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1 # 13th Generation International Geomagnetic Reference Field Schmidt semi-normalised spherical harmonic coefficients, degree n=1,13
2 # in units nanoTesla for IGRF and definitive DGRF main-field models (degree n=1,8 nanoTesla/year for secular variation (SV))
3 c/s deg ord IGRF IGRF IGRF IGRF IGRF IGRF IGRF IGRF IGRF DGRF DGRF DGRF DGRF DGRF DGRF DGRF DGRF DGRF DGRF DGRF DGRF DGRF DGRF DGRF IGRF SV
4 g/h n m 1900.0 1905.0 1910.0 1915.0 1920.0 1925.0 1930.0 1935.0 1940.0 1945.0 1950.0 1955.0 1960.0 1965.0 1970.0 1975.0 1980.0 1985.0 1990.0 1995.0 2000.0 2005.0 2010.0 2015.0 2020.0 2020-25
5 g 1 0 -31543 -31464 -31354 -31212 -31060 -30926 -30805 -30715 -30654 -30594 -30554 -30500 -30421 -30334 -30220 -30100 -29992 -29873 -29775 -29692 -29619.4 -29554.63 -29496.57 -29441.46 -29404.8 5.7
6 g 1 1 -2298 -2298 -2297 -2306 -2317 -2318 -2316 -2306 -2292 -2285 -2250 -2215 -2169 -2119 -2068 -2013 -1956 -1905 -1848 -1784 -1728.2 -1669.05 -1586.42 -1501.77 -1450.9 7.4
7 h 1 1 5922 5909 5898 5875 5845 5817 5808 5812 5821 5810 5815 5820 5791 5776 5737 5675 5604 5500 5406 5306 5186.1 5077.99 4944.26 4795.99 4652.5 -25.9
8 g 2 0 -677 -728 -769 -802 -839 -893 -951 -1018 -1106 -1244 -1341 -1440 -1555 -1662 -1781 -1902 -1997 -2072 -2131 -2200 -2267.7 -2337.24 -2396.06 -2445.88 -2499.6 -11.0
9 g 2 1 2905 2928 2948 2956 2959 2969 2980 2984 2981 2990 2998 3003 3002 2997 3000 3010 3027 3044 3059 3070 3068.4 3047.69 3026.34 3012.20 2982.0 -7.0
10 h 2 1 -1061 -1086 -1128 -1191 -1259 -1334 -1424 -1520 -1614 -1702 -1810 -1898 -1967 -2016 -2047 -2067 -2129 -2197 -2279 -2366 -2481.6 -2594.50 -2708.54 -2845.41 -2991.6 -30.2
11 g 2 2 924 1041 1176 1309 1407 1471 1517 1550 1566 1578 1576 1581 1590 1594 1611 1632 1663 1687 1686 1681 1670.9 1657.76 1668.17 1676.35 1677.0 -2.1
12 h 2 2 1121 1065 1000 917 823 728 644 586 528 477 381 291 206 114 25 -68 -200 -306 -373 -413 -458.0 -515.43 -575.73 -642.17 -734.6 -22.4
13 g 3 0 1022 1037 1058 1084 1111 1140 1172 1206 1240 1282 1297 1302 1302 1297 1287 1276 1281 1296 1314 1335 1339.6 1336.30 1339.85 1350.33 1363.2 2.2
14 g 3 1 -1469 -1494 -1524 -1559 -1600 -1645 -1692 -1740 -1790 -1834 -1889 -1944 -1992 -2038 -2091 -2144 -2180 -2208 -2239 -2267 -2288.0 -2305.83 -2326.54 -2352.26 -2381.2 -5.9
15 h 3 1 -330 -357 -389 -421 -445 -462 -480 -494 -499 -499 -476 -462 -414 -404 -366 -333 -336 -310 -284 -262 -227.6 -198.86 -160.40 -115.29 -82.1 6.0
16 g 3 2 1256 1239 1223 1212 1205 1202 1205 1215 1232 1255 1274 1288 1289 1292 1278 1260 1251 1247 1248 1249 1252.1 1246.39 1232.10 1225.85 1236.2 3.1
17 h 3 2 3 34 62 84 103 119 133 146 163 186 206 216 224 240 251 262 271 284 293 302 293.4 269.72 251.75 245.04 241.9 -1.1
18 g 3 3 572 635 705 778 839 881 907 918 916 913 896 882 878 856 838 830 833 829 802 759 714.5 672.51 633.73 581.69 525.7 -12.0
19 h 3 3 523 480 425 360 293 229 166 101 43 -11 -46 -83 -130 -165 -196 -223 -252 -297 -352 -427 -491.1 -524.72 -537.03 -538.70 -543.4 0.5
20 g 4 0 876 880 884 887 889 891 896 903 914 944 954 958 957 957 952 946 938 936 939 940 932.3 920.55 912.66 907.42 903.0 -1.2
21 g 4 1 628 643 660 678 695 711 727 744 762 776 792 796 800 804 800 791 782 780 780 780 786.8 797.96 808.97 813.68 809.5 -1.6
22 h 4 1 195 203 211 218 220 216 205 188 169 144 136 133 135 148 167 191 212 232 247 262 272.6 282.07 286.48 283.54 281.9 -0.1
23 g 4 2 660 653 644 631 616 601 584 565 550 544 528 510 504 479 461 438 398 361 325 290 250.0 210.65 166.58 120.49 86.3 -5.9
24 h 4 2 -69 -77 -90 -109 -134 -163 -195 -226 -252 -276 -278 -274 -278 -269 -266 -265 -257 -249 -240 -236 -231.9 -225.23 -211.03 -188.43 -158.4 6.5
25 g 4 3 -361 -380 -400 -416 -424 -426 -422 -415 -405 -421 -408 -397 -394 -390 -395 -405 -419 -424 -423 -418 -403.0 -379.86 -356.83 -334.85 -309.4 5.2
26 h 4 3 -210 -201 -189 -173 -153 -130 -109 -90 -72 -55 -37 -23 3 13 26 39 53 69 84 97 119.8 145.15 164.46 180.95 199.7 3.6
27 g 4 4 134 146 160 178 199 217 234 249 265 304 303 290 269 252 234 216 199 170 141 122 111.3 100.00 89.40 70.38 48.0 -5.1
28 h 4 4 -75 -65 -55 -51 -57 -70 -90 -114 -141 -178 -210 -230 -255 -269 -279 -288 -297 -297 -299 -306 -303.8 -305.36 -309.72 -329.23 -349.7 -5.0
29 g 5 0 -184 -192 -201 -211 -221 -230 -237 -241 -241 -253 -240 -229 -222 -219 -216 -218 -218 -214 -214 -214 -218.8 -227.00 -230.87 -232.91 -234.3 -0.3
30 g 5 1 328 328 327 327 326 326 327 329 334 346 349 360 362 358 359 356 357 355 353 352 351.4 354.41 357.29 360.14 363.2 0.5
