diff --git a/.codecov.yml b/.codecov.yml new file mode 100644 index 0000000..b026bb3 --- /dev/null +++ b/.codecov.yml @@ -0,0 +1,29 @@ +codecov: + notify: + require_ci_to_pass: yes + +coverage: + precision: 2 + round: down + range: "70...100" + + status: + project: yes + patch: yes + changes: no + +parsers: + gcov: + branch_detection: + conditional: yes + loop: yes + method: no + macro: no + +comment: + layout: "header, diff" + behavior: default + require_changes: no + +ignore: + - "src/clustering/shape_based/*" diff --git a/.travis.yml b/.travis.yml index 5cdadd5..5a00670 100644 --- a/.travis.yml +++ b/.travis.yml @@ -9,8 +9,6 @@ julia: notifications: email: false -after_success: - jobs: include: - stage: "Testing" @@ -18,14 +16,15 @@ jobs: os: linux script: - julia --project=test/ -e 'using Pkg; Pkg.develop(PackageSpec(path=pwd())); Pkg.instantiate()' - - julia --project=test/ test/clustering.jl - - julia --project=test/ test/test_workflow_introduction.jl - after_success: + - julia --project=test/ --code-coverage test/runtests.jl - stage: "Documentation" julia: 1.0 os: linux script: - julia --project=docs/ -e 'using Pkg; Pkg.develop(PackageSpec(path=pwd())); Pkg.instantiate()' - julia --project=docs/ docs/make.jl - after_success: -services: docker + after_success: skip + +after_success: + - julia -e 'using Pkg; Pkg.add("Coverage")' + - julia -e 'using Coverage; Codecov.submit(process_folder())' diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 0000000..7153bec --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,4 @@ +## How to contribute to ClustForOpt.jl +Welcome! Thank you for considering to contribute to `ClustForOpt.jl`. If you have a comment, question, feature request, or bug report, please open a new [issue](https://github.com/holgerteichgraeber/ClustForOpt.jl/issues). + +If you like to file a bug report, or like to contribute to the documentation or the code (always welcome!), the [JuMP.jl Contributing.md](https://github.com/JuliaOpt/JuMP.jl/blob/master/CONTRIBUTING.md) has some great tips on how to get started. diff --git a/Manifest.toml b/Manifest.toml deleted file mode 100644 index 3d572b5..0000000 --- a/Manifest.toml +++ /dev/null @@ -1,316 +0,0 @@ -[[Base64]] -uuid = "2a0f44e3-6c83-55bd-87e4-b1978d98bd5f" - -[[BinDeps]] -deps = ["Compat", "Libdl", "SHA", "URIParser"] -git-tree-sha1 = "12093ca6cdd0ee547c39b1870e0c9c3f154d9ca9" -uuid = "9e28174c-4ba2-5203-b857-d8d62c4213ee" -version = "0.8.10" - -[[BinaryProvider]] -deps = ["Libdl", "Pkg", "SHA", "Test"] -git-tree-sha1 = "055eb2690182ebc31087859c3dd8598371d3ef9e" -uuid = "b99e7846-7c00-51b0-8f62-c81ae34c0232" -version = "0.5.3" - -[[CSV]] -deps = ["CategoricalArrays", "DataFrames", "DataStreams", "Dates", "Mmap", "Parsers", "Profile", "Random", "Tables", "Test", "Unicode", "WeakRefStrings"] -git-tree-sha1 = "b92c6f626a044cc9619156d54994b94084d40abe" -uuid = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b" -version = "0.4.3" - 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The package can be used in conjunction with the multi-node capacity expansion model [CapacityExpansion](https://github.com/YoungFaithful/CapacityExpansion.jl). -The package has two main purposes: 1) Provide a simple process of clustering time-series input data, with clustered data output in a generalized type system 2) provide an interface between clustered data and optimization problem. +[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. -The package follows the clustering framework presented in [Teichgraeber and Brandt, 2019](https://doi.org/10.1016/j.apenergy.2019.02.012). -The package is actively developed, and new features are continuously added. For a reproducible version of the methods and data of the original paper by [Teichgraeber and Brandt, 2019](https://doi.org/10.1016/j.apenergy.2019.02.012), please refer to release [v0.1](https://github.com/holgerteichgraeber/ClustForOpt.jl/tree/v0.1). +The package has three main purposes: +1) Provide a simple process of finding representative periods for time-series input data, with implementations of the most commonly used clustering methods and extreme value selection methods. +2) Provide an interface between representative period data and optimization problem by having representative period data stored in a generalized type system. +3) Provide a generalized import feature for time series, where variable names, attributes, and node names are automatically stored and can then be used in the definition of sets of the optimization problem later. + +An example energy systems optimization problem that uses ClustForOpt for its input data is the package [CapacityExpansion](https://github.com/YoungFaithful/CapacityExpansion.jl), which implements a scalable generation and transmission capacity expansion problem. + +The ClustForOpt package follows the clustering framework presented in [Teichgraeber and Brandt, 2019](https://doi.org/10.1016/j.apenergy.2019.02.012). +The package is actively developed, and new features are continuously added. +For a reproducible version of the methods and data of the original paper by [Teichgraeber and Brandt, 2019](https://doi.org/10.1016/j.apenergy.2019.02.012), please refer to [v0.1](https://github.com/holgerteichgraeber/ClustForOpt.jl/tree/v0.1) (including shape based methods such as `k-shape` and `dynamic time warping barycenter averaging`). This package is developed by Holger Teichgraeber [@holgerteichgraeber](https://github.com/holgerteichgraeber) and Elias Kuepper [@YoungFaithful](https://github.com/youngfaithful). +## Installation +This package runs under julia v1.0 and higher. +Install using: + +```julia +import Pkg +Pkg.add("ClustForOpt") +``` + +## Documentation +[Documentation (Stable)](https://holgerteichgraeber.github.io/ClustForOpt.jl/stable): Please refer to this documentation for details on how to use ClustForOpt the current version of ClustForOpt. This is the documentation of the default version of the package. + +[Documentation (Development)](https://holgerteichgraeber.github.io/ClustForOpt.jl/dev): If you like to try the development version of ClustForOpt, please refer to this documentation. + +**See [NEWS](NEWS.md) for significant breaking changes when updating from one version of ClustForOpt to another.** + +## Citing ClustForOpt If you find ClustForOpt useful in your work, we kindly request that you cite the following paper ([link](https://doi.org/10.1016/j.apenergy.2019.02.012)): ``` @@ -28,82 +54,43 @@ If you find ClustForOpt useful in your work, we kindly request that you cite the } ``` -## Installation -This package runs under julia v1.0 and higher. -Install using: +## Quick Start Guide -```julia -] -add ClustForOpt -``` -where `]` opens the julia package manager. - -**See [NEWS](NEWS.md) for significant breaking changes when updating from one version of ClustForOpt to another.** - -## Documentation -[Stable](https://holgerteichgraeber.github.io/ClustForOpt.jl/stable) +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). -[Development](https://holgerteichgraeber.github.io/ClustForOpt.jl/dev) - -## Workflow - -Generally, the workflow requires three steps: +Generally, the workflow consists of three steps: - load data -- clustering +- find representative periods (clustering + extreme period selection) - optimization -An example workflow with examples on how to use the different functions can be found in [`examples/workflow_introduction.jl`](examples/workflow_introduction.jl) - -```julia +## Example Workflow +After ClustForOpt is installed, you can use it by saying: +```@repl workflow using ClustForOpt - -# load data (electricity price day ahead market) -ts_input_data, = load_timeseries_data("DAM", "GER";K=365, T=24) #DAM - -# run standard kmeans clustering algorithm to cluster into 5 representative periods, with 1000 initial starting points -clust_res = run_clust(ts_input_data;method="kmeans",representation="centroid",n_clust=5,n_init=1000) - -# battery operations optimization on the clustered data -opt_res = run_opt(clust_res) ``` -### Load data -`load_timeseries_data()` loads the data for a given `application` and `region`. -Possible applications are -- `DAM`: Day ahead market price data -- `CEP`: Capacity Expansion Problem data - -Possible regions are: -- `GER`: Germany -- `CA`: California -- `TX`: Texas - -The optional input parameters to `load_timeseries_data()` are the number of periods `K` and the number of time steps per period `T`. By default, they are chosen such that they result in daily time slices. - - -### Clustering -`run_clust()` takes the full `data` and gives a struct with the clustered data as the output. - -The input parameter `n_clust` determines the number of clusters,i.e., representative periods. - -#### Supported clustering methods - -The following combinations of clustering method and representations are supported by [run\_clust()](src/clustering/run_clust.jl): - -Name | method | representation ----- | --------------- | ----------------------- -k-means clustering | `` | `` -k-means clustering with medoid representation | `` | `` -k-medoids clustering (partitional) | `` | `` -k-medoids clustering (exact) [requires Gurobi] | `` | `` -hierarchical clustering with centroid representation | `` | `` -hierarchical clustering with medoid representation | `` | `` - -For use of DTW barycenter averaging (DBA) and k-shape clustering on single-attribute data (e.g. electricity prices), please use branch `v0.1-appl_energy-framework-comp`. - - +The first step is to load the data. The following example loads hourly wind, solar, and demand data for Germany (1 region) for one year. +```@repl workflow +ts_input_data = load_timeseries_data(:CEP_GER1) +``` +The output `ts_input_data` is a `ClustData` data struct that contains the data and additional information about the data. +```@repl workflow +ts_input_data.data # a dictionary with the data. +ts_input_data.data["wind-germany"] # the wind data (choose solar, el_demand as other options in this example) +ts_input_data.K # number of periods +``` -### Optimization -The function `run_opt()` runs the optimization problem and gives as an output a struct that contains optimal objective function value, decision