Charting Functions

Overview

MCHammer offers the most important charts for building and analyzing Monte-Carlo Results. MCH_Charts contains standard simulation charts for sensitivity, density, trends (time series with confidence bands) for simulation arrays, vectors and dataframes.

Functions

MCHammer.density_chrtFunction
density_chrt(Data, x_label="Sim. Values")

Data is your array, either simulated or historical. x_label [optional] allows you to customize your X axis label.

dist = rand(Normal(),1000)
density_chrt(dist, "The Standard Normal")
The Standard Normal -16 -14 -12 -10 -8 -6 -4 -2 0 2 4 6 8 10 12 14 16 18 -14.0 -13.5 -13.0 -12.5 -12.0 -11.5 -11.0 -10.5 -10.0 -9.5 -9.0 -8.5 -8.0 -7.5 -7.0 -6.5 -6.0 -5.5 -5.0 -4.5 -4.0 -3.5 -3.0 -2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 8.5 9.0 9.5 10.0 10.5 11.0 11.5 12.0 12.5 13.0 13.5 14.0 14.5 15.0 15.5 16.0 -20 -10 0 10 20 -14 -13 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 h,j,k,l,arrows,drag to pan i,o,+,-,scroll,shift-drag to zoom r,dbl-click to reset c for coordinates ? for help ? -0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 -0.50 -0.48 -0.46 -0.44 -0.42 -0.40 -0.38 -0.36 -0.34 -0.32 -0.30 -0.28 -0.26 -0.24 -0.22 -0.20 -0.18 -0.16 -0.14 -0.12 -0.10 -0.08 -0.06 -0.04 -0.02 0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.18 0.20 0.22 0.24 0.26 0.28 0.30 0.32 0.34 0.36 0.38 0.40 0.42 0.44 0.46 0.48 0.50 0.52 0.54 0.56 0.58 0.60 0.62 0.64 0.66 0.68 0.70 0.72 0.74 0.76 0.78 0.80 0.82 0.84 0.86 0.88 0.90 0.92 0.94 0.96 0.98 1.00 -0.5 0.0 0.5 1.0 -0.50 -0.45 -0.40 -0.35 -0.30 -0.25 -0.20 -0.15 -0.10 -0.05 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60 0.65 0.70 0.75 0.80 0.85 0.90 0.95 1.00 Frequency
MCHammer.histogram_chrtFunction
histogram_chrt(Data, x_label="Sim. Values")

Data is your array, either simulated or historical. x_label [optional] allows you to customize your X axis label.

histogram_chrt(dist, "The Standard Normal")
The Standard Normal -14 -12 -10 -8 -6 -4 -2 0 2 4 6 8 10 12 14 -12.0 -11.5 -11.0 -10.5 -10.0 -9.5 -9.0 -8.5 -8.0 -7.5 -7.0 -6.5 -6.0 -5.5 -5.0 -4.5 -4.0 -3.5 -3.0 -2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 8.5 9.0 9.5 10.0 10.5 11.0 11.5 12.0 -20 -10 0 10 20 -12.0 -11.5 -11.0 -10.5 -10.0 -9.5 -9.0 -8.5 -8.0 -7.5 -7.0 -6.5 -6.0 -5.5 -5.0 -4.5 -4.0 -3.5 -3.0 -2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 8.5 9.0 9.5 10.0 10.5 11.0 11.5 12.0 h,j,k,l,arrows,drag to pan i,o,+,-,scroll,shift-drag to zoom r,dbl-click to reset c for coordinates ? for help ? -30 -25 -20 -15 -10 -5 0 5 10 15 20 25 30 35 40 45 50 55 -25 -24 -23 -22 -21 -20 -19 -18 -17 -16 -15 -14 -13 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 -25 0 25 50 -26 -24 -22 -20 -18 -16 -14 -12 -10 -8 -6 -4 -2 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 Frequency
MCHammer.sensitivity_chrtFunction
sensitivity_chrt(ArrayName, TargetCol, Chrt_Type=1)

TargetCol: used to select the output against which the other variables are analyzed for influence.

