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dedebenui
2024-09-17 11:42:44 +02:00
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commit 81799c5eec
22 changed files with 3377 additions and 37 deletions
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.python-version Normal file
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data/2022-04-20 Argon_696_15.CSV Normal file
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70CSV
// AQ6370 OPTICAL SPECTRUM ANALYZER //
25
"CTRWL", 696.400000
"SPAN", 0.600000
"START WL", 696.100000
"STOP WL", 696.700000
"WLFREQ", 0
"REFL", -60.000
"LSCL",-2.0
"BASEL",0.00000000000
"RESLN",0.100
"AVG", 1
"SMPLAUTO", 1
"SMPL", 301
"SMPLINTVL",0.0020
"HIGH3"
"RAVG", 20
"LSUNT",1
"NMSKH","OFF"
"RESCOR",0
"RESPARM",10177
"FREQPARM",10177
# Complementary values
"GAS FLOW",500
"VOLTAGE",7900
"FREQUENCY",38800
"[TRACE DATA]"
696.1000,7.338E-009
696.1020,7.591E-009
696.1040,9.639E-009
696.1060,8.682E-009
696.1080,5.240E-009
696.1100,2.041E-008
696.1120,4.934E-009
696.1140,7.068E-009
696.1160,2.225E-008
696.1180,1.875E-008
696.1200,1.231E-008
696.1220,2.037E-008
696.1240,2.496E-008
696.1260,1.785E-008
696.1280,1.672E-008
696.1300,2.225E-008
696.1320,4.458E-009
696.1340,2.409E-008
696.1360,1.587E-008
696.1380,8.883E-009
696.1400,9.415E-009
696.1420,4.547E-009
696.1440,1.358E-008
696.1460,2.302E-008
696.1480,1.370E-008
696.1500,5.619E-009
696.1520,2.849E-008
696.1540,1.897E-008
696.1560,2.569E-008
696.1580,7.989E-009
696.1600,1.109E-008
696.1620,2.978E-008
696.1640,3.374E-008
696.1660,4.647E-009
696.1680,1.471E-008
696.1700,2.971E-008
696.1720,1.358E-008
696.1740,2.331E-008
696.1760,6.827E-010
696.1780,8.818E-009
696.1800,2.340E-008
696.1820,1.988E-008
696.1840,3.221E-008
696.1860,1.569E-008
696.1880,2.894E-008
696.1900,1.944E-008
696.1920,2.562E-008
696.1940,1.391E-008
696.1960,2.334E-008
696.1980,2.119E-008
696.2000,1.438E-008
696.2020,1.362E-008
696.2040,2.040E-008
696.2060,2.266E-008
696.2080,3.277E-008
696.2100,1.639E-008
696.2120,2.603E-008
696.2140,2.250E-008
696.2160,1.801E-008
696.2180,2.338E-008
696.2200,1.527E-008
696.2220,2.057E-008
696.2240,2.250E-008
696.2260,3.479E-008
696.2280,2.111E-008
696.2300,2.669E-008
696.2320,2.601E-008
696.2340,3.034E-008
696.2360,2.754E-008
696.2380,3.175E-008
696.2400,1.585E-008
696.2420,3.666E-008
696.2440,2.487E-008
696.2460,1.633E-008
696.2480,2.161E-008
696.2500,2.462E-008
696.2520,1.165E-008
696.2540,2.986E-008
696.2560,2.710E-008
696.2580,1.281E-008
696.2600,3.003E-008
696.2620,2.028E-008
696.2640,4.174E-008
696.2660,2.350E-008
696.2680,4.735E-008
696.2700,4.693E-008
696.2720,3.607E-008
696.2740,4.648E-008
696.2760,5.627E-008
696.2780,3.976E-008
696.2800,4.811E-008
696.2820,5.326E-008
696.2840,5.671E-008
696.2860,6.082E-008
696.2880,6.519E-008
696.2900,6.144E-008
696.2920,6.282E-008
696.2940,7.846E-008
696.2960,9.078E-008
696.2980,7.970E-008
696.3000,7.075E-008
696.3020,8.600E-008
696.3040,9.402E-008
696.3060,9.820E-008
696.3080,1.017E-007
696.3100,1.074E-007
696.3120,1.174E-007
696.3140,1.083E-007
696.3160,1.169E-007
696.3180,1.267E-007
696.3200,1.252E-007
696.3220,1.545E-007
696.3240,1.492E-007
696.3260,1.611E-007
696.3280,1.730E-007
696.3300,1.523E-007
696.3320,1.797E-007
696.3340,2.001E-007
696.3360,1.936E-007
696.3380,1.940E-007
696.3400,2.151E-007
696.3420,1.997E-007
696.3440,2.122E-007
696.3460,2.186E-007
696.3480,2.179E-007
696.3500,2.212E-007
696.3520,2.357E-007
696.3540,2.452E-007
696.3560,2.565E-007
696.3580,2.388E-007
696.3600,2.591E-007
696.3620,2.553E-007
696.3640,2.612E-007
696.3660,2.628E-007
696.3680,2.748E-007
696.3700,2.864E-007
696.3720,2.698E-007
696.3740,2.803E-007
696.3760,2.809E-007
696.3780,2.827E-007
696.3800,3.039E-007
696.3820,2.903E-007
696.3840,3.197E-007
696.3860,3.081E-007
696.3880,3.171E-007
696.3900,3.201E-007
696.3920,3.154E-007
696.3940,3.119E-007
696.3960,3.128E-007
696.3980,3.215E-007
696.4000,3.178E-007
696.4020,3.341E-007
696.4040,3.088E-007
696.4060,3.054E-007
696.4080,3.143E-007
696.4100,3.173E-007
696.4120,3.203E-007
696.4140,3.235E-007
696.4160,3.110E-007
696.4180,3.091E-007
696.4200,3.180E-007
696.4220,3.152E-007
696.4240,3.091E-007
696.4260,3.110E-007
696.4280,3.067E-007
696.4300,2.975E-007
696.4320,2.991E-007
696.4340,2.985E-007
696.4360,2.946E-007
696.4380,2.847E-007
696.4400,2.906E-007
696.4420,2.777E-007
696.4440,2.713E-007
696.4460,2.834E-007
696.4480,2.714E-007
696.4500,2.474E-007
696.4520,2.759E-007
696.4540,2.481E-007
696.4560,2.476E-007
696.4580,2.455E-007
696.4600,2.646E-007
696.4620,2.473E-007
696.4640,2.289E-007
696.4660,2.390E-007
696.4680,2.323E-007
696.4700,2.220E-007
696.4720,2.133E-007
696.4740,2.128E-007
696.4760,1.975E-007
696.4780,1.954E-007
696.4800,1.911E-007
696.4820,1.957E-007
696.4840,1.738E-007
696.4860,1.734E-007
696.4880,1.748E-007
696.4900,1.684E-007
696.4920,1.565E-007
696.4940,1.355E-007
696.4960,1.414E-007
696.4980,1.400E-007
696.5000,1.272E-007
696.5020,1.316E-007
696.5040,1.267E-007
696.5060,1.181E-007
696.5080,1.138E-007
696.5100,1.010E-007
696.5120,1.101E-007
696.5140,1.065E-007
696.5160,8.187E-008
696.5180,8.912E-008
696.5200,9.110E-008
696.5220,8.377E-008
696.5240,8.179E-008
696.5260,8.251E-008
696.5280,7.279E-008
696.5300,6.516E-008
696.5320,7.550E-008
696.5340,5.438E-008
696.5360,6.593E-008
696.5380,5.066E-008
696.5400,4.232E-008
696.5420,4.845E-008
696.5440,6.049E-008
696.5460,4.499E-008
696.5480,3.859E-008
696.5500,3.534E-008
696.5520,5.310E-008
696.5540,4.068E-008
696.5560,2.990E-008
696.5580,2.782E-008
696.5600,2.569E-008
696.5620,2.951E-008
696.5640,3.309E-008
696.5660,2.027E-008
696.5680,3.353E-008
696.5700,4.190E-008
696.5720,2.809E-008
696.5740,2.238E-008
696.5760,2.847E-008
696.5780,3.224E-008
696.5800,2.280E-008
696.5820,2.183E-008
696.5840,2.698E-008
696.5860,2.896E-008
696.5880,1.712E-008
696.5900,1.085E-008
696.5920,2.582E-008
696.5940,7.439E-009
696.5960,2.304E-008
696.5980,2.363E-008
696.6000,9.333E-010
696.6020,2.625E-008
696.6040,2.148E-008
696.6060,2.759E-008
696.6080,2.690E-008
696.6100,1.554E-008
696.6120,1.041E-008
696.6140,1.773E-008
696.6160,1.559E-008
696.6180,1.580E-008
696.6200,2.129E-008
696.6220,2.126E-008
696.6240,4.100E-009
696.6260,2.839E-008
696.6280,2.455E-008
