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Extreme value search among one attribute and multiple nodes #124

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Extreme value search among one attribute and multiple nodes #124

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15 changes: 15 additions & 0 deletions examples/workflow_example_extr.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -6,6 +6,7 @@ using TimeSeriesClustering
data_path=normpath(joinpath(dirname(@__FILE__),"..","data","TS_GER_18"))
ts_input_data = load_timeseries_data(data_path; T=24, years=[2015])

################## Example 1
# define simple extreme days of interest
ev1 = SimpleExtremeValueDescr("wind-dena42","max","absolute")
ev2 = SimpleExtremeValueDescr("solar-dena42","min","integral")
Expand All @@ -19,3 +20,17 @@ ts_clust_res = run_clust(ts_input_data_mod;method="kmeans",representation="centr

# representation modification
ts_clust_extr = representation_modification(extr_vals,ts_clust_res.clust_data)


################## Example 2
# find the minimum wind day among all nodes
ev4 = SimpleExtremeValueDescr("wind","min","absolute")
# simple extreme day selection
ts_input_data_mod_2,extr_vals_2,extr_idcs_2 = simple_extr_val_sel(ts_input_data,ev4;rep_mod_method="feasibility")

# run clustering
ts_clust_res_2 = run_clust(ts_input_data_mod;method="kmeans",representation="centroid",n_init=10,n_clust=5) # default k-means

# representation modification
ts_clust_extr_2 = representation_modification(extr_vals_2,ts_clust_res.clust_data)

14 changes: 13 additions & 1 deletion src/clustering/extreme_vals.jl
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Expand Up @@ -92,7 +92,19 @@ function simple_extr_val_ident(clust_data::ClustData,
extremum::String="max",
peak_def::String="absolute",
consecutive_periods::Int=1)
data=clust_data.data[data_type]
# all attribute-node pairs in clust_data
data_types = [k for k in keys(clust_data.data)]
attribute_nodes = data_types[occursin.(data_type, data_types)]
if isempty(attribute_nodes)
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Instead of leaving the if statement open, we could just return the error and end the if statement after line 99. What do you think?

error("data_type=$data_type is neither an attribute-node pair nor is it an attribute")
# if data_type is an attribute only, aggregate data among all nodes that contain that attribute
# this contains the special case that data_type is an attribute-node pair
else
data = zeros(clust_data.T*length(attribute_nodes), clust_data.K)
for i in 1:length(attribute_nodes)
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Alternative could be:

for (i, attribute_node) in enumerate(attribute_nodes)

That can generally simplify the readability of the code as you can now use attribute_node instead of attribute_nodes[i]

data[1+(i-1)*clust_data.T:i*clust_data.T, :] = clust_data.data[attribute_nodes[i]]
end
end
delta_period=consecutive_periods-1
# set data to be compared
if peak_def=="absolute" && consecutive_periods==1
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