diff --git a/CoLo-AT/Cooperative_Localization_Journal1.py b/CoLo-AT/Cooperative_Localization_Journal1.py
index 59f954dff36053322ed47bb2aa8f50996a1e470d..476e7ab24a60cb535a8d8a73e66cb2624ed6e67b 100644
--- a/CoLo-AT/Cooperative_Localization_Journal1.py
+++ b/CoLo-AT/Cooperative_Localization_Journal1.py
@@ -110,3 +110,4 @@ def all_algorithms_comp(dataset_path, robot_labels, duration, graph_name, robots
 	analyzer.calculate_loc_err_and_trace_state_variance_per_run(state_recorder_GS_SCI, plot_graphs = False)
 
 	analyzer.algos_comparison([state_recorder, state_recorder_LS_cen, state_recorder_LS_BDA, state_recorder_LS_CI, state_recorder_GS_SCI], graph_name = graph_name)
+	analyzer.generate_result_files([state_recorder, state_recorder_LS_cen, state_recorder_LS_BDA, state_recorder_LS_CI, state_recorder_GS_SCI])
\ No newline at end of file
diff --git a/CoLo-AT/RMSE.txt b/CoLo-AT/RMSE.txt
new file mode 100644
index 0000000000000000000000000000000000000000..50f780a6dcee67177bfdca83b45c5fd9c7cfbb0f
--- /dev/null
+++ b/CoLo-AT/RMSE.txt
@@ -0,0 +1,363 @@
+#Time	GS_CI	LS_cen	LS_BDA	LS_CI	GS_SCI	
+0.2500 0.0243 0.0245 0.0243 0.0245 0.0270
+0.7500 0.0243 0.0245 0.0243 0.0245 0.0270
+1.2500 0.1080 0.1084 0.1080 0.1084 0.1142
+1.7500 0.1118 0.0892 0.1118 0.0892 0.1137
+2.2500 0.1138 0.0895 0.1138 0.0895 0.1144
+2.7500 0.1144 0.0898 0.1144 0.0898 0.1148
+3.2500 0.1148 0.0900 0.1148 0.0900 0.1150
+3.7500 0.1148 0.0900 0.1148 0.0900 0.1150
+4.2500 0.1148 0.0900 0.1148 0.0900 0.1150
+4.7500 0.1149 0.0900 0.1149 0.0900 0.1150
+5.2500 0.1147 0.0898 0.1148 0.0898 0.1149
+5.7500 0.1144 0.0894 0.1146 0.0894 0.1147
+6.2500 0.1131 0.0875 0.1142 0.0875 0.1143
+6.7500 0.1086 0.0866 0.1149 0.0866 0.1124
+7.2500 0.1141 0.1367 0.1554 0.1367 0.1171
+7.7500 0.1170 0.1476 0.1615 0.1476 0.1164
+8.2500 0.1175 0.1481 0.1612 0.1481 0.1153
+8.7500 0.1148 0.1457 0.1610 0.1457 0.1145
+9.2500 0.1111 0.1443 0.1605 0.1443 0.1129
+9.7500 0.1246 0.1993 0.2211 0.2739 0.2456
+10.2500 0.1385 0.2118 0.2429 0.7455 0.8428
+10.7500 0.1327 0.2069 0.2380 0.9715 1.0623
+11.2500 0.1326 0.2043 0.2362 1.0945 0.7889
+11.7500 0.1336 0.2049 0.2367 1.4088 0.6018
+12.2500 0.1596 0.2121 0.2406 2.6125 0.5307
+12.7500 0.1930 0.2280 0.2518 3.1142 0.8934
+13.2500 0.1996 0.2271 0.2572 3.2703 1.4446
+13.7500 0.2351 0.1973 0.2510 3.6581 2.6093
+14.2500 0.2487 0.1914 0.2476 3.4365 11.1776
+14.7500 0.2746 0.2233 0.2590 3.1315 14.8190
+15.2500 0.2579 0.2291 0.2740 2.8246 3.2029
+15.7500 0.2950 0.2351 0.2765 2.7717 1.6135
+16.2500 0.3163 0.2363 0.2667 2.7776 4.2896
+16.7500 0.3370 0.2316 0.2571 2.6857 1.3709
+17.2500 0.3771 0.2490 0.2565 2.8613 2.2913
+17.7500 0.4223 0.2354 0.2526 2.7425 6.0694
+18.2500 0.4814 0.2266 0.2375 2.8782 3.2293
+18.7500 0.5182 0.2463 0.2308 2.7515 1.7145
+19.2500 0.6230 0.2338 0.2281 2.8268 2.5234
+19.7500 0.6894 0.2225 0.2218 2.7151 2.1639
+20.2500 0.7125 0.2250 0.2194 2.7988 2.0709
+20.7500 0.7386 0.2276 0.2155 2.5438 1.8764
+21.2500 0.7968 0.2305 0.2119 2.7549 2.2856
+21.7500 0.8439 0.2279 0.2105 2.8068 2.0953
+22.2500 0.8651 0.2222 0.2044 2.8410 2.1645
+22.7500 0.9025 0.2286 0.2003 2.6366 2.4590
+23.2500 0.9070 0.2206 0.1981 2.5259 2.0750
+23.7500 0.9706 0.2305 0.1990 2.9184 2.5018
+24.2500 0.9799 0.2431 0.1978 2.7933 2.4360
+24.7500 1.0137 0.2539 0.1977 2.5007 2.3808
+25.2500 1.0326 0.2407 0.1970 2.7118 2.3073
+25.7500 1.0875 0.2324 0.1935 2.3686 2.4666
+26.2500 1.0816 0.2316 0.1928 2.5382 3.3246
+26.7500 1.1373 0.2238 0.1920 2.1973 2.8829
+27.2500 1.2023 0.2379 0.1889 2.4256 2.6777
+27.7500 1.2222 0.2399 0.1900 2.5572 2.5683
+28.2500 1.2409 0.2308 0.1902 2.2937 2.3962
+28.7500 1.3163 0.2319 0.1913 2.3072 2.6928
+29.2500 1.3495 0.2260 0.1853 2.2428 3.0214
+29.7500 1.3662 0.2034 0.1742 1.6770 2.5837
+30.2500 1.4026 0.1795 0.1695 1.8541 3.1073
+30.7500 1.4306 0.1800 0.1768 1.8643 3.1605
+31.2500 1.4803 0.2010 0.1835 1.6076 3.3308
+31.7500 1.4235 0.2071 0.1912 1.7966 2.7990
+32.2500 0.9407 0.2087 0.1927 2.7962 3.0732
+32.7500 0.7362 0.2000 0.1887 3.3557 3.4634
+33.2500 0.6961 0.2128 0.2062 3.5602 3.4431
+33.7500 0.7519 0.2166 0.2199 3.5490 3.8141
+34.2500 0.7279 0.2164 0.2175 3.5669 4.1171
+34.7500 0.8893 0.2058 0.2158 3.1486 4.4179
+35.2500 0.8963 0.1969 0.2041 2.7886 3.5451
+35.7500 0.9139 0.2110 0.2273 2.5307 3.6251
+36.2500 0.8879 0.2250 0.2320 2.2855 3.3841
+36.7500 0.8119 0.2459 0.2250 2.4592 4.2985
+37.2500 0.8514 0.2627 0.2319 2.5499 7.0222
+37.7500 0.8886 0.2600 0.2331 2.5253 6.7393
+38.2500 0.9701 0.2597 0.2363 2.5064 8.2508
+38.7500 1.0099 0.2444 0.2240 2.3776 6.6932
+39.2500 1.1739 0.1461 0.1733 1.5501 5.3680
+39.7500 1.4986 0.1513 0.1756 1.3269 5.1625
+40.2500 1.5217 0.1311 0.1417 1.0468 4.9258
+40.7500 1.4877 0.0756 0.0910 0.9975 4.5625
+41.2500 1.5305 0.0622 0.0892 0.9925 4.1628
+41.7500 1.6370 0.0637 0.0931 0.8559 4.1495
+42.2500 1.5597 0.0772 0.1008 0.6530 6.9059
