVvU109FZMphW3bVhAXd4j4+ES++up5/vAHO3fcMZsnn3z0\nnPl79dX3qK//M9AghFtfD3/72/udavh0Oh2jRnVny5btaDTxSFIV6elmIiI6t9GpTqdj0qQ01q7d\nRm1tCGBj4sRePvWY9qA1xd+BGI1q2LS139WpU5UkJo4nMRHy8w9QU1PI2LHRpKam8tZbizh1qjfh\n4Tupq/svERFXEhJSjdO5F5drPDab7YIlX3TGuhLIGKoybBqNptM3JI3H/JPHd5Giq8KHKg2/rq4O\nSZIwGo1drh/ZEgI1plXDJ+2BIAiUlxfjdNYAwUACipLBwYP/ZNaszZSVbaa4uJhevb4jH/gb3rVr\n17Fz5wFcrt8BTo4fn8stt8ygb98bfcd369aNjRu/pLy8nBde+AuffroLmH32+jvo0SOeAwcOUFGR\nTErKeEDh9OnxnDjxB7Zt+x8ezxIghI8+uoeQkNf54x8f83VucLmcgL/XrcfrDVx/1NZ5Ue9VEAQG\nDuxHdHQ4tbUWzOb4Tu/qrj7DhIR4Zs+OwG5v8PRa6+21Fc0p0agkGrVUqDWybBqNi5ycnSQmjmTg\nwCkUFa1l0CAzeXl5HDyokJJyK9HRE1mz5ikKC9+hqioYrTae++9/kyFDEnn22QdIOdvT7UJBVysB\nqc+guQ1Je6TxGo/barWSlJTUZffyQ+OSMnz+D11lXHVm6Mk/Yd24Fq8jaK+RDlQM31F1BoCTJ0+y\ncuVx4F202iw8nm3I8hu4XAouVy2SVHuOx+GvQmM2m3n11XdxOpNQlM/Qaq9CUR5ElnPO+T/qR96j\nRw+effb3bN58DWfOTEcQdISEnODZZ7+goKAAqEeWvYiiFo1GwmLJx+P5DYIwFFBwu59n+fJfs3Dh\nrzh9+jRz5tzL6dPHgP2AAHgJCnqc++57vcWdusViobS0DI1GpHv37s2KZ6uIjY3tkn53hw7lsGtX\nEbKsJT5ew6RJmVitdWzceARFgSFDkujdu3M6rDcHf89EkiR0Oh0ajeacMF2g3oaHDh3h0KF6ysuL\nOXkyj7CwOmbO7EVm5jhOnTqFKDa02AoJSSQpaSS1tb3R66diMg2mvv5jCgtLeO21Rbz22uPtGvf5\nVEHpLDRFHmpuQ9JUw99AJJrGHl9r5QR/jLikDJ8/VCmozoC/zqcoimf7n7U/1NQYbTV8jTsWBDLA\nHfkwCwsLMRoHoSjv4/G8DMSgKIdRFDtbt97KbbeNJT4+3scYVVs66fV6cnJyOHz4OB7PcwhCErL8\nAnp9PDpdZJPXi46OZvv2tSxZsoQdO3bQq9ccFEVh0KBBpKauo6DgP+j1PdDpNhEVpcHhOAkoCAIo\nyknCwkLRarXMn/8L8vNvQZYXAm8gig+QltaXxx57lXHjxmG325tkOFZVVbFs2R7c7hRk2UVExLdc\neeUQtFptl4ggNIeysjK2bTtDXNwVaLU6ysqO8dVXGzhzxkxU1HBAYN26A2i1Wrp3T2LPnkNkZ5eh\n02nIyupDSkqDJFRpaakvZNiZcm+tUUL5/PMdxMbOJjHRhMVSSFXVFkaPHoBWqyUxMZHQ0K8pL99L\nUFAcR458jN2uw2Y7jE73CBpNHwTBQlHRyU4b88WGxiUW0LwaDTRsUMvKyrDZbB1idS5ZsoS7774b\nq9UKXHjd1+ESM3yNPb7OyJs1rsVzuVxdyhZtzmC1RWi7I/efmJhIbe1mNJr7kKTrgMXARvT6B3G7\ng1i69FOuueYqevbs+b0GuUuXfoFW+wBe7xQUJRRZfhqP5ybmzfu02fEVFxfzxBN/wuGYhqKU8Ne/\nXsWGDV/yzDP3snjxSmpq9jFkyBBSU6/h6qtvxG6vRpbDCApaxFNPfYBGoyE7ezeKsunsWGah1a5g\n0qR+TJ8+/Rymb6DGs9u2HcLrHUBsbDKCILB79xmOHv2CxMS+hIW5uPrqzPOWE7FYrGg08Wi1DQta\nZGR3DhzYSK9e0zGZGsbgdvfl1KlT1NTUsXu3TELCFDweJ6tW7eCGG4I4fjyf/fudaDRxSNIxsrLO\nMGxYepeN2T9v2DDH4HbXsn37UpxOGUUpprKyu0884ec/v56lS9fz2WdfUFOTjCzPRVHOUFz8MJGR\no4EI+vdvf+i4qzy+86mu0lY0RWRSRTQAdu7cyXPPPUdJSQkbN25kzJgxDBkyhKysrFaJBRw/fpy/\n/OUv5/x2oXVfh0vM8PmjIwu/fy2eVqs9pxZPDaF2JlpShGlcG9gapmZH7j8mJobQ0Ag8nuF4PGtx\nu1eiKAvxesditWoRBD2LF3/BSy/94Xvj0Go16HQyRmMoDkc9slxHjx7dGTFiRMBrybLMSy+9yp/+\n9Cb19V7gQ0Chvl7LxIkzOHBgE7/+9V2+efB4PGzcuJLPPvsMRYEZM1aRmJiI2+0mPDyOmpqtKIqA\noszG7U7lH//4jJKSGt5661UA3y5Z9fhUY+jxCBgMJiRJoqamhlOnvKSnZxATM4KammI2bNjHdddN\naNd8thUmUxBeb6kvj2O1VhIdbcTj+U6v1ONxYjBoOXWqiujoTLRaHVqtDp2uJ3l5BezeXUFy8rVo\nNFokqR/bt39Nv36pmM3mLhu3LMssWvQJK1Zspbr6DFVVh4iNfQKDIRi7fTVbtx7niiuuQFEUjEYj\n99xzIy+//B4azV8RxXi8XheSlI3X+wkDB17HQw8t6LKxXojoKjKe+o0aDAZmzZrFrFmzuP3227nr\nrrsoKipiz549rWoX5HQ6mT9/Pu+//z6Zmd/V8F5o3dfhEjR8/gakrQu/f52TKnnWOMR1PlRW/Mfj\nz9Q8XyG3oKAgoqJMeDwfU1NTh6J4AD2CEI3Xq6O62oUsE9D4zp59I++8MxubLZSgoG5otX/h8cd/\n/r3j1Hl86613ee21tdTXLwO2Aq/R0KEhmqqqx7jvvof53//+dU4+MyUlhUceecRntLxeL16vlzfe\neJm7755Ffb0MvA9Mx+12snJlFuvWrWPy5Mm+HTA0FIerosT9+sWxbl0ORuMwbLZqBKGc+PgMAEJD\nu1FcvBu73Q40kE5aUuXIy8tn1648vF6ZQYPiSU8f0OpFLSkpiYyMSg4f3oAoBhEaamfy5CtYv/4I\nRUVOBEEkKKiQQYNGUFd3hIqKOkymBpEAj8eCVisCBjSahndFo9EiCEaffm170dLC/PbbH/D66zvR\n6x/Ebi/Aan2BmJiDREQkM3r0NGprF+FwOHzqSmVlZXi9IqIIOl0YiqJQX+/i6qvH8uKLv0EQBN+3\neL57GzaFC6lMorUINGa73c7kyZPbRJT6xS9+wYIFC+jfv/85v19o3dfhEjR8KtpqoPyZkc2VJnQl\nW9T/vIGYmh05X1tgMBi47rqRvPjiDhTlVWA1sAiPJwi9Phqv9w2uv/67cEdFRQXZ2dlERkaSnp7O\nqlWLef3197Fas5k9+zGmTp3S5LX++99VeL2PIwh9UZSvgHlAPCAiCL9k584p2O32cwhF8J3REgQB\nnU6HKIpMmjSJtWuXMXbsZQjCVARBBEx4vVmcPHnyHG3Fxo1Ke/fuidvt5vDhbzGb6+jXTyQkJAqN\nRoPFUkpyciQmk8n3TJoSkNZoNJSXl7N69Wmio0eh1+vYtu0AWu1x0tL6BpwDRVE4ffo0dns9ERFh\nxMfHM2bMMAYMqEWSJEJDQ9HpdMyYEUpxcTEA8fGjCA4OJjOzH8uX76W4+AyK4iIxsY60tEyys9dT\nWZlHREQiNTXFREZ6mlXEqaqqYteuo7jdEv37J9C3b58mj20KixevwmR6Hb2+JzrdcKzWXCIiihk9\nejoORzUGg+econRRFElIiKSw8HHgThSlAI1mLbfc8jQhISHNEjha6m14sRiorjqv0+kMSN5qqvv6\nwoULAbj11lsDrisXUvd1uAQNn7/H15qQZEvMyKbO39nwlxhzOBytHk9XjTMjYyDJyRJer0Jx8QgE\nIQG3+/fo9f3p3j2Eyy5rUG3ZvXs3d975GyRpEJKUz7XXDuXVV5/htdeeb9V1IiJCkeVCRHE0khQL\nLANkQEEU9xAT07CTVPNrOTk51NRYiIqKoFevXr48rMfTsKhmZGTQt+8Qjh9/G/g5ilKERrOKESPe\nxmAw+BZQNVfrH/ocPHgQGRkNTLcDB46wffsaFMVARISbrKxM3/uk9k+EwLVvJ04UIAjJaLUNveDC\nwnpz6tSRgIZPURS2bdvPqVNBaLWRSFIeWVkWBg3q/z3ygclkok+fcw1SVFQUN900hsrKSjSaEKKj\nG3bjU6aMYNeuXMrLDxEXF8yECaOaZDjX1tayePFmZHkIen0QR48e4NprJdLS+gc8vik0sD0bFHd0\nOi1BQRJW606KihKAIubNm4BGo2Hdum9Ytmwzer2WceP6sXlzKVVVLyMITm6+eQITJ04E2tfbsPGm\nprPxYzWogRDoPprqvv7yyy+zfv16Xx8+h8NBWloaBw4cuOC6r8MlaPhUtLTwN9cmqCPn7QhUhqTR\naOy0Uom2fqgN4aZ6kpKSMJkWYTbfgijGUFi4kuDgYHr2tPP226/5jn3ooT/gcj2PyTQRWXbyxRdz\nuPba9VxxxRXNXkedxyef/DU7dsxBUY4hSdXAdmAcGk0SwcEHeOutjzCZGnJvX375DXv2SGi1iUhS\nLuPHlzF8eAY6nY7g4GBf6PWjj97i+utvo7b2JSTJyu9+9xhjx44F8NU4ejwejEbjOcXC/qHPgQP7\n0adPT5/8nCAIvt2qaujU+1Dp5ur1IyJCUZSGfJwsy9hsFkJCvL6wnb+3UltbS26ui549xyMIAl5v\nD3buXEf//r1bDGtbrVbsdjvBwcGkpKSwY8c+vvzyMLIs0KOHnquvntCq0Hh+fgFOZ2+SknqdnSMj\nu3dvabPhu//+m3j22Udxue5GlksIC1tDQsJIamq2MGFCOn379mHt2m945pkVyPLNVFYex+X6B2PH\npjJixI0MH57GuHHNiww0xyj1r3NT+8apKig/ht6GXWlUO1rm9Mgjj/DII9/1SAwJCSE7Oxu48Lqv\nwyVo+PxrVgI94EDdxtvysnW24VMNjaqy0pEO8f5o6wfUmEAzaNAgXn55AU899ThBQQ6mTYvj5psf\nZuTIkWzbto2XX36dqiqZnJzjREcPP3sOHS5Xwy6wJcOnYvDgwWzYsIxly77AZhMIC/stVVVVDBgw\ngAkT/uIrm6isrGTfPis9etyALCs4nSls2PA/Ro4c6kuel5SUMGvWfI4c2YvRaOappxZyxx23nxPi\nKywsYvnyPXg8ZnQ6O9ddN4KkpO/Yg/6hNTWE6nQ6AXylLGqeyX/BBXyLbc+eyeTm7qC8XAK0mEwl\njBkz1Odx+hchN7TRMiBJaghPhyxr8Hq9zRqt48dPsm7dKQQhAqihb18DBw9q6d59+tm/7yQ29jAj\nRw5p8RmIokCDl/3dHDT81jbcfPNNREaGs3r1FiTJicVyNcnJ96LTmdixYwUxMZtZtuxbZHkOxcUy\nNtthJOlu1q0rxenMYfbs6e169wMp0aieoFar7dTehl0ZjuwqNDXmjjJIVVxo3dfhEjR8KhqHOlUD\n43K5WmwT1NJ5O+MlbWxodDoder2+U4yeiuaYov7jUAvQGwqKvyPQXHbZONasGYskSWi1WsrKynjw\nwd+zdauA09kTvX4bJlMKlZUfEhFxFxUVucBX/PnPCoJg5Fe/apqV5/98evXqxYIFP/Pl8dQ8kOpZ\nCYJwtuzAiMfjRVFkjEYzOp35nGd8yy33kp09CVhPfX02Tz01nXHjxpKR0UBScblcLF++F5PpCszm\ncOz2Gj755EtmzhxNVFSU751wu91YrVZ0Op0vrKmG2tRwpiqOoP7zZ+YajUamTh1FaWkDMzMmZpjP\n+Pp7huoiHBJyhMrKfMzmKKqr8+neveF5+eck/Z9hfX0969efJDr6CvR6Iy6Xg6+/fo8ePa5GFDUo\nikxYWA9KS78LMzWHnj1TCA7+hpISPWVleZw8uYfBg7tx6FA26elp57wrLS2WV111JVdddSUrV65j\n7dpumEyRyLJEQcFpHn74S7xeD06nHqezBre7N9ALjyeZ4uIqli1bxwMP3NmqMbcGTRV+N6eC8kP3\nNjwfOT63293hDiJqDR9ceN3X4RI2fP5U9ba2CWoO/jv99rykTTFHA6ntdzVaozXqv3isW7eOQ4ci\ncDh+jizH4fXuIyTkBTSaDygt/SuCAGFhdxAc/BB///v1TJky0ZcTCAR1M+JfJ6kaRP9QoqoIExJS\nTWVlLlFRKZSVZdOjh95HdpFlmQMHdiAIXyMIIoIwCEWZzs6dO32Gz2634/EEYzCYyc7eTm7uYU6f\nPoLFYiYmRsvUqf0JDjaxbNkeHA4zklTL5Ml9yMgYGJA84S/jpS6mqiE0GAykpqY26xmqxJwpU4Zz\n8GAeNTX5ZGQEM2zYKF8INhChw263I8sm9PoGcoLBYMJgCMZmKwV6A2CzVdK3b+tEFkJCQpgzZyLL\nlq0iO9tJVtZ8wsLCWL58IyaTkV69Ult1Hn+EhprweCoB2Lt3Efv35xMW9nfMZg/l5fchy0bgFkQx\nDSijsrKY+vqOsU790ZyX05IKimoMA5Fofoz1gY1xset0wiVo+PxfHlmWsVgsnVoK0JGXs7G0l/+u\nqytyh82Fe/2VX1qjNVpbW8u77/4fhYUjkGUJQdAAvampqUanSweMKMo92GxfERxchkYziMLCwoCG\nTzUa6s4zNDTUJ4LsbxSAs9qbLnQ6HXPnTmPdup2Ulx8lPT2cK6+c5DuuIUwcjcWyF0EYhaJ40GgO\nEBd3ue+6ZrMZjaaOffvWUVISRmFhb0SxL2VlVaSkjGHVqm8RRTda7TgSE7uhKDKbNq2jR4+k7xFN\n1AU0kKai+k/1DNVF0997U4/1eDwEBwczceJwnweoeo9NEToaOkNUU1lZTFhYDDZbFcnJEcTGusjP\nXwdoiIqyMHz45TRGVVUV+/fvx2QyMXLkSN8mMCwsDJMpmmHDMggLa1B5MZkGcepUQbsM3/DhQ/n0\n0+d47723qKgoQqt9lm7d+mA0GikvH0NtbR2i+AUaTTRQj9u9kvHjf93m63QG2kKiUY/3J9NcyHlD\nCNyZ4SfDd5HBv/YN2lcK0BJaE0L0R2uYmufD8Pkr0bSk/NIYL7zwJlVVWSjKUuAmFKUQQXgfWZbw\neCQgHXgbr/ckRUVL0GgsLFx4GBCYPPkqAI4dO8Y///kv6uoczJw5mbFjxxIcHOwzeOqcCoKAx+Px\nqdU3GKyGjhM33dR0acQ//vEqd911E3AVXu9BkpKCSEjojsfjQafTYTAYmDYtg9/+dhFa7TQUpY6B\nA8fidh/C4bBSVyehKF7690+gpqYEl6sep1OH3W5vlbxTW4yh+mxU79A/QhHIM1QXZ/U+Zs4czcqV\neygvh+BghcmTBxMeHk56ejWCIBAaGoperz/nPc3NzWXmzLtwuwcgyxUMHRrJ4sXv+FoABQfrKSiw\nEhYWj8fjYdeubRw4sI+TJ/OYPXtam/rj5efns2PHKUymRzEa/019vYXKymq6d08gOFiLTiei1w/B\nZnsfjcbKoEHac4qiOwp1E9FeNEWiUdnAauSmcdf19rYS6mpFmMZNaH8yfBcZVKNnNBqx2+1dUvDd\nWiPVmEjTHFOzK9mi6mbA4XC0O9y7d+8xunf/K6dPL8XjeQxFsRAU1BOXS0QQxiII/0OSrgWcQE80\nml9SV+diwYJf8vnncej1eqZOnYPdfi+CEMbKlU/x978/ytChQ3E6ncTHx2M2m5EkCafTiSzLGI3G\ns95N6xaEadOm8dln4bz++hIiI39OcvJonntuOcnJ35Censp1142lZ88Uxo3rhyjGc/AgaDQyHk8d\ntbUKcXE69HoDmzcvpahIQZZDcLu3MHVqtzZ3XlAUBavV6jNC6nuohpdVIob6bFRyk3/OsDljGBkZ\nyW23XeXLi/qHhFUih//mQRRFfv3rP2K1PoLBcDsgsXv3HXz88cfceWdDXm3UqEGcPLmewsJatm7d\nxZkzFQwadC9bthRz8uR7/O53d7eoUetyudi7dy/Ll3+JwzEGRdmHTqfH4fgbpaVHMRjC6dmzlAED\nerJ79y7i47thNJby6qtPtGl+fwj4e3rqZsH/+TTWx2wLieZ8hzov5l58cAkaPnX3LAgNqg9dJQPU\nnJHy96xaS6Rpbd1hW8fp8Xh84swdCffGxkZQXHyc1NSfcfr0diTpdkJDixDFw5w5sxiPJxZBuBtF\n+Q/wBbIso9PF4XDMZOPGjeTlFWO3343Z/EsAXK4Efv/7PzJ+/B1oNKGEhKxl/vwpREZGYjQaO1C/\nqGfo0AXExvZhw4YDGI0zcLuP4nCks