replace with error(). The former was used for logging, the latter is used to throw exceptions.

Author: holgerteichgraeber

src/clustering/extreme_vals.jl

@@ -20,7 +20,7 @@ function simple_extr_val_sel(data::ClustData,
   elseif rep_mod_method=="append"
     data_mod = input_data_modification(data,idcs)
   else
-    @error("rep_mod_method - "*rep_mod_method*" - does not exist")
+    error("rep_mod_method - "*rep_mod_method*" - does not exist")
   end
   return data_mod,extr_vals,idcs
 end
@@ -104,15 +104,15 @@ function simple_extr_val_ident(clust_data::ClustData,
       data_eval[1,k] = sum(data[:,k:(k+delta_period)])
     end
   else
-    @error("peak_def - "*peak_def*" and consecutive_periods $consecutive_periods - not defined")
+    error("peak_def - "*peak_def*" and consecutive_periods $consecutive_periods - not defined")
   end
   # find minimum or maximum index. Second argument returns cartesian indices, second argument of that is the column (period) index
   if extremum=="max"
     idx_k = findmax(data_eval)[2][2]
   elseif extremum=="min"
     idx_k = findmin(data_eval)[2][2]
   else
-    @error("extremum - "*extremum*" - not defined")
+    error("extremum - "*extremum*" - not defined")
   end
   idx=collect(idx_k:(idx_k+delta_period))
   return idx
@@ -143,8 +143,8 @@ function input_data_modification(data::ClustData,
   k_ids_dn=deepcopy(data.k_ids)
   #check for uniqueness and right sorting (however just those one representing)
   k_ids_check=k_ids_dn[findall(k_ids_dn.!=0)]
-  allunique(k_ids_check) || @error "the provided clust_data.k_ids are not unique - The clust_data is probably the result of a clustering already."
-  sort(k_ids_check)==k_ids_check || @error "the provided clust_data.k_ids are not monoton increasing - The clust_data is probably the result of a clustering already."
+  allunique(k_ids_check) || error("the provided clust_data.k_ids are not unique - The clust_data is probably the result of a clustering already.")
+  sort(k_ids_check)==k_ids_check || error("the provided clust_data.k_ids are not monoton increasing - The clust_data is probably the result of a clustering already.")
   #get all k-ids that are represented within this clust-data
   k_ids_dn_data=k_ids_dn[findall(data.k_ids.!=0)]
   for k in sort(extr_val_idcs)
@@ -203,7 +203,7 @@ function extreme_val_output(data::ClustData,
     # assign it to the full original time-series
     k_ids_ed[index_k_ids_data]=k_ids_ed_data
   else
-    @error("rep_mod_method - "*rep_mod_method*" - does not exist")
+    error("rep_mod_method - "*rep_mod_method*" - does not exist")
   end
   delta_t_ed=data.delta_t[:,unique_extr_val_idcs]
   extr_vals = ClustData(data.region,data.years,K_ed,data.T,data_ed,weights_ed,k_ids_ed;delta_t=delta_t_ed,mean=data.mean,sdv=data.sdv)

src/clustering/run_clust.jl

@@ -375,7 +375,7 @@ function run_clust_kmedoids_exact_medoid(
     kmexact_optimizer=0
     )
 
-    (typeof(kmexact_optimizer)==Int) && @error("Please provide a kmexact_optimizer (Gurobi Environment). See test file for example")
+    (typeof(kmexact_optimizer)==Int) && error("Please provide a kmexact_optimizer (Gurobi Environment). See test file for example")
 
     # TODO: optional in future: pass distance metric as kwargs
     dist = SqEuclidean()

src/utils/datastructs.jl

@@ -190,7 +190,7 @@ function ClustData(region::String,
                        mean::Dict{String,Array}=Dict{String,Array}(),
                        sdv::Dict{String,Array}=Dict{String,Array}()
                        )
- isempty(data) && @error("Need to provide at least one input data stream")
+ isempty(data) && error("Need to provide at least one input data stream")
  mean_sdv_provided = ( !isempty(mean) && !isempty(sdv))
  if !mean_sdv_provided
    for (k,v) in data
@@ -282,7 +282,7 @@ function ClustDataMerged(data::ClustData)
    push!(data_type,k)
  end
  if maximum(data.delta_t)!=1
-   throw(@error "You cannot recluster data with different Δt")
+   error("You cannot recluster data with different Δt")
  end
  ClustDataMerged(data.region,data.years,data.K,data.T,data_merged,data_type,data.weights,data.mean,data.sdv,data.delta_t,data.k_ids)
 end