+42.7500 1.3186 0.0897 0.1028 0.7313 13.4644
+43.2500 1.1614 0.1094 0.1111 0.7431 9.0438
+43.7500 1.4805 0.1604 0.1460 0.5670 8.0915
+44.2500 1.4028 0.1559 0.1390 0.3417 8.4868
+44.7500 1.1016 0.1548 0.1394 0.9866 3.7361
+45.2500 1.0464 0.1861 0.1606 1.7759 5.4407
+45.7500 1.1083 0.1983 0.1709 1.9824 3.9081
+46.2500 1.4901 0.2109 0.1684 1.1012 5.1909
+46.7500 1.6232 0.2242 0.1793 0.5243 5.9437
+47.2500 1.6138 0.2185 0.1823 0.4988 4.7884
+47.7500 1.5538 0.2248 0.1911 0.5101 4.2418
+48.2500 1.6344 0.2315 0.1922 0.4964 19.6284
+48.7500 1.6055 0.2446 0.1883 0.4631 3.4900
+49.2500 1.6023 0.2397 0.1953 0.4590 4.5005
+49.7500 1.7328 0.2383 0.2097 0.2984 7.0022
+50.2500 1.6923 0.2282 0.2168 0.3850 5.5203
+50.7500 1.7533 0.2229 0.2199 0.3745 4.2464
+51.2500 1.8256 0.2300 0.2219 0.2691 3.2309
+51.7500 1.8442 0.2373 0.2169 0.3016 3.9114
+52.2500 1.8496 0.2363 0.2154 0.3200 4.3624
+52.7500 1.8046 0.2275 0.2072 0.3794 2.8191
+53.2500 1.9563 0.2169 0.2046 0.2128 3.0487
+53.7500 2.0230 0.2096 0.2078 0.1672 3.2687
+54.2500 2.0948 0.2184 0.2189 0.1852 3.3365
+54.7500 2.1097 0.2087 0.2100 0.1829 3.4243
+55.2500 2.0927 0.2145 0.2167 0.1908 3.2383
+55.7500 2.0841 0.2197 0.2224 0.1966 3.2223
+56.2500 2.0033 0.2156 0.2172 0.1921 3.0756
+56.7500 1.8138 0.2460 0.2153 0.2336 2.8883
+57.2500 1.8162 0.2310 0.1868 0.2239 2.7616
+57.7500 1.8189 0.2269 0.1840 0.2203 2.7654
+58.2500 1.8189 0.2327 0.1898 0.2257 2.7643
+58.7500 1.8189 0.2361 0.1688 0.3074 2.7841
+59.2500 2.0239 0.2658 0.1598 0.2426 3.3252
+59.7500 2.0354 0.2947 0.1718 0.2428 3.3511
+60.2500 2.0731 0.3155 0.1869 0.2583 3.4009
+60.7500 2.0096 0.3335 0.1781 0.2519 3.1142
+61.2500 1.4863 0.3793 0.1938 0.4365 3.9467
+61.7500 1.3647 0.4087 0.2332 0.5701 4.2837
+62.2500 1.3331 0.4243 0.2729 0.5314 5.4766
+62.7500 1.3120 0.4116 0.2930 0.4761 3.9769
+63.2500 1.2114 0.3892 0.3232 0.3672 3.8858
+63.7500 1.1957 0.3760 0.3440 0.3698 4.0410
+64.2500 1.2935 0.3565 0.3246 0.2710 4.1224
+64.7500 1.3469 0.3426 0.3326 0.3496 4.0879
+65.2500 1.2545 0.3330 0.3406 0.3668 4.2761
+65.7500 1.1956 0.3260 0.3425 0.3493 4.3219
+66.2500 1.2562 0.3157 0.3391 0.3426 4.1785
+66.7500 1.1256 0.3094 0.3360 0.3834 4.3449
+67.2500 1.1348 0.3028 0.3314 0.3710 4.5382
+67.7500 1.2456 0.2906 0.3180 0.4341 4.3712
+68.2500 1.0984 0.2789 0.3060 0.4158 4.5364
+68.7500 1.1098 0.2743 0.2988 0.3729 4.3016
+69.2500 1.2491 0.2618 0.2850 0.3575 4.1439
+69.7500 1.0417 0.2485 0.2682 0.3584 4.9163
+70.2500 1.0640 0.2442 0.2610 0.3532 4.4940
+70.7500 0.9920 0.2322 0.2492 0.3957 4.6038
+71.2500 0.8721 0.2126 0.2338 0.3971 4.5569
+71.7500 0.8070 0.2135 0.2167 0.4272 4.4659
+72.2500 0.7734 0.2094 0.2133 0.3993 4.5130
+72.7500 0.8308 0.2104 0.1957 0.4240 4.3504
+73.2500 0.8334 0.2063 0.2078 0.3506 4.3260
+73.7500 0.8262 0.2099 0.1831 0.4131 4.2863
+74.2500 0.8266 0.2059 0.1646 0.4220 4.2714
+74.7500 0.8810 0.2045 0.1593 0.4502 4.2643
+75.2500 0.8487 0.2028 0.1544 0.4639 4.5583
+75.7500 0.8686 0.1939 0.1501 0.4513 5.1719
+76.2500 0.8856 0.1810 0.1451 0.4329 4.2136
+76.7500 0.9101 0.1691 0.1408 0.4140 4.0539
+77.2500 0.8948 0.1644 0.1398 0.4064 4.4440
+77.7500 0.9507 0.1645 0.1410 0.4084 3.9536
+78.2500 0.9516 0.1680 0.1409 0.4063 3.9612
+78.7500 0.9901 0.1730 0.1385 0.4016 3.8954
+79.2500 1.0135 0.1779 0.1370 0.4057 3.8544
+79.7500 1.0238 0.1804 0.1366 0.4144 3.8380
+80.2500 1.0911 0.1851 0.1406 0.4239 3.7907
+80.7500 1.1107 0.1969 0.1458 0.4312 3.7947
+81.2500 1.1323 0.2058 0.1478 0.4315 3.7439
+81.7500 1.1199 0.1989 0.1470 0.4007 3.8052
+82.2500 1.1413 0.1893 0.1474 0.3379 3.7782
+82.7500 1.1522 0.1780 0.1647 0.2569 3.7535
+83.2500 1.0882 0.1002 0.1727 0.1423 3.9285
+83.7500 1.0640 0.0668 0.0933 0.1224 3.9634
+84.2500 1.0832 0.0683 0.0629 0.1067 4.0876
+84.7500 1.1065 0.0905 0.0905 0.1779 10.2564
+85.2500 1.1593 0.1232 0.1314 0.2186 5.2053
+85.7500 1.1766 0.1220 0.1285 0.1885 7.4368
+86.2500 1.2122 0.1171 0.1203 0.1615 5.2218
+86.7500 1.2007 0.1202 0.1189 0.1528 3.3549
+87.2500 1.2534 0.1678 0.1136 0.1809 3.2044
+87.7500 1.2684 0.2063 0.1174 0.2082 3.2789
+88.2500 1.2651 0.2065 0.1249 0.2055 3.3929
+88.7500 1.3121 0.2127 0.1306 0.2121 3.1452
+89.2500 1.2511 0.2097 0.1336 0.3244 3.4139
+89.7500 1.1946 0.1848 0.1431 0.8527 3.4324
+90.2500 1.2024 0.1594 0.1532 0.9782 3.5940
+90.7500 1.1781 0.1491 0.1564 0.7515 10.8026
+91.2500 1.1751 0.1634 0.1288 0.9892 6.3209
+91.7500 1.1813 0.1785 0.1383 1.3679 3.2423
+92.2500 1.1880 0.1977 0.1577 1.4221 3.3751
+92.7500 1.1945 0.2154 0.1671 1.3459 3.3490
+93.2500 1.1977 0.2035 0.1659 1.0027 3.4825
+93.7500 1.1962 0.1794 0.1742 0.4732 3.8239
+94.2500 1.2063 0.1857 0.1861 0.3798 4.0569
+94.7500 1.2069 0.1747 0.1731 0.3279 3.8883
+95.2500 1.2138 0.1825 0.1782 0.2040 4.0586
+95.7500 1.2120 0.1937 0.1909 0.2012 3.9849
+96.2500 1.2119 0.2145 0.1936 0.2205 3.6912
+96.7500 1.2060 0.2329 0.1881 0.2351 3.5909