696.6300,9.609E-009
696.6320,1.258E-008
696.6340,1.437E-008
696.6360,2.004E-008
696.6380,2.800E-008
696.6400,2.104E-008
696.6420,1.205E-008
696.6440,2.049E-009
696.6460,2.654E-008
696.6480,1.741E-008
696.6500,2.169E-008
696.6520,1.174E-008
696.6540,2.097E-008
696.6560,1.105E-008
696.6580,1.130E-008
696.6600,2.942E-008
696.6620,2.298E-008
696.6640,1.540E-008
696.6660,2.112E-008
696.6680,1.575E-008
696.6700,1.168E-008
696.6720,1.591E-008
696.6740,3.348E-008
696.6760,1.864E-008
696.6780,7.248E-009
696.6800,7.188E-009
696.6820,1.960E-008
696.6840,1.900E-008
696.6860,1.921E-008
696.6880,1.618E-008
696.6900,1.524E-008
696.6920,2.712E-008
696.6940,2.281E-008
696.6960,8.831E-009
696.6980,1.317E-008
696.7000,2.163E-008
1 70CSV
2 // AQ6370 OPTICAL SPECTRUM ANALYZER //
3 25
4 CTRWL 696.400000
5 SPAN 0.600000
6 START WL 696.100000
7 STOP WL 696.700000
8 WLFREQ 0
9 REFL -60.000
10 LSCL -2.0
11 BASEL 0.00000000000
12 RESLN 0.100
13 AVG 1
14 SMPLAUTO 1
15 SMPL 301
16 SMPLINTVL 0.0020
17 HIGH3
18 RAVG 20
19 LSUNT 1
20 NMSKH OFF
21 RESCOR 0
22 RESPARM 10177
23 FREQPARM 10177
24 # Complementary values
25 GAS FLOW 500
26 VOLTAGE 7900
27 FREQUENCY 38800
28 [TRACE DATA]
29 696.1000 7.338E-009
30 696.1020 7.591E-009
31 696.1040 9.639E-009
32 696.1060 8.682E-009
33 696.1080 5.240E-009
34 696.1100 2.041E-008
35 696.1120 4.934E-009
36 696.1140 7.068E-009
37 696.1160 2.225E-008
38 696.1180 1.875E-008
39 696.1200 1.231E-008
40 696.1220 2.037E-008
41 696.1240 2.496E-008
42 696.1260 1.785E-008
43 696.1280 1.672E-008
44 696.1300 2.225E-008
45 696.1320 4.458E-009
46 696.1340 2.409E-008
47 696.1360 1.587E-008
48 696.1380 8.883E-009
49 696.1400 9.415E-009
50 696.1420 4.547E-009
51 696.1440 1.358E-008
52 696.1460 2.302E-008
53 696.1480 1.370E-008
54 696.1500 5.619E-009
55 696.1520 2.849E-008
56 696.1540 1.897E-008
57 696.1560 2.569E-008
58 696.1580 7.989E-009
59 696.1600 1.109E-008
60 696.1620 2.978E-008
61 696.1640 3.374E-008
62 696.1660 4.647E-009
63 696.1680 1.471E-008
64 696.1700 2.971E-008
65 696.1720 1.358E-008
66 696.1740 2.331E-008
67 696.1760 6.827E-010
68 696.1780 8.818E-009
69 696.1800 2.340E-008
70 696.1820 1.988E-008
71 696.1840 3.221E-008
72 696.1860 1.569E-008
73 696.1880 2.894E-008
74 696.1900 1.944E-008
75 696.1920 2.562E-008
76 696.1940 1.391E-008
77 696.1960 2.334E-008
78 696.1980 2.119E-008
79 696.2000 1.438E-008
80 696.2020 1.362E-008
81 696.2040 2.040E-008
82 696.2060 2.266E-008
83 696.2080 3.277E-008
84 696.2100 1.639E-008
85 696.2120 2.603E-008
86 696.2140 2.250E-008
87 696.2160 1.801E-008
88 696.2180 2.338E-008
89 696.2200 1.527E-008
90 696.2220 2.057E-008
91 696.2240 2.250E-008
92 696.2260 3.479E-008
93 696.2280 2.111E-008
94 696.2300 2.669E-008
95 696.2320 2.601E-008
96 696.2340 3.034E-008
97 696.2360 2.754E-008
98 696.2380 3.175E-008
99 696.2400 1.585E-008
100 696.2420 3.666E-008
101 696.2440 2.487E-008
102 696.2460 1.633E-008
103 696.2480 2.161E-008
104 696.2500 2.462E-008
105 696.2520 1.165E-008
106 696.2540 2.986E-008
107 696.2560 2.710E-008
108 696.2580 1.281E-008
109 696.2600 3.003E-008
110 696.2620 2.028E-008
111 696.2640 4.174E-008
112 696.2660 2.350E-008
113 696.2680 4.735E-008
114 696.2700 4.693E-008
115 696.2720 3.607E-008
116 696.2740 4.648E-008
117 696.2760 5.627E-008
118 696.2780 3.976E-008
119 696.2800 4.811E-008
120 696.2820 5.326E-008
121 696.2840 5.671E-008
122 696.2860 6.082E-008
123 696.2880 6.519E-008
124 696.2900 6.144E-008
125 696.2920 6.282E-008
126 696.2940 7.846E-008
127 696.2960 9.078E-008
128 696.2980 7.970E-008
129 696.3000 7.075E-008
130 696.3020 8.600E-008
131 696.3040 9.402E-008
132 696.3060 9.820E-008
133 696.3080 1.017E-007
134 696.3100 1.074E-007
135 696.3120 1.174E-007
136 696.3140 1.083E-007
137 696.3160 1.169E-007
138 696.3180 1.267E-007
139 696.3200 1.252E-007
140 696.3220 1.545E-007
141 696.3240 1.492E-007
142 696.3260 1.611E-007
143 696.3280 1.730E-007
144 696.3300 1.523E-007
145 696.3320 1.797E-007
146 696.3340 2.001E-007
147 696.3360 1.936E-007
148 696.3380 1.940E-007
149 696.3400 2.151E-007
150 696.3420 1.997E-007
151 696.3440 2.122E-007
152 696.3460 2.186E-007
153 696.3480 2.179E-007
154 696.3500 2.212E-007
155 696.3520 2.357E-007
156 696.3540 2.452E-007
157 696.3560 2.565E-007
158 696.3580 2.388E-007
159 696.3600 2.591E-007
160 696.3620 2.553E-007
161 696.3640 2.612E-007
162 696.3660 2.628E-007
163 696.3680 2.748E-007
164 696.3700 2.864E-007
165 696.3720 2.698E-007
166 696.3740 2.803E-007
167 696.3760 2.809E-007
168 696.3780 2.827E-007
169 696.3800 3.039E-007
170 696.3820 2.903E-007
171 696.3840 3.197E-007
172 696.3860 3.081E-007
173 696.3880 3.171E-007
174 696.3900 3.201E-007
175 696.3920 3.154E-007
176 696.3940 3.119E-007
177 696.3960 3.128E-007
178 696.3980 3.215E-007
179 696.4000 3.178E-007
180 696.4020 3.341E-007
181 696.4040 3.088E-007
182 696.4060 3.054E-007
183 696.4080 3.143E-007
184 696.4100 3.173E-007
185 696.4120 3.203E-007
186 696.4140 3.235E-007
187 696.4160 3.110E-007
188 696.4180 3.091E-007
189 696.4200 3.180E-007
190 696.4220 3.152E-007
191 696.4240 3.091E-007
192 696.4260 3.110E-007
193 696.4280 3.067E-007
194 696.4300 2.975E-007
195 696.4320 2.991E-007
196 696.4340 2.985E-007
197 696.4360 2.946E-007
198 696.4380 2.847E-007
199 696.4400 2.906E-007
200 696.4420 2.777E-007
201 696.4440 2.713E-007
202 696.4460 2.834E-007
203 696.4480 2.714E-007
204 696.4500 2.474E-007
205 696.4520 2.759E-007
206 696.4540 2.481E-007
207 696.4560 2.476E-007
208 696.4580 2.455E-007
209 696.4600 2.646E-007
210 696.4620 2.473E-007
211 696.4640 2.289E-007
212 696.4660 2.390E-007
213 696.4680 2.323E-007
214 696.4700 2.220E-007
215 696.4720 2.133E-007
216 696.4740 2.128E-007
217 696.4760 1.975E-007
218 696.4780 1.954E-007
219 696.4800 1.911E-007
220 696.4820 1.957E-007
221 696.4840 1.738E-007
222 696.4860 1.734E-007
223 696.4880 1.748E-007
224 696.4900 1.684E-007
225 696.4920 1.565E-007
226 696.4940 1.355E-007
227 696.4960 1.414E-007
228 696.4980 1.400E-007
229 696.5000 1.272E-007
230 696.5020 1.316E-007
231 696.5040 1.267E-007
232 696.5060 1.181E-007
233 696.5080 1.138E-007
234 696.5100 1.010E-007
235 696.5120 1.101E-007
236 696.5140 1.065E-007
237 696.5160 8.187E-008
238 696.5180 8.912E-008
239 696.5200 9.110E-008
240 696.5220 8.377E-008