3TpFubPn87kyRl89dUBYmLM7NnzJqGh5URFjWTmzCsoKytj\n6dIVmEzXIooKaWl38tVX35Ke3nqdS5fLxddfb6WwUAFkBgwwM358pq/NkNoRvTHF3L/9j/qvJWOo\n0+nwer2cOHGK3NwSjEYtQ4f2JSQkhKCgoHOOLSoqQaMZe/bdFXG7R5OXV4jX60UURcLDw5k7dzJH\njx5l69YcRo9+BZ3OTETEAIqLc8jJyaFfv36Eh4f7CqD9N1A2m417713IiRMmLBaJiop30evnI4pz\nkOUydLpl9O07hl/+8gGGDx9OdnY2FouFXr1uIS4urs3P+odAY+/J3xg2Pq653obnk0TTeA38yeO7\nCOEfYugqL6qp83aESNPZY/VfPNVwb0c+sMceu5cHH3wJszmd+HgLZvO/mDnzSubM+R9ziQT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8nOXk5NTSccDqiq+g9XXTWqzmZMjpXL52hqw9mWkmFr4U/nWVvVHSrvQRRF+vXr1+hvsrKycDqd\nXHvttWRmZtK9e3fef/99YmJiyMvLIyYmxv3dkJAQjEYj+fn5day76OhoCgoKfHszDaBDEl9LC9g9\nm9PW507yF/HBrxNAWSbRUr3R1l6nZ/KMvFDJ7qmIiAg++eQttm/fzsaNZSxfvoH8/KNAOHA7tZ0C\nTgJvAceB5ej1pSQmJpGffx+CINGtW3cWLnyep59+nqKidcAYYBmCUI1KdSWvvDKDuXNnu5/PCy+8\nxuLFn2GxRGO3n0AUL0KvP0xFxRn++Mfb6No1AbVaza5duwgJCeHWW0dht9sJC+vh7hB9zTVX8PHH\nqUjSOGAbEycO9yqmXVZWxsqVezCZEtm+/UP27dsM3ERAwFpWr17PihX/dH/X0zJsyE26detWHI5r\nEcUpvwiGP43ZPJaioqns37+VQ4ee4o03/oRWq0Wr1dYpet++/SBnzsTSpcsQ7HYrGzZsJCoqnISE\nhCa/1xkzriYnJ5u//e1NBOFy9Pr5qFSvc9119+FyuRq0Yp1OJ4cPH6aqqoqEhASWLFnFunV56PVd\n6dGjMyEhEzEaX0CS9jB48A38/PPXiOJx5syZzPTpl+N0Ot3JM7IbVRYol48vx7uUliH8mtmoTPNX\nSrL5wkXaFLRnYXxzUR+hevts0qRJlJSUnPe9d999l06dOvH5558TFBTEokWLmDdvnrsVkedmXG7Y\nXN/nbYEOSXwymrrwN7c5rT/rA+XkAl+USbT0OpUbAPkaALcGpMPhcC8yUJvtuWpVOnb7I0hSNrAZ\nCKTW4nsSSAVqF7OxYyexenUtYdhsNjIyMrjxxvsoK+sNFAIBiKIVrTYUQdBgs1ndRdV79uxh0aLV\nmEyfIwidkKQd2Gy3YbP1QxQvQaebwnvvvc833+zF4bgWtfoA3buv5NtvV7pdLhaLhXXrNqLR/AlB\nCAAmsmPHX8nNzT0vOeTkyQzy8vScPZvG7t2fI0kHUas743AIHD48hkOHDjF27Fj3YizHxepzk8pk\nWDvW0pEkK7VKNvuAUYjifbhc09i48Xn27PmRK64Yd97ice5cKVFRKQBoNDrU6kSMxtImEZ/FYuHr\nrzdz8mQBISGRvPrqo3zxxRaczk+58857uPLK6Q1usJxOJw8+uIAtW4pRqzthNu8gOXkmQUE3YjAk\nk5b2L8LDuxIcnMC11yazffs+eveO54YbbqFv377uTZNcpA2/jlHPnobK1lTNJUP5+SvdqL7oovC/\naPF5HrehJJrvv//e6+enTp1Co9G4cxquueYa3n//fQDi4uIoKipybyrLy8uJiYkhLi6OU6d+7QdZ\nVFTEoEGDfHJPTcEF4mtgsLY0dib/1peQa3Kqq6tb1BneF5ATgqxWKzqdzp1oo2yHExwcjMtVWyBt\ntVoRBIEPPvgWk+kegoKuxumspqqqEtgGxABJQAUq1UaCggaj0agoLy8nLCwMrVbLk0++SnX1I+j1\nV1JTcwaL5QoEYQMuV38k6R0mTRqPSqXCbDYzf/4TmEwDgDgkyQwMAoKBjTidq7Fa/8m336pwuT5E\npxuOJEmkp9/Ol19+yZgxY3jllbfIyMjGZHKi1f4BQah916L4GWlpaXWIz+l0UlhYwPbthwgIuArQ\nIAhdcbmcSJIKQUikvLy8Tpq5t8XYkwxzcnJ4441/YrerkKQ5QA/gA0TxFWpJ0IUoGqipOX/HDBAZ\nGUhOTiEBAYZfzlGEwdCpSe/36683c+JEDJ07T8dkKiEjYy1vvfUMarWaQ4dO8eWXG+nduzMGgwG9\nXk9cXBwul4sfftjGrl0/c/bsCX74oQKN5g0EQU1V1RDOnl1DaOgIXC4JQehNbu4eBg0KYfr0KVx5\n5RV1zi+77tVqtXt8e+tpWF8bJ2WdYX2dK+TxKiejKWveALwl0DRnnv0vWXtwPvFVV1c3Oxu9Z8+e\nFBcXc+DAAVJTU1m3bh0XX3wxABMmTGDlypU899xzrF+/nv79+6NWqxk3bhyvvfYazz77LE6nk+++\n+46HHnrIp/fWEDok8ckvWulCUaKhou+mHt+XZRKyygaAwWDwWWZXU6/TM3NV2R9PuTMH3O4lmQR/\n3SjUtqxRqwXgAJAAHAWmASGoVBpqavL58cdejBt3My+/PJ8pUyaTl1eEVpuCRqMjODgRh+N6tNoF\nBAcbmDZtHM899wwmk4nNmzeTk6Ol1nVaDcQCnwCdAAOSNBtJehWXKwy1urv7mu32bqxYsYL58xdg\nt09DFGdjseQDfwBeB3KAvdx++0k+/XQJo0aNwmq1YjQaOXo0/5fYZjwaTTJW61MIwh+x2fYCO8jJ\nGY/FYjlvIWmIDD/9dBVm8x0EBz+EzfYhDsc+HI5y4BR2+25EcS3Dh8eTlBTp9V1ddtlQVq3aSk5O\nFpJkYeDAAJKTk93nqKysRBTF8zKOJUni5MkCOneejiRJaDQhOJ2J5OTk8MMPadjtwzGbrSxa9Am9\neiURHq5n0qRENBo1X35ZTFzczaSnv4HJ1JmoqHBEMYDq6v6Ul7/NwIFBZGRso6ZmPdHRDv7wh/l1\nFtv6SjCAVvc09EaG8phX1ry1pgBceW3+QlvF+FrSi0+lUrFixQruueceqqur6dOnD++99x7wa5lC\nr169iIqK4qOParOwk5OTefjhhxk6tLbP5GOPPUZiYqLP76c+dEjik+Ft4ZfjVoIgtCp21looE1fk\nIme5i4Kv0BTiU8bxZBUPuUhfPobsgq2pqQE4r17t5psncfz4v6muduBwbKC2N9+z1Fp8rwGLkaRq\nunT5kJCQyVitZ/jzn+9hyJBBjBgxkLVr/4UoPktAgJmIiJ9YtOglpk6d6l4stVotNTU1aDRDMBj6\nUl09EYgETgEfExSkRa3OJDAwirFjR7NhwzOI4jNYraeoqvqArVsH4HA8gCCsoaZmAPAhMAJIo5ac\nTRiNNzBlylyefvohZs6ciMlkQqsdxvDhkZw9e4TBg5/h5MknMZneIzIykTvvXIHJpGHXrkNMmDC6\nwWecn59PaWkpoaGhWK12JCkMlSoEne5+1Oq9JCScJCnpCDk5++nbtwezZo2kZ89kd4q7cjEOCwvj\n9tunYDQaUavVxMTEuN2nn322gXPn7ICD0aO7MH786Drx7uBgHeXl+ej1EYiiiEpVRXa2hZqaQSQk\nDGLDhj0EBs7BZDpG//7XsGHDp2i1JYSHX09BwXFqalzAdqzWG1CreyII67DbjRw8+B4uVwETJ0ax\nZMlrdchG3igpM24bG7ONkaHszvTWuUImS/nvMhnCr9ZefQXgsitfdgfKhKhMoPGXO9Jf8CTUlhav\nDx48mL179573uV6vd8f6PHH33Xc3WibhL3RI4vMcnPLEaShTs7nHl3eQLTlGffqe/oodeoMcx5N3\n4XIcz7MhrBz/lOvjvD23WbNuRKPRsGzZv8jIyKCk5HagCFBTG98LxOmMwuUKpbLyW7TarkhSAhkZ\nGTz++B8pKHiGffsGoVLB/fffyYQJE6iurq6zWA4bNgxBeA2dbiYazVqqqxciCGcJDPw7Gs0QBGED\nr732NBMnTuChh/7Cxo0X43Ta0Wi6IgirfylBuRGYDFwHhABPUxuPfBO4HEmKZMsWHU7nfsaOTQBE\nRoyYTlDQLnJzMxg4sD8XX/wu3bvX1tpVVRVTULCvwee8f/9hvv46DZUqAZfrFBddlIxO9xY220UI\nQgSi+AR3330DDz10v3sBVi7a9cUM4+Li6rjlN27czblzXUlISMXpdLB163ckJKTRs2dPNwFNnjyQ\nzz77DpOpB05nGUOGiISFxQKqX96znYCAsF/OpUGligPy2bPnE0ymPjgcE1CpjmO13gxE4nTm0afP\nG3Tu3A+Xy0l29t/JysoiOTnZPb4AdwlGS1EfGSrLK+QSCPk51RL7r2USnhafDG9kqIwZKtVQ5Plp\nt9vdv/P1RtXX8IzpdYTODNBBiU+GPJCqq6vdC3xrCn89j9tcNJY16mviq69EQo7jybFEwKtbU05t\n98ws9IRKpeKmm2Zy000z+e679dxyy1Ks1lHUDr+vgH7AXrKzX0SrHY0kvU9o6Fm6d/8b0dHRfPrp\nEiorK4HaRctms7kXSrvdjiiKJCYm8u9/v8b8+X+mpKSYyZOH8fbbOzl8+DBFRUUMGbKEqKgoioqK\nWLToRcxmM/fd9wrbtmkAHTabGUkKAaqA2dRmnY4DtgOh1MYijxAS0hmHIxpJMhMZmUNxcQBdunQj\nMrKaQYOmc/RoqZucKioy6d3b++65srKStWu38PHHe4iPv4FBg/ogSYNJT/+UV199jGXL3sJstjBw\nYG+s1jD++c8vmT59BKIouoUMIiIi3O+ssQSazMwSwsNTf/lMRK3uTFpaJp07d3b/vk+fPjz0UCcK\nCwvR6+NJTEykpKSErVu/pahIj05XitH4Gf36TcRiKQeySElJZN264wQGDiUoyE6XLnfgdH5IZKSW\nc+fMBAWp0eu7IAgqzOZkiouL6dSpU4sL+5sKOa6nVqvdHTdEUXR3nVc+J083aUNk6On6VEp+yYld\nvu6v11ZuTugYcmXQgYlPjitA7cIcFhbmFzdiU47pLWmkLcoklMfzjOOFhoa6LbqG4njNLdC+/PLJ\n3HnnFpYtuxVJCgAiEIQA1Op4HI4/IQjxwDg0msfrJBfodDrkrutyfFEpQA2QkpLCjh1r67i3pkyZ\nAsA333zPhx8eRaUKR6fbwh13jKN7965s3/5vHI5vEcWLcDhe+eV5HAS6AD2pzSTtAWxDEAR69eqN\ny5WHwRDIrbdO5ujRk5jNVXTvPpCuXbsSELCDQ4e+ANR066Zl1KjLznsGLpeLVas2ceCABaMxFJMJ\nzp3bTGBgFNXVZcTGhvHUU/P58MONFBf3JTFxNKWlZv72t/eIiEhGr+8KHOSaawbQt2/vJiXQREcH\ncvx4Omr1AMrLc9i2bRXZ2bHs2HGWm28exYAB/REEgcjISCIjI7FYLPz44484HA5uvHE4J0+eJSHB\nxJkzJhyOnVRU2LnttlG4XE4GDnSh1wuo1QGEhk5k/foVhIZehU7n4OTJPVgshXTqNB44RUTEJJxO\nZ4sL+5sDeV55ix0qv1NfT0NPwpLfnWfcUJl1qlKp3FqqnsdW9tdrLRn6Cp5rVEcQqIYOSnwul4uK\nigq0Wq1bV9PXA68pJKVMXGlqY1p/uDqVcU1lHM8zM66hOF5TIQgCb7zxEmPHXsyDD76ORnMbJtMG\nKitViGIUAQGQlDQEqzWJsrIyDAaDV+vAU77JarVy7tw5nE4n8fHx7oVKrVaTnZ3N5s1GunadhVot\nUl6exZo1P3D//ddhNlewZs0TmEw2+vQZSWTkcrZv/xLYDwykNg4ZgiAcZvTo29m8eS1JSbns2NEf\nlwtGjkytM3YmT76UkSOrcLlc6PV6srOzcTqddO7c2W09V1ZWsm9fOidPihQX52OzHcLhCCAlpStx\ncV04dy6K/ft34XT2IjJyBt999yEGg5bsbDMpKQLjx4/C4bDx9ddfctFF3dwEp0xY8STDqVPHUl6+\nnry8LHbu3El8/JX063cxFksFn3yyhquuqkQQatVWoqKiWLJkNZmZEeTmnsXpzOTuuydw990zEQQB\nk8lEQEAAoihSUVFBdPQW7HYjBkMn0tI+RxRDSUqaRVSUiX37DpKZ+QIGwwbmz7+RpKQkn8tueUJW\nEZLnVUPeCKVlqPy9krBky1D5XU8ylEslVCoVdru9jrUnu1WVx/ZMomlIks2fFp834lMWnP9e0SGJ\nT6VSuUmmsrLSbzV3DR1XqXbS1Ma0vh788u5V1i1sTRyvubj66quJiIjkvvuexmgchChWIYrF1NQk\nk5f3NYmJ5YSEhDRaMA21GqHvvPMpZ8/WZk8GB2cSHByFzSaRmppEbGwYkhSN3e7AbncQEBBNfn45\nsbGxvPHGkwwZ0p+8vGEcOVKEw9GDlJQAfvzRiCRdgyBE/UIg/yI+vpATJw6RmHgfVmsCn3++E4Ah\nQ+rWHwUHB2Oz2fjkk285dy4ElUqHwXCIO+6YRGRkJFqtlmPHTmG13kJc3CwKCi9hNL0AACAASURB\nVFZhMm3D6Sxg1Kg7OHYsC4cjGr1ei9F4ELO5O4GB3dDpulFeDhkZh+jV62IcjgA2bNjCwYPFSJKa\n3r1DuPbaieh0uvOeUXBwMLNnX8WpU6coLS2gV69LEUWRgAA9hw5Z+OCDYwQH98Pl2kT37nYyMrqS\nlZWF2XwxNtsoFixYwRdfrOW666YyevToX6xBJ/369eXRR2/k8883YjRuZ9QocDj6IAgCBoOBMWMu\nJjOzGx988Be/9dZTQh6r3kTBm4rmkqE8R2RXqrLWUN48KjeRMrl5U6HxJsmmvAZ/P7+Kigq6d+/e\n+Bf/x9Fhic9fPelk1HfcxuTOWnLM5kLpWoVaGaH63JpNjeO1BGPGXMLYsWM4cmQGarWBtLS/YbFk\nolY7efPNd4mMjGzSwrV+/Rb27jUQGTkag0Fiw4aT9OzZj379BrFu3Q+MHl2JKJoBKzpdCLm5B0hO\njsRqteJ0OomJMXDs2GksliCCg4OxWo8hSV1RqQYBecBAXC4BSdISG9ufhISUX4h4NAcObD+P+AB+\n+uk4mZmxJCWNAaCo6Gc2bdrP9ddPQafTodNpMJtjcbkCCA2dgMsl0qNHEsHBMTgcBwgMVDNgwFC+\n/fY/2O2pOBw5qFRFZGVFUlp6gIoKGxER59i9+yKSkmahUomcOrWTLVv2cPnldXv2yW5sq9VKt27d\niI09hMVSSnBwDEVFueTn55GauoDAwHCs1mHs2vUUen0MJlMXgoOHcfjwF1RVTSQr6yu++24ZsbHv\n0L//XARBR2XlX5g0aRhjxgxl8ODBWK1Wzpx5lezsrwkI6EZV1Q6mTBns947eynvUarUEBgb6PHTh\nSYZyTN7lcqHRaNzzCuomGiktt/rI0LPWUEmGMhHKWd2+lGTzZvGFh5/fFPn3hg5JfOAboeqmHF+G\nUv2lpUk0rSU+T9dqaGgo5eXlbstPPge0Lo7XHPTpk8DevVuIinqMAQM+pqjoRebMiWTQoEE4HA6y\nsrIwGAzuRA5PWCwWXnzxH2RlXYkonkaSdhIdPRJJikKvDyc+fiInTy6nSxcda9f+CZ1Ow6WX9uXW\nW29Ap9NhsVgYOTKVgweXk5X1I5WV79G5cyiRkTGUlW1CpQrH5VpGfHw1MTE/Ehub6n4WDocVrdb7\nc6mqqkGn+7XWLjAwkvLyY+7kpUsv7cv27efQ6w2AGoOhgtLSH1i+/L/U1FQSEyNSXZ1It24GNJpj\n9O49nrQ0PcXFh9FqS8nM3MhFF0VjNvdCra5dLCMj+5CZuanOdcguP7k8R61Wc+ut4/joo2+orAyj\nvPwsPXsmERhYu9jpdAZCQiIxm4/jcPSmpORnqqr0uFyjUalOYbc/SHb2PLp311JYeIri4ospKgpi\n69aN3HVXEVOmTGTBgnv4z3++oajoNIMGdWPGjKmtHSYNwpcZok2BJ8kq53JTEo08ybCxwnv533L3\ndTmjVKlP6hkzbI7Qhi/KGf7X0GGJT4bco83XUJY0yOovOp2uWeov3o7ZUpKuL44nCALV1dXuFG/A\nrbjSUldRczB79i0cO/YMBw/WqrKPHt2Zu+9+iNzcXObNe5aiIg0uVzlz5kzh7rtvP+/3n3yyksLC\naNTqWERxCmazkfz8w4wcOfiXe6ngzJkMKiouZ+TIGZSXZ2Ey7XMTu16vJzc3l/LySKZPf57MzCLO\nnv2BpKSf6NdvGIKgIzhYYPbsufTv34cPP/yes2d3oVbX9pQbN65ujZ6sNdm