+97.2500 1.2180 0.2463 0.1817 0.2465 3.7095
+97.7500 1.1905 0.2427 0.1774 0.2448 3.7275
+98.2500 1.1993 0.2435 0.1773 0.2470 3.7578
+98.7500 1.1819 0.2417 0.1742 0.2461 3.7599
+99.2500 1.1352 0.2393 0.1732 0.2528 3.6389
+99.7500 0.9405 0.2333 0.1838 0.3432 3.5783
+100.2500 0.8839 0.2229 0.1868 0.4744 3.3913
+100.7500 0.9328 0.2053 0.1833 0.4722 4.1450
+101.2500 0.8366 0.2060 0.1900 0.5620 3.3921
+101.7500 0.8870 0.2062 0.1939 0.5265 3.4564
+102.2500 0.9230 0.2062 0.1997 0.3654 3.8714
+102.7500 0.9341 0.2182 0.2167 0.2940 3.8384
+103.2500 0.8972 0.2196 0.2263 0.2594 3.5907
+103.7500 0.9030 0.2251 0.2305 0.2505 3.5051
+104.2500 0.9903 0.2310 0.2392 0.2511 3.7091
+104.7500 0.9258 0.2295 0.2358 0.2469 3.6564
+105.2500 0.9255 0.2406 0.2392 0.2587 3.6220
+105.7500 0.9066 0.2512 0.2399 0.2699 3.6047
+106.2500 0.9433 0.2579 0.2418 0.2767 3.6121
+106.7500 0.9429 0.2715 0.2475 0.2901 3.6975
+107.2500 0.9623 0.2631 0.2188 0.2745 3.5434
+107.7500 0.9555 0.2681 0.2202 0.2772 3.5291
+108.2500 0.9052 0.2701 0.2199 0.2788 3.4615
+108.7500 0.9262 0.2399 0.1735 0.2448 3.3855
+109.2500 1.0315 0.1954 0.1332 0.1987 3.4881
+109.7500 0.9902 0.1758 0.1246 0.1782 3.3423
+110.2500 1.0418 0.1574 0.1233 0.1591 3.3755
+110.7500 1.0608 0.1468 0.1221 0.1480 3.4138
+111.2500 1.0811 0.1460 0.1206 0.1469 3.4747
+111.7500 1.0759 0.1587 0.1215 0.1593 3.3521
+112.2500 1.0627 0.1608 0.1214 0.1612 3.3851
+112.7500 1.1276 0.1678 0.1211 0.1681 3.3943
+113.2500 1.1566 0.1566 0.1199 0.1569 3.3956
+113.7500 1.3112 0.1468 0.1331 0.1470 3.3536
+114.2500 1.2741 0.1431 0.1433 0.1433 3.4953
+114.7500 1.2540 0.1369 0.1396 0.1370 3.3900
+115.2500 1.3268 0.1316 0.1322 0.1316 3.4516
+115.7500 1.2582 0.1192 0.1210 0.1193 3.3772
+116.2500 1.4197 0.1247 0.1131 0.1247 3.5349
+116.7500 1.3916 0.1269 0.1060 0.1269 3.2387
+117.2500 1.3602 0.1233 0.1034 0.1233 3.1370
+117.7500 1.5351 0.1126 0.1011 0.1126 3.2230
+118.2500 1.4540 0.1015 0.0990 0.1015 3.1637
+118.7500 1.4274 0.0973 0.0978 0.0973 3.0950
+119.2500 1.5787 0.0977 0.0953 0.0977 3.2126
+119.7500 1.5311 0.0962 0.0919 0.0962 3.1741
+120.2500 1.5591 0.0910 0.0865 0.0910 3.1778
+120.7500 1.7846 0.0825 0.0840 0.0825 3.3770
+121.2500 1.8195 0.1020 0.1007 0.1020 3.3795
+121.7500 1.7418 0.1097 0.1059 0.1097 3.3500
+122.2500 1.7694 0.0902 0.0891 0.0902 3.3621
+122.7500 1.7784 0.0906 0.0877 0.0906 3.3599
+123.2500 1.8229 0.0926 0.0873 0.0926 3.3690
+123.7500 1.7606 0.0978 0.0931 0.0978 3.3411
+124.2500 1.7628 0.0853 0.0822 0.0853 3.3453
+124.7500 1.8409 0.0822 0.0796 0.0822 3.3665
+125.2500 1.7795 0.0808 0.0778 0.0808 3.3406
+125.7500 1.8596 0.0827 0.0784 0.0827 3.3579
+126.2500 1.7997 0.0803 0.0757 0.0803 3.3318
+126.7500 1.7714 0.1125 0.0968 0.1125 3.1880
+127.2500 1.6293 0.1278 0.1121 0.1278 3.1074
+127.7500 1.6913 0.1318 0.1193 0.1318 3.1156
+128.2500 1.7119 0.1349 0.1243 0.1349 3.1078
+128.7500 1.5864 0.1334 0.1269 0.1334 3.0489
+129.2500 1.5708 0.1284 0.1264 0.1284 2.9726
+129.7500 1.6420 0.1257 0.1254 0.1257 3.0486
+130.2500 1.6693 0.1221 0.1233 0.1221 3.0463
+130.7500 1.6936 0.1215 0.1212 0.1215 3.0428
+131.2500 1.7180 0.1201 0.1195 0.1201 3.0417
+131.7500 1.7426 0.1288 0.1270 0.1288 3.0428
+132.2500 1.8586 0.1225 0.1189 0.1225 3.2274
+132.7500 1.6808 0.1574 0.1455 0.1637 3.1732
+133.2500 1.7290 0.1653 0.1407 0.1492 3.2693
+133.7500 1.6952 0.1525 0.1270 0.1766 3.1950
+134.2500 1.6769 0.1549 0.1281 0.3023 2.7141
+134.7500 1.6963 0.1994 0.1418 0.4497 2.6860
+135.2500 1.7182 0.2194 0.1599 0.5584 2.6720
+135.7500 1.6675 0.2644 0.2092 0.6638 2.5224
+136.2500 1.7466 0.2851 0.2294 0.7089 2.5301
+136.7500 1.8008 0.3160 0.2416 0.7022 2.6158
+137.2500 1.8068 0.3597 0.2514 0.6155 3.9275
+137.7500 1.8881 0.3652 0.2664 0.5079 3.4906
+138.2500 1.9197 0.3688 0.2888 0.5100 3.2328
+138.7500 1.8995 0.3676 0.3086 0.4946 3.0520
+139.2500 1.9011 0.3223 0.2716 0.3593 2.9053
+139.7500 1.8688 0.2960 0.2542 0.3030 2.7314
+140.2500 1.9124 0.2831 0.2409 0.2801 2.9222
+140.7500 1.8667 0.2640 0.2246 0.2605 2.6153
+141.2500 1.8922 0.2531 0.2227 0.2509 2.7418
+141.7500 1.8932 0.2567 0.2213 0.2536 2.8316
+142.2500 1.8688 0.2554 0.2210 0.2532 2.7121
+142.7500 1.9097 0.2502 0.2261 0.2482 2.8156
+143.2500 1.8517 0.2401 0.2298 0.2390 2.5360
+143.7500 1.8780 0.2284 0.2210 0.2278 2.4387
+144.2500 1.9203 0.2281 0.2127 0.2278 2.5136
+144.7500 1.9100 0.2388 0.2177 0.2385 2.4812
+145.2500 1.9208 0.2481 0.2121 0.2480 2.4090
+145.7500 1.9931 0.2485 0.2025 0.2485 2.5939
+146.2500 1.9778 0.2444 0.1995 0.2444 2.7113
+146.7500 1.4424 0.2584 0.1937 1.9827 2.8970
+147.2500 1.2116 0.2948 0.2215 3.3234 6.0417
+147.7500 1.3242 0.3148 0.2387 3.1370 8.0638
+148.2500 1.3159 0.3240 0.2403 2.6061 2.7689
+148.7500 1.2768 0.2917 0.2333 2.2273 2.7711
+149.2500 1.0704 0.2474 0.2253 3.1282 4.8483
+149.7500 1.1573 0.2587 0.2432 3.0619 3.8413
+150.2500 1.0850 0.2691 0.2526 3.0279 3.9666