241 696.5240 8.179E-008
242 696.5260 8.251E-008
243 696.5280 7.279E-008
244 696.5300 6.516E-008
245 696.5320 7.550E-008
246 696.5340 5.438E-008
247 696.5360 6.593E-008
248 696.5380 5.066E-008
249 696.5400 4.232E-008
250 696.5420 4.845E-008
251 696.5440 6.049E-008
252 696.5460 4.499E-008
253 696.5480 3.859E-008
254 696.5500 3.534E-008
255 696.5520 5.310E-008
256 696.5540 4.068E-008
257 696.5560 2.990E-008
258 696.5580 2.782E-008
259 696.5600 2.569E-008
260 696.5620 2.951E-008
261 696.5640 3.309E-008
262 696.5660 2.027E-008
263 696.5680 3.353E-008
264 696.5700 4.190E-008
265 696.5720 2.809E-008
266 696.5740 2.238E-008
267 696.5760 2.847E-008
268 696.5780 3.224E-008
269 696.5800 2.280E-008
270 696.5820 2.183E-008
271 696.5840 2.698E-008
272 696.5860 2.896E-008
273 696.5880 1.712E-008
274 696.5900 1.085E-008
275 696.5920 2.582E-008
276 696.5940 7.439E-009
277 696.5960 2.304E-008
278 696.5980 2.363E-008
279 696.6000 9.333E-010
280 696.6020 2.625E-008
281 696.6040 2.148E-008
282 696.6060 2.759E-008
283 696.6080 2.690E-008
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280
data/Argon_696_11.CSV Normal file
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70CSV
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1 70CSV
2 // AQ6370 OPTICAL SPECTRUM ANALYZER //
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696.7960,2.886E-008
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696.8000,2.910E-008
1 70CSV
2 // AQ6370 OPTICAL SPECTRUM ANALYZER //
3 25
4 CTRWL 696.400000
5 SPAN 0.800000
6 START WL 696.000000
7 STOP WL 696.800000
8 WLFREQ 0
9 REFL -66.990
10 LSCL -2.0
11 BASEL 0.00000000000
12 RESLN 0.100
13 AVG 1
14 SMPLAUTO 1
15 SMPL 401
16 SMPLINTVL 0.0020
17 HIGH3
18 RAVG 20
19 LSUNT 1
20 NMSKH OFF
21 RESCOR 0
22 RESPARM 10177
23 FREQPARM 10177
24 [TRACE DATA]
25 696.0000 4.249E-008
26 696.0020 4.205E-008
27 696.0040 7.176E-008
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18
deploy.nu Normal file
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@@ -0,0 +1,18 @@
#!/usr/bin/env nu
let version = (open pyproject.toml).project.version
let zipfile = $"linemeasurement-($version).zip"
let filelist = (
git ls-files |
lines |
where {not ($in | str ends-with ".afphoto")} |
where {$in != "deploy.nu"}
)
^zip $zipfile ...$filelist
scp -O $zipfile $"fibnas:/volume1/web/($zipfile)"
scp -O $zipfile $"fibnas:/volume1/web/linemeasurement-latest.zip"
rm $zipfile

138
extract_points_balcon.py Normal file
View File

@@ -0,0 +1,138 @@
from typing import Union
from xml.dom import minidom
import numpy as np
import matplotlib.pyplot as plt
MX = [485.4, 485.4, 486.8]
MY = [1, 0, 0]
def str_pair_to_float(s: str) -> np.ndarray:
return np.array([float(el) for el in s.split(",")])
def parse_buffer(s: str) -> Union[float, complex]:
x, *y = s.split(",")
if y:
return float(x) + 1j * float(y[0])
else:
return float(x)
def transform_matrix_and_offset(
x: tuple[float, float, float],
y: tuple[float, float, float],
rx: tuple[float, float, float],
ry: tuple[float, float, float],
) -> tuple[np.ndarray, np.ndarray]:
a = -(
rx[0] * y[1] - rx[0] * y[2] - rx[1] * y[0] + rx[1] * y[2] + rx[2] * y[0] - rx[2] * y[1]
) / (x[1] * y[0] - x[2] * y[0] - x[0] * y[1] + x[2] * y[1] + x[0] * y[2] - x[1] * y[2])
b = -(
rx[0] * x[1] - rx[0] * x[2] - rx[1] * x[0] + rx[1] * x[2] + rx[2] * x[0] - rx[2] * x[1]
) / (-x[1] * y[0] + x[2] * y[0] + x[0] * y[1] - x[2] * y[1] - x[0] * y[2] + x[1] * y[2])
c = -(
ry[0] * y[1] - ry[0] * y[2] - ry[1] * y[0] + ry[1] * y[2] + ry[2] * y[0] - ry[2] * y[1]
) / (x[1] * y[0] - x[2] * y[0] - x[0] * y[1] + x[2] * y[1] + x[0] * y[2] - x[1] * y[2])
d = -(
-ry[0] * x[1] + ry[0] * x[2] + ry[1] * x[0] - ry[1] * x[2] - ry[2] * x[0] + ry[2] * x[1]
) / (x[1] * y[0] - x[2] * y[0] - x[0] * y[1] + x[2] * y[1] + x[0] * y[2] - x[1] * y[2])
e = -(
rx[0] * x[2] * y[1]
- rx[0] * x[1] * y[2]
- rx[1] * x[2] * y[0]
+ rx[1] * x[0] * y[2]
+ rx[2] * x[1] * y[0]
- rx[2] * x[0] * y[1]
) / (-x[1] * y[0] + x[2] * y[0] + x[0] * y[1] - x[2] * y[1] - x[0] * y[2] + x[1] * y[2])
f = -(
-ry[0] * x[2] * y[1]
+ ry[0] * x[1] * y[2]
+ ry[1] * x[2] * y[0]
- ry[1] * x[0] * y[2]
- ry[2] * x[1] * y[0]
+ ry[2] * x[0] * y[1]
) / (x[1] * y[0] - x[2] * y[0] - x[0] * y[1] + x[2] * y[1] + x[0] * y[2] - x[1] * y[2])
return np.array([[a, b], [c, d]]), np.array([e, f])
def parse_path(s: str) -> list[tuple[np.ndarray, np.ndarray]]:
INSTRUCTIONS = "mMlLhHvV"
lines: list[list[complex]] = []
current_line: list[complex] = []
pos = 0 + 0j
def add_line_point(new_pos):
nonlocal pos
if not current_line:
current_line.append(pos)
pos = new_pos
current_line.append(pos)
s = iter(s + "M")
instr = next(s)
buffer = ""
for char in s:
if char not in INSTRUCTIONS:
buffer += char
continue
arg = parse_buffer(buffer)
buffer = ""
if instr == "m":
if current_line:
lines.append(current_line)
current_line = []
pos += arg
elif instr == "M":
if current_line:
lines.append(current_line)
current_line = []
pos = arg
elif instr == "l":
add_line_point(pos + arg)
elif instr == "L":
add_line_point(arg)
elif instr == "v":
add_line_point(pos + 1j * arg)
elif instr == "V":
add_line_point(0 + 1j * arg)
elif instr == "h":
add_line_point(pos + arg)
elif instr == "H":
add_line_point(arg)
instr = char
if current_line:
lines.append(current_line)
lines = np.array(lines)
return [(line.real, line.imag) for line in lines]
def main():
with minidom.parse("./data/Balcon2008.svg") as doc:
rects = [
(
float(rect.getAttribute("x")) + float(rect.getAttribute("width")) / 2,
float(rect.getAttribute("y")) + float(rect.getAttribute("height")) / 2,
)
for rect in doc.getElementsByTagName("rect")
]
path_strings = [path.getAttribute("d") for path in doc.getElementsByTagName("path")]
px, py = parse_path(path_strings[0])[0]
M, off = transform_matrix_and_offset(px, py, MX, MY)
x, y = (np.array(rects).dot(M.T) + off).T
s = x.argsort()
x = x[s]
y = y[s]
np.savetxt("data/Balcon2008.csv", np.c_[x, y], fmt="%.5f")
plt.plot(x, y)
plt.show()
if __name__ == "__main__":
main()

135
extract_points_dong.py Normal file
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from typing import Union
from xml.dom import minidom
import numpy as np
import matplotlib.pyplot as plt
MX = [696, 696, 697.2]
MY = [1.2, 0, 0]