1axybNv2I1ZqIWq2j\noOAQY8aEIEkSBoOBe++dicXyJYWFFjSaGi69NITDhx3ASKKju2M0fk9+/noeeugm0tJyWb16PWp1\nHCNHJtGv3+1UVRUC31FTk4Mk1boWKytz6N/fAOBWz7Hb7ee5p5OSknj00VjKy8tRqS5m8eJvMBoz\ncDhsrF37HJWVZwkP12O17qKqahguV09Uqu/QaofjcEQhSS7OndtJaakBQRiK1WoiJmYyK1a8zOjR\nIwgICGDOnJvrLPT+gLyR83eGqBKNkWxTEo08yVBJWsrvyxtSOfTg2bnC89hKSTZvZChfm/L5eeKC\nxddB4M/aOJlsmpq40hhacq3KprSya1X+XF6EZXdKTU2N+/hqtdodpG9MVLmlkCfpwoVPYTQaEUWR\nTp06oVKpePrpNyksvJ7IyCtxOCp57735pKT0JyUlpc4xsrIK0OmmodOJmM1/R6UqQqvdDAzl3Lks\nJGk/Wm04XbtOpaoqF6vVzE8/lZOZmcnQoUMRBIEzZ7IQxUHExSURF5fEgAHJCMKXTJ48iMpKMwkJ\no0hOTsblcnH33dP46aeTWCyVJCcPJSIiArPZzJkzZ1mzZg8Wi4uePaOYOXMSM2Z047vvVlFTYyM1\ntRPjxl3mzviLi4vjkUeupaSkBI1GQ1ZWPt9/X0CXLuNRqUQCA6+lsPAfBAcH07//RXz77V4KCzNR\nqxMQRS0WSyHDhvWhtLSaY8c+R60OIDq6issuuxybzdZoUbher3crytx77zQ+/PBbli37CJPpLuz2\nVMrKfkCj+Rq9fgsazfe4XLMQhDE4na8DejIzz2GzgUYzFZtNw/HjZ4mLc7q7RMiLvOxOVroKlQki\nLYWyFVNbZYjKVl5zSdYbGULdNk7yXJDnm/x85DkoW5VNkWTzpkKjJMOGiu4vWHy/c/jT1Wm327Hb\n7e4Av7+z2LxBLteQ6/EMhl8tAc84nqxIIbttmuKqac3C5XT+2pVczg707PR8+vQ5wsImACCKIdhs\ng/n2229Rq9UMHDjQfe3Dhg3gs89WExi4lOBggerqF5gxYyIzZ0ZSU2MlKWkGr7/+Bfn5R9i3bxuQ\ngtkczKpVm+jfvz8BAQHY7RZyc/MQhDhiY2Mxm0tJTAwmJaVu01e1Wk1UVBTjxtXG7eTnVFhYyMcf\n7yUk5EqiosI4efIwK1eu4+abp5KcnIRWq63TFDgtLZ1Vq3ZgsUBiooEbbphMaWk5TmclLpeESgV2\neyVarZrCwkK+/TaTXr3mYbVmc+TIMcrK3qR/fwMaTS969uzEqFG1AsARERFuspGt9fT0dH78MZ3A\nQA0XX5zi3s2XlJRQU1NDVFQU8fHxXHbZQFasSKGmZho2WxiiOAibbTNOZzQqVRoBAWuw2T5DrdYR\nGzsWo3EQgpCHWv0tBsMcMjIWcd11Pd2JNQ0lhrSGDGUPilzrKopim1h5soKSL0nWWxsnZVG8sluB\nrGHaHLHupkiyQW24YMuWLVRXVyOLZjQV69at48knn6SmpoaEhASWL19OfHz8b7bzuowOS3wyfGnx\nKa0r2bXkS9JrbomELKzd3Hq8xnan3shQFMVGA+3KRauxXXNSUjznzu0hPHw8RmMe5859x+rVifz3\nv/9kxoxePP74/QiCwPTp0zh+PI0PPpgAqBk9eiBPP/2im+idTifjx/fihReWolLdg91ehCRVsXWr\nkeXLP2Hy5PH897+nyM0t5/TpckJDJYYPF5g69aomvQ+Xy8WmTVtJT9fQo4eD0FAdnTqlcvr0fhwO\nByqVyv285Czijz7aTVjYNURFRZKbe5SVK7/DYNBhsWxn165KoqK6Exqawdixhl/ed18iIjpxySUx\nFBbGUlDwTwoLQ/jmmwDs9izgS7TaCERRxfTpQxk5cgSCIHDs2DHee+8AOt3F2O1V7NnzGbNmXcaO\nHfvZv78MUQwnLKySP/7xGgICArDZijCZLLhc4TidRqAGQeiKKIqEhMRjMBxBFK9iyJCr2bTpZ1Sq\nAZjNL6HTrUavP8N11z1Q73NqLEuyKWQoN36WN0r+JjylK9VX2cxNgVz8bjAY3CEJZcan7Nb01tnD\nW8ywPjKUN6AajYby8nK+/PJLDhw4QFRUFEOGDCElJYXbbruNwYMHe71Op9PJnDlzOHz4MLGxsbz6\n6qs8++yzLFmyhIULF5KcnMyKFSvYsGEDDz74oLvz+quvvlqn8/q0adParPO6jA5LfEozv7XE59m2\nyGAwuCeyL9HYtTa3Hk/e3TUljue5O/VcuGpqas5TwVAuds1NlnnuuQe5//6/UVr6FWlp+4mJuZyE\nhKeRJBtffnk/U6f+xIABAxAEgT//eT4PPngvdrudkJAQ9/XJJDtlynjWrt1LcXEBZ88eISxsFvn5\nmbz66of8859rSE19nssvH4DRmEZOziYmTepFp06NdzRwuVx8/PHX7NkDRUUOysvLKCkpp2vXUKKj\ng90uI6UFXVBQgM0Wj1odSFlZGfn5KjZu3MhFFw3jhhuWcuzYV5w+/RVDhyZy++0zKS8vx+ksBmoF\nm/V6gaKiSrp3v57AwAh+/nkbu3eHctllV6PXG/joozWEhYXQt29fvvvuMOHh0wgJ6YQkSfzwwyn2\n7n2TgoIooqKmMXhwT44e3c+jj77BU0/NoVMnO3l5f8XpHECtkHgyKpWFiIgxREToGTHCRXGxhcBA\nkaAgC0bjWuLjhxEcHMDgwYPPa9vUEJpLhjJ0Ol2bEFB7uFLlOeKZMNOQbFpLyVA+hkyGN998Mzfd\ndBNTp07liy++4MiRIxw8eNBdt+sN8ka/rKyM2NhY4uLiyM3NBX67nddldFjik9Ea4lMmrni2LfJH\n7LC+YzYWx/NHPV5TFi5l7zPZ+m1qbOSiiy7i88/f5eTJk8yde5LOnZ/+5T50qNXJlJaW1vm+HLNS\nLiBKkp0+/WKWLt2LXj+B3NyzFBSsRJJUFBRUk5a2nX79yunaNZaQkP40dY0rLCzk9GkXAwbcisu1\njrNnT3D8eB5RUUHcdtvEOs9K1pKMjIxErU7H6XSwa9cJqqsDMRprXZXh4ccQxRAEIZX09AAWL97A\n7bePokePMtLTv0WtNiCK6XTpEo1OF4LNZiMv7xxBQaPIyyth9+4z2GzBvPDCv7j11gmkp58kPHwU\nWm0QOTlnyc+X6NFjGCZTMhZLZ9au3U14+ACysrbw2mvr6dEjkTNncqio2ITVWoIkhREUdBsxMddQ\nXb2MUaNSUKkkvv56Ib17S9jtRURGRtO/fzfuumt+q8nIc0zJcTX5XcoxL7m5rNLb0JxMxoag3DC1\nlZUnz0tJkpqclaocUzKaQ4YOh8NdcK+crxUVFcTExDB58mQmT57c4DVotVqWLFnC0KFDuf7668nL\ny2PFihUAv9nO6zIuEF8LCEqekA21LWoL4vNmacqft2U9nvL65AkmxxidTqd79yoTtOdk9FSqkBEU\nFMTQoUPp0yeJs2e/ITLySiyWDAThR7p3v/G888sZd94s2WnTJnL06E98+eV+CgtPIEk3IggqJOll\nHI4izp4NxuWyIQifk5DwYJPut5bkwWq10afPRBIS8sjO/pj77ru2TuNXSZJ4883FvP/+KkBi5Mih\n5OXlUVISRni4moEDJ1FSEsepUz9gt0eh10+mc2ctWq3Ip5+u4dFHZ7lVaRISprJ+/Q42b/6SsLBB\n2O2VGI1HyMqKRae7GKu1hPLyM2zdehaDIZHS0psxGHrhdEbicOQwbNj95OVlYLGEY7F0Iji4mOjo\n7uj1o9m27S/odPeQmDiU3Nz92GzLMBhKKC1dRM+e57j00huJiIjg6qunIkmSW/DAH1BmT8ouP+Xz\n9OZtkMnAW9lAUyBbeWq1ut2tvJagOWQItWGK7777js6dOyOKIk8++SSjRo0677j1dV5fu3YtS5Ys\nYevWrRiNRp5++mlWr17N3Llz3cdX4rfQeV1GhyW+lro6lW16Gmpb5E/ia04crz0ms5z9Jmezek5m\nz8kou129xQsFQWDhwid47LGXSE9/D4NBw0sv/bGOW02SJMxmM59//hXbtx8jPNzA3Lk30KNHD/d3\nRFHkgQf+j9zc1/j5ZzWwCUlKBsYCx3E4/o3N1pnoaEOT7lNWlImPN1FQsJ/Q0CSqq9O47LK+53U7\n//TT//Duu7vQ6VYDAps3P8hVV6np0yeWnj2nIggBbN++k5KS0wiCjm7dbCQl9UClUpGdXeve69Wr\nl3vhEkWJ3NztnDy5G4cjF4fDjCBMRxB+RKXaTUlJLMHBLxIQEEhNzTfY7Z8RHj4Xq3ULZ84cZsCA\nkaxb9ypVVVFIUiRW63ACAtIRxXh69x7IuXO5REcbqKqKpGvXo/Tr14VHH32K2NjYNomrNZY92Rxv\ngzfXu7fxqEyYaYtktJZYeS2BkgzltUBWmHG5XOzYsYPt27eTnp5OcnIyNpuNt99+m9tuu82t2Vlf\n5/UDBw4giiKpqbXi54mJiVx//fXMnTv3N9t5XUaHJT4ZTc3qVLoTZXmvhhYBf5VJSJJERUUFco++\n+uJ4DVk//oK8e1WpVA1OZm8704aSZyIiIvj3v99wF/8r653kc3722RqWLTuB0zkDu72YvXuf5dNP\nX6ZLly7uc4SGhjJ//q2sXTuPsrLZCMKVSNJxIAGr9TC5uYPIzv6Qt976J2+++ZLX9+tZO3bvvTew\nZcseCgr2MnJkJGPGXHHebzZs2I3LdTOgQa2ORBDuJTv7A/r3r6KsLBOdLpikpBxuuGEUu3YV0rlz\nLGq1mry8A/ToEYVOp6O8vJxTp05htVrZvr2QSy55BZVKQ15eBtu3v4BG8yMuVxAQisuVjEolUFlp\nQq2+GFH8iB49UsjO1nLmzMP07KkhKqqaoKCuBAVdB+g4eXIh3bpJREVp6do1Bau1jIKCOJ57bhbJ\nycltQgatyZ5sKRnK77Op/QBbi9aURbTmnPKYVRJ7WloaaWlp3Hnnndx///2cPHmSQ4cOcfDgQXe/\nzYaQkJDA8ePHycrKomvXruzfv58+ffoAv93O6zI6LPF51rDUp4PnzZ3YlIHqa+JzOp2YTCaANovj\nNefalOUJLVkkvSXPeJZVOBwOd7G9TO5Qa3mvXr2V/Py7cDgSgWQKCn5izZovmTfv/jrn6dmzJ5Mm\npbBhg43q6oNIUiFOZwUulxWrNQO1OobPP9/HbbcddO9kZSiJXbkwT5s2od77kiSJsrIyKit/wGIp\nRRQFRLGGTp1i+eMfr2bbtgOYzXYGDRrEgAH9GTbsZ1auXE1WloPu3SO47rqJpKenM2fO4xQXh2K3\nlxMSEsCNN/4BtVokLi4Jp9OF3W7Fas3EYjHidG5GFK+gqkqHw/EpISHdKSsrx2g8TkCAmW3b1pCb\na0UUc4iN/Q+dO/cmJqYLt97ag3Xr3sFojMPlymHOnHH07NmzTRZmf8TVGiJDuYRHmfAhe0Za4iZt\nClwuVx1vkb8VZsD7ZsLpdPLee++xevVqFi9ezMCBAwFITU09b8w3hNjYWBYtWsQVV1yBJEkkJyf/\n5juvyxAkf1Vv/8YhqxwAlJWVnaeo4ulOVFoaTT1+RUVFq1UQPKXOzGYzYWFh7gmsnJzyrk6j0dSp\nG/MXmlOe4KvzeWaQysr248bNIjv7KQIDa2vsqqtfZsyYA6xZ8/F5x9m6dQeLFh0mNHQ6giCxatVj\nQAyiOJKAgEk4nf/gqaeimTp1Kps27cflkhgxog8XXXRRs4n9yJEjvPHGIbZsEbDZknE4jhEc/Amb\nN69w1zV5g9VqJT8/n6KiIp5//m327r0EnW42TqcZi+URxo8fyYgR93LgwHp2734Vp/M2RFFHXFwA\nFRUfUVx8BpUqBLu9Bp0uAZerJ6KYSUCAltLSYQhCd9TqYCTpPUaOvIPIyC28+urd2Gw2jEYjnTp1\nIjY2trmvqNlQlijILaf8CaWXQJ4nUNfjIP9pipu0qedsTytPuZnIzs5m3rx5XHzxxSxYsMCvcdrf\nMjq8xSf/XeZ/eWKYzeZ6E1eaevzW7Cnqi+NZLBb3QvGrZuSvcby20ipsKy1P5TmVscOgoKA6RbyD\nB3fl3Ll3sVrNSFIhWu0+dLoId9NaZfLMmDGjqKys5p13/kp6eh6SVIwgzEaluhyL5TCiCC4XPP/8\nagRhIiCwc+d6/vznK+nVq1ed66qpqXFvnLwV/hYUGAkPH8zs2f3IyMigpmYI3bs76yU9+b0fOHCQ\n5ct3Iwjd2LXrNKL4V7TaECAEq3UMOTmrMRgKSUs7QUzMEGy2ZHJysqmqOofT6aJ375e55JJRlJXV\nsGHDn6ipOUxo6A2UlGxBFOcgCCdQq3VYrWFkZDzHY4/9Bb1eT1hYGF26dPH7+5STnxrqleePc9bX\nucGXMUPPc7aHlSfrs8pz0+Vy8dFHH7F8+XLefPNNRowY4ffr+C2jwxKfEvICKndOkAPOvpiM9blQ\nG/q+J/EqG6/qdDq3m0ZJrPJO0t8Lli968rXknJ5CyzJkd9bs2Tdz4MDbOBy70GhCEcWuTJzYD4fD\ngdVqPS+T1Gg0Ul7eCY3mHgICjNTULMbh0CEIGuLjs7FYorDZhhMd3Y3AwEiOHDnA3Xe/TLduXRk7\ntjezZ99IenoG77zzNTU1IWg05fTvH0F+vg2DQcvMmZfRo0cPYmMjsNnS0OtTGDRoIDk5O+nTJ8Hr\nfZpMJr7+ej3nzpWwbduP9O37LHp9FGr1Mszmb1GruxMQIAHfEBCg4cSJPeTklCGKBioq5iMIYwET\nKpWR7GwTZrMLk8mMShVHaGgUgYHXY7evRxAy0esDCQvrTXm5hSlTUkhNHeouF7BarXUWeHmT5Qsr\nxdPiaqu4mjJ7simdG1qbQAO0i5Xn7ZwFBQU89NBDdO/enU2bNrlr6DoyOqyrUx4kUKtPJ8eMAgMD\nfTZIvblQG4JnyyKtVut273nG8WQXo+zCUCa4NEdNpalQ7tDbsr6pOef84ouvee+9r7DbnVx99SXc\ne++ddSSblJmkN900H5NpAaWlesrLAzGZPiUo6HuCgjozfXoYWVlFHDgAOl0kQUEuysp0qFSpmEwO\n7PYfGDvWhNMZSlnZVQQHd8LhMJKR8S8mTHgSSXJgta7h2WdvIT4+nlWrvmXHjjwEQUdCgot77pnp\nLrSH2ve+e/duli9fQ0FBf+LjU9i06QMEYRJ2uwur9SfM5h8QhEBEsQJBcNGt27NkZtZgs21Crb4I\nqzUVQXgDUfwDovg1KpWFUaNu59y508AZIiK6UFhYQ1nZSaqrM9HrL0cQsoiLO8Xatf88L0vW0+3X\n1BKUxt6nbHHJcmP+hqeV52uLy5MM5T/wa+2qRqPxS8xQCWX5R2BgoFt8/4svvuDtt99m4cKFXHrp\npX6fs/8r6LAWn5JAZImxsLAwnw6Mpro7vbUsAtwLjkx4UBv7qa9UQE4IkdPeG1JTaep9eroY2zrz\nrTl1h9deexXXXutdakyZSarT6TAYAjGbLcTGdqGqKhe12kJsbCJ9+gSQnNyZr75Ko6xMi1ptpqjI\nBkiIYiBBQRPQaIayefMtiGIy8fGDKC21UlLyM+Hh/RBFLUZjJjt37uXHH3dz551TuO++/2PCBCM/\n//yzOyvX6XTywQefsWPHISoqSrDZkjl16hhOZyZa7WbMZhFJykOvH4xanYxef5DAQC01NdnExj5M\nSMgoNBojgjAIs3kBKtUdSBKEhvbF6SxGFN+kW7fNaDS5iOKNdOlyGeXlaWRnw+jRCUAVISGJ3Hrr\nX4iMjKz3WSnfiXLj4M2Kro8MPd+nr3vleUNbxdWUlqF8TqfT6fa+KNcYX8UMlajvPo1GI48++ihh\nYWFs3LjxPC3cjo4OS3ySJFFZWYkois1SFGkOGiM+b8ovDdXjNVYqIBOkMmCt3JEqM9masmA1tTzB\nl1BKRfnznHPnzuDJJ18DZhIWVkBExAb++MebmDRpAn/4w9OYzeOIjr6d6uqzmM2voFIVo9H0QxBE\nnM4srFaJmppcsrM3EhU1BIcjCIvlFLm5e9m06S2cznE4HL149dVd2GzvEhfXmTVr0lGpkrDZ1lFV\nlcaRI04cjkhstioEYTeCcAsu1/VYreuAt4E0zOaVBAb2R5LCCAycg9W6jJKSXGJjNYAJURQICNCh\nVh/GarXhcBzC6fyY+fNv4JFHHiQrK4tnn11OTk4FLpeJXr0qmTv3/4iKimrWs20pGQqC4FZaaesY\nF7R9XA3OL7gH38UMlfAWP5QkiXXr1vHyyy/z3HPPMWXKlAtWnhd0WFcn4J6QJpMJtVrtzvDyFSor\nK70G7j3jeLJrwpPwPOvxWloq4AlPV5bD4UCSpDquUbkdiq/O2Rhkq1euk2