+150.7500 1.1035 0.2694 0.2558 2.9870 11.9608
+151.2500 1.0974 0.2703 0.2586 2.9334 5.8747
+151.7500 1.1166 0.2659 0.2571 2.3102 18.8993
+152.2500 1.1133 0.2469 0.2401 1.9055 2.7608
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+154.2500 1.2213 0.2298 0.2137 1.4518 2.9868
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+156.2500 0.7357 0.1828 0.1959 2.1051 5.1777
+156.7500 0.6965 0.1731 0.1807 1.6882 6.3452
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+157.7500 0.9669 0.2378 0.1923 2.2342 12.8916
+158.2500 0.9438 0.1759 0.1596 2.1791 5.9866
+158.7500 1.1231 0.1626 0.1562 1.7818 7.2871
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+164.7500 1.1043 0.0873 0.1426 1.1741 5.0163
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+167.7500 0.8777 0.1203 0.1586 1.5698 12.4972
+168.2500 0.8469 0.1102 0.1579 1.4779 17.9553
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+170.7500 1.0391 0.0939 0.1318 0.4285 33.6102
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+171.7500 1.0328 0.1048 0.1681 0.3331 3.6520
+172.2500 1.0556 0.1158 0.1821 0.3241 6.3629
+172.7500 1.0361 0.1249 0.2025 0.3328 10.8015
+173.2500 1.0603 0.1329 0.2309 0.3600 17.1107
+173.7500 1.0762 0.1458 0.2407 0.3637 17.3135
+174.2500 1.0810 0.1456 0.2424 0.3505 15.6878
+174.7500 1.0988 0.1581 0.2601 0.3305 15.0611
+175.2500 0.9403 0.1772 0.2635 0.3208 14.3937
+175.7500 0.9225 0.1648 0.1740 0.2704 7.2402
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+177.2500 0.8747 0.1478 0.1195 0.6353 3.2343
+177.7500 0.8835 0.2323 0.1474 0.6797 3.1761
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+178.7500 0.9080 0.2539 0.1782 0.6766 3.1270
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+180.2500 0.9498 0.2527 0.1799 0.7843 16.6372
+180.7500 0.9622 0.2476 0.1813 0.7492 2.3525
diff --git a/CoLo-AT/RMTE.txt b/CoLo-AT/RMTE.txt
new file mode 100644
index 0000000000000000000000000000000000000000..4d2dcdbfcd64821c616bc5d35925400ec729da45
--- /dev/null
+++ b/CoLo-AT/RMTE.txt
@@ -0,0 +1,363 @@
+#Time	GS_CI	LS_cen	LS_BDA	LS_CI	GS_SCI	
+0.2500 0.1560 0.1567 0.1560 0.1567 0.1642
+0.7500 0.1560 0.1567 0.1560 0.1567 0.1642
+1.2500 0.3287 0.3293 0.3287 0.3293 0.3379
+1.7500 0.3344 0.2986 0.3344 0.2986 0.3372
+2.2500 0.3374 0.2991 0.3374 0.2991 0.3383
+2.7500 0.3383 0.2996 0.3383 0.2996 0.3388
+3.2500 0.3388 0.3000 0.3388 0.3000 0.3391
+3.7500 0.3388 0.3000 0.3388 0.3000 0.3391
+4.2500 0.3388 0.3000 0.3388 0.3000 0.3391
+4.7500 0.3389 0.3000 0.3389 0.3000 0.3391
+5.2500 0.3387 0.2997 0.3388 0.2997 0.3390
+5.7500 0.3382 0.2990 0.3385 0.2990 0.3386
+6.2500 0.3363 0.2958 0.3380 0.2958 0.3380
+6.7500 0.3296 0.2942 0.3389 0.2942 0.3353
+7.2500 0.3378 0.3697 0.3942 0.3697 0.3423
+7.7500 0.3420 0.3842 0.4019 0.3842 0.3412
+8.2500 0.3429 0.3848 0.4015 0.3848 0.3395
+8.7500 0.3389 0.3817 0.4012 0.3817 0.3383
+9.2500 0.3332 0.3798 0.4007 0.3798 0.3359
+9.7500 0.3530 0.4464 0.4702 0.5234 0.4956
+10.2500 0.3721 0.4602 0.4928 0.8634 0.9180
+10.7500 0.3642 0.4548 0.4878 0.9856 1.0307
+11.2500 0.3641 0.4520 0.4860 1.0462 0.8882
+11.7500 0.3654 0.4526 0.4865 1.1869 0.7757
+12.2500 0.3995 0.4606 0.4905 1.6163 0.7285
+12.7500 0.4393 0.4775 0.5018 1.7647 0.9452
+13.2500 0.4467 0.4766 0.5071 1.8084 1.2019
+13.7500 0.4849 0.4441 0.5010 1.9126 1.6153
+14.2500 0.4987 0.4374 0.4976 1.8538 3.3433
+14.7500 0.5241 0.4726 0.5089 1.7696 3.8495
+15.2500 0.5078 0.4786 0.5234 1.6806 1.7897
+15.7500 0.5431 0.4848 0.5258 1.6648 1.2702
+16.2500 0.5624 0.4861 0.5164 1.6666 2.0711
+16.7500 0.5805 0.4812 0.5070 1.6388 1.1708
+17.2500 0.6141 0.4990 0.5064 1.6915 1.5137
+17.7500 0.6498 0.4852 0.5025 1.6561 2.4636
+18.2500 0.6938 0.4761 0.4874 1.6965 1.7970
+18.7500 0.7199 0.4963 0.4804 1.6588 1.3094
+19.2500 0.7893 0.4835 0.4776 1.6813 1.5885
+19.7500 0.8303 0.4717 0.4710 1.6478 1.4710
+20.2500 0.8441 0.4743 0.4684 1.6730 1.4391
+20.7500 0.8594 0.4771 0.4642 1.5949 1.3698
+21.2500 0.8927 0.4801 0.4603 1.6598 1.5118
+21.7500 0.9186 0.4774 0.4588 1.6754 1.4475
+22.2500 0.9301 0.4714 0.4521 1.6855 1.4712
+22.7500 0.9500 0.4781 0.4476 1.6237 1.5681
+23.2500 0.9524 0.4697 0.4451 1.5893 1.4405
+23.7500 0.9852 0.4801 0.4461 1.7083 1.5817
+24.2500 0.9899 0.4930 0.4447 1.6713 1.5608
+24.7500 1.0068 0.5039 0.4446 1.5814 1.5430
+25.2500 1.0161 0.4906 0.4439 1.6468 1.5190
+25.7500 1.0428 0.4821 0.4399 1.5390 1.5705
+26.2500 1.0400 0.4812 0.4391 1.5932 1.8234
+26.7500 1.0665 0.4730 0.4382 1.4823 1.6979
+27.2500 1.0965 0.4877 0.4346 1.5574 1.6364
+27.7500 1.1055 0.4898 0.4359 1.5991 1.6026
+28.2500 1.1140 0.4804 0.4361 1.5145 1.5480
+28.7500 1.1473 0.4816 0.4374 1.5190 1.6410
+29.2500 1.1617 0.4754 0.4304 1.4976 1.7382
+29.7500 1.1688 0.4510 0.4174 1.2950 1.6074
+30.2500 1.1843 0.4237 0.4118 1.3617 1.7628
+30.7500 1.1961 0.4243 0.4205 1.3654 1.7778
+31.2500 1.2167 0.4483 0.4284 1.2679 1.8250
