From df551b6d66ef2d7989eb57d7d928f18f42f9a40a Mon Sep 17 00:00:00 2001 From: CaitlinA Date: Tue, 15 Sep 2020 02:49:35 -0700 Subject: [PATCH] Updates to getWeather data other minor changes --- .DS_Store | Bin 14340 -> 16388 bytes CurrentAnomalyPPTData.csv | 2652 ++++++++--------- CurrentAnomalyTempData.csv | 2652 ++++++++--------- EC2_Ubuntu18_withUserData.yml | 24 +- ExampleData/Future_Shriver2018.csv | 902 +++--- ExampleData/HistData_Norm_Stats.csv | 732 ++--- ExampleData/Hist_Shriver2018.csv | 82 +- ExampleData/MonthlyAnoms.csv | 2414 +-------------- ExampleData/generatedAnomData | Bin 2530 -> 14066 bytes ExampleData/generatedAnomData_BiasCorrected | Bin 2523 -> 14055 bytes ExampleData/wdata.csv | 1458 ++++----- STDF_Plumber.R | 6 +- TempMonthlys.csv | 14 + figureCode/.DS_Store | Bin 10244 -> 10244 bytes figureCode/FigureDataPrep.R | 24 +- figureCode/presentationFigs/.DS_Store | Bin 8196 -> 8196 bytes figureCode/presentationFigs/Temperature.R | 37 +- figureCode/presentationFigs/VWC.R | 4 +- ...P_ObservedAndFuture_Median_DIFFERENCES.png | Bin 398153 -> 420313 bytes ...EMP_ObservedAndFuture_Median_Quantiles.png | Bin 343626 -> 362810 bytes figureCode/testFigs/TempExplore.R | 41 +- functions/GetDataOffline.R | 8 +- functions/HelperFunctions.R | 33 +- functions/Outputs.R | 36 +- functions/simulationFunctions.R | 114 +- functions/weatherFunctions.R | 368 +-- 26 files changed, 4597 insertions(+), 7004 deletions(-) create mode 100644 TempMonthlys.csv diff --git a/.DS_Store b/.DS_Store index 3cc50f68c2f86d58e58a6c389155ea215923573a..33700c7fef4a78fdbfeaaca0526f7163accdbb72 100644 GIT binary patch delta 785 zcmZ`%&1(}u6n~o!o!vCaByE#T3GLFfP1Kg=tELCPnpUb<>A{55Pe{6JI~aG{Y+6wf zbI4UR4E_b4MMCJM7x82dLJb5w2;#wmQt;NhGaDO3abV`n{N}yin|be7->BDwF#r&< zTWSJemTeK6+~Pg}hXkN78+SFvPC1zaI2yTB^J~pc<7tP}#sS*)%WlT#SWQdoPJj_O3&{oA1D4Zd9e4}x;REc$N4n@IbT={~_G(Fg_#?K(qoR$_djFv} z6qEzfoHQ@;I$)sZLrJw&A2MWe-_x zOr|oe&&sfeE-Ur_Hg=}bSrA-INTJp^b{`jjYlB`6`snLeh?>!c!ibNt$;PaUEf@cm nXS01^r{44w*RrLyTGv{_V+&WRHP{}}_4Lcu;lNqjzlnYU-r>Lp delta 269 zcmZo^U~DN+VPIfjbSh0TWMD7=GC6=4L<{f$i8clX1rW_JQAL|o2S_td3^AT;V8g=7 z2;{I#zGx)DnNpmbla!yI!!X%UL3r~*sVvrw4L2D%K?;C?gBvI$&azpZ=Ls{T7*K=c zW>o`Tc1Fg@|AfRQdzuSQ{x2lD*_&lC6G&jQl0YoW#| diff --git a/CurrentAnomalyPPTData.csv b/CurrentAnomalyPPTData.csv index 73640a2..da2f711 100644 --- a/CurrentAnomalyPPTData.csv +++ b/CurrentAnomalyPPTData.csv @@ -1,1327 +1,1327 @@ "YEAR","MN","LEAD","CD","ForecastedMEAN","ClimatologicalMEAN","ForecastedSD","ClimatologicalSD","PO" -2020,7,1,1,11.67,11.67,0.2,0.2,0.33 -2020,7,2,1,11.06,11.06,0.14,0.14,0.28 -2020,7,3,1,11.06,11.06,0.18,0.18,0.28 -2020,7,4,1,9.72,9.72,0.48,0.48,0.58 -2020,7,5,1,8.29,8.29,1.38,1.38,0.94 -2020,7,6,1,7.92,7.92,2,2,1.04 -2020,7,7,1,7.98,7.98,0.31,0.31,0.48 -2020,7,8,1,9.24,9.24,0.29,0.29,0.45 -2020,7,9,1,11.26,11.02,1.3612,1.38,0.83 -2020,7,10,1,12.1,11.77,0.1679,0.17,0.35 -2020,7,11,1,12.55,12.29,0.1,0.1,0.29 -2020,7,12,1,11.98,11.98,2.29,2.29,1.07 -2020,7,13,1,11.67,11.67,0.2,0.2,0.33 -2020,7,1,2,11.3,11.3,0.18,0.18,0.29 -2020,7,2,2,12.35,12.35,0.18,0.18,0.31 -2020,7,3,2,12.7,12.7,0.19,0.19,0.29 -2020,7,4,2,11.87,11.87,0.54,0.54,0.58 -2020,7,5,2,10.01,10.01,0.62,0.62,0.65 -2020,7,6,2,10.33,10.33,0.41,0.41,0.49 -2020,7,7,2,11.1,11.1,0.15,0.15,0.29 -2020,7,8,2,11.69,11.69,0.16,0.16,0.29 -2020,7,9,2,12.04,11.47,0.2954,0.3,0.39 -2020,7,10,2,12.04,11.46,0.1294,0.13,0.25 -2020,7,11,2,12.2,11.71,0.11,0.11,0.25 -2020,7,12,2,11.21,11.21,0.18,0.18,0.33 -2020,7,13,2,11.3,11.3,0.18,0.18,0.29 -2020,7,1,3,12.04,12.04,0.29,0.29,0.47 -2020,7,2,3,11.68,11.68,0.48,0.48,0.56 -2020,7,3,3,10.78,10.78,0.3,0.3,0.43 -2020,7,4,3,10.06,10.06,0.27,0.27,0.49 -2020,7,5,3,8.5,8.5,0.81,0.81,0.82 -2020,7,6,3,8.15,8.15,0.25,0.25,0.47 -2020,7,7,3,8.65,8.65,0.12,0.12,0.3 -2020,7,8,3,9.91,9.91,0.39,0.39,0.54 -2020,7,9,3,11.46,10.96,0.5464,0.57,0.58 -2020,7,10,3,12.44,11.99,0.51,0.51,0.57 -2020,7,11,3,12.58,12.21,0.2198,0.22,0.41 -2020,7,12,3,12.26,12.26,0.33,0.33,0.56 -2020,7,13,3,12.04,12.04,0.29,0.29,0.47 -2020,7,1,4,12.6,12.6,0.79,0.79,0.61 -2020,7,2,4,12.11,12.11,0.38,0.38,0.46 -2020,7,3,4,12.54,12.54,0.2,0.2,0.31 -2020,7,4,4,11.55,11.55,1.06,1.06,0.76 -2020,7,5,4,10.54,10.54,0.88,0.88,0.72 -2020,7,6,4,10.86,10.86,0.2,0.2,0.34 -2020,7,7,4,10.92,10.92,0.15,0.15,0.29 -2020,7,8,4,12.26,12.26,0.18,0.18,0.32 -2020,7,9,4,13.27,12.51,0.36,0.36,0.42 -2020,7,10,4,13.38,12.61,0.29,0.29,0.38 -2020,7,11,4,13.25,12.64,0.71,0.71,0.61 -2020,7,12,4,12.04,12.04,0.42,0.42,0.5 -2020,7,13,4,12.6,12.6,0.79,0.79,0.61 -2020,7,1,5,11.11,11.11,0.42,0.42,0.59 -2020,7,2,5,11.11,11.11,2.16,2.16,1.01 -2020,7,3,5,10.28,10.28,0.27,0.27,0.47 -2020,7,4,5,9.33,9.33,0.83,0.83,0.8 -2020,7,5,5,8.08,8.08,0.35,0.35,0.57 -2020,7,6,5,7.93,7.83,0.22,0.22,0.47 -2020,7,7,5,8.25,8.17,0.3066,0.31,0.57 -2020,7,8,5,9.68,9.59,0.33,0.33,0.54 -2020,7,9,5,11.58,11.02,0.2595,0.26,0.42 -2020,7,10,5,12.22,11.73,1.09,1.09,0.75 -2020,7,11,5,12.05,11.74,0.7667,0.77,0.69 -2020,7,12,5,10.99,10.99,0.11,0.11,0.27 -2020,7,13,5,11.11,11.11,0.42,0.42,0.59 -2020,7,1,6,9.38,9.38,1.19,1.19,0.81 -2020,7,2,6,9.21,9.21,2.81,2.81,1.05 -2020,7,3,6,9.16,9.16,0.31,0.31,0.46 -2020,7,4,6,8.89,8.89,1.96,1.96,1 -2020,7,5,6,7.99,7.84,0.3238,0.33,0.5 -2020,7,6,6,8.79,8.1,0.1999,0.2,0.35 -2020,7,7,6,9.1,8.64,0.09,0.09,0.27 -2020,7,8,6,11.46,11.15,0.4564,0.46,0.58 -2020,7,9,6,13.08,12.18,2.8095,2.81,0.97 -2020,7,10,6,13.65,12.83,2.5415,2.56,0.95 -2020,7,11,6,12.41,11.76,0.3188,0.33,0.5 -2020,7,12,6,11.09,10.47,0.1197,0.12,0.28 -2020,7,13,6,9.71,9.38,1.158,1.19,0.81 -2020,7,1,7,11.57,11.26,0.1694,0.17,0.32 -2020,7,2,7,11.4,11.4,2.1,2.1,0.86 -2020,7,3,7,10.4,10.4,0.59,0.59,0.52 -2020,7,4,7,9.88,9.88,0.25,0.25,0.4 -2020,7,5,7,9.35,9.35,0.26,0.26,0.41 -2020,7,6,7,9.83,9.83,0.15,0.15,0.29 -2020,7,7,7,9.9,9.9,0.32,0.32,0.43 -2020,7,8,7,11.82,11.82,0.26,0.26,0.39 -2020,7,9,7,12.06,11.14,0.2649,0.27,0.39 -2020,7,10,7,13.21,12.18,0.3406,0.35,0.43 -2020,7,11,7,12.72,11.92,0.7516,0.78,0.67 -2020,7,12,7,12.29,12,0.307,0.31,0.42 -2020,7,13,7,11.26,11.26,0.17,0.17,0.32 -2020,7,1,8,10.58,10.58,0.33,0.33,0.47 -2020,7,2,8,10.48,10.48,0.9,0.9,0.72 -2020,7,3,8,9.85,9.85,0.75,0.75,0.67 -2020,7,4,8,9.44,9.44,2.37,2.37,1.05 -2020,7,5,8,8.35,8.35,1.26,1.26,0.89 -2020,7,6,8,8.64,8.51,0.6394,0.65,0.68 -2020,7,7,8,9.23,9.13,0.95,0.95,0.77 -2020,7,8,8,10.9,10.82,0.62,0.62,0.68 -2020,7,9,8,11.92,11.27,0.1944,0.2,0.37 -2020,7,10,8,12.74,12.16,1.0364,1.04,0.74 -2020,7,11,8,12.57,12.06,1.0646,1.14,0.8 -2020,7,12,8,12.04,11.64,0.1365,0.14,0.27 -2020,7,13,8,10.58,10.58,0.33,0.33,0.47 -2020,7,1,9,10.21,9.89,0.3051,0.32,0.46 -2020,7,2,9,9.91,9.91,0.85,0.85,0.64 -2020,7,3,9,9.87,9.87,2.64,2.64,1 -2020,7,4,9,10.16,10.16,1.63,1.63,0.94 -2020,7,5,9,9.58,9.58,0.2,0.2,0.38 -2020,7,6,9,10.3,9.97,0.1492,0.15,0.31 -2020,7,7,9,10.68,10.52,2.3546,2.38,1.03 -2020,7,8,9,12.21,12.16,1.8371,1.84,0.97 -2020,7,9,9,13.46,12.42,0.6561,0.67,0.61 -2020,7,10,9,14.8,13.82,2.1685,2.17,0.9 -2020,7,11,9,13.34,12.68,2.5778,2.69,1.04 -2020,7,12,9,12.39,11.69,0.12,0.12,0.29 -2020,7,13,9,10.63,9.89,0.3151,0.32,0.46 -2020,7,1,10,13.34,12.43,0.1884,0.19,0.3 -2020,7,2,10,11.07,11.07,0.49,0.49,0.44 -2020,7,3,10,10.3,10.3,0.8,0.8,0.59 -2020,7,4,10,10.36,10.36,0.16,0.16,0.29 -2020,7,5,10,9.9,9.9,0.33,0.33,0.43 -2020,7,6,10,10.18,10.18,0.34,0.34,0.42 -2020,7,7,10,10.21,10.21,0.4,0.4,0.44 -2020,7,8,10,11.29,11.29,0.48,0.48,0.5 -2020,7,9,10,12.35,11.21,0.37,0.37,0.46 -2020,7,10,10,13.5,12.41,0.2374,0.24,0.39 -2020,7,11,10,14.49,13.42,0.8178,0.82,0.64 -2020,7,12,10,13.92,13.23,0.1852,0.19,0.3 -2020,7,13,10,13.13,12.43,0.1831,0.19,0.3 -2020,7,1,11,11.2,10.54,0.7625,0.77,0.63 -2020,7,2,11,10.42,10.42,0.36,0.36,0.43 -2020,7,3,11,11.14,11.14,3.28,3.28,1.06 -2020,7,4,11,12.37,12.37,2.07,2.07,0.99 -2020,7,5,11,11.99,11.99,0.65,0.65,0.6 -2020,7,6,11,12.26,12.26,0.52,0.52,0.54 -2020,7,7,11,12.32,12.32,0.58,0.58,0.56 -2020,7,8,11,12.81,12.81,1.09,1.09,0.74 -2020,7,9,11,13.43,12.38,0.2994,0.3,0.43 -2020,7,10,11,15.22,13.99,0.2794,0.28,0.41 -2020,7,11,11,14.15,13.36,0.3445,0.35,0.48 -2020,7,12,11,12.83,12.36,0.2172,0.22,0.38 -2020,7,13,11,11.29,10.54,0.766,0.77,0.63 -2020,7,1,12,16.58,15.16,0.1486,0.15,0.29 -2020,7,2,12,12.15,11.84,0.7442,0.75,0.57 -2020,7,3,12,9.94,9.94,3.66,3.66,1.02 -2020,7,4,12,9.99,9.99,0.45,0.45,0.47 -2020,7,5,12,10.24,9.99,0.2689,0.27,0.36 -2020,7,6,12,10.6,10.42,0.166,0.17,0.3 -2020,7,7,12,10.36,10.36,0.17,0.17,0.29 -2020,7,8,12,10.56,10.56,0.45,0.45,0.5 -2020,7,9,12,12.48,11.54,0.2999,0.3,0.46 -2020,7,10,12,15.2,14.28,3.3556,3.38,1.05 -2020,7,11,12,17.38,16.88,3.6188,3.65,1.06 -2020,7,12,12,17.67,17.46,0.7051,0.73,0.62 -2020,7,13,12,15.57,15.16,0.1449,0.15,0.29 -2020,7,1,13,12.88,11.9,1.1341,1.15,0.72 -2020,7,2,13,10.79,10.79,3.03,3.03,0.97 -2020,7,3,13,10.08,10.08,0.81,0.81,0.58 -2020,7,4,13,10.74,10.74,0.9,0.9,0.65 -2020,7,5,13,11.18,11.01,1.0282,1.03,0.67 -2020,7,6,13,11.8,11.86,3.2521,3.38,1.02 -2020,7,7,13,10.84,10.84,0.98,0.98,0.66 -2020,7,8,13,10.69,10.69,0.53,0.53,0.5 -2020,7,9,13,12,10.92,0.4897,0.49,0.53 -2020,7,10,13,13.42,12.18,1.2385,1.27,0.73 -2020,7,11,13,14.03,13.28,1.4743,1.5,0.78 -2020,7,12,13,13.12,12.91,1.5217,1.57,0.83 -2020,7,13,13,12.47,11.9,1.1051,1.15,0.72 -2020,7,1,14,10.17,10.17,9.45,9.45,1.5 -2020,7,2,14,9.37,9.37,2.24,2.24,1.03 -2020,7,3,14,7.81,7.81,2.13,2.13,1.04 -2020,7,4,14,6.21,6.19,1.8497,1.89,1.06 -2020,7,5,14,5.1,4.96,0.864,0.89,0.88 -2020,7,6,14,4.95,4.72,0.1883,0.19,0.41 -2020,7,7,14,5.84,5.57,0.3,0.3,0.49 -2020,7,8,14,7.6,7.31,0.2098,0.21,0.41 -2020,7,9,14,9.31,8.89,0.81,0.81,0.73 -2020,7,10,14,9.88,9.57,0.557,0.61,0.66 -2020,7,11,14,9.97,9.97,1.85,1.85,1.05 -2020,7,12,14,10.2,10.2,2.53,2.53,1.06 -2020,7,13,14,10.17,10.17,9.45,9.45,1.5 -2020,7,1,15,9.37,9.09,7.0897,7.09,1.5 -2020,7,2,15,7.02,7.02,2.04,2.04,1.04 -2020,7,3,15,5.01,4.67,0.8275,0.88,0.74 -2020,7,4,15,3.07,2.9,0.3281,0.33,0.51 -2020,7,5,15,2.22,2.09,0.0969,0.1,0.27 -2020,7,6,15,2.48,2.32,0.1792,0.18,0.4 -2020,7,7,15,3.38,3.27,0.4787,0.48,0.67 -2020,7,8,15,5.92,5.82,1.56,1.56,1.03 -2020,7,9,15,8.92,8.78,1.4304,1.47,0.92 -2020,7,10,15,10.98,10.92,2.0376,2.16,1.03 -2020,7,11,15,11.03,11.03,1.07,1.07,0.82 -2020,7,12,15,10.15,10.15,2.6,2.6,1.03 -2020,7,13,15,9.09,9.09,7.09,7.09,1.5 -2020,7,1,16,6.69,6.11,0.49,0.52,0.62 -2020,7,2,16,4.32,4.32,0.46,0.46,0.51 -2020,7,3,16,3.68,2.84,0.2096,0.22,0.28 -2020,7,4,16,2.07,1.78,0.1881,0.2,0.33 -2020,7,5,16,1.62,1.48,0.1169,0.12,0.29 -2020,7,6,16,1.97,1.91,0.697,0.7,1 -2020,7,7,16,2.59,2.47,0.2096,0.21,0.41 -2020,7,8,16,4.96,4.77,0.2769,0.28,0.45 -2020,7,9,16,7.54,7.3,0.6187,0.62,0.64 -2020,7,10,16,9.16,9.16,0.34,0.34,0.46 -2020,7,11,16,8.82,8.82,0.19,0.19,0.36 -2020,7,12,16,7.56,7.56,2.14,2.14,1.06 -2020,7,13,16,6.11,6.11,0.52,0.52,0.62 -2020,7,1,17,4.68,4.21,0.1905,0.2,0.34 -2020,7,2,17,3.04,3.04,0.2,0.2,0.29 -2020,7,3,17,2.72,2.03,0.1982,0.2,0.27 -2020,7,4,17,1.74,1.39,0.159,0.17,0.29 -2020,7,5,17,1.36,1.22,0.1089,0.11,0.25 -2020,7,6,17,1.66,1.5,0.1299,0.13,0.28 -2020,7,7,17,2.5,2.35,0.117,0.12,0.27 -2020,7,8,17,4.43,4.28,0.34,0.34,0.52 -2020,7,9,17,6.93,6.75,0.5099,0.51,0.63 -2020,7,10,17,8.05,8.05,0.59,0.59,0.61 -2020,7,11,17,7.55,7.55,0.22,0.22,0.39 -2020,7,12,17,5.61,5.61,0.25,0.25,0.43 -2020,7,13,17,4.21,4.21,0.2,0.2,0.34 -2020,7,1,18,3.47,3.33,0.3612,0.37,0.58 -2020,7,2,18,2.63,2.48,0.1381,0.14,0.28 -2020,7,3,18,2.09,1.72,0.1546,0.16,0.29 -2020,7,4,18,1.41,1.21,0.2676,0.29,0.6 -2020,7,5,18,1.1,1.02,0.1975,0.2,0.55 -2020,7,6,18,1.36,1.25,0.1356,0.14,0.39 -2020,7,7,18,2.18,2.05,0.23,0.23,0.47 -2020,7,8,18,4.22,3.99,0.34,0.34,0.49 -2020,7,9,18,6.11,5.94,0.4655,0.47,0.57 -2020,7,10,18,6.47,6.47,0.42,0.42,0.53 -2020,7,11,18,5.6,5.6,0.14,0.14,0.28 -2020,7,12,18,4.17,4.17,0.13,0.13,0.26 -2020,7,13,18,3.33,3.33,0.37,0.37,0.58 -2020,7,1,19,3.28,3.28,0.13,0.13,0.24 -2020,7,2,19,2.71,2.52,0.123,0.13,0.25 -2020,7,3,19,1.98,1.8,0.5476,0.58,0.96 -2020,7,4,19,1.71,1.43,0.1261,0.14,0.29 -2020,7,5,19,1.31,1.22,0.1054,0.11,0.29 -2020,7,6,19,1.59,1.49,0.2799,0.28,0.75 -2020,7,7,19,2.27,2.1,0.0972,0.1,0.29 -2020,7,8,19,3.89,3.89,0.35,0.35,0.57 -2020,7,9,19,5.8,5.8,0.18,0.18,0.35 -2020,7,10,19,6.23,6.23,0.41,0.41,0.52 -2020,7,11,19,5.33,5.33,0.2,0.2,0.31 -2020,7,12,19,3.86,3.86,0.17,0.17,0.28 -2020,7,13,19,3.28,3.28,0.13,0.13,0.24 -2020,7,1,20,3.74,3.74,0.29,0.29,0.54 -2020,7,2,20,3.23,3.23,1.02,1.02,1.05 -2020,7,3,20,2.66,2.46,0.3645,0.37,0.67 -2020,7,4,20,2.05,1.87,0.1094,0.12,0.33 -2020,7,5,20,1.77,1.67,0.12,0.12,0.34 -2020,7,6,20,2.18,2,0.1386,0.14,0.4 -2020,7,7,20,3.25,3.03,0.6495,0.66,0.91 -2020,7,8,20,5.24,5.09,0.21,0.21,0.42 -2020,7,9,20,6.7,6.7,0.89,0.89,0.83 -2020,7,10,20,6.73,6.73,0.57,0.57,0.62 -2020,7,11,20,5.18,5.18,0.27,0.27,0.39 -2020,7,12,20,4.06,4.06,0.14,0.14,0.28 -2020,7,13,20,3.74,3.74,0.29,0.29,0.54 -2020,7,1,21,3.7,3.7,0.13,0.13,0.31 -2020,7,2,21,4.29,4.22,1.2953,1.35,1.06 -2020,7,3,21,4.45,4.22,0.4298,0.46,0.65 -2020,7,4,21,4.45,4.22,0.2274,0.24,0.42 -2020,7,5,21,3.75,3.64,0.3238,0.33,0.54 -2020,7,6,21,3.58,3.45,0.1598,0.16,0.39 -2020,7,7,21,3.79,3.61,0.2,0.2,0.54 -2020,7,8,21,4.79,4.79,0.35,0.35,0.65 -2020,7,9,21,6.07,6.07,0.17,0.17,0.39 -2020,7,10,21,5.89,5.89,0.16,0.16,0.35 -2020,7,11,21,4.8,4.8,0.37,0.37,0.5 -2020,7,12,21,3.72,3.72,0.5,0.5,0.61 -2020,7,13,21,3.7,3.7,0.13,0.13,0.31 -2020,7,1,22,10.28,10.28,0.23,0.23,0.4 -2020,7,2,22,9.63,9.63,0.35,0.35,0.47 -2020,7,3,22,8.43,8.43,1.85,1.85,0.95 -2020,7,4,22,7.19,7.19,1.79,1.79,0.99 -2020,7,5,22,5.95,5.7,0.3787,0.38,0.51 -2020,7,6,22,5.95,5.52,0.2633,0.27,0.42 -2020,7,7,22,6.81,6.43,0.2499,0.25,0.45 -2020,7,8,22,8.21,7.79,0.1399,0.14,0.31 -2020,7,9,22,9.83,9.37,2.4153,2.5,1.07 -2020,7,10,22,9.75,9.44,2.5062,2.51,1.07 -2020,7,11,22,10.02,9.9,0.0792,0.08,0.28 -2020,7,12,22,9.87,9.87,0.16,0.16,0.36 -2020,7,13,22,10.28,10.28,0.23,0.23,0.4 -2020,7,1,23,9.82,9.82,1.48,1.48,0.85 -2020,7,2,23,9.15,9.15,1.09,1.09,0.79 -2020,7,3,23,8.39,8.39,1.34,1.34,0.86 -2020,7,4,23,7.53,7.53,2.3,2.3,1.07 -2020,7,5,23,6.58,6.41,0.7912,0.8,0.72 -2020,7,6,23,6.98,6.24,0.1964,0.2,0.33 -2020,7,7,23,7.88,7.32,0.29,0.29,0.48 -2020,7,8,23,9.71,9.46,7.4872,7.57,1.5 -2020,7,9,23,11.31,10.64,2.4346,2.52,1.02 -2020,7,10,23,11.83,11.11,0.5534,0.56,0.59 -2020,7,11,23,11.3,11,2.1739,2.22,1.05 -2020,7,12,23,10.87,10.56,0.3816,0.39,0.54 -2020,7,13,23,10.01,9.82,1.4676,1.48,0.85 -2020,7,1,24,9.94,9.94,0.61,0.61,0.61 -2020,7,2,24,9.98,9.98,1.67,1.67,0.86 -2020,7,3,24,9.46,9.46,0.64,0.64,0.57 -2020,7,4,24,8.79,8.79,3.06,3.06,1.07 -2020,7,5,24,7.25,7.17,1.4478,1.45,0.88 -2020,7,6,24,7.84,7.37,0.9154,0.95,0.71 -2020,7,7,24,9.38,8.79,0.9879,0.99,0.8 -2020,7,8,24,12.11,11.43,3.3777,3.38,1.06 -2020,7,9,24,13.79,12.74,3.7068,3.71,1.02 -2020,7,10,24,13.97,13.09,3.7338,3.92,1.05 -2020,7,11,24,12.9,12.07,3.3679,3.41,1.06 -2020,7,12,24,11.71,10.73,0.2293,0.23,0.36 -2020,7,13,24,10.77,9.94,0.5962,0.61,0.61 -2020,7,1,25,10.12,10.12,0.32,0.32,0.46 -2020,7,2,25,8.34,8.34,0.17,0.17,0.31 -2020,7,3,25,7.26,7.26,0.53,0.53,0.53 -2020,7,4,25,6,6,2.31,2.31,1.07 -2020,7,5,25,5.03,4.82,1.2168,1.26,0.91 -2020,7,6,25,5.49,5.03,0.9236,0.93,0.82 -2020,7,7,25,7.91,7.19,0.27,0.27,0.42 -2020,7,8,25,10.76,9.75,0.8573,0.87,0.67 -2020,7,9,25,13.33,12.08,3.2075,3.32,0.97 -2020,7,10,25,13.22,12.14,0.3763,0.38,0.44 -2020,7,11,25,13.79,12.88,0.3989,0.4,0.46 -2020,7,12,25,11.86,11.39,0.1939,0.2,0.37 -2020,7,13,25,10.58,10.12,0.3092,0.32,0.46 -2020,7,1,26,10.9,10.8,2.3646,2.43,0.97 -2020,7,2,26,8.81,8.81,2.76,2.76,1.04 -2020,7,3,26,6.26,6.22,1.4523,1.48,0.93 -2020,7,4,26,4.51,4.42,1.2231,1.31,0.94 -2020,7,5,26,3.41,3.22,0.6199,0.64,0.83 -2020,7,6,26,3.85,3.6,0.3375,0.34,0.59 -2020,7,7,26,5.71,5.5,1.7399,1.74,1.05 -2020,7,8,26,8.6,8.07,0.3308,0.34,0.5 -2020,7,9,26,11.07,10.28,0.49,0.49,0.54 -2020,7,10,26,12.1,11.64,0.3352,0.34,0.47 -2020,7,11,26,12.86,12.65,0.2095,0.21,0.39 -2020,7,12,26,12.09,12.09,0.3,0.3,0.43 -2020,7,13,26,10.8,10.8,2.43,2.43,0.97 -2020,7,1,27,10.55,10.2,2.9468,2.98,1.02 -2020,7,2,27,7.73,7.73,1.14,1.14,0.77 -2020,7,3,27,5.36,5.09,0.4625,0.48,0.53 -2020,7,4,27,3.71,3.54,0.7766,0.83,0.75 -2020,7,5,27,2.72,2.47,0.318,0.32,0.56 -2020,7,6,27,3.44,3.23,0.3065,0.31,0.62 -2020,7,7,27,5.42,5.22,0.77,0.77,0.73 -2020,7,8,27,8.44,7.96,0.6577,0.66,0.65 -2020,7,9,27,11.66,10.99,2.3152,2.32,0.93 -2020,7,10,27,12.41,12.18,3.2204,3.28,1.02 -2020,7,11,27,13.11,12.85,0.3446,0.35,0.47 -2020,7,12,27,11.6,11.6,2.86,2.86,1.02 -2020,7,13,27,10.27,10.2,2.8578,2.98,1.02 -2020,7,1,28,8.57,7.98,1.16,1.16,0.79 -2020,7,2,28,5.99,5.99,0.8,0.8,0.72 -2020,7,3,28,4.14,3.7,0.3495,0.36,0.41 -2020,7,4,28,2.48,2.2,0.2768,0.29,0.4 -2020,7,5,28,1.96,1.67,0.0998,0.1,0.22 -2020,7,6,28,2.7,2.57,0.0987,0.1,0.27 -2020,7,7,28,4.24,4.24,0.15,0.15,0.29 -2020,7,8,28,7.3,6.81,0.3358,0.34,0.49 -2020,7,9,28,10.26,9.35,0.9991,1,0.71 -2020,7,10,28,10.59,10.31,0.1549,0.16,0.32 -2020,7,11,28,10.77,10.57,0.3792,0.38,0.53 -2020,7,12,28,9.36,9.36,0.38,0.38,0.52 -2020,7,13,28,8.13,7.98,1.1381,1.16,0.79 -2020,7,1,29,5.82,5.48,0.47,0.47,0.55 -2020,7,2,29,3.89,3.89,0.22,0.22,0.36 -2020,7,3,29,2.79,2.52,0.1798,0.19,0.3 -2020,7,4,29,1.63,1.48,0.1345,0.14,0.29 -2020,7,5,29,1.56,1.28,0.1394,0.14,0.27 -2020,7,6,29,2.17,2.07,0.0886,0.09,0.25 -2020,7,7,29,3.65,3.65,0.34,0.34,0.52 -2020,7,8,29,6.72,6.2,1.288,1.29,0.84 -2020,7,9,29,9.24,8.55,2.7731,2.8,1.01 -2020,7,10,29,9.26,9.23,2.8677,2.88,1.07 -2020,7,11,29,8.33,8.26,2.273,2.3,1.06 -2020,7,12,29,6.66,6.66,1.86,1.86,1.06 -2020,7,13,29,5.66,5.48,0.4593,0.47,0.55 -2020,7,1,30,4.52,4.24,0.1557,0.16,0.29 -2020,7,2,30,3.27,3.27,0.18,0.18,0.29 -2020,7,3,30,2.59,2.29,0.1588,0.17,0.26 -2020,7,4,30,1.5,1.32,0.1347,0.14,0.28 -2020,7,5,30,1.4,1.19,0.1162,0.12,0.28 -2020,7,6,30,2.05,1.82,0.1394,0.14,0.29 -2020,7,7,30,3.45,3.33,0.42,0.42,0.56 -2020,7,8,30,6.51,5.89,0.71,0.71,0.68 -2020,7,9,30,8.23,7.82,1.4473,1.47,0.83 -2020,7,10,30,8.1,8.1,0.52,0.52,0.53 -2020,7,11,30,6.81,6.81,0.63,0.63,0.63 -2020,7,12,30,5.28,5.28,0.77,0.77,0.81 -2020,7,13,30,4.24,4.24,0.16,0.16,0.29 -2020,7,1,31,3.59,3.59,0.14,0.14,0.28 -2020,7,2,31,3.17,3.17,0.12,0.12,0.26 -2020,7,3,31,2.48,2.35,0.104,0.11,0.27 -2020,7,4,31,1.67,1.6,0.2646,0.29,0.75 -2020,7,5,31,1.55,1.44,0.3989,0.4,0.9 -2020,7,6,31,2.06,1.88,0.0965,0.1,0.29 -2020,7,7,31,3.18,3.11,0.7085,0.71,0.98 -2020,7,8,31,5.73,5.11,0.1794,0.18,0.35 -2020,7,9,31,6.49,6.42,1.3363,1.35,0.87 -2020,7,10,31,6.38,6.38,0.35,0.35,0.49 -2020,7,11,31,4.97,4.97,0.51,0.51,0.64 -2020,7,12,31,3.95,3.95,0.41,0.41,0.64 -2020,7,13,31,3.59,3.59,0.14,0.14,0.28 -2020,7,1,32,3.02,2.99,0.427,0.43,0.69 -2020,7,2,32,2.97,2.97,0.21,0.21,0.46 -2020,7,3,32,2.77,2.71,0.3245,0.33,0.66 -2020,7,4,32,2.46,2.33,0.1444,0.16,0.39 -2020,7,5,32,2.26,2.12,0.1888,0.19,0.42 -2020,7,6,32,2.38,2.24,0.3623,0.37,0.77 -2020,7,7,32,2.83,2.7,0.1492,0.15,0.46 -2020,7,8,32,4.19,3.92,0.2292,0.23,0.47 -2020,7,9,32,4.64,4.64,0.46,0.46,0.64 -2020,7,10,32,4.51,4.51,0.38,0.38,0.55 -2020,7,11,32,3.39,3.39,0.4,0.4,0.53 -2020,7,12,32,2.97,2.97,0.24,0.24,0.46 -2020,7,13,32,2.99,2.99,0.43,0.43,0.69 -2020,7,1,33,10.05,10.05,0.34,0.34,0.46 -2020,7,2,33,8.08,8.08,0.34,0.34,0.47 -2020,7,3,33,6.29,6.29,0.54,0.54,0.55 -2020,7,4,33,4.69,4.69,1.62,1.62,0.98 -2020,7,5,33,3.82,3.59,0.7507,0.76,0.83 -2020,7,6,33,4.58,4.31,0.5621,0.57,0.68 -2020,7,7,33,7.14,6.79,0.5896,0.59,0.62 -2020,7,8,33,11.38,10.45,2.1892,2.25,0.92 -2020,7,9,33,14.4,13.14,2.9985,3.12,0.95 -2020,7,10,33,15.36,14.15,0.19,0.19,0.28 -2020,7,11,33,14.49,13.27,0.13,0.13,0.24 -2020,7,12,33,12.52,11.99,0.1783,0.18,0.31 -2020,7,13,33,10.66,10.05,0.34,0.34,0.46 -2020,7,1,34,8.74,8.46,0.2867,0.29,0.41 -2020,7,2,34,6.59,6.59,2.06,2.06,1.07 -2020,7,3,34,4.24,4.24,0.43,0.43,0.48 -2020,7,4,34,2.97,2.86,0.6307,0.68,0.75 -2020,7,5,34,2.39,2.03,0.1353,0.14,0.27 -2020,7,6,34,3.16,3.02,0.2292,0.23,0.42 -2020,7,7,34,5.54,5.54,0.58,0.58,0.58 -2020,7,8,34,9.54,8.83,1.9686,2.02,0.91 -2020,7,9,34,12.49,11.21,0.386,0.4,0.46 -2020,7,10,34,11.92,11.49,0.41,0.41,0.47 -2020,7,11,34,11.6,11.11,0.1089,0.11,0.26 -2020,7,12,34,9.74,9.74,0.98,0.98,0.72 -2020,7,13,34,8.95,8.46,0.288,0.29,0.41 -2020,7,1,35,6.32,6.32,1.73,1.73,1.03 -2020,7,2,35,4.5,4.5,0.61,0.61,0.71 -2020,7,3,35,3,3,0.48,0.48,0.57 -2020,7,4,35,1.89,1.74,0.1891,0.21,0.36 -2020,7,5,35,1.62,1.41,0.1286,0.13,0.25 -2020,7,6,35,2.37,2.22,0.1388,0.14,0.27 -2020,7,7,35,4.1,4.1,0.23,0.23,0.38 -2020,7,8,35,7.66,7.07,0.2876,0.3,0.41 -2020,7,9,35,9.93,9.51,0.8386,0.84,0.69 -2020,7,10,35,10.3,10,0.1365,0.14,0.29 -2020,7,11,35,9.4,9.15,0.13,0.13,0.29 -2020,7,12,35,7.68,7.68,1.15,1.15,0.84 -2020,7,13,35,6.32,6.32,1.73,1.73,1.03 -2020,7,1,36,8.22,8.22,0.24,0.24,0.37 -2020,7,2,36,6.14,6.14,0.18,0.18,0.33 -2020,7,3,36,4.41,4.41,1.62,1.62,0.97 -2020,7,4,36,2.86,2.86,0.53,0.53,0.64 -2020,7,5,36,2.32,2.16,0.1843,0.19,0.37 -2020,7,6,36,3.13,3.13,0.2,0.2,0.32 -2020,7,7,36,5.27,5.27,0.38,0.38,0.48 -2020,7,8,36,9.81,9.25,1.5881,1.66,0.81 -2020,7,9,36,12.1,11.21,0.2125,0.22,0.35 -2020,7,10,36,12.29,11.89,0.1297,0.13,0.28 -2020,7,11,36,11.49,10.88,0.1191,0.12,0.26 -2020,7,12,36,9.42,9.42,0.19,0.19,0.3 -2020,7,13,36,8.92,8.22,0.2383,0.24,0.37 -2020,7,1,37,4.2,4.23,1.14,1.15,1.03 -2020,7,2,37,3.24,3.15,0.22,0.22,0.41 -2020,7,3,37,2.1,2.1,0.15,0.15,0.29 -2020,7,4,37,1.38,1.33,0.1557,0.16,0.4 -2020,7,5,37,1.25,1.22,0.3861,0.39,0.93 -2020,7,6,37,1.82,1.71,0.156,0.16,0.34 -2020,7,7,37,3.16,3.16,0.34,0.34,0.53 -2020,7,8,37,5.6,5.42,1.8041,1.82,1.07 -2020,7,9,37,7.1,6.93,0.8167,0.83,0.68 -2020,7,10,37,7.35,7.35,0.57,0.57,0.6 -2020,7,11,37,6.29,6.29,0.74,0.74,0.75 -2020,7,12,37,5.16,5.16,0.35,0.35,0.63 -2020,7,13,37,4.23,4.23,1.15,1.15,1.03 -2020,7,1,38,9.88,9.59,0.4069,0.41,0.48 -2020,7,2,38,9.69,9.69,1.7,1.7,0.82 -2020,7,3,38,10.6,10.6,0.74,0.74,0.66 -2020,7,4,38,10.9,10.9,0.36,0.36,0.56 -2020,7,5,38,10.54,10.54,0.13,0.13,0.29 -2020,7,6,38,11.46,11.02,0.2248,0.23,0.37 -2020,7,7,38,11.66,11.11,0.2479,0.25,0.4 -2020,7,8,38,13.16,13.03,3.5218,3.54,1.04 -2020,7,9,38,14.61,13.39,0.9153,0.92,0.65 -2020,7,10,38,14.83,13.89,3.6701,3.73,1.05 -2020,7,11,38,13.12,12.43,0.53,0.53,0.62 -2020,7,12,38,12.04,11.21,0.3654,0.37,0.48 -2020,7,13,38,10.66,9.59,0.4013,0.41,0.48 -2020,7,1,39,10.09,10.09,0.66,0.66,0.56 -2020,7,2,39,11.11,11.11,1.38,1.38,0.74 -2020,7,3,39,12.03,12.03,1.7,1.7,0.81 -2020,7,4,39,12.21,12.21,3.02,3.02,1.06 -2020,7,5,39,11.37,11.37,0.79,0.79,0.64 -2020,7,6,39,11.32,10.86,0.1487,0.15,0.28 -2020,7,7,39,12.9,12.09,0.1399,0.14,0.29 -2020,7,8,39,14.97,14.26,0.6132,0.62,0.53 -2020,7,9,39,14.83,13.59,0.6367,0.65,0.55 -2020,7,10,39,14.57,13.7,1.533,1.55,0.83 -2020,7,11,39,12.42,11.68,2.54,2.54,1 -2020,7,12,39,11.59,10.69,0.826,0.84,0.63 -2020,7,13,39,11.28,10.09,0.6537,0.66,0.56 -2020,7,1,40,10.66,10.66,0.25,0.25,0.37 -2020,7,2,40,11.65,11.65,0.37,0.37,0.4 -2020,7,3,40,12.23,11.99,0.6224,0.64,0.51 -2020,7,4,40,11.26,11.31,2.9036,2.97,0.99 -2020,7,5,40,9.75,9.75,3.07,3.07,1.06 -2020,7,6,40,9.51,9.51,0.24,0.24,0.35 -2020,7,7,40,11.27,10.66,0.13,0.13,0.26 -2020,7,8,40,14.81,13.34,0.1785,0.18,0.29 -2020,7,9,40,14.4,13.72,3.4593,3.52,1.01 -2020,7,10,40,14.07,13.43,0.1495,0.15,0.3 -2020,7,11,40,11.79,11.06,0.6096,0.61,0.6 -2020,7,12,40,10.92,10.61,1.07,1.07,0.74 -2020,7,13,40,11.43,10.66,0.2472,0.25,0.37 -2020,7,1,41,10.09,10.09,0.15,0.15,0.28 -2020,7,2,41,9.85,9.85,0.67,0.67,0.56 -2020,7,3,41,8.8,8.8,0.25,0.25,0.33 -2020,7,4,41,7.77,7.77,1.21,1.21,0.74 -2020,7,5,41,6.53,6.46,1.6617,1.72,1 -2020,7,6,41,6.99,6.71,0.46,0.46,0.55 -2020,7,7,41,9.14,8.58,0.27,0.27,0.43 -2020,7,8,41,12.48,11.37,1.41,1.41,0.77 -2020,7,9,41,14.31,13.09,3.7511,3.81,0.98 -2020,7,10,41,13.88,12.5,0.2896,0.29,0.34 -2020,7,11,41,13.27,11.69,0.19,0.19,0.29 -2020,7,12,41,12.24,11.11,0.17,0.17,0.26 -2020,7,13,41,11.16,10.09,0.1498,0.15,0.28 -2020,7,1,42,11.42,11.42,0.19,0.19,0.32 -2020,7,2,42,10.27,10.27,0.4,0.4,0.42 -2020,7,3,42,8.17,8.17,1.16,1.16,0.7 -2020,7,4,42,6.04,6.04,0.45,0.45,0.48 -2020,7,5,42,5.24,5.16,0.8792,0.91,0.81 -2020,7,6,42,5.83,5.83,0.13,0.13,0.27 -2020,7,7,42,8.73,8.32,0.1499,0.15,0.28 -2020,7,8,42,13.09,12.14,0.5816,0.59,0.57 -2020,7,9,42,15.52,14.57,4.5882,4.59,1.05 -2020,7,10,42,16.21,15.18,0.41,0.41,0.41 -2020,7,11,42,14.59,13.27,0.64,0.64,0.54 -2020,7,12,42,13.23,12.32,0.1888,0.19,0.27 -2020,7,13,42,12.55,11.42,0.189,0.19,0.32 -2020,7,1,43,10.92,10.92,0.17,0.17,0.29 -2020,7,2,43,9.38,9.42,0.2154,0.22,0.32 -2020,7,3,43,7.57,7.67,1.4059,1.41,0.78 -2020,7,4,43,5.38,5.4,0.3485,0.35,0.42 -2020,7,5,43,4.65,4.65,0.21,0.21,0.33 -2020,7,6,43,5.83,5.83,0.16,0.16,0.27 -2020,7,7,43,8.36,8.36,0.31,0.31,0.42 -2020,7,8,43,12.32,12.08,0.588,0.59,0.59 -2020,7,9,43,14.84,14.45,0.2987,0.3,0.42 -2020,7,10,43,15.45,15.14,0.65,0.65,0.56 -2020,7,11,43,13.7,13.5,3.9273,3.93,1.05 -2020,7,12,43,11.43,11.43,0.78,0.78,0.57 -2020,7,13,43,11.49,10.92,0.1634,0.17,0.29 -2020,7,1,44,8.13,7.91,0.1681,0.17,0.3 -2020,7,2,44,5.65,5.73,0.1578,0.16,0.28 -2020,7,3,44,4.37,4.38,1.0348,1.07,0.75 -2020,7,4,44,2.86,2.79,0.3398,0.35,0.44 -2020,7,5,44,2.45,2.45,0.35,0.35,0.43 -2020,7,6,44,3.65,3.65,0.18,0.18,0.26 -2020,7,7,44,5.73,5.73,0.16,0.16,0.28 -2020,7,8,44,9.01,8.8,0.3697,0.37,0.48 -2020,7,9,44,11.03,10.92,1.3452,1.35,0.85 -2020,7,10,44,12.2,11.87,0.27,0.27,0.38 -2020,7,11,44,11.4,11.07,0.3,0.3,0.41 -2020,7,12,44,9.19,9.19,0.46,0.46,0.48 -2020,7,13,44,8.27,7.91,0.17,0.17,0.3 -2020,7,1,45,5.83,5.83,0.2158,0.22,0.37 -2020,7,2,45,3.64,3.87,0.371,0.38,0.53 -2020,7,3,45,2.9,2.71,0.3565,0.36,0.4 -2020,7,4,45,1.88,1.75,0.1752,0.18,0.28 -2020,7,5,45,1.61,1.61,0.15,0.15,0.22 -2020,7,6,45,2.29,2.29,0.18,0.18,0.27 -2020,7,7,45,3.6,3.6,0.28,0.28,0.4 -2020,7,8,45,6.16,6.16,1.92,1.92,1.03 -2020,7,9,45,7.83,7.83,0.88,0.88,0.79 -2020,7,10,45,9.04,9.04,1.68,1.68,0.95 -2020,7,11,45,8.8,8.8,0.72,0.72,0.66 -2020,7,12,45,7.2,7.2,0.51,0.51,0.57 -2020,7,13,45,5.83,5.83,0.22,0.22,0.37 -2020,7,1,46,4.49,4.69,0.4762,0.48,0.68 -2020,7,2,46,3.03,3.24,0.9468,0.99,1.06 -2020,7,3,46,2.36,2.36,0.18,0.18,0.35 -2020,7,4,46,1.75,1.75,0.11,0.11,0.28 -2020,7,5,46,1.46,1.46,0.1,0.1,0.25 -2020,7,6,46,2.16,2.16,0.12,0.12,0.28 -2020,7,7,46,3.52,3.52,0.46,0.46,0.63 -2020,7,8,46,5.44,5.44,0.62,0.62,0.67 -2020,7,9,46,6.26,6.26,0.21,0.21,0.35 -2020,7,10,46,6.86,6.86,0.32,0.32,0.49 -2020,7,11,46,6.75,6.75,0.28,0.28,0.52 -2020,7,12,46,5.96,5.96,0.98,0.98,0.92 -2020,7,13,46,4.69,4.69,0.48,0.48,0.68 -2020,7,1,47,4.47,4.66,0.326,0.33,0.53 -2020,7,2,47,2.5,2.73,0.2979,0.31,0.51 -2020,7,3,47,2.14,2.07,0.1959,0.2,0.34 -2020,7,4,47,1.68,1.66,0.1217,0.13,0.36 -2020,7,5,47,1.48,1.48,0.16,0.16,0.38 -2020,7,6,47,2.02,2.02,0.14,0.14,0.35 -2020,7,7,47,3,2.88,0.2564,0.26,0.42 -2020,7,8,47,4.24,4.24,0.17,0.17,0.29 -2020,7,9,47,4.74,4.74,0.16,0.16,0.29 -2020,7,10,47,6.03,6.03,0.31,0.31,0.51 -2020,7,11,47,6.73,6.73,0.2,0.2,0.43 -2020,7,12,47,6.11,6.11,1.32,1.32,0.98 -2020,7,13,47,4.66,4.66,0.33,0.33,0.53 -2020,7,1,48,4.84,5.05,0.37,0.37,0.59 -2020,7,2,48,4.32,4.6,0.3565,0.36,0.52 -2020,7,3,48,4.16,4.16,0.89,0.89,0.86 -2020,7,4,48,3.69,3.69,0.25,0.25,0.45 -2020,7,5,48,3.35,3.35,0.15,0.15,0.29 -2020,7,6,48,3.8,3.68,0.1054,0.11,0.28 -2020,7,7,48,3.87,3.87,0.61,0.61,0.86 -2020,7,8,48,4.03,3.92,0.201,0.21,0.41 -2020,7,9,48,3.55,3.55,0.34,0.34,0.49 -2020,7,10,48,3.71,3.71,0.23,0.23,0.4 -2020,7,11,48,4.16,4.16,0.28,0.28,0.53 -2020,7,12,48,5.11,5.11,0.92,0.92,0.93 -2020,7,13,48,5.05,5.05,0.37,0.37,0.59 -2020,7,1,49,2.77,2.81,0.4082,0.41,0.68 -2020,7,2,49,2.6,2.56,0.2264,0.23,0.5 -2020,7,3,49,2.09,2.09,0.29,0.29,0.54 -2020,7,4,49,1.67,1.67,0.16,0.16,0.39 -2020,7,5,49,1.57,1.55,0.3535,0.37,0.88 -2020,7,6,49,1.77,1.71,0.0959,0.1,0.37 -2020,7,7,49,2.36,2.28,0.0983,0.1,0.29 -2020,7,8,49,3.31,3.16,0.1647,0.17,0.37 -2020,7,9,49,3.57,3.57,0.37,0.37,0.5 -2020,7,10,49,3.48,3.48,0.34,0.34,0.48 -2020,7,11,49,2.93,2.93,0.87,0.87,0.89 -2020,7,12,49,2.74,2.74,0.13,0.13,0.29 -2020,7,13,49,2.81,2.81,0.41,0.41,0.68 -2020,7,1,50,11.3,11,1.0961,1.11,0.72 -2020,7,2,50,11.9,11.9,0.86,0.86,0.64 -2020,7,3,50,13.86,13.86,1.13,1.13,0.7 -2020,7,4,50,14.69,14.69,1.02,1.02,0.7 -2020,7,5,50,14.46,14.46,0.24,0.24,0.34 -2020,7,6,50,14.08,14.08,0.19,0.19,0.31 -2020,7,7,50,14.52,14.16,0.3592,0.37,0.44 -2020,7,8,50,14.92,14.92,1.03,1.03,0.64 -2020,7,9,50,14.96,14.09,0.47,0.47,0.48 -2020,7,10,50,15.73,14.78,0.3872,0.39,0.45 -2020,7,11,50,13.01,12.37,0.8307,0.85,0.66 -2020,7,12,50,12.37,12.04,0.6677,0.67,0.59 -2020,7,13,50,11.48,11,1.1027,1.11,0.72 -2020,7,1,51,10.38,10.38,0.37,0.37,0.4 -2020,7,2,51,12.57,12.57,0.5,0.5,0.48 -2020,7,3,51,14.05,14.41,0.8939,0.94,0.58 -2020,7,4,51,14.33,14.33,0.76,0.76,0.59 -2020,7,5,51,14.22,14.22,2.06,2.06,0.84 -2020,7,6,51,12.61,12.61,0.27,0.27,0.38 -2020,7,7,51,14.27,14.27,0.17,0.17,0.3 -2020,7,8,51,15.29,14.83,0.2593,0.27,0.36 -2020,7,9,51,14.57,13.95,0.2199,0.22,0.34 -2020,7,10,51,13.79,13.49,0.176,0.18,0.33 -2020,7,11,51,11.29,10.94,0.4423,0.45,0.52 -2020,7,12,51,10.29,10.29,2.47,2.47,0.92 -2020,7,13,51,10.38,10.38,0.37,0.37,0.4 -2020,7,1,52,11.47,11.47,0.32,0.32,0.39 -2020,7,2,52,12.95,12.99,1.0591,1.08,0.64 -2020,7,3,52,11.61,12.36,3.2851,3.42,0.92 -2020,7,4,52,10.23,10.63,0.7626,0.77,0.58 -2020,7,5,52,9.37,9.37,2.57,2.57,1.01 -2020,7,6,52,9.48,9.48,0.18,0.18,0.29 -2020,7,7,52,11.58,11.58,0.13,0.13,0.24 -2020,7,8,52,14.42,13.87,0.1278,0.13,0.25 -2020,7,9,52,14.44,14.13,0.6429,0.65,0.59 -2020,7,10,52,13.89,13.73,0.7779,0.78,0.65 -2020,7,11,52,11.38,11.25,1.5541,1.57,0.9 -2020,7,12,52,11,11,1.87,1.87,0.88 -2020,7,13,52,11.91,11.47,0.3151,0.32,0.39 -2020,7,1,53,10.13,10.13,0.35,0.35,0.44 -2020,7,2,53,8.59,9.05,0.2697,0.27,0.38 -2020,7,3,53,6.76,7.28,0.7282,0.75,0.57 -2020,7,4,53,4.87,5.15,0.3882,0.41,0.42 -2020,7,5,53,4.65,4.65,0.23,0.23,0.31 -2020,7,6,53,5.75,5.75,0.28,0.28,0.37 -2020,7,7,53,7.45,7.45,0.18,0.18,0.29 -2020,7,8,53,11.13,11.13,3.56,3.56,1.04 -2020,7,9,53,12.24,12.24,0.18,0.18,0.3 -2020,7,10,53,11.86,11.86,0.17,0.17,0.27 -2020,7,11,53,10.54,10.54,0.21,0.21,0.29 -2020,7,12,53,9.28,9.28,2.25,2.25,0.9 -2020,7,13,53,10.13,10.13,0.35,0.35,0.44 -2020,7,1,54,7.36,7.36,0.2,0.2,0.3 -2020,7,2,54,6.32,6.62,0.3071,0.32,0.39 -2020,7,3,54,4.41,4.63,0.2479,0.25,0.29 -2020,7,4,54,3.25,3.14,0.3689,0.38,0.36 -2020,7,5,54,3.16,3.16,0.22,0.22,0.28 -2020,7,6,54,4.2,4.05,0.5794,0.58,0.58 -2020,7,7,54,4.79,4.79,0.21,0.21,0.3 -2020,7,8,54,7.26,7.26,0.77,0.77,0.66 -2020,7,9,54,9.13,9.13,0.57,0.57,0.51 -2020,7,10,54,9.07,9.07,0.6,0.6,0.54 -2020,7,11,54,8.45,8.45,3.3,3.3,1.05 -2020,7,12,54,7.3,7.3,3.87,3.87,1.07 -2020,7,13,54,7.36,7.36,0.2,0.2,0.3 -2020,7,1,55,6.6,6.38,0.1733,0.18,0.28 -2020,7,2,55,4.43,4.54,0.1564,0.16,0.25 -2020,7,3,55,2.96,3,0.242,0.25,0.28 -2020,7,4,55,2.04,1.89,0.2061,0.22,0.26 -2020,7,5,55,1.88,1.88,0.21,0.21,0.29 -2020,7,6,55,2.58,2.37,0.2581,0.26,0.34 -2020,7,7,55,2.89,2.89,0.23,0.23,0.29 -2020,7,8,55,4.87,4.87,0.23,0.23,0.35 -2020,7,9,55,6.75,6.75,0.97,0.97,0.71 -2020,7,10,55,7.93,7.93,2.55,2.55,1.04 -2020,7,11,55,8.02,8.02,1.09,1.09,0.79 -2020,7,12,55,7.18,7.18,1.94,1.94,0.91 -2020,7,13,55,6.38,6.38,0.18,0.18,0.28 -2020,7,1,56,13.04,11.58,0.2085,0.21,0.32 -2020,7,2,56,11.36,10.8,0.37,0.37,0.38 -2020,7,3,56,10.9,10.9,0.32,0.32,0.37 -2020,7,4,56,12.37,12.37,0.94,0.94,0.66 -2020,7,5,56,12.98,13.13,1.4531,1.49,0.78 -2020,7,6,56,13.71,13.77,1.3792,1.38,0.8 -2020,7,7,56,12.6,12.6,0.74,0.74,0.61 -2020,7,8,56,11.57,11.57,1.82,1.82,0.8 -2020,7,9,56,12.06,11.17,0.9268,0.93,0.65 -2020,7,10,56,13.16,11.9,0.3889,0.39,0.42 -2020,7,11,56,13.41,12.63,0.19,0.19,0.3 -2020,7,12,56,13.08,13.08,0.34,0.34,0.45 -2020,7,13,56,11.99,11.58,0.2096,0.21,0.32 -2020,7,1,57,11.88,10.8,0.2957,0.3,0.4 -2020,7,2,57,11.38,11.38,0.28,0.28,0.36 -2020,7,3,57,13.7,13.43,0.1787,0.18,0.3 -2020,7,4,57,14.91,14.91,0.79,0.79,0.62 -2020,7,5,57,14.72,14.85,0.5531,0.56,0.55 -2020,7,6,57,16.07,16,1.1739,1.18,0.71 -2020,7,7,57,15.27,15.27,0.47,0.47,0.48 -2020,7,8,57,14.36,14.36,0.32,0.32,0.37 -2020,7,9,57,14.05,13.23,0.318,0.32,0.37 -2020,7,10,57,13.72,13.13,0.24,0.24,0.34 -2020,7,11,57,13.45,13.08,0.37,0.37,0.44 -2020,7,12,57,12.47,12.47,0.76,0.76,0.61 -2020,7,13,57,10.8,10.8,0.3,0.3,0.4 -2020,7,1,58,11.98,11.1,0.1958,0.2,0.29 -2020,7,2,58,12.34,12.34,0.47,0.47,0.48 -2020,7,3,58,13.22,13.76,0.3876,0.39,0.44 -2020,7,4,58,15.92,16.16,0.2868,0.29,0.37 -2020,7,5,58,16.49,16.47,1.0184,1.02,0.62 -2020,7,6,58,16.05,16.05,2.3,2.3,0.86 -2020,7,7,58,15.74,15.74,0.32,0.32,0.39 -2020,7,8,58,14.8,14.8,0.17,0.17,0.27 -2020,7,9,58,13.77,13.77,0.19,0.19,0.27 -2020,7,10,58,14.39,14.39,0.16,0.16,0.28 -2020,7,11,58,12.86,12.86,0.27,0.27,0.41 -2020,7,12,58,12.63,12.63,0.95,0.95,0.65 -2020,7,13,58,11.1,11.1,0.2,0.2,0.29 -2020,7,1,59,11.96,11.05,0.1866,0.19,0.24 -2020,7,2,59,13.61,13.13,0.2167,0.22,0.29 -2020,7,3,59,14.25,15.23,0.6361,0.64,0.46 -2020,7,4,59,14.1,14.88,1.3527,1.42,0.7 -2020,7,5,59,14.64,14.67,4.894,4.91,1.06 -2020,7,6,59,13.81,13.81,0.7,0.7,0.56 -2020,7,7,59,14.71,14.71,0.18,0.18,0.3 -2020,7,8,59,15.02,15.02,0.23,0.23,0.32 -2020,7,9,59,14.36,13.89,0.572,0.58,0.49 -2020,7,10,59,12.7,12.7,0.18,0.18,0.29 -2020,7,11,59,10.78,10.78,0.17,0.17,0.31 -2020,7,12,59,9.87,9.87,0.28,0.28,0.36 -2020,7,13,59,11.05,11.05,0.19,0.19,0.24 -2020,7,1,60,11.06,11.06,0.22,0.22,0.28 -2020,7,2,60,12.51,12.43,0.4026,0.41,0.4 -2020,7,3,60,12.66,14,2.0348,2.05,0.74 -2020,7,4,60,11.73,12.75,1.0497,1.16,0.65 -2020,7,5,60,12,12.11,4.6802,4.71,1.07 -2020,7,6,60,11.7,11.7,2.75,2.75,0.92 -2020,7,7,60,10.98,10.98,0.34,0.34,0.45 -2020,7,8,60,12.33,12.33,4.61,4.61,1.06 -2020,7,9,60,12.98,12.98,3.09,3.09,0.88 -2020,7,10,60,12.38,12.38,0.66,0.66,0.52 -2020,7,11,60,10.72,10.72,0.66,0.66,0.6 -2020,7,12,60,9.05,9.05,0.3,0.3,0.38 -2020,7,13,60,11.06,11.06,0.22,0.22,0.28 -2020,7,1,61,8.84,8.84,0.31,0.31,0.37 -2020,7,2,61,9.2,9.73,3.1862,3.27,0.96 -2020,7,3,61,7.91,8.69,1.0762,1.08,0.6 -2020,7,4,61,6.56,6.86,0.4983,0.52,0.48 -2020,7,5,61,6.79,6.79,0.21,0.21,0.28 -2020,7,6,61,7.78,7.76,2.1367,2.14,0.94 -2020,7,7,61,8.65,8.65,0.18,0.18,0.3 -2020,7,8,61,10.61,10.61,0.6,0.6,0.54 -2020,7,9,61,11.28,11.28,0.56,0.56,0.48 -2020,7,10,61,10.66,10.66,0.21,0.21,0.28 -2020,7,11,61,8.55,8.55,0.28,0.28,0.38 -2020,7,12,61,7.32,7.32,0.98,0.98,0.66 -2020,7,13,61,8.84,8.84,0.31,0.31,0.37 -2020,7,1,62,9.96,9.96,0.23,0.23,0.27 -2020,7,2,62,10.48,10.01,0.3097,0.32,0.32 -2020,7,3,62,8.48,8.33,0.4373,0.44,0.35 -2020,7,4,62,7.09,7.24,0.4623,0.48,0.44 -2020,7,5,62,6.07,5.83,0.1761,0.18,0.23 -2020,7,6,62,6.84,6.47,0.3906,0.4,0.4 -2020,7,7,62,6.61,6.61,0.24,0.24,0.32 -2020,7,8,62,8.6,8.6,2.46,2.46,0.88 -2020,7,9,62,9.54,9.54,1.08,1.08,0.58 -2020,7,10,62,9.86,9.86,0.58,0.58,0.45 -2020,7,11,62,8.49,8.49,0.22,0.22,0.28 -2020,7,12,62,8.53,8.53,0.71,0.71,0.5 -2020,7,13,62,9.96,9.96,0.23,0.23,0.27 -2020,7,1,63,7.51,7.51,0.13,0.13,0.23 -2020,7,2,63,6.86,6.86,0.17,0.17,0.26 -2020,7,3,63,5.05,4.78,0.3988,0.4,0.39 -2020,7,4,63,3.49,3.36,0.2898,0.3,0.34 -2020,7,5,63,3.5,3.18,0.22,0.22,0.24 -2020,7,6,63,3.62,3.62,0.51,0.51,0.47 -2020,7,7,63,3.91,3.91,0.31,0.31,0.34 -2020,7,8,63,5.52,5.52,0.61,0.61,0.5 -2020,7,9,63,6.67,6.67,0.91,0.91,0.55 -2020,7,10,63,7.38,7.38,0.26,0.26,0.28 -2020,7,11,63,6.49,6.49,0.24,0.24,0.29 -2020,7,12,63,7.23,7.23,0.27,0.27,0.28 -2020,7,13,63,7.51,7.51,0.13,0.13,0.23 -2020,7,1,64,7.3,7.3,0.21,0.21,0.29 -2020,7,2,64,6.8,6.73,0.7997,0.81,0.59 -2020,7,3,64,5.16,5.12,0.2598,0.27,0.28 -2020,7,4,64,3.75,3.52,0.27,0.27,0.29 -2020,7,5,64,3.65,3.23,0.1923,0.2,0.23 -2020,7,6,64,4.41,4.16,0.3089,0.31,0.36 -2020,7,7,64,4.65,4.65,0.26,0.26,0.33 -2020,7,8,64,6.36,6.36,0.2,0.2,0.29 -2020,7,9,64,7.59,7.59,0.21,0.21,0.29 -2020,7,10,64,7.88,7.88,0.2,0.2,0.29 -2020,7,11,64,6.95,6.95,0.25,0.25,0.36 -2020,7,12,64,6.55,6.55,0.63,0.63,0.54 -2020,7,13,64,7.3,7.3,0.21,0.21,0.29 -2020,7,1,65,4.72,4.72,0.17,0.17,0.27 -2020,7,2,65,3.39,3.39,0.16,0.16,0.24 -2020,7,3,65,2.25,2.03,0.2445,0.25,0.27 -2020,7,4,65,1.51,1.27,0.2529,0.26,0.28 -2020,7,5,65,1.53,1.38,0.1823,0.19,0.24 -2020,7,6,65,1.34,1.27,0.1987,0.2,0.28 -2020,7,7,65,1.28,1.28,0.21,0.21,0.27 -2020,7,8,65,2.1,1.86,0.1725,0.18,0.24 -2020,7,9,65,3.11,3.11,0.34,0.34,0.42 -2020,7,10,65,4.82,4.82,1.65,1.65,1.05 -2020,7,11,65,5.78,5.78,1.87,1.87,1.05 -2020,7,12,65,5.45,5.45,0.26,0.26,0.35 -2020,7,13,65,4.72,4.72,0.17,0.17,0.27 -2020,7,1,66,15.83,14.33,0.2497,0.25,0.38 -2020,7,2,66,11.26,10.55,0.3958,0.4,0.44 -2020,7,3,66,8.74,8.74,0.32,0.32,0.34 -2020,7,4,66,10,9.93,0.4958,0.5,0.48 -2020,7,5,66,10.12,11.09,1.3864,1.44,0.71 -2020,7,6,66,11.94,12.7,0.417,0.42,0.44 -2020,7,7,66,11.21,11.21,1.02,1.02,0.66 -2020,7,8,66,10.05,10.05,0.51,0.51,0.44 -2020,7,9,66,11.98,11.1,0.73,0.73,0.58 -2020,7,10,66,14.94,14.29,2.6907,2.7,0.94 -2020,7,11,66,17.96,17.67,1.4694,1.47,0.8 -2020,7,12,66,16.62,16.62,0.14,0.14,0.31 -2020,7,13,66,14.33,14.33,0.25,0.25,0.38 -2020,7,1,67,19.27,17.82,0.1164,0.12,0.28 -2020,7,2,67,13.49,12.49,1.3426,1.36,0.74 -2020,7,3,67,7.45,7.45,0.19,0.19,0.27 -2020,7,4,67,7.37,7.19,0.1761,0.18,0.26 -2020,7,5,67,6.98,7.27,0.1759,0.18,0.24 -2020,7,6,67,8.39,8.86,0.2963,0.3,0.32 -2020,7,7,67,8.66,8.66,0.24,0.24,0.29 -2020,7,8,67,9.02,9.02,0.54,0.54,0.45 -2020,7,9,67,13.89,12.99,0.44,0.44,0.44 -2020,7,10,67,19.34,18.57,0.21,0.21,0.35 -2020,7,11,67,22.37,22,0.4587,0.46,0.52 -2020,7,12,67,20.83,20.83,0.11,0.11,0.27 -2020,7,13,67,17.82,17.82,0.12,0.12,0.28 -2020,7,1,68,20.49,19.03,0.1275,0.13,0.29 -2020,7,2,68,15.37,13.9,0.5496,0.55,0.52 -2020,7,3,68,7.68,7.68,0.32,0.32,0.38 -2020,7,4,68,6.15,5.85,0.274,0.29,0.32 -2020,7,5,68,5.6,5.44,0.2238,0.23,0.27 -2020,7,6,68,7.33,7.09,0.1857,0.19,0.24 -2020,7,7,68,7.43,7.43,0.31,0.31,0.37 -2020,7,8,68,9.52,9.52,0.46,0.46,0.46 -2020,7,9,68,15.56,14.88,0.29,0.29,0.38 -2020,7,10,68,21.72,21.06,0.1381,0.14,0.29 -2020,7,11,68,25.72,25.34,0.6645,0.68,0.58 -2020,7,12,68,23.61,23.61,0.16,0.16,0.34 -2020,7,13,68,19.03,19.03,0.13,0.13,0.29 -2020,7,1,69,15.59,14.15,0.7355,0.74,0.58 -2020,7,2,69,13.72,13.1,0.58,0.58,0.47 -2020,7,3,69,12.79,12.55,0.3093,0.33,0.37 -2020,7,4,69,14.04,14.64,0.4815,0.49,0.47 -2020,7,5,69,13.79,15.04,1.4146,1.42,0.7 -2020,7,6,69,16,16.2,1.7373,1.74,0.78 -2020,7,7,69,14.79,14.79,1.03,1.03,0.65 -2020,7,8,69,13.93,13.93,0.35,0.35,0.37 -2020,7,9,69,14.52,13.95,0.8058,0.81,0.54 -2020,7,10,69,17.19,16.53,0.2074,0.21,0.3 -2020,7,11,69,18.76,18.76,0.25,0.25,0.35 -2020,7,12,69,16.9,16.9,0.22,0.22,0.35 -2020,7,13,69,14.15,14.15,0.74,0.74,0.58 -2020,7,1,70,15.42,14.39,0.1856,0.19,0.28 -2020,7,2,70,13.78,13.78,0.18,0.18,0.29 -2020,7,3,70,14.17,14.46,0.1984,0.2,0.29 -2020,7,4,70,14.37,15.49,0.9087,0.92,0.58 -2020,7,5,70,14.29,14.95,0.3299,0.33,0.37 -2020,7,6,70,14.67,14.67,0.51,0.51,0.46 -2020,7,7,70,14.19,14.19,0.34,0.34,0.38 -2020,7,8,70,14.16,14.16,0.17,0.17,0.25 -2020,7,9,70,14.8,14.8,0.2,0.2,0.27 -2020,7,10,70,15.83,15.83,0.2,0.2,0.3 -2020,7,11,70,17.75,17.75,0.17,0.17,0.3 -2020,7,12,70,16.47,16.47,2.15,2.15,0.85 -2020,7,13,70,14.39,14.39,0.19,0.19,0.28 -2020,7,1,71,14.12,14.12,0.33,0.33,0.34 -2020,7,2,71,14.75,14.33,0.3437,0.35,0.38 -2020,7,3,71,12.99,13.56,0.208,0.22,0.29 -2020,7,4,71,13.2,14.23,4.3386,4.48,1.04 -2020,7,5,71,12.44,12.91,0.71,0.72,0.54 -2020,7,6,71,12.23,12.23,1.21,1.21,0.66 -2020,7,7,71,10.62,10.62,0.22,0.22,0.31 -2020,7,8,71,11.75,11.75,2.25,2.25,0.81 -2020,7,9,71,13.4,13.4,0.33,0.33,0.35 -2020,7,10,71,14.38,14.38,0.27,0.27,0.33 -2020,7,11,71,14.76,14.76,0.55,0.55,0.49 -2020,7,12,71,13.79,13.79,0.31,0.31,0.37 -2020,7,13,71,14.12,14.12,0.33,0.33,0.34 -2020,7,1,72,2.82,2.82,0.59,0.59,0.73 -2020,7,2,72,5.12,4.64,0.5222,0.54,0.65 -2020,7,3,72,6.61,6.46,0.2467,0.25,0.43 -2020,7,4,72,7.26,7.26,0.32,0.32,0.48 -2020,7,5,72,6.29,6.29,1.43,1.43,0.91 -2020,7,6,72,5.3,5.3,0.61,0.61,0.69 -2020,7,7,72,4.73,4.63,0.4006,0.42,0.66 -2020,7,8,72,4.97,4.97,1.41,1.41,1.06 -2020,7,9,72,4.85,4.85,0.39,0.39,0.56 -2020,7,10,72,4.4,4.4,0.22,0.22,0.37 -2020,7,11,72,3.27,3.27,0.42,0.42,0.55 -2020,7,12,72,2.37,2.37,0.44,0.44,0.58 -2020,7,13,72,2.82,2.82,0.59,0.59,0.73 -2020,7,1,73,2.89,2.89,1.18,1.18,1.04 -2020,7,2,73,4.41,4.3,1.4657,1.48,1.07 -2020,7,3,73,5.65,5.51,0.3735,0.38,0.53 -2020,7,4,73,6.2,6.2,0.2,0.2,0.38 -2020,7,5,73,5.42,5.42,0.53,0.53,0.62 -2020,7,6,73,5.18,5.18,0.91,0.91,0.84 -2020,7,7,73,5,4.89,1.0324,1.1,1 -2020,7,8,73,5.48,5.48,1.17,1.17,0.96 -2020,7,9,73,5.18,5.18,0.47,0.47,0.63 -2020,7,10,73,4.23,4.23,0.19,0.19,0.38 -2020,7,11,73,3,3,0.48,0.48,0.63 -2020,7,12,73,2.25,2.25,0.53,0.53,0.67 -2020,7,13,73,2.89,2.89,1.18,1.18,1.04 -2020,7,1,74,2.28,2.28,0.88,0.88,0.9 -2020,7,2,74,4.98,4.62,1.5557,1.59,1 -2020,7,3,74,6.68,6.68,0.7,0.7,0.67 -2020,7,4,74,8.02,8.02,1.6,1.6,0.9 -2020,7,5,74,7.22,7.22,2.65,2.65,1.07 -2020,7,6,74,5.84,5.84,0.59,0.59,0.65 -2020,7,7,74,4.37,4.37,0.14,0.14,0.36 -2020,7,8,74,3.76,3.76,0.88,0.88,0.94 -2020,7,9,74,3.01,3.01,0.6,0.6,0.78 -2020,7,10,74,2.26,2.26,0.32,0.32,0.56 -2020,7,11,74,1.57,1.57,0.52,0.52,0.84 -2020,7,12,74,1.26,1.26,0.26,0.26,0.49 -2020,7,13,74,2.28,2.28,0.88,0.88,0.9 -2020,7,1,75,10.58,10.58,5.11,5.11,1.07 -2020,7,2,75,22.33,20.81,4.5232,4.66,0.93 -2020,7,3,75,27.84,27.84,4.17,4.17,0.88 -2020,7,4,75,31.97,31.97,5.21,5.21,0.92 -2020,7,5,75,26.89,26.89,8.51,8.51,1.03 -2020,7,6,75,24.02,24.02,2.68,2.68,0.81 -2020,7,7,75,18.95,18.95,0.2,0.2,0.35 -2020,7,8,75,16.17,16.17,0.29,0.29,0.41 -2020,7,9,75,11.68,11.68,0.15,0.15,0.28 -2020,7,10,75,7.88,7.88,0.32,0.32,0.44 -2020,7,11,75,5.62,5.62,0.54,0.54,0.54 -2020,7,12,75,5.35,5.35,0.29,0.29,0.37 -2020,7,13,75,10.58,10.58,5.11,5.11,1.07 -2020,7,1,76,11.91,11.91,2.33,2.33,0.79 -2020,7,2,76,24.17,22.43,3.1075,3.18,0.83 -2020,7,3,76,30.86,30.861,0.01001,0.01,1.07 -2020,7,4,76,35.84,35.847,0.29007,0.29,1.5 -2020,7,5,76,30.53,30.53,4.51,4.51,0.86 -2020,7,6,76,26.86,26.86,0.9,0.9,0.55 -2020,7,7,76,21.68,21.68,0.21,0.21,0.33 -2020,7,8,76,17.82,17.82,0.13,0.13,0.28 -2020,7,9,76,12.26,12.26,0.18,0.18,0.32 -2020,7,10,76,7.83,7.83,0.63,0.63,0.58 -2020,7,11,76,5.67,5.67,0.99,0.99,0.7 -2020,7,12,76,5.65,5.65,0.24,0.24,0.32 -2020,7,13,76,11.91,11.91,2.33,2.33,0.79 -2020,7,1,77,2.68,2.63,0.2693,0.27,0.42 -2020,7,2,77,3.13,3.13,0.27,0.27,0.46 -2020,7,3,77,3.55,3.55,0.33,0.33,0.46 -2020,7,4,77,3.69,3.69,0.34,0.34,0.45 -2020,7,5,77,3.44,3.44,0.59,0.59,0.67 -2020,7,6,77,3.36,3.36,0.97,0.97,0.94 -2020,7,7,77,3.54,3.38,0.1597,0.16,0.36 -2020,7,8,77,4.16,4.16,0.69,0.69,0.74 -2020,7,9,77,4.16,4.16,0.38,0.38,0.5 -2020,7,10,77,3.72,3.72,0.97,0.97,0.8 -2020,7,11,77,2.54,2.54,0.39,0.39,0.47 -2020,7,12,77,2.22,2.22,0.2,0.2,0.31 -2020,7,13,77,2.63,2.63,0.27,0.27,0.42 -2020,7,1,78,3.51,3.51,0.61,0.61,0.67 -2020,7,2,78,5.59,5.61,4.9775,5.11,1.5 -2020,7,3,78,7.38,7.11,0.5644,0.58,0.59 -2020,7,4,78,8.61,8.22,0.2262,0.24,0.35 -2020,7,5,78,7.4,7.17,0.4899,0.49,0.51 -2020,7,6,78,6.81,6.76,2.2935,2.33,1.06 -2020,7,7,78,6.12,5.89,1.0633,1.07,0.91 -2020,7,8,78,6.24,6.24,4.91,4.91,1.5 -2020,7,9,78,5.88,5.88,0.38,0.38,0.55 -2020,7,10,78,4.93,4.93,0.28,0.28,0.45 -2020,7,11,78,3.62,3.62,0.58,0.58,0.64 -2020,7,12,78,2.69,2.69,0.28,0.28,0.41 -2020,7,13,78,3.51,3.51,0.61,0.61,0.67 -2020,7,1,79,1.46,1.46,0.42,0.42,0.61 -2020,7,2,79,2.51,2.51,0.98,0.98,1.03 -2020,7,3,79,3.39,3.39,0.41,0.41,0.5 -2020,7,4,79,3.92,3.92,0.59,0.59,0.61 -2020,7,5,79,3.71,3.71,1.1,1.1,0.87 -2020,7,6,79,3.34,3.34,0.5,0.5,0.6 -2020,7,7,79,3.3,3.11,0.1388,0.14,0.29 -2020,7,8,79,3.43,3.43,0.17,0.17,0.29 -2020,7,9,79,3.2,3.2,0.26,0.26,0.42 -2020,7,10,79,2.41,2.41,0.17,0.17,0.28 -2020,7,11,79,1.45,1.45,0.3,0.3,0.47 -2020,7,12,79,1.04,1.04,0.16,0.16,0.27 -2020,7,13,79,1.46,1.46,0.42,0.42,0.61 -2020,7,1,80,1.67,1.67,0.24,0.24,0.37 -2020,7,2,80,2.71,2.71,0.54,0.54,0.66 -2020,7,3,80,3.7,3.7,0.2,0.2,0.31 -2020,7,4,80,4.08,4.08,0.21,0.21,0.33 -2020,7,5,80,3.74,3.74,0.25,0.25,0.38 -2020,7,6,80,3.39,3.39,0.58,0.58,0.66 -2020,7,7,80,3.21,3.21,0.33,0.33,0.56 -2020,7,8,80,3.36,3.36,0.19,0.19,0.34 -2020,7,9,80,3.15,3.15,0.25,0.25,0.42 -2020,7,10,80,2.51,2.51,0.21,0.21,0.35 -2020,7,11,80,1.71,1.71,0.23,0.23,0.37 -2020,7,12,80,1.32,1.32,0.15,0.15,0.28 -2020,7,13,80,1.67,1.67,0.24,0.24,0.37 -2020,7,1,81,5.29,5.29,2.29,2.29,0.99 -2020,7,2,81,12.16,11.75,1.3591,1.36,0.7 -2020,7,3,81,18.27,18.27,1.33,1.33,0.65 -2020,7,4,81,21.58,21.58,3.01,3.01,0.81 -2020,7,5,81,19.05,19.05,5.2,5.2,0.95 -2020,7,6,81,16.12,16.12,1.86,1.86,0.76 -2020,7,7,81,13.31,13.31,0.35,0.35,0.46 -2020,7,8,81,11.19,11.19,0.67,0.67,0.6 -2020,7,9,81,7.74,7.74,0.17,0.17,0.29 -2020,7,10,81,4.97,4.97,0.2,0.2,0.32 -2020,7,11,81,2.74,2.74,0.2,0.2,0.32 -2020,7,12,81,2.41,2.41,0.17,0.17,0.28 -2020,7,13,81,5.29,5.29,2.29,2.29,0.99 -2020,7,1,82,8.02,8.02,1.69,1.69,0.73 -2020,7,2,82,19.51,19.03,7.8904,8.1,1.07 -2020,7,3,82,29.42,29.421,0.14001,0.14,1.05 -2020,7,4,82,35.86,35.861,1.89001,1.89,1.04 -2020,7,5,82,32.08,32.08,7.73,7.73,0.95 -2020,7,6,82,28.33,28.33,2.62,2.62,0.75 -2020,7,7,82,22.55,22.55,0.26,0.26,0.36 -2020,7,8,82,17.82,17.82,1.11,1.11,0.68 -2020,7,9,82,11.89,11.89,0.16,0.16,0.28 -2020,7,10,82,6.81,6.81,0.52,0.52,0.5 -2020,7,11,82,3.86,3.86,0.2,0.2,0.28 -2020,7,12,82,3.24,3.24,0.2,0.2,0.28 -2020,7,13,82,8.02,8.02,1.69,1.69,0.73 -2020,7,1,83,4.01,4.29,0.3221,0.33,0.43 -2020,7,2,83,4.8,5.05,0.2737,0.28,0.39 -2020,7,3,83,5.33,5.33,0.33,0.33,0.42 -2020,7,4,83,5.3,5.3,0.48,0.48,0.53 -2020,7,5,83,5.15,5.15,0.78,0.78,0.67 -2020,7,6,83,5.23,5.23,0.18,0.18,0.31 -2020,7,7,83,5.28,5.28,0.13,0.13,0.29 -2020,7,8,83,5.79,5.79,0.47,0.47,0.58 -2020,7,9,83,5.11,5.11,0.56,0.56,0.6 -2020,7,10,83,4.09,4.09,0.5,0.5,0.56 -2020,7,11,83,3.04,3.04,0.22,0.22,0.34 -2020,7,12,83,3.26,3.26,0.15,0.15,0.26 -2020,7,13,83,4.29,4.29,0.33,0.33,0.43 -2020,7,1,84,3.28,3.39,0.191,0.2,0.38 -2020,7,2,84,2.76,2.94,0.3461,0.35,0.43 -2020,7,3,84,2.41,2.41,0.21,0.21,0.29 -2020,7,4,84,2.03,2.03,0.17,0.17,0.28 -2020,7,5,84,1.96,1.96,0.18,0.18,0.27 -2020,7,6,84,2.1,2.1,0.19,0.19,0.29 -2020,7,7,84,2.26,2.26,0.75,0.75,1.02 -2020,7,8,84,2.14,2.14,0.34,0.34,0.56 -2020,7,9,84,1.68,1.68,0.29,0.29,0.43 -2020,7,10,84,1.86,1.86,0.23,0.23,0.41 -2020,7,11,84,2.32,2.32,0.33,0.33,0.55 -2020,7,12,84,3.04,3.04,0.42,0.42,0.65 -2020,7,13,84,3.39,3.39,0.2,0.2,0.38 -2020,7,1,85,2.26,2.22,0.1582,0.16,0.25 -2020,7,2,85,2.08,2.09,0.1636,0.18,0.27 -2020,7,3,85,2.08,2.08,0.32,0.32,0.48 -2020,7,4,85,1.76,1.76,0.29,0.29,0.52 -2020,7,5,85,1.74,1.74,0.18,0.18,0.39 -2020,7,6,85,1.99,1.99,0.13,0.13,0.3 -2020,7,7,85,2.38,2.38,0.37,0.37,0.69 -2020,7,8,85,2.69,2.69,0.31,0.31,0.53 -2020,7,9,85,2.47,2.47,0.25,0.25,0.41 -2020,7,10,85,2.19,2.19,0.34,0.34,0.51 -2020,7,11,85,1.88,1.88,0.16,0.16,0.29 -2020,7,12,85,1.91,1.91,0.14,0.14,0.23 -2020,7,13,85,2.22,2.22,0.16,0.16,0.25 -2020,7,1,86,2.21,2.08,0.1598,0.16,0.26 -2020,7,2,86,2.71,2.57,0.1651,0.17,0.27 -2020,7,3,86,2.8,2.8,0.2,0.2,0.32 -2020,7,4,86,3.12,3.12,0.74,0.74,0.75 -2020,7,5,86,2.86,2.86,0.45,0.45,0.61 -2020,7,6,86,3.06,3.06,0.26,0.26,0.48 -2020,7,7,86,3.12,3.12,0.18,0.18,0.43 -2020,7,8,86,3.59,3.59,0.28,0.28,0.46 -2020,7,9,86,3.3,3.3,0.2,0.2,0.34 -2020,7,10,86,2.62,2.62,0.24,0.24,0.4 -2020,7,11,86,1.79,1.79,0.29,0.29,0.41 -2020,7,12,86,1.66,1.66,0.16,0.16,0.26 -2020,7,13,86,2.08,2.08,0.16,0.16,0.26 -2020,7,1,87,1.26,1.26,0.24,0.24,0.29 -2020,7,2,87,2.15,2.15,0.21,0.21,0.27 -2020,7,3,87,3.2,3.2,0.32,0.32,0.36 -2020,7,4,87,3.92,3.92,0.42,0.42,0.43 -2020,7,5,87,4.05,4.05,0.35,0.35,0.4 -2020,7,6,87,3.92,3.92,0.3,0.3,0.38 -2020,7,7,87,3.41,3.41,0.18,0.18,0.28 -2020,7,8,87,2.83,2.83,0.17,0.17,0.26 -2020,7,9,87,2.16,2.16,0.16,0.16,0.28 -2020,7,10,87,1.52,1.52,0.18,0.18,0.29 -2020,7,11,87,1,1,0.31,0.31,0.46 -2020,7,12,87,0.81,0.81,0.18,0.18,0.25 -2020,7,13,87,1.26,1.26,0.24,0.24,0.29 -2020,7,1,88,2.12,2.12,0.28,0.28,0.22 -2020,7,2,88,6.75,6.75,0.32,0.32,0.29 -2020,7,3,88,12.97,12.97,0.76,0.76,0.46 -2020,7,4,88,17.27,17.27,1.76,1.76,0.62 -2020,7,5,88,18.6,18.6,1.98,1.98,0.67 -2020,7,6,88,17.67,17.67,0.29,0.29,0.29 -2020,7,7,88,13.83,13.83,0.55,0.55,0.4 -2020,7,8,88,9.17,9.17,0.3,0.3,0.28 -2020,7,9,88,4.52,4.52,0.31,0.31,0.32 -2020,7,10,88,2.21,2.21,0.2,0.2,0.24 -2020,7,11,88,0.79,0.79,0.31,0.31,0.41 -2020,7,12,88,0.64,0.64,0.26,0.26,0.26 -2020,7,13,88,2.12,2.12,0.28,0.28,0.22 -2020,7,1,89,2.48,2.48,0.31,0.31,0.28 -2020,7,2,89,7.84,7.84,0.27,0.27,0.28 -2020,7,3,89,15.73,15.73,1.52,1.52,0.61 -2020,7,4,89,20.8,20.8,6.76,6.76,0.94 -2020,7,5,89,21.89,21.89,4.7,4.7,0.86 -2020,7,6,89,17.82,17.82,0.25,0.25,0.28 -2020,7,7,89,13.48,13.48,0.31,0.31,0.31 -2020,7,8,89,9.06,9.06,0.31,0.31,0.31 -2020,7,9,89,4.69,4.69,0.3,0.3,0.32 -2020,7,10,89,2.21,2.21,0.18,0.18,0.23 -2020,7,11,89,0.89,0.89,0.19,0.19,0.27 -2020,7,12,89,0.7,0.7,0.22,0.22,0.26 -2020,7,13,89,2.48,2.48,0.31,0.31,0.28 -2020,7,1,90,2.13,1.93,0.199,0.2,0.29 -2020,7,2,90,1.94,1.78,0.2612,0.27,0.36 -2020,7,3,90,1.67,1.67,0.18,0.18,0.22 -2020,7,4,90,1.85,1.85,0.19,0.19,0.27 -2020,7,5,90,2.03,2.03,0.17,0.17,0.28 -2020,7,6,90,2.39,2.39,0.19,0.19,0.31 -2020,7,7,90,2.34,2.34,0.17,0.17,0.29 -2020,7,8,90,2.15,2.15,0.34,0.34,0.45 -2020,7,9,90,1.64,1.64,0.22,0.22,0.35 -2020,7,10,90,1.62,1.62,0.22,0.22,0.36 -2020,7,11,90,1.73,1.73,0.15,0.15,0.27 -2020,7,12,90,1.86,1.86,0.19,0.19,0.28 -2020,7,13,90,1.93,1.93,0.2,0.2,0.29 -2020,7,1,91,1.19,1.19,0.29,0.29,0.28 -2020,7,2,91,3.24,3.24,0.3,0.3,0.28 -2020,7,3,91,6.18,6.18,0.56,0.56,0.45 -2020,7,4,91,8.9,8.9,0.73,0.73,0.51 -2020,7,5,91,10.03,10.03,0.31,0.31,0.34 -2020,7,6,91,10.09,10.09,0.52,0.52,0.42 -2020,7,7,91,8.08,8.08,0.63,0.63,0.47 -2020,7,8,91,5.12,5.12,0.3,0.3,0.28 -2020,7,9,91,2.28,2.28,0.26,0.26,0.28 -2020,7,10,91,0.95,0.95,0.15,0.15,0.2 -2020,7,11,91,0.28,0.28,0.22,0.22,0.29 -2020,7,12,91,0.33,0.33,0.2,0.2,0.21 -2020,7,13,91,1.19,1.19,0.29,0.29,0.28 -2020,7,1,92,1,1,0.28,0.28,0.27 -2020,7,2,92,3.36,3.36,0.34,0.34,0.34 -2020,7,3,92,7.47,7.47,1.63,1.63,0.75 -2020,7,4,92,10.42,10.42,2.32,2.32,0.8 -2020,7,5,92,11.89,11.89,0.51,0.51,0.43 -2020,7,6,92,11.32,11.32,0.67,0.67,0.45 -2020,7,7,92,8.36,8.36,0.64,0.64,0.45 -2020,7,8,92,4.67,4.67,0.31,0.31,0.28 -2020,7,9,92,1.77,1.77,0.28,0.28,0.29 -2020,7,10,92,0.53,0.53,0.21,0.21,0.22 -2020,7,11,92,0.13,0.13,0.16,0.16,0.23 -2020,7,12,92,0.16,0.16,0.13,0.13,0.13 -2020,7,13,92,1,1,0.28,0.28,0.27 -2020,7,1,93,0.82,0.82,0.21,0.21,0.21 -2020,7,2,93,1.94,1.94,0.24,0.24,0.25 -2020,7,3,93,3.76,3.76,0.3,0.3,0.27 -2020,7,4,93,6.3,6.3,0.41,0.41,0.34 -2020,7,5,93,7.87,7.87,0.26,0.26,0.26 -2020,7,6,93,8.32,8.32,0.38,0.38,0.28 -2020,7,7,93,6.1,6.1,0.38,0.38,0.3 -2020,7,8,93,3.08,3.08,0.35,0.35,0.28 -2020,7,9,93,1.08,1.08,0.25,0.25,0.25 -2020,7,10,93,0.29,0.29,0.15,0.15,0.15 -2020,7,11,93,0.23,0.23,0.13,0.13,0.17 -2020,7,12,93,0.38,0.38,0.15,0.15,0.19 -2020,7,13,93,0.82,0.82,0.21,0.21,0.21 -2020,7,1,94,0.96,0.96,0.16,0.16,0.23 -2020,7,2,94,1.04,1.04,0.19,0.19,0.26 -2020,7,3,94,1.49,1.49,0.25,0.25,0.26 -2020,7,4,94,2.28,2.28,0.27,0.27,0.28 -2020,7,5,94,2.94,2.94,0.24,0.24,0.25 -2020,7,6,94,2.92,2.92,0.32,0.32,0.28 -2020,7,7,94,2.1,2.1,0.32,0.32,0.29 -2020,7,8,94,1.11,1.11,0.26,0.26,0.29 -2020,7,9,94,0.44,0.44,0.15,0.15,0.23 -2020,7,10,94,0.42,0.42,0.15,0.15,0.26 -2020,7,11,94,0.61,0.61,0.13,0.13,0.21 -2020,7,12,94,0.81,0.81,0.17,0.17,0.24 -2020,7,13,94,0.96,0.96,0.16,0.16,0.23 -2020,7,1,95,1.18,1.18,0.2,0.2,0.29 -2020,7,2,95,1.03,1.03,0.22,0.22,0.29 -2020,7,3,95,1.26,1.26,0.24,0.24,0.25 -2020,7,4,95,1.52,1.52,0.27,0.27,0.27 -2020,7,5,95,1.83,1.83,0.25,0.25,0.26 -2020,7,6,95,1.98,1.98,0.29,0.29,0.28 -2020,7,7,95,1.62,1.62,0.3,0.3,0.29 -2020,7,8,95,0.96,0.96,0.24,0.24,0.26 -2020,7,9,95,0.47,0.47,0.14,0.14,0.2 -2020,7,10,95,0.6,0.6,0.18,0.18,0.23 -2020,7,11,95,1,1,0.11,0.11,0.17 -2020,7,12,95,1.2,1.2,0.17,0.17,0.22 -2020,7,13,95,1.18,1.18,0.2,0.2,0.29 -2020,7,1,96,3.04,3.04,0.15,0.15,0.29 -2020,7,2,96,2.22,2.22,0.21,0.21,0.29 -2020,7,3,96,2.28,2.28,0.22,0.22,0.28 -2020,7,4,96,2.35,2.35,0.29,0.29,0.27 -2020,7,5,96,3.17,2.66,0.2891,0.3,0.26 -2020,7,6,96,3.27,2.84,0.2794,0.28,0.28 -2020,7,7,96,2.51,2.42,0.3272,0.33,0.38 -2020,7,8,96,1.59,1.5,0.1865,0.19,0.28 -2020,7,9,96,0.56,0.56,0.18,0.18,0.24 -2020,7,10,96,1.32,1.32,0.16,0.16,0.28 -2020,7,11,96,2.6,2.6,0.2,0.2,0.36 -2020,7,12,96,3.41,3.41,0.15,0.15,0.28 -2020,7,13,96,3.04,3.04,0.15,0.15,0.29 -2020,7,1,97,5.21,5.2,0.5044,0.51,0.68 -2020,7,2,97,3.77,3.66,0.2957,0.3,0.4 -2020,7,3,97,3.33,3.33,0.25,0.25,0.28 -2020,7,4,97,3.3,3.3,0.24,0.24,0.27 -2020,7,5,97,3.85,3.42,0.2304,0.24,0.25 -2020,7,6,97,3.94,3.68,0.2292,0.23,0.28 -2020,7,7,97,3.27,3.27,0.3971,0.4,0.51 -2020,7,8,97,2.55,2.49,0.36,0.36,0.48 -2020,7,9,97,1.43,1.43,0.2,0.2,0.29 -2020,7,10,97,2.76,2.76,0.36,0.36,0.54 -2020,7,11,97,4.72,4.72,0.3,0.3,0.49 -2020,7,12,97,5.89,5.89,0.48,0.48,0.61 -2020,7,13,97,5.2,5.2,0.51,0.51,0.68 -2020,7,1,98,5.48,5.48,0.14,0.14,0.28 -2020,7,2,98,2.94,2.94,0.17,0.17,0.25 -2020,7,3,98,2.6,2.6,0.29,0.29,0.29 -2020,7,4,98,2.58,2.58,0.28,0.28,0.26 -2020,7,5,98,2.96,2.84,0.2921,0.3,0.28 -2020,7,6,98,2.78,2.77,0.31,0.31,0.33 -2020,7,7,98,2.1,2.18,0.3699,0.37,0.45 -2020,7,8,98,1.34,1.38,0.19,0.19,0.27 -2020,7,9,98,0.97,0.97,0.22,0.22,0.29 -2020,7,10,98,3.56,3.56,0.25,0.25,0.39 -2020,7,11,98,6.62,6.62,0.13,0.13,0.29 -2020,7,12,98,7.34,7.34,0.24,0.24,0.42 -2020,7,13,98,5.48,5.48,0.14,0.14,0.28 -2020,7,1,99,5.16,5.25,0.2681,0.28,0.56 -2020,7,2,99,3.42,3.59,0.3398,0.34,0.51 -2020,7,3,99,2.88,2.86,0.4029,0.41,0.57 -2020,7,4,99,2.47,2.34,0.1564,0.16,0.29 -2020,7,5,99,2.4,2.27,0.2882,0.29,0.42 -2020,7,6,99,2.54,2.53,0.3495,0.35,0.56 -2020,7,7,99,2.65,2.75,0.8192,0.84,0.94 -2020,7,8,99,2.95,2.98,0.7775,0.78,0.81 -2020,7,9,99,2.84,2.84,0.39,0.39,0.52 -2020,7,10,99,3.98,3.98,0.36,0.36,0.59 -2020,7,11,99,5.46,5.46,0.48,0.48,0.67 -2020,7,12,99,6.08,6.08,1.37,1.37,1.04 -2020,7,13,99,5.25,5.25,0.28,0.28,0.56 -2020,7,1,100,5.85,5.74,0.1518,0.16,0.29 -2020,7,2,100,3.77,3.69,0.18,0.18,0.29 -2020,7,3,100,2.36,2.22,0.2484,0.25,0.29 -2020,7,4,100,1.62,1.34,0.2376,0.24,0.26 -2020,7,5,100,1.51,1.32,0.2093,0.21,0.28 -2020,7,6,100,1.53,1.48,0.1787,0.18,0.29 -2020,7,7,100,1.79,1.79,0.17,0.17,0.24 -2020,7,8,100,2.86,2.86,0.23,0.23,0.32 -2020,7,9,100,4.19,4.19,0.35,0.35,0.41 -2020,7,10,100,6.02,6.02,1.9,1.9,1.02 -2020,7,11,100,7.08,7.08,2.08,2.08,1.01 -2020,7,12,100,6.86,6.86,0.78,0.78,0.64 -2020,7,13,100,5.74,5.74,0.16,0.16,0.29 -2020,7,1,101,6.06,5.97,0.0974,0.1,0.27 -2020,7,2,101,3.77,3.74,0.2222,0.23,0.38 -2020,7,3,101,2.96,2.86,0.2952,0.3,0.38 -2020,7,4,101,2.34,2.16,0.2343,0.25,0.31 -2020,7,5,101,2.19,2.11,0.3753,0.38,0.46 -2020,7,6,101,1.93,1.98,0.1565,0.16,0.28 -2020,7,7,101,1.76,1.77,0.1761,0.18,0.29 -2020,7,8,101,1.94,1.88,0.2374,0.24,0.34 -2020,7,9,101,2.07,2.07,0.23,0.23,0.35 -2020,7,10,101,4.12,4.12,0.65,0.65,0.74 -2020,7,11,101,6.23,6.23,0.13,0.13,0.29 -2020,7,12,101,6.85,6.85,0.28,0.28,0.47 -2020,7,13,101,5.97,5.97,0.1,0.1,0.27 -2020,7,1,102,4.93,4.93,0.13,0.13,0.25 -2020,7,2,102,3.14,3.14,0.28,0.28,0.4 -2020,7,3,102,2.79,2.62,0.3122,0.32,0.35 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-2020,7,13,1,54.31,53.54,1.1148,1.16 -2020,7,1,2,58.06,57.19,1.0597,1.19 -2020,7,2,2,48.2,47.41,1.189,1.31 -2020,7,3,2,37.15,36.51,1.8676,1.99 -2020,7,4,2,27.92,27.14,2.2738,2.43 -2020,7,5,2,22.92,22.12,2.3801,2.57 -2020,7,6,2,24.23,24.04,2.52,2.64 -2020,7,7,2,32.15,32.15,2.38,2.38 -2020,7,8,2,42.42,42.34,1.8531,1.94 -2020,7,9,2,53.07,52.95,1.253,1.33 -2020,7,10,2,61.51,61.34,1.2476,1.3 -2020,7,11,2,66.56,65.76,1.1349,1.26 -2020,7,12,2,65.15,64.33,1.1415,1.25 -2020,7,13,2,57.98,57.19,1.1166,1.19 -2020,7,1,3,57.87,56.81,1.2884,1.45 -2020,7,2,3,47.97,47.06,1.3067,1.46 -2020,7,3,3,36.46,35.94,2.1748,2.26 -2020,7,4,3,27.37,26.64,2.6418,2.85 -2020,7,5,3,22.03,21.53,2.7602,2.95 -2020,7,6,3,23.56,23.33,2.7896,2.91 -2020,7,7,3,31.6,31.6,2.43,2.43 -2020,7,8,3,42.49,42.35,2.1044,2.2 -2020,7,9,3,53.65,53.48,1.3614,1.47 -2020,7,10,3,61.92,61.65,1.2799,1.3 -2020,7,11,3,66.3,65.58,1.3727,1.47 -2020,7,12,3,64.73,63.93,1.1973,1.33 -2020,7,13,3,57.7,56.81,1.4043,1.45 -2020,7,1,4,61.85,60.83,1.2567,1.39 -2020,7,2,4,52.23,51.43,1.209,1.33 -2020,7,3,4,41.69,41.09,1.96,2.02 -2020,7,4,4,33.43,32.73,2.3123,2.54 -2020,7,5,4,29.08,28.4,2.4092,2.65 -2020,7,6,4,30.47,30.23,2.4581,2.58 -2020,7,7,4,37.59,37.54,1.9836,2.01 -2020,7,8,4,47.13,46.91,1.6613,1.83 -2020,7,9,4,57.27,56.96,1.3172,1.38 -2020,7,10,4,65.39,64.99,1.1844,1.22 -2020,7,11,4,70.06,69.2,1.2448,1.36 -2020,7,12,4,68.67,67.8,1.2593,1.33 -2020,7,13,4,61.74,60.83,1.3311,1.39 -2020,7,1,5,60.04,59.17,1.3078,1.43 -2020,7,2,5,50.67,49.87,1.2406,1.37 -2020,7,3,5,39.72,39.34,2.0841,2.2 -2020,7,4,5,31.4,30.85,2.7278,2.89 -2020,7,5,5,26.59,26.3,2.7387,2.89 -2020,7,6,5,28.13,27.98,2.6781,2.82 -2020,7,7,5,35.34,35.34,2.15,2.15 -2020,7,8,5,45.32,45.11,1.9718,2.18 -2020,7,9,5,55.74,55.54,1.4861,1.6 -2020,7,10,5,63.63,63.33,1.4589,1.47 -2020,7,11,5,67.9,67.35,1.4722,1.56 -2020,7,12,5,66.52,65.88,1.2878,1.3 -2020,7,13,5,59.87,59.17,1.3196,1.43 -2020,7,1,6,63.52,62.91,1.4722,1.62 -2020,7,2,6,53.88,53.29,1.3711,1.53 -2020,7,3,6,42.73,42.38,2.2602,2.3 -2020,7,4,6,34.32,33.88,2.725,2.96 -2020,7,5,6,30.17,30.04,2.7229,2.87 -2020,7,6,6,32.79,32.79,2.8,2.8 -2020,7,7,6,40.66,40.66,1.95,1.95 -2020,7,8,6,50.64,50.28,1.9797,2.18 -2020,7,9,6,60.56,60.17,1.7915,1.82 -2020,7,10,6,67.99,67.52,1.5866,1.6 -2020,7,11,6,71.82,71.32,1.5002,1.66 -2020,7,12,6,70.23,69.66,1.497,1.56 -2020,7,13,6,63.5,62.91,1.5361,1.62 -2020,7,1,7,66.05,65.27,1.3617,1.47 -2020,7,2,7,56.69,55.99,1.2135,1.35 -2020,7,3,7,46.31,45.81,1.9591,2 -2020,7,4,7,38.54,38.01,2.3462,2.6 -2020,7,5,7,34.77,34.39,2.3791,2.5 -2020,7,6,7,36.83,36.56,2.3232,2.42 -2020,7,7,7,43.73,43.5,1.5663,1.73 -2020,7,8,7,52.74,52.35,1.6453,1.77 -2020,7,9,7,62.48,61.9,1.4716,1.49 -2020,7,10,7,70.28,69.51,1.3043,1.35 -2020,7,11,7,74.32,73.52,1.2353,1.33 -2020,7,12,7,72.89,72.07,1.3061,1.36 -2020,7,13,7,66.15,65.27,1.378,1.47 -2020,7,1,8,61.17,60.33,1.379,1.46 -2020,7,2,8,51.74,51,1.2235,1.33 -2020,7,3,8,41.06,40.64,2.0705,2.15 -2020,7,4,8,33.23,32.66,2.4809,2.8 -2020,7,5,8,29.09,28.76,2.6361,2.79 -2020,7,6,8,31.12,30.92,2.5817,2.71 -2020,7,7,8,38.3,38.3,1.94,1.94 -2020,7,8,8,47.91,47.63,1.9243,2.02 -2020,7,9,8,57.84,57.48,1.4545,1.52 -2020,7,10,8,65.4,64.84,1.2876,1.44 -2020,7,11,8,69.42,68.7,1.4927,1.52 -2020,7,12,8,67.84,67.14,1.2472,1.35 -2020,7,13,8,61.09,60.33,1.3908,1.46 -2020,7,1,9,63.44,62.92,1.3249,1.46 -2020,7,2,9,54.37,53.8,1.4976,1.54 -2020,7,3,9,44.2,43.81,2.2125,2.25 -2020,7,4,9,36.84,36.41,2.3667,2.65 -2020,7,5,9,33.57,33.3,2.3057,2.38 -2020,7,6,9,36.07,35.89,2.1297,2.25 -2020,7,7,9,43.09,42.94,1.4641,1.52 -2020,7,8,9,51.98,51.46,1.6845,1.76 -2020,7,9,9,60.99,60.36,1.5146,1.59 -2020,7,10,9,67.84,67.11,1.3392,1.4 -2020,7,11,9,71.37,70.81,1.2187,1.31 -2020,7,12,9,69.93,69.35,1.3486,1.36 -2020,7,13,9,63.55,62.92,1.3658,1.46 -2020,7,1,10,68.71,68.14,1.3703,1.49 -2020,7,2,10,60.03,59.44,1.4146,1.48 -2020,7,3,10,50.39,49.92,1.9006,2.05 -2020,7,4,10,43.39,42.97,2.1318,2.44 -2020,7,5,10,40.39,40.05,2.2213,2.26 -2020,7,6,10,42.69,42.37,1.9924,2.12 -2020,7,7,10,49.18,48.85,1.3811,1.52 -2020,7,8,10,57.38,56.77,1.4953,1.58 -2020,7,9,10,66.28,65.43,1.5088,1.57 -2020,7,10,10,73.11,72.26,1.2349,1.35 -2020,7,11,10,76.67,75.94,1.2012,1.21 -2020,7,12,10,75.28,74.52,1.2439,1.36 -2020,7,13,10,68.98,68.14,1.4449,1.49 -2020,7,1,11,65.53,65.06,1.3848,1.51 -2020,7,2,11,56.76,56.29,1.5694,1.69 -2020,7,3,11,47.26,46.82,2.1343,2.18 -2020,7,4,11,40.35,40,2.1122,2.32 -2020,7,5,11,38.04,37.6,1.9664,2.06 -2020,7,6,11,40.96,40.5,1.9707,2.08 -2020,7,7,11,47.39,47.03,1.3216,1.46 -2020,7,8,11,55.46,54.74,1.3909,1.53 -2020,7,9,11,63.58,62.71,1.4906,1.57 -2020,7,10,11,69.86,69.02,1.3028,1.36 -2020,7,11,11,73.05,72.47,1.0876,1.14 -2020,7,12,11,71.73,71.11,1.1755,1.27 -2020,7,13,11,65.8,65.06,1.44,1.51 -2020,7,1,12,72.51,72,1.248,1.39 -2020,7,2,12,64.6,64.09,1.5069,1.58 -2020,7,3,12,55.78,55.28,1.9866,2.08 -2020,7,4,12,49.17,48.89,2.197,2.31 -2020,7,5,12,47.17,46.48,2.0584,2.11 -2020,7,6,12,49.61,48.88,1.9899,2.09 -2020,7,7,12,55.32,54.79,1.5097,1.67 -2020,7,8,12,62.84,61.97,1.36,1.43 -2020,7,9,12,70.63,69.63,1.5071,1.53 -2020,7,10,12,76.55,75.76,1.1183,1.24 -2020,7,11,12,79.49,78.89,0.9034,0.92 -2020,7,12,12,78.35,77.66,1.0328,1.14 -2020,7,13,12,72.86,72,1.3014,1.39 -2020,7,1,13,70.43,69.92,1.3742,1.53 -2020,7,2,13,61.69,61.23,1.4799,1.58 -2020,7,3,13,52.42,51.93,1.8666,2.06 -2020,7,4,13,45.67,45.37,2.1083,2.23 -2020,7,5,13,43.71,43.11,1.9655,2.04 -2020,7,6,13,46.65,45.98,1.8317,1.97 -2020,7,7,13,52.85,52.39,1.4053,1.51 -2020,7,8,13,60.78,59.97,1.3343,1.43 -2020,7,9,13,69,67.98,1.5945,1.61 -2020,7,10,13,75.26,74.34,1.3621,1.44 -2020,7,11,13,78.36,77.68,1.15,1.15 -2020,7,12,13,76.91,76.17,1.2476,1.36 -2020,7,13,13,70.76,69.92,1.4321,1.53 -2020,7,1,14,56.33,55.74,1.4422,1.53 -2020,7,2,14,45.55,45.07,1.7273,1.9 -2020,7,3,14,33.28,32.96,2.1864,2.42 -2020,7,4,14,23.3,23.01,3.0021,3.13 -2020,7,5,14,18.25,18.25,3.35,3.35 -2020,7,6,14,20.66,20.66,3.23,3.23 -2020,7,7,14,29.12,29.12,3.02,3.02 -2020,7,8,14,40.03,40.03,2.78,2.78 -2020,7,9,14,51.27,51.27,2.16,2.16 -2020,7,10,14,59.79,59.79,1.87,1.87 -2020,7,11,14,64.31,64.25,1.9458,2.03 -2020,7,12,14,63.17,62.86,1.4447,1.6 -2020,7,13,14,56.02,55.74,1.394,1.53 -2020,7,1,15,54.63,54.41,1.3397,1.46 -2020,7,2,15,42.11,41.79,2.2897,2.46 -2020,7,3,15,27.84,27.38,2.8857,3.23 -2020,7,4,15,15.31,15.31,4.09,4.09 -2020,7,5,15,10.58,10.58,4.57,4.57 -2020,7,6,15,15.05,15.05,4.32,4.32 -2020,7,7,15,25.97,26.37,3.8049,3.89 -2020,7,8,15,39.47,39.47,3.23,3.23 -2020,7,9,15,51.71,51.71,2.33,2.33 -2020,7,10,15,60.45,60.45,2.02,2.02 -2020,7,11,15,64.63,64.63,2.13,2.13 -2020,7,12,15,62.76,62.54,1.4495,1.62 -2020,7,13,15,54.5,54.41,1.4014,1.46 -2020,7,1,16,55.87,55.67,1.4638,1.6 -2020,7,2,16,42.56,42.24,2.3978,2.59 -2020,7,3,16,28.05,27.5,3.0173,3.43 -2020,7,4,16,15.63,15.63,4.4,4.4 -2020,7,5,16,11.44,11.44,5.11,5.11 -2020,7,6,16,16.01,16.01,4.69,4.69 -2020,7,7,16,26.64,27.49,3.7488,3.81 -2020,7,8,16,40.73,40.8,3.2567,3.26 -2020,7,9,16,53.41,53.41,2.42,2.42 -2020,7,10,16,62.27,62.27,2.11,2.11 -2020,7,11,16,66.58,66.58,2.25,2.25 -2020,7,12,16,64.53,64.3,1.6721,1.8 -2020,7,13,16,55.71,55.67,1.5294,1.6 -2020,7,1,17,56.19,55.95,1.6576,1.83 -2020,7,2,17,43.01,42.71,2.3846,2.54 -2020,7,3,17,29.48,28.99,3.1078,3.31 -2020,7,4,17,18.82,18.65,4.1006,4.34 -2020,7,5,17,15.44,15.44,5.07,5.07 -2020,7,6,17,19.62,19.89,4.3915,4.69 -2020,7,7,17,28.97,29.91,3.3476,3.53 -2020,7,8,17,41.43,41.62,2.9738,3.13 -2020,7,9,17,53.04,53.04,2.35,2.35 -2020,7,10,17,61.9,61.9,2.14,2.14 -2020,7,11,17,66.71,66.71,2.35,2.35 -2020,7,12,17,64.99,64.69,1.899,2.07 -2020,7,13,17,55.95,55.95,1.83,1.83 -2020,7,1,18,57.39,57.12,1.7603,1.92 -2020,7,2,18,44.29,43.96,2.5061,2.65 -2020,7,3,18,31.25,30.79,3.3518,3.36 -2020,7,4,18,21.55,21.25,3.8657,4.06 -2020,7,5,18,18.75,18.75,4.79,4.79 -2020,7,6,18,23.32,23.59,4.2731,4.53 -2020,7,7,18,32.02,32.83,3.3454,3.4 -2020,7,8,18,43.26,43.48,2.7743,2.98 -2020,7,9,18,53.88,53.88,2.25,2.25 -2020,7,10,18,62.74,62.74,2.15,2.15 -2020,7,11,18,67.82,67.82,2.27,2.27 -2020,7,12,18,66.23,65.89,2.109,2.22 -2020,7,13,18,57.12,57.12,1.92,1.92 -2020,7,1,19,55.22,54.93,1.8221,1.98 -2020,7,2,19,43.42,43.02,2.6063,2.87 -2020,7,3,19,31.9,31.42,3.4024,3.44 -2020,7,4,19,23.66,23.15,3.9,4.16 -2020,7,5,19,21.09,21.09,4.74,4.74 -2020,7,6,19,24.89,25.12,4.3248,4.49 -2020,7,7,19,32,32.92,3.2867,3.42 -2020,7,8,19,42.27,42.35,2.9815,3.03 -2020,7,9,19,51.67,51.67,2.12,2.12 -2020,7,10,19,59.73,59.73,1.94,1.94 -2020,7,11,19,64.48,64.32,1.8231,1.96 -2020,7,12,19,63.19,62.72,2.1733,2.28 -2020,7,13,19,55.17,54.93,1.8404,1.98 -2020,7,1,20,55.77,55.43,1.644,1.81 -2020,7,2,20,44.64,44.23,2.4958,2.62 -2020,7,3,20,34,33.55,2.902,2.91 -2020,7,4,20,27,26.54,2.8548,3.15 -2020,7,5,20,24.9,24.9,3.7,3.7 -2020,7,6,20,28.57,28.57,3.6,3.6 -2020,7,7,20,34.58,34.81,2.7661,2.82 -2020,7,8,20,42.84,42.84,2.51,2.51 -2020,7,9,20,51.28,51.28,2.1,2.1 -2020,7,10,20,59.47,59.47,1.94,1.94 -2020,7,11,20,64.55,64.32,1.8181,1.96 -2020,7,12,20,63.55,62.99,1.8921,2.1 -2020,7,13,20,55.78,55.43,1.6753,1.81 -2020,7,1,21,53.44,53.18,1.4745,1.54 -2020,7,2,21,42.67,42.33,1.8913,2.09 -2020,7,3,21,32,31.67,2.22,2.22 -2020,7,4,21,25.45,25.1,2.1317,2.35 -2020,7,5,21,23.79,23.79,2.79,2.79 -2020,7,6,21,27.98,27.98,2.79,2.79 -2020,7,7,21,33.97,34.23,2.3395,2.39 -2020,7,8,21,42.03,42.03,2.01,2.01 -2020,7,9,21,49.7,49.7,1.81,1.81 -2020,7,10,21,57.23,57.23,1.56,1.56 -2020,7,11,21,61.84,61.62,1.4284,1.53 -2020,7,12,21,61.07,60.49,1.7468,1.83 -2020,7,13,21,53.62,53.18,1.4187,1.54 -2020,7,1,22,59.31,58.61,1.5222,1.58 -2020,7,2,22,49.22,48.65,1.5195,1.7 -2020,7,3,22,37.88,37.64,2.1765,2.18 -2020,7,4,22,29.02,28.65,2.6216,2.86 -2020,7,5,22,24.06,24.06,2.94,2.94 -2020,7,6,22,25.91,25.91,2.84,2.84 -2020,7,7,22,33.61,33.61,2.51,2.51 -2020,7,8,22,43.85,43.85,2.48,2.48 -2020,7,9,22,54.73,54.73,2.04,2.04 -2020,7,10,22,63.03,62.98,1.7659,1.85 -2020,7,11,22,67.44,67.23,1.9152,2 -2020,7,12,22,66.04,65.63,1.5223,1.61 -2020,7,13,22,59.02,58.61,1.4883,1.58 -2020,7,1,23,62.07,61.5,1.5199,1.55 -2020,7,2,23,52.02,51.5,1.5185,1.6 -2020,7,3,23,40.42,40.09,2.1776,2.28 -2020,7,4,23,31.38,31.03,2.9291,3.03 -2020,7,5,23,26.77,26.77,3.07,3.07 -2020,7,6,23,29.46,29.46,2.86,2.86 -2020,7,7,23,37.63,37.63,2.19,2.19 -2020,7,8,23,48.03,47.91,2.2735,2.35 -2020,7,9,23,58.66,58.54,1.8067,1.88 -2020,7,10,23,66.67,66.45,1.6394,1.72 -2020,7,11,23,70.69,70.38,1.7446,1.83 -2020,7,12,23,69.03,68.57,1.4145,1.54 -2020,7,13,23,61.96,61.5,1.4731,1.55 -2020,7,1,24,64.29,64,1.6559,1.71 -2020,7,2,24,54.19,53.88,1.5344,1.67 -2020,7,3,24,42.4,41.98,2.2867,2.34 -2020,7,4,24,33.01,32.66,3.0212,3.11 -2020,7,5,24,28.85,28.85,3.14,3.14 -2020,7,6,24,32.45,32.45,2.88,2.88 -2020,7,7,24,41.19,41.19,2.16,2.16 -2020,7,8,24,51.87,51.5,2.2754,2.36 -2020,7,9,24,62.14,61.75,1.8632,1.95 -2020,7,10,24,69.59,69.26,1.5094,1.61 -2020,7,11,24,73.23,72.88,1.7586,1.83 -2020,7,12,24,71.43,70.99,1.6045,1.69 -2020,7,13,24,64.44,64,1.6079,1.71 -2020,7,1,25,61.34,61,1.453,1.63 -2020,7,2,25,50.56,50.22,1.7411,1.86 -2020,7,3,25,38.01,37.63,2.5267,2.54 -2020,7,4,25,27.92,27.6,3.0682,3.34 -2020,7,5,25,23.44,23.44,3.58,3.58 -2020,7,6,25,27.2,27.2,3.23,3.23 -2020,7,7,25,36.48,36.48,2.63,2.63 -2020,7,8,25,47.44,47.41,2.4193,2.46 -2020,7,9,25,58.24,58.24,1.96,1.96 -2020,7,10,25,66.48,66.36,1.5754,1.72 -2020,7,11,25,70.55,70.36,1.8958,2 -2020,7,12,25,68.79,68.46,1.5393,1.69 -2020,7,13,25,61.32,61,1.4649,1.63 -2020,7,1,26,57.06,56.72,1.4178,1.5 -2020,7,2,26,45.57,45.21,2.058,2.14 -2020,7,3,26,32.3,31.95,2.7751,2.8 -2020,7,4,26,21.35,21.17,3.535,3.66 -2020,7,5,26,16.72,16.72,3.91,3.91 -2020,7,6,26,20.57,20.57,3.57,3.57 -2020,7,7,26,30.61,30.61,3.23,3.23 -2020,7,8,26,42.52,42.52,2.88,2.88 -2020,7,9,26,54.08,54.08,2.16,2.16 -2020,7,10,26,62.5,62.5,1.82,1.82 -2020,7,11,26,66.49,66.49,2.01,2.01 -2020,7,12,26,64.76,64.5,1.4461,1.58 -2020,7,13,26,56.93,56.72,1.3908,1.5 -2020,7,1,27,58.62,58.35,1.3787,1.55 -2020,7,2,27,46.47,46.13,2.2803,2.38 -2020,7,3,27,32.5,32.06,3.1128,3.16 -2020,7,4,27,20.64,20.64,3.92,3.92 -2020,7,5,27,16.39,16.39,4.35,4.35 -2020,7,6,27,20.84,20.84,3.87,3.87 -2020,7,7,27,31.69,31.69,3.43,3.43 -2020,7,8,27,44.3,44.3,2.98,2.98 -2020,7,9,27,56.33,56.33,2.2,2.2 -2020,7,10,27,64.88,64.88,1.9,1.9 -2020,7,11,27,68.74,68.74,2.04,2.04 -2020,7,12,27,66.73,66.49,1.4938,1.63 -2020,7,13,27,58.51,58.35,1.4609,1.55 -2020,7,1,28,58.67,58.39,1.4569,1.64 -2020,7,2,28,46,45.62,2.3554,2.5 -2020,7,3,28,31.92,31.37,3.2572,3.33 -2020,7,4,28,20.05,20.05,4.19,4.19 -2020,7,5,28,16.15,16.15,4.92,4.92 -2020,7,6,28,20.65,20.65,4.28,4.28 -2020,7,7,28,31.36,31.36,3.59,3.59 -2020,7,8,28,43.98,43.98,3.04,3.04 -2020,7,9,28,56.1,56.1,2.28,2.28 -2020,7,10,28,64.95,64.95,2.03,2.03 -2020,7,11,28,69.04,69.04,2.18,2.18 -2020,7,12,28,67.09,66.81,1.6601,1.82 -2020,7,13,28,58.52,58.39,1.5367,1.64 -2020,7,1,29,60.46,60.09,1.7207,1.86 -2020,7,2,29,47.46,47.04,2.2907,2.44 -2020,7,3,29,34.12,33.42,3.178,3.32 -2020,7,4,29,23.79,23.51,3.6712,4.07 -2020,7,5,29,20.57,20.57,4.8,4.8 -2020,7,6,29,24.88,24.88,4.02,4.02 -2020,7,7,29,33.9,34.04,2.8638,3.02 -2020,7,8,29,45.11,45.11,2.73,2.73 -2020,7,9,29,56.26,56.26,2.25,2.25 -2020,7,10,29,65.52,65.52,2.12,2.12 -2020,7,11,29,70.62,70.53,2.2109,2.38 -2020,7,12,29,69.11,68.8,1.9947,2.08 -2020,7,13,29,60.26,60.09,1.7361,1.86 -2020,7,1,30,59.77,59.36,1.7199,1.89 -2020,7,2,30,47.04,46.59,2.3715,2.45 -2020,7,3,30,34.63,33.96,3.1278,3.28 -2020,7,4,30,25.8,25.42,3.8067,3.95 -2020,7,5,30,23.06,23.06,4.5,4.5 -2020,7,6,30,27.02,27.02,3.81,3.81 -2020,7,7,30,34.8,34.8,2.86,2.86 -2020,7,8,30,44.65,44.65,2.69,2.69 -2020,7,9,30,55,55,2.33,2.33 -2020,7,10,30,64.28,64.28,2.23,2.23 -2020,7,11,30,69.74,69.66,2.2765,2.4 -2020,7,12,30,68.46,68.09,2.0551,2.18 -2020,7,13,30,59.48,59.36,1.7882,1.89 -2020,7,1,31,57.58,57.08,1.6482,1.83 -2020,7,2,31,45.31,44.78,2.3686,2.45 -2020,7,3,31,33.69,33.03,2.6008,2.89 -2020,7,4,31,26.1,25.62,3.0561,3.29 -2020,7,5,31,23.7,23.7,3.68,3.68 -2020,7,6,31,27.8,27.8,3.28,3.28 -2020,7,7,31,34.48,34.48,2.49,2.49 -2020,7,8,31,43.16,43.16,2.33,2.33 -2020,7,9,31,52.37,52.37,2.33,2.33 -2020,7,10,31,61.61,61.61,2.08,2.08 -2020,7,11,31,67.4,67.21,1.9753,2.13 -2020,7,12,31,66.15,65.74,1.7698,1.97 -2020,7,13,31,57.26,57.08,1.7296,1.83 -2020,7,1,32,54.47,53.96,1.4995,1.65 -2020,7,2,32,42.85,42.29,2.1746,2.27 -2020,7,3,32,31.09,30.58,2.3964,2.46 -2020,7,4,32,23.27,22.81,2.5123,2.76 -2020,7,5,32,20.89,20.89,3.2,3.2 -2020,7,6,32,25.5,25.5,2.89,2.89 -2020,7,7,32,32.73,32.73,2.4,2.4 -2020,7,8,32,41.46,41.46,2.17,2.17 -2020,7,9,32,50.12,50.12,2.22,2.22 -2020,7,10,32,58.59,58.51,1.7548,1.9 -2020,7,11,32,63.64,63.4,1.7136,1.85 -2020,7,12,32,62.44,61.95,1.636,1.79 -2020,7,13,32,54.25,53.96,1.5579,1.65 -2020,7,1,33,61.92,61.63,1.4772,1.62 -2020,7,2,33,50.56,50.25,1.8462,1.94 -2020,7,3,33,37.59,37.07,2.5163,2.72 -2020,7,4,33,26.8,26.57,3.3521,3.54 -2020,7,5,33,22.61,22.61,3.95,3.95 -2020,7,6,33,26.98,26.98,3.52,3.52 -2020,7,7,33,36.93,36.93,2.87,2.87 -2020,7,8,33,48.47,48.47,2.58,2.58 -2020,7,9,33,59.52,59.52,2.01,2.01 -2020,7,10,33,67.68,67.6,1.6146,1.7 -2020,7,11,33,71.59,71.42,1.7687,1.93 -2020,7,12,33,69.58,69.29,1.6356,1.74 -2020,7,13,33,61.89,61.63,1.4691,1.62 -2020,7,1,34,61.64,61.3,1.5734,1.76 -2020,7,2,34,49.52,49.13,2.1339,2.29 -2020,7,3,34,36.2,35.57,2.8966,3.06 -2020,7,4,34,25.38,25.19,3.4175,3.76 -2020,7,5,34,21.82,21.82,4.3,4.3 -2020,7,6,34,26.41,26.41,3.76,3.76 -2020,7,7,34,36.14,36.14,3.03,3.03 -2020,7,8,34,47.69,47.69,2.83,2.83 -2020,7,9,34,59,59,2.28,2.28 -2020,7,10,34,67.52,67.52,1.88,1.88 -2020,7,11,34,71.66,71.55,1.7945,2 -2020,7,12,34,69.68,69.36,1.6181,1.84 -2020,7,13,34,61.53,61.3,1.5835,1.76 -2020,7,1,35,62.05,61.38,1.7119,1.92 -2020,7,2,35,50,49.39,2.1763,2.31 -2020,7,3,35,38.2,37.19,2.5274,2.78 -2020,7,4,35,29.42,28.83,2.9344,3.28 -2020,7,5,35,26.56,26.56,3.7,3.7 -2020,7,6,35,30.64,30.64,2.98,2.98 -2020,7,7,35,38.29,38.29,2.28,2.28 -2020,7,8,35,47.94,47.84,2.1673,2.34 -2020,7,9,35,58,57.93,2.092,2.16 -2020,7,10,35,66.84,66.75,1.6252,1.77 -2020,7,11,35,71.68,71.52,1.8569,1.96 -2020,7,12,35,70.02,69.67,1.6744,1.87 -2020,7,13,35,61.65,61.38,1.7558,1.92 -2020,7,1,36,63.88,63.29,1.776,1.93 -2020,7,2,36,52.05,51.57,1.9119,2.11 -2020,7,3,36,39.79,38.9,2.6031,2.65 -2020,7,4,36,30.05,29.53,3.0332,3.28 -2020,7,5,36,26.62,26.62,3.71,3.71 -2020,7,6,36,31,31,3.26,3.26 -2020,7,7,36,39.76,39.76,2.54,2.54 -2020,7,8,36,50.35,50.22,2.2569,2.47 -2020,7,9,36,60.81,60.71,2.0512,2.15 -2020,7,10,36,69.15,69.05,1.5751,1.66 -2020,7,11,36,73.5,73.27,1.7998,1.92 -2020,7,12,36,71.55,71.19,1.7001,1.9 -2020,7,13,36,63.61,63.29,1.7467,1.93 -2020,7,1,37,59.42,58.67,1.5406,1.69 -2020,7,2,37,47.88,47.13,1.8896,2.11 -2020,7,3,37,37.02,36.01,2.248,2.38 -2020,7,4,37,29.78,29.19,2.5864,2.72 -2020,7,5,37,27.47,27.38,3.0183,3.17 -2020,7,6,37,30.97,30.97,2.62,2.62 -2020,7,7,37,37,37,1.99,1.99 -2020,7,8,37,45.44,45.32,1.8291,2.04 -2020,7,9,37,54.64,54.53,2.0477,2.16 -2020,7,10,37,63.59,63.5,1.5878,1.73 -2020,7,11,37,68.81,68.59,1.5721,1.74 -2020,7,12,37,67.33,66.99,1.3961,1.56 -2020,7,13,37,58.94,58.67,1.5315,1.69 -2020,7,1,38,65.94,65.53,1.5691,1.66 -2020,7,2,38,56.58,56.18,1.5484,1.63 -2020,7,3,38,46.28,45.87,2.1052,2.23 -2020,7,4,38,38.59,38.21,2.5209,2.71 -2020,7,5,38,35.42,35.19,2.3787,2.43 -2020,7,6,38,38.34,38.16,2.2619,2.38 -2020,7,7,38,45.62,45.48,1.4694,1.55 -2020,7,8,38,54.75,54.14,1.7235,1.89 -2020,7,9,38,63.71,63.01,1.626,1.76 -2020,7,10,38,70.52,69.81,1.3793,1.5 -2020,7,11,38,74.06,73.52,1.3934,1.48 -2020,7,12,38,72.64,72.04,1.3702,1.52 -2020,7,13,38,66.18,65.53,1.5168,1.66 -2020,7,1,39,67.76,67.45,1.6097,1.71 -2020,7,2,39,58.06,57.74,1.5094,1.67 -2020,7,3,39,47.29,46.87,2.0281,2.16 -2020,7,4,39,39.08,38.71,2.5328,2.69 -2020,7,5,39,35.76,35.63,2.4821,2.53 -2020,7,6,39,39.21,39.04,2.2807,2.44 -2020,7,7,39,46.94,46.86,1.5941,1.66 -2020,7,8,39,56.53,55.96,1.8379,1.96 -2020,7,9,39,65.74,65.1,1.7381,1.83 -2020,7,10,39,72.61,72.07,1.3587,1.47 -2020,7,11,39,76.19,75.73,1.4499,1.54 -2020,7,12,39,74.68,74.12,1.4736,1.62 -2020,7,13,39,68.04,67.45,1.5613,1.71 -2020,7,1,40,67.65,67.28,1.5664,1.66 -2020,7,2,40,57.63,57.31,1.5363,1.68 -2020,7,3,40,46.82,46.28,1.9412,2.03 -2020,7,4,40,38.52,38.08,2.4885,2.64 -2020,7,5,40,35.29,35.11,2.4916,2.6 -2020,7,6,40,39.11,38.89,2.2437,2.43 -2020,7,7,40,47.01,46.91,1.7955,1.89 -2020,7,8,40,56.63,56.05,1.9637,2 -2020,7,9,40,65.8,65.2,1.8096,1.86 -2020,7,10,40,72.69,72.29,1.3286,1.45 -2020,7,11,40,76.48,76.06,1.3785,1.54 -2020,7,12,40,74.72,74.26,1.5262,1.6 -2020,7,13,40,67.78,67.28,1.4774,1.66 -2020,7,1,41,65.81,65.48,1.5743,1.71 -2020,7,2,41,55.38,55.05,1.5491,1.74 -2020,7,3,41,43.62,43.11,2.2093,2.3 -2020,7,4,41,34.21,33.81,2.8045,3.03 -2020,7,5,41,30.22,30.22,3.16,3.16 -2020,7,6,41,34.12,34.12,2.91,2.91 -2020,7,7,41,42.99,42.99,2.32,2.32 -2020,7,8,41,53.45,53.17,2.3694,2.38 -2020,7,9,41,63.53,63.19,1.8318,2 -2020,7,10,41,70.98,70.73,1.4669,1.55 -2020,7,11,41,74.91,74.58,1.654,1.81 -2020,7,12,41,73.07,72.69,1.5874,1.74 -2020,7,13,41,65.86,65.48,1.5336,1.71 -2020,7,1,42,66.43,66.01,1.6795,1.83 -2020,7,2,42,55.65,55.31,1.7132,1.82 -2020,7,3,42,44.09,43.42,2.196,2.35 -2020,7,4,42,34.77,34.33,2.7767,2.94 -2020,7,5,42,31.01,31.01,3.13,3.13 -2020,7,6,42,35.1,35.1,2.9,2.9 -2020,7,7,42,43.75,43.75,2.32,2.32 -2020,7,8,42,53.93,53.66,2.2854,2.3 -2020,7,9,42,63.71,63.4,1.9068,2.01 -2020,7,10,42,71.32,71.1,1.4336,1.49 -2020,7,11,42,75.55,75.22,1.6131,1.77 -2020,7,12,42,73.74,73.38,1.6916,1.82 -2020,7,13,42,66.38,66.01,1.6641,1.83 -2020,7,1,43,69.16,68.37,1.6938,1.89 -2020,7,2,43,58.16,57.53,1.6578,1.81 -2020,7,3,43,46.73,45.78,2.1954,2.2 -2020,7,4,43,37.81,37.03,2.6308,2.74 -2020,7,5,43,34.41,34.15,2.7067,2.91 -2020,7,6,43,38.58,38.34,2.4788,2.68 -2020,7,7,43,46.69,46.56,2.0045,2.19 -2020,7,8,43,56.45,55.95,2.101,2.11 -2020,7,9,43,65.71,65.24,1.771,1.9 -2020,7,10,43,73.23,72.9,1.2659,1.4 -2020,7,11,43,77.66,77.22,1.5128,1.64 -2020,7,12,43,76.11,75.62,1.668,1.84 -2020,7,13,43,68.87,68.37,1.7398,1.89 -2020,7,1,44,68.59,67.67,1.8891,2 -2020,7,2,44,56.8,56.03,1.896,1.96 -2020,7,3,44,44.84,43.71,2.2762,2.28 -2020,7,4,44,35.85,34.97,2.6173,2.74 -2020,7,5,44,32.62,32.38,2.9538,3.13 -2020,7,6,44,36.9,36.66,2.6652,2.89 -2020,7,7,44,44.74,44.67,2.2652,2.38 -2020,7,8,44,54.69,54.22,2.0731,2.27 -2020,7,9,44,64.56,64.11,1.9873,2.15 -2020,7,10,44,72.84,72.54,1.533,1.56 -2020,7,11,44,77.69,77.24,1.641,1.8 -2020,7,12,44,75.99,75.51,1.708,1.89 -2020,7,13,44,68.17,67.67,1.862,2 -2020,7,1,45,67.31,66.26,1.74,1.85 -2020,7,2,45,55.83,54.86,1.7439,1.9 -2020,7,3,45,44.53,43.19,1.9692,2 -2020,7,4,45,36.5,35.39,2.4105,2.51 -2020,7,5,45,33.67,33.26,2.6886,2.93 -2020,7,6,45,37.49,37.18,2.3356,2.5 -2020,7,7,45,44.35,44.27,1.9602,2.01 -2020,7,8,45,53.58,53.18,2.0402,2.05 -2020,7,9,45,63.24,62.74,1.9378,2.12 -2020,7,10,45,71.5,71.16,1.4819,1.57 -2020,7,11,45,76.26,75.79,1.536,1.69 -2020,7,12,45,74.58,74.08,1.5556,1.71 -2020,7,13,45,66.78,66.26,1.706,1.85 -2020,7,1,46,59.04,57.95,1.5862,1.68 -2020,7,2,46,48.57,47.4,1.8653,1.99 -2020,7,3,46,38.3,36.85,2.0597,2.1 -2020,7,4,46,31.25,30.18,2.3788,2.52 -2020,7,5,46,28.72,28.29,2.6944,2.89 -2020,7,6,46,31.88,31.61,2.2076,2.37 -2020,7,7,46,37.34,37.28,1.7888,1.8 -2020,7,8,46,45.42,45.14,1.8244,1.85 -2020,7,9,46,54.28,53.89,1.8526,2.02 -2020,7,10,46,62.62,62.32,1.4396,1.58 -2020,7,11,46,67.29,66.93,1.5108,1.61 -2020,7,12,46,66,65.51,1.4073,1.59 -2020,7,13,46,58.41,57.95,1.5463,1.68 -2020,7,1,47,60.6,59.57,1.3104,1.45 -2020,7,2,47,50.42,49.12,1.8126,1.83 -2020,7,3,47,39.88,38.38,1.9316,1.95 -2020,7,4,47,32.82,31.6,2.0372,2.18 -2020,7,5,47,30.34,29.74,2.4251,2.56 -2020,7,6,47,33.71,33.18,2.2216,2.33 -2020,7,7,47,39.33,39.12,1.7962,1.8 -2020,7,8,47,47.61,47.14,1.7853,1.96 -2020,7,9,47,56.72,56.05,1.9251,2.07 -2020,7,10,47,64.62,64.2,1.4135,1.5 -2020,7,11,47,69.01,68.53,1.2647,1.4 -2020,7,12,47,67.54,67.02,1.2054,1.35 -2020,7,13,47,60.1,59.57,1.3549,1.45 -2020,7,1,48,56.45,55.5,1.2461,1.31 -2020,7,2,48,46.02,44.88,1.5288,1.61 -2020,7,3,48,34.8,33.62,1.6678,1.69 -2020,7,4,48,26.68,25.59,1.9932,2.08 -2020,7,5,48,24.13,23.54,2.5967,2.76 -2020,7,6,48,28.15,27.64,2.5218,2.64 -2020,7,7,48,35.26,34.72,2.0701,2.11 -2020,7,8,48,43.46,43.07,1.7921,1.94 -2020,7,9,48,52.27,51.69,1.9685,2.08 -2020,7,10,48,60.23,59.71,1.5668,1.67 -2020,7,11,48,64.59,64.06,1.3485,1.49 -2020,7,12,48,63.33,62.72,1.2874,1.38 -2020,7,13,48,56.07,55.5,1.2057,1.31 -2020,7,1,49,52.94,52.26,1.3033,1.43 -2020,7,2,49,41.73,40.89,1.8587,1.98 -2020,7,3,49,30.23,29.4,2.1824,2.19 -2020,7,4,49,22.11,21.52,2.5477,2.71 -2020,7,5,49,19.36,19.15,2.9038,3.13 -2020,7,6,49,23.12,23.12,2.82,2.82 -2020,7,7,49,30.16,30.04,2.3496,2.48 -2020,7,8,49,39.09,38.97,1.976,2.18 -2020,7,9,49,48.13,47.94,2.0365,2.22 -2020,7,10,49,56.64,56.46,1.6496,1.75 -2020,7,11,49,61.64,61.35,1.4666,1.64 -2020,7,12,49,60.47,59.98,1.4307,1.51 -2020,7,13,49,52.63,52.26,1.3451,1.43 -2020,7,1,50,68.71,68.34,1.5374,1.63 -2020,7,2,50,59.42,59.15,1.6069,1.65 -2020,7,3,50,49.73,49.22,1.9222,2.09 -2020,7,4,50,42.48,42.08,2.2623,2.44 -2020,7,5,50,40.09,39.64,2.1832,2.24 -2020,7,6,50,43.36,42.82,2.1339,2.27 -2020,7,7,50,50.03,49.73,1.3602,1.47 -2020,7,8,50,58.57,57.78,1.4751,1.64 -2020,7,9,50,66.85,65.96,1.633,1.68 -2020,7,10,50,73.24,72.48,1.3915,1.44 -2020,7,11,50,76.62,76.03,1.2911,1.44 -2020,7,12,50,75.31,74.66,1.4772,1.57 -2020,7,13,50,69.01,68.34,1.5655,1.63 -2020,7,1,51,71.38,70.9,1.4051,1.57 -2020,7,2,51,61.88,61.58,1.5214,1.61 -2020,7,3,51,51.89,51.33,1.7757,1.87 -2020,7,4,51,44.37,43.9,2.2186,2.35 -2020,7,5,51,41.98,41.38,2.1733,2.24 -2020,7,6,51,45.45,44.88,2.0689,2.19 -2020,7,7,51,52.48,52.2,1.515,1.58 -2020,7,8,51,61.47,60.62,1.4682,1.63 -2020,7,9,51,69.94,69.07,1.6289,1.65 -2020,7,10,51,76.16,75.59,1.2056,1.3 -2020,7,11,51,79.5,78.95,1.231,1.37 -2020,7,12,51,77.96,77.34,1.4512,1.54 -2020,7,13,51,71.53,70.9,1.5038,1.57 -2020,7,1,52,70.11,69.44,1.5609,1.72 -2020,7,2,52,60.12,59.6,1.6002,1.69 -2020,7,3,52,49.75,49.03,1.8438,1.89 -2020,7,4,52,42.07,41.37,2.2316,2.37 -2020,7,5,52,39.29,38.84,2.3743,2.39 -2020,7,6,52,42.9,42.53,2.0659,2.25 -2020,7,7,52,50.06,49.88,1.6962,1.78 -2020,7,8,52,58.84,58.17,1.5391,1.69 -2020,7,9,52,67.12,66.5,1.5116,1.63 -2020,7,10,52,73.76,73.34,1.2793,1.35 -2020,7,11,52,77.93,77.39,1.3647,1.55 -2020,7,12,52,76.65,76.04,1.6045,1.76 -2020,7,13,52,70.02,69.44,1.6796,1.72 -2020,7,1,53,72.51,71.66,1.5981,1.68 -2020,7,2,53,61.8,61.04,1.4931,1.62 -2020,7,3,53,50.77,49.83,1.6969,1.81 -2020,7,4,53,42.89,41.89,2.1723,2.23 -2020,7,5,53,40.28,39.55,2.4044,2.43 -2020,7,6,53,43.97,43.47,2.1655,2.34 -2020,7,7,53,51.11,50.83,1.8464,2.02 -2020,7,8,53,60.17,59.45,1.6873,1.83 -2020,7,9,53,68.86,68.26,1.6886,1.78 -2020,7,10,53,76.23,75.73,1.411,1.52 -2020,7,11,53,80.7,80.06,1.4447,1.64 -2020,7,12,53,79.29,78.65,1.5835,1.69 -2020,7,13,53,72.29,71.66,1.6134,1.68 -2020,7,1,54,73.98,73.13,1.3593,1.49 -2020,7,2,54,64.02,63.14,1.4552,1.61 -2020,7,3,54,53.63,52.59,1.6199,1.69 -2020,7,4,54,46.56,45.3,2.0047,2.11 -2020,7,5,54,44.28,43.24,2.1432,2.17 -2020,7,6,54,47.94,47.14,1.9633,2.15 -2020,7,7,54,54.46,54.02,1.814,1.89 -2020,7,8,54,63.03,62.29,1.6768,1.76 -2020,7,9,54,71.3,70.6,1.7931,1.9 -2020,7,10,54,78.16,77.43,1.6951,1.82 -2020,7,11,54,81.95,81.15,1.4529,1.6 -2020,7,12,54,80.35,79.63,1.3586,1.51 -2020,7,13,54,73.85,73.13,1.4454,1.49 -2020,7,1,55,69.83,68.88,1.3705,1.52 -2020,7,2,55,59.98,59.02,1.4914,1.6 -2020,7,3,55,49.82,48.65,1.5424,1.61 -2020,7,4,55,42.92,41.63,1.8495,2.03 -2020,7,5,55,40.85,39.89,2.0804,2.15 -2020,7,6,55,44.44,43.63,1.7575,1.9 -2020,7,7,55,50.65,50.19,1.6444,1.73 -2020,7,8,55,58.9,58.26,1.6254,1.79 -2020,7,9,55,67.6,66.8,1.8417,1.96 -2020,7,10,55,74.37,73.7,1.476,1.6 -2020,7,11,55,77.88,77.19,1.2305,1.32 -2020,7,12,55,76.17,75.42,1.4124,1.51 -2020,7,13,55,69.65,68.88,1.4713,1.52 -2020,7,1,56,71.66,71.21,1.2209,1.33 -2020,7,2,56,63.29,62.94,1.3816,1.54 -2020,7,3,56,54.49,53.97,1.8987,2.02 -2020,7,4,56,48.01,47.63,1.9981,2.21 -2020,7,5,56,46.46,45.57,1.8646,1.95 -2020,7,6,56,49.58,48.5,1.924,2.02 -2020,7,7,56,55.21,54.48,1.4808,1.6 -2020,7,8,56,62.5,61.61,1.3474,1.39 -2020,7,9,56,69.92,68.96,1.4287,1.49 -2020,7,10,56,75.9,75.02,1.277,1.4 -2020,7,11,56,78.92,78.2,1.1432,1.22 -2020,7,12,56,77.65,76.94,1.1909,1.29 -2020,7,13,56,71.95,71.21,1.2823,1.33 -2020,7,1,57,72.19,71.65,1.4116,1.51 -2020,7,2,57,63.39,63.05,1.4922,1.62 -2020,7,3,57,54.31,53.75,1.8625,1.97 -2020,7,4,57,47.67,47.24,2.0027,2.17 -2020,7,5,57,46.22,45.22,1.8929,1.98 -2020,7,6,57,49.45,48.27,1.9761,2.13 -2020,7,7,57,55.22,54.42,1.4548,1.57 -2020,7,8,57,62.69,61.73,1.4654,1.48 -2020,7,9,57,70.11,69.15,1.4419,1.51 -2020,7,10,57,76.09,75.28,1.185,1.3 -2020,7,11,57,79.3,78.54,1.2394,1.34 -2020,7,12,57,78.21,77.44,1.3433,1.42 -2020,7,13,57,72.47,71.65,1.3696,1.51 -2020,7,1,58,73.85,73.3,1.2488,1.33 -2020,7,2,58,65.36,64.97,1.4423,1.5 -2020,7,3,58,56.48,55.86,1.8405,1.88 -2020,7,4,58,50.13,49.45,1.9828,2.13 -2020,7,5,58,48.67,47.46,1.9113,2.07 -2020,7,6,58,51.71,50.51,2.0053,2.14 -2020,7,7,58,57.46,56.7,1.4877,1.56 -2020,7,8,58,64.9,63.99,1.3677,1.38 -2020,7,9,58,72.15,71.25,1.41,1.41 -2020,7,10,58,77.64,76.97,1.0338,1.16 -2020,7,11,58,80.62,79.94,1.1284,1.24 -2020,7,12,58,79.52,78.83,1.2293,1.28 -2020,7,13,58,74.01,73.3,1.2,1.33 -2020,7,1,59,73.66,73.06,1.3696,1.45 -2020,7,2,59,64.55,64.06,1.4185,1.47 -2020,7,3,59,55,54.34,1.6689,1.73 -2020,7,4,59,48.37,47.57,2.0025,2.09 -2020,7,5,59,46.37,45.43,1.8726,2.01 -2020,7,6,59,49.55,48.81,1.8533,2.01 -2020,7,7,59,55.89,55.46,1.4842,1.53 -2020,7,8,59,63.94,63.13,1.39,1.39 -2020,7,9,59,71.62,70.81,1.4828,1.49 -2020,7,10,59,77.47,76.91,1.1252,1.25 -2020,7,11,59,80.97,80.32,1.2912,1.37 -2020,7,12,59,79.79,79.08,1.4895,1.53 -2020,7,13,59,73.74,73.06,1.426,1.45 -2020,7,1,60,75.75,75.09,1.2624,1.34 -2020,7,2,60,67.19,66.47,1.3733,1.45 -2020,7,3,60,57.87,57.08,1.6327,1.67 -2020,7,4,60,51.66,50.55,1.9569,2.04 -2020,7,5,60,49.67,48.5,1.8561,2.03 -2020,7,6,60,52.8,51.87,1.964,2.11 -2020,7,7,60,58.57,58.03,1.6384,1.67 -2020,7,8,60,66.11,65.25,1.4398,1.44 -2020,7,9,60,73.11,72.4,1.4769,1.48 -2020,7,10,60,78.89,78.25,1.2544,1.38 -2020,7,11,60,82.38,81.64,1.2691,1.39 -2020,7,12,60,81.36,80.63,1.3244,1.43 -2020,7,13,60,75.78,75.09,1.231,1.34 -2020,7,1,61,76.05,75.33,1.2573,1.32 -2020,7,2,61,66.83,66.06,1.2661,1.4 -2020,7,3,61,56.96,56.09,1.5249,1.57 -2020,7,4,61,50.36,49.1,1.9204,2.04 -2020,7,5,61,48.22,46.96,1.9798,2.13 -2020,7,6,61,51.54,50.56,2.1067,2.17 -2020,7,7,61,57.58,57.01,1.6732,1.79 -2020,7,8,61,65.45,64.64,1.4977,1.5 -2020,7,9,61,72.82,72.19,1.5778,1.58 -2020,7,10,61,79.21,78.49,1.484,1.59 -2020,7,11,61,83.12,82.24,1.4876,1.57 -2020,7,12,61,81.94,81.17,1.3236,1.47 -2020,7,13,61,76.03,75.33,1.2151,1.32 -2020,7,1,62,78.67,78.24,0.729,0.81 -2020,7,2,62,71.45,70.86,1.0389,1.15 -2020,7,3,62,63.17,62.34,1.558,1.58 -2020,7,4,62,57.29,56.08,1.8579,1.93 -2020,7,5,62,55.47,54.09,2.0667,2.13 -2020,7,6,62,58.58,57.13,2.2228,2.34 -2020,7,7,62,63.89,62.81,1.8789,1.94 -2020,7,8,62,70.39,69.45,1.5994,1.6 -2020,7,9,62,76.45,75.83,1.6156,1.62 -2020,7,10,62,81.44,80.61,1.3171,1.41 -2020,7,11,62,84.02,83.28,1.0718,1.14 -2020,7,12,62,83.13,82.49,0.9679,1.07 -2020,7,13,62,78.73,78.24,0.7483,0.81 -2020,7,1,63,80.66,80.3,0.6688,0.72 -2020,7,2,63,73.8,73.31,0.9196,1.02 -2020,7,3,63,65.88,65.15,1.5206,1.55 -2020,7,4,63,60.14,59.16,1.8477,1.91 -2020,7,5,63,58.69,57.44,2.0466,2.16 -2020,7,6,63,62.09,60.87,2.2124,2.32 -2020,7,7,63,67.86,66.71,2.0572,2.08 -2020,7,8,63,74.06,73.16,1.83,1.83 -2020,7,9,63,79.63,79.05,1.8479,1.87 -2020,7,10,63,84.05,83.18,1.4321,1.57 -2020,7,11,63,86.03,85.31,1.1008,1.17 -2020,7,12,63,84.96,84.3,0.9964,1.12 -2020,7,13,63,80.73,80.3,0.6648,0.72 -2020,7,1,64,75.59,75.08,0.8556,0.88 -2020,7,2,64,67.18,66.53,1.1143,1.19 -2020,7,3,64,58.02,57.23,1.3372,1.34 -2020,7,4,64,51.84,50.71,1.582,1.72 -2020,7,5,64,50.16,48.97,1.835,1.9 -2020,7,6,64,53.88,52.67,1.8992,1.97 -2020,7,7,64,59.98,59.07,1.739,1.8 -2020,7,8,64,67.45,66.56,1.79,1.79 -2020,7,9,64,74.41,73.68,1.6995,1.9 -2020,7,10,64,80.01,79.03,1.5762,1.73 -2020,7,11,64,82.6,81.75,1.2656,1.32 -2020,7,12,64,81.08,80.36,1.0624,1.19 -2020,7,13,64,75.62,75.08,0.8238,0.88 -2020,7,1,65,74.03,73.1,1.3034,1.4 -2020,7,2,65,65.49,64.59,1.442,1.51 -2020,7,3,65,56.19,55.23,1.2906,1.37 -2020,7,4,65,49.83,48.78,1.4977,1.6 -2020,7,5,65,48.46,47.55,1.664,1.71 -2020,7,6,65,52.53,51.35,1.7956,1.83 -2020,7,7,65,58.93,57.8,1.8436,1.85 -2020,7,8,65,66.06,65.39,1.8154,1.9 -2020,7,9,65,74.14,73.1,1.7518,1.93 -2020,7,10,65,79.38,78.41,1.5068,1.58 -2020,7,11,65,81.24,80.43,1.209,1.23 -2020,7,12,65,79.44,78.45,1.4273,1.54 -2020,7,13,65,74.01,73.1,1.3015,1.4 -2020,7,1,66,75.97,75.44,1.0174,1.12 -2020,7,2,66,68.99,68.59,1.4313,1.5 -2020,7,3,66,61.14,60.6,1.9059,1.98 -2020,7,4,66,55.1,54.71,2.012,2.24 -2020,7,5,66,54.04,52.8,1.8671,1.9 -2020,7,6,66,56.66,55.23,2.0821,2.2 -2020,7,7,66,61.12,60.31,1.7412,1.85 -2020,7,8,66,67.55,66.56,1.4496,1.47 -2020,7,9,66,73.79,72.93,1.2313,1.33 -2020,7,10,66,78.83,78.11,1.0099,1.1 -2020,7,11,66,81.22,80.6,0.8857,0.94 -2020,7,12,66,80.41,79.81,0.882,0.94 -2020,7,13,66,76.16,75.44,1.0465,1.12 -2020,7,1,67,79.59,79.01,0.7616,0.85 -2020,7,2,67,74.66,74.34,1.114,1.2 -2020,7,3,67,68.72,68.26,1.7481,1.82 -2020,7,4,67,63.65,63.32,1.959,2.14 -2020,7,5,67,62.79,61.49,1.9124,1.99 -2020,7,6,67,64.18,62.82,2.1053,2.33 -2020,7,7,67,67.14,66.44,1.8792,1.98 -2020,7,8,67,72.01,71.04,1.5454,1.57 -2020,7,9,67,76.62,75.86,1.1268,1.17 -2020,7,10,67,80.24,79.67,0.8123,0.86 -2020,7,11,67,82.02,81.54,0.6671,0.73 -2020,7,12,67,81.87,81.44,0.6184,0.66 -2020,7,13,67,79.58,79.01,0.8339,0.85 -2020,7,1,68,81.06,80.57,0.6033,0.67 -2020,7,2,68,77.17,76.92,0.9401,1.02 -2020,7,3,68,72.36,71.98,1.5049,1.56 -2020,7,4,68,67.94,67.72,1.7249,1.91 -2020,7,5,68,67.19,66.1,1.8305,1.84 -2020,7,6,68,67.86,66.97,2.0115,2.19 -2020,7,7,68,70.32,69.8,1.8483,1.94 -2020,7,8,68,74.03,73.34,1.4287,1.49 -2020,7,9,68,77.84,77.23,0.9709,1.03 -2020,7,10,68,80.9,80.41,0.7294,0.77 -2020,7,11,68,82.53,82.15,0.5681,0.62 -2020,7,12,68,82.6,82.26,0.5246,0.56 -2020,7,13,68,80.98,80.57,0.6372,0.67 -2020,7,1,69,75.2,74.61,1.1073,1.24 -2020,7,2,69,67.61,67.22,1.4121,1.48 -2020,7,3,69,59.44,58.86,1.8838,1.92 -2020,7,4,69,53.48,52.91,1.9924,2.2 -2020,7,5,69,52.32,51.04,1.9031,1.92 -2020,7,6,69,55.05,53.62,2.0534,2.17 -2020,7,7,69,59.97,58.95,1.6872,1.75 -2020,7,8,69,66.57,65.6,1.3938,1.46 -2020,7,9,69,73.15,72.28,1.2645,1.35 -2020,7,10,69,78.37,77.61,1.074,1.14 -2020,7,11,69,80.9,80.21,0.9586,1.08 -2020,7,12,69,80.04,79.35,1.035,1.1 -2020,7,13,69,75.39,74.61,1.1732,1.24 -2020,7,1,70,76.66,76.09,1.0549,1.17 -2020,7,2,70,69.33,68.82,1.3362,1.45 -2020,7,3,70,61.17,60.53,1.6928,1.75 -2020,7,4,70,55.42,54.58,1.8961,2.01 -2020,7,5,70,54,52.67,1.9679,1.97 -2020,7,6,70,56.62,55.29,1.9807,2.12 -2020,7,7,70,61.75,60.82,1.5489,1.64 -2020,7,8,70,68.39,67.49,1.285,1.39 -2020,7,9,70,74.81,74.02,1.1963,1.29 -2020,7,10,70,79.63,78.99,0.9948,1.03 -2020,7,11,70,82.07,81.46,0.8903,1 -2020,7,12,70,81.3,80.66,0.9937,1.06 -2020,7,13,70,76.79,76.09,1.0762,1.17 -2020,7,1,71,77.45,76.91,0.9738,1.06 -2020,7,2,71,70.34,69.73,1.1984,1.29 -2020,7,3,71,62.18,61.44,1.5297,1.62 -2020,7,4,71,56.5,55.43,1.7905,1.9 -2020,7,5,71,54.7,53.39,1.9694,1.98 -2020,7,6,71,57.45,56.15,2.0589,2.16 -2020,7,7,71,62.57,61.66,1.6136,1.71 -2020,7,8,71,69.09,68.24,1.2717,1.35 -2020,7,9,71,75.35,74.67,1.243,1.29 -2020,7,10,71,80.18,79.56,1.0121,1.07 -2020,7,11,71,82.72,82.13,0.8847,0.96 -2020,7,12,71,81.93,81.32,0.9683,1.05 -2020,7,13,71,77.54,76.91,1.0082,1.06 -2020,7,1,72,57.58,57.33,1.2899,1.38 -2020,7,2,72,46.7,46.41,1.7935,1.91 -2020,7,3,72,36.03,35.68,2.0696,2.2 -2020,7,4,72,29.72,29.35,2.2956,2.48 -2020,7,5,72,28.59,28.38,2.6189,2.87 -2020,7,6,72,32.85,32.85,2.67,2.67 -2020,7,7,72,39.19,39.32,2.0174,2.14 -2020,7,8,72,46.86,46.86,1.69,1.69 -2020,7,9,72,54.19,54.11,1.5607,1.71 -2020,7,10,72,61.47,61.21,1.4992,1.64 -2020,7,11,72,65.81,65.54,1.3239,1.44 -2020,7,12,72,65.38,64.68,1.5669,1.66 -2020,7,13,72,57.9,57.33,1.2343,1.38 -2020,7,1,73,57.72,57.29,1.3399,1.5 -2020,7,2,73,47.7,47.29,1.7501,1.92 -2020,7,3,73,37.92,37.54,2.1646,2.25 -2020,7,4,73,32.07,31.66,2.3017,2.49 -2020,7,5,73,30.93,30.66,2.6112,2.87 -2020,7,6,73,34.57,34.57,2.76,2.76 -2020,7,7,73,40.02,40.02,2.04,2.04 -2020,7,8,73,46.55,46.49,1.6046,1.69 -2020,7,9,73,53.24,53.07,1.6161,1.72 -2020,7,10,73,60.57,60.16,1.5109,1.67 -2020,7,11,73,65.07,64.68,1.4956,1.58 -2020,7,12,73,64.92,64.05,1.7252,1.83 -2020,7,13,73,58.01,57.29,1.3521,1.5 -2020,7,1,74,60.11,59.77,1.2848,1.38 -2020,7,2,74,49.87,49.54,1.7579,1.91 -2020,7,3,74,39.6,39.24,2.1761,2.27 -2020,7,4,74,33.67,33.3,2.4203,2.51 -2020,7,5,74,32.64,32.39,2.3658,2.6 -2020,7,6,74,36.7,36.7,2.37,2.37 -2020,7,7,74,42.37,42.42,1.6486,1.74 -2020,7,8,74,49.16,49.16,1.52,1.52 -2020,7,9,74,56.03,55.88,1.4881,1.57 -2020,7,10,74,63.26,62.89,1.4126,1.51 -2020,7,11,74,67.58,67.24,1.3527,1.44 -2020,7,12,74,67.43,66.63,1.5683,1.63 -2020,7,13,74,60.45,59.77,1.3132,1.38 -2020,7,1,75,57.25,56.94,1.088,1.13 -2020,7,2,75,49.84,49.6,1.5004,1.55 -2020,7,3,75,42.5,42.26,1.6246,1.66 -2020,7,4,75,38.51,38.26,1.4653,1.58 -2020,7,5,75,37.74,37.57,1.5709,1.69 -2020,7,6,75,39.88,39.88,1.93,1.93 -2020,7,7,75,43.09,43.11,1.6369,1.66 -2020,7,8,75,47.63,47.63,1.64,1.64 -2020,7,9,75,52.85,52.67,1.4735,1.51 -2020,7,10,75,58.43,58.03,1.2342,1.4 -2020,7,11,75,61.91,61.57,1.2454,1.3 -2020,7,12,75,62.27,61.61,1.2065,1.28 -2020,7,13,75,57.53,56.94,1.0366,1.13 -2020,7,1,76,56.82,56.57,0.7585,0.81 -2020,7,2,76,51.18,50.97,1.1316,1.22 -2020,7,3,76,45.28,45.07,1.4153,1.44 -2020,7,4,76,42.02,41.81,1.3969,1.45 -2020,7,5,76,41.41,41.22,1.4842,1.65 -2020,7,6,76,42.95,42.95,1.84,1.84 -2020,7,7,76,45.34,45.34,1.5,1.5 -2020,7,8,76,48.85,48.85,1.46,1.46 -2020,7,9,76,53.08,52.9,1.2025,1.27 -2020,7,10,76,57.4,57.06,1.0532,1.17 -2020,7,11,76,60.04,59.78,0.9342,0.96 -2020,7,12,76,60.39,59.94,0.7883,0.88 -2020,7,13,76,57,56.57,0.7576,0.81 -2020,7,1,77,56.95,56.28,1.4284,1.56 -2020,7,2,77,45.74,44.97,1.7839,1.92 -2020,7,3,77,34.16,33.55,2.137,2.21 -2020,7,4,77,26.13,25.64,2.5411,2.7 -2020,7,5,77,23.8,23.45,3.0657,3.35 -2020,7,6,77,27.74,27.74,3.52,3.52 -2020,7,7,77,35.25,34.95,2.8312,2.99 -2020,7,8,77,43.61,43.54,2.2434,2.27 -2020,7,9,77,52.04,51.83,2.1642,2.19 -2020,7,10,77,60.34,59.97,1.7414,1.84 -2020,7,11,77,65.23,64.87,1.6689,1.75 -2020,7,12,77,64.61,63.86,1.7153,1.76 -2020,7,13,77,56.93,56.28,1.4698,1.56 -2020,7,1,78,54.53,54.12,1.3678,1.47 -2020,7,2,78,43.82,43.42,1.6909,1.77 -2020,7,3,78,33.05,32.66,2.0431,2.14 -2020,7,4,78,26.06,25.64,2.2622,2.5 -2020,7,5,78,24.27,24.06,2.5174,2.75 -2020,7,6,78,28.36,28.36,2.54,2.54 -2020,7,7,78,34.87,34.92,2.2521,2.28 -2020,7,8,78,42.68,42.63,1.9013,1.99 -2020,7,9,78,50.29,50.11,1.7702,1.92 -2020,7,10,78,57.89,57.6,1.5329,1.68 -2020,7,11,78,62.48,62.15,1.5428,1.56 -2020,7,12,78,62.01,61.25,1.6002,1.78 -2020,7,13,78,54.74,54.12,1.3762,1.47 -2020,7,1,79,61.46,60.83,1.551,1.65 -2020,7,2,79,50.18,49.55,1.8835,2.01 -2020,7,3,79,39.12,38.6,2.4067,2.55 -2020,7,4,79,32.03,31.5,2.6277,2.93 -2020,7,5,79,30.69,30.37,3.2075,3.32 -2020,7,6,79,35.14,35.14,3.11,3.11 -2020,7,7,79,41.94,41.87,2.2312,2.26 -2020,7,8,79,49.47,49.42,1.847,1.89 -2020,7,9,79,57.21,56.88,1.9706,2.12 -2020,7,10,79,65.54,64.98,1.8422,1.94 -2020,7,11,79,70.3,69.76,1.8644,1.88 -2020,7,12,79,69.58,68.64,1.6648,1.87 -2020,7,13,79,61.7,60.83,1.5423,1.65 -2020,7,1,80,57.39,56.78,1.3764,1.5 -2020,7,2,80,47.83,47.28,1.821,2 -2020,7,3,80,38.47,38.1,2.1366,2.15 -2020,7,4,80,32.95,32.55,1.9551,2.19 -2020,7,5,80,31.91,31.62,2.3544,2.46 -2020,7,6,80,35.03,34.97,2.3546,2.44 -2020,7,7,80,39.53,39.53,1.82,1.82 -2020,7,8,80,45.5,45.36,1.773,1.84 -2020,7,9,80,52.1,51.7,1.8071,1.96 -2020,7,10,80,59.6,59.04,1.5376,1.6 -2020,7,11,80,64.16,63.61,1.5185,1.54 -2020,7,12,80,64.08,63.17,1.4933,1.6 -2020,7,13,80,57.65,56.78,1.3637,1.5 -2020,7,1,81,61.04,60.57,1.0155,1.12 -2020,7,2,81,53.23,52.9,1.4511,1.49 -2020,7,3,81,45.55,45.29,1.5356,1.56 -2020,7,4,81,41.27,41.01,1.2821,1.44 -2020,7,5,81,40.64,40.44,1.4958,1.57 -2020,7,6,81,42.98,42.89,1.7237,1.78 -2020,7,7,81,46.21,46.21,1.45,1.45 -2020,7,8,81,50.72,50.55,1.5701,1.63 -2020,7,9,81,55.97,55.62,1.4453,1.61 -2020,7,10,81,61.96,61.47,1.3685,1.39 -2020,7,11,81,65.81,65.34,1.2819,1.3 -2020,7,12,81,66.27,65.52,1.1996,1.29 -2020,7,13,81,61.22,60.57,1.0395,1.12 -2020,7,1,82,58.43,58.1,0.7757,0.86 -2020,7,2,82,53.66,53.45,1.0993,1.13 -2020,7,3,82,48.5,48.29,1.32,1.33 -2020,7,4,82,45.44,45.2,1.1845,1.33 -2020,7,5,82,44.86,44.65,1.3987,1.54 -2020,7,6,82,45.98,45.91,1.6303,1.73 -2020,7,7,82,47.57,47.57,1.47,1.47 -2020,7,8,82,50.29,50.2,1.4782,1.54 -2020,7,9,82,53.86,53.58,1.2269,1.36 -2020,7,10,82,57.83,57.45,1.1546,1.17 -2020,7,11,82,60.45,60.1,0.9015,1.01 -2020,7,12,82,61.17,60.63,0.8889,0.95 -2020,7,13,82,58.59,58.1,0.796,0.86 -2020,7,1,83,58.54,57.53,1.3421,1.49 -2020,7,2,83,47.59,46.37,1.6427,1.8 -2020,7,3,83,36.23,35.15,2.0054,2.01 -2020,7,4,83,28.55,27.38,2.4175,2.63 -2020,7,5,83,25.78,25.18,2.9366,3.1 -2020,7,6,83,29.56,29.14,2.8492,3.09 -2020,7,7,83,36.51,35.85,2.4174,2.45 -2020,7,8,83,44.5,44.12,2.1244,2.17 -2020,7,9,83,53.1,52.55,2.0929,2.27 -2020,7,10,83,61.74,61.09,1.8841,1.94 -2020,7,11,83,66.83,66.17,1.796,1.84 -2020,7,12,83,65.95,65.14,1.5384,1.64 -2020,7,13,83,58.3,57.53,1.4384,1.49 -2020,7,1,84,65.62,64.62,1.2185,1.36 -2020,7,2,84,54.21,52.95,1.6129,1.64 -2020,7,3,84,42.25,41.05,1.6478,1.73 -2020,7,4,84,34.39,33.01,2.1711,2.35 -2020,7,5,84,32.31,31.56,2.7662,2.98 -2020,7,6,84,37,36.34,2.346,2.63 -2020,7,7,84,44.61,43.83,1.7752,1.83 -2020,7,8,84,52.76,52.32,1.9536,1.97 -2020,7,9,84,62.09,61.32,2.0412,2.26 -2020,7,10,84,70.8,69.94,1.9751,2.01 -2020,7,11,84,75.36,74.55,1.6397,1.71 -2020,7,12,84,73.59,72.84,1.3161,1.42 -2020,7,13,84,65.37,64.62,1.2349,1.36 -2020,7,1,85,62.43,61.55,1.1817,1.27 -2020,7,2,85,50.64,49.62,1.4067,1.56 -2020,7,3,85,38.98,37.95,1.9098,1.94 -2020,7,4,85,31.6,30.23,2.6641,2.74 -2020,7,5,85,29.48,28.81,3.1057,3.26 -2020,7,6,85,34.17,33.63,2.7554,3.03 -2020,7,7,85,41.64,40.81,1.9012,2.12 -2020,7,8,85,49.36,48.85,1.9729,2.08 -2020,7,9,85,57.8,57.2,2.32,2.32 -2020,7,10,85,66.89,66.02,1.799,1.9 -2020,7,11,85,72.13,71.3,1.6594,1.74 -2020,7,12,85,70.74,69.95,1.3242,1.4 -2020,7,13,85,62.29,61.55,1.1645,1.27 -2020,7,1,86,57.56,56.71,1.2931,1.4 -2020,7,2,86,46.86,45.92,1.6902,1.75 -2020,7,3,86,36.27,35.45,1.8014,1.95 -2020,7,4,86,29.45,28.54,2.4574,2.6 -2020,7,5,86,27.5,26.97,2.89,3.06 -2020,7,6,86,31.02,30.7,2.6636,2.82 -2020,7,7,86,37.14,36.6,1.8771,2.05 -2020,7,8,86,44.38,43.89,1.9471,2.17 -2020,7,9,86,52.24,51.67,2.2064,2.35 -2020,7,10,86,60.99,60.12,1.9186,1.94 -2020,7,11,86,65.97,65.21,1.4994,1.7 -2020,7,12,86,64.99,64.25,1.2186,1.29 -2020,7,13,86,57.55,56.71,1.3194,1.4 -2020,7,1,87,60.58,59.63,1.4044,1.53 -2020,7,2,87,50.41,49.51,1.7856,1.92 -2020,7,3,87,40.34,39.65,2.0069,2.03 -2020,7,4,87,34.02,33.37,2.22,2.31 -2020,7,5,87,32.74,32.28,2.238,2.48 -2020,7,6,87,36.15,35.85,2.1705,2.27 -2020,7,7,87,41.41,41.09,1.5163,1.59 -2020,7,8,87,48.09,47.49,1.9912,2.22 -2020,7,9,87,55.29,54.73,2.2448,2.28 -2020,7,10,87,63.75,62.76,1.9057,1.95 -2020,7,11,87,68.37,67.51,1.5348,1.68 -2020,7,12,87,67.57,66.69,1.3008,1.45 -2020,7,13,87,60.58,59.63,1.4372,1.53 -2020,7,1,88,66.43,65.39,1.4691,1.54 -2020,7,2,88,58.12,57.37,1.85,1.95 -2020,7,3,88,49.38,48.83,1.6696,1.81 -2020,7,4,88,43.75,43.33,1.3842,1.52 -2020,7,5,88,42.52,42.26,1.3783,1.42 -2020,7,6,88,44.9,44.61,1.4558,1.56 -2020,7,7,88,48.55,48.22,1.4708,1.62 -2020,7,8,88,54.01,53.37,1.9141,2.12 -2020,7,9,88,60.22,59.65,2.0324,2.07 -2020,7,10,88,67.36,66.49,1.5239,1.7 -2020,7,11,88,71.19,70.45,1.288,1.39 -2020,7,12,88,71.32,70.41,1.3801,1.46 -2020,7,13,88,66.35,65.39,1.4306,1.54 -2020,7,1,89,63.41,62.83,0.8865,0.95 -2020,7,2,89,57.17,56.78,1.0749,1.2 -2020,7,3,89,50.46,50.12,1.3808,1.41 -2020,7,4,89,46.04,45.76,1.319,1.33 -2020,7,5,89,45.26,45.01,1.363,1.44 -2020,7,6,89,47.31,47.05,1.4791,1.58 -2020,7,7,89,50.08,49.87,1.3276,1.49 -2020,7,8,89,54.05,53.61,1.5912,1.66 -2020,7,9,89,58.54,58.13,1.4331,1.47 -2020,7,10,89,63.58,63.1,0.9782,1.08 -2020,7,11,89,66.58,66.18,0.7829,0.82 -2020,7,12,89,66.87,66.33,0.814,0.87 -2020,7,13,89,63.42,62.83,0.9025,0.95 -2020,7,1,90,63.14,62.11,1.299,1.37 -2020,7,2,90,52.5,51.5,1.7341,1.82 -2020,7,3,90,41.85,40.98,1.5949,1.77 -2020,7,4,90,35.4,34.25,2.1247,2.17 -2020,7,5,90,33.75,33.15,2.5395,2.54 -2020,7,6,90,37.47,36.99,2.0336,2.16 -2020,7,7,90,43.44,42.76,1.6166,1.69 -2020,7,8,90,50.47,49.74,2.1149,2.25 -2020,7,9,90,58.43,57.74,2.2489,2.37 -2020,7,10,90,67.16,66.07,1.7881,1.88 -2020,7,11,90,71.65,70.77,1.3472,1.52 -2020,7,12,90,70.32,69.58,1.1311,1.2 -2020,7,13,90,62.97,62.11,1.352,1.37 -2020,7,1,91,70.76,69.7,1.3385,1.41 -2020,7,2,91,62.24,61.51,1.6183,1.79 -2020,7,3,91,53.24,52.61,1.7109,1.73 -2020,7,4,91,47.19,46.67,1.4749,1.48 -2020,7,5,91,46.05,45.7,1.2863,1.37 -2020,7,6,91,48.86,48.47,1.4034,1.46 -2020,7,7,91,53.09,52.61,1.4076,1.55 -2020,7,8,91,58.47,57.84,2.092,2.16 -2020,7,9,91,64.72,64.11,2.0347,2.11 -2020,7,10,91,71.86,70.84,1.5753,1.76 -2020,7,11,91,75.58,74.77,1.2688,1.33 -2020,7,12,91,75.61,74.73,1.3668,1.39 -2020,7,13,91,70.59,69.7,1.3447,1.41 -2020,7,1,92,65.28,64.43,1.0717,1.12 -2020,7,2,92,60.72,60.3,1.1332,1.17 -2020,7,3,92,55.3,54.88,1.3,1.3 -2020,7,4,92,51.34,50.92,1.2891,1.3 -2020,7,5,92,50.41,50.07,1.3264,1.33 -2020,7,6,92,52.02,51.63,1.417,1.42 -2020,7,7,92,54.3,53.92,1.36,1.36 -2020,7,8,92,57.12,56.66,1.6499,1.65 -2020,7,9,92,60.39,59.99,1.437,1.44 -2020,7,10,92,64.14,63.41,1.2481,1.25 -2020,7,11,92,66.21,65.67,0.88,0.88 -2020,7,12,92,66.88,66.22,1.0144,1.05 -2020,7,13,92,65.13,64.43,1.0848,1.12 -2020,7,1,93,70.29,69.4,1.0773,1.22 -2020,7,2,93,64.73,64.27,1.1398,1.25 -2020,7,3,93,58.74,58.3,1.1264,1.19 -2020,7,4,93,54.92,54.43,1.3168,1.34 -2020,7,5,93,53.62,53.26,1.2819,1.33 -2020,7,6,93,54.76,54.39,1.1444,1.26 -2020,7,7,93,56.86,56.4,1.2985,1.31 -2020,7,8,93,59.93,59.47,1.5921,1.77 -2020,7,9,93,63.85,63.34,1.667,1.73 -2020,7,10,93,68.67,67.77,1.509,1.51 -2020,7,11,93,71.91,71.07,1.2249,1.35 -2020,7,12,93,72.85,72.02,1.2392,1.38 -2020,7,13,93,70.15,69.4,1.1862,1.22 -2020,7,1,94,78.04,76.99,1.2744,1.43 -2020,7,2,94,68.02,67.19,1.6591,1.82 -2020,7,3,94,57.64,56.87,1.5803,1.64 -2020,7,4,94,51.27,50.47,1.6731,1.73 -2020,7,5,94,49.77,49.25,1.7717,1.84 -2020,7,6,94,53.03,52.57,1.5035,1.57 -2020,7,7,94,58.4,57.54,1.9518,1.99 -2020,7,8,94,64.92,64.22,2.286,2.51 -2020,7,9,94,72.66,71.85,2.4379,2.53 -2020,7,10,94,80.68,79.5,1.8895,1.89 -2020,7,11,94,84.76,83.86,1.3833,1.42 -2020,7,12,94,84.04,83.32,1.1224,1.14 -2020,7,13,94,77.89,76.99,1.3941,1.43 -2020,7,1,95,78.16,76.67,1.822,2.03 -2020,7,2,95,67.09,65.83,2.0492,2.25 -2020,7,3,95,55.84,54.79,1.8258,1.91 -2020,7,4,95,49.07,48.03,1.751,1.84 -2020,7,5,95,47.45,46.91,1.6794,1.82 -2020,7,6,95,51.22,50.73,1.6365,1.74 -2020,7,7,95,57.49,56.49,1.8479,1.87 -2020,7,8,95,64.73,63.96,2.3012,2.54 -2020,7,9,95,73.24,72.43,2.4285,2.48 -2020,7,10,95,82.15,80.7,2.33,2.33 -2020,7,11,95,86.52,85.17,2.1017,2.17 -2020,7,12,95,85.34,84.03,1.9061,2.13 -2020,7,13,95,77.95,76.67,1.9449,2.03 -2020,7,1,96,80.53,79.49,1.2708,1.42 -2020,7,2,96,70.85,69.83,1.5916,1.78 -2020,7,3,96,60.32,59.38,1.4051,1.47 -2020,7,4,96,53.74,52.96,1.3391,1.38 -2020,7,5,96,52.11,51.65,1.4309,1.49 -2020,7,6,96,55.34,54.82,1.316,1.41 -2020,7,7,96,60.82,59.89,1.5606,1.61 -2020,7,8,96,67.3,66.7,1.8922,2.1 -2020,7,9,96,75.55,74.73,1.9409,2.08 -2020,7,10,96,83.28,82.22,1.6665,1.67 -2020,7,11,96,87.3,86.4,1.3234,1.41 -2020,7,12,96,86.63,85.74,1.2917,1.41 -2020,7,13,96,80.41,79.49,1.3905,1.42 -2020,7,1,97,62.99,62.12,1.055,1.19 -2020,7,2,97,53.54,52.51,1.2652,1.4 -2020,7,3,97,43.48,42.38,1.4041,1.44 -2020,7,4,97,36.88,35.83,1.5535,1.64 -2020,7,5,97,34.98,34.4,1.8159,1.92 -2020,7,6,97,38.28,37.68,1.6622,1.76 -2020,7,7,97,44.15,43.17,1.5403,1.56 -2020,7,8,97,50.75,50.22,1.8375,1.91 -2020,7,9,97,59.23,58.37,2.027,2.07 -2020,7,10,97,66.98,65.99,1.6799,1.68 -2020,7,11,97,70.79,69.98,1.2169,1.35 -2020,7,12,97,69.48,68.8,1.0279,1.14 -2020,7,13,97,62.87,62.12,1.1429,1.19 -2020,7,1,98,73.74,72.74,1.2101,1.36 -2020,7,2,98,65.39,64.43,1.4726,1.64 -2020,7,3,98,56.03,55.02,1.3746,1.41 -2020,7,4,98,49.85,49,1.3612,1.42 -2020,7,5,98,48.14,47.64,1.4166,1.51 -2020,7,6,98,51.11,50.41,1.4953,1.58 -2020,7,7,98,56.27,55.25,1.6052,1.68 -2020,7,8,98,62.27,61.73,1.8198,1.89 -2020,7,9,98,70.61,69.66,1.9876,2 -2020,7,10,98,77.24,76.18,1.6247,1.67 -2020,7,11,98,80.23,79.35,1.257,1.39 -2020,7,12,98,79.11,78.17,1.3611,1.47 -2020,7,13,98,73.62,72.74,1.3061,1.36 -2020,7,1,99,58.35,57.5,1.0222,1.17 -2020,7,2,99,48.54,47.55,1.2116,1.35 -2020,7,3,99,38.09,36.96,1.4383,1.49 -2020,7,4,99,31.04,29.73,1.9533,2.05 -2020,7,5,99,28.86,28.08,2.3606,2.51 -2020,7,6,99,32.61,31.76,2.1389,2.26 -2020,7,7,99,38.59,38.01,1.4564,1.62 -2020,7,8,99,46.07,45.64,1.5691,1.65 -2020,7,9,99,54.9,54.01,1.8771,1.88 -2020,7,10,99,62.21,61.57,1.3826,1.52 -2020,7,11,99,66.11,65.54,1.07,1.17 -2020,7,12,99,64.79,64.21,1.0928,1.16 -2020,7,13,99,58.1,57.5,1.1498,1.17 -2020,7,1,100,68.55,67.48,1.3139,1.52 -2020,7,2,100,59.01,57.98,1.4471,1.59 -2020,7,3,100,49.18,47.91,1.6322,1.67 -2020,7,4,100,42.58,41.29,1.8566,1.98 -2020,7,5,100,40.66,39.77,2.0176,2.07 -2020,7,6,100,44.33,43.38,1.7335,1.85 -2020,7,7,100,50.15,49.51,1.4357,1.58 -2020,7,8,100,57.8,57.24,1.6359,1.74 -2020,7,9,100,66.74,65.71,1.9892,1.99 -2020,7,10,100,73.28,72.48,1.5974,1.62 -2020,7,11,100,76.41,75.69,1.2141,1.26 -2020,7,12,100,74.66,73.83,1.4222,1.5 -2020,7,13,100,68.32,67.48,1.3642,1.52 -2020,7,1,101,62.69,61.79,1.0878,1.23 -2020,7,2,101,53.46,52.52,1.202,1.3 -2020,7,3,101,43.82,42.77,1.3007,1.41 -2020,7,4,101,37.5,36.43,1.4788,1.65 -2020,7,5,101,35.88,35.22,1.7433,1.79 -2020,7,6,101,39.34,38.51,1.5357,1.63 -2020,7,7,101,44.87,44.14,1.2663,1.4 -2020,7,8,101,51.66,51.2,1.448,1.53 -2020,7,9,101,60.42,59.39,1.7788,1.78 -2020,7,10,101,67.11,66.33,1.4118,1.43 -2020,7,11,101,70.37,69.7,1.0207,1.13 -2020,7,12,101,68.83,68.11,1.1499,1.21 -2020,7,13,101,62.53,61.79,1.1246,1.23 -2020,7,1,102,70.16,69.1,1.2894,1.45 -2020,7,2,102,60.78,59.88,1.2675,1.38 -2020,7,3,102,51,49.98,1.3009,1.34 -2020,7,4,102,44.58,43.61,1.3633,1.49 -2020,7,5,102,43.23,42.56,1.5726,1.61 -2020,7,6,102,46.98,46.1,1.4211,1.53 -2020,7,7,102,52.75,51.85,1.3823,1.53 -2020,7,8,102,59.6,59.04,1.5867,1.74 -2020,7,9,102,68.48,67.33,1.9326,1.94 -2020,7,10,102,74.96,74.01,1.4707,1.6 -2020,7,11,102,77.86,77.01,1.2474,1.32 -2020,7,12,102,76.22,75.25,1.4489,1.52 -2020,7,13,102,70.04,69.1,1.3841,1.45 +2020,8,1,1,44.07,43.31,1.2364,1.38 +2020,8,2,1,32.27,31.54,2.0257,2.19 +2020,8,3,1,22.03,21.16,2.4155,2.69 +2020,8,4,1,16.13,15.32,2.6706,2.77 +2020,8,5,1,17.52,17.26,2.6584,2.93 +2020,8,6,1,26.3,26.3,2.74,2.74 +2020,8,7,1,37.84,37.84,2.17,2.17 +2020,8,8,1,49.73,49.65,1.3415,1.47 +2020,8,9,1,58.6,58.47,1.2499,1.38 +2020,8,10,1,63.46,62.75,1.203,1.22 +2020,8,11,1,61.76,60.99,1.1494,1.16 +2020,8,12,1,54.29,53.54,1.0503,1.16 +2020,8,13,1,44.22,43.31,1.2972,1.38 +2020,8,1,2,48.17,47.41,1.1777,1.31 +2020,8,2,2,37.18,36.51,1.8655,1.99 +2020,8,3,2,27.91,27.14,2.3163,2.43 +2020,8,4,2,22.92,22.12,2.3513,2.57 +2020,8,5,2,24.27,24.04,2.4236,2.64 +2020,8,6,2,32.15,32.15,2.38,2.38 +2020,8,7,2,42.41,42.34,1.8704,1.94 +2020,8,8,2,53.06,52.95,1.2698,1.33 +2020,8,9,2,61.51,61.34,1.2403,1.3 +2020,8,10,2,66.59,65.76,1.2314,1.26 +2020,8,11,2,65.15,64.33,1.1251,1.25 +2020,8,12,2,57.97,57.19,1.0868,1.19 +2020,8,13,2,48.28,47.41,1.2363,1.31 +2020,8,1,3,47.92,47.06,1.3149,1.46 +2020,8,2,3,36.62,35.94,2.0188,2.26 +2020,8,3,3,27.34,26.64,2.741,2.85 +2020,8,4,3,22.05,21.53,2.6977,2.95 +2020,8,5,3,23.62,23.33,2.6322,2.91 +2020,8,6,3,31.6,31.6,2.43,2.43 +2020,8,7,3,42.46,42.35,2.1402,2.2 +2020,8,8,3,53.63,53.48,1.4096,1.47 +2020,8,9,3,61.94,61.65,1.1844,1.3 +2020,8,10,3,66.3,65.58,1.4422,1.47 +2020,8,11,3,64.73,63.93,1.2091,1.33 +2020,8,12,3,57.69,56.81,1.3057,1.45 +2020,8,13,3,48.01,47.06,1.4134,1.46 +2020,8,1,4,52.34,51.43,1.2207,1.33 +2020,8,2,4,41.76,41.09,1.8517,2.02 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+2020,8,2,102,51,49.98,1.2869,1.34 +2020,8,3,102,44.59,43.61,1.4317,1.49 +2020,8,4,102,43.23,42.56,1.568,1.61 +2020,8,5,102,46.98,46.1,1.4161,1.53 +2020,8,6,102,52.78,51.85,1.4644,1.53 +2020,8,7,102,59.59,59.04,1.5536,1.74 +2020,8,8,102,68.46,67.33,1.8026,1.94 +2020,8,9,102,74.98,74.01,1.5733,1.6 +2020,8,10,102,77.85,77.01,1.2016,1.32 +2020,8,11,102,76.21,75.25,1.3903,1.52 +2020,8,12,102,70.04,69.1,1.3855,1.45 +2020,8,13,102,60.78,59.88,1.3176,1.38 diff --git a/EC2_Ubuntu18_withUserData.yml b/EC2_Ubuntu18_withUserData.yml index 67edefb..fac6016 100644 --- a/EC2_Ubuntu18_withUserData.yml +++ b/EC2_Ubuntu18_withUserData.yml @@ -204,7 +204,6 @@ Resources: if grep -q " 6" /etc/centos-release; then yum update curl -y; fi; cd /root/ - exec > >(tee /var/log/user-data.log|logger -t user-data -s 2>/dev/console) 2>&1 echo BEGIN @@ -212,7 +211,7 @@ Resources: # setup development account sudo adduser devel --gecos "First Last,RoomNumber,WorkPhone,HomePhone" --disabled-password - echo "devel:stdf" | sudo chpasswd + echo devel:stdf | sudo chpasswd # https://askubuntu.com/questions/319714/proper-way-to-add-a-user-account-via-bash-script # https://community.rstudio.com/t/rserver-1692-error-system-error-13-permission-denied/46972/2 @@ -249,10 +248,19 @@ Resources: # # xml apt-get -y install libxml2-dev + # # + apt-get -y install libsodium-dev + apt-get -y install libcurl4-openssl-dev + + # # need for rstudio server + apt-get -y install lib32stdc++6 libclang-6.0-dev + apt-get -y install libclang-dev + # install RStudio-Server - sudo apt-get install gdebi-core - wget https://download2.rstudio.org/server/bionic/amd64/rstudio-server-1.2.5019-amd64.deb - echo y | gdebi rstudio-server-1.2.5019-amd64.deb + # https://rstudio.com/products/rstudio/download-server/debian-ubuntu + apt-get install gdebi-core + wget https://download2.rstudio.org/server/bionic/amd64/rstudio-server-1.3.1073-amd64.deb + gdebi -n rstudio-server-1.3.1073-amd64.deb # install R packages/dependecies echo package download begin @@ -297,6 +305,12 @@ Resources: chown -R :appallow /srv/shiny-server/shorttermdroughtforecaster chmod -R g+rwx /srv/shiny-server/shorttermdroughtforecaster + #download Data + mkdir WeatherData + cd WeatherData + wget -r -l1 -np "https://www.northwestknowledge.net/metdata/data" -P /tmp -A "tmmn_*.nc" + wget -r -l1 -np "https://www.northwestknowledge.net/metdata/data" -P /tmp -A "tmmx_*.nc" + wget -r -l1 -np "https://www.northwestknowledge.net/metdata/data" -P /tmp -A "pr_*.nc" #aws cli #apt-get install -y awscli diff --git a/ExampleData/Future_Shriver2018.csv b/ExampleData/Future_Shriver2018.csv index 36f46d6..905ef77 100644 --- a/ExampleData/Future_Shriver2018.csv +++ b/ExampleData/Future_Shriver2018.csv @@ -1,451 +1,451 @@ -run,Prob -1_1981,0.479371044556208 -1_1981,0.449348836969454 -1_1981,0.587329235137601 -1_1982,0.479371044556208 -1_1982,0.448545256588263 -1_1982,0.505751211561394 -1_1983,0.479371044556208 -1_1983,0.463236210658834 -1_1983,0.59583528735646 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--git a/TempMonthlys.csv b/TempMonthlys.csv new file mode 100644 index 0000000..d3729ae --- /dev/null +++ b/TempMonthlys.csv @@ -0,0 +1,14 @@ +"","Date","Hist.avg_C.mean.med","Hist.avg_C.mean.10","Hist.avg_C.mean.90","NearFut.avg_C.mean.med","NearFut.avg_C.mean.10","NearFut.avg_C.mean.90","NearFut.avg_C.Diffs.Med","NearFut.avg_C.Diffs.10","NearFut.avg_C.Diffs.90" +"1",2020-09-15,15.2748558333333,13.5282736666667,16.2967613333333,16.4464191666667,15.0872115,17.429317,1.17156333333333,-0.187644333333333,2.15446116666667 +"2",2020-10-15,9.37801290322581,7.59928064516129,10.8777582258065,10.8649451612903,9.08621177419355,12.3646859677419,1.48693225806452,-0.291801129032256,2.98667306451613 +"3",2020-11-15,3.469435,1.98168083333333,5.4019735,4.68046833333333,3.192707,6.61300766666667,1.21103333333333,-0.276727999999999,3.14357266666667 +"4",2020-12-15,-0.481197580645161,-2.5135435483871,1.55668790322581,0.798062903225806,-1.23427983870968,2.83594677419355,1.27926048387097,-0.753082258064516,3.31714435483871 +"5",2021-01-15,-0.683727419354839,-2.29696193548387,0.947621774193549,0.246400806451613,-1.36682758064516,1.87774483870968,0.930128225806452,-0.683100161290322,2.56147225806452 +"6",2021-02-15,0.85205775862069,-0.739091459359606,2.91909142857143,1.90954107142857,0.410258392857143,4.06457339285714,1.05748331280788,-0.441799365763547,3.21251563423645 +"7",2021-03-15,4.04238870967742,2.36359967741936,5.64644032258065,5.23849112903226,3.5597,6.842545,1.19610241935484,-0.48268870967742,2.80015629032258 +"8",2021-04-15,7.23055416666667,5.37445483333333,9.12003583333333,8.69127333333333,6.83517783333333,10.5807588333333,1.46071916666667,-0.395376333333332,3.35020466666667 +"9",2021-05-15,11.8533524193548,9.80823193548387,13.9617879032258,13.3767717741935,11.3316533870968,15.4852135483871,1.52341935483871,-0.521699032258063,3.63186112903226 +"10",2021-06-15,17.0133833333333,14.7934111666667,17.9918266666667,17.8086516666667,15.5886838333333,18.7870851666667,0.795268333333333,-1.4246995,1.77370183333333 +"11",2021-07-15,19.695985483871,18.4706420967742,20.7850379032258,20.3859524193548,19.1606059677419,21.4750069354839,0.689966935483866,-0.535379516129034,1.77902145161291 +"12",2021-08-15,18.6881540322581,17.7612185483871,19.7305983870968,19.3951137096774,18.468180483871,20.4375664516129,0.706959677419352,-0.219973548387095,1.74941241935484 +"13",2021-09-15,15.3307108333333,13.5282736666667,16.4093123333333,16.6483108333333,14.845877,17.7269141666667,1.3176,-0.484833833333335,2.39620333333333 diff --git a/figureCode/.DS_Store b/figureCode/.DS_Store index 8b014a83a5c93559cf564c5968ee99d6c8aa8239..2e4bae7a7547b98f5782c512af31dab3964c0c63 100644 GIT binary patch delta 161 zcmZn(XbIS`TY&M@WJd|%$>#-fwIr*n4Gm3n6bwxbYIPK<4ULU~9CIUs+FDKyaaBWG z&xG8{s_L5Bx>=K7O2{+Lo_tVTg(IanIVUMUKWFj*an;S&1hd${1~z~UGd4B@8fIh$ iHq6M>SVzIa(gderAH_vB%St|H+sv-;i)E82Gcy2tN;E+L delta 156 zcmZn(XbIS`TY&N8MyMwW)PIttZ>My5cHnXz$gEhmS#s-dlCLT+VM zbxm#E%*k&gX956(0W$Rf diff --git a/figureCode/FigureDataPrep.R b/figureCode/FigureDataPrep.R index 61375c6..a78690e 100644 --- a/figureCode/FigureDataPrep.R +++ b/figureCode/FigureDataPrep.R @@ -4,14 +4,13 @@ library(ggplot2) ###################################################################################### ################# Data organization ################# ###################################################################################### -readIn <- 1 +readIn <- 0 -currMonth <- month(Sys.Date()) currDate <- Sys.Date() # Historical Data ------------------------------------------------------------------- if(readIn){ - HistDataNormMean <- fread('ExampleData/HistData_Norm_Stats.csv') + HistData_Norm_Stats <- fread('ExampleData/HistData_Norm_Stats.csv') MonthlyAnoms <- fread('ExampleData/MonthlyAnoms.csv') TempAnoms <- fread('ExampleData/TempAnoms.csv') PPTAnoms <- fread('ExampleData/PPTAnoms.csv') @@ -25,31 +24,32 @@ todayMonthDay <- format(Sys.Date() , format="%m-%d") # Get data for prior 6 months sixMonthsAgo <- format(Sys.Date() - 183, format="%m-%d") -r1 <- which(grepl(todayMonthDay, HistDataNormMean$Date)) -r2 <- which(grepl(sixMonthsAgo, HistDataNormMean$Date)) +r1 <- which(grepl(todayMonthDay, HistData_Norm_Stats$Date)) +r2 <- which(grepl(sixMonthsAgo, HistData_Norm_Stats$Date)) dateIndex <- if( r2 > r1) { c(r2:366, 1:(r1- 1)) } else{ c(r2:(r1 - 1)) } -HistDataNormMean_18MNs <- HistDataNormMean[dateIndex,] +HistDataNormMean_18MNs <- HistData_Norm_Stats[dateIndex,] -HistDataNormMean_18MNs$Year <- ifelse(HistDataNormMean_18MNs$Date > todayMonthDay, 2019, 2020) +HistDataNormMean_18MNs$Year <- ifelse(HistDataNormMean_18MNs$Date > todayMonthDay, currYear - 1 , currYear) HistDataNormMean_18MNs$Date <- as.Date(paste(HistDataNormMean_18MNs$Year, HistDataNormMean_18MNs$Date, sep = '-')) HistDataNormMean_18MNs <- setorder(HistDataNormMean_18MNs, Date) # Format historical record 1 year forward fromt today's date ---------------------------------------------- -HistDataNormMean$Year <- ifelse(HistDataNormMean$Date > todayMonthDay, 2020, 2021) +HistData_Norm_Stats$Year <- ifelse(HistData_Norm_Stats$Date > todayMonthDay, currYear, currYear + 1) # when you convert to Date, elimantes Feb. 29 if that year does not have a 2-29 -HistDataNormMean$Date <- as.Date(paste(HistDataNormMean$Year, HistDataNormMean$Date, sep = '-')) -HistDataNormMean <- setorder(HistDataNormMean, Date) +HistData_Norm_Stats$Date <- as.Date(paste(HistData_Norm_Stats$Year, HistData_Norm_Stats$Date, sep = '-')) +HistData_Norm_Stats <- setorder(HistData_Norm_Stats, Date) # Append -HistDataNormMean_18MNs <- rbind(HistDataNormMean_18MNs, HistDataNormMean) +HistDataNormMean_18MNs <- rbind(HistDataNormMean_18MNs, HistData_Norm_Stats) HistDataNormMean_18MNs <- HistDataNormMean_18MNs[complete.cases(HistDataNormMean_18MNs), ] # Go to the first day of the same month of the next year -HistDataNormMean_18MNs <- HistDataNormMean_18MNs[HistDataNormMean_18MNs$Date < as.Date(paste0('2021-',currMonth + 1,'-01')),] # 06 should be currMonth +HistDataNormMean_18MNs <- HistDataNormMean_18MNs[ + HistDataNormMean_18MNs$Date < as.Date(paste0(currYear + 1, '-' ,currMonth + 1,'-01')),] # 06 should be currMonth ggplot() + # geom_line(data = AllOut2, aes(Date, avgC_rollmean, color = run_Year)) + diff --git a/figureCode/presentationFigs/.DS_Store b/figureCode/presentationFigs/.DS_Store index eeb8f51bca6b22c26ca263cb24316b2ac1f79291..2015efe97cd353bbb5a59cd552c38e82697dcb93 100644 GIT binary patch literal 8196 zcmeHMO>h)N6n<|BFdcrSGrJ+g zU{H^uRsJ2YN~O3N4tk(U^rq!c;l;92g;;vgDi2<~c<^AUue)a{2)k9D)RO6{>942X zdo$f%e>1&r767oTXeI#~0YIZeAYVjPg(B?YxUIy(9!7{14`9L`uwexJ?D%coQAfl; 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a/figureCode/testFigs/TempExplore.R +++ b/figureCode/testFigs/TempExplore.R @@ -36,13 +36,13 @@ ggplot() + theme_bw() + geom_hline(yintercept = 0) + geom_vline(xintercept = c(as.Date('1982-01-01'),as.Date('1983-01-01'), as.Date('1984-01-01'), as.Date('1985-01-01'))) + - scale_x_date(date_breaks = "2 months", date_labels = "%m", expand = c(0,0)) - + scale_x_date(date_breaks = "2 months", date_labels = "%m", expand = c(0,0)) -HistDataNormMean$Year <- ifelse(HistDataNormMean$Day < yday(Sys.Date()), 2021, 2020) + +HistDataNormMean$Year <- ifelse(HistDataNormMean$Day < currDOY, 2021, 2020) HistDataNormMean$Date <- as.Date(strptime(paste(HistDataNormMean$Year, HistDataNormMean$Day), format="%Y %j"), format="%m-%d-%Y") -HistDataNormMean <- setorder(HistDataNormMean, Date) +HistDataNormMean <- setorder(HistDataNormMean, Date) y <- 1985 @@ -65,7 +65,7 @@ year2$Year <- as.integer(currYear) # change year # add observed data where appropriate year2[1:currDOY,] <- thisYearObservedWData[1:currDOY,] # this works even when currDOY is 366 and year2 DF only has 365 rows -# if "year2" is 365 days but current year is 366 days +# if "year2" is 365 days but current year is 366 days ## what to do? if(days == 366 & nrow(year2) != 366) { year2 <- rbind(year2, year2[365,]) @@ -74,7 +74,7 @@ if(days == 366 & nrow(year2) != 366) { ### --------- ## year 3 ... forecasts that run into next year (aka 2021) and then scratch data for the rest of 2021 ### --------- -year3 <- year2Fut +year3 <- year2Fut if(days == 366) year3 <- year3[1:365,] #if this year is 366 days, next year needs to be 365 year3$Year <- as.integer(currYear + 1) @@ -83,7 +83,7 @@ p1 <- ggplot() + theme_bw() p1 -p2 <- p1 + geom_line(data = hist, aes(DOY, Tmax_C), color = 'red') # data for +p2 <- p1 + geom_line(data = hist, aes(DOY, Tmax_C), color = 'red') # data for p2 p2 <- p2 + geom_line(data = year2Fut, aes(DOY, Tmax_C), color = 'darkgreen') # future @@ -96,7 +96,7 @@ ggplot() + geom_line(data = year2Fut, aes(DOY, Tmax_C), color = 'pink') + geom_line(data = year3, aes(DOY, Tmax_C), color = 'purple', lty = 'dashed') + theme_bw() - + # SW weathAnomOneSim <- rbind(year1, year2, year3) @@ -106,7 +106,7 @@ weathAnomOneSim$Date <- as.Date(strptime(paste(weathAnomOneSim$Year, weathAnomOn ggplot() + geom_hline(yintercept = c(seq(-15, 25, by = 0.5)), color = 'gray', alpha = .1) + - + geom_line(data = weathAnomOneSim, aes(Date, avg_C), color = 'purple') + geom_line(data = HistDataNormMean, aes(Date, avgC_rollmean.med), color = 'red') + @@ -118,9 +118,9 @@ weathAnomOneSim2 <- weathAnomOneSim[weathAnomOneSim$Year >= 2020, ] weathAnomOneSim2[360:390,] ggplot() + geom_line(data = weathAnomOneSim2, aes(Date, avg_C), color = 'purple') + - + geom_line(data = HistDataNormMean, aes(Date, avgC_rollmean.med), color = 'red') + - + geom_line(data = weathAnomOneSim2, aes(Date, rollmean_avgC)) + theme_bw() @@ -151,9 +151,9 @@ ggplot() + geom_line(data = HistDataNormMean, aes(Date, avgC_rollmean.med)) + geom_line(data = AllOut2, aes(Date, avgC_rollmean , color = runy)) + - + geom_line(data = AnomRunStats, aes(Date, avgC_rollmean.med), color = 'black', size = 1.1) + - + theme_bw() + theme(legend.position = 'none') @@ -182,7 +182,7 @@ year2[1:currDOY,] <- thisYearObservedWData[1:currDOY,] # this works even when cu ### --------- ## year 3 ... forecasts that run into next year (aka 2021) and then scratch data for the rest of 2021 ### --------- -year3 <- year2Fut +year3 <- year2Fut if(days == 366) year3 <- year3[1:365,] #if this year is 366 days, next year needs to be 365 year3$Year <- as.integer(currYear + 1) @@ -191,8 +191,8 @@ p1 <- ggplot() + geom_line(data = thisYearObservedWData, aes(DOY, Tmax_C), color = 'blue') + theme_bw() p1 - -p2 <- p1 + geom_line(data = histdata, aes(DOY, Tmax_C), color = 'red') # data for + +p2 <- p1 + geom_line(data = histdata, aes(DOY, Tmax_C), color = 'red') # data for p2 p2 <- p2 + geom_line(data = year2Fut, aes(DOY, Tmax_C), color = 'darkgreen') # future @@ -222,12 +222,7 @@ wdata4 <- wdata3[wdata3$Year %in% 1990:1994, ] p1 <- ggplot(wdata4, aes(Date)) + geom_line(aes(y=avg_C)) + geom_line(aes(y = avgC_RollMean), color = 'red') + - theme_bw() - + theme_bw() + p1 ggplotly(p1) - - - - - diff --git a/functions/GetDataOffline.R b/functions/GetDataOffline.R index f4af524..b2d6179 100644 --- a/functions/GetDataOffline.R +++ b/functions/GetDataOffline.R @@ -69,13 +69,13 @@ getCPCData <- function(url, var) { } tempurl <- 'https://www.cpc.ncep.noaa.gov/pacdir/NFORdir/HUGEdir2/cpcllftd.dat' -#tempData <- getCPCData(tempurl, 'temp') +tempData <- getCPCData(tempurl, 'temp') summary(tempData) ppturl <- 'https://www.cpc.ncep.noaa.gov/pacdir/NFORdir/HUGEdir2/cpcllfpd.dat' -#pptData <- getCPCData(ppturl, 'ppt') +pptData <- getCPCData(ppturl, 'ppt') summary(pptData) -#write.csv(tempData, 'CurrentAnomalyTempData.csv', row.names = FALSE) # farenheit -#write.csv(pptData, 'CurrentAnomalyPPTData.csv', row.names = FALSE) # inches +write.csv(tempData, 'CurrentAnomalyTempData.csv', row.names = FALSE) # farenheit +write.csv(pptData, 'CurrentAnomalyPPTData.csv', row.names = FALSE) # inches diff --git a/functions/HelperFunctions.R b/functions/HelperFunctions.R index b6db524..9733cb9 100644 --- a/functions/HelperFunctions.R +++ b/functions/HelperFunctions.R @@ -1,13 +1,12 @@ # year 1 is last years data # thisYearObservedWData is this year's observed weather data -makeWeathOneSim <- function(y, year1, thisYearObservedWData, weathAnomAll) { +makeWeathOneSim <- function(y, year1, thisYearObservedWData, weathAnomAll, + currDOY, currYear) { - currYear <- year(Sys.Date()) days <- dim(thisYearObservedWData)[1] daysNextYear <- yearDays(as.Date(paste0(currYear + 1,'-01-01'))) LeapYear <- days == 366 - currDOY <- yday(Sys.Date()) ### --------- ### year 2 - observed data for this year until today's date and then future, weathAnom data ### --------- @@ -21,8 +20,8 @@ makeWeathOneSim <- function(y, year1, thisYearObservedWData, weathAnomAll) { year2 <- plyr::join(year2, year2Fut[,c('Date', 'Tmax_C', 'Tmin_C', 'PPT_cm')], by = 'Date') - # fill in with this years' oberserved data - # should have the corrrect number of days always since they are good representation of the same year + # fill in with this years' observed data + # should have the correct number of days always since they are good representation of the same year year2[1:currDOY, c('Tmax_C', 'Tmin_C', 'PPT_cm')] <- thisYearObservedWData[1:currDOY, c('Tmax_C', 'Tmin_C', 'PPT_cm')] if(any(is.na(year2))) { @@ -31,11 +30,11 @@ makeWeathOneSim <- function(y, year1, thisYearObservedWData, weathAnomAll) { year2$Tmin_C <- zoo::na.fill( year2$Tmin_C, "extend") year2$PPT_cm <- zoo::na.fill( year2$PPT_cm, "extend") } - # ggplot() + - # geom_line(data = year2, aes(Date, Tmax_C, group = 1)) + - # geom_line(data = thisYearObservedWData, aes(Date, Tmax_C, group = 1), color = 'purple') + - # geom_line(data = year2Fut, aes(Date, Tmax_C, group = 1), color = 'green') - # + # ggplot() + + # geom_line(data = year2, aes(Date, Tmax_C, group = 1)) + + # geom_line(data = thisYearObservedWData, aes(Date, Tmax_C, group = 1), color = 'purple') + + # geom_line(data = year2Fut, aes(Date, Tmax_C, group = 1), color = 'green') + # ### --------- ## year 3 ... forecasts that run into next year (aka 2021) and then scratch data for the rest of 2021 @@ -49,6 +48,16 @@ makeWeathOneSim <- function(y, year1, thisYearObservedWData, weathAnomAll) { year3 <- plyr::join(year3, year3Fut[,c('Date', 'Tmax_C', 'Tmin_C', 'PPT_cm')], by = 'Date') + # year3OG <- wdata[wdata$Year == y + 1, ] + # year3OG <- makeDateMonthDat(year3OG, 'DOY') + # + # ggplot() + + # geom_line(data = year3OG, aes(Date, Tmax_C, group = 1), color = 'green') + + # geom_line(data = year3, aes(Date, Tmax_C, group = 1), lty = 'dashed') + # + # year3$Tmax_C - year3OG$Tmax_C + # + # Make one simulation's worth of data year2$Date <- year3$Date <- NULL weathAnomOneSim <- rbind(year1, year2, year3) @@ -72,9 +81,7 @@ yearDays <- function(time) { } } -makeMonthLeadRelationshipTable <- function(TempAnoms) { - - currMonth <- month(Sys.Date()) +makeMonthLeadRelationshipTable <- function(TempAnoms, currMonth) { monthLeads <- data.frame(TempAnoms[,'LEAD']) diff --git a/functions/Outputs.R b/functions/Outputs.R index dae2450..27808e2 100644 --- a/functions/Outputs.R +++ b/functions/Outputs.R @@ -1,4 +1,4 @@ -getOutputs <- function(sw_out, sw_in, calc_Shriver = TRUE, calc_GISSM = FALSE) { +getOutputs <- function(sw_out, sw_in, calc_Shriver = TRUE, calc_GISSM = TRUE) { # Temp and Precip Temp1 <- data.table(sw_out@TEMP@Day) @@ -31,7 +31,7 @@ getOutputs <- function(sw_out, sw_in, calc_Shriver = TRUE, calc_GISSM = FALSE) { ) } # Format VWC ------------------------------------------- - VWC1 <- VWC1[variable != 'Lyr_1', ] + #VWC1 <- VWC1[variable != 'Lyr_1', ] VWC1 <- merge(VWC1, SoilsDF)#, by = 'variable') VWC1 <- setDT(VWC1)[,.(VWC = weighted.mean(value, width)), @@ -42,6 +42,11 @@ getOutputs <- function(sw_out, sw_in, calc_Shriver = TRUE, calc_GISSM = FALSE) { # Join up Data <- merge(Temp1, PPT1, by = c('Year', 'Day')) Data <- merge(Data, VWC1, by = c('Year', 'Day')) + + # test - eliminate leap year dat ---------------------- + # step one - make data + #Data$Date <- makeDateMonthDat(Data, 'Day') + #Data <- Data[Data$Date != 02-29, ] if(!calc_GISSM && !calc_Shriver) return(list(Data)) if(!calc_GISSM && calc_Shriver) return(list(Data, Shriver2018Vars)) @@ -131,7 +136,6 @@ getShriver2018Vars <- function(TempDF, VWCDF) { # fall 2019 -2020 data # fall 2020 - 2021 data - Temp_1_250 <- TempDF[Day %in% 1:250, .(Temp_mean = mean(avg_C)), .(Year)] VWC_top_70_100 <- VWCDF[Day %in% 70:100 & variable == 'Lyr_1', .(VWC_mean = mean(value)), .(Year)] @@ -142,3 +146,29 @@ getShriver2018Vars <- function(TempDF, VWCDF) { return(vars) } + +formatShriver2018 <- function(Hist_Shriver2018, Future_Shriver2018, currYear) { + + Hist_Shriver2018$TP <- 'Historical' + + Future_Shriver2018$run_year <- sapply(strsplit(Future_Shriver2018$run, '_'), '[', 2) + Future_Shriver2018$run_sim <- sapply(strsplit(Future_Shriver2018$run, '_'), '[', 1) + Future_Shriver2018 <- Future_Shriver2018[Year %in% (currYear-1):currYear] + Future_ShriverMeds <- Future_Shriver2018[, .(Prob = median(Prob)), .(run_sim, Year)] + Future_ShriverMeds$TP <- as.character(Future_ShriverMeds$Year) + + return(bind(Hist_Shriver2018,Future_ShriverMeds)) +} + +formatGISSM <- function(Hist_GISSM, Future_GISSM) { + + Hist_GISSM$TP <- 'Historical' + + Future_GISSM$run_year <- sapply(strsplit(Future_GISSM$run, '_'), '[', 2) + Future_GISSM$run_sim <- sapply(strsplit(Future_GISSM$run, '_'), '[', 1) + Future_GISSM <- Future_GISSM[, .(Prob = mean(SeedlingSurvival_1stSeason)), .(run_sim, ryear)] + Future_GISSM$TP <- as.character(Future_GISSM$ryear) + + return(bind(Hist_GISSM,Future_GISSM)) +} + diff --git a/functions/simulationFunctions.R b/functions/simulationFunctions.R index 031da0b..2393a53 100644 --- a/functions/simulationFunctions.R +++ b/functions/simulationFunctions.R @@ -7,27 +7,28 @@ library(lubridate) library(raster) library(zoo) library(caTools) -# weather -#library(geoknife) # app library(plumber) library(rvest) -source('Git/shorttermdroughtforecaster/functions/weatherFunctions.R') -source('Git/shorttermdroughtforecaster/functions/soilsAndComp.R') -source('Git/shorttermdroughtforecaster/functions/Outputs.R') -source('Git/shorttermdroughtforecaster/functions/HelperFunctions.R') -source('Git/shorttermdroughtforecaster/functions/ecoIndicators.R') -# -# lat <- 43.3737 -# lng <- -116.6323 +#source('functions/weatherFunctions.R') +source('functions/soilsAndComp.R') +source('functions/Outputs.R') +source('functions/HelperFunctions.R') +source('functions/ecoIndicators.R') + +# +# lat <- 35.1983 +# lng <- -111.6513 # soils <- 2 # sand <- 50 # clay <- 15 -# comp <- 1 -# shrubs <- .5 +# comp <- 2 # grasses <- .5 -# forbs <- bg <- trees <- 0 +# shrubs <- .5 +# trees <- 0 +# forbs <- 0 +# bg <- 0 #* gather user data, execute soilwat simulations, and return outputs #* @param lat latitude of site @@ -35,39 +36,38 @@ source('Git/shorttermdroughtforecaster/functions/ecoIndicators.R') #* @param soils binary decision of user 1 is extract from a gridded source 2 is user input #* @param sand user input of sand texture #* @param clay user input of clay texture -#* @param comp binary decision of user 1 is generate composition from climate 2 is user inputs -#* @param trees user input of tree composition -#* @param shrubs user input of shrub composition -#* @param grasses user input of grass composition -#* @param forbs user input of forb composition -#* @param bg user input of bareground composition + #* @get /gatherDataAndExecuteSW gatherDataAndExecuteSW <- function(lat, lng, - soils, sand = 33, clay = 33, - comp, trees = 0, shrubs = 0.5, grasses = 0.5, forbs = 0, bg = 0){ + soils, sand = 33, clay = 33, comp = 1) { ################### ---------------------------------------------------------------- - # Part 0 - format data from HTTP request + # Part 0 - Setup - format data from HTTP request, get dates ################### ---------------------------------------------------------------- lat <- as.numeric(lat) lng <- as.numeric(lng) print(lat) print(lng) - + sand <- as.numeric(sand) clay <- as.numeric(clay) + currDOY <- yday(Sys.Date()) + currMonth <- month(Sys.Date()) + currYear <- year(Sys.Date()) + ################### ---------------------------------------------------------------- # Part 1 - Getting and formatting weather data for the historical and future runs ################### ---------------------------------------------------------------- # get historical weather data -> using geoknife ... this takes very long and needs to be changed - # print(Sys.time()) - #wdata <- getWeatherData(lat, lng) - # print(Sys.time()) - #write.csv(wdata2, 'ExampleData/wdata_ReynoldsCreek.csv', row.names = FALSE) - wdata <- fread('ExampleData/wdata_ReynoldsCreek.csv') + print(Sys.time()) + wdata <- getWeatherData(lat, lng, currYear, + dir = '~/Desktop/www.northwestknowledge.net/metdata/data/') + print(Sys.time()) + # write.csv(wdata2, 'ExampleData/wdata.csv', row.names = FALSE) + #wdata <- fread('ExampleData/wdata.csv') ################### ---------------------------------------------------------------- # Part 2 - Sets soils and veg and lat @@ -82,7 +82,7 @@ source('Git/shorttermdroughtforecaster/functions/ecoIndicators.R') # 4 set latitude. Used in GISSM calculations swSite_IntrinsicSiteParams(sw_in0)[["Latitude"]] <- lat * pi/180 - + ################### ---------------------------------------------------------------- # Part 3 - Run Soilwat ################### ---------------------------------------------------------------- @@ -90,30 +90,32 @@ source('Git/shorttermdroughtforecaster/functions/ecoIndicators.R') # Soils info formatting ---------------------------------------------------- SoilsDF <- data.frame(depth_cm = c(1:250), Depth = c(rep('Shallow', 15), rep('Intermediate', 50), rep('Deep',185))) - + Soils <- data.frame(sw_in0@soils@Layers) Soils <- Soils[,c('depth_cm', 'sand_frac', 'clay_frac')] Soils$width <- diff(c(0, Soils$depth_cm)) SoilsDF <- merge(Soils, SoilsDF, by = 'depth_cm') SoilsDF$variable <- paste0('Lyr_',1:dim(SoilsDF)[1]) - + # write.csv(SoilsDF, 'Tests/TestExample/SoilsDF.csv') # -------------------------------------------------------------------------- # Run 1 - with observed historical data ------------------------------------ # -------------------------------------------------------------------------- print('Running Historical') swCarbon_Use_Bio(sw_in0) <- FALSE swCarbon_Use_WUE(sw_in0) <- FALSE - swYears_EndYear(sw_in0) <- year(Sys.Date()) - 1 + swYears_EndYear(sw_in0) <- currYear - 1 - weath <- dbW_dataframe_to_weatherData(wdata[wdata$Year %in% c(1979:2019), + weath <- dbW_dataframe_to_weatherData(wdata[wdata$Year %in% c(1979:(currYear - 1)), c('Year', 'DOY', 'Tmax_C', 'Tmin_C', 'PPT_cm')], round = 4) - sw_out0 <- sw_exec(inputData = sw_in0, weatherList = weath, quiet = TRUE) + sw_out0 <- sw_exec(inputData = sw_in0, weatherList = weath, quiet = FALSE) HistDataAll <- getOutputs(sw_out0, sw_in0) - + # format outputs HistDataAll1 <- setorder(HistDataAll[[1]], Year, Day) + #fwrite(HistDataAll1, 'Tests/TestExample/HistData.csv') + HistDataAll1 <- getRolling(HistDataAll1) - + HistData_Norm_Stats <- HistDataAll1[HistDataAll1$Year %in% 1981:2010, ] # historical normal HistData_Norm_Stats <- makeDateMonthDat(HistData_Norm_Stats, 'Day') # Get date without the year HistData_Norm_Stats$Year <- HistData_Norm_Stats$Day <- NULL @@ -124,44 +126,48 @@ source('Git/shorttermdroughtforecaster/functions/ecoIndicators.R') c('Date', sapply(names(HistData_Norm_Stats)[-c(11)], paste0, c(".med", ".10", ".90"))))# get all means and sds!!! HistData_Norm_Stats <- getSWP(HistData_Norm_Stats, SoilsDF) HistData_Norm_Stats <- setorder(HistData_Norm_Stats, Date) - + # eco vars ------------------------------------------------------------------ - Hist_Shriver2018 <- data.table(Year = HistDataAll[[2]]$PredictingForYear, + Hist_Shriver2018 <- data.table(Year = HistDataAll[[2]]$PlantedinYear, Prob = p_Shriver2018(HistDataAll[[2]]$Temp_mean, HistDataAll[[2]]$VWC_mean)) - - #Hist_GISSM <- data.table(HistDataAll[[3]]) - + + Hist_GISSM <- data.table(HistDataAll[[3]]) + # -------------------------------------------------------------------------- # Run 2 - with future anomaly data # -------------------------------------------------------------------------- print('Running Future') print(Sys.time()) - AnomalyData1 <- runFutureSWwithAnomalies(lat, lng, sw_in0, wdata, res2, n = 30, SoilsDF) - + AnomalyData1 <- runFutureSWwithAnomalies(lat, lng, sw_in0, wdata, res2, n = 1, SoilsDF, + currDOY, currMonth, currYear) + AllOut <- AnomalyData1[[1]] + #fwrite(AllOut, 'Tests/TestExample/FutureData.csv') MonthlyAnoms <- AnomalyData1[[3]] - + AnomRunStats <- formatOutputsFuture(AllOut, SoilsDF) - + # eco vars ------------------------------------------------------------------ - Future_Shriver2018 <- data.table(Year = AnomalyData1[[2]]$PredictingForYear, run = AnomalyData1[[2]]$run, + Future_Shriver2018 <- data.table(Year = AnomalyData1[[2]]$PlantedinYear, run = AnomalyData1[[2]]$run, Prob = p_Shriver2018(AnomalyData1[[2]]$Temp_mean, AnomalyData1[[2]]$VWC_mean)) - - #Future_GISSM <- data.table(AnomalyData1[[3]]) + + Future_GISSM <- data.table(AnomalyData1[[3]]) # format ecovars for writing out ------------------------------------------- + Shriver_Stats <- formatShriver2018(Hist_Shriver2018, Future_Shriver2018, currYear) + GISSM_Stats <- formatGISSM(Hist_GISSM, Future_GISSM) + ################### ---------------------------------------------------------------- # Part 4 - Write out formatted outputs ################### ---------------------------------------------------------------- - # fwrite(HistData_Norm_Stats, 'ExampleData/HistData_Norm_Stats.csv') - # #fwrite(MonthlyAnoms, 'ExampleData/MonthlyAnoms.csv') - # fwrite(AnomRunStats, 'ExampleData/AnomRun_Stats.csv') - # + #fwrite(HistData_Norm_Stats, 'ExampleData/HistData_Norm_Stats.csv') + # fwrite(MonthlyAnoms, 'ExampleData/MonthlyAnoms.csv') + #fwrite(AnomRunStats, 'ExampleData/AnomRun_Stats.csv') + # # fwrite(Hist_Shriver2018, 'ExampleData/Hist_Shriver2018.csv') # fwrite(Future_Shriver2018, 'ExampleData/Future_Shriver2018.csv') # #fwrite(Hist_GISSM, 'ExampleData/Hist_GISSM.csv') # #fwrite(Future_GISSM, 'ExampleData/Future_GISSM.csv') - return(list(HistData_Norm_Stats, AnomRunStats, Shriver_Stats, HistDataAll1)) # AnomalyData, HistData + return(list(HistData_Norm_Stats, AnomRunStats, Shriver_Stats, GISSM_Stats))#, HistDataAll1)) } - diff --git a/functions/weatherFunctions.R b/functions/weatherFunctions.R index b65b766..651b848 100644 --- a/functions/weatherFunctions.R +++ b/functions/weatherFunctions.R @@ -1,100 +1,76 @@ -getWeatherData <- function(lat, lng) { - - stencil <- simplegeom(c(lng, lat)) - fabric = webdata(url='https://cida.usgs.gov/thredds/dodsC/UofIMETDATA') - variables(fabric) <- c('precipitation_amount', 'min_air_temperature', 'max_air_temperature') - times(fabric) <- c('1979-01-01', paste(Sys.Date())) - job <- geoknife(stencil, fabric, wait = TRUE) - - # format - data <- result(job) - data$statistic <- NULL - - wdata <- reshape2::dcast(data, DateTime ~ variable, value.var = 'bufferedPoint') - # Year DOY Tmax_C Tmin_C PPT_cm - wdata$Year <- year(wdata$DateTime) - wdata$DOY <- yday(wdata$DateTime) - - wdata$DateTime <- NULL - wdata <- wdata[,c('Year', 'DOY', 'max_air_temperature', 'min_air_temperature', 'precipitation_amount' )] - names(wdata) <- c('Year', 'DOY','Tmax_C', 'Tmin_C', 'PPT_cm') - - # 2019 and 2020 for temp are incorrect ... - #summary(wdata2) - wdata2 <- wdata[wdata$Year %in% 1979:2018, ] - #wdata2$Date <- as.Date(wdata2$Date) - +getWeatherData <- function(lat, lng, currYear, dir) { + # Make data.frame of just years and days ------------------------------------------- - currYear <- year(Sys.time()) - wdataFrame <- data.frame(Date = seq(from = as.Date('1979-01-01'), - to = as.Date(paste0(year(Sys.Date()),'-12-31')), by="day")) - wdataFrame$Year <- year(wdataFrame$Date) - wdataFrame$DOY <- yday(wdataFrame$Date) - - # Join with weather data to make NAs ------------------------------------------- - wdata2 <- plyr::join(wdataFrame, wdata2) - + wdata <- data.frame(Date = seq(from = as.Date('1979-01-01'), + to = as.Date(paste0(currYear,'-12-31')), by="day")) + wdata$Year <- year(wdata$Date) + wdata$DOY <- yday(wdata$Date) + wdata$Tmax_C <- wdata$Tmin_C <- wdata$PPT_cm <- c() + # Get info from netcdfs .... # https://www.northwestknowledge.net/metdata/data/ + files <- list.files(dir, full.names = TRUE) + # Tmax_C - files <- list.files('~/Desktop/Projects/STDF/wdatanc/tmax/', full.names = TRUE) - for(f in 1:length(files)){ - year <- as.numeric(substr(files[f], nchar(files[f]) - 6, nchar(files[f]) -3)) - - nc <- suppressWarnings(brick(files[f], varname = 'air_temperature')) + tmmxfiles <- grep('tmmx_', files, value = TRUE) + for(f in 1:length(tmmxfiles)){ + year <- as.numeric(substr(tmmxfiles[f], nchar(tmmxfiles[f]) - 6, nchar(tmmxfiles[f]) -3)) + + nc <- suppressWarnings(brick(tmmxfiles[f], varname = 'air_temperature')) vals <- extract(nc, matrix(c(lng, lat), ncol = 2))[1,] - wdata2[wdata2$Year == year, 'Tmax_C'][1:length(vals)] <- vals - } + wdata[wdata$Year == year, 'Tmax_C'][1:length(vals)] <- vals + } + # Tmin_C - files <- list.files('~/Desktop/Projects/STDF/wdatanc/tmin/', full.names = TRUE) - for(f in 1:length(files)){ - year <- as.numeric(substr(files[f], nchar(files[f]) - 6, nchar(files[f]) -3)) - - nc <- suppressWarnings(brick(files[f], varname = 'air_temperature')) + tmmnfiles <- grep('tmmn_', files, value = TRUE) + for(f in 1:length(tmmnfiles)){ + year <- as.numeric(substr(tmmnfiles[f], nchar(tmmnfiles[f]) - 6, nchar(tmmnfiles[f]) -3)) + + nc <- suppressWarnings(brick(tmmnfiles[f], varname = 'air_temperature')) vals <- extract(nc, matrix(c(lng, lat), ncol = 2))[1,] - wdata2[wdata2$Year == year, 'Tmin_C'][1:length(vals)] <- vals + wdata[wdata$Year == year, 'Tmin_C'][1:length(vals)] <- vals } - + #PPT_cm - files <- list.files('~/Desktop/Projects/STDF/wdatanc/pr/', full.names = TRUE) - for(f in 1:length(files)){ - year <- as.numeric(substr(files[f], nchar(files[f]) - 6, nchar(files[f]) -3)) - - nc <- suppressWarnings(brick(files[f], varname = 'precipitation_amount')) + prfiles <- grep('pr_', files, value = TRUE) + for(f in 1:length(prfiles)){ + year <- as.numeric(substr(prfiles[f], nchar(prfiles[f]) - 6, nchar(prfiles[f]) -3)) + + nc <- suppressWarnings(brick(prfiles[f], varname = 'precipitation_amount')) vals <- extract(nc, matrix(c(lng, lat), ncol = 2))[1,] - wdata2[wdata2$Year == year, 'PPT_cm'][1:length(vals)] <- vals - } + wdata[wdata$Year == year, 'PPT_cm'][1:length(vals)] <- vals + } - # fix values - wdata2$Tmax_C <- wdata2$Tmax_C - 273.15 - wdata2$Tmin_C <- wdata2$Tmin_C - 273.15 - wdata2$PPT_cm <- wdata2$PPT_cm/10 - # if there isn't 365 days in each year ... na.locf for temp and put 0 for ppt? - # fill in missing with weather generator when running SOILWAT - wdata2$Tmax_C <- zoo::na.locf(wdata2$Tmax_C) - wdata2$Tmin_C <- zoo::na.locf(wdata2$Tmin_C) - wdata2[is.na(wdata2$PPT_cm), 'PPT_cm'] <- 0 - - return(wdata2) + # fill in missing with weather generator when running SOILWAT? + wdata$Tmax_C <- zoo::na.locf(wdata$Tmax_C) + wdata$Tmin_C <- zoo::na.locf(wdata$Tmin_C) + wdata[is.na(wdata$PPT_cm), 'PPT_cm'] <- 0 + + # convert + wdata$Tmax_C <- wdata$Tmax_C - 273.15 + wdata$Tmin_C <- wdata$Tmin_C - 273.15 + wdata$PPT_cm <- wdata$PPT_cm /10 + return(wdata) } -runFutureSWwithAnomalies <- function(lat, lng, sw_in0, wdata, res2, n, SoilsDF){ - +runFutureSWwithAnomalies <- function(lat, lng, sw_in0, wdata, res2, n, SoilsDF, + currDOY, currMonth, currYear){ + Nleads <- 12 - + MonthlyAnoms <- data.frame() # for testing AllOut1 <- Shriver_Out <- GISSM_Out <- data.frame() # Determine Region from coordinates and shapefile ------------------------------------------ CD102 <- shapefile(x = 'CD102/CD102.shp') points <- data.frame(x = lng, y = lat) - - coordinates(points) <- ~ x + y + + coordinates(points) <- ~ x + y proj4string(points) <- CRS("+proj=longlat +datum=WGS84 +no_defs") - + CDRegion <- as.numeric(over(points, CD102)$ID4) - + # Read in and subset anomaly data ----------------------------------------------------------- TempAnomsWhole <- fread('CurrentAnomalyTempData.csv') TempAnoms <- subset(TempAnomsWhole, CD == CDRegion) @@ -104,40 +80,37 @@ runFutureSWwithAnomalies <- function(lat, lng, sw_in0, wdata, res2, n, SoilsDF){ PPTAnoms <- PPTAnoms[1:Nleads,] TempAnoms <- TempAnoms[1:Nleads] - + # Establish date and time info ------------------------------------------------------------- # Establish current month and how the 'LEAD's relate to months # A LEAD of 1 relates to the forecast beginning where the currmonth + 1 - currMonth <- month(Sys.Date()) - currDOY <- yday(Sys.Date()) - 1 - currYear <- year(Sys.Date()) - - monthLeads <- makeMonthLeadRelationshipTable(TempAnoms) - + + monthLeads <- makeMonthLeadRelationshipTable(TempAnoms, currMonth) + # Prepare historical data ------------------------------------------------------------------- ## Calculate mean daily temperature wdata$Tmean_C <- rowMeans(wdata[,c('Tmax_C', 'Tmin_C')]) - + # Determine months wdata$Month <- month(wdata$Date) wdata$Date <- NULL - + # Aggregate to monthly values monthlyWdata <- wdata[,.(Tmean_C = mean(Tmean_C), PPT_cm = sum(PPT_cm)), .(Month, Year)] - - # Convert monthly PPT to inches + + # Convert monthly PPT to inches monthlyWdata$PPT_in <- monthlyWdata$PPT_cm / 2.54 - + # Aggregate to moving left-aligned 3-month periods (NWS lead seasons) monthlyWdata$Tmean_C_rollMean <- zoo::rollmean(x = monthlyWdata[,c("Tmean_C")], - k = 3, FUN = mean, fill = NA, + k = 3, FUN = mean, fill = NA, partial = TRUE, align = "left") monthlyWdata$PPT_in_rollSum <- zoo::rollsum(x = monthlyWdata[,c("PPT_in")], - k = 3, fill = NA, + k = 3, fill = NA, partial = TRUE, align = "left") # Norm monthlyWdata <- monthlyWdata[monthlyWdata$Year %in% 1981:2010, ] - + # Convert moving left-aligned 3-month periods to NWS leads monthLeads2 <- monthLeads[1:Nleads,] monthlyWdata <- merge(monthlyWdata, monthLeads2[,1:2]) @@ -145,67 +118,67 @@ runFutureSWwithAnomalies <- function(lat, lng, sw_in0, wdata, res2, n, SoilsDF){ # Transform PPT_in_rollingSum using powers (PO) from NWS long-long ppt forecasts monthlyWdata <- merge(monthlyWdata, PPTAnoms[,c('LEAD', 'PO')], by = 'LEAD') monthlyWdata$PPT_PO_rollSum <- monthlyWdata$PPT_in_rollSum ^ monthlyWdata$PO - + # Begin generating futures! ---------------------------------------------------------------------- # Step 1 - generate an anomaly for each lead ------------------------------------------------- - + # Temp TempAnoms$ClimatologicalMEAN_Temp_C <- (TempAnoms$ClimatologicalMEAN - 32) * (5/9) TempAnoms$ForecastedMEAN_Temp_C <- (TempAnoms$ForecastedMEAN - 32) * (5/9) - + TempAnoms$ForecastedSD_Temp_C <- (TempAnoms$ForecastedSD) * (5/9) TempAnoms$Anom_F <- TempAnoms$ForecastedMEAN - TempAnoms$ClimatologicalMEAN TempAnoms$Anom_C <- TempAnoms$Anom_F * (5/9) - + # PPT - keep in transformed units backT <- 1/PPTAnoms$PO - + # convert PPTAnoms$ClimatatologicalMEAN_PPT_PO <- PPTAnoms$ClimatologicalMEAN ^ PPTAnoms$PO PPTAnoms$ClimatatologicalMEAN_PPT_cm <- PPTAnoms$ClimatologicalMEAN * 2.54 - + PPTAnoms$ForecastedMEAN_PPT_PO <- PPTAnoms$ForecastedMEAN ^ PPTAnoms$PO PPTAnoms$ForecastedMEAN_PPT_cm <- PPTAnoms$ForecastedMEAN * 2.54 - + PPTAnoms$ForecastedSD_PPT_in <- as.numeric(PPTAnoms$ForecastedSD) ^ backT PPTAnoms$ForecastedSD_PPT_cm <- PPTAnoms$ForecastedSD_PPT_in * 2.54 - + # Anomalies NEEDS to be in transformed units for MV sampling PPTAnoms$Anom_PO <- PPTAnoms$ForecastedMEAN_PPT_PO - PPTAnoms$ClimatatologicalMEAN_PPT_PO - + # Anomalies for comparison PPTAnoms$Anom_cm <- PPTAnoms$ForecastedMEAN_PPT_cm - PPTAnoms$ClimatatologicalMEAN_PPT_cm PPTAnoms$Anom_CF <- PPTAnoms$ForecastedMEAN / PPTAnoms$ClimatologicalMEAN - + # fwrite(PPTAnoms, 'ExampleData/PPTAnoms.csv') # fwrite(TempAnoms, 'ExampleData/TempAnoms.csv') - + # Step 2 - n samples, multivariate sampling for each lead ------------------------------------------------- - generatedAnomData <- generateAnomalyData(monthlyWdata, TempAnoms, PPTAnoms, - leads = seq_len(Nleads), Nleads = Nleads, - n = n) - saveRDS(generatedAnomData, 'ExampleData/generatedAnomData') - + generatedAnomData <- generateAnomalyData(monthlyWdata, TempAnoms, PPTAnoms, + leads = seq_len(Nleads), Nleads = Nleads, + n = 30) + #saveRDS(generatedAnomData, 'ExampleData/generatedAnomData') + # Step 2.1 - Correction factor to the correction factor based on mean ------------------------------- - + ### Temperature tempGenAnoms <- data.table(generatedAnomData [, , "dT_C"]) tempGenAnoms$LEAD <- row.names(tempGenAnoms) tempGenAnoms <- melt(tempGenAnoms, id.vars = 'LEAD') tempGenAnoms <- tempGenAnoms[,.(MVGenMean = mean(value)),.(LEAD)] - - TempBiasCF <- TempAnoms$Anom_C - tempGenAnoms$MVGenMean + + TempBiasCF <- TempAnoms$Anom_C - tempGenAnoms$MVGenMean #print(cbind(NWSAnom = TempAnoms$Anom_C , MVAnom = tempGenAnoms$MVGenMean, TempBiasCF)) - + generatedAnomData [, , "dT_C"] <- generatedAnomData [, , "dT_C"] + TempBiasCF - + ### Precipitation pptGenAnoms <- data.table(generatedAnomData [, , "PPT_CF"]) pptGenAnoms$LEAD <- row.names(pptGenAnoms) pptGenAnoms <- melt(pptGenAnoms, id.vars = 'LEAD') pptGenAnomsMean <- pptGenAnoms[,.(MVGenMean = mean(value)),.(LEAD)] - - PPTBiasCF <- PPTAnoms$Anom_CF / pptGenAnomsMean$MVGenMean + + PPTBiasCF <- PPTAnoms$Anom_CF / pptGenAnomsMean$MVGenMean #print(cbind(NWSAnom = PPTAnoms$Anom_CF, MVAnom = pptGenAnomsMean$MVGenMean, PPTBiasCF)) generatedAnomData [, , "PPT_CF"] <- generatedAnomData [, , "PPT_CF"] * PPTBiasCF @@ -214,96 +187,97 @@ runFutureSWwithAnomalies <- function(lat, lng, sw_in0, wdata, res2, n, SoilsDF){ ## ----- need for figures but not used anywhere in analysis generatedAnomData[, , 'PPT_GenForecasted_cm'] <- generatedAnomData [, , "PPT_CF"] * PPTAnoms$ClimatatologicalMEAN_PPT_cm - - saveRDS(generatedAnomData, 'ExampleData/generatedAnomData_BiasCorrected') - + + #saveRDS(generatedAnomData, 'Git/shorttermdroughtforecaster/ExampleData/generatedAnomData_BiasCorrected') + for(nn in 1:n){ print(nn) - + # Step 3 Get monthly averages across leads -------------------------------------------------- OneYearAnom <- generatedAnomData[ , nn, ] OneYearAnom <- cbind(OneYearAnom, Climatological_MEAN_cm = PPTAnoms$ClimatatologicalMEAN_PPT_cm[1:12]) OneYearAnom <- cbind(OneYearAnom, Anom_cm = OneYearAnom[,4] - OneYearAnom[,6]) - + yearlydat <- data.frame(matrix(nrow = 12, ncol = 3)) names(yearlydat) <- c('tempAnom', 'pptAnom_cm', 'pptAnom_CF') yearlydat$Month <- as.numeric(row.names(yearlydat)) - + for(m in c(yearlydat$Month)){ # for each month, m, in a year, nn - + mLeads <- c(t(monthLeads[monthLeads$Month == m, 3:5])) #temp - yearlydat[m, 1] <- mean(OneYearAnom[mLeads, 'dT_C'], na.rm = TRUE) - + yearlydat[m, 1] <- mean(OneYearAnom[mLeads, 'dT_C'], na.rm = TRUE) + # ppt cm yearlydat[m, 2] <- mean(OneYearAnom[mLeads, 'Anom_cm'], na.rm = TRUE)/3 - + # ppt correction factor yearlydat[m, 3] <- mean(OneYearAnom[mLeads, 'PPT_CF'], na.rm = TRUE) } - - + + MonthlyAnoms <- rbind(MonthlyAnoms, yearlydat) #} - #fwrite(MonthlyAnoms, 'ExampleData/MonthlyAnoms.csv') - - + fwrite(MonthlyAnoms, 'ExampleData/MonthlyAnoms.csv') + + # Step 4 ---------------------------------------------------------------------------------------------- # Create future weather / integrate anomaly data into historical weather ---------------------------------------------------------- years <- 1981:2011 wdata2 <- wdata[wdata$Year %in% years, ] weathAnomAll <- suppressWarnings(integrateAnomalyData(wdata2, yearlydat)) - # Make weather data for one simulation - ## Three years worth of data: - # 1) One year ago - # 2) Observed until today's date + ## Three years worth of data: + # 1) One year ago + # 2) Observed until today's date # 3) Future data integrated with historical data (weath Anom) ### year 1 - The year prior to current year's observed data - year1 <- wdata[wdata$Year == (currYear - 1), c('Year', 'DOY', 'Tmax_C', 'Tmin_C', 'PPT_cm')] - + year1 <- wdata[wdata$Year == currYear - 1, c('Year', 'DOY', 'Tmax_C', 'Tmin_C', 'PPT_cm')] + # observed weather ----------------------------------------------- thisYearObservedWData <- wdata[wdata$Year == currYear, c('Year', 'DOY', 'Tmax_C', 'Tmin_C', 'PPT_cm')] thisYearObservedWData <- makeDateMonthDat(thisYearObservedWData, 'DOY') - + for(y in 1981:2010) { #print(y) - weathAnomOneSim <- makeWeathOneSim(y, year1, thisYearObservedWData, weathAnomAll) + weathAnomOneSim <- makeWeathOneSim(y, year1, thisYearObservedWData, weathAnomAll, + currDOY, currYear) # run SOILWAT2 for future years ---------------------------------------------------------- weathAnomOneSim <- dbW_dataframe_to_weatherData(weathAnomOneSim, round = 4) - + swYears_EndYear(sw_in0) <- currYear + 1 swYears_StartYear(sw_in0) <- currYear - 1 - + sw_out <- sw_exec(inputData = sw_in0, weatherList = weathAnomOneSim, quiet = TRUE) - + # Grab Data I want for this run ---------------------------------------------------------- - Out1 <- getOutputs(sw_out, sw_in0) + Out1 <- getOutputs(sw_out, sw_in0, calc_GISSM = FALSE) AllOut1 <- rbind(AllOut1, cbind(Out1[[1]], run = paste(nn, y, sep = '_'))) Shriver_Out <- rbind(Shriver_Out, cbind(Out1[[2]], run = paste(nn, y, sep = '_'))) #GISSM_Out <- rbind(GISSM_Out, cbind(Out1[[3]], run = paste(nn, y, sep = '_'))) - + } } - - return(list(AllOut1, Shriver_Out, GISSM_Out, MonthlyAnoms)) + + return(list(AllOut1, Shriver_Out, #GISSM_Out, + MonthlyAnoms)) } -generateAnomalyData <- function(monthlyWdata, TempAnoms, PPTAnoms, +generateAnomalyData <- function(monthlyWdata, TempAnoms, PPTAnoms, leads, Nleads, n = 30) { - + set.seed(NULL) - + # one table forecast_NWS <- merge(TempAnoms[,c('LEAD','ClimatologicalMEAN_Temp_C', 'ForecastedSD_Temp_C', 'Anom_C')], PPTAnoms[,c('LEAD','ClimatatologicalMEAN_PPT_PO', 'ForecastedSD', 'Anom_PO')]) - + #------ Calculate anomalies from historical data corresponding to NWS deviations ids <- monthlyWdata[["LEAD"]] meteo_anomalies_leads <- data.frame( @@ -311,7 +285,7 @@ generateAnomalyData <- function(monthlyWdata, TempAnoms, PPTAnoms, dT_C = monthlyWdata[["Tmean_C_rollMean"]] - forecast_NWS[["ClimatologicalMEAN_Temp_C"]][ids], dPPT_PO = monthlyWdata[["PPT_PO_rollSum"]] - forecast_NWS[["ClimatatologicalMEAN_PPT_PO"]][ids] ) - + # Within-lead covariances among dT and dPPT from historical data cov_anomalies_leads <- by( data = meteo_anomalies_leads[c("dT_C", "dPPT_PO")], # why covariance on the difference? @@ -326,14 +300,14 @@ generateAnomalyData <- function(monthlyWdata, TempAnoms, PPTAnoms, dim = c( length(leads), n, 5), dimnames = list(NULL, NULL, c("dT_C", "dPPT_PO", 'PT_GenForecasted_PO', 'PPT_GenForecasted_cm', 'PPT_CF')) ) - + for (k in seq_along(leads)) { # Put together covariances from historical meteo data and # variances from NWS forecasts kcov <- cov_anomalies_leads[[k]] kcov["dT_C", "dT_C"] <- t(forecast_NWS[k, "ForecastedSD_Temp_C"] ^ 2) kcov["dPPT_PO", "dPPT_PO"] <- t(forecast_NWS[k, "ForecastedSD"] ^ 2) - + # Draw multivariate normal anomalies: Sigma may not be positive definite # write message that confirms that mvrnorm worked for each lead tmp <- tryCatch( @@ -343,22 +317,22 @@ generateAnomalyData <- function(monthlyWdata, TempAnoms, PPTAnoms, Sigma = kcov, empirical = TRUE ), - + error = function(e) { # Try & Make sigma positive definite message(paste('Attempting to make lead', k, 'positive definite')) print(kcov) - + kcov2 <- Matrix::nearPD( x = kcov, keepDiag = FALSE, maxit = 1000 ) if (kcov2[["converged"]]) { - + message(paste('kcov for lead', k, 'successfuly converged')) print(kcov2$mat) - + tmp <- MASS::mvrnorm( n = n, mu = as.numeric(forecast_NWS[k, c("Anom_C", "Anom_PO")]), # Means as forecasted NWS anoms @@ -378,108 +352,48 @@ generateAnomalyData <- function(monthlyWdata, TempAnoms, PPTAnoms, } ) - + gen_anomalies_leads[k, , c(1,2)] <- tmp } - + # generate forecast in transformed units - gen_anomalies_leads[, , 'PT_GenForecasted_PO'] <- + gen_anomalies_leads[, , 'PT_GenForecasted_PO'] <- gen_anomalies_leads[, , 'dPPT_PO'] + PPTAnoms$ClimatatologicalMEAN_PPT_PO[1:12] - + # convert ppt forecast to cm backT <- 1/PPTAnoms$PO[1:12] gen_anomalies_leads[, , 'PPT_GenForecasted_cm'] <- ((gen_anomalies_leads[, , 'PT_GenForecasted_PO']) ^ backT) * 2.54 - + # calculate correction factor gen_anomalies_leads[, , 'PPT_CF'] <- gen_anomalies_leads[, , 'PPT_GenForecasted_cm'] / PPTAnoms$ClimatatologicalMEAN_PPT_cm[1:12] - - + + return(gen_anomalies_leads) - + } integrateAnomalyData <- function(wdata, yearlydat) { - + # This function alters the historical weather data so that every year in the historical record # now has future anomolies applied ## Temp -> additive ## PPT -> multiplicative scaled - + ### Step 1 - find month in wdata and merge anomalies # wdata$Month <- month(as.Date(strptime(paste(wdata$Year, wdata$DOY), format="%Y %j"), format="%m-%d-%Y")) #test <- wdata[wdata$DOY == 366,] wdata <- merge(wdata, yearlydat) - - ### Temp ------------------------------------------------------------------------------------ + + ### Temp ------------------------------------------------------------------------------------ wdata$Tmax_C <- wdata$Tmax_C + wdata$tempAnom wdata$Tmin_C <- wdata$Tmin_C + wdata$tempAnom - + ### Precip ---------------------------------------------------------------------------- wdata$PPT_cm <- wdata$PPT_cm * wdata$pptAnom_CF - + #That's it? wdata <- wdata[,c('Year', 'DOY', 'Tmax_C', 'Tmin_C', 'PPT_cm')] wdata <- setorder(wdata, Year, DOY) return(wdata) - -} -# year 1 is last years data -# thisYearObservedWData is this year's observed weather data -makeWeathOneSim <- function(y, year1, thisYearObservedWData, weathAnomAll) { - - currDOY <- yday(Sys.Date()) - currYear <- year(Sys.Date()) - - days <- dim(thisYearObservedWData)[1] - daysNextYear <- yearDays(as.Date(paste0(currYear + 1,'-01-01'))) - LeapYear <- days == 366 - - - ### --------- - ### year 2 - observed data for this year until today's date and then future, weathAnom data - ### --------- - - year2 <- data.frame(Year = currYear, DOY = 1:days) - year2 <- makeDateMonthDat(year2, 'DOY') - - # join future data to the structure of this year's data - year2Fut <- weathAnomAll[weathAnomAll$Year == y , ] - year2Fut <- makeDateMonthDat(year2Fut, 'DOY') - year2 <- plyr::join(year2, year2Fut[,c('Date', 'Tmax_C', 'Tmin_C', 'PPT_cm')], - by = 'Date') - - # fill in with this years' observed data - # should have the corrrect number of days always since they are good representation of the same year - year2[1:currDOY, c('Tmax_C', 'Tmin_C', 'PPT_cm')] <- thisYearObservedWData[1:currDOY, c('Tmax_C', 'Tmin_C', 'PPT_cm')] - - if(any(is.na(year2))) { - print('NAs present in year2 data') - year2$Tmax_C <- zoo::na.fill( year2$Tmax_C, "extend") - year2$Tmin_C <- zoo::na.fill( year2$Tmin_C, "extend") - year2$PPT_cm <- zoo::na.fill( year2$PPT_cm, "extend") - } - # ggplot() + - # geom_line(data = year2, aes(Date, Tmax_C, group = 1)) + - # geom_line(data = thisYearObservedWData, aes(Date, Tmax_C, group = 1), color = 'purple') + - # geom_line(data = year2Fut, aes(Date, Tmax_C, group = 1), color = 'green') - # - - ### --------- - ## year 3 ... forecasts that run into next year (aka 2021) and then scratch data for the rest of 2021 - ### --------- - year3 <- data.frame(Year = currYear + 1, DOY = 1:daysNextYear) - year3 <- makeDateMonthDat(year3, 'DOY') - - # join future data to the structure of this year's data - year3Fut <- weathAnomAll[weathAnomAll$Year == y + 1, ] - year3Fut <- makeDateMonthDat(year3Fut, 'DOY') - year3 <- plyr::join(year3, year3Fut[,c('Date', 'Tmax_C', 'Tmin_C', 'PPT_cm')], - by = 'Date') - - # Make one simulation's worth of data - year2$Date <- year3$Date <- NULL - weathAnomOneSim <- rbind(year1, year2, year3) - - return(weathAnomOneSim) }

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