Create aggreg.md

Author: JuliusBeranek

docs/src/aggreg.md

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+# Aggreg.jl
+
+## Inputdata
+
+Contains time series data (i.e., respecting a temporal order) with the following attributes:
+* region: optional information to specify the region data belongs to
+* period_from: date(year,month,day,hour) from which the data is from
+* period_to: date(year,month,day,hour) until which the data is
+* resolution: resolution of the original series (e.g., hours, days, weeks, etc.)
+* nseries: number of time series inputed (treated separately as if independent from each other)
+* data: Dictionary with an entry for each attribute `[file name (attribute: e.g technology)]-[column name (node: e.g. location)]`, Each entry of the dictionary is a 2-dimensional `time-steps T x periods K`-Array holding the data
+  
+
+## SeriesInstance
+
+Contains the series and respective attributes needed for performing the aggregation with the following attributes: 
+* series: original time series
+* block_size: number of clusters to be aggregated at once each iteration
+* stopping_k: reduced number of clusters pre-established
+* current_k: current number of clusters
+* dm: discrepancy metric to be used to measure the difference between clusters to be aggregated
+* rep_value: function to assign a representative value for each cluster (by default, the mean of values inside the cluster)
+* lseries: length of series
+* nseries: number of series
+
+## ClustInstance
+
+Constains clusters information.
+
+* k_cent: matrix with the representative points for each cluster
+* weigths: 1-dimensional `periods K`-Array with the absolute weight for each period. E.g. for a yearly resolution time series of 365 days, sum(weights) = 365
+* series_clust: clusters assignment for the original series (vector with the from-to relationship between the original series and the clusters)
+* nclusters: current number of clusters
+* search_range: range in which the clusters can be merged, considering the amount of clusters and the block_size (generally equals to 1: (nclusters-block_size+1))
+
+## AggregInstance:
+
+Contains the aggregation instance temporary values.
+
+* merging_ clust
+* series_comp
+* k_cent_comp
+
+## aggreg1D
+
+The function aggregates N one-dimensional vectors concomitantly following a marker gave as the input of the function. It assumes the collection of 1D vectors is vertically concatanated so forming a 2D matrix with the first dimension equal to the size of the vectors analysed and the second dimension equal to the number of vectors analysed.
+
+The function can use the following input:
+* series: A collection of vectors‚
+* marker: A vector indicating the assignment of the series
+* rep_value: Value to be used for representation of the aggregated interval (we use mean by default)
+
+
+## cdad
+
+Function to compute the cumulative distribution absolute difference from vector `u`
+to vector `v` assuming both have an equal mass (i.e., both normalised) and
+they are ordered.
+
+## load_series_instance
+
+Function to load the data provided and fit into a struct SeriesInstance().
+
+## load_clust_instance
+
+Function to load the clusters attributes.
+
+## search_min_dist
+
+Search minimal distance and return the marker, the min_dist position, and the clusters to be merged.
+
+## find_clusters!
+
+Function calling the necessary functions for the data aggregation procedure (to be enveloped in a loop with the necessary conditions, e.g., while nclusters > stopping_k).
+
+## compute_dist!
+
+Compute the distances between two matrices (series_comp and k_cent_comp).
+
+
+## update_marker
+
+Update the clusters assignment (Bool marker indicating which positions are to be merged).
+
+## replace_lines
+
+Replace lines of a matrix/vector by another matrix/vector with the same number of columns than the original matrix/vector.
+
+## update_clust!
+
+Updates clusters attributes in the _ClustInstance object.
+
+## update_k! 
+
+Updates number of clusters in the _SeriesInstance object.
+