31 h 5 1 -210 -193 -172 -148 -122 -96 -72 -51 -33 -12 3 15 16 19 26 31 46 47 46 46 43.8 42.72 44.58 46.98 47.7 0.0
32 g 5 2 264 259 253 245 236 226 218 211 208 194 211 230 242 254 262 264 261 253 245 235 222.3 208.95 200.26 192.35 187.8 -0.6
33 h 5 2 53 56 57 58 58 58 60 64 71 95 103 110 125 128 139 148 150 150 154 165 171.9 180.25 189.01 196.98 208.3 2.5
34 g 5 3 5 -1 -9 -16 -23 -28 -32 -33 -33 -20 -20 -23 -26 -31 -42 -59 -74 -93 -109 -118 -130.4 -136.54 -141.05 -140.94 -140.7 0.2
35 h 5 3 -33 -32 -33 -34 -38 -44 -53 -64 -75 -67 -87 -98 -117 -126 -139 -152 -151 -154 -153 -143 -133.1 -123.45 -118.06 -119.14 -121.2 -0.6
36 g 5 4 -86 -93 -102 -111 -119 -125 -131 -136 -141 -142 -147 -152 -156 -157 -160 -159 -162 -164 -165 -166 -168.6 -168.05 -163.17 -157.40 -151.2 1.3
37 h 5 4 -124 -125 -126 -126 -125 -122 -118 -115 -113 -119 -122 -121 -114 -97 -91 -83 -78 -75 -69 -55 -39.3 -19.57 -0.01 15.98 32.3 3.0
38 g 5 5 -16 -26 -38 -51 -62 -69 -74 -76 -76 -82 -76 -69 -63 -62 -56 -49 -48 -46 -36 -17 -12.9 -13.55 -8.03 4.30 13.5 0.9
39 h 5 5 3 11 21 32 43 51 58 64 69 82 80 78 81 81 83 88 92 95 97 107 106.3 103.85 101.04 100.12 98.9 0.3
40 g 6 0 63 62 62 61 61 61 60 59 57 59 54 47 46 45 43 45 48 53 61 68 72.3 73.60 72.78 69.55 66.0 -0.5
41 g 6 1 61 60 58 57 55 54 53 53 54 57 57 57 58 61 64 66 66 65 65 67 68.2 69.56 68.69 67.57 65.5 -0.3
42 h 6 1 -9 -7 -5 -2 0 3 4 4 4 6 -1 -9 -10 -11 -12 -13 -15 -16 -16 -17 -17.4 -20.33 -20.90 -20.61 -19.1 0.0
43 g 6 2 -11 -11 -11 -10 -10 -9 -9 -8 -7 6 4 3 1 8 15 28 42 51 59 68 74.2 76.74 75.92 72.79 72.9 0.4
44 h 6 2 83 86 89 93 96 99 102 104 105 100 99 96 99 100 100 99 93 88 82 72 63.7 54.75 44.18 33.30 25.1 -1.6
45 g 6 3 -217 -221 -224 -228 -233 -238 -242 -246 -249 -246 -247 -247 -237 -228 -212 -198 -192 -185 -178 -170 -160.9 -151.34 -141.40 -129.85 -121.5 1.3
46 h 6 3 2 4 5 8 11 14 19 25 33 16 33 48 60 68 72 75 71 69 69 67 65.1 63.63 61.54 58.74 52.8 -1.3
47 g 6 4 -58 -57 -54 -51 -46 -40 -32 -25 -18 -25 -16 -8 -1 4 2 1 4 4 3 -1 -5.9 -14.58 -22.83 -28.93 -36.2 -1.4
48 h 6 4 -35 -32 -29 -26 -22 -18 -16 -15 -15 -9 -12 -16 -20 -32 -37 -41 -43 -48 -52 -58 -61.2 -63.53 -66.26 -66.64 -64.5 0.8
49 g 6 5 59 57 54 49 44 39 32 25 18 21 12 7 -2 1 3 6 14 16 18 19 16.9 14.58 13.10 13.14 13.5 0.0
50 h 6 5 36 32 28 23 18 13 8 4 0 -16 -12 -12 -11 -8 -6 -4 -2 -1 1 1 0.7 0.24 3.02 7.35 8.9 0.0
51 g 6 6 -90 -92 -95 -98 -101 -103 -104 -106 -107 -104 -105 -107 -113 -111 -112 -111 -108 -102 -96 -93 -90.4 -86.36 -78.09 -70.85 -64.7 0.9
52 h 6 6 -69 -67 -65 -62 -57 -52 -46 -40 -33 -39 -30 -24 -17 -7 1 11 17 21 24 36 43.8 50.94 55.40 62.41 68.1 1.0
53 g 7 0 70 70 71 72 73 73 74 74 74 70 65 65 67 75 72 71 72 74 77 77 79.0 79.88 80.44 81.29 80.6 -0.1
54 g 7 1 -55 -54 -54 -54 -54 -54 -54 -53 -53 -40 -55 -56 -56 -57 -57 -56 -59 -62 -64 -72 -74.0 -74.46 -75.00 -75.99 -76.7 -0.2
55 h 7 1 -45 -46 -47 -48 -49 -50 -51 -52 -52 -45 -35 -50 -55 -61 -70 -77 -82 -83 -80 -69 -64.6 -61.14 -57.80 -54.27 -51.5 0.6
56 g 7 2 0 0 1 2 2 3 4 4 4 0 2 2 5 4 1 1 2 3 2 1 0.0 -1.65 -4.55 -6.79 -8.2 0.0
57 h 7 2 -13 -14 -14 -14 -14 -14 -15 -17 -18 -18 -17 -24 -28 -27 -27 -26 -27 -27 -26 -25 -24.2 -22.57 -21.20 -19.53 -16.9 0.6
58 g 7 3 34 33 32 31 29 27 25 23 20 0 1 10 15 13 14 16 21 24 26 28 33.3 38.73 45.24 51.82 56.5 0.7
59 h 7 3 -10 -11 -12 -12 -13 -14 -14 -14 -14 2 0 -4 -6 -2 -4 -5 -5 -2 0 4 6.2 6.82 6.54 5.59 2.2 -0.8
60 g 7 4 -41 -41 -40 -38 -37 -35 -34 -33 -31 -29 -40 -32 -32 -26 -22 -14 -12 -6 -1 5 9.1 12.30 14.00 15.07 15.8 0.1
61 h 7 4 -1 0 1 2 4 5 6 7 7 6 10 8 7 6 8 10 16 20 21 24 24.0 25.35 24.96 24.45 23.5 -0.2
62 g 7 5 -21 -20 -19 -18 -16 -14 -12 -11 -9 -10 -7 -11 -7 -6 -2 0 1 4 5 4 6.9 9.37 10.46 9.32 6.4 -0.5
63 h 7 5 28 28 28 28 28 29 29 29 29 28 36 28 23 26 23 22 18 17 17 17 14.8 10.93 7.03 3.27 -2.2 -1.1
64 g 7 6 18 18 18 19 19 19 18 18 17 15 5 9 17 13 13 12 11 10 9 8 7.3 5.42 1.64 -2.88 -7.2 -0.8
65 h 7 6 -12 -12 -13 -15 -16 -17 -18 -19 -20 -17 -18 -20 -18 -23 -23 -23 -23 -23 -23 -24 -25.4 -26.32 -27.61 -27.50 -27.2 0.1
66 g 7 7 6 6 6 6 6 6 6 6 5 29 19 18 8 1 -2 -5 -2 0 0 -2 -1.2 1.94 4.92 6.61 9.8 0.8
67 h 7 7 -22 -22 -22 -22 -22 -21 -20 -19 -19 -22 -16 -18 -17 -12 -11 -12 -10 -7 -4 -6 -5.8 -4.64 -3.28 -2.32 -1.8 0.3
68 g 8 0 11 11 11 11 11 11 11 11 11 13 22 11 15 13 14 14 18 21 23 25 24.4 24.80 24.41 23.98 23.7 0.0
69 g 8 1 8 8 8 8 7 7 7 7 7 7 15 9 6 5 6 6 6 6 5 6 6.6 7.62 8.21 8.89 9.7 0.1
70 h 8 1 8 8 8 8 8 8 8 8 8 12 5 10 11 7 7 6 7 8 10 11 11.9 11.20 10.84 10.04 8.4 -0.2