+31.7500 1.1931 0.4551 0.4373 1.3404 1.6730
+32.2500 0.9699 0.4568 0.4389 1.6722 1.7531
+32.7500 0.8580 0.4473 0.4344 1.8319 1.8610
+33.2500 0.8343 0.4613 0.4541 1.8869 1.8555
+33.7500 0.8671 0.4654 0.4690 1.8839 1.9530
+34.2500 0.8532 0.4652 0.4663 1.8886 2.0291
+34.7500 0.9430 0.4537 0.4645 1.7744 2.1019
+35.2500 0.9467 0.4437 0.4517 1.6699 1.8829
+35.7500 0.9560 0.4593 0.4767 1.5908 1.9040
+36.2500 0.9423 0.4744 0.4817 1.5118 1.8396
+36.7500 0.9010 0.4958 0.4743 1.5682 2.0733
+37.2500 0.9227 0.5126 0.4815 1.5969 2.6499
+37.7500 0.9427 0.5099 0.4828 1.5891 2.5960
+38.2500 0.9849 0.5096 0.4861 1.5832 2.8724
+38.7500 1.0049 0.4943 0.4733 1.5420 2.5871
+39.2500 1.0835 0.3822 0.4163 1.2450 2.3169
+39.7500 1.2242 0.3890 0.4191 1.1519 2.2721
+40.2500 1.2336 0.3621 0.3765 1.0231 2.2194
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+41.2500 1.2371 0.2495 0.2987 0.9962 2.0403
+41.7500 1.2794 0.2524 0.3050 0.9251 2.0370
+42.2500 1.2489 0.2779 0.3175 0.8081 2.6279
+42.7500 1.1483 0.2994 0.3207 0.8552 3.6694
+43.2500 1.0777 0.3307 0.3333 0.8620 3.0073
+43.7500 1.2168 0.4004 0.3820 0.7530 2.8446
+44.2500 1.1844 0.3948 0.3729 0.5846 2.9132
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+45.2500 1.0229 0.4314 0.4007 1.3326 2.3325
+45.7500 1.0528 0.4453 0.4134 1.4080 1.9769
+46.2500 1.2207 0.4592 0.4104 1.0494 2.2784
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+47.2500 1.2704 0.4674 0.4270 0.7062 2.1882
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+48.2500 1.2784 0.4812 0.4384 0.7046 4.4304
+48.7500 1.2671 0.4945 0.4339 0.6805 1.8681
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+52.7500 1.3433 0.4770 0.4552 0.6160 1.6790
+53.2500 1.3987 0.4657 0.4523 0.4614 1.7461
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+54.2500 1.4473 0.4673 0.4678 0.4303 1.8266
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+116.2500 1.1915 0.3531 0.3363 0.3532 1.8801
+116.7500 1.1797 0.3563 0.3256 0.3563 1.7996
+117.2500 1.1663 0.3511 0.3216 0.3511 1.7712
+117.7500 1.2390 0.3355 0.3180 0.3355 1.7953
+118.2500 1.2058 0.3185 0.3147 0.3186 1.7787
+118.7500 1.1948 0.3120 0.3127 0.3120 1.7593
+119.2500 1.2565 0.3126 0.3088 0.3126 1.7924
+119.7500 1.2374 0.3101 0.3031 0.3101 1.7816
+120.2500 1.2486 0.3016 0.2942 0.3016 1.7826
+120.7500 1.3359 0.2872 0.2898 0.2872 1.8377
+121.2500 1.3489 0.3194 0.3173 0.3194 1.8383
+121.7500 1.3198 0.3311 0.3254 0.3311 1.8303
+122.2500 1.3302 0.3003 0.2985 0.3003 1.8336
+122.7500 1.3336 0.3010 0.2961 0.3010 1.8330
+123.2500 1.3502 0.3042 0.2954 0.3042 1.8355
+123.7500 1.3269 0.3128 0.3051 0.3128 1.8279
+124.2500 1.3277 0.2921 0.2867 0.2921 1.8290
+124.7500 1.3568 0.2867 0.2821 0.2867 1.8348
+125.2500 1.3340 0.2843 0.2789 0.2843 1.8277
+125.7500 1.3637 0.2875 0.2800 0.2875 1.8325
+126.2500 1.3415 0.2834 0.2752 0.2834 1.8253
+126.7500 1.3309 0.3355 0.3111 0.3355 1.7855
+127.2500 1.2764 0.3575 0.3348 0.3575 1.7628
+127.7500 1.3005 0.3631 0.3454 0.3631 1.7651
+128.2500 1.3084 0.3673 0.3526 0.3673 1.7629
+128.7500 1.2595 0.3653 0.3563 0.3653 1.7461
+129.2500 1.2533 0.3584 0.3555 0.3584 1.7241
+129.7500 1.2814 0.3545 0.3541 0.3545 1.7460
+130.2500 1.2920 0.3494 0.3511 0.3494 1.7454
+130.7500 1.3014 0.3486 0.3481 0.3486 1.7443
+131.2500 1.3107 0.3465 0.3457 0.3465 1.7440
+131.7500 1.3201 0.3590 0.3564 0.3590 1.7444
+132.2500 1.3633 0.3500 0.3448 0.3500 1.7965
+132.7500 1.2964 0.3967 0.3815 0.4046 1.7814
+133.2500 1.3149 0.4066 0.3751 0.3863 1.8081
+133.7500 1.3020 0.3905 0.3564 0.4202 1.7875
+134.2500 1.2950 0.3936 0.3579 0.5498 1.6474
+134.7500 1.3024 0.4465 0.3765 0.6706 1.6389
+135.2500 1.3108 0.4684 0.3998 0.7473 1.6346
+135.7500 1.2913 0.5142 0.4574 0.8147 1.5882
+136.2500 1.3216 0.5340 0.4789 0.8419 1.5906
+136.7500 1.3419 0.5621 0.4915 0.8380 1.6174
+137.2500 1.3442 0.5998 0.5014 0.7845 1.9818
+137.7500 1.3741 0.6043 0.5161 0.7127 1.8683
+138.2500 1.3855 0.6073 0.5374 0.7141 1.7980
+138.7500 1.3782 0.6063 0.5555 0.7033 1.7470
+139.2500 1.3788 0.5677 0.5212 0.5994 1.7045
+139.7500 1.3670 0.5440 0.5041 0.5505 1.6527
+140.2500 1.3829 0.5320 0.4908 0.5293 1.7095
+140.7500 1.3663 0.5138 0.4739 0.5104 1.6172
+141.2500 1.3756 0.5030 0.4719 0.5009 1.6558
+141.7500 1.3759 0.5066 0.4704 0.5035 1.6827
+142.2500 1.3670 0.5054 0.4701 0.5031 1.6468
+142.7500 1.3819 0.5002 0.4756 0.4982 1.6780
+143.2500 1.3608 0.4900 0.4793 0.4889 1.5925
+143.7500 1.3704 0.4779 0.4701 0.4773 1.5616
+144.2500 1.3858 0.4776 0.4612 0.4773 1.5854
+144.7500 1.3820 0.4887 0.4665 0.4884 1.5752
+145.2500 1.3859 0.4980 0.4605 0.4979 1.5521
+145.7500 1.4118 0.4985 0.4500 0.4985 1.6106
+146.2500 1.4063 0.4944 0.4466 0.4944 1.6466
+146.7500 1.2010 0.5084 0.4401 1.4081 1.7021
+147.2500 1.1007 0.5429 0.4706 1.8230 2.4580
+147.7500 1.1507 0.5611 0.4886 1.7711 2.8397
+148.2500 1.1471 0.5692 0.4902 1.6143 1.6640