wL1f0DBw6wceMuAgJE\nZs68mk6dOiEIApdccgsFBc8SHNwfECgpWYRW+x7V1dPQ6frgdH5PVZVAQMCNCMJ+HI4iAgIKiIoy\nkp1twWrVAz3RaiEwMIbo6AO4XFHYbHcSGAiVlcspKDgF3IxaPRiHowxYCvQD7ga0wOuoVJOAzahU\npwgKep7w8ChiYkpJT19AePgMIJz8/PeIiwvCai2na9du6PVaZs+ezLXXXu2+z6KiIo4ePQrUpqzL\nGyx/wHNxV6qEKFVVlF4MX8GzxrI942pN/W1LsknrU3yprKzkiSeewOFwsGjRonqbN19AB7b44Ncm\ntE0tYm8uvFl83hJoGovj+XoS11dArty1y9+TXTe+jBcqoVys2toN1rNnT/r163de6UePHomYTFmY\nzU4kSSAw8DTPP/84y5Z9RmFhPuXlRgIDryEoqCuiOBCzeRORkZ8QEhJNTo4euBNRHIvDcQiz+XPy\n8s6i06USHDyGnJznsdkuQZJAEKbicJQjCH2RpDzgZ+B74BrAiCCEolJNQpK2Ex1tpmtXLYmJQ9Hr\nb6BXr+N06ZJESsrjJCUlERMT47WOUq6NGzVqVJuUC8gbOFkHMigoCKBecQJPr0NL0Zri99acszWW\nZUsSaFQqFQ6HA5fLVcfK27ZtG0899RSPP/44M2fOvGDlNYIOTXwyMclFnf46PrQ8jtdWMTXZlSqK\nYp1SAV/GCz1RX0G4P9FQhqiMBx64geee+w/V1f1wufIYNaoT11xzDVOnTuXw4cOsWrWO06ej0eli\nKS6uICCgihkzRrNnjxGDwYLNlojLZQO0OJ0GQkO1aDROHI5MXK5y4BpE8RAOx1HgYsAKpAPhCMIu\nJGk/anV3NBoDBsMBhg0biM32I5GRSRiNBwkNPcWf/jSfuLi4eu+zodR9lhHxegAAIABJREFUf6Eh\n6a/mdCRvDhnWV6/mT/gzflgfGcrPyWq1uufm1VdfTVRUFFVVVVgsFj777DO6devmk+swm8089thj\nbNiwAZvNxtGjR3E4HMyaNYuzZ8+SnJzMp59+6q5TfuGFF/joo4/QaDQsXLiQKVOm+OQ6/IUO7eqU\nXTFWqxW73e4WefYVTCaTe+LW1NSg0+nc1kVjcbyAgIA2i6nJ5QlyU9j64BkvbGm2ny+UXpqL5maI\n5uTk8PPPP2MwGEhJSanzXAoLC3nkkYUYjX0AF3Fx6bzyyiM8+ODLHDump7w8hZqacbhcB+jUaSVB\nQZVERj7D2bNrKC7ejcMxjUGDRnH8+CtYLJ3QaCSSknpRUWFEpdqC1WrD6exNly6d6NOnmtdf/xM7\nd+5n584ThITomTXrCpKTk71et2ciSWuFj5sKuRC9peIJnmQojy8lEcjuUuV8kePBzRWYaCmUVp5e\nr2+TOapMkJHDAC6Xi88//5zPPvsMu91OWVkZJ06c4KKLLuKrr76qd3w0FXPmzCEhIYFnnnnG/dld\nd93FyJEj+b//+z+WLVvG8ePHWbRoEdu2beMvf/kL27Zto7CwkLFjx3L8+PE22Wy1FB2a+OTJJS8U\nvsx8kiSJqqoqt5uwsTjeb50I6kNj8UIlGbZ3DMaXijYVFRUcPXoUQRAYPHgwBoOBtLQ0/vznNzl6\nNAOzWaBr1zDmzbsWm83Bxx+fQJKGU1Ozk5qaE0RFjcBiycViOUNo6BACAsLo1s3CDTfUyp85HA6C\ngoLo16+f213YGNprUfZXiUJ9MTBlOKCt6lfbQ30FvG8orFYrL774IidOnGDp0qVuPVqbzcZPP/1E\nv3793B6llqCgoICJEydy7NixOveYlJTEsWPHCA4OpqKigtTUVNLS0vjrX/9KVFQU8+bNA+Daa6/l\nkUceYfTo0a27eT/it0vJbYjWqqx4Qo7jyaQXHBzc5nG8+uDr8oT64oXe3FiyW7mtXFJKa9bX2X2h\noaGMGTOmzmc9evRgxYq/k5+fj8vlIjo6GoOhtot6//4Hycw8R2zsNFJSniIrKwu9Xk98fDxnzpzB\n6XSSkJCAWq12L+7y5sHhcDToUvaX1mVDaElReHPhze0nJ4XJrlM5Lu2PUgEZ7ZElKkmSO9lL6ao+\nevQoDz/8MLfddhuvvPJKHcLXarWkpKS0+tw//fQTgiAwfvx48vPzSU1NZenSpRiNRrdxEBoa6u6J\nmZ+f7xanhvbrsdccXCA+fEd8cnqxHONwuVxuwmsojtdW8a22Kk9QCk7LSQeSJKHT6dzEK7uo/LFY\nKd3X7RH3iY2NreNiFAThPAFg5UKh/Lt8rKamvitbzfyvJHW0BDIROByOOiLt8v/5ulRAPm57W3my\nKL7dbufNN99k+/btfPjhh3Tv3t1v5y8qKqJnz56sXLkStVrNY489xrPPPnvee1b20Wvo/36L6NDE\np1yYWqurKdfj6XQ6dx+rmpoad5akvHt3Op3uHWpb1hi1tSu1MQHr1tQXNnTOtpbDgqYlzDQHjWX7\neXYWkC1uOU7mr3tuD1c11CUCb++0oeelDGfIguZKN3x946u9yF2eM0or7/Tp0zz00ENceeWVrF+/\n3u/XEhERUUeyccaMGSxcuJCwsDBMJhNBQUFUVFS4yyXi4uIoLi52/764uJj4+Hi/XmNr0aFjfPIi\n0tJOCkrrxVs9njz55PRu2eUnW0O+SONu7Praw5WqFLBuTgp9c+KFnmiP+FZ7uxg1Go070cGX0mLe\n0B6JJN6SOlqDpkixyeUCbU3ucnhEFEX0er079r9kyRK++eYb/vGPf9CvXz+/XwfU1h8PHDiQrVu3\nkpiYyBNPPIHBYCA9PZ0xY8Zw1113sXTpUvbt28f777/P1q1beeaZZ9i4cSOFhYWMHDmSEydOeBVt\n/63ggsVH3c7mTR3kDdXjyTtLecGRyVXOmlTWzPlDysiTfNrDBdaSFPrmxAuVz8put7ephmh7WZZN\nsULkMehwONzXCC2vmfstkHtTe2A2hsbUZ+RxBHXrEX21efCG+kpAMjMzeeCBBxgzZgwbN25sEy+N\njJCQEN5//32uvvpq7HY7o0aN4rnnnqOiooJZs2bxyiuv0K1bNz755BMAxo4dy/jx4+nbty9qtZrF\nixf/pkkPOrjF11hPvvp+I8fxZCFp+XPP/njKJBKdTuf12L4qEZDRnPIEX6EtY2pKPVIlEfozuUEJ\npZJOWwktt9bFWJ+IcmNk2Na98uRrbWsha89YnkajqfPMZM+Dt7KK1owxZTsx+fm6XC6WL1/OJ598\nwltvvdWsxrAX0HRcsPgUf2+qrqYyjuetPKE5SSTK+IRMop670Kbs2tuje4K3nbm/F0c5G1ZuyyQ/\nX1/HCz3RXvEt5eLY0udbX3d7eaPl6XmQ3X1yRmFbxYT9nSXqDZ4am/LzlWOBMjyTZ+qbk00hQ+VY\nUlp5+fn5PPjgg/Tt25dNmzb5XELxAn5Fh7b45J0e1NZlBQUFnbfDVMbxZP97U+vxfKk36bkDVS5U\n8iKm1WrbpPM61LUs2yOm1hj5tCZe6Al5oVPuzP2Ntm7UqlQHsdvtdVSN/G1JK8WW23IstTZjsyWW\ntFJaTV5LJEli1apVLF68mNdff51LLrnkguSYn9GhLT4l6tPVNJlMAHVIURnHk39nsVj8mkTiuWuX\n3bRWq9X9/7Lrz9eJDUq0t2XZ1JhlS+KFnnqkSrdbW2bDtkf8ULZCXC6Xu7tAfZmRvrKk26tcoD4r\nr7moz5Kub4zJ/6fT6dzlLsXFxTz88MPExcWxadMmn6pHLVy4kA8//JBjx45hNBp/N3JjvkCHtvgA\nN3FUVVW5J588MXwVx/M16itP8Cb5BL4RA/aXAkpjUN6rP5RBlC4/+Y/8fFwul/tef69yWM0ln6Zk\nRjYl/vW/cK++OqfsHZEt6DVr1vD888/Tq1cvTp06xf3338/cuXN92k1h586dzJs3D4fDwdGjR39X\ncmO+QIcnPjlWVF1d7c64lCeGXq8HvMfxlLVbv0VXn4yGXKRNdV+11722Z80Y4I4d+qtEQEZ73auv\nyMfb5gG8b7h+C/cqlx35G/URbWlpKQsWLMBkMhETE8OxY8c4fPgwEydOZM2aNa0+b0lJCdOmTeOd\nd97hrrvu4tixY78ruTFf4PdN602E7NKRi2RDQkLaJY7X0PW1tDzBmzvG0+VX38IuE60cZ2qre20P\nNZKG0vY9LWl5996SeKEnlPVxbXmvvuxoIAgCGo2mjiv4tyJb91sgWvm9SpLE5s2beeaZZ3jyySeZ\nMWOG+1qcTiclJSWtPq8kSdx5550sXLiQmJgY9+e/J7kxX6DDE5/D4aC6utrt1lL2D2vrOF591ycn\nkfiivUx9KheeGWuyI0BO6PBXeYASyk1FW7bSaSym5ot4oSfaOnlFhi+yRJsC5YZL3lTYbDY32flb\ntg58F8trDuqz8kwmE0899RRGo5H//ve/REdH1/mdWq0mNja21ef/+9//zqhRo7j00kvJzMysc3wl\n/pflxnyBDk98VquVgIAAnE5nHaUVQRDcE0XW1WxLC6Qtk0iUC7tKpXKXYsiLlr8XqfbelUuS1Gyi\nbaxEoL7+hXKpQHskr8hal+1BtCqViuDg4Dpzpykam/KYbK7GZlv36IO62anKJrG7d+/miSee4MEH\nH+SWW27x67VkZmayYcMGPvroI+x2Ozk5OVx66aW/K7kxX6DDx/jkySbX6MkEIMd3ampq2i221Zb9\n1GRXr1Ieqj5tTaXUW2tjX8qax7aSwmoroq1PnABq9TVl2Tp/qYLI16C0aNsqKak+RZKm/K6x5JmG\nnpm8mZHnbFuNJ2UNojxna2pqeOGFF/j5559ZunQpnTp18vu1KHHu3DmmT5/OsWPHmD179u9GbswX\n6PAW39GjR0lMTESr1bo7KlRXV7tJTi5kbQsBYGUj2rYUxlUSbX2Fw425SJUu2aZkkbZHqQDUrcnz\nt/UuPzM5XuxwONDpdO5Nla/jhZ5ojy7sUFd3srnPuDFZMVnqT0mGslUo1yC2tZUnP2PlnD18+DCP\nPvoos2fPZuHChW1CwJ5QrlcLFy783ciN+QId3uJ7/PHH2bt3Ly6Xi169elFVVcWmTZvYvXs3sbGx\ndeqYlG4YeaHyxW69vvIEf8KTaH21O24si1R29dnt9naxaNuaaJsi7txYIXRj8UJPKONMbfmM29Kd\n6m2cAXWel682EN5Qn5Vnt9t57bXX2LNnD0uXLm11J/QL8A86PPFB7eK0bNkynn76afr06UPXrl3J\nyMggMjKSYcOGMXz4cFJSUtxNRZVEKO88WzLZWlKe4Au0JdEq3X2eIsBNaQ/ji/P/FkigORmxDdUX\nNhZjbY/6OPDeKdzf8IzlyZa0rzYQ9aG+rhEnT55k/vz5XHPNNTzwwANt9uwvoPm4QHxATk4Od911\nFy+99BJDhw4FaidVYWEhe/bsYc+ePRw4cACz2UyvXr3cZNirVy93iYNy9ym7a+pboDzLE9qqQLq9\nkki8xQ89nxn4ptBeifaqP/RHTK0xMXOVSuXeXAQEBLTpu/Vl66Cmwpv0lyc849LN2UDUB28E73Q6\neffdd1m3bh1Lliw5r7HwBfz2cIH4mgGHw8Hx48fdZHjq1CmCgoIYOnQow4cPZ9iwYURERJwXk1Au\nULI7BGrLE9p6MW5rom2qteW5QDVlA9HQedu6lY58D23ZWUAeZ3a7vU4Kuj/ihd7O3V5JM63J2GyM\nDOsLYcgWvCfBnzlzhgceeIAJEybwpz/96XevePJ7wQXiawUkSaKiooJ9+/axe/du9u3bh9FoJCkp\nyW0VDhgwAI1GQ0FBAUajkYSEBKBusoivXDDeoEzZb8sduTK2JbuhmoOWZJG252LcHpqTnpmTchKW\nL+OF3tAerYOgaVZeS9BYJingrkGUm8S6XC7+9a9/sXLlShYvXszgwYNbfR1Wq5Vp06aRmZmJWq3m\nzjvv5Iknnrigs+kHXCA+H8PlcpGRkcHu3bvZs2cPR44cobCwkNLSUmbNmsW8efPo3Lkz8GsHeH8k\nzrRX/NCzMNuXai+eC5Sy44LscmpJTV5r0B6dyaFurzx5MfaG1sQLvR3LW0KHv9EedXnyM5NbNgmC\nwKlTp1iwYAH9+/dn//79jBgxgjfeeMNn7YOsViu7du3isssuw2q1MmLECJYvX86iRYsu6Gz6GBeI\nz4/YuHEj99xzD7179+bWW28lOzubvXv3kpOTQ1xcHMOGDWPYsGEMGTLEXUrhbdfZnMSZ/wW3pi8h\nJ+o4HA532UBLXaTNQXu6U1sbU2ssXujNmm6vpBl/WXmNQVmSIW8sysvLWbp0Kbt27cJisZCeno7L\n5WL06NF88cUXPn//M2fOZO7cucydO/eCzqaPcWFr4EeIosi7777L5ZdfXudzSZLIyclhz549rF+/\nnpdeegmbzUa/fv1ITU1l+PDhdO/eHaCOLJa8uNe3U/cU4m1rq0cQhDarP4S6NXmyKkh9WqS+yiJV\naom2tfKK0tpqyMprDEo3u7fmx8r6QvldOp112+n4G+2lvlJf4X1RUREPP/wwXbp04auvviIwMNA9\nj0+fPu3za5MT695///0LOpt+wAXi8yPGjh3r9XNBEEhISCAhIYHrr78eqF3Ef/zxR/bs2cNrr71G\neno6YWFh7sSZ1NRUQkNDz+v3JS/qUGsNyG7N35usmrfzOp3O88oxmlpo35Ki8fYqCFduaPy1sfBW\nOK7UbVWr1dhsNmw2m99j00orr60kAsG7jqkkSXz99de88cYbvPzyy4wfP959v8p57EvU1NRw/fXX\n8+KLLxIaGnre++7oOpu+wAXi+41Ao9GQmppKamoq999/P5IkYTQa2bt3L3v27GHx4sVUVFTQo0cP\nd+JMz549+eSTT0hKSmLEiBGIotgmi5PSrdnWVo/SndpUq6cpItMNZZF6nrc+dRtfo73KT+o7b1P1\nSFs63trTypPPq7TyysrKeOyxx9Dr9fzwww+Ehob6/VqsViszZ85k2rRp3H777UCtlXdBZ9O3uBDj\n+x+C0+nk9OnT7N69m6+++opt27bRpUsXJkyYwMiRIxk+fLi7FYm3ZAZfuPraozauLc7bUJq7HDds\nq9ZM0H5JM82NqbUkXuiL8/oK3s4rSRIbN27kb3/7G08//TTTp09vk3duNpv/v71zj4qy3P74h5uK\neCEhoVaGojgyiicE1EzzkiKJWXi8VHaU1PB0Sk1L+xGaiseOOmYCxxQTrczQo2WWkuARb5WA4v2C\nWQlaoqglKiDBzPP7g/W+Z4abIMwMwvNZy7XqBd7LoO9+9rO/+7sJCQnhqaeeYubMmepx6bNZ+8jA\ndx8ya9Ys1q5dy8KFC3n22WdJT0/nwIEDpKamcvnyZdq0aaMKZx577DEaNWpUrnCmOlt91trWtJaI\nxHgMlTKlQlGNmtMWy5rPW1vZVmXq29J/3+pClmd83Vu3bhEREUF+fj7R0dG4urqa/V4U9uzZQ1BQ\nEO3atVOPDR8+nGnTpjFmzBgyMzNVn00XFxcA5s+fz/r167Gzs0On0zFkyBCL3e/9jAx89yHHjx+n\nbdu2tGjRoszXDAYDWVlZaiA8evQoBoOBrl27qsIZDw8PAJMXU0VbVoDVeuOscV0wbRUorYqtzUb7\n6lzXnFgiu6zIj1SIksG0jRs3tljQU2qmxhMchBB8//33zJo1i+nTpzN69GiL/X2TWJ56H/i+/fZb\n3nnnHe7cuUObNm345JNPeOihhygoKGD8+PEcPnwYV1dXtVYGsGbNGpYsWQKUmFiHhoZa7wFqiLKy\nPXLkiOo4k5WVVaEPaektKyipkRm/mMz9QrBW0/29NGZXtkVa1a1law2lvdfRQbV1XePBtLVhJ1aV\n65ZnNFBQUEBkZCRZWVmsWLFC1skaAPU68On1etq0acORI0dwc3Nj8eLF/PLLL6xcuZLIyEgKCwtZ\nsGABSUlJLF++nK1bt5KZmUlQUBBHjhzBYDAQEBDA3r17y0xMvp+5mw+pt7c369ato0OHDkyaNMnk\nxQRlHfBr88VkLTFHbfYgVuYEUvpzq4tZrTmpLLusrXpheZRu9VGum56ezowZMwgLCyM0NNQq44Mk\nlqdeqzqVwvUff/yBm5sb7u7u/PbbbwAkJycTExMDQGBgIOPGjQNg9+7dBAcH4+joCEBQUBBJSUmM\nGTPGOg9hBmxsbHB3d+e5557jueeeA0peSCdOnGDZsmW89dZb+Pn5kZ2dTW5urlovVOoKykvJeGJ2\nTYUzxgNpLSlhN0erQFVVpAqNGjWyaLZljUnsVanlVbe/sCp11oqyvD///JNFixZx+PBhNmzYoO72\nSBoG9TrwNWrUiJUrV+Ln58fIkSO5dOkS8fHxAFy6dElVQAK0aNGC69evk52dbZLdNZSmUHt7e9at\nW8fp06fZu3cv/v7+Jj6ka9eu5dq1a7Rr166MD2lNeuQq68kzJ5YWkdja2qoLBCXwKRPYjT8Dc45r\nMp4sYKkWFDDN8mpjMG3pRURFfqRCCPLz8wHTRc2pU6eYNm0ao0ePZsGCBTLLa4DUi8A3aNAgrl27\nZnLMxsaGbdu2sXLlSvbu3cv169d59913+eKLLwgLCwMqbv5sqE2hERERODs7q8/v7OxMYGAggYGB\ngKkPaXx8POHh4djb29O1a1c1GD7yyCMmq3TF69C4dqO0BBQVFVW7J682MN7ms1Z22axZszJ/z0pn\nN8aTxmuiIjUOrJZsvDeXYlNZRCiLpIr6C5Xv/emnn1RnpI8++oj//ve/xMXFodFoanwvdyMhIYG3\n336boqIixo0bR3h4uNmvKbk79SLw7dy5s9zjhw4dwt7eHn9/fwA8PDwYOXIkYWFhuLu7k5OTo8qV\nb9y4QevWrXF3dycjI0M9R05ODn/5y19MzqvX65kzZw6bNm2isLCQb775Bh8fn/teMKNsZVaEra0t\nXl5eeHl5MXbsWHXbLD09nZSUFCIiIkx8SP39/enWrRvNmzc3Ec7cuXNHPaeS9QghzB74rOW8UtUA\nUN3s5m511tLK2GbNmt0XWV51UQRXStuOothUzKPT09NZuXIlv/zyCw8++CDDhg0jNTUVe3t72rdv\nb7b7ysvL4x//+AdpaWm4uLjQv39/goKC8PX1Nds1JVWjXgS+imjTpg2nTp3iwoULPProoxw8eFD1\ntnvqqafYsGEDkZGRJCYm0qVLF+zs7OjXrx9Llixh3rx56PV6duzYwRtvvGFy3vfee4/Lly+rTuiK\nPkin0+Hp6Ul8fDxJSUlMnTpVFcwsXrzYRDATHBx83wtmbGxsaNq0KX369KFPnz6AqQ9pUlISCxcu\nVFfbWq2WgwcPkpOTw3/+8x91eGp1X+jVxVrOK1DzAFBedmMcDCuqs0KJ9VVdDfLmuK6xl6nyOzYY\nDBgMBlq1asWaNWsoKioiLS2N7du3k5GRwXvvvWe2e0pLS6Nbt25qSWXEiBEkJCTIwFcHqNeBz83N\njaioKJ5++mmEEHh6erJ69Wrgf1mXRqPB1dWVdevWAeDp6cn06dPx8/NDCMGMGTPUvjco2fZcu3at\nyfgP5R92QxbMKJTnQ6oICebMmYNWq8XBwYEXXnihjA8p1L5wxhI+l+VhrlaBu3mRGvtr2tjYqM33\nxp6u5sKSWZ4xFW3lXrx4kcmTJ9O9e3eSk5NVwYyySDM3pXUEDz74IOfOnbPItSWVU68DH8CoUaMY\nNWpUmeOOjo5s3Lix3J+ZOHEiEydOLPdrFy5cQK/XM3z4cDIzM+nQoQNxcXG0bt1aCmYqICEhgS+/\n/JLExER69uyJEILff/+d1NRUDhw4UMaHNCAgAK1Wi729fRnhDPxPxGBcLyyNtTIPsLyIRNkiVeqm\nyvBfJSBaIqOuC5+1spVrMBhYv349H3/8McuWLaNHjx4WuZfSKIsUYxqKXqCuU+8D371SkWBm+fLl\nPPzww2zevBknJyeioqKYPHmyGkSlYKYsw4YNY+jQoSYZsouLC0OGDFEtlox9SNesWcPp06dp3Lgx\nvr6+qnDGzc2tjHBG8Vk0DoSKwMHS4hVr1hArmgBflS1S42BY3YzaWlmeUl8uneVdvnyZadOm4enp\nSXJysrrLYg1Km0jn5OTI5vg6ggx8FVCRYCYjIwMHBwecnJwACAkJIS4uDqBGghljNm3axIQJE7h5\n8ybAfS+aUepUlWFnZ4dWq0Wr1TJhwgSEENy+fZtDhw6pKtIrV67wyCOPmPiQKnPR9Ho9eXl56nWU\nF7ixus9cWLOGWNWt3KqMa6qOitRari9QfpYnhGDLli1ER0ezePFi+vbta3XLse7duzNhwgSuXr3K\nAw88wBdffMGCBQusek+SEuq1c4s5MBgMdO7cmXXr1uHv709sbCzp6emsWrVKFcQogpnFixeza9cu\nfvnlF4YOHUp6ejp6vZ6AgAB27NhhUjtUOHfuHOPGjePkyZNq4GvILjPGGAwGLly4wIEDB0hJSVF9\nSH18fLC1tWXDhg1s2bIFX1/fcn0hzTGV3TjwWHJahblcbiry1CzdH6cMALak64txsDXO8n7//Xfe\nfPNNnJ2d0el05XrYWovt27er7Qx/+9vfmDVrlrVvSYIMfPfE0aNHmTRpErdv38bb25vVq1fj7OxM\nQUEBoaGhHD16VBXMeHp6ArB69WqWLl2qCmbGjx9f5rx37txh0KBBfPTRRwQEBHDr1i0A+vXrR0xM\nDD4+PgA89NBDZGdns3btWk6ePMn7778PoIpy6qNopjyEEBw9epSXX36Z3NxcevXqxc8//1ypD6nx\nn5pYYVnLXg0sO7KovC1SRTijtF7UdqN9eRQXF5Ofn4+9vb3a8ymEIDExkX/961/MmzePp59+2upZ\nnuT+QG513gOPPfYYqampZY7fq2BGYcqUKbz66qt06tTJ5LgUzVTMG2+8waRJkwgLC1OzEcWHdN++\nfSxdutTEh7R79+5oNBq1leJehDPWrGtZemSRskWqBHp7e3saN25s0pdpvEVaul5YUyraUr158ybh\n4eEUFRWRmJioDmeVSKqCDHwWpjzRDKAOnnzxxRcpLwmXopmy2NjYsGfPHpOXf0U+pKdPn+bAgQMs\nX76cjIwMnJyc8PPzU+uFrq6uZV7mxsIZe3t7bG1tVbsxS6sXlWBrZ2dntWBbupZXW432FVHeMwsh\n2L9/P7Nnz2bmzJmMGDFCZnmSaiMDn4WpSDSj0+nYvXu32mCfn5+PVqvl2LFjNRbNjBo1ivT0dOzt\n7Rk8eDDR0dHA/S+aAar00lNs1bp27cqkSZMQQlTZh9RgMFBcXExubq7aB6b4axYXF9f6INrSWFNE\nUp1gW51G+7upSI23kY2fOT8/n7lz53Lp0iW2bduGm5ub+R5eUq+RNb46SvPmzdUaX01FMwkJCQwZ\nMgSDwcDQoUP5+9//zrBhw6RoxghjH9KUlBSOHz+Ovb09HTp04OLFi/z222/s37+fRo0aqU325hbO\nGHuKWtLL1FzBtrJxTUowBNQpHcb1y7S0NN5++21ee+01XnrppVpfbNTnxaGkLDLw1VFatGhh0s5Q\nE9GMMW+99RYajYZXXnlFimYqwWAwsHr1av7v//6PgIAAWrRowcWLF8v4kDo6OpZpCyg9Q06pFVY1\ncFlrMC2YZnmWUGyWzgqV4cc//vgjycnJdOvWjd27d/Pjjz8SGxtLmzZtzHIfcnHYsJBbnXUUJehB\nzUUzCvn5+WzdupWEhARAimYqIysrizVr1rBz5078/PyAyn1I/f396d69Ox06dDAZ3KtYiEHZsTnl\nBUJrjQ6y1paqUrcrLCzE1tZW7YFs3LgxWVlZbNy4kfPnz9OxY0fmzZtHr1697rq4uxcUIwVbW1u0\nWi1XrlwBpA1hfUUGvnpCRaKZhIQEHnroIYQQjB8/nrFjx+Ll5aV+XYpmyqddu3YcOHCgjHCmtA9p\nUVERx44dIyUlhSVLlvDTTz/h7Oxs4kPq7OxcZjpF6XqXjY0NhYXAb0mqAAAOHElEQVSFCCEs6voC\n1hXOlOc4U1xcTGJiIhcvXuSbb77Bw8ODEydOkJqayo8//mjWe5KLw4aBDHz1hIpEM1Dygpk0aRKt\nWrVi9uzZ6vHacpoxpj7NH6tKtuXg4IC/vz/+/v68/vrr1fIhNRgMFBUVcePGDRo3bgyULDiKi4vV\nKeP1VThTejah8pxnz57ljTfeYOjQoSQlJakLMD8/PzXzvlfk4lCiIANfPUev1zN+/HhatGihbtko\n1GQ0U3nI+WPV8yHt0KEDp0+fxsHBga1bt5r0FiptE0qjeG0LZ+palqfX64mNjWXr1q2sWLGCLl26\n1Pq168riUGJ9pLilnpOZmUn79u3RaDRqf2CPHj34+OOPa1U0AyV1j5iYGL788ksAoqOjuXXrFhER\nEeZ7wPsQvV5PTEwMc+fOpW/fvgDl+pA2atSo1oUz1szyDAYD+fn5gKm9W2ZmJlOmTKF3795ERERY\n9J6g8sVhbdgQSuoeMvBJao3169ezf/9+Vq5cCUB8fDw//PBDmZdJQ+fYsWO89tprrFq1Cq1WC1Ts\nQ9q1a1f8/f0JCAigbdu2JsIZpa0CqiacsbRiU6H0kNjGjRur44M+/fRTPvvsM6KioggICLDI/ZTG\nkotDSd1ABj5JrfH555/z/fffs3z5cqAk8O3Zs4fY2Fgr31ndQ/G7rOzrhYWFHDlyhJSUFFJSUsjK\nyirXhxQwCYSlhTPGjjPWyPKMRzUpWV52djZTp07F29ub+fPn06RJE4vdk0Qia3ySWqOm88cKCwsJ\nDg4mMzMTOzs7QkNDCQ8P5/r164wZM4bz58/j6enJ559/zgMPPADAggULWLduHQ4ODuh0OoKCgmr9\nuczB3bYmbWxsaNKkCY8//jiPP/44wD35kN66dUtViNrZ2ZlkiOYWzhhneUqbghCCzZs38+GHH7Jk\nyRJ69+4tLcckFkdmfJJa4/bt2/j4+JCWlsYDDzzAgAEDWLBgAX369KnSzxcWFvLDDz/Qv39/CgsL\n6dGjB5988glRUVE8/vjjvPLKK6xatYpTp04RFRXFvn37iIiIYN++fVy5coW+ffty6tQpi7YCWBtj\nH9KUlBTVh9THx4dff/2VY8eO8cMPP9CkSRMTx5Ti4mKTAb61KZxRGvD1ej2Ojo7q7+PatWtMnz6d\n1q1bs2jRIpo3b17ja0kk94IMfJJapTbnj40YMYKwsDDCwsI4ceIEzZs3Jzc3F39/f86dO8ecOXNw\ndXVl8uTJAAwfPpw333yTJ554orYe575DCMG2bdsICwvDw8MDNzc3Ll++XKEPaW0KZ8C0Ab9JkyZq\nlrd9+3Z0Oh3//Oc/CQwMlFmexKo0nKWxxCIEBwcTHBxc4/MoW3pxcXFcv35dzQ5atmzJ77//DpTU\niRRTb5BNxFAylHXGjBnExcWp7RTGPqTx8fGEh4erxt1KMHzkkUeA/9UKSzvO3G3CghCCgoIC9Hq9\nSQN+bm4ub7/9NgBJSUnqFrVEYk1k4JPUOe7cucPIkSN57733aNmyZaWNwrKJ2BQXFxdOnTpl8rnY\n2tri5eWFl5cXY8eOVYNUeno6KSkpRERE8Ouvv5bxIVWEM4poxnjCgnEg1Ov13LlzBwcHB5o1a6Zm\neXv27GHu3LmEh4cTEhJitixv06ZNTJgwwcTbVhpLSypDBj5JnaKwsJARI0YQHBzM2LFjgZIsLy8v\nDycnJ3Jzc9Who6XFNFevXr2rmEan0/Hpp59y4sSJeimagbKLgdLY2NjQtGlT+vTpo9Zf78WH9M6d\nO6r8387Ojv3791NUVISPjw/Lli3j+vXrJCQkmNW4+dy5c3zwwQcmx3Q6HZ6ensTHx5OUlMTUqVNV\nY+nFixebGEsHBwdLY+mGiJBI6gh5eXkiMDBQLFq0yOR4aGioiIuLE0IIsXLlSjF+/HghhBB79uwR\n/fr1E3q9Xly6dEl4eHiIvLy8Cs//3XffCV9fX+Hj4yOEEOLll18Wq1atEkIIERsbK6ZMmSKEEGLv\n3r2id+/ewmAwiOzsbNGxY0dRVFRU689b1/nzzz/FwYMHRUxMjBgzZozo0aOHGDx4sHjnnXeETqcT\njz76qNi2bZu4efOmuHHjhoiJiRE9e/YUjo6Owt3dXbz00kvi3//+tzhx4oRZ7q+goED07t1bnDlz\nRjRr1kw93rdvX3H8+HH1/93d3YUQQqxZs0ZMnz5dPT5t2jTx2WefmeXeJHUbmfFJ6gxpaWns3buX\nCxcusHbtWqBEsKLT6RgzZgyLFi2iXbt2rF+/HoC+ffsyYMAAtFotdnZ2fPjhhzRt2rTccyuKwtjY\nWLXRODk5maioKABGjx6Nv78/UVFR7Nq1i1GjRqnT3Dt37kxqamqDE82U50P622+/8frrr5OcnMyg\nQYOYP38+Xl5e+Pr6cvLkSVxcXMjIyODmzZukpKSQmppKTk6OWSzIpkyZwquvvkqnTp1Mjktjacnd\nkIFPUmfo16+fKqgoTWJiYrnHZ8+ebeKtWB5CCEJDQ9HpdCYvRCmaqR42NjaEh4fj6OjI+fPncXFx\nUX1Id+zYgZOTE19//bXaH9ilS5cqj8yqiIqMpWfOnAnAiy++qG63GiONpSWVIQOfpN7zwQcf0KtX\nL5588kkyMzPV41I0U31WrFihil6g5HPSarWq9VptU5GxtE6nY/fu3eoCJT8/H61Wy7Fjx6SxtOSu\nWMasTyKxIpmZmXz66ad4e3szcOBAzp07x5NPPomzszN5eXkANRLN5Ofn89prr+Hl5YWHhwe5ublc\nv36doKAgNBoNTz/9NH/88Yf6/QsWLKBTp074+PiwY8cOMz21eTAOetZkxowZnDt3jjNnznDmzBma\nNm2qTrpQpo4AZaaObNu2jYKCAm7fvs2OHTvo37+/lZ9EYhWsXGOUSCxKZmam6NKlixCi9kQz48eP\nF3PmzDE5JoUzlqV58+bqf+fn54tRo0aJjh07il69eomff/5Z/dpHH30kvL29RadOndTfvaThIQOf\npEFx/vx5VdV59epVERgYKDp27CgGDx4srl27pn5fZGSk0Gg0QqvViu3bt1d4vuzsbNG5c2dhMBhM\njnt4eIibN28KIYS4ceOG6NChgxBCiHfffVdER0er3xcSEiK+++67Wns+iURyd2SNT9KgaNu2LceP\nHwfA1dW1RqIZgJMnT2JjY8OAAQPIzs7G39+f2NhYKZyRSOowssYnkdSAnJwcOnbsSFJSEqdPn8bN\nzY158+ZJ4YxEUoeRgU8iqQGtWrXCyckJBwcHbG1tee6558jIyKg14QzAJ598go+PDxqNhpEjR5KX\nl1dvxTMSiSWQgU8iqQG9evVi3759ZGVlAZCQkECPHj3o378/GzduBGDDhg0MHDgQgKeeeopNmzZh\nMBjIzs7m8OHDdO/evcLzX7lyhcjISFJSUjh79iytW7cmJiaGGTNm8Ne//pWzZ88SEhLC3LlzAdi3\nbx87duzgzJkz7Ny5k6lTp1JcXGzeD0Eiuc+QgU8iqQEtWrQgLi6OZ599ls6dO3Pt2jVmzpyJTqdj\n48aNaDQatmzZwuLFiwFTt5mBAwdW6jYDJdugeXl53Lp1CyjJGBs1akRycjLPP/88UOI6k5CQAFCh\n60xdQa/XM2vWLDQaDW3btuXEiRNAibH0Cy+8gEaj4YknnjDpt1yzZo3aK/jxxx9b58Yl9Qtrq2sk\nEknlLFy4UDg7O4uJEyeKZ555RhQUFJh4UwohRKtWrYQQQrzyyisiPj5ePR4WFiY2b95s0futjMjI\nSDFhwgS1hUNRw86bN0+88847QgghEhMTxbBhw4QQJSpcjUYj8vPzxe3bt4W3t7fIycmxzs1L6g0y\n45NI6jC5ubl8/fXXpKSkMHjwYM6fP8+uXbvuS/HMn3/+ydq1a4mJiVHn9Smjiowz2MDAQNLS0gDY\nvXs3wcHBODo64uTkRFBQEElJSdZ5AEm9QQY+iaQOs3PnTry9vdFoNIwYMQKdTseKFStqJJ45fPiw\niVXXvQhlDh06hK+vLxqNhqlTp5brl1maCxcuoNfrGT58ON7e3jzzzDPk5OQA0lhaYllk4JNI6jDt\n27dn//79ajA6ePAg3t7eDBgw4J7EM2+++SaBgYEmgao6Qhm9Xg/ASy+9xOeff87Zs2e5evUqX331\nlXq+QYMG4evra/KnW7duXL16lYcffpjNmzdz5swZBg4cyOTJk9Wfk8bSEkshG9glkjqMr68vr7/+\nOj179sTOzg5fX19WrVpFQUHBPY1qev/995kyZQpDhw5Vj1VnPFNKSgoPP/wwTZs2VRvxn3/+eb75\n5htCQkKAio2lMzIycHBwwMnJCYCQkBDi4uIApLG0xKLIjE8iqeNMnjyZs2fPcvr0adavX4+Tk5Pq\nOqOMBHJxcVG/f/bs2WRkZHDq1CmGDBlS5nyltyUrc5kpb5sxOzvbZFvS1dW1StuPHTt25OrVqxw6\ndAiAb7/9lp49ewJIY2mJRZEZn0TSwLkXocy9bD/a2toSHx/PpEmTuH37Nt7e3qxevRooma8XGhqK\nRqPB1dWVdevWAeDp6cn06dPx8/NDCMGMGTPw8PCo/kNKJEbIwCeRNHBatmxJXl4eTk5OVRLKlHfc\n3d29Std67LHHyu0rdHR0VGuWpZk4cWKNB9pKJMbIrU6JpIFTXaGMp6cnubm5nD17tszPSCT3Azai\nKjpkiURSL5gzZw5fffUVP/30E1qtlqVLl+Lt7c2YMWPIzMxUhTJKzXD+/PmsX78eOzs7dDqdWjM8\nePAgYWFh5OXlMXjwYKKjo9WePImkriMDn0QikUgaFHKrUyKRSCQNChn4JBKJRNKgkIFPIpFIJA0K\nGfgkEolE0qD4f8dRvT8AD1ypAAAAAElFTkSuQmCC\n", |