71 g 8 2 -4 -4 -4 -4 -3 -3 -3 -3 -3 -8 -4 -6 -4 -4 -2 -1 0 0 -1 -6 -9.2 -11.73 -14.50 -16.78 -17.6 -0.1
72 h 8 2 -14 -15 -15 -15 -15 -15 -15 -15 -14 -21 -22 -15 -14 -12 -15 -16 -18 -19 -19 -21 -21.5 -20.88 -20.03 -18.26 -15.3 0.6
73 g 8 3 -9 -9 -9 -9 -9 -9 -9 -9 -10 -5 -1 -14 -11 -14 -13 -12 -11 -11 -10 -9 -7.9 -6.88 -5.59 -3.16 -0.5 0.4
74 h 8 3 7 7 6 6 6 6 5 5 5 -12 0 5 7 9 6 4 4 5 6 8 8.5 9.83 11.83 13.18 12.8 -0.2
75 g 8 4 1 1 1 2 2 2 2 1 1 9 11 6 2 0 -3 -8 -7 -9 -12 -14 -16.6 -18.11 -19.34 -20.56 -21.1 -0.1
76 h 8 4 -13 -13 -13 -13 -14 -14 -14 -15 -15 -7 -21 -23 -18 -16 -17 -19 -22 -23 -22 -23 -21.5 -19.71 -17.41 -14.60 -11.7 0.5
77 g 8 5 2 2 2 3 4 4 5 6 6 7 15 10 10 8 5 4 4 4 3 9 9.1 10.17 11.61 13.33 15.3 0.4
78 h 8 5 5 5 5 5 5 5 5 5 5 2 -8 3 4 4 6 6 9 11 12 15 15.5 16.22 16.71 16.16 14.9 -0.3
79 g 8 6 -9 -8 -8 -8 -7 -7 -6 -6 -5 -10 -13 -7 -5 -1 0 0 3 4 4 6 7.0 9.36 10.85 11.76 13.7 0.3
80 h 8 6 16 16 16 16 17 17 18 18 19 18 17 23 23 24 21 18 16 14 12 11 8.9 7.61 6.96 5.69 3.6 -0.4
81 g 8 7 5 5 5 6 6 7 8 8 9 7 5 6 10 11 11 10 6 4 2 -5 -7.9 -11.25 -14.05 -15.98 -16.5 -0.1
82 h 8 7 -5 -5 -5 -5 -5 -5 -5 -5 -5 3 -4 -4 1 -3 -6 -10 -13 -15 -16 -16 -14.9 -12.76 -10.74 -9.10 -6.9 0.5
83 g 8 8 8 8 8 8 8 8 8 7 7 2 -1 9 8 4 3 1 -1 -4 -6 -7 -7.0 -4.87 -3.54 -2.02 -0.3 0.4
84 h 8 8 -18 -18 -18 -18 -19 -19 -19 -19 -19 -11 -17 -13 -20 -17 -16 -17 -15 -11 -10 -4 -2.1 -0.06 1.64 2.26 2.8 0.0
85 g 9 0 8 8 8 8 8 8 8 8 8 5 3 4 4 8 8 7 5 5 4 4 5.0 5.58 5.50 5.33 5.0 0.0
86 g 9 1 10 10 10 10 10 10 10 10 10 -21 -7 9 6 10 10 10 10 10 9 9 9.4 9.76 9.45 8.83 8.4 0.0
87 h 9 1 -20 -20 -20 -20 -20 -20 -20 -20 -21 -27 -24 -11 -18 -22 -21 -21 -21 -21 -20 -20 -19.7 -20.11 -20.54 -21.77 -23.4 0.0
88 g 9 2 1 1 1 1 1 1 1 1 1 1 -1 -4 0 2 2 2 1 1 1 3 3.0 3.58 3.45 3.02 2.9 0.0
89 h 9 2 14 14 14 14 14 14 14 15 15 17 19 12 12 15 16 16 16 15 15 15 13.4 12.69 11.51 10.76 11.0 0.0
90 g 9 3 -11 -11 -11 -11 -11 -11 -12 -12 -12 -11 -25 -5 -9 -13 -12 -12 -12 -12 -12 -10 -8.4 -6.94 -5.27 -3.22 -1.5 0.0
91 h 9 3 5 5 5 5 5 5 5 5 5 29 12 7 2 7 6 7 9 9 11 12 12.5 12.67 12.75 11.74 9.8 0.0
92 g 9 4 12 12 12 12 12 12 12 11 11 3 10 2 1 10 10 10 9 9 9 8 6.3 5.01 3.13 0.67 -1.1 0.0
93 h 9 4 -3 -3 -3 -3 -3 -3 -3 -3 -3 -9 2 6 0 -4 -4 -4 -5 -6 -7 -6 -6.2 -6.72 -7.14 -6.74 -5.1 0.0
94 g 9 5 1 1 1 1 1 1 1 1 1 16 5 4 4 -1 -1 -1 -3 -3 -4 -8 -8.9 -10.76 -12.38 -13.20 -13.2 0.0
95 h 9 5 -2 -2 -2 -2 -2 -2 -2 -3 -3 4 2 -2 -3 -5 -5 -5 -6 -6 -7 -8 -8.4 -8.16 -7.42 -6.88 -6.3 0.0
96 g 9 6 -2 -2 -2 -2 -2 -2 -2 -2 -2 -3 -5 1 -1 -1 0 -1 -1 -1 -2 -1 -1.5 -1.25 -0.76 -0.10 1.1 0.0
97 h 9 6 8 8 8 8 9 9 9 9 9 9 8 10 9 10 10 10 9 9 9 8 8.4 8.10 7.97 7.79 7.8 0.0
98 g 9 7 2 2 2 2 2 2 3 3 3 -4 -2 2 -2 5 3 4 7 7 7 10 9.3 8.76 8.43 8.68 8.8 0.0
99 h 9 7 10 10 10 10 10 10 10 11 11 6 8 7 8 10 11 11 10 9 8 5 3.8 2.92 2.14 1.04 0.4 0.0
100 g 9 8 -1 0 0 0 0 0 0 0 1 -3 3 2 3 1 1 1 2 1 1 -2 -4.3 -6.66 -8.42 -9.06 -9.3 0.0
101 h 9 8 -2 -2 -2 -2 -2 -2 -2 -2 -2 1 -11 -6 0 -4 -2 -3 -6 -7 -7 -8 -8.2 -7.73 -6.08 -3.89 -1.4 0.0
102 g 9 9 -1 -1 -1 -1 -1 -1 -2 -2 -2 -4 8 5 -1 -2 -1 -2 -5 -5 -6 -8 -8.2 -9.22 -10.08 -10.54 -11.9 0.0
103 h 9 9 2 2 2 2 2 2 2 2 2 8 -7 5 5 1 1 1 2 2 2 3 4.8 6.01 7.01 8.44 9.6 0.0
104 g 10 0 -3 -3 -3 -3 -3 -3 -3 -3 -3 -3 -8 -3 1 -2 -3 -3 -4 -4 -3 -3 -2.6 -2.17 -1.94 -2.01 -1.9 0.0
105 g 10 1 -4 -4 -4 -4 -4 -4 -4 -4 -4 11 4 -5 -3 -3 -3 -3 -4 -4 -4 -6 -6.0 -6.12 -6.24 -6.26 -6.2 0.0
106 h 10 1 2 2 2 2 2 2 2 2 2 5 13 -4 4 2 1 1 1 1 2 1 1.7 2.19 2.73 3.28 3.4 0.0
107 g 10 2 2 2 2 2 2 2 2 2 2 1 -1 -1 4 2 2 2 2 3 2 2 1.7 1.42 0.89 0.17 -0.1 0.0
108 h 10 2 1 1 1 1 1 1 1 1 1 1 -2 0 1 1 1 1 0 0 1 0 0.0 0.10 -0.10 -0.40 -0.2 0.0
109 g 10 3 -5 -5 -5 -5 -5 -5 -5 -5 -5 2 13 2 0 -5 -5 -5 -5 -5 -5 -4 -3.1 -2.35 -1.07 0.55 1.7 0.0
110 h 10 3 2 2 2 2 2 2 2 2 2 -20 -10 -8 0 2 3 3 3 3 3 4 4.0 4.46 4.71 4.55 3.6 0.0
111 g 10 4 -2 -2 -2 -2 -2 -2 -2 -2 -2 -5 -4 -3 -1 -2 -1 -2 -2 -2 -2 -1 -0.5 -0.15 -0.16 -0.55 -0.9 0.0
112 h 10 4 6 6 6 6 6 6 6 6 6 -1 2 -2 2 6 4 4 6 6 6 5 4.9 4.76 4.44 4.40 4.8 0.0
113 g 10 5 6 6 6 6 6 6 6 6 6 -1 4 7 4 4 6 5 5 5 4 4 3.7 3.06 2.45 1.70 0.7 0.0
114 h 10 5 -4 -4 -4 -4 -4 -4 -4 -4 -4 -6 -3 -4 -5 -4 -4 -4 -4 -4 -4 -5 -5.9 -6.58 -7.22 -7.92 -8.6 0.0
115 g 10 6 4 4 4 4 4 4 4 4 4 8 12 4 6 4 4 4 3 3 3 2 1.0 0.29 -0.33 -0.67 -0.9 0.0
116 h 10 6 0 0 0 0 0 0 0 0 0 6 6 1 1 0 0 -1 0 0 0 -1 -1.2 -1.01 -0.96 -0.61 -0.1 0.0
117 g 10 7 0 0 0 0 0 0 0 0 0 -1 3 -2 1 0 1 1 1 1 1 2 2.0 2.06 2.13 2.13 1.9 0.0