def str_pair_to_float(s: str) -> np.ndarray:
return np.array([float(el) for el in s.split(",")])
def parse_buffer(s: str) -> Union[float, complex]:
x, *y = s.split(",")
if y:
return float(x) + 1j * float(y[0])
else:
return float(x)
def transform_matrix_and_offset(
x: tuple[float, float, float],
y: tuple[float, float, float],
rx: tuple[float, float, float],
ry: tuple[float, float, float],
) -> tuple[np.ndarray, np.ndarray]:
a = -(
rx[0] * y[1] - rx[0] * y[2] - rx[1] * y[0] + rx[1] * y[2] + rx[2] * y[0] - rx[2] * y[1]
) / (x[1] * y[0] - x[2] * y[0] - x[0] * y[1] + x[2] * y[1] + x[0] * y[2] - x[1] * y[2])
b = -(
rx[0] * x[1] - rx[0] * x[2] - rx[1] * x[0] + rx[1] * x[2] + rx[2] * x[0] - rx[2] * x[1]
) / (-x[1] * y[0] + x[2] * y[0] + x[0] * y[1] - x[2] * y[1] - x[0] * y[2] + x[1] * y[2])
c = -(
ry[0] * y[1] - ry[0] * y[2] - ry[1] * y[0] + ry[1] * y[2] + ry[2] * y[0] - ry[2] * y[1]
) / (x[1] * y[0] - x[2] * y[0] - x[0] * y[1] + x[2] * y[1] + x[0] * y[2] - x[1] * y[2])
d = -(
-ry[0] * x[1] + ry[0] * x[2] + ry[1] * x[0] - ry[1] * x[2] - ry[2] * x[0] + ry[2] * x[1]
) / (x[1] * y[0] - x[2] * y[0] - x[0] * y[1] + x[2] * y[1] + x[0] * y[2] - x[1] * y[2])
e = -(
rx[0] * x[2] * y[1]
- rx[0] * x[1] * y[2]
- rx[1] * x[2] * y[0]
+ rx[1] * x[0] * y[2]
+ rx[2] * x[1] * y[0]
- rx[2] * x[0] * y[1]
) / (-x[1] * y[0] + x[2] * y[0] + x[0] * y[1] - x[2] * y[1] - x[0] * y[2] + x[1] * y[2])
f = -(
-ry[0] * x[2] * y[1]
+ ry[0] * x[1] * y[2]
+ ry[1] * x[2] * y[0]
- ry[1] * x[0] * y[2]
- ry[2] * x[1] * y[0]
+ ry[2] * x[0] * y[1]
) / (x[1] * y[0] - x[2] * y[0] - x[0] * y[1] + x[2] * y[1] + x[0] * y[2] - x[1] * y[2])
return np.array([[a, b], [c, d]]), np.array([e, f])
def parse_path(s: str) -> list[tuple[np.ndarray, np.ndarray]]:
INSTRUCTIONS = "mMlLhHvV"
lines: list[list[complex]] = []
current_line: list[complex] = []
pos = 0 + 0j
def add_line_point(new_pos):
nonlocal pos
if not current_line:
current_line.append(pos)
pos = new_pos
current_line.append(pos)
s = iter(s + "M")
instr = next(s)
buffer = ""
for char in s:
if char not in INSTRUCTIONS:
buffer += char
continue
arg = parse_buffer(buffer)
buffer = ""
if instr == "m":
if current_line:
lines.append(current_line)
current_line = []
pos += arg
elif instr == "M":
if current_line:
lines.append(current_line)
current_line = []
pos = arg
elif instr == "l":
add_line_point(pos + arg)
elif instr == "L":
add_line_point(arg)
elif instr == "v":
add_line_point(pos + 1j * arg)
elif instr == "V":
add_line_point(0 + 1j * arg)
elif instr == "h":
add_line_point(pos + arg)
elif instr == "H":
add_line_point(arg)
instr = char
if current_line:
lines.append(current_line)
lines = np.array(lines)
return [(line.real, line.imag) for line in lines]
def main():
with minidom.parse("./data/Dong2005.svg") as doc:
circles = [
(float(circ.getAttribute("cx")), float(circ.getAttribute("cy")))
for circ in doc.getElementsByTagName("circle")
]
path_strings = [path.getAttribute("d") for path in doc.getElementsByTagName("path")]
px, py = parse_path(path_strings[0])[0]
M, off = transform_matrix_and_offset(px, py, MX, MY)
x, y = (np.array(circles).dot(M.T) + off).T
s = x.argsort()
x = x[s]
y = y[s]
np.savetxt("data/Dong2005.csv", np.c_[x, y], fmt="%.5f")
plt.plot(x, y)
plt.show()
if __name__ == "__main__":
main()

50
playground.py Normal file
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@@ -0,0 +1,50 @@
from collections import namedtuple
import itertools
from scipy.optimize import root
Args = namedtuple("Args", ["alpha", "w", "Te"])
ARG1 = Args(0.04, 0.00409, 5_000)
ARG2 = Args(0.032, 0.00537, 10_000)
ARG3 = Args(0.023, 0.00873, 40_000)
def L_stark(Ne: float, alpha: float, w: float, Te: float) -> float:
"""computes stark broadening
Parameters
----------
Ne : float
free electron density in 1e15 / cm^3
alpha : float
static ion-broadening parameter
w : float
electron impact parameter in nm/1e16/cm^3
Te : float
electron temperature in K
Returns
-------
float
Stark broadening in nm
"""
Ne = 1e15 * Ne
return (
2e-16
* (1 + 1.75e-4 * (Ne ** 0.25) * alpha * (1 - 0.068 * (Te ** -0.5) * (Ne ** (1 / 6))))
* w
* Ne
)
def main():
for ls, args in itertools.product([0.001, 0.003, 0.009], [ARG1, ARG2, ARG3]):
def to_root(Ne):
return L_stark(Ne, *args) - ls
print(ls, args.Te, root(to_root, 1.0).x)
if __name__ == "__main__":
main()

18
pyproject.toml Normal file
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@@ -0,0 +1,18 @@
[build-system]
requires = ["setuptools >= 65.0.0"]
build-backend = "setuptools.build_meta"
[project]
name = "linemeasurement"
version = "0.1.0"
description = "Add your description here"
readme = "README.md"
requires-python = ">=3.10"
dependencies = ["click", "numpy", "plotapp", "scipy"]
[project.scripts]
linemeasurement = "linemeasurement.__main__:main"
[tool.uv.sources]
plotapp = { url = "http://130.92.113.172/plotapp-0.1.1.zip" }

296
src/linemeasurement/__main__.py Normal file
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@@ -0,0 +1,296 @@
import click
import numpy as np
from linemeasurement.osa import load as osaload
from linemeasurement.profiles import (
VoigtFitResult,
add_noise,
convolve,
elevated_gaussian,
fft_gaussian,
fft_lorentzian,
fft_voigt,
gaussian,
linear_direct_voigt_fit,
linear_fft_voigt_fit,
log_direct_voigt_fit,
lorentzian,
single_func_fit,
voigt,
)
from plotapp import PlotApp
from scipy.interpolate import interp1d
from scipy.signal import wiener
LN2 = np.log(2)
def setup_axes(n: int, lims=(-10, 10)) -> tuple[np.ndarray, float, np.ndarray, float]:
"""Creates a time-like and a frequency-like array and returns their respective spacing"""
x, dx = np.linspace(*lims, n, retstep=True)
f = np.fft.fftfreq(n, dx)
return x, dx, f, f[1] - f[0]
def freq_axis(x: np.ndarray) -> tuple[np.ndarray, np.ndarray, float]:
"""
given an array with equally spaced values, returns a corresponding frequency array,
an array of indices to automatically sort ffts and the x spacing
"""
dx = np.diff(x).mean()
f = np.fft.fftfreq(len(x), dx)
ind = np.argsort(f)
return f[ind], ind, dx
@click.group()
def main():
pass
@main.command(help="fit a lorentzian with a gaussian")
def demo1():
x = np.linspace(-10, 10, 501)
with PlotApp(
FWHM=np.geomspace(0.1, 10),
relative_noise=np.linspace(0, 2),