+148.7500 1.1300 0.5401 0.4830 1.4924 1.6647
+149.2500 1.0346 0.4973 0.4746 1.7687 2.2019
+149.7500 1.0758 0.5087 0.4931 1.7498 1.9599
+150.2500 1.0416 0.5187 0.5026 1.7401 1.9916
+150.7500 1.0505 0.5191 0.5058 1.7283 3.4584
+151.2500 1.0476 0.5199 0.5085 1.7127 2.4238
+151.7500 1.0567 0.5156 0.5071 1.5199 4.3473
+152.2500 1.0551 0.4969 0.4900 1.3804 1.6616
+152.7500 1.0588 0.4845 0.4820 1.2683 2.2233
+153.2500 1.0661 0.4758 0.4764 1.0109 1.7112
+153.7500 1.0660 0.4804 0.4777 1.2839 1.8597
+154.2500 1.1051 0.4794 0.4623 1.2049 1.7282
+154.7500 0.7578 0.4327 0.4591 1.4400 2.1925
+155.2500 0.7616 0.4223 0.4504 1.5976 2.4550
+155.7500 0.7771 0.4259 0.4507 1.5295 2.7938
+156.2500 0.8577 0.4275 0.4426 1.4509 2.2755
+156.7500 0.8346 0.4161 0.4250 1.2993 2.5190
+157.2500 0.8315 0.4598 0.4461 1.3780 3.1923
+157.7500 0.9833 0.4877 0.4385 1.4947 3.5905
+158.2500 0.9715 0.4194 0.3996 1.4762 2.4467
+158.7500 1.0598 0.4033 0.3952 1.3348 2.6995
+159.2500 1.1104 0.4075 0.3890 1.0913 2.7771
+159.7500 1.1160 0.4081 0.3973 1.0659 2.7216
+160.2500 1.1927 0.4123 0.3856 0.6557 2.2046
+160.7500 1.2222 0.4068 0.3987 0.5611 2.1964
+161.2500 1.2047 0.4088 0.4149 0.5133 2.1928
+161.7500 1.2094 0.4122 0.4302 0.4905 2.1888
+162.2500 1.2369 0.4082 0.4372 0.4720 2.1808
+162.7500 1.1895 0.3909 0.4326 0.4370 2.2572
+163.2500 0.9198 0.3198 0.3531 0.8825 2.3869
+163.7500 1.0437 0.2830 0.3501 1.0064 2.0716
+164.2500 1.0222 0.3077 0.3656 1.0124 2.0893
+164.7500 1.0508 0.2955 0.3777 1.0836 2.2397
+165.2500 0.9803 0.2987 0.3947 1.1279 2.7911
+165.7500 0.9645 0.3144 0.4155 0.9660 2.5016
+166.2500 0.8709 0.3127 0.3850 1.2168 3.2418
+166.7500 0.8114 0.3067 0.3614 1.3759 2.5447
+167.2500 0.9262 0.3309 0.3933 1.1856 3.1070
+167.7500 0.9368 0.3469 0.3983 1.2529 3.5351
+168.2500 0.9203 0.3319 0.3973 1.2157 4.2374
+168.7500 1.0176 0.3335 0.3893 1.1852 1.9913
+169.2500 0.9901 0.3181 0.3794 1.1864 1.9026
+169.7500 0.9582 0.3299 0.3843 1.1814 2.0143
+170.2500 0.9837 0.3364 0.3846 1.0767 2.4002
+170.7500 1.0194 0.3064 0.3630 0.6546 5.7974
+171.2500 0.9995 0.2985 0.3825 0.5773 8.4237
+171.7500 1.0163 0.3237 0.4100 0.5772 1.9110
+172.2500 1.0274 0.3403 0.4267 0.5693 2.5225
+172.7500 1.0179 0.3534 0.4500 0.5769 3.2866
+173.2500 1.0297 0.3646 0.4805 0.6000 4.1365
+173.7500 1.0374 0.3819 0.4907 0.6031 4.1609
+174.2500 1.0397 0.3816 0.4923 0.5920 3.9608
+174.7500 1.0482 0.3976 0.5100 0.5749 3.8809
+175.2500 0.9697 0.4210 0.5133 0.5664 3.7939
+175.7500 0.9605 0.4059 0.4171 0.5200 2.6908
+176.2500 0.9346 0.3872 0.3547 0.5266 1.6513
+176.7500 0.9447 0.3670 0.3297 0.6175 1.7862
+177.2500 0.9353 0.3844 0.3457 0.7970 1.7984
+177.7500 0.9399 0.4819 0.3839 0.8244 1.7822
+178.2500 0.9428 0.5021 0.4053 0.8243 1.7781
+178.7500 0.9529 0.5039 0.4222 0.8225 1.7683
+179.2500 0.9439 0.4929 0.4272 0.8640 1.7747
+179.7500 0.9579 0.4962 0.4342 0.8979 1.6315
+180.2500 0.9746 0.5027 0.4242 0.8856 4.0789
+180.7500 0.9809 0.4976 0.4257 0.8656 1.5338
diff --git a/CoLo-AT/__pycache__/Cooperative_Localization_Journal1.cpython-36.pyc b/CoLo-AT/__pycache__/Cooperative_Localization_Journal1.cpython-36.pyc
index f61628b9573c970d4e070e8d112414736c57bca8..3af8e2339cde74a005acc5b2cf61285fe765bc9b 100644
Binary files a/CoLo-AT/__pycache__/Cooperative_Localization_Journal1.cpython-36.pyc and b/CoLo-AT/__pycache__/Cooperative_Localization_Journal1.cpython-36.pyc differ
diff --git a/CoLo-AT/colo_d3.pdf b/CoLo-AT/colo_d3.pdf
index 7930a0632892d5b1bebaf4ca89ac69b8548b81ee..c28e5758b46a4d52da8339a58d3fb7f9521c89cc 100644
Binary files a/CoLo-AT/colo_d3.pdf and b/CoLo-AT/colo_d3.pdf differ
diff --git a/CoLo-AT/data_analysis/__pycache__/data_analyzer.cpython-36.pyc b/CoLo-AT/data_analysis/__pycache__/data_analyzer.cpython-36.pyc
index 18812b6a3f8fed8faf5b81d65bea7d299bf7cf94..a8472c46abecf8674485f09fe856ba4425cf189f 100644
Binary files a/CoLo-AT/data_analysis/__pycache__/data_analyzer.cpython-36.pyc and b/CoLo-AT/data_analysis/__pycache__/data_analyzer.cpython-36.pyc differ
diff --git a/CoLo-AT/data_analysis/data_analyzer.py b/CoLo-AT/data_analysis/data_analyzer.py
index 2614c75e193fdd66dd858563a9a60381fb00b967..00bdcfde124e00335b03a8dc8deccb3842ae16bc 100644
--- a/CoLo-AT/data_analysis/data_analyzer.py
+++ b/CoLo-AT/data_analysis/data_analyzer.py
@@ -275,7 +275,7 @@ class Analyzer():
 		for data_recorder in arr_data_recorder:
 			loc_err_per_run, state_err_per_run, trace_per_run, time_stamps = self.calculate_loc_err_and_trace_state_variance_per_run(data_recorder, plot_graphs = False, selected_labels = selected_labels)
 			if only_trace == None or data_recorder.name not in only_trace:
-				arr_loc_err.append([time_stamps, loc_err_per_run, data_recorder.name])
+				arr_loc_err.append([time_stamps, state_err_per_run, data_recorder.name])
 			arr_trace.append([time_stamps, trace_per_run, data_recorder.name] )
 