118 h 10 7 -2 -2 -2 -2 -2 -2 -2 -1 -1 -4 -3 -3 -1 -2 -1 -1 -1 -1 -2 -2 -2.9 -3.47 -3.95 -4.16 -4.3 0.0
119 g 10 8 2 2 2 1 1 1 1 2 2 -3 2 6 -1 2 0 0 2 2 3 5 4.2 3.77 3.09 2.33 1.4 0.0
120 h 10 8 4 4 4 4 4 4 4 4 4 -2 6 7 6 3 3 3 4 4 3 1 0.2 -0.86 -1.99 -2.85 -3.4 0.0
121 g 10 9 2 2 2 2 3 3 3 3 3 5 10 -2 2 2 3 3 3 3 3 1 0.3 -0.21 -1.03 -1.80 -2.4 0.0
122 h 10 9 0 0 0 0 0 0 0 0 0 0 11 -1 0 0 1 1 0 0 -1 -2 -2.2 -2.31 -1.97 -1.12 -0.1 0.0
123 g 10 10 0 0 0 0 0 0 0 0 0 -2 3 0 0 0 -1 -1 0 0 0 0 -1.1 -2.09 -2.80 -3.59 -3.8 0.0
124 h 10 10 -6 -6 -6 -6 -6 -6 -6 -6 -6 -2 8 -3 -7 -6 -4 -5 -6 -6 -6 -7 -7.4 -7.93 -8.31 -8.72 -8.8 0.0
125 g 11 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2.7 2.95 3.05 3.00 3.0 0.0
126 g 11 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1.7 -1.60 -1.48 -1.40 -1.4 0.0
127 h 11 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.1 0.26 0.13 0.00 0.0 0.0
128 g 11 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1.9 -1.88 -2.03 -2.30 -2.5 0.0
129 h 11 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1.3 1.44 1.67 2.11 2.5 0.0
130 g 11 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1.5 1.44 1.65 2.08 2.3 0.0
131 h 11 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -0.9 -0.77 -0.66 -0.60 -0.6 0.0
132 g 11 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -0.1 -0.31 -0.51 -0.79 -0.9 0.0
133 h 11 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -2.6 -2.27 -1.76 -1.05 -0.4 0.0
134 g 11 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.1 0.29 0.54 0.58 0.3 0.0
135 h 11 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.9 0.90 0.85 0.76 0.6 0.0
136 g 11 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -0.7 -0.79 -0.79 -0.70 -0.7 0.0
137 h 11 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -0.7 -0.58 -0.39 -0.20 -0.2 0.0
138 g 11 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.7 0.53 0.37 0.14 -0.1 0.0
139 h 11 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -2.8 -2.69 -2.51 -2.12 -1.7 0.0
140 g 11 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1.7 1.80 1.79 1.70 1.4 0.0
141 h 11 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -0.9 -1.08 -1.27 -1.44 -1.6 0.0
142 g 11 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.1 0.16 0.12 -0.22 -0.6 0.0
143 h 11 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1.2 -1.58 -2.11 -2.57 -3.0 0.0
144 g 11 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1.2 0.96 0.75 0.44 0.2 0.0
145 h 11 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1.9 -1.90 -1.94 -2.01 -2.0 0.0
146 g 11 11 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4.0 3.99 3.75 3.49 3.1 0.0
147 h 11 11 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -0.9 -1.39 -1.86 -2.34 -2.6 0.0
148 g 12 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -2.2 -2.15 -2.12 -2.09 -2.0 0.0
149 g 12 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -0.3 -0.29 -0.21 -0.16 -0.1 0.0
150 h 12 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -0.4 -0.55 -0.87 -1.08 -1.2 0.0
151 g 12 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.2 0.21 0.30 0.46 0.5 0.0
152 h 12 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.3 0.23 0.27 0.37 0.5 0.0
153 g 12 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.9 0.89 1.04 1.23 1.3 0.0
154 h 12 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2.5 2.38 2.13 1.75 1.4 0.0
155 g 12 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -0.2 -0.38 -0.63 -0.89 -1.2 0.0
156 h 12 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -2.6 -2.63 -2.49 -2.19 -1.8 0.0
157 g 12 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.9 0.96 0.95 0.85 0.7 0.0
158 h 12 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.7 0.61 0.49 0.27 0.1 0.0
159 g 12 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -0.5 -0.30 -0.11 0.10 0.3 0.0
160 h 12 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.3 0.40 0.59 0.72 0.8 0.0
161 g 12 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.3 0.46 0.52 0.54 0.5 0.0
162 h 12 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.01 0.00 -0.09 -0.2 0.0
163 g 12 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -0.3 -0.35 -0.39 -0.37 -0.3 0.0
164 h 12 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 0.02 0.13 0.29 0.6 0.0
165 g 12 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -0.4 -0.36 -0.37 -0.43 -0.5 0.0
166 h 12 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.3 0.28 0.27 0.23 0.2 0.0