| 283 |
"text": [ |
283 |
"text": [ |
| 284 |
"<matplotlib.figure.Figure at 0x7fe4581e1e50>" |
284 |
"<matplotlib.figure.Figure at 0x7f5b0cca34d0>" |
| 285 |
] |
285 |
] |
| 286 |
} |
286 |
} |
| 287 |
], |
287 |
], |
| 288 |
"prompt_number": 4 |
288 |
"prompt_number": 8 |
| 289 |
}, |
289 |
}, |
| 290 |
{ |
290 |
{ |
| 291 |
"cell_type": "markdown", |
291 |
"cell_type": "markdown", |
| 292 |
"metadata": {}, |
292 |
"metadata": {}, |
| 293 |
"source": [ |
293 |
"source": [ |
| 294 |
"Pro lep\u0161\u00ed zobrazen\u00ed jednotliv\u00fdch zkreslen\u00ed vykresl\u00edme i 2D pr\u016fm\u011bt dat." |
294 |
"Pro lep\u0161\u00ed zobrazen\u00ed jednotliv\u00fdch zkreslen\u00ed vykresl\u00edme i 2D pr\u016fm\u011bt dat." |
| 295 |
] |
295 |
] |
| 296 |
}, |
296 |
}, |
| 297 |
{ |
297 |
{ |
| 298 |
"cell_type": "code", |
298 |
"cell_type": "code", |
| 299 |
"collapsed": false, |
299 |
"collapsed": false, |
| 300 |
"input": [ |
300 |
"input": [ |
| 301 |
"plt.plot(x, y,'.')" |
301 |
"plt.plot(x, y,'.')" |
| 302 |
], |
302 |
], |
| 303 |
"language": "python", |
303 |
"language": "python", |
| 304 |
"metadata": {}, |
304 |
"metadata": {}, |
| 305 |
"outputs": [ |
305 |
"outputs": [ |
| 306 |
{ |
306 |
{ |
| 307 |
"metadata": {}, |
307 |
"metadata": {}, |
| 308 |
"output_type": "pyout", |
308 |
"output_type": "pyout", |
| 309 |
"prompt_number": 5, |
309 |
"prompt_number": 6, |
| 310 |
"text": [ |
310 |
"text": [ |
| 311 |
"[<matplotlib.lines.Line2D at 0x7fe45330c850>]" |
- |
|
| 312 |
] |
- |
|
| 313 |
}, |
- |
|
| 314 |
{ |
- |
|
| 315 |
"metadata": {}, |
- |
|
| 316 |
"output_type": "display_data", |
- |
|
| 317 |
"png": 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7Vmow2dnSiEwURRCvISe7kVdWSqvr5uURLV4c+5p6ys4JvVenIkpdjodRvena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\n", |
311 |
"[<matplotlib.lines.Line2D at 0x7f5b0cc2a750>]" |
| 318 |
"text": [ |
- |
|
| 319 |
"<matplotlib.figure.Figure at 0x7fe458176d10>" |
- |
|
| 320 |
] |
312 |
] |
| 321 |
} |
313 |
} |
| 322 |
], |
314 |
], |
| 323 |
"prompt_number": 5 |
315 |
"prompt_number": 6 |
| 324 |
}, |
316 |
}, |
| 325 |
{ |
317 |
{ |
| 326 |
"cell_type": "markdown", |
318 |
"cell_type": "markdown", |
| 327 |
"metadata": {}, |
319 |
"metadata": {}, |
| 328 |
"source": [ |
320 |
"source": [ |
| 329 |
"Z grafu je vid\u011bt, \u017ee se uplat\u0148uj\u00ed oba zn\u00e1m\u00e9 deforma\u010dn\u00ed jevy - soft-iron a hard-iron. Pokus\u00edme se je proto kompenzovat. Nejd\u0159\u00edve zkus\u00edme odstranit Hard-iron efekty zp\u016fsobuj\u00edc\u00ed offsety." |
321 |
"Z grafu je vid\u011bt, \u017ee se uplat\u0148uj\u00ed oba zn\u00e1m\u00e9 deforma\u010dn\u00ed jevy - soft-iron a hard-iron. Pokus\u00edme se je proto kompenzovat. Nejd\u0159\u00edve zkus\u00edme odstranit Hard-iron efekty zp\u016fsobuj\u00edc\u00ed offsety." |
| 330 |
] |
322 |
] |
| 331 |
}, |
323 |
}, |
| 332 |
{ |
324 |
{ |
| 333 |
"cell_type": "code", |
325 |
"cell_type": "code", |
| 334 |
"collapsed": false, |
326 |
"collapsed": false, |
| 335 |
"input": [ |
327 |
"input": [ |
| 336 |
"xoffset = (min(x) + max(x))/2\n", |
328 |
"xoffset = (min(x) + max(x))/2\n", |
| 337 |
"print xoffset" |
329 |
"print xoffset" |
| 338 |
], |
330 |
], |
| 339 |
"language": "python", |
331 |
"language": "python", |
| 340 |
"metadata": {}, |
332 |
"metadata": {}, |
| 341 |
"outputs": [ |
333 |
"outputs": [ |
| 342 |
{ |
334 |
{ |
| 343 |
"output_type": "stream", |
335 |
"output_type": "stream", |
| 344 |
"stream": "stdout", |
336 |
"stream": "stdout", |
| 345 |
"text": [ |
337 |
"text": [ |
| 346 |
"34.675\n" |
338 |
"34.675\n" |
| 347 |
] |
339 |
] |
| 348 |
} |
340 |
} |
| 349 |
], |
341 |
], |
| 350 |
"prompt_number": 37 |
342 |
"prompt_number": 37 |
| 351 |
}, |
343 |
}, |
| 352 |
{ |
344 |
{ |
| 353 |
"cell_type": "code", |
345 |
"cell_type": "code", |
| 354 |
"collapsed": false, |
346 |
"collapsed": false, |
| 355 |
"input": [ |
347 |
"input": [ |
| 356 |
"yoffset = (min(y) + max(y))/2\n", |
348 |
"yoffset = (min(y) + max(y))/2\n", |
| 357 |
"print yoffset" |
349 |
"print yoffset" |
| 358 |
], |
350 |
], |
| 359 |
"language": "python", |
351 |
"language": "python", |
| 360 |
"metadata": {}, |
352 |
"metadata": {}, |
| 361 |
"outputs": [ |
353 |
"outputs": [ |
| 362 |
{ |
354 |
{ |
| 363 |
"output_type": "stream", |
355 |
"output_type": "stream", |
| 364 |
"stream": "stdout", |
356 |
"stream": "stdout", |
| 365 |
"text": [ |
357 |
"text": [ |
| 366 |
"-53.29\n" |
358 |
"-53.29\n" |
| 367 |
] |
359 |
] |
| 368 |
} |
360 |
} |
| 369 |
], |
361 |
], |
| 370 |
"prompt_number": 39 |
362 |
"prompt_number": 39 |
| 371 |
}, |
363 |
}, |
| 372 |
{ |
364 |
{ |
| 373 |
"cell_type": "code", |
365 |
"cell_type": "code", |
| 374 |
"collapsed": false, |
366 |
"collapsed": false, |
| 375 |
"input": [ |
367 |
"input": [ |
| 376 |
"plt.plot(x-xoffset, y-yoffset,'.')" |
368 |
"plt.plot(x-xoffset, y-yoffset,'.')" |
| 377 |
], |
369 |
], |
| 378 |
"language": "python", |
370 |
"language": "python", |
| 379 |
"metadata": {}, |
371 |
"metadata": {}, |
| 380 |
"outputs": [ |
372 |
"outputs": [ |
| 381 |
{ |
373 |
{ |
| 382 |
"metadata": {}, |
374 |
"metadata": {}, |
| 383 |
"output_type": "pyout", |
375 |
"output_type": "pyout", |
| 384 |
"prompt_number": 41, |
376 |
"prompt_number": 41, |
| 385 |
"text": [ |
377 |
"text": [ |
| 386 |
"[<matplotlib.lines.Line2D at 0x7f5bf860a410>]" |
378 |
"[<matplotlib.lines.Line2D at 0x7f5bf860a410>]" |
| 387 |
] |
379 |
] |
| 388 |
}, |
380 |
}, |
| 389 |
{ |
381 |
{ |
| 390 |
"metadata": {}, |
382 |
"metadata": {}, |
| 391 |
"output_type": "display_data", |
383 |
"output_type": "display_data", |
| 392 |
"png": 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mqiQi8pXZIJOjMZZ2hkLAgX/Xrl1YvXr18P22tjaUlJSgvLwczz777PDyZ555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\nDL4AAAAASUVORK5CYII=\n", |
| 393 |
"text": [ |
385 |
"text": [ |
| 394 |
"<matplotlib.figure.Figure at 0x7f5bf86a8fd0>" |
386 |
"<matplotlib.figure.Figure at 0x7f5bf86a8fd0>" |
| 395 |
] |
387 |
] |
| 396 |
} |
388 |
} |
| 397 |
], |
389 |
], |
| 398 |
"prompt_number": 41 |
390 |
"prompt_number": 41 |
| 399 |
}, |
391 |
}, |
| 400 |
{ |
392 |
{ |
| 401 |
"cell_type": "markdown", |
393 |
"cell_type": "markdown", |
| 402 |
"metadata": {}, |
394 |
"metadata": {}, |
| 403 |
"source": [ |
395 |
"source": [ |
| 404 |
"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." |
396 |
"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." |
| 405 |
] |
397 |
] |
| 406 |
}, |
398 |
}, |
| 407 |
{ |
399 |
{ |
| 408 |
"cell_type": "code", |
400 |
"cell_type": "code", |
| 409 |
"collapsed": false, |
401 |
"collapsed": false, |
| 410 |
"input": [ |
402 |
"input": [ |
| 411 |
"try:\n", |
403 |
"try:\n", |
| 412 |
"\n", |
404 |
"\n", |
| 413 |
" for n in range(MEASUREMENTS):\n", |
405 |
" for n in range(MEASUREMENTS):\n", |
| 414 |
" (x, y, z) = mag_sensor.axes()\n", |
406 |
" (x, y, z) = mag_sensor.axes()\n", |
| 415 |
" phi = np.arctan2(x-xoffset, y-yoffset)\n", |
407 |
" phi = np.arctan2(x-xoffset, y-yoffset)\n", |
| 416 |
" clear_output()\n", |
408 |
" clear_output()\n", |
| 417 |
" print ((phi*180)/pi+180)\n", |
409 |
" print ((phi*180)/pi+180)\n", |
| 418 |
" time.sleep(0.2)\n", |
410 |
" time.sleep(0.2)\n", |
| 419 |
" sys.stdout.flush()\n", |
411 |
" sys.stdout.flush()\n", |
| 420 |
"except KeyboardInterrupt:\n", |
412 |
"except KeyboardInterrupt:\n", |
| 421 |
" sys.exit(0)" |
413 |
" sys.exit(0)" |
| 422 |
], |
414 |
], |
| 423 |
"language": "python", |
415 |
"language": "python", |
| 424 |
"metadata": {}, |
416 |
"metadata": {}, |
| 425 |
"outputs": [ |
417 |
"outputs": [ |
| 426 |
{ |
418 |
{ |
| 427 |
"output_type": "stream", |
419 |
"output_type": "stream", |
| 428 |
"stream": "stdout", |
420 |
"stream": "stdout", |
| 429 |
"text": [ |
421 |
"text": [ |
| 430 |
"83.1246570299\n" |
422 |
"83.1246570299\n" |
| 431 |
] |
423 |
] |
| 432 |
} |
424 |
} |
| 433 |
], |
425 |
], |
| 434 |
"prompt_number": 44 |
426 |
"prompt_number": 44 |
| 435 |
}, |
427 |
}, |
| 436 |
{ |
428 |
{ |
| 437 |
"cell_type": "markdown", |
429 |
"cell_type": "markdown", |
| 438 |
"metadata": {}, |
430 |
"metadata": {}, |
| 439 |
"source": [ |
431 |
"source": [ |
| 440 |
"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", |
432 |
"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", |
| 441 |
"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", |