 		print('Plotting Comparison Graphs')
@@ -306,9 +306,41 @@ class Analyzer():
 
 
 
+	def generate_result_files(self, arr_data_recorder):
+		state_err_arr = [] 
+		trace_arr = []
+		algo_name_arr= [] 
+		for data_recorder in arr_data_recorder:
+			recorder_name = data_recorder.get_name()
+			loc_err_per_run, state_err_per_run, trace_per_run, time_stamps = self.calculate_loc_err_and_trace_state_variance_per_run(data_recorder, plot_graphs = False)
+			algo_name_arr.append(recorder_name)
+			state_err_arr.append(state_err_per_run)
+			trace_arr.append(state_err_per_run)
+		RMSE_file = open("RMSE.txt", "w")
+		RMTE_file = open("RMTE.txt", "w")
+
+		RMSE_file.write('#Time' + '\t')
+		for algo_name in algo_name_arr:
+			RMSE_file.write(algo_name + '\t')
+
+		RMSE_file.write('\n')
+
+
+		for idx, time_stamp in enumerate(time_stamps): 
+			result_str = '{:2.4f} {:2.4f} {:2.4f} {:2.4f} {:2.4f} {:2.4f}\n'.format(time_stamp, state_err_arr[0][idx], state_err_arr[1][idx], state_err_arr[2][idx], state_err_arr[3][idx], state_err_arr[4][idx])
+			RMSE_file.write(result_str)
 