167 g 12 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -0.1 0.08 0.21 0.22 0.1 0.0
168 h 12 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -0.9 -0.87 -0.86 -0.89 -0.9 0.0
169 g 12 11 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -0.2 -0.49 -0.77 -0.94 -1.1 0.0
170 h 12 11 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -0.4 -0.34 -0.23 -0.16 0.0 0.0
171 g 12 12 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -0.4 -0.08 0.04 -0.03 -0.3 0.0
172 h 12 12 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.8 0.88 0.87 0.72 0.5 0.0
173 g 13 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -0.2 -0.16 -0.09 -0.02 0.1 0.0
174 g 13 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -0.9 -0.88 -0.89 -0.92 -0.9 0.0
175 h 13 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -0.9 -0.76 -0.87 -0.88 -0.9 0.0
176 g 13 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.3 0.30 0.31 0.42 0.5 0.0
177 h 13 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.2 0.33 0.30 0.49 0.6 0.0
178 g 13 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.1 0.28 0.42 0.63 0.7 0.0
179 h 13 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1.8 1.72 1.66 1.56 1.4 0.0
180 g 13 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -0.4 -0.43 -0.45 -0.42 -0.3 0.0
181 h 13 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -0.4 -0.54 -0.59 -0.50 -0.4 0.0
182 g 13 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1.3 1.18 1.08 0.96 0.8 0.0
183 h 13 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1.0 -1.07 -1.14 -1.24 -1.3 0.0
184 g 13 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -0.4 -0.37 -0.31 -0.19 0.0 0.0
185 h 13 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -0.1 -0.04 -0.07 -0.10 -0.1 0.0
186 g 13 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.7 0.75 0.78 0.81 0.8 0.0
187 h 13 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.7 0.63 0.54 0.42 0.3 0.0
188 g 13 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -0.4 -0.26 -0.18 -0.13 0.0 0.0
189 h 13 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.3 0.21 0.10 -0.04 -0.1 0.0
190 g 13 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.3 0.35 0.38 0.38 0.4 0.0
191 h 13 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.6 0.53 0.49 0.48 0.5 0.0
192 g 13 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -0.1 -0.05 0.02 0.08 0.1 0.0
193 h 13 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.3 0.38 0.44 0.48 0.5 0.0
194 g 13 11 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.4 0.41 0.42 0.46 0.5 0.0
195 h 13 11 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -0.2 -0.22 -0.25 -0.30 -0.4 0.0
196 g 13 12 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0 -0.10 -0.26 -0.35 -0.5 0.0
197 h 13 12 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -0.5 -0.57 -0.53 -0.43 -0.4 0.0
198 g 13 13 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.1 -0.18 -0.26 -0.36 -0.4 0.0
199 h 13 13 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -0.9 -0.82 -0.79 -0.71 -0.6 0.0
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@@ -935,6 +935,7 class Parameters(Spectra):
935 data_pre = None # Data Pre Parametrization
935 data_pre = None # Data Pre Parametrization
936 data_SNR = None # Signal to Noise Ratio
936 data_SNR = None # Signal to Noise Ratio
937 data_outlier = None
937 data_outlier = None
938 data_vdrift = None
938 abscissaList = None # Abscissa, can be velocities, lags or time
939 abscissaList = None # Abscissa, can be velocities, lags or time
939 utctimeInit = None # Initial UTC time
940 utctimeInit = None # Initial UTC time
940 paramInterval = None # Time interval to calculate Parameters in seconds
941 paramInterval = None # Time interval to calculate Parameters in seconds
Show file before | Show file after | Hide comments
@@ -213,7 +213,7 class GenericRTIPlot(Plot):
213 if self.zlimits is not None:
213 if self.zlimits is not None:
214 self.zmin, self.zmax = self.zlimits[n]
214 self.zmin, self.zmax = self.zlimits[n]
215
215
216 ax.plt = ax.pcolormesh(x, y, z[n].T * self.factors[n],
216 ax.plt = ax.pcolormesh(x, y, z[n].T ,
217 vmin=self.zmin,
217 vmin=self.zmin,
218 vmax=self.zmax,
218 vmax=self.zmax,
219 cmap=self.cmaps[n]
219 cmap=self.cmaps[n]
@@ -222,7 +222,7 class GenericRTIPlot(Plot):
222 if self.zlimits is not None:
222 if self.zlimits is not None:
223 self.zmin, self.zmax = self.zlimits[n]
223 self.zmin, self.zmax = self.zlimits[n]