433 |
"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", |
| 442 |
"\n", |
434 |
"\n", |
| 443 |
"Zpracov\u00e1n\u00ed dat 3D\n", |
435 |
"Zpracov\u00e1n\u00ed dat 3D\n", |
| 444 |
"-----------\n", |
436 |
"-----------\n", |
| 445 |
"\n", |
437 |
"\n", |
| 446 |
"K tomu pou\u017eijeme datov\u00fd set, kde jsou body rozprost\u0159en\u00e9 po cel\u00e9m povrchu koule. " |
438 |
"K tomu pou\u017eijeme datov\u00fd set, kde jsou body rozprost\u0159en\u00e9 po cel\u00e9m povrchu koule. " |
| 447 |
] |
439 |
] |
| 448 |
}, |
440 |
}, |
| 449 |
{ |
441 |
{ |
| 450 |
"cell_type": "code", |
442 |
"cell_type": "code", |
| 451 |
"collapsed": false, |
443 |
"collapsed": false, |
| 452 |
"input": [ |
444 |
"input": [ |
| 453 |
"data = np.load('./calibration_data_3Dset.npz')\n", |
445 |
"data = np.load('./calibration_data_3Dset.npz')\n", |
| 454 |
"list_meas = data['data']\n", |
446 |
"list_meas = data['data']\n", |
| 455 |
"x = list_meas[:, 0]\n", |
447 |
"x = list_meas[:, 0]\n", |
| 456 |
"y = list_meas[:, 1]\n", |
448 |
"y = list_meas[:, 1]\n", |
| 457 |
"z = list_meas[:, 2]" |
449 |
"z = list_meas[:, 2]" |
| 458 |
], |
450 |
], |
| 459 |
"language": "python", |
451 |
"language": "python", |
| 460 |
"metadata": {}, |
452 |
"metadata": {}, |
| 461 |
"outputs": [], |
453 |
"outputs": [], |
| 462 |
"prompt_number": 1 |
454 |
"prompt_number": 1 |
| 463 |
}, |
455 |
}, |
| 464 |
{ |
456 |
{ |
| 465 |
"cell_type": "code", |
457 |
"cell_type": "code", |
| 466 |
"collapsed": false, |
458 |
"collapsed": false, |
| 467 |
"input": [ |
459 |
"input": [ |
| 468 |
"from mpl_toolkits.mplot3d.axes3d import Axes3D\n", |
460 |
"from mpl_toolkits.mplot3d.axes3d import Axes3D\n", |
| 469 |
"#%pylab qt ## po odkomentovani a zakomentovani nasledujiciho radku udela vystup do QT okna. \n", |
461 |
"#%pylab qt ## po odkomentovani a zakomentovani nasledujiciho radku udela vystup do QT okna. \n", |
| 470 |
"%pylab inline\n", |
462 |
"%pylab inline\n", |
| 471 |
"fig = plt.figure()\n", |
463 |
"fig = plt.figure()\n", |
| 472 |
"ax = Axes3D(fig)\n", |
464 |
"ax = Axes3D(fig)\n", |
| 473 |
"p = ax.scatter(x,y,z)" |
465 |
"p = ax.scatter(x,y,z)" |
| 474 |
], |
466 |
], |
| 475 |
"language": "python", |
467 |
"language": "python", |
| 476 |
"metadata": {}, |
468 |
"metadata": {}, |
| 477 |
"outputs": [ |
469 |
"outputs": [ |
| 478 |
{ |
470 |
{ |
| 479 |
"output_type": "stream", |
471 |
"output_type": "stream", |
| 480 |
"stream": "stdout", |
472 |
"stream": "stdout", |
| 481 |
"text": [ |
473 |
"text": [ |
| 482 |
"Populating the interactive namespace from numpy and matplotlib\n" |
474 |
"Populating the interactive namespace from numpy and matplotlib\n" |
| 483 |
] |
475 |
] |
| 484 |
}, |
476 |
}, |
| 485 |
{ |
477 |
{ |
| 486 |
"metadata": {}, |
478 |
"metadata": {}, |
| 487 |
"output_type": "display_data", |
479 |
"output_type": "display_data", |
| 488 |
"png": 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Px1Eulw2TntF7ymEHYQ5GxUYBXAEg8Pm/L0M2u9mU9mbUD2lWS1Q6x0tChsMa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\nQyqVIjg4GCKRyCJfb0uBFL4XGIqi0NnZiZqaGlRVVUEikeD+/fvw9fV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|
480 |
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Px1Eulw2TntF7ymEHYQ5GxUYBXAEg8Pm/L0M2u9mU9mbUD2lWS1Q6x0tChsMa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\ndK1KAERdWwAAAABJRU5ErkJggg==\n", |
| 489 |
"text": [ |
481 |
"text": [ |
| 490 |
"<matplotlib.figure.Figure at 0x7f5bf86dd450>" |
482 |
"<matplotlib.figure.Figure at 0x7f5bf86dd450>" |
| 491 |
] |
483 |
] |
| 492 |
} |
484 |
} |
| 493 |
], |
485 |
], |
| 494 |
"prompt_number": 23 |
486 |
"prompt_number": 23 |
| 495 |
}, |
487 |
}, |
| 496 |
{ |
488 |
{ |
| 497 |
"cell_type": "code", |
489 |
"cell_type": "code", |
| 498 |
"collapsed": false, |
490 |
"collapsed": false, |
| 499 |
"input": [ |
491 |
"input": [ |
| 500 |
"import scipy\n", |
492 |
"import scipy\n", |
| 501 |
"from scipy import optimize\n", |
493 |
"from scipy import optimize\n", |
| 502 |
"import calibration_utils\n", |
494 |
"import calibration_utils\n", |
| 503 |
"\n", |
495 |
"\n", |
| 504 |
"sensor_ref = 1.\n", |
496 |
"sensor_ref = 1.\n", |
| 505 |
"sensor_res = 0.73\n", |
497 |
"sensor_res = 0.73\n", |
| 506 |
"noise_window = 10\n", |
498 |
"noise_window = 10\n", |
| 507 |
"noise_threshold = 1000" |
499 |
"noise_threshold = 1000" |
| 508 |
], |
500 |
], |
| 509 |
"language": "python", |
501 |
"language": "python", |
| 510 |
"metadata": {}, |
502 |
"metadata": {}, |
| 511 |
"outputs": [], |
503 |
"outputs": [], |
| 512 |
"prompt_number": 3 |
504 |
"prompt_number": 3 |
| 513 |
}, |
505 |
}, |
| 514 |
{ |
506 |
{ |
| 515 |
"cell_type": "markdown", |
507 |
"cell_type": "markdown", |
| 516 |
"metadata": {}, |
508 |
"metadata": {}, |
| 517 |
"source": [ |
509 |
"source": [ |
| 518 |
"Uprav\u00edme strukuturu pole m\u011b\u0159en\u00fdch hodnot do sn\u00e1ze indexovateln\u00e9ho form\u00e1tu." |
510 |
"Uprav\u00edme strukuturu pole m\u011b\u0159en\u00fdch hodnot do sn\u00e1ze indexovateln\u00e9ho form\u00e1tu." |
| 519 |
] |
511 |
] |
| 520 |
}, |
512 |
}, |
| 521 |
{ |
513 |
{ |
| 522 |
"cell_type": "code", |
514 |
"cell_type": "code", |
| 523 |
"collapsed": false, |
515 |
"collapsed": false, |
| 524 |
"input": [ |
516 |
"input": [ |
| 525 |
"measurements = np.array(list_meas)" |
517 |
"measurements = np.array(list_meas)" |
| 526 |
], |
518 |
], |
| 527 |
"language": "python", |
519 |
"language": "python", |
| 528 |
"metadata": {}, |
520 |
"metadata": {}, |
| 529 |
"outputs": [], |
521 |
"outputs": [], |
| 530 |
"prompt_number": 4 |
522 |
"prompt_number": 4 |
| 531 |
}, |
523 |
}, |
| 532 |
{ |
524 |
{ |
| 533 |
"cell_type": "markdown", |
525 |
"cell_type": "markdown", |
| 534 |
"metadata": {}, |
526 |
"metadata": {}, |
| 535 |
"source": [ |
527 |
"source": [ |
| 536 |
"Spo\u010d\u00edt\u00e1me medi\u00e1n magnitudy zm\u011b\u0159en\u00fdch vektor\u016f. A nastav\u00edme podle n\u011bj rozhodovac\u00ed \u00farove\u0148 pro filtraci a ofiltrujeme nam\u011b\u0159en\u00e1 kalibra\u010dn\u00ed data. " |
528 |
"Spo\u010d\u00edt\u00e1me medi\u00e1n magnitudy zm\u011b\u0159en\u00fdch vektor\u016f. A nastav\u00edme podle n\u011bj rozhodovac\u00ed \u00farove\u0148 pro filtraci a ofiltrujeme nam\u011b\u0159en\u00e1 kalibra\u010dn\u00ed data. " |
| 537 |
] |
529 |
] |
| 538 |
}, |
530 |
}, |
| 539 |
{ |
531 |
{ |
| 540 |
"cell_type": "code", |
532 |
"cell_type": "code", |
| 541 |
"collapsed": false, |
533 |
"collapsed": false, |
| 542 |
"input": [ |
534 |
"input": [ |
| 543 |
"meas_median=scipy.median(scipy.array([scipy.linalg.norm(v) for v in measurements]))\n", |
535 |
"meas_median=scipy.median(scipy.array([scipy.linalg.norm(v) for v in measurements]))\n", |
| 544 |
"noise_threshold = meas_median * 0.8\n", |
536 |
"noise_threshold = meas_median * 0.8\n", |
| 545 |
"print noise_threshold\n", |
537 |
"print noise_threshold\n", |
| 546 |
"flt_meas, flt_idx = calibration_utils.filter_meas(measurements, noise_window, noise_threshold)\n", |
538 |
"flt_meas, flt_idx = calibration_utils.filter_meas(measurements, noise_window, noise_threshold)\n", |
| 547 |
"print(\"remaining \"+str(len(flt_meas))+\" after filtering\")" |
539 |
"print(\"remaining \"+str(len(flt_meas))+\" after filtering\")" |
| 548 |
], |
540 |
], |
| 549 |
"language": "python", |
541 |
"language": "python", |
| 550 |
"metadata": {}, |
542 |
"metadata": {}, |
| 551 |
"outputs": [ |
543 |
"outputs": [ |
| 552 |
{ |
544 |
{ |
| 553 |
"output_type": "stream", |
545 |
"output_type": "stream", |
| 554 |
"stream": "stdout", |
546 |
"stream": "stdout", |
| 555 |
"text": [ |
547 |
"text": [ |
| 556 |
"460.895326306\n", |
548 |
"460.895326306\n", |
| 557 |
"remaining 346 after filtering\n" |
549 |
"remaining 346 after filtering\n" |
| 558 |
] |
550 |
] |
| 559 |
} |
551 |
} |
| 560 |
], |
552 |
], |
| 561 |
"prompt_number": 5 |
553 |
"prompt_number": 5 |
| 562 |
}, |
554 |
}, |
| 563 |
{ |
555 |
{ |
| 564 |
"cell_type": "markdown", |
556 |
"cell_type": "markdown", |
| 565 |
"metadata": {}, |
557 |
"metadata": {}, |
| 566 |
"source": [ |
558 |
"source": [ |
| 567 |
"Spo\u010d\u00edt\u00e1me odhad elipsoidu z nam\u011b\u0159en\u00fdch minim\u00e1ln\u00edch a maxim\u00e1ln\u00edch hodnot." |
559 |
"Spo\u010d\u00edt\u00e1me odhad elipsoidu z nam\u011b\u0159en\u00fdch minim\u00e1ln\u00edch a maxim\u00e1ln\u00edch hodnot." |
| 568 |
] |
560 |
] |
| 569 |
}, |
561 |
}, |
| 570 |
{ |
562 |
{ |
| 571 |
"cell_type": "code", |
563 |
"cell_type": "code", |