src/utils/load_data.jl

@@ -49,7 +49,7 @@ function load_timeseries_data(data_path::String;
       data_name=split(full_data_name,".")[1]
       K=add_timeseries_data!(dt, data_name, dirname(data_path); K=K, T=T, years=years)
   else
-      throw(@error("The path $data_path is neither recognized as a directory nor as a file"))
+      error("The path $data_path is neither recognized as a directory nor as a file")
   end
   # Store the data
   ts_input_data =  ClustData(FullInputData(region, years, num, dt),K,T)
@@ -94,7 +94,7 @@ function load_timeseries_data(existing_data::Symbol;
   elseif existing_data == :CEP_GER18
       data_path=joinpath(data_path,"TS_GER_18")
   else
-      @error("The symbol - $existing_data - does not exist")
+      error("The symbol - $existing_data - does not exist")
   end
   return load_timeseries_data(data_path;region=region,T=T,years=years,att=att)
 end
@@ -135,7 +135,7 @@ function add_timeseries_data!(dt::Dict{String,Array},
         if !(column[1] in [time_name, year_name])
             K_calc=Int(floor(length(column[2])/T))
             if K_calc!=K && K!=0
-                @error("The time_series $(column[1]) has K=$K_calc != K=$K of the previous")
+                error("The time_series $(column[1]) has K=$K_calc != K=$K of the previous")
             else
                 K=K_calc
             end
@@ -158,7 +158,7 @@ function find_column_name(df::DataFrame, name_itr::Array{Symbol,1}; error::Bool=
         end
     end
     if error
-        col_name!=:none || throw(@error "No $(name_itr) in $(repr(df)).")
+        col_name!=:none || error("No $(name_itr) in $(repr(df)).")
     else
         col_name!=:none || @warn "No $(name_itr) in $(repr(df))."
     end
@@ -174,8 +174,8 @@ If the number of periods of the `ts_weather` data isn't a multiple of the `ts`-t
 """
 function combine_timeseries_weather_data(ts::ClustData,
                                         ts_weather::ClustData)
-    ts.T==ts_weather.T || throw(@error "The number of timesteps per period is not the same: `ts.T=$(ts.T)≢$(ts_weather.T)=ts_weather.T`")
-    ts.K<=ts_weather.K || throw(@error "The number of timesteps in the `ts`-timeseries isn't shorter or equal to the ones in the `ts_weather`-timeseries.")
+    ts.T==ts_weather.T || error("The number of timesteps per period is not the same: `ts.T=$(ts.T)≢$(ts_weather.T)=ts_weather.T`")
+    ts.K<=ts_weather.K || error("The number of timesteps in the `ts`-timeseries isn't shorter or equal to the ones in the `ts_weather`-timeseries.")
     ts_weather.K%ts.K==0 || @warn "The number of periods of the `ts_weather` data isn't a multiple of the other `ts`-timeseries: periods 1 to $(ts_weather.K%ts.K) are attached to the end of the new combined timeseries."
     ts_data=deepcopy(ts_weather.data)
     ts_mean=deepcopy(ts_weather.mean)

src/utils/utils.jl

@@ -92,7 +92,7 @@ function z_normalize(data::Array;
     end
     return data_norm, hourly_mean, hourly_sdv #TODO change the output here to an immutable struct with three fields - use struct - "composite type"
   else
-    @error("scope _ ",scope," _ not defined.")
+    error("scope _ ",scope," _ not defined.")
   end
 end # function z_normalize
 
@@ -102,7 +102,7 @@ provide idx should usually be done as default within function call in order to e
 """
 function undo_z_normalize(data_norm_merged::Array,mn::Dict{String,Array},sdv::Dict{String,Array};idx=[])
   T = div(size(data_norm_merged)[1],length(keys(mn))) # number of time steps in one period. div() is integer division like in c++, yields integer (instead of float as in normal division)
-  0 != rem(size(data_norm_merged)[1],length(keys(mn))) && @error("dimension mismatch") # rem() checks the remainder. If not zero, throw error.
+  0 != rem(size(data_norm_merged)[1],length(keys(mn))) && error("dimension mismatch") # rem() checks the remainder. If not zero, throw error.
   data_merged = zeros(size(data_norm_merged))
   i=0
   for (attr,mn_a) in mn
@@ -137,7 +137,7 @@ function undo_z_normalize(data_norm::Array, mn::Array, sdv::Array; idx=[])
     data = data_norm * Diagonal(summed_sdv) +  ones(size(data_norm,1)) * summed_mean'
     return data
   elseif isempty(idx)
-    @error("no idx provided in undo_z_normalize")
+    error("no idx provided in undo_z_normalize")
   end
 end
 
@@ -269,7 +269,7 @@ This is the DEFAULT resize medoids function
 Takes in centers (typically medoids) and normalizes them such that the yearly average of the clustered data is the same as the yearly average of the original data.
 """
 function resize_medoids(data::ClustData,centers::Array,weights::Array)
-    (data.T * length(keys(data.data)) != size(centers,1) ) && @error("dimension missmatch between full input data and centers")
+    (data.T * length(keys(data.data)) != size(centers,1) ) && error("dimension missmatch between full input data and centers")
     centers_res = zeros(size(centers))
     # go through the attributes within data
     i=0