Chrt_Type: allows to change the chart metric: Spearman (1), PPMC (2) and Contribution to Variance % (3)

sensitivity_chrt(Trials,1)
Rank Correlation -3.5 -3.0 -2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 -3.0 -2.9 -2.8 -2.7 -2.6 -2.5 -2.4 -2.3 -2.2 -2.1 -2.0 -1.9 -1.8 -1.7 -1.6 -1.5 -1.4 -1.3 -1.2 -1.1 -1.0 -0.9 -0.8 -0.7 -0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2.0 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3.0 -4 -2 0 2 4 -3.0 -2.8 -2.6 -2.4 -2.2 -2.0 -1.8 -1.6 -1.4 -1.2 -1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6 2.8 3.0 Positive Negative impact h,j,k,l,arrows,drag to pan i,o,+,-,scroll,shift-drag to zoom r,dbl-click to reset c for coordinates ? for help ? x2 x3 Input Variables with Biggest Impact
MCHammer.trend_chrtFunction
trend_chrt(SimTimeArray, PeriodRange, quantiles=[0.05,0.5,0.95])

trend_chrt allows the visualization of a simulated time series. These can be generated using the GBMM function.

PeriodRange must constructed using the Dates package and use the following syntax :

  dr = collect(Date(2019,1,01):Dates.Year(1):Date(2023,01,01))
ts_trials =[]
dr = collect(Date(2019,1,01):Dates.Month(1):Date(2019,12,01))

#To setup a TimeSeries simulation with MCHammer
for i = 1:1000
    Monthly_Sales = GBMM(100000, 0.05,0.05,12)
    Monthly_Expenses = GBMM(50000, 0.03,0.02,12)
    MonthlyCOGS = Monthly_Sales .* 0.3
    MonthlyProfit = Monthly_Sales - Monthly_Expenses - MonthlyCOGS
    push!(ts_trials, MonthlyProfit)
end

trend_chrt(ts_trials, dr)
timestamp Jan 1, 2019 Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan 1, 2018 2019 2020 2021 Jan 1, 2018 2019 2020 2021 Jan 1, 2018 2019 2020 2021 LowerBound p50 UpperBound variable h,j,k,l,arrows,drag to pan i,o,+,-,scroll,shift-drag to zoom r,dbl-click to reset c for coordinates ? for help ? -1.2×10⁵ -1.0×10⁵ -8.0×10⁴ -6.0×10⁴ -4.0×10⁴ -2.0×10⁴ 0 2.0×10⁴ 4.0×10⁴ 6.0×10⁴ 8.0×10⁴ 1.0×10⁵ 1.2×10⁵ 1.4×10⁵ 1.6×10⁵ 1.8×10⁵ 2.0×10⁵ 2.2×10⁵ -1.00×10⁵ -9.50×10⁴ -9.00×10⁴ -8.50×10⁴ -8.00×10⁴ -7.50×10⁴ -7.00×10⁴ -6.50×10⁴ -6.00×10⁴ -5.50×10⁴ -5.00×10⁴ -4.50×10⁴ -4.00×10⁴ -3.50×10⁴ -3.00×10⁴ -2.50×10⁴ -2.00×10⁴ -1.50×10⁴ -1.00×10⁴ -5.00×10³ 0 5.00×10³ 1.00×10⁴ 1.50×10⁴ 2.00×10⁴ 2.50×10⁴ 3.00×10⁴ 3.50×10⁴ 4.00×10⁴ 4.50×10⁴ 5.00×10⁴ 5.50×10⁴ 6.00×10⁴ 6.50×10⁴ 7.00×10⁴ 7.50×10⁴ 8.00×10⁴ 8.50×10⁴ 9.00×10⁴ 9.50×10⁴ 1.00×10⁵ 1.05×10⁵ 1.10×10⁵ 1.15×10⁵ 1.20×10⁵ 1.25×10⁵ 1.30×10⁵ 1.35×10⁵ 1.40×10⁵ 1.45×10⁵ 1.50×10⁵ 1.55×10⁵ 1.60×10⁵ 1.65×10⁵ 1.70×10⁵ 1.75×10⁵ 1.80×10⁵ 1.85×10⁵ 1.90×10⁵ 1.95×10⁵ 2.00×10⁵ -1×10⁵ 0 1×10⁵ 2×10⁵ -1.0×10⁵ -9.0×10⁴ -8.0×10⁴ -7.0×10⁴ -6.0×10⁴ -5.0×10⁴ -4.0×10⁴ -3.0×10⁴ -2.0×10⁴ -1.0×10⁴ 0 1.0×10⁴ 2.0×10⁴ 3.0×10⁴ 4.0×10⁴ 5.0×10⁴ 6.0×10⁴ 7.0×10⁴ 8.0×10⁴ 9.0×10⁴ 1.0×10⁵ 1.1×10⁵ 1.2×10⁵ 1.3×10⁵ 1.4×10⁵ 1.5×10⁵ 1.6×10⁵ 1.7×10⁵ 1.8×10⁵ 1.9×10⁵ 2.0×10⁵ value