absolute_noise=[0, *np.geomspace(0.001, 1)],
) as app:
app.set_antialiasing(True)
app.params["FWHM"].value = 3.7
@app.update
def draw(FWHM, relative_noise, absolute_noise):
lo = lorentzian(x, 1, FWHM, 0)
lo = add_noise(lo, absolute_noise, relative_noise * 0.04)
fit = single_func_fit(x, lo, gaussian)
app[0].set_line_data("Lorentzian", x, lo, width=2)
app[0].set_line_data(
"gaus fit", x, fit.curve, width=2, label=f"Gaussian fit : FWHM = {fit.width}"
)
@main.command(name="default", help="illustrate the influence of noises and fit a voigt profile")
def demo2():
with PlotApp(
n=np.arange(256, 2046),
lorentz_fwhm=np.geomspace(0.1, 1),
gauss_fwhm=np.linspace(1, 10),
relative_noise=np.linspace(0, 2),
absolute_noise=[0, *np.geomspace(1e-6, 1, 201)],
) as app:
app.set_antialiasing(True)
v_ax = app["Voigt"]
l_ax = app["Lorentz"]
g_ax = app["Gauss"]
l_ax.link_x(v_ax)
g_ax.link_x(v_ax)
app.params["gauss_fwhm"].value = 5
app.params["n"].value = 1024
app.params_layout.setSpacing(0)
@app.update
def draw(n, lorentz_fwhm, gauss_fwhm, absolute_noise, relative_noise):
x, dx, f, df = setup_axes(n, (-20, 20))
gaus = gaussian(x, 1, gauss_fwhm, 0)
lore = lorentzian(x, 1, lorentz_fwhm, 0)
g_ax.set_line_data("Gaussian", x, gaus / gaus.max(), width=2)
l_ax.set_line_data("Lorentz", x, lore / lore.max(), width=2)
conv = convolve(gaus, lore)
conv /= conv.max()
conv_noise = add_noise(conv, absolute_noise, relative_noise * 0.1)
gaus_noise = add_noise(gaus, absolute_noise, relative_noise * 0.1)
v_ax.set_line_data("Voigt", x, conv, width=2)
v_ax.set_line_data("Noisy Voigt", x, conv_noise, width=2)
g_ax.set_line_data("Noisy Gaussian", x, gaus_noise, width=2)
lin_fit = linear_direct_voigt_fit(x, conv_noise)
log_fit = log_direct_voigt_fit(x, conv_noise)
transfo = np.fft.fft(np.fft.fftshift(conv_noise)).real
fft_fit = linear_fft_voigt_fit(f, transfo, x, dx)
for fit in lin_fit, log_fit, fft_fit:
display_fit(x, fit)
def display_fit(x: np.ndarray, fit: VoigtFitResult):
v_ax.set_line_data(fit.name, x, fit.curve)
g_ax.set_line_data(
fit.name + "gauss",
x,
gaussian(x, fit.amp, fit.gaussian_width, 0),
label=f"{fit.name} - Gaussian {fit.gaussian_width:.2f}",
width=1.5,
)
l_ax.set_line_data(
fit.name + "loren",
x,
lorentzian(x, fit.amp, fit.lorentzian_width, 0),
label=f"{fit.name} - Lorentzian {fit.lorentzian_width:.2f}",
width=1.5,
)
@main.command(help="Explore fft fitting")
def demo3():
x, dx, f, df = setup_axes(1001, (-20, 20))
with PlotApp(
lorentz_fwhm=np.geomspace(0.1, 1),
gauss_fwhm=np.linspace(1, 10),
relative_noise=np.linspace(0, 2),
absolute_noise=[0, *np.geomspace(1e-6, 1, 201)],
) as app:
app.set_antialiasing(True)
ax = app[0]
ind = np.argsort(f)
@app.update
def draw(lorentz_fwhm, gauss_fwhm, absolute_noise, relative_noise):
gaus_clean = gaussian(x, 1, gauss_fwhm, 0)
lore_clean = lorentzian(x, 1, lorentz_fwhm, 0)
voig_clean = convolve(gaus_clean, lore_clean)
gaus = add_noise(gaus_clean, absolute_noise, relative_noise * 0.1)
lore = add_noise(lore_clean, absolute_noise, relative_noise * 0.1)
voig = add_noise(voig_clean, absolute_noise, relative_noise * 0.1)
gaus_f = np.fft.fft(np.fft.fftshift(gaus)).real
lore_f = np.fft.fft(np.fft.fftshift(lore)).real
voig_f = np.fft.fft(np.fft.fftshift(voig)).real
ax.set_line_data("Transformed Gaussian", f[ind], gaus_f[ind])
ax.set_line_data("Transformed Lorentz", f[ind], lore_f[ind])
ax.set_line_data("Transformed Voigt", f[ind], voig_f[ind])
gaus_ff = fft_gaussian(f, dx, 1, gauss_fwhm)
lore_ff = fft_lorentzian(f, dx, 1, lorentz_fwhm)
voig_ff = fft_voigt(f, dx, gauss_fwhm, lorentz_fwhm) * voig_clean.sum()
ax.set_line_data("Expected Gaussian", f[ind], gaus_ff[ind], width=1)
ax.set_line_data("Expected Lorentz", f[ind], lore_ff[ind], width=1)
ax.set_line_data("Expected Voigt", f[ind], voig_ff[ind], width=1)
@click.option("--all", "show_all", default=False, is_flag=True)
@click.option("--file", "-f", type=click.Path(True, dir_okay=False), required=True, prompt=True)
@main.command(name="fit")
def fit_measurement(file, show_all):
raw_wl, raw_intens = osaload(file, normalize=True)
_o = raw_wl.argsort()
raw_intens = raw_intens[_o]
raw_wl = raw_wl[_o]
del _o
if raw_wl[0] < 1:
raw_wl *= 1e9
with PlotApp(
center_wl=np.linspace(raw_wl.min(), raw_wl.max(), 8192),
span=np.geomspace(0.001, raw_wl[-1] - raw_wl[0], 1024),
filter=range(0, 30, 3),
) as app:
app.set_antialiasing(True)
ax1 = app["Measurement"]
ax2 = app["Fourier domain"]
baseline = 0.0
app.params["center_wl"].value = raw_wl[raw_intens.argmax()]
app.params["span"].value = app.params["span"].values()[-1]
@app.update
def draw(center_wl, span, filter):
nonlocal baseline
# Crop spectrum around peak
left_index = np.argmin(np.abs(raw_wl - (center_wl - span / 2)))
right_index = np.argmin(np.abs(raw_wl - (center_wl + span / 2)))
intens = raw_intens[left_index : right_index + 1]
wl = raw_wl[left_index : right_index + 1]
print(len(wl), len(intens))
# Fit rough gaussian to find peak
gauss_fit = single_func_fit(wl, intens, elevated_gaussian, extra_params=1)
wl0 = gauss_fit.x0
baseline = intens[:10].mean()
# intens -= baseline
# gauss_fit.curve -= baseline
intens[intens < 0] = 0
# center spectrum around 0
ax1.set_line_data("Measured spectrum", raw_wl - wl0, raw_intens, width=2, color="red")
# ax1.set_line_data(
# "Initial Guassian fit", raw_wl - wl0, gauss_fit.curve, width=2, color="blue"
# )
new_wl = np.linspace(-span / 2, span / 2, 1024)
intens = interp1d(wl - wl0, intens, bounds_error=False, fill_value=0)(new_wl)
wl = new_wl
# wl -= wl0
intens_fit = intens - baseline
if filter:
intens_fit = wiener(intens_fit, filter)
f, ind, dwl = freq_axis(wl)
ax1.set_line_data("Spectrum to fit", wl, intens_fit + baseline)
trans = np.abs(np.fft.fft(intens_fit))[ind]
ax2.set_line_data("Transformed", f, trans)
ax2.lines["Transformed"].setZValue(10)
display_fit(wl, dwl, f, linear_direct_voigt_fit(wl, intens_fit))
# display_fit(wl, dwl, f, linear_fft_voigt_fit(f, trans, wl, dwl))
# display_fit(wl, dwl, f, log_direct_voigt_fit(wl, intens_fit))
def display_fit(x: np.ndarray, dx: float, f: np.ndarray, fit: VoigtFitResult):
ax2.set_line_data(
fit.name,
f,
fft_voigt(f, dx, fit.amp, fit.gaussian_width, fit.lorentzian_width),
)
ax1.set_line_data(fit.name, x, fit.curve + baseline)
