Add functions for visualizing hurricane impacts

src/PlotImpact.jl

+#* Functions for plotting hurricane impacts modelled in Impact.jl
+#*------------------------------------------------------------------------------
+
+function plothist_tl_failures(
+        γ_values::Array{Float64,1}, 
+        N_runs::Int64, 
+        rstep = 20; # number of realizations that were saved together
+        datapath="", # path to folder containing different γ scans
+        plotpath="" # where to save the plot
+    )
+    
+    N_γ = length(γ_values) # number of given γ values
+    mean_Nf_arr = zeros(N_γ) # array for mean number of line failures
+    σ_arr = zeros(N_γ) # array for standard deviations
+
+    ### Go through γ values and plot the respective histogram
+    for i in 1:N_γ
+        mean_Nf, σ = plothist_tl_failures(
+            γ_values[i], N_runs, rstep, datapath=datapath, plotpath=plotpath
+        )
+        mean_Nf_arr[i] = mean_Nf # save mean number of failures
+        σ_arr[i] = σ # save standard deviation
+    end
+
+    return mean_Nf_arr, σ_arr
+end
+
+function plothist_tl_failures(
+        γ::Float64,
+        N_runs::Int64,
+        rstep = 20; # number of realizations that were saved together
+        datapath::String, # path to folder containing data files
+        plotpath::String # where to save the plot
+    )
+
+    Nf = zeros(Int64, N_runs) # array for number of failures
+    
+    ### Read data and save the number of failures that occured in each run
+    for i in rstep:rstep:N_runs
+        res = load(datapath * "gamma$γ/" * "results_$γ" * "_$i.jld2", "result")
+        for j in 1:rstep
+            Nf[i-rstep+j] = length(res[j])
+        end
+    end
+
+    ### Plot a historgram of the number of failures
+    N_bins = maximum(Nf) - minimum(Nf) # number of bins
+    n, bins, patches = plt[:hist](Nf, bins=N_bins, edgecolor="k")
+    
+    ### Calculate mean number of failures and standard deviation
+    mean_Nf = round(mean!([1.], Nf)[1], digits=1)
+    σ = round(std(Nf, mean=mean_Nf), digits=1)
+
+    ### Legend with γ, mean number of failures and standard deviation
+    mlines = pyimport("matplotlib.lines")
+    no_marker = mlines.Line2D([], [], ls="None")
+    plt.legend(
+        [no_marker for i in 1:4], 
+        [L"γ = " * "$γ", 
+        L"N_r = " * "$N_runs", 
+        L"\bar{N}_\mathrm{f} \approx" * "$mean_Nf", 
+        L"\sigma_\mathrm{f} \approx" * "$σ"], 
+        handletextpad = 0, handlelength = 0
+    )
+
+    ### Axes labels
+    plt.xlabel("Number of line failures " * L"N_\mathrm{f}")
+    plt.ylabel("Frequency")
+
+    ### Save and close figure
+    figpath = plotpath * "Texas_hist_tl_failures_gamma$γ.png"
+    plt.savefig(figpath, bbox_inches="tight")
+    plt.clf()
+
+    return mean_Nf, σ
+end