| 572 |
"collapsed": false, |
564 |
"collapsed": false, |
| 573 |
"input": [ |
565 |
"input": [ |
| 574 |
" p0 = calibration_utils.get_min_max_guess(flt_meas, sensor_ref)\n", |
566 |
" p0 = calibration_utils.get_min_max_guess(flt_meas, sensor_ref)\n", |
| 575 |
" cp0, np0 = calibration_utils.scale_measurements(flt_meas, p0)\n", |
567 |
" cp0, np0 = calibration_utils.scale_measurements(flt_meas, p0)\n", |
| 576 |
" print(\"initial guess : avg \"+str(np0.mean())+\" std \"+str(np0.std()))\n", |
568 |
" print(\"initial guess : avg \"+str(np0.mean())+\" std \"+str(np0.std()))\n", |
| 577 |
"\n", |
569 |
"\n", |
| 578 |
" def err_func(p, meas, y):\n", |
570 |
" def err_func(p, meas, y):\n", |
| 579 |
" cp, np = calibration_utils.scale_measurements(meas, p)\n", |
571 |
" cp, np = calibration_utils.scale_measurements(meas, p)\n", |
| 580 |
" err = y*scipy.ones(len(meas)) - np\n", |
572 |
" err = y*scipy.ones(len(meas)) - np\n", |
| 581 |
" return err" |
573 |
" return err" |
| 582 |
], |
574 |
], |
| 583 |
"language": "python", |
575 |
"language": "python", |
| 584 |
"metadata": {}, |
576 |
"metadata": {}, |
| 585 |
"outputs": [ |
577 |
"outputs": [ |
| 586 |
{ |
578 |
{ |
| 587 |
"output_type": "stream", |
579 |
"output_type": "stream", |
| 588 |
"stream": "stdout", |
580 |
"stream": "stdout", |
| 589 |
"text": [ |
581 |
"text": [ |
| 590 |
"initial guess : avg 0.999920050427 std 0.0671243703038\n" |
582 |
"initial guess : avg 0.999920050427 std 0.0671243703038\n" |
| 591 |
] |
583 |
] |
| 592 |
} |
584 |
} |
| 593 |
], |
585 |
], |
| 594 |
"prompt_number": 6 |
586 |
"prompt_number": 6 |
| 595 |
}, |
587 |
}, |
| 596 |
{ |
588 |
{ |
| 597 |
"cell_type": "markdown", |
589 |
"cell_type": "markdown", |
| 598 |
"metadata": {}, |
590 |
"metadata": {}, |
| 599 |
"source": [ |
591 |
"source": [ |
| 600 |
"Optimalizujeme odhad fitov\u00e1n\u00edm elipsoidu." |
592 |
"Optimalizujeme odhad fitov\u00e1n\u00edm elipsoidu." |
| 601 |
] |
593 |
] |
| 602 |
}, |
594 |
}, |
| 603 |
{ |
595 |
{ |
| 604 |
"cell_type": "code", |
596 |
"cell_type": "code", |
| 605 |
"collapsed": false, |
597 |
"collapsed": false, |
| 606 |
"input": [ |
598 |
"input": [ |
| 607 |
" p1, cov, info, msg, success = optimize.leastsq(err_func, p0[:], args=(flt_meas, sensor_ref), full_output=1)\n", |
599 |
" p1, cov, info, msg, success = optimize.leastsq(err_func, p0[:], args=(flt_meas, sensor_ref), full_output=1)\n", |
| 608 |
" if not success in [1, 2, 3, 4]:\n", |
600 |
" if not success in [1, 2, 3, 4]:\n", |
| 609 |
" print(\"Optimization error: \", msg)\n", |
601 |
" print(\"Optimization error: \", msg)\n", |
| 610 |
" print(\"Please try to provide a clean logfile.\")\n", |
602 |
" print(\"Please try to provide a clean logfile.\")\n", |
| 611 |
" sys.exit(1)\n", |
603 |
" sys.exit(1)\n", |
| 612 |
"\n", |
604 |
"\n", |
| 613 |
" cp1, np1 = calibration_utils.scale_measurements(flt_meas, p1)\n", |
605 |
" cp1, np1 = calibration_utils.scale_measurements(flt_meas, p1)\n", |
| 614 |
" print(\"optimized guess : avg \"+str(np1.mean())+\" std \"+str(np1.std()))" |
606 |
" print(\"optimized guess : avg \"+str(np1.mean())+\" std \"+str(np1.std()))" |
| 615 |
], |
607 |
], |
| 616 |
"language": "python", |
608 |
"language": "python", |
| 617 |
"metadata": {}, |
609 |
"metadata": {}, |
| 618 |
"outputs": [ |
610 |
"outputs": [ |
| 619 |
{ |
611 |
{ |
| 620 |
"output_type": "stream", |
612 |
"output_type": "stream", |
| 621 |
"stream": "stdout", |
613 |
"stream": "stdout", |
| 622 |
"text": [ |
614 |
"text": [ |
| 623 |
"optimized guess : avg 0.999137645687 std 0.0293532050592\n" |
615 |
"optimized guess : avg 0.999137645687 std 0.0293532050592\n" |
| 624 |
] |
616 |
] |
| 625 |
} |
617 |
} |
| 626 |
], |
618 |
], |
| 627 |
"prompt_number": 7 |
619 |
"prompt_number": 7 |
| 628 |
}, |
620 |
}, |
| 629 |
{ |
621 |
{ |
| 630 |
"cell_type": "markdown", |
622 |
"cell_type": "markdown", |
| 631 |
"metadata": {}, |
623 |
"metadata": {}, |
| 632 |
"source": [ |
624 |
"source": [ |
| 633 |
"Vykresl\u00edme v\u00fdsledek filtrace a fitov\u00e1n\u00ed. " |
625 |
"Vykresl\u00edme v\u00fdsledek filtrace a fitov\u00e1n\u00ed. " |
| 634 |
] |
626 |
] |
| 635 |
}, |
627 |
}, |
| 636 |
{ |
628 |
{ |
| 637 |
"cell_type": "code", |
629 |
"cell_type": "code", |
| 638 |
"collapsed": false, |
630 |
"collapsed": false, |
| 639 |
"input": [ |
631 |
"input": [ |
| 640 |
"%pylab qt\n", |
632 |
"%pylab qt\n", |
| 641 |
"#%pylab inline\n", |
633 |
"#%pylab inline\n", |
| 642 |
"calibration_utils.plot_results(False, measurements, flt_idx, flt_meas, cp0, np0, cp1, np1, sensor_ref)\n", |
634 |
"calibration_utils.plot_results(False, measurements, flt_idx, flt_meas, cp0, np0, cp1, np1, sensor_ref)\n", |
| 643 |
"calibration_utils.plot_mag_3d(flt_meas, cp1, p1)" |
635 |
"calibration_utils.plot_mag_3d(flt_meas, cp1, p1)" |
| 644 |
], |
636 |
], |
| 645 |
"language": "python", |
637 |
"language": "python", |
| 646 |
"metadata": {}, |
638 |
"metadata": {}, |
| 647 |
"outputs": [ |
639 |
"outputs": [ |
| 648 |
{ |
640 |
{ |
| 649 |
"output_type": "stream", |
641 |
"output_type": "stream", |
| 650 |
"stream": "stdout", |
642 |
"stream": "stdout", |
| 651 |
"text": [ |
643 |
"text": [ |
| 652 |
"Populating the interactive namespace from numpy and matplotlib\n" |
644 |
"Populating the interactive namespace from numpy and matplotlib\n" |
| 653 |
] |
645 |
] |
| 654 |
}, |
646 |
}, |
| 655 |
{ |
647 |
{ |
| 656 |
"output_type": "stream", |
648 |
"output_type": "stream", |
| 657 |
"stream": "stderr", |
649 |
"stream": "stderr", |
| 658 |
"text": [ |
650 |
"text": [ |
| 659 |
"WARNING: pylab import has clobbered these variables: ['cov', 'info']\n", |
651 |
"WARNING: pylab import has clobbered these variables: ['cov', 'info']\n", |
| 660 |
"`%pylab --no-import-all` prevents importing * from pylab and numpy\n" |
652 |
"`%pylab --no-import-all` prevents importing * from pylab and numpy\n" |
| 661 |
] |
653 |
] |
| 662 |
} |
654 |
} |
| 663 |
], |
655 |
], |
| 664 |
"prompt_number": 8 |
656 |
"prompt_number": 8 |
| 665 |
}, |
657 |
}, |
| 666 |
{ |
658 |
{ |
| 667 |
"cell_type": "markdown", |
659 |
"cell_type": "markdown", |
| 668 |
"metadata": {}, |
660 |
"metadata": {}, |
| 669 |
"source": [ |
661 |
"source": [ |
| 670 |
"Nyn\u00ed m\u016f\u017eeme z\u00edskan\u00e9 scale faktory a offsety pou\u017e\u00edt na kompenzaci libovoln\u00e9ho m\u011b\u0159en\u00ed a n\u00e1sledn\u011b vypo\u010d\u00edtat polohov\u00e9 \u00fahly platformy ve sf\u00e9rick\u00fdch sou\u0159adnic\u00edch." |
662 |
"Nyn\u00ed m\u016f\u017eeme z\u00edskan\u00e9 scale faktory a offsety pou\u017e\u00edt na kompenzaci libovoln\u00e9ho m\u011b\u0159en\u00ed a n\u00e1sledn\u011b vypo\u010d\u00edtat polohov\u00e9 \u00fahly platformy ve sf\u00e9rick\u00fdch sou\u0159adnic\u00edch." |
| 671 |
] |
663 |
] |
| 672 |
}, |
664 |
}, |
| 673 |
{ |
665 |
{ |
| 674 |
"cell_type": "code", |
666 |
"cell_type": "code", |
| 675 |
"collapsed": false, |
667 |
"collapsed": false, |
| 676 |
"input": [ |
668 |
"input": [ |
| 677 |
"for n in range(MEASUREMENTS):\n", |
669 |
"for n in range(MEASUREMENTS):\n", |
| 678 |
" m = mag_sensor.axes()\n", |
670 |
" m = mag_sensor.axes()\n", |
| 679 |
" sm = (m - p1[0:3])*p1[3:6]\n", |
671 |
" sm = (m - p1[0:3])*p1[3:6]\n", |
| 680 |
" r = norm(sm)\n", |
672 |
" r = norm(sm)\n", |
| 681 |
" theta = np.arccos(sm[2]/r)\n", |
673 |
" theta = np.arccos(sm[2]/r)\n", |
| 682 |
" phi = np.arctan2(sm[1],sm[0])\n", |
674 |
" phi = np.arctan2(sm[1],sm[0])\n", |
| 683 |
" clear_output()\n", |
675 |
" clear_output()\n", |
| 684 |
" print (r,(theta*180)/pi,(phi*180)/pi)\n", |
676 |
" print (r,(theta*180)/pi,(phi*180)/pi)\n", |
| 685 |
" sys.stdout.flush()" |
677 |
" sys.stdout.flush()" |
| 686 |
], |
678 |
], |
| 687 |
"language": "python", |
679 |
"language": "python", |
| 688 |
"metadata": {}, |
680 |
"metadata": {}, |
| 689 |
"outputs": [ |
681 |
"outputs": [ |
| 690 |
{ |
682 |
{ |
| 691 |
"output_type": "stream", |
683 |
"output_type": "stream", |
| 692 |
"stream": "stdout", |
684 |
"stream": "stdout", |
| 693 |
"text": [ |
685 |
"text": [ |
| 694 |
"(1.0033330291141624, 159.16410600293204, 61.66914855382452)\n" |
686 |
"(1.0033330291141624, 159.16410600293204, 61.66914855382452)\n" |
| 695 |
] |
687 |
] |
| 696 |
} |
688 |
} |
| 697 |
], |
689 |
], |
| 698 |
"prompt_number": 35 |
690 |
"prompt_number": 35 |
| 699 |
} |
691 |
} |
| 700 |
], |
692 |
], |
| 701 |
"metadata": {} |
693 |
"metadata": {} |
| 702 |
} |
694 |
} |
| 703 |
] |
695 |
] |
| 704 |
} |
696 |
} |