if show_all:
ax1.set_line_data(
fit.name + "gauss",
x,
gaussian(x, 1, fit.gaussian_width, 0) + baseline,
label=f"{fit.name} - Gaussian {fit.gaussian_width:.4g}",
width=1.5,
)
ax1.set_line_data(
fit.name + "lorentz",
x,
lorentzian(x, 1, fit.lorentzian_width, 0) + baseline,
label=f"{fit.name} - Lorentzian {fit.lorentzian_width:.4g}",
width=1.5,
)
@main.command(name="test", help="test scaling of voigt profile")
def test_amp():
x, dx, f, df = setup_axes(1001, (-20, 20))
with PlotApp(
lorentz_fwhm=np.geomspace(0.1, 1), gauss_fwhm=np.linspace(1, 10), amp=np.linspace(1, 7)
) as app:
app.set_antialiasing(True)
ax1 = app["Main"]
ax2 = app["Fourrier"]
ind = np.argsort(f)
@app.update
def draw(lorentz_fwhm, gauss_fwhm, amp):
vo = voigt(x, amp, gauss_fwhm, lorentz_fwhm)
trans = np.fft.fft(np.fft.fftshift(vo))
expected = fft_voigt(f, dx, amp, gauss_fwhm, lorentz_fwhm)
back_transformed = np.fft.fftshift(np.fft.ifft(expected).real)
ax1.set_line_data("original", x, vo)
ax1.set_line_data("back tranformed", x, back_transformed)
ax2.set_line_data("expected", f[ind], expected[ind])
ax2.set_line_data("original transformed", f[ind], trans[ind].real)
if __name__ == "__main__":
main()

156
deconvolution.py → src/linemeasurement/deconvolution.py
View File

@@ -2,11 +2,13 @@ from dataclasses import dataclass, field
from typing import Callable
import click
import labmodule as lab
import numpy as np
from customfunc.app import PlotApp
from scipy.optimize import curve_fit
from plotapp import PlotApp
from scipy.interpolate import interp1d
from scipy.optimize import curve_fit
from scipy.signal import wiener
from .osa import load as osaload
LN2 = np.log(2)
@@ -43,6 +45,56 @@ class VoigtFitResult:
)
def load_osa_spectrum(
data: Union[os.PathLike, str], lvl_col: int = 1, normalize: bool = True, ret_props=False
) -> tuple[np.ndarray, np.ndarray]:
"""Read files from Yokogawa optical spectrum analyzers
Parameters
----------
data : os.PathLike | str
path to the file or raw data
lvl_col : int, optional
index of the intensity column, by default 1
normalize : bool, optional
whether to shift the maximum to 0, by default True
Returns
-------
wavelength : np.ndarray
wavelength in m
intensity : np.ndarray
recorded intensity in dB
"""
props = {}
if isinstance(data, str) and not os.path.exists(data):
s = data.splitlines()
else:
with open(data) as f:
s = f.readlines()
s = iter(s)
for line in s:
if "[TRACE DATA]" in line:
break
if ret_props and line.startswith('"'):
k, v = parse_osa_prop_line(line)
props[k] = v
to_load = "\n".join([el.strip() for el in s])
wl, intens = np.loadtxt(
StringIO(to_load), delimiter=",", usecols=(0, lvl_col), unpack=True, comments='"'
)
if normalize:
if (intens < 0).sum() < len(intens) / 10:
intens -= intens.min()
intens /= intens.max()
else:
intens -= intens.max()
if ret_props:
return 1e-9 * wl, intens, props
else:
return 1e-9 * wl, intens
def setup_axes(n: int, lims=(-10, 10)) -> tuple[np.ndarray, float, np.ndarray, float]:
"""Creates a time-like and a frequency-like array and returns their respective spacing"""
x, dx = np.linspace(*lims, n, retstep=True)
@@ -61,6 +113,15 @@ def freq_axis(x: np.ndarray) -> tuple[np.ndarray, np.ndarray, float]:
return f[ind], ind, dx
def rough_fwhm(x: np.ndarray, y: np.ndarray) -> float:
m2 = y.max() / 2
ind = np.where(y >= m2)[0]
return (
x[np.minimum(ind + 1, len(x) - 1)][x[ind].argmax()]
- x[np.maximum(0, ind - 1)][x[ind].argmin()]
)
def lorentzian(x: np.ndarray, amp: float, width: float, x0: float) -> np.ndarray:
"""Lorentzian function of max `amp`, FWHM `width` and position `x0`"""
gamma = (width * 0.5) ** 2
@@ -89,13 +150,13 @@ def fft_lorentzian(f: np.ndarray, dx: float, amp: float, width: float) -> np.nda
def gaussian(x: np.ndarray, amp: float, width: float, x0: float) -> np.ndarray:
"""Gaussian function of max `amp`, FWHM `width` and position `x0`"""
sig2 = width ** 2 / (8 * LN2)
sig2 = width**2 / (8 * LN2)
return amp * np.exp(-((x - x0) ** 2) / (2 * sig2))
def elevated_gaussian(x: np.ndarray, amp: float, width: float, x0: float, y0: float) -> np.ndarray:
"""Gaussian function of max `amp`, FWHM `width`, position `x0` and baseline `y0`"""
sig2 = width ** 2 / (8 * LN2)
sig2 = width**2 / (8 * LN2)
return amp * np.exp(-((x - x0) ** 2) / (2 * sig2)) + y0
@@ -177,15 +238,25 @@ def single_func_fit(
fct = _fct
am = y.argmax()
xm = x.mean()
fwhm = rough_fwhm(x, y)
params, cov = curve_fit(
fct, x, y, sigma=1 / y if use_weights else None, p0=[y[am], 1, x[am]] + [0] * extra_params
fct,
x - xm,
y,
sigma=1 / y if use_weights else None,
p0=[y[am], fwhm, x[am] - xm] + [0] * extra_params,
)
params[2] += xm
curve = fct(x, *params)
return FitResult(curve, cov, params[0], params[1], params[2], tuple(params))
def linear_direct_voigt_fit(x: np.ndarray, y: np.ndarray, use_weights=False) -> VoigtFitResult:
params, cov = curve_fit(voigt, x, y, sigma=1 / y if use_weights else None)
fwhm = rough_fwhm(x, y)
params, cov = curve_fit(
voigt, x, y, sigma=1 / y if use_weights else None, p0=[y.max(), fwhm / 2, fwhm / 2]
)
curve = voigt(x, *params)
return VoigtFitResult("Linear direct fit", curve, cov, *params)
@@ -200,8 +271,13 @@ def log_direct_voigt_fit(x: np.ndarray, y: np.ndarray) -> VoigtFitResult:
def linear_fft_voigt_fit(f: np.ndarray, A: np.ndarray, x: np.ndarray, dx: float) -> VoigtFitResult:
fwhm = 4 * LN2 / (np.pi * rough_fwhm(f, A))
params, cov = curve_fit(
lambda _f, _a, _g, _l: fft_voigt(_f, dx, _a, _g, _l), f, A, bounds=[1e-12, np.inf]
lambda _f, _amp, _gau, _lor: fft_voigt(_f, dx, _amp, _gau, _lor),
f,
A,
bounds=[1e-12, np.inf],
p0=[A.max(), fwhm, fwhm / 10],
)
curve = voigt(x, *params)
return VoigtFitResult("Linear FFT fit", curve, cov, *params)
@@ -249,7 +325,11 @@ def demo2():
absolute_noise=[0, *np.geomspace(1e-6, 1, 201)],
) as app:
app.set_antialiasing(True)
ax = app["Main Plot"]
v_ax = app["Voigt"]
l_ax = app["Lorentz"]
g_ax = app["Gauss"]
l_ax.link_x(v_ax)
g_ax.link_x(v_ax)
app.params["gauss_fwhm"].value = 5
app.params["n"].value = 1024
@@ -260,37 +340,37 @@ def demo2():
x, dx, f, df = setup_axes(n, (-20, 20))
gaus = gaussian(x, 1, gauss_fwhm, 0)
lore = lorentzian(x, 1, lorentz_fwhm, 0)
ax.set_line_data("Gaussian", x, gaus / gaus.max(), width=2)