224 ax.collections.remove(ax.collections[0])
224 ax.collections.remove(ax.collections[0])
225 ax.plt = ax.pcolormesh(x, y, z[n].T * self.factors[n],
225 ax.plt = ax.pcolormesh(x, y, z[n].T ,
226 vmin=self.zmin,
226 vmin=self.zmin,
227 vmax=self.zmax,
227 vmax=self.zmax,
228 cmap=self.cmaps[n]
228 cmap=self.cmaps[n]
Show file before | Show file after | Hide comments
@@ -71,11 +71,8 class SpectraPlot(Plot):
71 norm = dataOut.nProfiles * dataOut.max_nIncohInt * dataOut.nCohInt * dataOut.windowOfFilter
71 norm = dataOut.nProfiles * dataOut.max_nIncohInt * dataOut.nCohInt * dataOut.windowOfFilter
72 noise = 10*numpy.log10(dataOut.getNoise()/norm)
72 noise = 10*numpy.log10(dataOut.getNoise()/norm)
73
73
74 <<<<<<< HEAD
75 =======
76
74
77
75
78 >>>>>>> 37cccf17c7b80521b59b978cb30e4ab2e6f37fce
79 z = numpy.zeros((dataOut.nChannels, dataOut.nFFTPoints, dataOut.nHeights))
76 z = numpy.zeros((dataOut.nChannels, dataOut.nFFTPoints, dataOut.nHeights))
80 for ch in range(dataOut.nChannels):
77 for ch in range(dataOut.nChannels):
81 if hasattr(dataOut.normFactor,'ndim'):
78 if hasattr(dataOut.normFactor,'ndim'):
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@@ -596,9 +596,9 class AMISRReader(ProcessingUnit):
596 # self.dataOut.channelIndexList = None
596 # self.dataOut.channelIndexList = None
597
597
598
598
599 #self.dataOut.azimuthList = numpy.roll( numpy.array(self.azimuthList) ,self.shiftChannels)
599 # #self.dataOut.azimuthList = numpy.roll( numpy.array(self.azimuthList) ,self.shiftChannels)
600 #self.dataOut.elevationList = numpy.roll(numpy.array(self.elevationList) ,self.shiftChannels)
600 # #self.dataOut.elevationList = numpy.roll(numpy.array(self.elevationList) ,self.shiftChannels)
601 #self.dataOut.codeList = numpy.roll(numpy.array(self.beamCode), self.shiftChannels)
601 # #self.dataOut.codeList = numpy.roll(numpy.array(self.beamCode), self.shiftChannels)
602
602
603 self.dataOut.azimuthList = self.azimuthList
603 self.dataOut.azimuthList = self.azimuthList
604 self.dataOut.elevationList = self.elevationList
604 self.dataOut.elevationList = self.elevationList
@@ -661,11 +661,7 class AMISRReader(ProcessingUnit):
661 self.dataOut.radarControllerHeaderObj.elevationList = self.elevationList
661 self.dataOut.radarControllerHeaderObj.elevationList = self.elevationList
662 self.dataOut.radarControllerHeaderObj.dtype = "Voltage"
662 self.dataOut.radarControllerHeaderObj.dtype = "Voltage"
663 self.dataOut.ippSeconds = self.ippSeconds
663 self.dataOut.ippSeconds = self.ippSeconds
664 <<<<<<< HEAD
665 self.dataOut.ippFactor = self.nchannels*self.nFFT
666 =======
667 self.dataOut.ippFactor = self.nFFT
664 self.dataOut.ippFactor = self.nFFT
668 >>>>>>> 37cccf17c7b80521b59b978cb30e4ab2e6f37fce
669 pass
665 pass
670
666
671 def readNextFile(self,online=False):
667 def readNextFile(self,online=False):
Show file before | Show file after | Hide comments
@@ -19,6 +19,7 from schainpy.model.data.jrodata import Parameters, hildebrand_sekhon
19 from scipy import asarray as ar,exp
19 from scipy import asarray as ar,exp
20 from scipy.optimize import curve_fit
20 from scipy.optimize import curve_fit
21 from schainpy.utils import log
21 from schainpy.utils import log
22 from scipy import signal
22 import warnings
23 import warnings
23 from numpy import NaN
24 from numpy import NaN
24 from scipy.optimize.optimize import OptimizeWarning
25 from scipy.optimize.optimize import OptimizeWarning
@@ -3990,4 +3991,69 class addTxPower(Operation):
3990 log.warning("No power available for the datetime {}, setting power to 0 w", self.name)
3991 log.warning("No power available for the datetime {}, setting power to 0 w", self.name)
3991 dataOut.txPower = 0
3992 dataOut.txPower = 0
3992
3993
3994 return dataOut
3995
3996
3997
3998
3999
4000
4001 class getVerticalDrifts(Operation):
4002 '''
4003 This operation is just for test AMISR Data
4004
4005 fmin = minimun range to measure drifts (KHz)
4006 fmax = maximun range to measure drifts (KHz)
4007
4008 '''
4009 isConfig = False
4010 vdrifts = None
4011 frange = None
4012 nfft = None
4013 i_min = 0
4014 i_max = -1
4015 fcoef = None
4016 def __init__(self):
4017
4018 Operation.__init__(self)
4019 self.isConfig = False
4020
4021
4022 def setup(self, dataOut, fmin=None, fmax=None):
4023
4024 self.vdrifts=numpy.zeros((dataOut.nChannels, dataOut.nHeights))
4025 dt1 = dataOut.processingHeaderObj.ipp
4026 self.nfft = dataOut.data_spc.shape[1]
