Merge branch 'testing' into dev

Author: holgerteichgraeber

Project.toml

@@ -4,7 +4,7 @@ keywords = ["clustering", "JuMP", "optimization"]
 license = "MIT"
 desc = "julia implementation of using different clustering methods for finding representative periods for the optimization of energy systems"
 author = ["Holger Teichgraeber"]
-version = "0.4.1"
+version = "0.4.2"
 
 [deps]
 CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"

README.md

@@ -4,6 +4,7 @@
 [![](https://img.shields.io/badge/docs-dev-blue.svg)](https://holgerteichgraeber.github.io/ClustForOpt.jl/dev)
 [![License](http://img.shields.io/badge/license-MIT-brightgreen.svg?style=flat)](LICENSE)
 [![Build Status](https://travis-ci.com/holgerteichgraeber/ClustForOpt.jl.svg?token=HRFemjSxM1NBCsbHGNDG&branch=master)](https://travis-ci.com/holgerteichgraeber/ClustForOpt.jl)
+[![codecov](https://codecov.io/gh/holgerteichgraeber/ClustForOpt.jl/branch/master/graph/badge.svg)](https://codecov.io/gh/holgerteichgraeber/ClustForOpt.jl)
 
 
 [ClustForOpt](https://github.com/holgerteichgraeber/ClustForOpt.jl) is a [julia](https://www.juliaopt.com) implementation of unsupervised machine learning methods for finding representative periods for energy systems optimization problems.
@@ -55,7 +56,7 @@ If you find ClustForOpt useful in your work, we kindly request that you cite the
 
 ## Quick Start Guide
 
-This quick start guide introduces the main concepts of using ClustForOpt. For more detail on the different functionalities that ClustForOpt provides, please refer to the subsequent chapters of the documentation or the examples in the [examples](https://github.com/holgerteichgraeber/ClustForOpt.jl/tree/master/examples) folder.
+This quick start guide introduces the main concepts of using ClustForOpt. For more detail on the different functionalities that ClustForOpt provides, please refer to the subsequent chapters of the documentation or the examples in the [examples](https://github.com/holgerteichgraeber/ClustForOpt.jl/tree/master/examples) folder, specifically [workflow_introduction.jl](https://github.com/holgerteichgraeber/ClustForOpt.jl/blob/master/examples/workflow_introduction.jl).
 
 Generally, the workflow consists of three steps:
 - load data

docs/src/index.md

@@ -3,6 +3,7 @@
 [![](https://img.shields.io/badge/docs-stable-blue.svg)](https://holgerteichgraeber.github.io/ClustForOpt.jl/stable)
 [![](https://img.shields.io/badge/docs-dev-blue.svg)](https://holgerteichgraeber.github.io/ClustForOpt.jl/dev)
 [![Build Status](https://travis-ci.com/holgerteichgraeber/ClustForOpt.jl.svg?token=HRFemjSxM1NBCsbHGNDG&branch=master)](https://travis-ci.com/holgerteichgraeber/ClustForOpt.jl)
+[![codecov](https://codecov.io/gh/holgerteichgraeber/ClustForOpt.jl/branch/master/graph/badge.svg)](https://codecov.io/gh/holgerteichgraeber/ClustForOpt.jl)
 
 [ClustForOpt](https://github.com/holgerteichgraeber/ClustForOpt.jl) is a [julia](https://www.juliaopt.com) implementation of unsupervised machine learning methods for finding representative periods for energy systems optimization problems.
 By reducing the number of time steps used in the optimization model, using representative periods leads to significant reductions in computational complexity.

docs/src/quickstart.md

@@ -1,7 +1,7 @@
 Quick Start Guide
 =================
 
-This quick start guide introduces the main concepts of using ClustForOpt. For more detail on the different functionalities that ClustForOpt provides, please refer to the subsequent chapters of the documentation or the examples in the [examples](https://github.com/holgerteichgraeber/ClustForOpt.jl/tree/master/examples) folder.
+This quick start guide introduces the main concepts of using ClustForOpt. For more detail on the different functionalities that ClustForOpt provides, please refer to the subsequent chapters of the documentation or the examples in the [examples](https://github.com/holgerteichgraeber/ClustForOpt.jl/tree/master/examples) folder, specifically [workflow_introduction.jl](https://github.com/holgerteichgraeber/ClustForOpt.jl/blob/master/examples/workflow_introduction.jl).
 
 Generally, the workflow consists of three steps:
 - load data

test/reference_generation/run_clust.jl

@@ -6,8 +6,8 @@ using Random
 reference_results = Dict{String,Any}()
 
 Random.seed!(1111)
-for data in ["CEP_GER1","CEP_GER18"]
-    ts_input_data = load_timeseries_data(Symbol(data))
+for data in [:CEP_GER1,:CEP_GER18]
+    ts_input_data = load_timeseries_data(data)
     #mr: method, representation, n_init
     mr = [["kmeans","centroid",1000],
     ["kmeans","medoid",1000],
@@ -47,8 +47,8 @@ for data in ["CEP_GER1","CEP_GER18"]
     end
 end
 
-data = "CEP_GER1"
-ts_input_data = load_timeseries_data(Symbol(data))
+data = :CEP_GER1
+ts_input_data = load_timeseries_data(data)
 method = "kmedoids_exact"
 repr = "medoid"
 using Cbc

test/run_clust.jl

@@ -11,8 +11,8 @@ using Random
 @load normpath(joinpath(dirname(@__FILE__),"reference_generation","run_clust.jld2")) reference_results
 
 Random.seed!(1111)
-@testset "run_clust $data" for data in ["CEP_GER1","CEP_GER18"] begin
-    ts_input_data = load_timeseries_data(Symbol(data))
+@testset "run_clust $data" for data in [:CEP_GER1,:CEP_GER18] begin
+    ts_input_data = load_timeseries_data(data)
     #mr: method, representation, n_init
     mr = [["kmeans","centroid",1000],
     ["kmeans","medoid",1000],
@@ -58,8 +58,8 @@ Random.seed!(1111)
 end
 
 # Use the same data for all subsequent tests
-data = "CEP_GER1"
-ts_input_data = load_timeseries_data(Symbol(data))
+data = :CEP_GER1
+ts_input_data = load_timeseries_data(data)
 
 using Cbc
 optimizer = Cbc.Optimizer