ax.set_line_data("Lorentz", x, lore / lore.max(), width=2)
g_ax.set_line_data("Gaussian", x, gaus / gaus.max(), width=2)
l_ax.set_line_data("Lorentz", x, lore / lore.max(), width=2)
conv = convolve(gaus, lore)
conv /= conv.max()
conv_noise = add_noise(conv, absolute_noise, relative_noise * 0.1)
gaus_noise = add_noise(gaus, absolute_noise, relative_noise * 0.1)
ax.set_line_data("Voigt", x, conv, width=2)
ax.set_line_data("Noisy Voigt", x, conv_noise, width=2)
ax.set_line_data("Noisy Gaussian", x, gaus_noise, width=2)
v_ax.set_line_data("Voigt", x, conv, width=2)
v_ax.set_line_data("Noisy Voigt", x, conv_noise, width=2)
g_ax.set_line_data("Noisy Gaussian", x, gaus_noise, width=2)
lin_fit = linear_direct_voigt_fit(x, conv_noise)
log_fit = log_direct_voigt_fit(x, conv_noise)
transfo = np.fft.fft(np.fft.fftshift(conv_noise)).real
fft_fit = linear_fft_voigt_fit(f, transfo / transfo.max(), x, dx)
fft_fit = linear_fft_voigt_fit(f, transfo, x, dx)
for fit in lin_fit, log_fit, fft_fit:
display_fit(x, fit)
def display_fit(x: np.ndarray, fit: VoigtFitResult):
ax.set_line_data(fit.name, x, fit.curve)
ax.set_line_data(
v_ax.set_line_data(fit.name, x, fit.curve)
g_ax.set_line_data(
fit.name + "gauss",
x,
gaussian(x, 1, fit.gaussian_width, 0),
gaussian(x, fit.amp, fit.gaussian_width, 0),
label=f"{fit.name} - Gaussian {fit.gaussian_width:.2f}",
width=1.5,
)
ax.set_line_data(
l_ax.set_line_data(
fit.name + "loren",
x,
lorentzian(x, 1, fit.lorentzian_width, 0),
lorentzian(x, fit.amp, fit.lorentzian_width, 0),
label=f"{fit.name} - Lorentzian {fit.lorentzian_width:.2f}",
width=1.5,
)
@@ -337,25 +417,26 @@ def demo3():
ax.set_line_data("Expected Voigt", f[ind], voig_ff[ind], width=1)
@click.argument("file", type=click.Path(True, dir_okay=False))
@click.option("--all", "show_all", default=False, is_flag=True)
@click.option("--file", "-f", type=click.Path(True, dir_okay=False), required=True, prompt=True)
@main.command(name="fit")
def fit_measurement(file, show_all):
raw_wl, intens = lab.osa.load_osa_spectrum(file)
raw_wl, intens = osaload(file, normalize=True)
if raw_wl[0] < 1:
raw_wl *= 1e9
gauss_fit = single_func_fit(raw_wl, intens, elevated_gaussian, extra_params=1)
wl0 = gauss_fit.x0
baseline = gauss_fit.params[-1]
intens -= baseline
gauss_fit.curve -= baseline
baseline = intens[:10].mean()
# intens -= baseline
# gauss_fit.curve -= baseline
intens[intens < 0] = 0
lim = np.max(np.abs(raw_wl - wl0))
new_wl = np.linspace(-lim, lim, 2048)
new_wl = np.linspace(-lim, lim, 1024)
intens = interp1d(raw_wl - wl0, intens, bounds_error=False, fill_value=0)(new_wl)
wl = new_wl
with PlotApp(n=np.arange(len(wl) // 2)) as app:
with PlotApp(num_skipped=np.arange(len(wl) // 2), filter=range(0, 30, 3)) as app:
app.set_antialiasing(True)
ax1 = app["Measurement"]
@@ -367,14 +448,17 @@ def fit_measurement(file, show_all):
# ax1.plot_widget.plotItem.setLogMode(y=True)
@app.update
def draw(n):
s = slice(n, -n) if n else slice(None)
def draw(num_skipped, filter):
n, r = divmod(num_skipped, 2)
s = slice(n + r, -n) if n else slice(None)
wl_fit = wl[s]
intens_fit = intens[s] - intens[s].min()
intens_fit = intens[s] - baseline
if filter:
intens_fit = wiener(intens_fit, filter)
f, ind, dwl = freq_axis(wl_fit)
ax1.set_line_data("Fitted spectrum", wl_fit, intens_fit)
ax1.set_line_data("Fitted spectrum", wl_fit, intens_fit + baseline)
trans = np.abs(np.fft.fft(intens_fit))[ind]
ax2.set_line_data("Transformed", f, trans)
@@ -392,20 +476,20 @@ def fit_measurement(file, show_all):
f,
fft_voigt(f, dx, fit.amp, fit.gaussian_width, fit.lorentzian_width),
)
ax1.set_line_data(fit.name, x, fit.curve)
ax1.set_line_data(fit.name, x, fit.curve + baseline)
if show_all:
ax1.set_line_data(
fit.name + "gauss",
x,
gaussian(x, 1, fit.gaussian_width, 0),
label=f"{fit.name} - Gaussian {fit.gaussian_width:.3f}",
gaussian(x, 1, fit.gaussian_width, 0) + baseline,
label=f"{fit.name} - Gaussian {fit.gaussian_width:.4g}",
width=1.5,
)
ax1.set_line_data(
fit.name + "lorentz",
x,
lorentzian(x, 1, fit.lorentzian_width, 0),
label=f"{fit.name} - Lorentzian {fit.lorentzian_width:.3f}",
lorentzian(x, 1, fit.lorentzian_width, 0) + baseline,
label=f"{fit.name} - Lorentzian {fit.lorentzian_width:.4g}",
width=1.5,
)

112
src/linemeasurement/osa.py Normal file
View File

@@ -0,0 +1,112 @@
import os
from functools import lru_cache
from io import StringIO
from pathlib import Path
from typing import Any, Union
import numpy as np
def parse_osa_prop_line(s: str) -> tuple[str, Any]:
vals = s.split('"')
if vals[0]:
raise ValueError()
key = vals[1].lower().replace(" ", "_")
if len(vals) <= 2:
return key, True
val_str = vals[2].replace(",", "").strip()
if val_str == '"OFF"':
return key, False
for fct in int, float:
try:
val = fct(val_str)
return key, val
except ValueError:
continue
return key, val_str
@lru_cache
def load_osa_spectrum(
data: Union[os.PathLike, str], lvl_col: int = 1, normalize: bool = True, ret_props=False
) -> tuple[np.ndarray, np.ndarray]:
"""Read files from Yokogawa optical spectrum analyzers
Parameters
----------
data : os.PathLike | str
path to the file or raw data
lvl_col : int, optional
index of the intensity column, by default 1
normalize : bool, optional
whether to shift the maximum to 0, by default True
Returns
-------
wavelength : np.ndarray
wavelength in m
intensity : np.ndarray
recorded intensity in dB
"""
props = {}
if isinstance(data, str) and not os.path.exists(data):
s = data.splitlines()
else:
with open(data) as f:
s = f.readlines()
s = iter(s)
for line in s:
if "[TRACE DATA]" in line:
break
if ret_props and line.startswith('"'):
k, v = parse_osa_prop_line(line)
props[k] = v
to_load = "\n".join([el.strip() for el in s])
wl, intens = np.loadtxt(
StringIO(to_load), delimiter=",", usecols=(0, lvl_col), unpack=True, comments='"'
)
if normalize:
if (intens < 0).sum() < len(intens) / 10:
intens -= intens.min()
intens /= intens.max()
else:
intens -= intens.max()
if ret_props:
return 1e-9 * wl, intens, props
else:
return 1e-9 * wl, intens
def load_avantes(
data: Union[os.PathLike, str], usecols=(0, 1), skiprows=8
) -> tuple[np.ndarray, np.ndarray]:
if isinstance(data, str):
s = data
else:
s = Path(data).read_text()
s = s.replace(",", ".")