4027 all_frange=numpy.fft.fftshift(numpy.fft.fftfreq(self.nfft,d=dt1)/1000)
4028 self.i_min = numpy.argwhere(all_frange < fmin)[-1][0]
4029 self.i_max = numpy.argwhere(all_frange > fmax)[0][0]
4030 #print(self.i_min, self.i_max)
4031 self.frange = all_frange[self.i_min:self.i_max]
4032 self.fcoef = signal.butter(3, 0.05)
4033 #print(self.frange)
4034 print("VELOCITY STEP RESOLUTION = {:2.2f} m/s".format(abs(self.frange[1]-self.frange[0])*1000*SPEED_OF_LIGHT /(2*dataOut.frequency)))
4035 self.isConfig = True
4036
4037 def run(self, dataOut, fmin=None, fmax=None):
4038
4039 dataOut.flagNoData = False
4040 # dataOut.data_spc
4041
4042 if not(self.isConfig):
4043 self.setup(dataOut, fmin=fmin, fmax=fmax)
4044
4045 spc = dataOut.data_spc[:, self.i_min:self.i_max, :]
4046 for ch in range(dataOut.nChannels):
4047 for h in range(dataOut.nHeights):
4048 #spcHf = signal.filtfilt(self.fcoef[0], self.fcoef[1], spc[ch,:,h])
4049 spcHf = spc[ch,:,h]
4050 im = numpy.argmax(spcHf)
4051
4052
4053 v = -self.frange[im]*1000*SPEED_OF_LIGHT /(2*dataOut.frequency)
4054
4055 self.vdrifts[ch,h] = v
4056 aux = signal.filtfilt(self.fcoef[0], self.fcoef[1], self.vdrifts[ch, :])
4057 self.vdrifts[ch,:]=aux
4058 dataOut.data_vdrift = self.vdrifts
3993 return dataOut No newline at end of file
4059 return dataOut
Show file before | Show file after | Hide comments
@@ -1597,7 +1597,7 class IntegrationFaradaySpectra(Operation):
1597
1597
1598 self.putData(*args)
1598 self.putData(*args)
1599
1599
1600 if self.__profIndex == self.n:
1600 if self.__profIndex >= self.n:
1601
1601
1602 avgdata_spc, avgdata_cspc, avgdata_dc, n = self.pushData()
1602 avgdata_spc, avgdata_cspc, avgdata_dc, n = self.pushData()
1603 self.n_ints = n
1603 self.n_ints = n
@@ -1699,7 +1699,6 class IntegrationFaradaySpectra(Operation):
1699 self.dataOut.data_cspc=self.dataOut.dataLag_cspc[:,:,:,self.dataOut.LagPlot]
1699 self.dataOut.data_cspc=self.dataOut.dataLag_cspc[:,:,:,self.dataOut.LagPlot]
1700 self.dataOut.data_dc=self.dataOut.dataLag_dc[:,:,self.dataOut.LagPlot]
1700 self.dataOut.data_dc=self.dataOut.dataLag_dc[:,:,self.dataOut.LagPlot]
1701
1701
1702
1703 self.dataOut.nIncohInt *= self.n_ints
1702 self.dataOut.nIncohInt *= self.n_ints
1704 #print("maxProfilesInt: ",self.maxProfilesInt)
1703 #print("maxProfilesInt: ",self.maxProfilesInt)
1705
1704
@@ -1764,21 +1763,30 class removeInterference(Operation):
1764
1763
1765
1764
1766 #dataOut.max_nIncohInt * dataOut.nCohInt
1765 #dataOut.max_nIncohInt * dataOut.nCohInt
1767 norm = self.dataOut.nIncohInt /self.dataOut.max_nIncohInt
1766 if hasattr(self.dataOut.nIncohInt, 'shape'):
1768 #print(norm.shape)
1767 norm = self.dataOut.nIncohInt.T /self.dataOut.max_nIncohInt
1768 norm = norm.T
1769 else:
1770 norm = self.dataOut.nIncohInt /self.dataOut.max_nIncohInt
1771 norm = norm
1772
1769 # Subrutina de Remocion de la Interferencia
1773 # Subrutina de Remocion de la Interferencia
1770 for ich in range(num_channel):
1774 for ich in range(num_channel):
1771 # Se ordena los espectros segun su potencia (menor a mayor)
1775 # Se ordena los espectros segun su potencia (menor a mayor)
1772 #power = jspectra[ich, mask_prof, :]
1776 #power = jspectra[ich, mask_prof, :]
1773 interf = jspectra[ich, :, minIndex:maxIndex]
1777 if hasattr(self.dataOut.nIncohInt, 'shape'):
1774 #print(interf.shape)
1778 interf = jspectra[ich, :, minIndex:maxIndex]/norm[ich,minIndex:maxIndex]
1779 else:
1780 interf = jspectra[ich, :, minIndex:maxIndex]/norm
1781 # print(interf.shape)
1775 inttef = interf.mean(axis=1)
1782 inttef = interf.mean(axis=1)
1776
1783
1777 for hei in range(num_hei):
1784 for hei in range(num_hei):
1778 temp = jspectra[ich,:, hei]
1785 temp = jspectra[ich,:, hei]#/norm[ich,hei]
1779 temp -= inttef
1786 temp -= inttef
1780 temp += jnoise[ich]*norm[ich,hei]
1787 temp += jnoise[ich]
1781 jspectra[ich,:, hei] = temp
1788 # print(jspectra.shape, temp.shape)
1789 jspectra[ich,:, hei] = temp
1782
1790
1783 # Guardar Resultados
1791 # Guardar Resultados
1784 self.dataOut.data_spc = jspectra
1792 self.dataOut.data_spc = jspectra
@@ -1821,6 +1829,7 class removeInterference(Operation):
1821 num_prof = jspectra.shape[1]
1829 num_prof = jspectra.shape[1]
1822 num_hei = jspectra.shape[2]
1830 num_hei = jspectra.shape[2]
1823
1831
1832 count_hei = nhei_interf
1824 # hei_interf
1833 # hei_interf
1825 if hei_interf is None:
1834 if hei_interf is None:
1826 count_hei = int(num_hei / 2) # a half of total ranges