+		RMSE_file.close()
 
+		RMTE_file.write('#Time' + '\t')
+		for algo_name in algo_name_arr:
+			RMTE_file.write(algo_name + '\t')
 
+		RMTE_file.write('\n')
 
 
+		for idx, time_stamp in enumerate(time_stamps): 
+			result_str = '{:2.4f} {:2.4f} {:2.4f} {:2.4f} {:2.4f} {:2.4f}\n'.format(time_stamp, np.sqrt(state_err_arr[0][idx]), np.sqrt(state_err_arr[1][idx]), np.sqrt(state_err_arr[2][idx]), np.sqrt(state_err_arr[3][idx]), np.sqrt(state_err_arr[4][idx]))
+			RMTE_file.write(result_str)
 
+		RMTE_file.close()
diff --git a/CoLo-AT/localization_algos/__pycache__/centralized_ekf.cpython-36.pyc b/CoLo-AT/localization_algos/__pycache__/centralized_ekf.cpython-36.pyc
index 712e9c467ad91a8c3ed5dc2086cc45ebe01529a6..b2a239a5028fbbf5b2893019f6e8f40ab48506de 100644
Binary files a/CoLo-AT/localization_algos/__pycache__/centralized_ekf.cpython-36.pyc and b/CoLo-AT/localization_algos/__pycache__/centralized_ekf.cpython-36.pyc differ
diff --git a/CoLo-AT/read_data_to_plot.py b/CoLo-AT/read_data_to_plot.py
new file mode 100644
index 0000000000000000000000000000000000000000..ea521801ce67df5de433ddb5050d213bbd22a576
--- /dev/null
+++ b/CoLo-AT/read_data_to_plot.py
@@ -0,0 +1,70 @@
+import matplotlib.pyplot as plt
+import numpy as np
+
+RMSE_file = open("RMSE.txt", "r")
+RMTE_file = open("RMTE.txt", "r")
+
+RMSE_arr = []
+RMTE_arr = []
+
+
+RMSE_line = RMSE_file.readline()
+names = RMSE_line.split('\t')
+del names[0]
+del names[-1]
+print(names)
+
+
+for RMSE_line in RMSE_file:
+	RMSE = RMSE_line.split(' ')
+	[float(i) for i in RMSE]
+	RMSE_arr.append(np.array(RMSE))
+
+RMSE_arr = np.array(RMSE_arr)
+
+
+RMTE_line = RMTE_file.readline()
+for RMTE_line in RMTE_file:
+	RMTE = RMTE_line.split(' ')
+	[float(i) for i in RMTE]
+	RMTE_arr.append(np.array(RMTE))
+
+RMTE_arr = np.array(RMTE_arr)
+
+
+RMSE_file.close()
+RMTE_file.close()
+
+print(RMSE_arr[0,:])
+
+
+
+fig = plt.figure()
+fig1 = fig.add_subplot(211)
+fig2 = fig.add_subplot(212)
+
+for idx, name in enumerate(names):
+	fig1.plot(RMSE_arr[:,0], RMSE_arr[:,idx+1], label = name)
+
+	fig2.plot(RMTE_arr[:,0], RMTE_arr[:,idx+1], label = name)
+
+fig1.set_title('RMSE')
+fig1.set_xlabel('Time [s]')
+fig1.set_ylabel('RMS [m]') 
+#fig1.set_ylim(0, 1)
+fig1.legend(loc=1)
+fig1.legend(loc='center left', bbox_to_anchor=(1, 0.5))
+#fig1.tick_params(labelsize=18)
+
+fig2.set_title('RMTE')
+fig2.set_xlabel('Time [s]')
+fig2.set_ylabel('RMTE [m]')
+#fig2.set_ylim(0, 1)
+fig2.legend(loc=1)
+fig2.legend(loc='center left', bbox_to_anchor=(1, 0.5))
+#fig2.tick_params(labelsize=18)
+
+plt.subplots_adjust(hspace=.6)
+plt.savefig('results.pdf')
+print('image saved')
+plt.show()
\ No newline at end of file
diff --git a/CoLo-AT/results.pdf b/CoLo-AT/results.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..985683c893f86b472a10b8d5631dc09658e504b9
Binary files /dev/null and b/CoLo-AT/results.pdf differ
diff --git a/CoLo-AT/simulation_process/__pycache__/state_recorder.cpython-36.pyc b/CoLo-AT/simulation_process/__pycache__/state_recorder.cpython-36.pyc
index 732abd46ebad50eb45b3176b5055226175851428..1a2804c5a5acc4c9c3f1a525d00d7c7318ec6bfa 100644
Binary files a/CoLo-AT/simulation_process/__pycache__/state_recorder.cpython-36.pyc and b/CoLo-AT/simulation_process/__pycache__/state_recorder.cpython-36.pyc differ
diff --git a/CoLo-AT/simulation_process/state_recorder.py b/CoLo-AT/simulation_process/state_recorder.py
index ad6a540a861e827f64668cd5e392325ef8ea9a22..84ec9372519fba1c09409b3fe9956863499bbfc1 100644
--- a/CoLo-AT/simulation_process/state_recorder.py
+++ b/CoLo-AT/simulation_process/state_recorder.py
@@ -19,13 +19,13 @@ class StatesRecorder():
 		self.updata_type_in_time_order = []
 		self.num_robots = len(self.dataset_labels)
 		self.loc_err_arr = {}
-		self.trace_sigma_s_arr = {}
+		self.trace_state_var_arr = {}
 		self.updata_type_arr = {}
 		self.gt_states = np.matrix(np.zeros((2*self.num_robots,1)))
 		for i, label in enumerate(self.dataset_labels):
 			self.recorded_data[label]=[]
 			self.loc_err_arr[label]=[]
-			self.trace_sigma_s_arr[label]=[]         
+			self.trace_state_var_arr[label]=[]         
 			self.updata_type_arr[label]=[]
 
 	def set_starting_state(self, stating_states):
@@ -44,7 +44,7 @@ class StatesRecorder():
 			self.data_in_time_order.append(recorded_dataline)	
 			self.recorded_data[label].append(recorded_dataline)	
 			self.loc_err_arr[label].append(0)
-			self.trace_sigma_s_arr[label].append(initial_trace_state_var)
+			self.trace_state_var_arr[label].append(initial_trace_state_var)
 			self.updata_type_arr[label].append('ini')
 			self.updata_type_in_time_order.append('ini')
 	
@@ -80,12 +80,12 @@ class StatesRecorder():
 			loc_err = sqrt((est_x_pos-gt_x_pos)*(est_x_pos-gt_x_pos)+(est_y_pos-gt_y_pos)*(est_y_pos-gt_y_pos))
 			recorded_dataline = [time, robot_label, est_x_pos, est_y_pos, trace_state_var, gt_x_pos, gt_y_pos, loc_err, est_states, self.gt_states.copy()] 
 		
-		#warning (optional)
+		#warnings (optional)
 		'''
 		if(trace_state_var<0):
 			print('TIME: ', time+self.start_time)
 			print(updata_type)
-			print('neg trace: ', recorded_dataline)
+			print('negative trace of state variances: ', recorded_dataline)
 		'''
 		'''
 		if(loc_err >= 1):
@@ -100,7 +100,7 @@ class StatesRecorder():
 		self.updata_type_in_time_order.append(updata_type)
 		self.recorded_data[robot_label].append(recorded_dataline)
 		self.loc_err_arr[robot_label].append(loc_err)
-		self.trace_sigma_s_arr[robot_label].append(trace_state_var)
+		self.trace_state_var_arr[robot_label].append(trace_state_var)
 		self.updata_type_arr[robot_label].append(updata_type)
 
 		
@@ -121,8 +121,8 @@ class StatesRecorder():
 	def get_loc_err_arr(self):
 		return self.loc_err_arr
 
-	def get_trace_sigma_s_arr(self):
-		return self.trace_sigma_s_arr
+	def get_trace_state_var_arr(self):
+		return self.trace_state_var_arr
 
 	def get_update_type_arr(self):
 		return self.updata_type_arr
@@ -133,22 +133,22 @@ class StatesRecorder():
 		fig1 = fig.add_subplot(211)
 		fig2 = fig.add_subplot(212)
 		loc_err_arr = self.get_loc_err_arr()
-		trace_sigma_s_arr = self.get_trace_sigma_s_arr()
+		trace_state_var_arr = self.get_trace_state_var_arr()
 		for i, label in enumerate(self.dataset_labels):
 			time_arr = self.get_time_arr(label)
 			fig1.plot(time_arr, loc_err_arr[label], label= 'Robot %d' %label)
-			fig2.plot(time_arr, trace_sigma_s_arr[label], label= 'Robot %d' %label)
+			fig2.plot(time_arr, np.sqrt(trace_state_var_arr[label]), label= 'Robot %d' %label)
 			print('Robot',label, 'loc err: ', sum(loc_err_arr[label])/len(loc_err_arr[label]))
-			print('Robot',label, 'trace Sigma_s: ', sum(trace_sigma_s_arr[label])/len(trace_sigma_s_arr[label]))
+			print('Robot',label, 'RMTE: ', sum(np.sqrt(trace_state_var_arr[label]))/len(trace_state_var_arr[label]))
 		fig1.set_title('Estimation deviation error')
 		fig1.set_xlabel('Time[s]')
-		fig1.set_ylabel('RMS[m]') 
+		fig1.set_ylabel('loc err [m]') 
 		#fig1.set_ylim(0, 6)
 		fig1.legend(loc='center left', bbox_to_anchor=(1, 0.5))
 
 		fig2.set_title('Trace of state variance')
 		fig2.set_xlabel('Time [s]')
-		fig2.set_ylabel('Sigma_s [m^2]')
+		fig2.set_ylabel('RMTE [m]')
 		#fig2.set_ylim(0, 0.08)
 		fig2.legend(loc='center left', bbox_to_anchor=(1, 0.5))
 			
@@ -157,3 +157,6 @@ class StatesRecorder():
 
 		return True
 
+
+
+