return np.loadtxt(StringIO(s), delimiter=";", usecols=usecols, skiprows=skiprows, unpack=True)
def load(path: os.PathLike, normalize=False) -> tuple[np.ndarray, np.ndarray]:
s = Path(path).read_text()
wl = None
if "[TRACE DATA]" in s:
wl, i = load_osa_spectrum(s, normalize=False)
elif "Integration time [ms]" in s:
wl, i = load_avantes(s)
else:
for lim in [None, ",", ";"]:
try:
wl, i = np.loadtxt(StringIO(s), delimiter=lim, unpack=True)
break
except ValueError:
continue
if wl is None:
raise ValueError(f"Could not load a spectrum from {path}")
if normalize:
i -= i.min()
i /= i.max()
return wl, i

214
src/linemeasurement/profiles.py Normal file
View File

@@ -0,0 +1,214 @@
from dataclasses import dataclass, field
from typing import Callable
import numpy as np
from scipy.optimize import curve_fit
LN2 = np.log(2)
@dataclass
class FitResult:
curve: np.ndarray = field(repr=False)
covariance: np.ndarray
amp: float
width: float
x0: float = 0
params: tuple[float, ...] = field(default_factory=tuple)
@dataclass
class VoigtFitResult:
name: str
curve: np.ndarray = field(repr=False)
covariance: np.ndarray
amp: float
gaussian_width: float
lorentzian_width: float
@property
def descr(self) -> str:
return (
f"{self.name} - Gaussian width : {self.gaussian_width:.2f}, "
f"Lorentzian width : {self.lorentzian_width:.2f}"
)
def rough_fwhm(x: np.ndarray, y: np.ndarray) -> float:
m2 = y.max() / 2
ind = np.where(y >= m2)[0]
return (
x[np.minimum(ind + 1, len(x) - 1)][x[ind].argmax()]
- x[np.maximum(0, ind - 1)][x[ind].argmin()]
)
def lorentzian(x: np.ndarray, amp: float, width: float, x0: float) -> np.ndarray:
"""Lorentzian function of max `amp`, FWHM `width` and position `x0`"""
gamma = (width * 0.5) ** 2
return amp * gamma / ((x - x0) ** 2 + gamma)
def unit_lorentzian(x: np.ndarray, width: float, x0: float) -> np.ndarray:
"""Normalized Lorentzian function of FWHM `width` and position `x0`"""
return lorentzian(x, 1 / (np.pi * width * 0.5), width, x0)
def peak_func(x: np.ndarray, amp: float, width: float, x0: float) -> np.ndarray:
"""2-sided decaying exp of max `amp`, FWHM `width` and position `x0`"""
a = 2 * LN2 / width
return amp * np.exp(-np.abs(x - x0) * a)
def fft_lorentzian(f: np.ndarray, dx: float, amp: float, width: float) -> np.ndarray:
"""
Same as `peak_func` but parametrized as the fourier transform
of a Lorentzian function of max `amp` and FWHM `width`
"""
gamma_sqrt = width / 2
return amp * np.pi * gamma_sqrt * np.exp(-2 * np.pi * gamma_sqrt * np.abs(f)) / dx
def gaussian(x: np.ndarray, amp: float, width: float, x0: float) -> np.ndarray:
"""Gaussian function of max `amp`, FWHM `width` and position `x0`"""
sig2 = width**2 / (8 * LN2)
return amp * np.exp(-((x - x0) ** 2) / (2 * sig2))
def elevated_gaussian(x: np.ndarray, amp: float, width: float, x0: float, y0: float) -> np.ndarray:
"""Gaussian function of max `amp`, FWHM `width`, position `x0` and baseline `y0`"""
sig2 = width**2 / (8 * LN2)
return amp * np.exp(-((x - x0) ** 2) / (2 * sig2)) + y0
def unit_gaussian(x: np.ndarray, width: float, x0: float) -> np.ndarray:
"""Normalized Gaussian function of FWHM `width` and position `x0`"""
return gaussian(x, np.sqrt(4 * LN2 / np.pi) / width, width, x0)
def fft_gaussian(f: np.ndarray, dx: float, amp: float, width: float) -> np.ndarray:
"""
Same as `gaussian` but parametrized as the fourier transform
of another Gaussian function of max `amp` and FWHM `width`
""" # exp_param =
freq_amp = amp * width / 2 * np.sqrt(np.pi / LN2) / dx
freq_width = 4 * LN2 / (width * np.pi)
return gaussian(f, freq_amp, freq_width, 0)
def voigt(x: np.ndarray, amp: float, gaussian_width: float, lorentzian_width: float) -> np.ndarray:
"""Voigt profile of max 1 with specified Gaussian and Lorentzian FWHM"""
conv = convolve(unit_gaussian(x, gaussian_width, 0), unit_lorentzian(x, lorentzian_width, 0))
if conv.max() > 0:
return conv * amp / conv.max()
return conv
def log_voigt(
x: np.ndarray, amp: float, gaussian_width: float, lorentzian_width: float
) -> np.ndarray:
"""log of `voigt`"""
return np.log(voigt(x, amp, gaussian_width, lorentzian_width))
def fft_voigt(
f: np.ndarray, dx: float, amp: float, gaussian_width: float, lorentzian_width: float
) -> np.ndarray:
"""
returns the product of a `peak_func` and a `gaussian`
parameterized as the Fourier transform of a Voigt profile
given by `gaussian_width` and `lorentzian_width`
"""
profile = fft_gaussian(f, dx, 1, gaussian_width) * fft_lorentzian(f, dx, 1, lorentzian_width)
fac = amp / np.abs(np.fft.ifft(np.fft.fftshift(profile))).max()
return profile * fac
def single_func_fit(
x: np.ndarray,
y: np.ndarray,
fct: Callable[[np.ndarray, float, float, float], np.ndarray],
force_center=False,
use_weights=False,
extra_params: int = 0,
) -> FitResult:
"""fits a single bell-like function (Gaussian or Lorentian, not Voigt)
Parameters
----------
x : np.ndarray
x input
y : np.ndarray
corresponding y data
fct : Callable[[np.ndarray, float, float, float], np.ndarray]
function to fit
force_center : bool, optional
assume the bell is centered at x=0, by default False
use_weights : bool, optional
give more importance to higher y values, by default False
Returns
-------
FitResult
resuts of the fit
"""
if force_center:
def _fct(_x: np.ndarray, amp: float, width: float):
return fct(_x, amp, width, 0)
fct = _fct
am = y.argmax()
xm = x.mean()
fwhm = rough_fwhm(x, y)
params, cov = curve_fit(
fct,
x - xm,
y,
sigma=1 / y if use_weights else None,
p0=[y[am], fwhm, x[am] - xm] + [0] * extra_params,
)
params[2] += xm
curve = fct(x, *params)
return FitResult(curve, cov, params[0], params[1], params[2], tuple(params))
def linear_direct_voigt_fit(x: np.ndarray, y: np.ndarray, use_weights=False) -> VoigtFitResult:
fwhm = rough_fwhm(x, y)
params, cov = curve_fit(
voigt, x, y, sigma=1 / y if use_weights else None, p0=[y.max(), fwhm / 2, fwhm / 2]
)
curve = voigt(x, *params)
return VoigtFitResult("Linear direct fit", curve, cov, *params)
def log_direct_voigt_fit(x: np.ndarray, y: np.ndarray) -> VoigtFitResult:
ind = y > 0
y_fit = np.log(y[ind])
x_fit = x[ind]
params, cov = curve_fit(log_voigt, x_fit, y_fit, bounds=[1e-12, np.inf])
curve = voigt(x, *params)
return VoigtFitResult("Log direct fit", curve, cov, *params)
def linear_fft_voigt_fit(f: np.ndarray, A: np.ndarray, x: np.ndarray, dx: float) -> VoigtFitResult:
fwhm = 4 * LN2 / (np.pi * rough_fwhm(f, A))
params, cov = curve_fit(
lambda _f, _amp, _gau, _lor: fft_voigt(_f, dx, _amp, _gau, _lor),
f,
A,
bounds=[1e-12, np.inf],
p0=[A.max(), fwhm, fwhm / 10],
)
curve = voigt(x, *params)
return VoigtFitResult("Linear FFT fit", curve, cov, *params)
def convolve(gau: np.ndarray, lor: np.ndarray) -> np.ndarray:
return np.convolve(gau, lor, mode="same")
def add_noise(arr: np.ndarray, abs_fac: float, rel_fac: float):
return (
arr + abs_fac * np.random.rand(len(arr)) + rel_fac * arr * (np.random.rand(len(arr)) - 0.5)
)

231
uv.lock generated Normal file
View File

@@ -0,0 +1,231 @@
version = 1
requires-python = ">=3.10"
[[package]]
name = "click"
version = "8.1.7"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "colorama", marker = "platform_system == 'Windows'" },
]
sdist = { url = "https://files.pythonhosted.org/packages/96/d3/f04c7bfcf5c1862a2a5b845c6b2b360488cf47af55dfa79c98f6a6bf98b5/click-8.1.7.tar.gz", hash = "sha256:ca9853ad459e787e2192211578cc907e7594e294c7ccc834310722b41b9ca6de", size = 336121 }
wheels = [
{ url = "https://files.pythonhosted.org/packages/00/2e/d53fa4befbf2cfa713304affc7ca780ce4fc1fd8710527771b58311a3229/click-8.1.7-py3-none-any.whl", hash = "sha256:ae74fb96c20a0277a1d615f1e4d73c8414f5a98db8b799a7931d1582f3390c28", size = 97941 },
]
[[package]]
name = "colorama"
version = "0.4.6"
source = { registry = "https://pypi.org/simple" }
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1
wolfram.txt Normal file
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@@ -0,0 +1 @@
{A = a * x_1 + b * y_1 + e; B = c * x_1 + d * y_1 + f; C = a * x_2 + b * y_2 + e; D = c * x_2 + d * y_2 + f; E = a * x_3 + b * y_3 + e; F = c * x_3 + d * y_3 + f} solve for a, b, c, d, e, f