1835 count_hei = int(num_hei / 2) # a half of total ranges
@@ -1993,7 +2002,7 class removeInterference(Operation):
1993 self.dataOut = dataOut
2002 self.dataOut = dataOut
1994
2003
1995 if mode == 1:
2004 if mode == 1:
1996 self.removeInterference(interf = 2,hei_interf = None, nhei_interf = None, offhei_interf = None)
2005 self.removeInterference(interf = 2,hei_interf = hei_interf, nhei_interf = nhei_interf, offhei_interf = offhei_interf)
1997 elif mode == 2:
2006 elif mode == 2:
1998 self.removeInterference2()
2007 self.removeInterference2()
1999 elif mode == 3:
2008 elif mode == 3:
Show file before | Show file after | Hide comments
@@ -79,9 +79,13 class VoltageProc(ProcessingUnit):
79
79
80 class selectChannels(Operation):
80 class selectChannels(Operation):
81
81
82 def run(self, dataOut, channelList=None):
82 def run(self, dataOut, channelList=[]):
83
84 if isinstance(channelList, int):
85 channelList = [channelList]
86
83 self.channelList = channelList
87 self.channelList = channelList
84 if self.channelList == None:
88 if len(self.channelList) == 0:
85 print("Missing channelList")
89 print("Missing channelList")
86 return dataOut
90 return dataOut
87 channelIndexList = []
91 channelIndexList = []
@@ -99,15 +103,16 class selectChannels(Operation):
99
103
100 index = dataOut.channelList.index(channel)
104 index = dataOut.channelList.index(channel)
101 channelIndexList.append(index)
105 channelIndexList.append(index)
106
102 dataOut = self.selectChannelsByIndex(dataOut,channelIndexList)
107 dataOut = self.selectChannelsByIndex(dataOut,channelIndexList)
103
108
104 #update Processing Header:
109 #update Processing Header:
105 dataOut.processingHeaderObj.channelList = dataOut.channelList
110 dataOut.processingHeaderObj.channelList = dataOut.channelList
106 dataOut.processingHeaderObj.elevationList = dataOut.elevationList
111 dataOut.processingHeaderObj.elevationList = dataOut.elevationList
107 dataOut.processingHeaderObj.azimuthList = dataOut.azimuthList
112 dataOut.processingHeaderObj.azimuthList = dataOut.azimuthList
108 dataOut.processingHeaderObj.codeList = dataOut.codeList
113 dataOut.processingHeaderObj.codeList = dataOut.codeList
109 dataOut.processingHeaderObj.nChannels = len(dataOut.channelList)
114 dataOut.processingHeaderObj.nChannels = len(dataOut.channelList)
110
115
111 return dataOut
116 return dataOut
112
117
113 def selectChannelsByIndex(self, dataOut, channelIndexList):
118 def selectChannelsByIndex(self, dataOut, channelIndexList):
@@ -145,7 +150,7 class selectChannels(Operation):
145 dataOut.data = data
150 dataOut.data = data
146 # dataOut.channelList = [dataOut.channelList[i] for i in channelIndexList]
151 # dataOut.channelList = [dataOut.channelList[i] for i in channelIndexList]
147 dataOut.channelList = [n for n in range(len(channelIndexList))]
152 dataOut.channelList = [n for n in range(len(channelIndexList))]
148
153
149 elif dataOut.type == 'Spectra':
154 elif dataOut.type == 'Spectra':
150 if hasattr(dataOut, 'data_spc'):
155 if hasattr(dataOut, 'data_spc'):
151 if dataOut.data_spc is None:
156 if dataOut.data_spc is None:
@@ -165,10 +170,16 class selectChannels(Operation):
165 # dataOut.channelList = [dataOut.channelList[i] for i in channelIndexList]
170 # dataOut.channelList = [dataOut.channelList[i] for i in channelIndexList]
166 dataOut.channelList = channelIndexList
171 dataOut.channelList = channelIndexList
167 dataOut = self.__selectPairsByChannel(dataOut,channelIndexList)
172 dataOut = self.__selectPairsByChannel(dataOut,channelIndexList)
168 if len(dataOut.elevationList>0):
173
174 # channelIndexList = numpy.asarray(channelIndexList)
175 dataOut.elevationList = numpy.asarray(dataOut.elevationList)
176 dataOut.azimuthList = numpy.asarray(dataOut.azimuthList)
177 dataOut.codeList = numpy.asarray(dataOut.codeList)
178 if (len(dataOut.elevationList) > 0):
169 dataOut.elevationList = dataOut.elevationList[channelIndexList]
179 dataOut.elevationList = dataOut.elevationList[channelIndexList]
170 dataOut.azimuthList = dataOut.azimuthList[channelIndexList]
180 dataOut.azimuthList = dataOut.azimuthList[channelIndexList]
171 dataOut.codeList = dataOut.codeList[channelIndexList]
181 dataOut.codeList = dataOut.codeList[channelIndexList]
182
172 return dataOut
183 return dataOut
173
184
174 def __selectPairsByChannel(self, dataOut, channelList=None):
185 def __selectPairsByChannel(self, dataOut, channelList=None):
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