variables, and additional info. The `run_opt()` function infers the optimization problem type from the input data. See the examples folder for further details. +The second step is to cluster the data into representative periods. Here, we use k-means clustering and get 5 representative periods. +```@repl workflow +clust_res = run_clust(ts_input_data;method="kmeans",n_clust=5) +ts_clust_data = clust_res.clust_data +``` +The `ts_clust_data` is a `ClustData` data struct, this time with clustered data (i.e. less representative periods). +```@repl workflow +ts_clust_data.data # the clustered data +ts_clust_data.data["wind-germany"] # the wind data. Note the dimensions compared to ts_input_data +ts_clust_data.K # number of periods +``` -A Capacity Expansion Optimization Problem that utilizes `ClustForOpt` can be found in the package [CapacityExpansion](https://github.com/YoungFaithful/CapacityExpansion.jl). +The clustered input data can be used as input to an optimization problem. +The optimization problem formulated in the package [CapacityExpansion](https://github.com/YoungFaithful/CapacityExpansion.jl) can be used with the data clustered in this example. diff --git a/data/DAM/CA/TS/el_price.csv b/data/DAM/CA/TS/el_price.csv deleted file mode 100644 index 6a36b98..0000000 --- a/data/DAM/CA/TS/el_price.csv +++ /dev/null @@ -1,8761 +0,0 @@ -Timestamp,CA -01/01/15 12:00 AM,36.60436 -01/01/15 01:00 AM,34.4529 -01/01/15 02:00 AM,33.02658 -01/01/15 03:00 AM,31.57161 -01/01/15 04:00 AM,33.37264 -01/01/15 05:00 AM,36.28938 -01/01/15 06:00 AM,38.05681 -01/01/15 07:00 AM,38.7898 -01/01/15 08:00 AM,35.17378 -01/01/15 09:00 AM,30.26771 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11:00 AM,31.86213 -09/29/15 12:00 PM,33.22693 -09/29/15 01:00 PM,33.75423 -09/29/15 02:00 PM,36.45701 -09/29/15 03:00 PM,38.22257 -09/29/15 04:00 PM,38.67066 -09/29/15 05:00 PM,43.88356 -09/29/15 06:00 PM,48.82307 -09/29/15 07:00 PM,46.64379 -09/29/15 08:00 PM,30.34537 -09/29/15 09:00 PM,26.94707 -09/29/15 10:00 PM,26.88829 -09/29/15 11:00 PM,27.97123 -09/30/15 12:00 AM,26.66761 -09/30/15 01:00 AM,25.6723 -09/30/15 02:00 AM,25.44508 -09/30/15 03:00 AM,25.17099 -09/30/15 04:00 AM,25.67103 -09/30/15 05:00 AM,27.88119 -09/30/15 06:00 AM,28.96717 -09/30/15 07:00 AM,30.86781 -09/30/15 08:00 AM,29.35566 -09/30/15 09:00 AM,28.88158 -09/30/15 10:00 AM,29.09528 -09/30/15 11:00 AM,30.84128 -09/30/15 12:00 PM,31.90288 -09/30/15 01:00 PM,32.02412 -09/30/15 02:00 PM,32.77633 -09/30/15 03:00 PM,33.21284 -09/30/15 04:00 PM,32.61504 -09/30/15 05:00 PM,37.73182 -09/30/15 06:00 PM,44.37211 -09/30/15 07:00 PM,45.98368 -09/30/15 08:00 PM,36.8513 -09/30/15 09:00 PM,30.04417 -09/30/15 10:00 PM,27.23404 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-2015-01-09T16:00:00,30.09 -2015-01-09T17:00:00,33.47 -2015-01-09T18:00:00,32.78 -2015-01-09T19:00:00,33.47 -2015-01-09T20:00:00,30.11 -2015-01-09T21:00:00,26.69 -2015-01-09T22:00:00,21.64 -2015-01-09T23:00:00,14.72 -2015-01-10T00:00:00,17.1 -2015-01-10T01:00:00,12.93 -2015-01-10T02:00:00,13.7 -2015-01-10T03:00:00,11.01 -2015-01-10T04:00:00,9.08 -2015-01-10T05:00:00,9.28 -2015-01-10T06:00:00,7.19 -2015-01-10T07:00:00,9.31 -2015-01-10T08:00:00,11.35 -2015-01-10T09:00:00,15.87 -2015-01-10T10:00:00,15.84 -2015-01-10T11:00:00,14.83 -2015-01-10T12:00:00,15.83 -2015-01-10T13:00:00,14.85 -2015-01-10T14:00:00,11.42 -2015-01-10T15:00:00,11.34 -2015-01-10T16:00:00,14.15 -2015-01-10T17:00:00,17.09 -2015-01-10T18:00:00,18.87 -2015-01-10T19:00:00,13.44 -2015-01-10T20:00:00,15.82 -2015-01-10T21:00:00,11.51 -2015-01-10T22:00:00,11.81 -2015-01-10T23:00:00,5.08 -2015-01-11T00:00:00,0.05 -2015-01-11T01:00:00,-2.06 -2015-01-11T02:00:00,-4.11 -2015-01-11T03:00:00,-5.06 -2015-01-11T04:00:00,-4.12 -2015-01-11T05:00:00,-0.74 -2015-01-11T06:00:00,-25.02 -2015-01-11T07:00:00,-16.94 -2015-01-11T08:00:00,-6.86 -2015-01-11T09:00:00,1.53 -2015-01-11T10:00:00,5.61 -2015-01-11T11:00:00,9.91 -2015-01-11T12:00:00,8.71 -2015-01-11T13:00:00,-0.09 -2015-01-11T14:00:00,-0.07 -2015-01-11T15:00:00,-1.8 -2015-01-11T16:00:00,0.61 -2015-01-11T17:00:00,13.51 -2015-01-11T18:00:00,11.44 -2015-01-11T19:00:00,14.88 -2015-01-11T20:00:00,9.63 -2015-01-11T21:00:00,9.29 -2015-01-11T22:00:00,10.18 -2015-01-11T23:00:00,-2 -2015-01-12T00:00:00,9.3 -2015-01-12T01:00:00,2.87 -2015-01-12T02:00:00,0.09 -2015-01-12T03:00:00,0.26 -2015-01-12T04:00:00,2.83 -2015-01-12T05:00:00,10.4 -2015-01-12T06:00:00,20.03 -2015-01-12T07:00:00,44.95 -2015-01-12T08:00:00,44.92 -2015-01-12T09:00:00,36.33 -2015-01-12T10:00:00,30.41 -2015-01-12T11:00:00,30.07 -2015-01-12T12:00:00,29.33 -2015-01-12T13:00:00,29.39 -2015-01-12T14:00:00,28.98 -2015-01-12T15:00:00,29.49 -2015-01-12T16:00:00,28.99 -2015-01-12T17:00:00,34.41 -2015-01-12T18:00:00,31.69 -2015-01-12T19:00:00,29.59 -2015-01-12T20:00:00,27.61 -2015-01-12T21:00:00,15.55 -2015-01-12T22:00:00,14.91 -2015-01-12T23:00:00,9.1 -2015-01-13T00:00:00,9.27 -2015-01-13T01:00:00,10.42 -2015-01-13T02:00:00,11.16 -2015-01-13T03:00:00,10.71 -2015-01-13T04:00:00,11.26 -2015-01-13T05:00:00,11.69 -2015-01-13T06:00:00,21.05 -2015-01-13T07:00:00,32.37 -2015-01-13T08:00:00,34.21 -2015-01-13T09:00:00,28.47 -2015-01-13T10:00:00,26.07 -2015-01-13T11:00:00,23.74 -2015-01-13T12:00:00,23.73 -2015-01-13T13:00:00,23.76 -2015-01-13T14:00:00,25.62 -2015-01-13T15:00:00,28.91 -2015-01-13T16:00:00,34.13 -2015-01-13T17:00:00,42.16 -2015-01-13T18:00:00,41.99 -2015-01-13T19:00:00,37.91 -2015-01-13T20:00:00,31.41 -2015-01-13T21:00:00,29.91 -2015-01-13T22:00:00,27.02 -2015-01-13T23:00:00,18.36 -2015-01-14T00:00:00,13.25 -2015-01-14T01:00:00,15.02 -2015-01-14T02:00:00,11.99 -2015-01-14T03:00:00,13.68 -2015-01-14T04:00:00,13.91 -2015-01-14T05:00:00,16.26 -2015-01-14T06:00:00,27.32 -2015-01-14T07:00:00,31.62 -2015-01-14T08:00:00,34.74 -2015-01-14T09:00:00,32.41 -2015-01-14T10:00:00,29.71 -2015-01-14T11:00:00,30.32 -2015-01-14T12:00:00,29.7 -2015-01-14T13:00:00,29.28 -2015-01-14T14:00:00,30.63 -2015-01-14T15:00:00,35.61 -2015-01-14T16:00:00,37.8 -2015-01-14T17:00:00,44.69 -2015-01-14T18:00:00,43.23 -2015-01-14T19:00:00,34.51 -2015-01-14T20:00:00,29.44 -2015-01-14T21:00:00,27.96 -2015-01-14T22:00:00,23.73 -2015-01-14T23:00:00,16.29 -2015-01-15T00:00:00,24.36 -2015-01-15T01:00:00,21.59 -2015-01-15T02:00:00,15.46 -2015-01-15T03:00:00,15.44 -2015-01-15T04:00:00,15.43 -2015-01-15T05:00:00,15.47 -2015-01-15T06:00:00,27.69 -2015-01-15T07:00:00,33.89 -2015-01-15T08:00:00,34.79 -2015-01-15T09:00:00,30.9 -2015-01-15T10:00:00,29.97 -2015-01-15T11:00:00,29.42 -2015-01-15T12:00:00,27.02 -2015-01-15T13:00:00,26.18 -2015-01-15T14:00:00,26.53 -2015-01-15T15:00:00,28.21 -2015-01-15T16:00:00,29.37 -2015-01-15T17:00:00,32.54 -2015-01-15T18:00:00,32.67 -2015-01-15T19:00:00,30.49 -2015-01-15T20:00:00,28.32 -2015-01-15T21:00:00,22.05 -2015-01-15T22:00:00,19.01 -2015-01-15T23:00:00,12.01 -2015-01-16T00:00:00,9.78 -2015-01-16T01:00:00,11.64 -2015-01-16T02:00:00,9.72 -2015-01-16T03:00:00,10.56 -2015-01-16T04:00:00,14.11 -2015-01-16T05:00:00,16.02 -2015-01-16T06:00:00,24.71 -2015-01-16T07:00:00,34.04 -2015-01-16T08:00:00,37.71 -2015-01-16T09:00:00,36.92 -2015-01-16T10:00:00,37.99 -2015-01-16T11:00:00,39.97 -2015-01-16T12:00:00,39.95 -2015-01-16T13:00:00,40.02 -2015-01-16T14:00:00,41.83 -2015-01-16T15:00:00,44.05 -2015-01-16T16:00:00,47.1 -2015-01-16T17:00:00,52.49 -2015-01-16T18:00:00,53.54 -2015-01-16T19:00:00,48.02 -2015-01-16T20:00:00,41.51 -2015-01-16T21:00:00,37.92 -2015-01-16T22:00:00,35.64 -2015-01-16T23:00:00,28.79 -2015-01-17T00:00:00,29.03 -2015-01-17T01:00:00,27.47 -2015-01-17T02:00:00,28.06 -2015-01-17T03:00:00,27.92 -2015-01-17T04:00:00,27.05 -2015-01-17T05:00:00,27.24 -2015-01-17T06:00:00,27.34 -2015-01-17T07:00:00,27.74 -2015-01-17T08:00:00,29.73 -2015-01-17T09:00:00,35.05 -2015-01-17T10:00:00,38.81 -2015-01-17T11:00:00,38.89 -2015-01-17T12:00:00,34.99 -2015-01-17T13:00:00,31.96 -2015-01-17T14:00:00,30.07 -2015-01-17T15:00:00,31.27 -2015-01-17T16:00:00,33.84 -2015-01-17T17:00:00,42.44 -2015-01-17T18:00:00,44.9 -2015-01-17T19:00:00,34.24 -2015-01-17T20:00:00,28.95 -2015-01-17T21:00:00,27.02 -2015-01-17T22:00:00,27.46 -2015-01-17T23:00:00,24.5 -2015-01-18T00:00:00,23.35 -2015-01-18T01:00:00,21.19 -2015-01-18T02:00:00,22.24 -2015-01-18T03:00:00,22.31 -2015-01-18T04:00:00,22.32 -2015-01-18T05:00:00,22.11 -2015-01-18T06:00:00,14.12 -2015-01-18T07:00:00,15.17 -2015-01-18T08:00:00,19.07 -2015-01-18T09:00:00,24.94 -2015-01-18T10:00:00,27.2 -2015-01-18T11:00:00,28.22 -2015-01-18T12:00:00,28.15 -2015-01-18T13:00:00,27.85 -2015-01-18T14:00:00,28.17 -2015-01-18T15:00:00,29.91 -2015-01-18T16:00:00,30.44 -2015-01-18T17:00:00,38.42 -2015-01-18T18:00:00,42.46 -2015-01-18T19:00:00,39.8 -2015-01-18T20:00:00,30.54 -2015-01-18T21:00:00,30.4 -2015-01-18T22:00:00,31 -2015-01-18T23:00:00,28.42 -2015-01-19T00:00:00,27.92 -2015-01-19T01:00:00,27.76 -2015-01-19T02:00:00,28.25 -2015-01-19T03:00:00,28.41 -2015-01-19T04:00:00,28.07 -2015-01-19T05:00:00,29.63 -2015-01-19T06:00:00,42.87 -2015-01-19T07:00:00,52.07 -2015-01-19T08:00:00,56.49 -2015-01-19T09:00:00,52.38 -2015-01-19T10:00:00,48.84 -2015-01-19T11:00:00,47.95 -2015-01-19T12:00:00,44.97 -2015-01-19T13:00:00,47.95 -2015-01-19T14:00:00,47.72 -2015-01-19T15:00:00,47.43 -2015-01-19T16:00:00,48.19 -2015-01-19T17:00:00,52.16 -2015-01-19T18:00:00,56.26 -2015-01-19T19:00:00,52.96 -2015-01-19T20:00:00,45 -2015-01-19T21:00:00,40 -2015-01-19T22:00:00,37.53 -2015-01-19T23:00:00,31.07 -2015-01-20T00:00:00,31.93 -2015-01-20T01:00:00,31.08 -2015-01-20T02:00:00,29.92 -2015-01-20T03:00:00,29.97 -2015-01-20T04:00:00,31.02 -2015-01-20T05:00:00,34.81 -2015-01-20T06:00:00,39.4 -2015-01-20T07:00:00,53.39 -2015-01-20T08:00:00,58.88 -2015-01-20T09:00:00,58.8 -2015-01-20T10:00:00,57.34 -2015-01-20T11:00:00,57.46 -2015-01-20T12:00:00,53.53 -2015-01-20T13:00:00,52.93 -2015-01-20T14:00:00,52.38 -2015-01-20T15:00:00,52.1 -2015-01-20T16:00:00,51.04 -2015-01-20T17:00:00,58.55 -2015-01-20T18:00:00,66.65 -2015-01-20T19:00:00,56.02 -2015-01-20T20:00:00,50.01 -2015-01-20T21:00:00,42.73 -2015-01-20T22:00:00,42.02 -2015-01-20T23:00:00,36.93 -2015-01-21T00:00:00,31.08 -2015-01-21T01:00:00,32.07 -2015-01-21T02:00:00,31.53 -2015-01-21T03:00:00,31.2 -2015-01-21T04:00:00,33.27 -2015-01-21T05:00:00,35.07 -2015-01-21T06:00:00,43.71 -2015-01-21T07:00:00,54.12 -2015-01-21T08:00:00,58.39 -2015-01-21T09:00:00,56.6 -2015-01-21T10:00:00,54.96 -2015-01-21T11:00:00,53.99 -2015-01-21T12:00:00,48.49 -2015-01-21T13:00:00,49.59 -2015-01-21T14:00:00,48 -2015-01-21T15:00:00,50 -2015-01-21T16:00:00,51.02 -2015-01-21T17:00:00,55.47 -2015-01-21T18:00:00,57.1 -2015-01-21T19:00:00,51.95 -2015-01-21T20:00:00,43.22 -2015-01-21T21:00:00,38.98 -2015-01-21T22:00:00,38.38 -2015-01-21T23:00:00,31.51 -2015-01-22T00:00:00,29.67 -2015-01-22T01:00:00,29.93 -2015-01-22T02:00:00,29 -2015-01-22T03:00:00,28.96 -2015-01-22T04:00:00,29.38 -2015-01-22T05:00:00,31.09 -2015-01-22T06:00:00,36.92 -2015-01-22T07:00:00,48.42 -2015-01-22T08:00:00,53.79 -2015-01-22T09:00:00,50.52 -2015-01-22T10:00:00,50.53 -2015-01-22T11:00:00,49.89 -2015-01-22T12:00:00,52.44 -2015-01-22T13:00:00,54.01 -2015-01-22T14:00:00,51.54 -2015-01-22T15:00:00,49.99 -2015-01-22T16:00:00,51.99 -2015-01-22T17:00:00,59.2 -2015-01-22T18:00:00,56.99 -2015-01-22T19:00:00,53.81 -2015-01-22T20:00:00,45.57 -2015-01-22T21:00:00,39.86 -2015-01-22T22:00:00,37.26 -2015-01-22T23:00:00,30.96 -2015-01-23T00:00:00,31.18 -2015-01-23T01:00:00,29.97 -2015-01-23T02:00:00,28.86 -2015-01-23T03:00:00,28.78 -2015-01-23T04:00:00,29.39 -2015-01-23T05:00:00,32.49 -2015-01-23T06:00:00,37.52 -2015-01-23T07:00:00,54.28 -2015-01-23T08:00:00,53.56 -2015-01-23T09:00:00,57.68 -2015-01-23T10:00:00,56.97 -2015-01-23T11:00:00,55.75 -2015-01-23T12:00:00,49.94 -2015-01-23T13:00:00,47.77 -2015-01-23T14:00:00,46.58 -2015-01-23T15:00:00,44.09 -2015-01-23T16:00:00,45.18 -2015-01-23T17:00:00,49.98 -2015-01-23T18:00:00,47.99 -2015-01-23T19:00:00,46.98 -2015-01-23T20:00:00,41.48 -2015-01-23T21:00:00,35.12 -2015-01-23T22:00:00,34.21 -2015-01-23T23:00:00,31.14 -2015-01-24T00:00:00,28.5 -2015-01-24T01:00:00,27.04 -2015-01-24T02:00:00,24.44 -2015-01-24T03:00:00,24.29 -2015-01-24T04:00:00,25.93 -2015-01-24T05:00:00,24.99 -2015-01-24T06:00:00,23.94 -2015-01-24T07:00:00,24.26 -2015-01-24T08:00:00,27.6 -2015-01-24T09:00:00,29.53 -2015-01-24T10:00:00,30.26 -2015-01-24T11:00:00,37.81 -2015-01-24T12:00:00,37.27 -2015-01-24T13:00:00,34.37 -2015-01-24T14:00:00,30.07 -2015-01-24T15:00:00,30.25 -2015-01-24T16:00:00,34.52 -2015-01-24T17:00:00,36.09 -2015-01-24T18:00:00,37.86 -2015-01-24T19:00:00,34.56 -2015-01-24T20:00:00,30.04 -2015-01-24T21:00:00,27.97 -2015-01-24T22:00:00,28.11 -2015-01-24T23:00:00,24.99 -2015-01-25T00:00:00,23.26 -2015-01-25T01:00:00,22.13 -2015-01-25T02:00:00,19.04 -2015-01-25T03:00:00,19.91 -2015-01-25T04:00:00,21.49 -2015-01-25T05:00:00,21.75 -2015-01-25T06:00:00,19.82 -2015-01-25T07:00:00,22.35 -2015-01-25T08:00:00,24.64 -2015-01-25T09:00:00,28.03 -2015-01-25T10:00:00,29.94 -2015-01-25T11:00:00,36.65 -2015-01-25T12:00:00,31.99 -2015-01-25T13:00:00,29.25 -2015-01-25T14:00:00,29.13 -2015-01-25T15:00:00,29.26 -2015-01-25T16:00:00,30.29 -2015-01-25T17:00:00,36.5 -2015-01-25T18:00:00,40.96 -2015-01-25T19:00:00,39.03 -2015-01-25T20:00:00,34.92 -2015-01-25T21:00:00,30.2 -2015-01-25T22:00:00,29.99 -2015-01-25T23:00:00,28.07 -2015-01-26T00:00:00,26.93 -2015-01-26T01:00:00,26.13 -2015-01-26T02:00:00,25.54 -2015-01-26T03:00:00,25.41 -2015-01-26T04:00:00,25.8 -2015-01-26T05:00:00,27.1 -2015-01-26T06:00:00,41.94 -2015-01-26T07:00:00,55.16 -2015-01-26T08:00:00,53.22 -2015-01-26T09:00:00,47.28 -2015-01-26T10:00:00,45.28 -2015-01-26T11:00:00,47.43 -2015-01-26T12:00:00,46.22 -2015-01-26T13:00:00,46 -2015-01-26T14:00:00,42.47 -2015-01-26T15:00:00,42.44 -2015-01-26T16:00:00,39.76 -2015-01-26T17:00:00,44.92 -2015-01-26T18:00:00,44.03 -2015-01-26T19:00:00,36.98 -2015-01-26T20:00:00,30.34 -2015-01-26T21:00:00,28.49 -2015-01-26T22:00:00,27.15 -2015-01-26T23:00:00,24.05 -2015-01-27T00:00:00,20.36 -2015-01-27T01:00:00,16.31 -2015-01-27T02:00:00,16.95 -2015-01-27T03:00:00,16.34 -2015-01-27T04:00:00,19.92 -2015-01-27T05:00:00,22.92 -2015-01-27T06:00:00,27.57 -2015-01-27T07:00:00,34.27 -2015-01-27T08:00:00,35.92 -2015-01-27T09:00:00,36.97 -2015-01-27T10:00:00,36.94 -2015-01-27T11:00:00,38.33 -2015-01-27T12:00:00,37.93 -2015-01-27T13:00:00,38.19 -2015-01-27T14:00:00,39.14 -2015-01-27T15:00:00,40.96 -2015-01-27T16:00:00,38.93 -2015-01-27T17:00:00,50.65 -2015-01-27T18:00:00,46.31 -2015-01-27T19:00:00,42.26 -2015-01-27T20:00:00,40 -2015-01-27T21:00:00,35.23 -2015-01-27T22:00:00,30.28 -2015-01-27T23:00:00,27.8 -2015-01-28T00:00:00,26.97 -2015-01-28T01:00:00,26.59 -2015-01-28T02:00:00,25.93 -2015-01-28T03:00:00,25.27 -2015-01-28T04:00:00,25.75 -2015-01-28T05:00:00,25.94 -2015-01-28T06:00:00,34.25 -2015-01-28T07:00:00,44.31 -2015-01-28T08:00:00,42.86 -2015-01-28T09:00:00,33.96 -2015-01-28T10:00:00,33.11 -2015-01-28T11:00:00,29.72 -2015-01-28T12:00:00,27.85 -2015-01-28T13:00:00,27.63 -2015-01-28T14:00:00,27.03 -2015-01-28T15:00:00,26.59 -2015-01-28T16:00:00,27 -2015-01-28T17:00:00,29.95 -2015-01-28T18:00:00,28.62 -2015-01-28T19:00:00,28.01 -2015-01-28T20:00:00,25.47 -2015-01-28T21:00:00,21.69 -2015-01-28T22:00:00,16.96 -2015-01-28T23:00:00,14.9 -2015-01-29T00:00:00,9.98 -2015-01-29T01:00:00,10.74 -2015-01-29T02:00:00,9.21 -2015-01-29T03:00:00,9.37 -2015-01-29T04:00:00,11.84 -2015-01-29T05:00:00,13.37 -2015-01-29T06:00:00,26.2 -2015-01-29T07:00:00,30 -2015-01-29T08:00:00,32.12 -2015-01-29T09:00:00,28.89 -2015-01-29T10:00:00,28.81 -2015-01-29T11:00:00,29.81 -2015-01-29T12:00:00,28.63 -2015-01-29T13:00:00,28.56 -2015-01-29T14:00:00,29.18 -2015-01-29T15:00:00,33.82 -2015-01-29T16:00:00,32.45 -2015-01-29T17:00:00,41.35 -2015-01-29T18:00:00,39.73 -2015-01-29T19:00:00,39 -2015-01-29T20:00:00,33.41 -2015-01-29T21:00:00,29.09 -2015-01-29T22:00:00,28.74 -2015-01-29T23:00:00,25.4 -2015-01-30T00:00:00,26.6 -2015-01-30T01:00:00,25.61 -2015-01-30T02:00:00,26.42 -2015-01-30T03:00:00,24.74 -2015-01-30T04:00:00,23.89 -2015-01-30T05:00:00,26.07 -2015-01-30T06:00:00,36.41 -2015-01-30T07:00:00,48.76 -2015-01-30T08:00:00,49.19 -2015-01-30T09:00:00,46.35 -2015-01-30T10:00:00,49.51 -2015-01-30T11:00:00,47.47 -2015-01-30T12:00:00,46 -2015-01-30T13:00:00,44.95 -2015-01-30T14:00:00,43.24 -2015-01-30T15:00:00,43.71 -2015-01-30T16:00:00,44 -2015-01-30T17:00:00,46.69 -2015-01-30T18:00:00,48.26 -2015-01-30T19:00:00,43.11 -2015-01-30T20:00:00,40.04 -2015-01-30T21:00:00,30.17 -2015-01-30T22:00:00,29.5 -2015-01-30T23:00:00,27.01 -2015-01-31T00:00:00,27.92 -2015-01-31T01:00:00,26.5 -2015-01-31T02:00:00,26.4 -2015-01-31T03:00:00,25.75 -2015-01-31T04:00:00,24.95 -2015-01-31T05:00:00,25.13 -2015-01-31T06:00:00,24.98 -2015-01-31T07:00:00,26.55 -2015-01-31T08:00:00,28.36 -2015-01-31T09:00:00,29.51 -2015-01-31T10:00:00,29.91 -2015-01-31T11:00:00,28.79 -2015-01-31T12:00:00,28.41 -2015-01-31T13:00:00,28.23 -2015-01-31T14:00:00,28.19 -2015-01-31T15:00:00,29.17 -2015-01-31T16:00:00,32.12 -2015-01-31T17:00:00,42.99 -2015-01-31T18:00:00,44.31 -2015-01-31T19:00:00,42.15 -2015-01-31T20:00:00,32.83 -2015-01-31T21:00:00,28.34 -2015-01-31T22:00:00,28.5 -2015-01-31T23:00:00,25.56 -2015-02-01T00:00:00,27.04 -2015-02-01T01:00:00,26.32 -2015-02-01T02:00:00,25.98 -2015-02-01T03:00:00,25.63 -2015-02-01T04:00:00,25.78 -2015-02-01T05:00:00,25.99 -2015-02-01T06:00:00,24.26 -2015-02-01T07:00:00,26.48 -2015-02-01T08:00:00,27.84 -2015-02-01T09:00:00,29.09 -2015-02-01T10:00:00,34.15 -2015-02-01T11:00:00,37.74 -2015-02-01T12:00:00,34.95 -2015-02-01T13:00:00,28.24 -2015-02-01T14:00:00,28.21 -2015-02-01T15:00:00,28.47 -2015-02-01T16:00:00,29.89 -2015-02-01T17:00:00,36.85 -2015-02-01T18:00:00,36.55 -2015-02-01T19:00:00,29.99 -2015-02-01T20:00:00,28.59 -2015-02-01T21:00:00,28.36 -2015-02-01T22:00:00,28.18 -2015-02-01T23:00:00,26.92 -2015-02-02T00:00:00,27.23 -2015-02-02T01:00:00,26.38 -2015-02-02T02:00:00,26.19 -2015-02-02T03:00:00,26.21 -2015-02-02T04:00:00,26.24 -2015-02-02T05:00:00,27.47 -2015-02-02T06:00:00,37.85 -2015-02-02T07:00:00,53.35 -2015-02-02T08:00:00,53.13 -2015-02-02T09:00:00,54.92 -2015-02-02T10:00:00,51.35 -2015-02-02T11:00:00,50.96 -2015-02-02T12:00:00,45 -2015-02-02T13:00:00,44.04 -2015-02-02T14:00:00,44.98 -2015-02-02T15:00:00,45.74 -2015-02-02T16:00:00,47.99 -2015-02-02T17:00:00,58.95 -2015-02-02T18:00:00,54.92 -2015-02-02T19:00:00,51.45 -2015-02-02T20:00:00,45 -2015-02-02T21:00:00,43.05 -2015-02-02T22:00:00,40 -2015-02-02T23:00:00,34.68 -2015-02-03T00:00:00,29.46 -2015-02-03T01:00:00,28.84 -2015-02-03T02:00:00,28.05 -2015-02-03T03:00:00,28.08 -2015-02-03T04:00:00,28.21 -2015-02-03T05:00:00,30.08 -2015-02-03T06:00:00,40 -2015-02-03T07:00:00,60.43 -2015-02-03T08:00:00,60.08 -2015-02-03T09:00:00,58.79 -2015-02-03T10:00:00,55.77 -2015-02-03T11:00:00,53.37 -2015-02-03T12:00:00,46.2 -2015-02-03T13:00:00,48.76 -2015-02-03T14:00:00,47.85 -2015-02-03T15:00:00,51.7 -2015-02-03T16:00:00,51.54 -2015-02-03T17:00:00,60.07 -2015-02-03T18:00:00,65.52 -2015-02-03T19:00:00,52.89 -2015-02-03T20:00:00,47.99 -2015-02-03T21:00:00,44.25 -2015-02-03T22:00:00,43.93 -2015-02-03T23:00:00,30.87 -2015-02-04T00:00:00,34.9 -2015-02-04T01:00:00,31.14 -2015-02-04T02:00:00,30.08 -2015-02-04T03:00:00,30.06 -2015-02-04T04:00:00,30.49 -2015-02-04T05:00:00,33.55 -2015-02-04T06:00:00,42.64 -2015-02-04T07:00:00,50.92 -2015-02-04T08:00:00,61.76 -2015-02-04T09:00:00,62.59 -2015-02-04T10:00:00,61.1 -2015-02-04T11:00:00,58.94 -2015-02-04T12:00:00,54.87 -2015-02-04T13:00:00,53.53 -2015-02-04T14:00:00,51.45 -2015-02-04T15:00:00,49.88 -2015-02-04T16:00:00,49.92 -2015-02-04T17:00:00,59.37 -2015-02-04T18:00:00,75.87 -2015-02-04T19:00:00,62.73 -2015-02-04T20:00:00,49.89 -2015-02-04T21:00:00,42.66 -2015-02-04T22:00:00,44.04 -2015-02-04T23:00:00,36.41 -2015-02-05T00:00:00,39.96 -2015-02-05T01:00:00,33.29 -2015-02-05T02:00:00,32.24 -2015-02-05T03:00:00,32.44 -2015-02-05T04:00:00,32.92 -2015-02-05T05:00:00,38.91 -2015-02-05T06:00:00,45.96 -2015-02-05T07:00:00,63.79 -2015-02-05T08:00:00,62.06 -2015-02-05T09:00:00,60.28 -2015-02-05T10:00:00,52.03 -2015-02-05T11:00:00,49.96 -2015-02-05T12:00:00,50.52 -2015-02-05T13:00:00,47.44 -2015-02-05T14:00:00,44.5 -2015-02-05T15:00:00,43.54 -2015-02-05T16:00:00,44.04 -2015-02-05T17:00:00,50.75 -2015-02-05T18:00:00,48.13 -2015-02-05T19:00:00,44.94 -2015-02-05T20:00:00,41.89 -2015-02-05T21:00:00,37.9 -2015-02-05T22:00:00,30.45 -2015-02-05T23:00:00,29.01 -2015-02-06T00:00:00,28.73 -2015-02-06T01:00:00,28.14 -2015-02-06T02:00:00,27.75 -2015-02-06T03:00:00,27.73 -2015-02-06T04:00:00,28.23 -2015-02-06T05:00:00,29.05 -2015-02-06T06:00:00,36 -2015-02-06T07:00:00,52.92 -2015-02-06T08:00:00,46.9 -2015-02-06T09:00:00,45.94 -2015-02-06T10:00:00,45.93 -2015-02-06T11:00:00,42.93 -2015-02-06T12:00:00,39.65 -2015-02-06T13:00:00,37.1 -2015-02-06T14:00:00,35.54 -2015-02-06T15:00:00,38.57 -2015-02-06T16:00:00,42.95 -2015-02-06T17:00:00,50.19 -2015-02-06T18:00:00,54.95 -2015-02-06T19:00:00,50.24 -2015-02-06T20:00:00,45 -2015-02-06T21:00:00,40.33 -2015-02-06T22:00:00,39.37 -2015-02-06T23:00:00,29.01 -2015-02-07T00:00:00,28.46 -2015-02-07T01:00:00,28 -2015-02-07T02:00:00,27.29 -2015-02-07T03:00:00,26.28 -2015-02-07T04:00:00,26.08 -2015-02-07T05:00:00,25.91 -2015-02-07T06:00:00,25.53 -2015-02-07T07:00:00,27.79 -2015-02-07T08:00:00,32.03 -2015-02-07T09:00:00,42.1 -2015-02-07T10:00:00,37.96 -2015-02-07T11:00:00,33.75 -2015-02-07T12:00:00,29.99 -2015-02-07T13:00:00,26.56 -2015-02-07T14:00:00,25.36 -2015-02-07T15:00:00,24.76 -2015-02-07T16:00:00,25.16 -2015-02-07T17:00:00,29.15 -2015-02-07T18:00:00,35.81 -2015-02-07T19:00:00,26.83 -2015-02-07T20:00:00,22.31 -2015-02-07T21:00:00,15.01 -2015-02-07T22:00:00,14.25 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-2015-02-09T12:00:00,31.21 -2015-02-09T13:00:00,30.96 -2015-02-09T14:00:00,30.69 -2015-02-09T15:00:00,32.81 -2015-02-09T16:00:00,32.3 -2015-02-09T17:00:00,37.66 -2015-02-09T18:00:00,43.93 -2015-02-09T19:00:00,44.57 -2015-02-09T20:00:00,39.91 -2015-02-09T21:00:00,32.84 -2015-02-09T22:00:00,29.43 -2015-02-09T23:00:00,27.12 -2015-02-10T00:00:00,25.36 -2015-02-10T01:00:00,25.32 -2015-02-10T02:00:00,25.19 -2015-02-10T03:00:00,25.57 -2015-02-10T04:00:00,26.02 -2015-02-10T05:00:00,27.75 -2015-02-10T06:00:00,38.22 -2015-02-10T07:00:00,52.95 -2015-02-10T08:00:00,49 -2015-02-10T09:00:00,45.97 -2015-02-10T10:00:00,47.97 -2015-02-10T11:00:00,49.97 -2015-02-10T12:00:00,47.27 -2015-02-10T13:00:00,46.95 -2015-02-10T14:00:00,46 -2015-02-10T15:00:00,47 -2015-02-10T16:00:00,50.38 -2015-02-10T17:00:00,61.41 -2015-02-10T18:00:00,84.94 -2015-02-10T19:00:00,62.82 -2015-02-10T20:00:00,51.66 -2015-02-10T21:00:00,44.09 -2015-02-10T22:00:00,44.54 -2015-02-10T23:00:00,33.95 -2015-02-11T00:00:00,30.57 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-2015-02-12T14:00:00,43.94 -2015-02-12T15:00:00,45.47 -2015-02-12T16:00:00,49.96 -2015-02-12T17:00:00,57.58 -2015-02-12T18:00:00,72 -2015-02-12T19:00:00,69.94 -2015-02-12T20:00:00,54.98 -2015-02-12T21:00:00,46.72 -2015-02-12T22:00:00,43.29 -2015-02-12T23:00:00,36.91 -2015-02-13T00:00:00,31.03 -2015-02-13T01:00:00,31.1 -2015-02-13T02:00:00,31.04 -2015-02-13T03:00:00,31.02 -2015-02-13T04:00:00,31.88 -2015-02-13T05:00:00,33 -2015-02-13T06:00:00,45.42 -2015-02-13T07:00:00,58.25 -2015-02-13T08:00:00,57.84 -2015-02-13T09:00:00,50.81 -2015-02-13T10:00:00,45.49 -2015-02-13T11:00:00,42.97 -2015-02-13T12:00:00,38.05 -2015-02-13T13:00:00,33.45 -2015-02-13T14:00:00,32.06 -2015-02-13T15:00:00,38.04 -2015-02-13T16:00:00,43.2 -2015-02-13T17:00:00,45.75 -2015-02-13T18:00:00,46.31 -2015-02-13T19:00:00,41.71 -2015-02-13T20:00:00,31.81 -2015-02-13T21:00:00,29.05 -2015-02-13T22:00:00,30.43 -2015-02-13T23:00:00,29.09 -2015-02-14T00:00:00,28.81 -2015-02-14T01:00:00,30.05 -2015-02-14T02:00:00,28.02 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-2015-02-17T05:00:00,31.08 -2015-02-17T06:00:00,44 -2015-02-17T07:00:00,55.03 -2015-02-17T08:00:00,55.55 -2015-02-17T09:00:00,49.98 -2015-02-17T10:00:00,47.91 -2015-02-17T11:00:00,45.53 -2015-02-17T12:00:00,43.31 -2015-02-17T13:00:00,43.32 -2015-02-17T14:00:00,43.51 -2015-02-17T15:00:00,44.54 -2015-02-17T16:00:00,44.92 -2015-02-17T17:00:00,49.6 -2015-02-17T18:00:00,62.51 -2015-02-17T19:00:00,56.92 -2015-02-17T20:00:00,49.94 -2015-02-17T21:00:00,42.72 -2015-02-17T22:00:00,40.08 -2015-02-17T23:00:00,31.97 -2015-02-18T00:00:00,30.3 -2015-02-18T01:00:00,30.09 -2015-02-18T02:00:00,30.03 -2015-02-18T03:00:00,29.75 -2015-02-18T04:00:00,31.03 -2015-02-18T05:00:00,33.58 -2015-02-18T06:00:00,47.91 -2015-02-18T07:00:00,54.62 -2015-02-18T08:00:00,57.8 -2015-02-18T09:00:00,53.94 -2015-02-18T10:00:00,54.02 -2015-02-18T11:00:00,50.44 -2015-02-18T12:00:00,47 -2015-02-18T13:00:00,42.98 -2015-02-18T14:00:00,42.92 -2015-02-18T15:00:00,43.99 -2015-02-18T16:00:00,44.71 -2015-02-18T17:00:00,51.3 -2015-02-18T18:00:00,58.85 -2015-02-18T19:00:00,56.68 -2015-02-18T20:00:00,44.94 -2015-02-18T21:00:00,37.49 -2015-02-18T22:00:00,32.27 -2015-02-18T23:00:00,30.09 -2015-02-19T00:00:00,30.9 -2015-02-19T01:00:00,29.95 -2015-02-19T02:00:00,28.07 -2015-02-19T03:00:00,28.55 -2015-02-19T04:00:00,29.93 -2015-02-19T05:00:00,30.95 -2015-02-19T06:00:00,36.93 -2015-02-19T07:00:00,47.93 -2015-02-19T08:00:00,50.45 -2015-02-19T09:00:00,49.94 -2015-02-19T10:00:00,47.07 -2015-02-19T11:00:00,43.96 -2015-02-19T12:00:00,37.61 -2015-02-19T13:00:00,36.69 -2015-02-19T14:00:00,36.28 -2015-02-19T15:00:00,40.9 -2015-02-19T16:00:00,44.87 -2015-02-19T17:00:00,49.59 -2015-02-19T18:00:00,48.99 -2015-02-19T19:00:00,40.03 -2015-02-19T20:00:00,37.39 -2015-02-19T21:00:00,31.01 -2015-02-19T22:00:00,30.03 -2015-02-19T23:00:00,27.44 -2015-02-20T00:00:00,25.5 -2015-02-20T01:00:00,24.33 -2015-02-20T02:00:00,22.85 -2015-02-20T03:00:00,23.2 -2015-02-20T04:00:00,23.48 -2015-02-20T05:00:00,25.2 -2015-02-20T06:00:00,29.14 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-2015-02-26T11:00:00,38.76 -2015-02-26T12:00:00,30.94 -2015-02-26T13:00:00,30.98 -2015-02-26T14:00:00,31.12 -2015-02-26T15:00:00,35.94 -2015-02-26T16:00:00,39.92 -2015-02-26T17:00:00,44.88 -2015-02-26T18:00:00,45.74 -2015-02-26T19:00:00,45.04 -2015-02-26T20:00:00,37.79 -2015-02-26T21:00:00,30.8 -2015-02-26T22:00:00,30.1 -2015-02-26T23:00:00,27.03 -2015-02-27T00:00:00,26.72 -2015-02-27T01:00:00,27.46 -2015-02-27T02:00:00,27.19 -2015-02-27T03:00:00,27.92 -2015-02-27T04:00:00,28.02 -2015-02-27T05:00:00,29.88 -2015-02-27T06:00:00,41.91 -2015-02-27T07:00:00,44.02 -2015-02-27T08:00:00,43.07 -2015-02-27T09:00:00,42.43 -2015-02-27T10:00:00,39.92 -2015-02-27T11:00:00,39.39 -2015-02-27T12:00:00,35.59 -2015-02-27T13:00:00,35.72 -2015-02-27T14:00:00,34.93 -2015-02-27T15:00:00,40.68 -2015-02-27T16:00:00,41.62 -2015-02-27T17:00:00,47.94 -2015-02-27T18:00:00,64.06 -2015-02-27T19:00:00,57.82 -2015-02-27T20:00:00,47.19 -2015-02-27T21:00:00,40.07 -2015-02-27T22:00:00,37.3 -2015-02-27T23:00:00,31.88 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-2015-03-03T02:00:00,8.4 -2015-03-03T03:00:00,9.48 -2015-03-03T04:00:00,13.77 -2015-03-03T05:00:00,15.65 -2015-03-03T06:00:00,27.01 -2015-03-03T07:00:00,31.92 -2015-03-03T08:00:00,30.16 -2015-03-03T09:00:00,26.68 -2015-03-03T10:00:00,24.55 -2015-03-03T11:00:00,23.01 -2015-03-03T12:00:00,21.9 -2015-03-03T13:00:00,24.33 -2015-03-03T14:00:00,26.08 -2015-03-03T15:00:00,29.97 -2015-03-03T16:00:00,34.8 -2015-03-03T17:00:00,44.32 -2015-03-03T18:00:00,55.9 -2015-03-03T19:00:00,57.69 -2015-03-03T20:00:00,40.09 -2015-03-03T21:00:00,30.16 -2015-03-03T22:00:00,28.72 -2015-03-03T23:00:00,24.92 -2015-03-04T00:00:00,23.31 -2015-03-04T01:00:00,16.25 -2015-03-04T02:00:00,15.37 -2015-03-04T03:00:00,15.5 -2015-03-04T04:00:00,18.04 -2015-03-04T05:00:00,25.52 -2015-03-04T06:00:00,29.64 -2015-03-04T07:00:00,38.03 -2015-03-04T08:00:00,34.07 -2015-03-04T09:00:00,28.12 -2015-03-04T10:00:00,25.73 -2015-03-04T11:00:00,17.99 -2015-03-04T12:00:00,15.57 -2015-03-04T13:00:00,15.68 -2015-03-04T14:00:00,21.64 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-2015-03-06T04:00:00,27.47 -2015-03-06T05:00:00,32.9 -2015-03-06T06:00:00,42.05 -2015-03-06T07:00:00,50.96 -2015-03-06T08:00:00,49.22 -2015-03-06T09:00:00,49.08 -2015-03-06T10:00:00,48.34 -2015-03-06T11:00:00,46 -2015-03-06T12:00:00,39.44 -2015-03-06T13:00:00,36.8 -2015-03-06T14:00:00,33.72 -2015-03-06T15:00:00,39.51 -2015-03-06T16:00:00,41.26 -2015-03-06T17:00:00,45.98 -2015-03-06T18:00:00,59.61 -2015-03-06T19:00:00,62 -2015-03-06T20:00:00,42.98 -2015-03-06T21:00:00,34.48 -2015-03-06T22:00:00,34.05 -2015-03-06T23:00:00,29.66 -2015-03-07T00:00:00,34.09 -2015-03-07T01:00:00,29.84 -2015-03-07T02:00:00,26.02 -2015-03-07T03:00:00,24.06 -2015-03-07T04:00:00,24.65 -2015-03-07T05:00:00,25.06 -2015-03-07T06:00:00,25.47 -2015-03-07T07:00:00,27.18 -2015-03-07T08:00:00,28.84 -2015-03-07T09:00:00,26.22 -2015-03-07T10:00:00,24.53 -2015-03-07T11:00:00,24.22 -2015-03-07T12:00:00,22.5 -2015-03-07T13:00:00,22.93 -2015-03-07T14:00:00,24.49 -2015-03-07T15:00:00,26.11 -2015-03-07T16:00:00,29.34 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-2015-03-23T03:00:00,28.07 -2015-03-23T04:00:00,28.12 -2015-03-23T05:00:00,31.64 -2015-03-23T06:00:00,46.99 -2015-03-23T07:00:00,57.59 -2015-03-23T08:00:00,55.68 -2015-03-23T09:00:00,44.2 -2015-03-23T10:00:00,36.98 -2015-03-23T11:00:00,34.01 -2015-03-23T12:00:00,30.4 -2015-03-23T13:00:00,29.97 -2015-03-23T14:00:00,30.39 -2015-03-23T15:00:00,36.92 -2015-03-23T16:00:00,41.66 -2015-03-23T17:00:00,51.34 -2015-03-23T18:00:00,55.93 -2015-03-23T19:00:00,75.61 -2015-03-23T20:00:00,58.59 -2015-03-23T21:00:00,49.36 -2015-03-23T22:00:00,44.94 -2015-03-23T23:00:00,34 -2015-03-24T00:00:00,32.95 -2015-03-24T01:00:00,31.82 -2015-03-24T02:00:00,31.95 -2015-03-24T03:00:00,32.68 -2015-03-24T04:00:00,34.2 -2015-03-24T05:00:00,36.13 -2015-03-24T06:00:00,48.01 -2015-03-24T07:00:00,57.96 -2015-03-24T08:00:00,58.97 -2015-03-24T09:00:00,50.64 -2015-03-24T10:00:00,44.95 -2015-03-24T11:00:00,44.23 -2015-03-24T12:00:00,44.39 -2015-03-24T13:00:00,46.01 -2015-03-24T14:00:00,47.87 -2015-03-24T15:00:00,50.57 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-2015-03-26T05:00:00,30.27 -2015-03-26T06:00:00,40.98 -2015-03-26T07:00:00,46.95 -2015-03-26T08:00:00,49.98 -2015-03-26T09:00:00,52.9 -2015-03-26T10:00:00,53 -2015-03-26T11:00:00,53.87 -2015-03-26T12:00:00,50.4 -2015-03-26T13:00:00,48.3 -2015-03-26T14:00:00,46.68 -2015-03-26T15:00:00,46.9 -2015-03-26T16:00:00,43.96 -2015-03-26T17:00:00,43.34 -2015-03-26T18:00:00,46.17 -2015-03-26T19:00:00,52.02 -2015-03-26T20:00:00,42.83 -2015-03-26T21:00:00,37.26 -2015-03-26T22:00:00,31.46 -2015-03-26T23:00:00,26.11 -2015-03-27T00:00:00,27.69 -2015-03-27T01:00:00,27.64 -2015-03-27T02:00:00,26.94 -2015-03-27T03:00:00,26.5 -2015-03-27T04:00:00,27.19 -2015-03-27T05:00:00,29.34 -2015-03-27T06:00:00,32.67 -2015-03-27T07:00:00,36.25 -2015-03-27T08:00:00,43.02 -2015-03-27T09:00:00,39.98 -2015-03-27T10:00:00,40.02 -2015-03-27T11:00:00,38.94 -2015-03-27T12:00:00,32.28 -2015-03-27T13:00:00,29.85 -2015-03-27T14:00:00,29.45 -2015-03-27T15:00:00,29.52 -2015-03-27T16:00:00,30.61 -2015-03-27T17:00:00,40.03 -2015-03-27T18:00:00,45.44 -2015-03-27T19:00:00,46.49 -2015-03-27T20:00:00,38.2 -2015-03-27T21:00:00,32.31 -2015-03-27T22:00:00,30.82 -2015-03-27T23:00:00,27.33 -2015-03-28T00:00:00,25.68 -2015-03-28T01:00:00,24.97 -2015-03-28T02:00:00,24.94 -2015-03-28T03:00:00,27.06 -2015-03-28T04:00:00,28.08 -2015-03-28T05:00:00,30.95 -2015-03-28T06:00:00,32.31 -2015-03-28T07:00:00,32.55 -2015-03-28T08:00:00,38.39 -2015-03-28T09:00:00,30.96 -2015-03-28T10:00:00,32.47 -2015-03-28T11:00:00,28.22 -2015-03-28T12:00:00,25.86 -2015-03-28T13:00:00,23.29 -2015-03-28T14:00:00,23.11 -2015-03-28T15:00:00,23.57 -2015-03-28T16:00:00,23.7 -2015-03-28T17:00:00,26.87 -2015-03-28T18:00:00,32.96 -2015-03-28T19:00:00,34.96 -2015-03-28T20:00:00,25.19 -2015-03-28T21:00:00,22.18 -2015-03-28T22:00:00,19.51 -2015-03-28T23:00:00,11.71 -2015-03-29T00:00:00,9.9 -2015-03-29T01:00:00,8.93 -2015-03-29T02:00:00,8.93 -2015-03-29T03:00:00,5.08 -2015-03-29T04:00:00,6.98 -2015-03-29T05:00:00,5.99 -2015-03-29T06:00:00,-0.08 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-2015-05-05T07:00:00,31.01 -2015-05-05T08:00:00,34.66 -2015-05-05T09:00:00,34.49 -2015-05-05T10:00:00,33.27 -2015-05-05T11:00:00,33.62 -2015-05-05T12:00:00,30 -2015-05-05T13:00:00,26.99 -2015-05-05T14:00:00,25.21 -2015-05-05T15:00:00,24.87 -2015-05-05T16:00:00,24.85 -2015-05-05T17:00:00,24.99 -2015-05-05T18:00:00,29.25 -2015-05-05T19:00:00,33.36 -2015-05-05T20:00:00,33.3 -2015-05-05T21:00:00,35.22 -2015-05-05T22:00:00,30.77 -2015-05-05T23:00:00,23.23 -2015-05-06T00:00:00,22.93 -2015-05-06T01:00:00,22.1 -2015-05-06T02:00:00,17.97 -2015-05-06T03:00:00,15.9 -2015-05-06T04:00:00,17.1 -2015-05-06T05:00:00,22.84 -2015-05-06T06:00:00,33.97 -2015-05-06T07:00:00,39.98 -2015-05-06T08:00:00,42 -2015-05-06T09:00:00,35.52 -2015-05-06T10:00:00,32.9 -2015-05-06T11:00:00,29.01 -2015-05-06T12:00:00,24 -2015-05-06T13:00:00,20.09 -2015-05-06T14:00:00,17.91 -2015-05-06T15:00:00,14.92 -2015-05-06T16:00:00,17.59 -2015-05-06T17:00:00,23.09 -2015-05-06T18:00:00,28.21 -2015-05-06T19:00:00,33.99 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-2015-05-08T09:00:00,34.63 -2015-05-08T10:00:00,32.32 -2015-05-08T11:00:00,33.12 -2015-05-08T12:00:00,32.23 -2015-05-08T13:00:00,27.45 -2015-05-08T14:00:00,25.08 -2015-05-08T15:00:00,25.43 -2015-05-08T16:00:00,25.86 -2015-05-08T17:00:00,30.07 -2015-05-08T18:00:00,33.93 -2015-05-08T19:00:00,37.09 -2015-05-08T20:00:00,35.82 -2015-05-08T21:00:00,33.16 -2015-05-08T22:00:00,31.2 -2015-05-08T23:00:00,26.47 -2015-05-09T00:00:00,21.99 -2015-05-09T01:00:00,20.04 -2015-05-09T02:00:00,18.41 -2015-05-09T03:00:00,15.72 -2015-05-09T04:00:00,15.75 -2015-05-09T05:00:00,15.66 -2015-05-09T06:00:00,16.86 -2015-05-09T07:00:00,18.42 -2015-05-09T08:00:00,22.06 -2015-05-09T09:00:00,21.11 -2015-05-09T10:00:00,19.01 -2015-05-09T11:00:00,17.86 -2015-05-09T12:00:00,17.2 -2015-05-09T13:00:00,13.92 -2015-05-09T14:00:00,9.85 -2015-05-09T15:00:00,7.99 -2015-05-09T16:00:00,9.89 -2015-05-09T17:00:00,14.23 -2015-05-09T18:00:00,17.7 -2015-05-09T19:00:00,19.79 -2015-05-09T20:00:00,20.28 -2015-05-09T21:00:00,20.95 -2015-05-09T22:00:00,20.99 -2015-05-09T23:00:00,19.03 -2015-05-10T00:00:00,8.1 -2015-05-10T01:00:00,-5.81 -2015-05-10T02:00:00,-5.4 -2015-05-10T03:00:00,-11.11 -2015-05-10T04:00:00,-14.16 -2015-05-10T05:00:00,-11.41 -2015-05-10T06:00:00,-5.13 -2015-05-10T07:00:00,-4.85 -2015-05-10T08:00:00,-0.08 -2015-05-10T09:00:00,-9.11 -2015-05-10T10:00:00,-10.31 -2015-05-10T11:00:00,-5.39 -2015-05-10T12:00:00,-7.45 -2015-05-10T13:00:00,-6.55 -2015-05-10T14:00:00,-14.93 -2015-05-10T15:00:00,-4.23 -2015-05-10T16:00:00,1.19 -2015-05-10T17:00:00,11.76 -2015-05-10T18:00:00,19.88 -2015-05-10T19:00:00,25.26 -2015-05-10T20:00:00,27.08 -2015-05-10T21:00:00,26.56 -2015-05-10T22:00:00,28.01 -2015-05-10T23:00:00,25.45 -2015-05-11T00:00:00,22.2 -2015-05-11T01:00:00,18.35 -2015-05-11T02:00:00,17.06 -2015-05-11T03:00:00,15.02 -2015-05-11T04:00:00,15.96 -2015-05-11T05:00:00,18.62 -2015-05-11T06:00:00,28.96 -2015-05-11T07:00:00,38.17 -2015-05-11T08:00:00,39.16 -2015-05-11T09:00:00,37.72 -2015-05-11T10:00:00,33 -2015-05-11T11:00:00,31.92 -2015-05-11T12:00:00,29.3 -2015-05-11T13:00:00,27.91 -2015-05-11T14:00:00,25.41 -2015-05-11T15:00:00,27.03 -2015-05-11T16:00:00,27.74 -2015-05-11T17:00:00,32.6 -2015-05-11T18:00:00,37.96 -2015-05-11T19:00:00,42.11 -2015-05-11T20:00:00,39.72 -2015-05-11T21:00:00,38.01 -2015-05-11T22:00:00,34.92 -2015-05-11T23:00:00,22.95 -2015-05-12T00:00:00,23.59 -2015-05-12T01:00:00,23 -2015-05-12T02:00:00,18.87 -2015-05-12T03:00:00,17.94 -2015-05-12T04:00:00,18.98 -2015-05-12T05:00:00,22.93 -2015-05-12T06:00:00,30.14 -2015-05-12T07:00:00,37.98 -2015-05-12T08:00:00,45.44 -2015-05-12T09:00:00,38.84 -2015-05-12T10:00:00,38.96 -2015-05-12T11:00:00,37.92 -2015-05-12T12:00:00,31.31 -2015-05-12T13:00:00,27.39 -2015-05-12T14:00:00,24.6 -2015-05-12T15:00:00,24.54 -2015-05-12T16:00:00,24.88 -2015-05-12T17:00:00,25.56 -2015-05-12T18:00:00,27.64 -2015-05-12T19:00:00,33.93 -2015-05-12T20:00:00,34 -2015-05-12T21:00:00,34.35 -2015-05-12T22:00:00,34.58 -2015-05-12T23:00:00,27.37 -2015-05-13T00:00:00,21.03 -2015-05-13T01:00:00,18.09 -2015-05-13T02:00:00,18.01 -2015-05-13T03:00:00,16.17 -2015-05-13T04:00:00,16.52 -2015-05-13T05:00:00,19.99 -2015-05-13T06:00:00,27.93 -2015-05-13T07:00:00,32.96 -2015-05-13T08:00:00,36.55 -2015-05-13T09:00:00,30.61 -2015-05-13T10:00:00,24.55 -2015-05-13T11:00:00,23.07 -2015-05-13T12:00:00,22.06 -2015-05-13T13:00:00,22.04 -2015-05-13T14:00:00,21.94 -2015-05-13T15:00:00,22.93 -2015-05-13T16:00:00,23.76 -2015-05-13T17:00:00,27.75 -2015-05-13T18:00:00,32.91 -2015-05-13T19:00:00,36.97 -2015-05-13T20:00:00,37.21 -2015-05-13T21:00:00,36.71 -2015-05-13T22:00:00,35.82 -2015-05-13T23:00:00,27.99 -2015-05-14T00:00:00,25.9 -2015-05-14T01:00:00,21.02 -2015-05-14T02:00:00,18.89 -2015-05-14T03:00:00,18.96 -2015-05-14T04:00:00,17.87 -2015-05-14T05:00:00,17.64 -2015-05-14T06:00:00,16.26 -2015-05-14T07:00:00,18.05 -2015-05-14T08:00:00,18.56 -2015-05-14T09:00:00,18.5 -2015-05-14T10:00:00,18.83 -2015-05-14T11:00:00,19.1 -2015-05-14T12:00:00,18.33 -2015-05-14T13:00:00,16.36 -2015-05-14T14:00:00,17.01 -2015-05-14T15:00:00,16.75 -2015-05-14T16:00:00,17.64 -2015-05-14T17:00:00,17.96 -2015-05-14T18:00:00,23.05 -2015-05-14T19:00:00,25.48 -2015-05-14T20:00:00,25.66 -2015-05-14T21:00:00,25.84 -2015-05-14T22:00:00,27.39 -2015-05-14T23:00:00,24 -2015-05-15T00:00:00,24.99 -2015-05-15T01:00:00,23.42 -2015-05-15T02:00:00,21.04 -2015-05-15T03:00:00,19.63 -2015-05-15T04:00:00,20.63 -2015-05-15T05:00:00,24.28 -2015-05-15T06:00:00,29.91 -2015-05-15T07:00:00,37.95 -2015-05-15T08:00:00,36.98 -2015-05-15T09:00:00,34.98 -2015-05-15T10:00:00,32.36 -2015-05-15T11:00:00,35.04 -2015-05-15T12:00:00,33.62 -2015-05-15T13:00:00,29.27 -2015-05-15T14:00:00,25.63 -2015-05-15T15:00:00,25.08 -2015-05-15T16:00:00,24.85 -2015-05-15T17:00:00,28.08 -2015-05-15T18:00:00,36.55 -2015-05-15T19:00:00,39.92 -2015-05-15T20:00:00,36.69 -2015-05-15T21:00:00,36.62 -2015-05-15T22:00:00,39.48 -2015-05-15T23:00:00,36.85 -2015-05-16T00:00:00,26.03 -2015-05-16T01:00:00,22.66 -2015-05-16T02:00:00,21.25 -2015-05-16T03:00:00,21.14 -2015-05-16T04:00:00,21.03 -2015-05-16T05:00:00,21.07 -2015-05-16T06:00:00,21.08 -2015-05-16T07:00:00,21.3 -2015-05-16T08:00:00,21.55 -2015-05-16T09:00:00,21.67 -2015-05-16T10:00:00,20.02 -2015-05-16T11:00:00,18.2 -2015-05-16T12:00:00,17.92 -2015-05-16T13:00:00,16.37 -2015-05-16T14:00:00,13.93 -2015-05-16T15:00:00,14.82 -2015-05-16T16:00:00,14.3 -2015-05-16T17:00:00,17.59 -2015-05-16T18:00:00,20.05 -2015-05-16T19:00:00,24.5 -2015-05-16T20:00:00,26 -2015-05-16T21:00:00,26.37 -2015-05-16T22:00:00,25.94 -2015-05-16T23:00:00,17.01 -2015-05-17T00:00:00,12.35 -2015-05-17T01:00:00,10.06 -2015-05-17T02:00:00,10.13 -2015-05-17T03:00:00,10.07 -2015-05-17T04:00:00,6.18 -2015-05-17T05:00:00,3.98 -2015-05-17T06:00:00,3.53 -2015-05-17T07:00:00,8.57 -2015-05-17T08:00:00,11.97 -2015-05-17T09:00:00,13.47 -2015-05-17T10:00:00,14.44 -2015-05-17T11:00:00,13.41 -2015-05-17T12:00:00,10.1 -2015-05-17T13:00:00,5.19 -2015-05-17T14:00:00,-2.02 -2015-05-17T15:00:00,-2.92 -2015-05-17T16:00:00,3.17 -2015-05-17T17:00:00,10.11 -2015-05-17T18:00:00,16.88 -2015-05-17T19:00:00,21.24 -2015-05-17T20:00:00,25.95 -2015-05-17T21:00:00,30.94 -2015-05-17T22:00:00,30.98 -2015-05-17T23:00:00,26.61 -2015-05-18T00:00:00,22.24 -2015-05-18T01:00:00,20.45 -2015-05-18T02:00:00,18.15 -2015-05-18T03:00:00,17.9 -2015-05-18T04:00:00,17.8 -2015-05-18T05:00:00,21.51 -2015-05-18T06:00:00,37.46 -2015-05-18T07:00:00,44.45 -2015-05-18T08:00:00,43.99 -2015-05-18T09:00:00,39.94 -2015-05-18T10:00:00,34.59 -2015-05-18T11:00:00,35.71 -2015-05-18T12:00:00,31.91 -2015-05-18T13:00:00,29.9 -2015-05-18T14:00:00,27.15 -2015-05-18T15:00:00,27.35 -2015-05-18T16:00:00,27.29 -2015-05-18T17:00:00,28.42 -2015-05-18T18:00:00,31.99 -2015-05-18T19:00:00,36.92 -2015-05-18T20:00:00,32.75 -2015-05-18T21:00:00,31.96 -2015-05-18T22:00:00,34.14 -2015-05-18T23:00:00,27.98 -2015-05-19T00:00:00,20.81 -2015-05-19T01:00:00,20.04 -2015-05-19T02:00:00,18 -2015-05-19T03:00:00,15.97 -2015-05-19T04:00:00,19.16 -2015-05-19T05:00:00,23.35 -2015-05-19T06:00:00,29.92 -2015-05-19T07:00:00,39.1 -2015-05-19T08:00:00,39.21 -2015-05-19T09:00:00,36.73 -2015-05-19T10:00:00,31.44 -2015-05-19T11:00:00,31.88 -2015-05-19T12:00:00,27.4 -2015-05-19T13:00:00,26.25 -2015-05-19T14:00:00,25.49 -2015-05-19T15:00:00,24.99 -2015-05-19T16:00:00,25.04 -2015-05-19T17:00:00,26.42 -2015-05-19T18:00:00,30.94 -2015-05-19T19:00:00,35.92 -2015-05-19T20:00:00,37.7 -2015-05-19T21:00:00,37.11 -2015-05-19T22:00:00,36.99 -2015-05-19T23:00:00,30.08 -2015-05-20T00:00:00,28.41 -2015-05-20T01:00:00,24.94 -2015-05-20T02:00:00,22.38 -2015-05-20T03:00:00,21.08 -2015-05-20T04:00:00,21.26 -2015-05-20T05:00:00,24.79 -2015-05-20T06:00:00,31.67 -2015-05-20T07:00:00,47.17 -2015-05-20T08:00:00,56.11 -2015-05-20T09:00:00,47.11 -2015-05-20T10:00:00,49.1 -2015-05-20T11:00:00,47 -2015-05-20T12:00:00,42.77 -2015-05-20T13:00:00,38.55 -2015-05-20T14:00:00,33.53 -2015-05-20T15:00:00,31.45 -2015-05-20T16:00:00,29.78 -2015-05-20T17:00:00,32.01 -2015-05-20T18:00:00,34.77 -2015-05-20T19:00:00,37.47 -2015-05-20T20:00:00,36.53 -2015-05-20T21:00:00,33.93 -2015-05-20T22:00:00,35.17 -2015-05-20T23:00:00,27.4 -2015-05-21T00:00:00,27.2 -2015-05-21T01:00:00,26.02 -2015-05-21T02:00:00,25.89 -2015-05-21T03:00:00,23.77 -2015-05-21T04:00:00,24.7 -2015-05-21T05:00:00,27.1 -2015-05-21T06:00:00,34.03 -2015-05-21T07:00:00,43 -2015-05-21T08:00:00,49.97 -2015-05-21T09:00:00,42.82 -2015-05-21T10:00:00,40.01 -2015-05-21T11:00:00,40.28 -2015-05-21T12:00:00,36.09 -2015-05-21T13:00:00,33.44 -2015-05-21T14:00:00,31.61 -2015-05-21T15:00:00,29.93 -2015-05-21T16:00:00,29.62 -2015-05-21T17:00:00,30.97 -2015-05-21T18:00:00,34.98 -2015-05-21T19:00:00,39.96 -2015-05-21T20:00:00,39.64 -2015-05-21T21:00:00,37 -2015-05-21T22:00:00,37.76 -2015-05-21T23:00:00,30.02 -2015-05-22T00:00:00,29.95 -2015-05-22T01:00:00,26.33 -2015-05-22T02:00:00,24.33 -2015-05-22T03:00:00,23.74 -2015-05-22T04:00:00,23.79 -2015-05-22T05:00:00,25.28 -2015-05-22T06:00:00,33.99 -2015-05-22T07:00:00,40.72 -2015-05-22T08:00:00,40.52 -2015-05-22T09:00:00,35.25 -2015-05-22T10:00:00,31.41 -2015-05-22T11:00:00,31 -2015-05-22T12:00:00,28.53 -2015-05-22T13:00:00,28.13 -2015-05-22T14:00:00,26.97 -2015-05-22T15:00:00,27.93 -2015-05-22T16:00:00,28.42 -2015-05-22T17:00:00,30.21 -2015-05-22T18:00:00,33.97 -2015-05-22T19:00:00,36.17 -2015-05-22T20:00:00,35.92 -2015-05-22T21:00:00,35.68 -2015-05-22T22:00:00,36.12 -2015-05-22T23:00:00,34.2 -2015-05-23T00:00:00,30.11 -2015-05-23T01:00:00,25.54 -2015-05-23T02:00:00,24.23 -2015-05-23T03:00:00,20.96 -2015-05-23T04:00:00,20.53 -2015-05-23T05:00:00,20.22 -2015-05-23T06:00:00,20.21 -2015-05-23T07:00:00,22.61 -2015-05-23T08:00:00,24.92 -2015-05-23T09:00:00,26.08 -2015-05-23T10:00:00,25.6 -2015-05-23T11:00:00,25.53 -2015-05-23T12:00:00,24.3 -2015-05-23T13:00:00,22.18 -2015-05-23T14:00:00,20.42 -2015-05-23T15:00:00,19.48 -2015-05-23T16:00:00,19.42 -2015-05-23T17:00:00,21.9 -2015-05-23T18:00:00,25.76 -2015-05-23T19:00:00,29.57 -2015-05-23T20:00:00,29.35 -2015-05-23T21:00:00,29.58 -2015-05-23T22:00:00,30 -2015-05-23T23:00:00,27.67 -2015-05-24T00:00:00,25.24 -2015-05-24T01:00:00,19.84 -2015-05-24T02:00:00,19.06 -2015-05-24T03:00:00,17.88 -2015-05-24T04:00:00,17.55 -2015-05-24T05:00:00,15.96 -2015-05-24T06:00:00,14.94 -2015-05-24T07:00:00,16.15 -2015-05-24T08:00:00,17.48 -2015-05-24T09:00:00,19.3 -2015-05-24T10:00:00,19.86 -2015-05-24T11:00:00,20.08 -2015-05-24T12:00:00,19.42 -2015-05-24T13:00:00,18.1 -2015-05-24T14:00:00,15.76 -2015-05-24T15:00:00,14.78 -2015-05-24T16:00:00,14.97 -2015-05-24T17:00:00,16.01 -2015-05-24T18:00:00,20.12 -2015-05-24T19:00:00,23.71 -2015-05-24T20:00:00,24.5 -2015-05-24T21:00:00,27.8 -2015-05-24T22:00:00,29.99 -2015-05-24T23:00:00,24.7 -2015-05-25T00:00:00,21.1 -2015-05-25T01:00:00,19.31 -2015-05-25T02:00:00,17.92 -2015-05-25T03:00:00,16.1 -2015-05-25T04:00:00,15.91 -2015-05-25T05:00:00,18.58 -2015-05-25T06:00:00,20.98 -2015-05-25T07:00:00,22.09 -2015-05-25T08:00:00,22.94 -2015-05-25T09:00:00,24.09 -2015-05-25T10:00:00,23.99 -2015-05-25T11:00:00,25.01 -2015-05-25T12:00:00,25 -2015-05-25T13:00:00,21.74 -2015-05-25T14:00:00,21.09 -2015-05-25T15:00:00,19.9 -2015-05-25T16:00:00,20.44 -2015-05-25T17:00:00,21.99 -2015-05-25T18:00:00,26.58 -2015-05-25T19:00:00,30.81 -2015-05-25T20:00:00,32.89 -2015-05-25T21:00:00,32.94 -2015-05-25T22:00:00,33.64 -2015-05-25T23:00:00,29.46 -2015-05-26T00:00:00,29.43 -2015-05-26T01:00:00,26.69 -2015-05-26T02:00:00,23.93 -2015-05-26T03:00:00,22.73 -2015-05-26T04:00:00,22.47 -2015-05-26T05:00:00,23.4 -2015-05-26T06:00:00,37.23 -2015-05-26T07:00:00,47.97 -2015-05-26T08:00:00,50.73 -2015-05-26T09:00:00,44.81 -2015-05-26T10:00:00,42.01 -2015-05-26T11:00:00,39.98 -2015-05-26T12:00:00,38.18 -2015-05-26T13:00:00,33.53 -2015-05-26T14:00:00,30.66 -2015-05-26T15:00:00,28.9 -2015-05-26T16:00:00,28.47 -2015-05-26T17:00:00,28.42 -2015-05-26T18:00:00,32.63 -2015-05-26T19:00:00,35.32 -2015-05-26T20:00:00,34.41 -2015-05-26T21:00:00,34.47 -2015-05-26T22:00:00,33.16 -2015-05-26T23:00:00,27.23 -2015-05-27T00:00:00,25.25 -2015-05-27T01:00:00,23.7 -2015-05-27T02:00:00,22.61 -2015-05-27T03:00:00,20.79 -2015-05-27T04:00:00,20.66 -2015-05-27T05:00:00,23.63 -2015-05-27T06:00:00,27.97 -2015-05-27T07:00:00,33.09 -2015-05-27T08:00:00,34.94 -2015-05-27T09:00:00,34 -2015-05-27T10:00:00,34.87 -2015-05-27T11:00:00,34.75 -2015-05-27T12:00:00,35.9 -2015-05-27T13:00:00,35 -2015-05-27T14:00:00,33.82 -2015-05-27T15:00:00,32.13 -2015-05-27T16:00:00,31.97 -2015-05-27T17:00:00,36.84 -2015-05-27T18:00:00,38.02 -2015-05-27T19:00:00,39.98 -2015-05-27T20:00:00,38.17 -2015-05-27T21:00:00,37.96 -2015-05-27T22:00:00,38.19 -2015-05-27T23:00:00,32.89 -2015-05-28T00:00:00,27.04 -2015-05-28T01:00:00,27.66 -2015-05-28T02:00:00,25.09 -2015-05-28T03:00:00,23.77 -2015-05-28T04:00:00,23.95 -2015-05-28T05:00:00,25.08 -2015-05-28T06:00:00,28.91 -2015-05-28T07:00:00,38 -2015-05-28T08:00:00,37.88 -2015-05-28T09:00:00,32.91 -2015-05-28T10:00:00,31.05 -2015-05-28T11:00:00,31.04 -2015-05-28T12:00:00,27.95 -2015-05-28T13:00:00,25.16 -2015-05-28T14:00:00,25.07 -2015-05-28T15:00:00,26.18 -2015-05-28T16:00:00,26.29 -2015-05-28T17:00:00,30.58 -2015-05-28T18:00:00,34.08 -2015-05-28T19:00:00,37.99 -2015-05-28T20:00:00,37.82 -2015-05-28T21:00:00,37.95 -2015-05-28T22:00:00,37.34 -2015-05-28T23:00:00,33.03 -2015-05-29T00:00:00,27.81 -2015-05-29T01:00:00,26.53 -2015-05-29T02:00:00,25.57 -2015-05-29T03:00:00,25.01 -2015-05-29T04:00:00,26.01 -2015-05-29T05:00:00,26.6 -2015-05-29T06:00:00,34.27 -2015-05-29T07:00:00,39.29 -2015-05-29T08:00:00,40.56 -2015-05-29T09:00:00,36.02 -2015-05-29T10:00:00,34.46 -2015-05-29T11:00:00,35.64 -2015-05-29T12:00:00,31.08 -2015-05-29T13:00:00,29.64 -2015-05-29T14:00:00,28.69 -2015-05-29T15:00:00,28.24 -2015-05-29T16:00:00,28.44 -2015-05-29T17:00:00,30.86 -2015-05-29T18:00:00,32.48 -2015-05-29T19:00:00,34.84 -2015-05-29T20:00:00,34.97 -2015-05-29T21:00:00,32.52 -2015-05-29T22:00:00,31.37 -2015-05-29T23:00:00,26.08 -2015-05-30T00:00:00,22.21 -2015-05-30T01:00:00,18.92 -2015-05-30T02:00:00,17.53 -2015-05-30T03:00:00,18.01 -2015-05-30T04:00:00,17.29 -2015-05-30T05:00:00,17.1 -2015-05-30T06:00:00,17.71 -2015-05-30T07:00:00,17.06 -2015-05-30T08:00:00,24.04 -2015-05-30T09:00:00,24.08 -2015-05-30T10:00:00,18.6 -2015-05-30T11:00:00,18.67 -2015-05-30T12:00:00,15.34 -2015-05-30T13:00:00,15.01 -2015-05-30T14:00:00,15.16 -2015-05-30T15:00:00,14.98 -2015-05-30T16:00:00,14.51 -2015-05-30T17:00:00,18.05 -2015-05-30T18:00:00,27.03 -2015-05-30T19:00:00,30.96 -2015-05-30T20:00:00,34.22 -2015-05-30T21:00:00,34.4 -2015-05-30T22:00:00,34.94 -2015-05-30T23:00:00,31.3 -2015-05-31T00:00:00,22.09 -2015-05-31T01:00:00,19.26 -2015-05-31T02:00:00,17.23 -2015-05-31T03:00:00,15.94 -2015-05-31T04:00:00,15.11 -2015-05-31T05:00:00,14.23 -2015-05-31T06:00:00,12.07 -2015-05-31T07:00:00,11.12 -2015-05-31T08:00:00,13.89 -2015-05-31T09:00:00,15.92 -2015-05-31T10:00:00,15.55 -2015-05-31T11:00:00,15.88 -2015-05-31T12:00:00,14.3 -2015-05-31T13:00:00,13.24 -2015-05-31T14:00:00,11.15 -2015-05-31T15:00:00,10.14 -2015-05-31T16:00:00,10.69 -2015-05-31T17:00:00,13.58 -2015-05-31T18:00:00,16.22 -2015-05-31T19:00:00,18 -2015-05-31T20:00:00,17.4 -2015-05-31T21:00:00,18.14 -2015-05-31T22:00:00,20.18 -2015-05-31T23:00:00,15.96 -2015-06-01T00:00:00,13.42 -2015-06-01T01:00:00,14.53 -2015-06-01T02:00:00,11.16 -2015-06-01T03:00:00,14.51 -2015-06-01T04:00:00,14 -2015-06-01T05:00:00,15.91 -2015-06-01T06:00:00,25.95 -2015-06-01T07:00:00,34.52 -2015-06-01T08:00:00,37.98 -2015-06-01T09:00:00,37.86 -2015-06-01T10:00:00,39.01 -2015-06-01T11:00:00,38.92 -2015-06-01T12:00:00,38.79 -2015-06-01T13:00:00,39.72 -2015-06-01T14:00:00,38.2 -2015-06-01T15:00:00,35.75 -2015-06-01T16:00:00,35.73 -2015-06-01T17:00:00,39.22 -2015-06-01T18:00:00,40.59 -2015-06-01T19:00:00,43.98 -2015-06-01T20:00:00,41.65 -2015-06-01T21:00:00,37.23 -2015-06-01T22:00:00,36.98 -2015-06-01T23:00:00,28.91 -2015-06-02T00:00:00,24.24 -2015-06-02T01:00:00,22.05 -2015-06-02T02:00:00,20.37 -2015-06-02T03:00:00,16.75 -2015-06-02T04:00:00,16 -2015-06-02T05:00:00,17.73 -2015-06-02T06:00:00,24.33 -2015-06-02T07:00:00,30.75 -2015-06-02T08:00:00,31.72 -2015-06-02T09:00:00,27.67 -2015-06-02T10:00:00,24.23 -2015-06-02T11:00:00,23.02 -2015-06-02T12:00:00,18.72 -2015-06-02T13:00:00,18.05 -2015-06-02T14:00:00,17.46 -2015-06-02T15:00:00,17.32 -2015-06-02T16:00:00,17.4 -2015-06-02T17:00:00,17.55 -2015-06-02T18:00:00,24.03 -2015-06-02T19:00:00,27.97 -2015-06-02T20:00:00,27.1 -2015-06-02T21:00:00,25.2 -2015-06-02T22:00:00,23.62 -2015-06-02T23:00:00,15.44 -2015-06-03T00:00:00,14.92 -2015-06-03T01:00:00,11.17 -2015-06-03T02:00:00,8.7 -2015-06-03T03:00:00,4.89 -2015-06-03T04:00:00,8.25 -2015-06-03T05:00:00,16.15 -2015-06-03T06:00:00,26.34 -2015-06-03T07:00:00,39.46 -2015-06-03T08:00:00,39.36 -2015-06-03T09:00:00,33.94 -2015-06-03T10:00:00,29.92 -2015-06-03T11:00:00,29.9 -2015-06-03T12:00:00,26.44 -2015-06-03T13:00:00,25.06 -2015-06-03T14:00:00,24.88 -2015-06-03T15:00:00,25.06 -2015-06-03T16:00:00,25.99 -2015-06-03T17:00:00,33.12 -2015-06-03T18:00:00,38.1 -2015-06-03T19:00:00,42.08 -2015-06-03T20:00:00,43.48 -2015-06-03T21:00:00,41.57 -2015-06-03T22:00:00,40.79 -2015-06-03T23:00:00,36.43 -2015-06-04T00:00:00,37.45 -2015-06-04T01:00:00,30.78 -2015-06-04T02:00:00,30.04 -2015-06-04T03:00:00,29.33 -2015-06-04T04:00:00,28.64 -2015-06-04T05:00:00,26.89 -2015-06-04T06:00:00,31.47 -2015-06-04T07:00:00,34.06 -2015-06-04T08:00:00,31.63 -2015-06-04T09:00:00,28.08 -2015-06-04T10:00:00,24.99 -2015-06-04T11:00:00,23.02 -2015-06-04T12:00:00,21.12 -2015-06-04T13:00:00,19.72 -2015-06-04T14:00:00,18.73 -2015-06-04T15:00:00,19.03 -2015-06-04T16:00:00,20.01 -2015-06-04T17:00:00,27.94 -2015-06-04T18:00:00,33.79 -2015-06-04T19:00:00,36.92 -2015-06-04T20:00:00,37.79 -2015-06-04T21:00:00,36.34 -2015-06-04T22:00:00,36.63 -2015-06-04T23:00:00,29.9 -2015-06-05T00:00:00,25.37 -2015-06-05T01:00:00,24.01 -2015-06-05T02:00:00,22.89 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-2015-06-08T05:00:00,18.1 -2015-06-08T06:00:00,31.54 -2015-06-08T07:00:00,40.39 -2015-06-08T08:00:00,44.91 -2015-06-08T09:00:00,39.6 -2015-06-08T10:00:00,37.49 -2015-06-08T11:00:00,38.19 -2015-06-08T12:00:00,32.61 -2015-06-08T13:00:00,31.98 -2015-06-08T14:00:00,30.62 -2015-06-08T15:00:00,30.28 -2015-06-08T16:00:00,29.98 -2015-06-08T17:00:00,32.92 -2015-06-08T18:00:00,34.73 -2015-06-08T19:00:00,36.97 -2015-06-08T20:00:00,34.97 -2015-06-08T21:00:00,33.99 -2015-06-08T22:00:00,32.91 -2015-06-08T23:00:00,25.87 -2015-06-09T00:00:00,25.01 -2015-06-09T01:00:00,22.68 -2015-06-09T02:00:00,19.05 -2015-06-09T03:00:00,17.94 -2015-06-09T04:00:00,17.34 -2015-06-09T05:00:00,22.04 -2015-06-09T06:00:00,31.61 -2015-06-09T07:00:00,38.06 -2015-06-09T08:00:00,40.49 -2015-06-09T09:00:00,39.92 -2015-06-09T10:00:00,40.33 -2015-06-09T11:00:00,47.01 -2015-06-09T12:00:00,44.94 -2015-06-09T13:00:00,41.73 -2015-06-09T14:00:00,40.91 -2015-06-09T15:00:00,39.46 -2015-06-09T16:00:00,34.96 -2015-06-09T17:00:00,35.04 -2015-06-09T18:00:00,36.13 -2015-06-09T19:00:00,38.1 -2015-06-09T20:00:00,38.16 -2015-06-09T21:00:00,35.29 -2015-06-09T22:00:00,34.91 -2015-06-09T23:00:00,29.65 -2015-06-10T00:00:00,27.99 -2015-06-10T01:00:00,25.59 -2015-06-10T02:00:00,24.59 -2015-06-10T03:00:00,23.05 -2015-06-10T04:00:00,23.75 -2015-06-10T05:00:00,24.1 -2015-06-10T06:00:00,32.97 -2015-06-10T07:00:00,41.44 -2015-06-10T08:00:00,41.96 -2015-06-10T09:00:00,36.67 -2015-06-10T10:00:00,35.06 -2015-06-10T11:00:00,34.26 -2015-06-10T12:00:00,31.16 -2015-06-10T13:00:00,31.13 -2015-06-10T14:00:00,29.7 -2015-06-10T15:00:00,30.77 -2015-06-10T16:00:00,32.94 -2015-06-10T17:00:00,34.96 -2015-06-10T18:00:00,38.46 -2015-06-10T19:00:00,39.7 -2015-06-10T20:00:00,37.08 -2015-06-10T21:00:00,35.13 -2015-06-10T22:00:00,35.92 -2015-06-10T23:00:00,29.95 -2015-06-11T00:00:00,28.31 -2015-06-11T01:00:00,25.98 -2015-06-11T02:00:00,25.91 -2015-06-11T03:00:00,24.02 -2015-06-11T04:00:00,24.06 -2015-06-11T05:00:00,26.69 -2015-06-11T06:00:00,31.52 -2015-06-11T07:00:00,42.29 -2015-06-11T08:00:00,45.97 -2015-06-11T09:00:00,42.96 -2015-06-11T10:00:00,40.4 -2015-06-11T11:00:00,39.9 -2015-06-11T12:00:00,35.21 -2015-06-11T13:00:00,34.04 -2015-06-11T14:00:00,32.77 -2015-06-11T15:00:00,33.94 -2015-06-11T16:00:00,35.94 -2015-06-11T17:00:00,43.06 -2015-06-11T18:00:00,42.91 -2015-06-11T19:00:00,44.96 -2015-06-11T20:00:00,41.79 -2015-06-11T21:00:00,41.19 -2015-06-11T22:00:00,40.96 -2015-06-11T23:00:00,33.39 -2015-06-12T00:00:00,30.03 -2015-06-12T01:00:00,29.46 -2015-06-12T02:00:00,26.9 -2015-06-12T03:00:00,25.88 -2015-06-12T04:00:00,24.89 -2015-06-12T05:00:00,26.54 -2015-06-12T06:00:00,33.14 -2015-06-12T07:00:00,42.41 -2015-06-12T08:00:00,44.92 -2015-06-12T09:00:00,44.45 -2015-06-12T10:00:00,41.85 -2015-06-12T11:00:00,40.75 -2015-06-12T12:00:00,35.15 -2015-06-12T13:00:00,32.02 -2015-06-12T14:00:00,30.14 -2015-06-12T15:00:00,31.95 -2015-06-12T16:00:00,32.18 -2015-06-12T17:00:00,35.4 -2015-06-12T18:00:00,40.72 -2015-06-12T19:00:00,40.05 -2015-06-12T20:00:00,40.08 -2015-06-12T21:00:00,40.91 -2015-06-12T22:00:00,41.02 -2015-06-12T23:00:00,34 -2015-06-13T00:00:00,27.3 -2015-06-13T01:00:00,26.59 -2015-06-13T02:00:00,25.09 -2015-06-13T03:00:00,23.52 -2015-06-13T04:00:00,23.08 -2015-06-13T05:00:00,23.58 -2015-06-13T06:00:00,24.92 -2015-06-13T07:00:00,24.9 -2015-06-13T08:00:00,27.97 -2015-06-13T09:00:00,30.61 -2015-06-13T10:00:00,29.63 -2015-06-13T11:00:00,29.49 -2015-06-13T12:00:00,28.24 -2015-06-13T13:00:00,26.08 -2015-06-13T14:00:00,23.5 -2015-06-13T15:00:00,21.99 -2015-06-13T16:00:00,21.82 -2015-06-13T17:00:00,23.33 -2015-06-13T18:00:00,27.59 -2015-06-13T19:00:00,29.63 -2015-06-13T20:00:00,29.73 -2015-06-13T21:00:00,29.05 -2015-06-13T22:00:00,31.43 -2015-06-13T23:00:00,27.07 -2015-06-14T00:00:00,19.08 -2015-06-14T01:00:00,13.55 -2015-06-14T02:00:00,15.77 -2015-06-14T03:00:00,14.86 -2015-06-14T04:00:00,13.38 -2015-06-14T05:00:00,10.24 -2015-06-14T06:00:00,10.24 -2015-06-14T07:00:00,14.53 -2015-06-14T08:00:00,15.66 -2015-06-14T09:00:00,16.64 -2015-06-14T10:00:00,16.74 -2015-06-14T11:00:00,17.61 -2015-06-14T12:00:00,15.09 -2015-06-14T13:00:00,17.03 -2015-06-14T14:00:00,15.74 -2015-06-14T15:00:00,13.93 -2015-06-14T16:00:00,14.59 -2015-06-14T17:00:00,18.02 -2015-06-14T18:00:00,24.85 -2015-06-14T19:00:00,27.06 -2015-06-14T20:00:00,29.93 -2015-06-14T21:00:00,30.21 -2015-06-14T22:00:00,31.97 -2015-06-14T23:00:00,27.04 -2015-06-15T00:00:00,22.87 -2015-06-15T01:00:00,20.01 -2015-06-15T02:00:00,17.34 -2015-06-15T03:00:00,16.35 -2015-06-15T04:00:00,16 -2015-06-15T05:00:00,18.55 -2015-06-15T06:00:00,29.78 -2015-06-15T07:00:00,37.64 -2015-06-15T08:00:00,40.34 -2015-06-15T09:00:00,40.1 -2015-06-15T10:00:00,38.99 -2015-06-15T11:00:00,38.4 -2015-06-15T12:00:00,39.9 -2015-06-15T13:00:00,38.85 -2015-06-15T14:00:00,34 -2015-06-15T15:00:00,30.38 -2015-06-15T16:00:00,31.01 -2015-06-15T17:00:00,36.09 -2015-06-15T18:00:00,40.54 -2015-06-15T19:00:00,40.54 -2015-06-15T20:00:00,37.05 -2015-06-15T21:00:00,35.2 -2015-06-15T22:00:00,36.86 -2015-06-15T23:00:00,28.89 -2015-06-16T00:00:00,27.64 -2015-06-16T01:00:00,26.02 -2015-06-16T02:00:00,25.22 -2015-06-16T03:00:00,24.42 -2015-06-16T04:00:00,25.03 -2015-06-16T05:00:00,26.04 -2015-06-16T06:00:00,35.65 -2015-06-16T07:00:00,48.15 -2015-06-16T08:00:00,50.97 -2015-06-16T09:00:00,46.66 -2015-06-16T10:00:00,46.77 -2015-06-16T11:00:00,49.7 -2015-06-16T12:00:00,42.73 -2015-06-16T13:00:00,40.67 -2015-06-16T14:00:00,39.49 -2015-06-16T15:00:00,36.94 -2015-06-16T16:00:00,36.41 -2015-06-16T17:00:00,38.18 -2015-06-16T18:00:00,40.02 -2015-06-16T19:00:00,44 -2015-06-16T20:00:00,40.6 -2015-06-16T21:00:00,38.94 -2015-06-16T22:00:00,38.92 -2015-06-16T23:00:00,30.02 -2015-06-17T00:00:00,26.2 -2015-06-17T01:00:00,24.92 -2015-06-17T02:00:00,24.57 -2015-06-17T03:00:00,22.56 -2015-06-17T04:00:00,24.09 -2015-06-17T05:00:00,24.91 -2015-06-17T06:00:00,30.96 -2015-06-17T07:00:00,41.3 -2015-06-17T08:00:00,43.21 -2015-06-17T09:00:00,38.65 -2015-06-17T10:00:00,30.43 -2015-06-17T11:00:00,30.92 -2015-06-17T12:00:00,29.21 -2015-06-17T13:00:00,28.45 -2015-06-17T14:00:00,27.09 -2015-06-17T15:00:00,26.92 -2015-06-17T16:00:00,27.72 -2015-06-17T17:00:00,31.96 -2015-06-17T18:00:00,36.6 -2015-06-17T19:00:00,38.13 -2015-06-17T20:00:00,39.73 -2015-06-17T21:00:00,39.26 -2015-06-17T22:00:00,36.46 -2015-06-17T23:00:00,28.41 -2015-06-18T00:00:00,28.88 -2015-06-18T01:00:00,26.06 -2015-06-18T02:00:00,24.57 -2015-06-18T03:00:00,22.91 -2015-06-18T04:00:00,22.95 -2015-06-18T05:00:00,24.92 -2015-06-18T06:00:00,32.76 -2015-06-18T07:00:00,39.86 -2015-06-18T08:00:00,40.64 -2015-06-18T09:00:00,40.01 -2015-06-18T10:00:00,36.46 -2015-06-18T11:00:00,38.96 -2015-06-18T12:00:00,35.68 -2015-06-18T13:00:00,38.31 -2015-06-18T14:00:00,37.42 -2015-06-18T15:00:00,32.98 -2015-06-18T16:00:00,31.92 -2015-06-18T17:00:00,29.53 -2015-06-18T18:00:00,32.34 -2015-06-18T19:00:00,35.22 -2015-06-18T20:00:00,35.09 -2015-06-18T21:00:00,34.19 -2015-06-18T22:00:00,32.58 -2015-06-18T23:00:00,27.56 -2015-06-19T00:00:00,27.91 -2015-06-19T01:00:00,26.79 -2015-06-19T02:00:00,24.77 -2015-06-19T03:00:00,23.95 -2015-06-19T04:00:00,23.72 -2015-06-19T05:00:00,26.02 -2015-06-19T06:00:00,31.99 -2015-06-19T07:00:00,37.88 -2015-06-19T08:00:00,41.8 -2015-06-19T09:00:00,40.71 -2015-06-19T10:00:00,36.01 -2015-06-19T11:00:00,34.33 -2015-06-19T12:00:00,33.29 -2015-06-19T13:00:00,34.99 -2015-06-19T14:00:00,30.46 -2015-06-19T15:00:00,29.44 -2015-06-19T16:00:00,29.88 -2015-06-19T17:00:00,30.68 -2015-06-19T18:00:00,35.07 -2015-06-19T19:00:00,37.47 -2015-06-19T20:00:00,36.81 -2015-06-19T21:00:00,35.51 -2015-06-19T22:00:00,38.03 -2015-06-19T23:00:00,30.01 -2015-06-20T00:00:00,33.22 -2015-06-20T01:00:00,27.05 -2015-06-20T02:00:00,25.97 -2015-06-20T03:00:00,24.88 -2015-06-20T04:00:00,24.72 -2015-06-20T05:00:00,22.44 -2015-06-20T06:00:00,24.69 -2015-06-20T07:00:00,26.48 -2015-06-20T08:00:00,30.86 -2015-06-20T09:00:00,32.83 -2015-06-20T10:00:00,32.11 -2015-06-20T11:00:00,30.94 -2015-06-20T12:00:00,28.71 -2015-06-20T13:00:00,24.64 -2015-06-20T14:00:00,20.09 -2015-06-20T15:00:00,21.9 -2015-06-20T16:00:00,22.02 -2015-06-20T17:00:00,24.73 -2015-06-20T18:00:00,30.23 -2015-06-20T19:00:00,33.74 -2015-06-20T20:00:00,33.67 -2015-06-20T21:00:00,34.05 -2015-06-20T22:00:00,36.17 -2015-06-20T23:00:00,27.73 -2015-06-21T00:00:00,25.97 -2015-06-21T01:00:00,22.65 -2015-06-21T02:00:00,20.02 -2015-06-21T03:00:00,18.15 -2015-06-21T04:00:00,18.96 -2015-06-21T05:00:00,15.84 -2015-06-21T06:00:00,15.84 -2015-06-21T07:00:00,18.96 -2015-06-21T08:00:00,20.48 -2015-06-21T09:00:00,22.63 -2015-06-21T10:00:00,23.77 -2015-06-21T11:00:00,26.21 -2015-06-21T12:00:00,23.71 -2015-06-21T13:00:00,15.52 -2015-06-21T14:00:00,15.53 -2015-06-21T15:00:00,16.94 -2015-06-21T16:00:00,15.33 -2015-06-21T17:00:00,22.47 -2015-06-21T18:00:00,28.99 -2015-06-21T19:00:00,31.95 -2015-06-21T20:00:00,37.88 -2015-06-21T21:00:00,36.47 -2015-06-21T22:00:00,36.99 -2015-06-21T23:00:00,31.95 -2015-06-22T00:00:00,24.87 -2015-06-22T01:00:00,24.33 -2015-06-22T02:00:00,22.3 -2015-06-22T03:00:00,19.08 -2015-06-22T04:00:00,18.88 -2015-06-22T05:00:00,21 -2015-06-22T06:00:00,30.35 -2015-06-22T07:00:00,41.09 -2015-06-22T08:00:00,43.47 -2015-06-22T09:00:00,43.53 -2015-06-22T10:00:00,43.72 -2015-06-22T11:00:00,43.52 -2015-06-22T12:00:00,38.24 -2015-06-22T13:00:00,39.7 -2015-06-22T14:00:00,35.31 -2015-06-22T15:00:00,34.66 -2015-06-22T16:00:00,33.04 -2015-06-22T17:00:00,35.09 -2015-06-22T18:00:00,35.59 -2015-06-22T19:00:00,37.69 -2015-06-22T20:00:00,38.99 -2015-06-22T21:00:00,35.5 -2015-06-22T22:00:00,31.04 -2015-06-22T23:00:00,27.05 -2015-06-23T00:00:00,27.07 -2015-06-23T01:00:00,26.87 -2015-06-23T02:00:00,26.36 -2015-06-23T03:00:00,24.88 -2015-06-23T04:00:00,24.87 -2015-06-23T05:00:00,26.85 -2015-06-23T06:00:00,30.46 -2015-06-23T07:00:00,38.28 -2015-06-23T08:00:00,42.28 -2015-06-23T09:00:00,40.28 -2015-06-23T10:00:00,40.42 -2015-06-23T11:00:00,40.39 -2015-06-23T12:00:00,32.84 -2015-06-23T13:00:00,31 -2015-06-23T14:00:00,30.35 -2015-06-23T15:00:00,29.34 -2015-06-23T16:00:00,27.51 -2015-06-23T17:00:00,29.88 -2015-06-23T18:00:00,33.92 -2015-06-23T19:00:00,35.9 -2015-06-23T20:00:00,35 -2015-06-23T21:00:00,33.4 -2015-06-23T22:00:00,29.95 -2015-06-23T23:00:00,26.68 -2015-06-24T00:00:00,24.85 -2015-06-24T01:00:00,23.05 -2015-06-24T02:00:00,20.36 -2015-06-24T03:00:00,17.94 -2015-06-24T04:00:00,22.08 -2015-06-24T05:00:00,25.65 -2015-06-24T06:00:00,29.12 -2015-06-24T07:00:00,36.34 -2015-06-24T08:00:00,34.93 -2015-06-24T09:00:00,33.63 -2015-06-24T10:00:00,31.04 -2015-06-24T11:00:00,30.83 -2015-06-24T12:00:00,29.46 -2015-06-24T13:00:00,29.43 -2015-06-24T14:00:00,29.45 -2015-06-24T15:00:00,30.65 -2015-06-24T16:00:00,29.5 -2015-06-24T17:00:00,32.64 -2015-06-24T18:00:00,36.42 -2015-06-24T19:00:00,37.86 -2015-06-24T20:00:00,38.04 -2015-06-24T21:00:00,39.04 -2015-06-24T22:00:00,38.99 -2015-06-24T23:00:00,32.8 -2015-06-25T00:00:00,31.15 -2015-06-25T01:00:00,29.62 -2015-06-25T02:00:00,27.8 -2015-06-25T03:00:00,26.1 -2015-06-25T04:00:00,26.86 -2015-06-25T05:00:00,29.67 -2015-06-25T06:00:00,35.04 -2015-06-25T07:00:00,45.97 -2015-06-25T08:00:00,48.96 -2015-06-25T09:00:00,43.21 -2015-06-25T10:00:00,38.89 -2015-06-25T11:00:00,40.04 -2015-06-25T12:00:00,34.96 -2015-06-25T13:00:00,35.22 -2015-06-25T14:00:00,33.7 -2015-06-25T15:00:00,37.81 -2015-06-25T16:00:00,35.59 -2015-06-25T17:00:00,39.22 -2015-06-25T18:00:00,42.17 -2015-06-25T19:00:00,42.99 -2015-06-25T20:00:00,43.03 -2015-06-25T21:00:00,39.4 -2015-06-25T22:00:00,37.06 -2015-06-25T23:00:00,30.7 -2015-06-26T00:00:00,31.91 -2015-06-26T01:00:00,31 -2015-06-26T02:00:00,30.69 -2015-06-26T03:00:00,28.91 -2015-06-26T04:00:00,28.91 -2015-06-26T05:00:00,31.72 -2015-06-26T06:00:00,37.42 -2015-06-26T07:00:00,44.51 -2015-06-26T08:00:00,45.05 -2015-06-26T09:00:00,41.33 -2015-06-26T10:00:00,37.91 -2015-06-26T11:00:00,36.97 -2015-06-26T12:00:00,35.98 -2015-06-26T13:00:00,35.44 -2015-06-26T14:00:00,33.93 -2015-06-26T15:00:00,35.01 -2015-06-26T16:00:00,36.6 -2015-06-26T17:00:00,39.56 -2015-06-26T18:00:00,41.26 -2015-06-26T19:00:00,41.03 -2015-06-26T20:00:00,37.28 -2015-06-26T21:00:00,35.7 -2015-06-26T22:00:00,37.94 -2015-06-26T23:00:00,34.38 -2015-06-27T00:00:00,30.28 -2015-06-27T01:00:00,27.2 -2015-06-27T02:00:00,26.73 -2015-06-27T03:00:00,25.54 -2015-06-27T04:00:00,25.1 -2015-06-27T05:00:00,24.07 -2015-06-27T06:00:00,25.13 -2015-06-27T07:00:00,26.08 -2015-06-27T08:00:00,33.21 -2015-06-27T09:00:00,35.48 -2015-06-27T10:00:00,34.91 -2015-06-27T11:00:00,34.13 -2015-06-27T12:00:00,32.34 -2015-06-27T13:00:00,28.14 -2015-06-27T14:00:00,26.43 -2015-06-27T15:00:00,25.05 -2015-06-27T16:00:00,25.06 -2015-06-27T17:00:00,27.01 -2015-06-27T18:00:00,29.41 -2015-06-27T19:00:00,32.5 -2015-06-27T20:00:00,32.76 -2015-06-27T21:00:00,32.65 -2015-06-27T22:00:00,34.98 -2015-06-27T23:00:00,30.9 -2015-06-28T00:00:00,21.26 -2015-06-28T01:00:00,18.59 -2015-06-28T02:00:00,16.94 -2015-06-28T03:00:00,19.08 -2015-06-28T04:00:00,16.3 -2015-06-28T05:00:00,15.27 -2015-06-28T06:00:00,15.08 -2015-06-28T07:00:00,15.06 -2015-06-28T08:00:00,15.3 -2015-06-28T09:00:00,15.56 -2015-06-28T10:00:00,15.04 -2015-06-28T11:00:00,16.07 -2015-06-28T12:00:00,15.23 -2015-06-28T13:00:00,14.94 -2015-06-28T14:00:00,14.3 -2015-06-28T15:00:00,14.68 -2015-06-28T16:00:00,15.55 -2015-06-28T17:00:00,17.42 -2015-06-28T18:00:00,28.72 -2015-06-28T19:00:00,35.02 -2015-06-28T20:00:00,36.92 -2015-06-28T21:00:00,35.53 -2015-06-28T22:00:00,38.92 -2015-06-28T23:00:00,36.86 -2015-06-29T00:00:00,33.51 -2015-06-29T01:00:00,29.23 -2015-06-29T02:00:00,26.9 -2015-06-29T03:00:00,25.94 -2015-06-29T04:00:00,25.34 -2015-06-29T05:00:00,27.08 -2015-06-29T06:00:00,34.98 -2015-06-29T07:00:00,43.93 -2015-06-29T08:00:00,47.62 -2015-06-29T09:00:00,43.93 -2015-06-29T10:00:00,41.55 -2015-06-29T11:00:00,40.02 -2015-06-29T12:00:00,34.55 -2015-06-29T13:00:00,32.62 -2015-06-29T14:00:00,31.69 -2015-06-29T15:00:00,32.17 -2015-06-29T16:00:00,34.5 -2015-06-29T17:00:00,39.46 -2015-06-29T18:00:00,39.13 -2015-06-29T19:00:00,43.96 -2015-06-29T20:00:00,43.94 -2015-06-29T21:00:00,41.8 -2015-06-29T22:00:00,41.18 -2015-06-29T23:00:00,31.96 -2015-06-30T00:00:00,29.44 -2015-06-30T01:00:00,29.36 -2015-06-30T02:00:00,29.49 -2015-06-30T03:00:00,27.52 -2015-06-30T04:00:00,27.58 -2015-06-30T05:00:00,29.91 -2015-06-30T06:00:00,36.07 -2015-06-30T07:00:00,42.55 -2015-06-30T08:00:00,42.61 -2015-06-30T09:00:00,37.93 -2015-06-30T10:00:00,36.07 -2015-06-30T11:00:00,35.72 -2015-06-30T12:00:00,32.91 -2015-06-30T13:00:00,31.88 -2015-06-30T14:00:00,31.23 -2015-06-30T15:00:00,31.97 -2015-06-30T16:00:00,33.92 -2015-06-30T17:00:00,40.02 -2015-06-30T18:00:00,43.9 -2015-06-30T19:00:00,46.79 -2015-06-30T20:00:00,44.54 -2015-06-30T21:00:00,41.34 -2015-06-30T22:00:00,36.93 -2015-06-30T23:00:00,33.62 -2015-07-01T00:00:00,30.62 -2015-07-01T01:00:00,29.98 -2015-07-01T02:00:00,28.05 -2015-07-01T03:00:00,27.7 -2015-07-01T04:00:00,28.36 -2015-07-01T05:00:00,28.67 -2015-07-01T06:00:00,35.32 -2015-07-01T07:00:00,42.02 -2015-07-01T08:00:00,41.54 -2015-07-01T09:00:00,36.9 -2015-07-01T10:00:00,34.22 -2015-07-01T11:00:00,31.99 -2015-07-01T12:00:00,30.04 -2015-07-01T13:00:00,28.77 -2015-07-01T14:00:00,29.43 -2015-07-01T15:00:00,29.99 -2015-07-01T16:00:00,30.04 -2015-07-01T17:00:00,34.94 -2015-07-01T18:00:00,37.28 -2015-07-01T19:00:00,42.58 -2015-07-01T20:00:00,42.42 -2015-07-01T21:00:00,39.68 -2015-07-01T22:00:00,36.98 -2015-07-01T23:00:00,28.39 -2015-07-02T00:00:00,28.74 -2015-07-02T01:00:00,28.08 -2015-07-02T02:00:00,27.31 -2015-07-02T03:00:00,26.07 -2015-07-02T04:00:00,27.49 -2015-07-02T05:00:00,28.19 -2015-07-02T06:00:00,35.11 -2015-07-02T07:00:00,42.45 -2015-07-02T08:00:00,43.12 -2015-07-02T09:00:00,35.91 -2015-07-02T10:00:00,32.52 -2015-07-02T11:00:00,28.72 -2015-07-02T12:00:00,28.15 -2015-07-02T13:00:00,27.54 -2015-07-02T14:00:00,26.73 -2015-07-02T15:00:00,28.49 -2015-07-02T16:00:00,33.1 -2015-07-02T17:00:00,40 -2015-07-02T18:00:00,44.28 -2015-07-02T19:00:00,48.26 -2015-07-02T20:00:00,44.2 -2015-07-02T21:00:00,40.97 -2015-07-02T22:00:00,37.95 -2015-07-02T23:00:00,30.14 -2015-07-03T00:00:00,29.72 -2015-07-03T01:00:00,30.07 -2015-07-03T02:00:00,29.18 -2015-07-03T03:00:00,30.38 -2015-07-03T04:00:00,31.84 -2015-07-03T05:00:00,35.39 -2015-07-03T06:00:00,46.39 -2015-07-03T07:00:00,53.02 -2015-07-03T08:00:00,56.62 -2015-07-03T09:00:00,55.78 -2015-07-03T10:00:00,47.56 -2015-07-03T11:00:00,41.64 -2015-07-03T12:00:00,39.03 -2015-07-03T13:00:00,33.95 -2015-07-03T14:00:00,35.34 -2015-07-03T15:00:00,39.75 -2015-07-03T16:00:00,47.43 -2015-07-03T17:00:00,60 -2015-07-03T18:00:00,58.94 -2015-07-03T19:00:00,57.34 -2015-07-03T20:00:00,55.99 -2015-07-03T21:00:00,53.79 -2015-07-03T22:00:00,57.09 -2015-07-03T23:00:00,47.99 -2015-07-04T00:00:00,37.04 -2015-07-04T01:00:00,31.76 -2015-07-04T02:00:00,31 -2015-07-04T03:00:00,32.09 -2015-07-04T04:00:00,29.74 -2015-07-04T05:00:00,28.46 -2015-07-04T06:00:00,27.58 -2015-07-04T07:00:00,30.82 -2015-07-04T08:00:00,35.03 -2015-07-04T09:00:00,38.94 -2015-07-04T10:00:00,31.35 -2015-07-04T11:00:00,28.68 -2015-07-04T12:00:00,28.15 -2015-07-04T13:00:00,28.12 -2015-07-04T14:00:00,27.67 -2015-07-04T15:00:00,28.68 -2015-07-04T16:00:00,30.91 -2015-07-04T17:00:00,39.29 -2015-07-04T18:00:00,43.99 -2015-07-04T19:00:00,44.14 -2015-07-04T20:00:00,40.05 -2015-07-04T21:00:00,38.99 -2015-07-04T22:00:00,39.89 -2015-07-04T23:00:00,34.5 -2015-07-05T00:00:00,30.44 -2015-07-05T01:00:00,29.47 -2015-07-05T02:00:00,29.43 -2015-07-05T03:00:00,27.94 -2015-07-05T04:00:00,28.97 -2015-07-05T05:00:00,26.06 -2015-07-05T06:00:00,25.25 -2015-07-05T07:00:00,25.58 -2015-07-05T08:00:00,26.08 -2015-07-05T09:00:00,26.1 -2015-07-05T10:00:00,24.77 -2015-07-05T11:00:00,24.01 -2015-07-05T12:00:00,24.36 -2015-07-05T13:00:00,23.08 -2015-07-05T14:00:00,22.72 -2015-07-05T15:00:00,24.02 -2015-07-05T16:00:00,24.74 -2015-07-05T17:00:00,28.65 -2015-07-05T18:00:00,32.56 -2015-07-05T19:00:00,33.91 -2015-07-05T20:00:00,34.95 -2015-07-05T21:00:00,35.07 -2015-07-05T22:00:00,39.98 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-2015-07-12T03:00:00,27.03 -2015-07-12T04:00:00,27.44 -2015-07-12T05:00:00,25.62 -2015-07-12T06:00:00,25.05 -2015-07-12T07:00:00,24.07 -2015-07-12T08:00:00,23.9 -2015-07-12T09:00:00,24.09 -2015-07-12T10:00:00,21.06 -2015-07-12T11:00:00,22.98 -2015-07-12T12:00:00,24.08 -2015-07-12T13:00:00,22.92 -2015-07-12T14:00:00,19.42 -2015-07-12T15:00:00,20.08 -2015-07-12T16:00:00,23.91 -2015-07-12T17:00:00,27.07 -2015-07-12T18:00:00,30.03 -2015-07-12T19:00:00,35.74 -2015-07-12T20:00:00,36.58 -2015-07-12T21:00:00,36.6 -2015-07-12T22:00:00,39.99 -2015-07-12T23:00:00,38.49 -2015-07-13T00:00:00,32.24 -2015-07-13T01:00:00,28.94 -2015-07-13T02:00:00,26.86 -2015-07-13T03:00:00,24.79 -2015-07-13T04:00:00,24.98 -2015-07-13T05:00:00,27.43 -2015-07-13T06:00:00,36.52 -2015-07-13T07:00:00,44.81 -2015-07-13T08:00:00,45.84 -2015-07-13T09:00:00,45.97 -2015-07-13T10:00:00,46.69 -2015-07-13T11:00:00,52.38 -2015-07-13T12:00:00,52.87 -2015-07-13T13:00:00,47.67 -2015-07-13T14:00:00,48.19 -2015-07-13T15:00:00,47.06 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-2015-07-18T07:00:00,34.43 -2015-07-18T08:00:00,36.93 -2015-07-18T09:00:00,41.93 -2015-07-18T10:00:00,36.92 -2015-07-18T11:00:00,36.57 -2015-07-18T12:00:00,35.05 -2015-07-18T13:00:00,31.26 -2015-07-18T14:00:00,31.72 -2015-07-18T15:00:00,32.52 -2015-07-18T16:00:00,34.33 -2015-07-18T17:00:00,38.1 -2015-07-18T18:00:00,43.37 -2015-07-18T19:00:00,46.93 -2015-07-18T20:00:00,45.92 -2015-07-18T21:00:00,44.92 -2015-07-18T22:00:00,46.94 -2015-07-18T23:00:00,43 -2015-07-19T00:00:00,40.52 -2015-07-19T01:00:00,35.87 -2015-07-19T02:00:00,31.05 -2015-07-19T03:00:00,28.88 -2015-07-19T04:00:00,27.1 -2015-07-19T05:00:00,25.45 -2015-07-19T06:00:00,25.4 -2015-07-19T07:00:00,27.07 -2015-07-19T08:00:00,29.5 -2015-07-19T09:00:00,33.1 -2015-07-19T10:00:00,34.97 -2015-07-19T11:00:00,34.97 -2015-07-19T12:00:00,30.79 -2015-07-19T13:00:00,27.58 -2015-07-19T14:00:00,25.44 -2015-07-19T15:00:00,22.74 -2015-07-19T16:00:00,21.42 -2015-07-19T17:00:00,22.24 -2015-07-19T18:00:00,26.76 -2015-07-19T19:00:00,31.9 -2015-07-19T20:00:00,32.83 -2015-07-19T21:00:00,33.45 -2015-07-19T22:00:00,36.03 -2015-07-19T23:00:00,31.04 -2015-07-20T00:00:00,25.41 -2015-07-20T01:00:00,23.26 -2015-07-20T02:00:00,24.24 -2015-07-20T03:00:00,23.12 -2015-07-20T04:00:00,22.63 -2015-07-20T05:00:00,25.44 -2015-07-20T06:00:00,37.34 -2015-07-20T07:00:00,45.12 -2015-07-20T08:00:00,47.99 -2015-07-20T09:00:00,47.11 -2015-07-20T10:00:00,45.92 -2015-07-20T11:00:00,48.19 -2015-07-20T12:00:00,48.82 -2015-07-20T13:00:00,48.73 -2015-07-20T14:00:00,47.94 -2015-07-20T15:00:00,48.05 -2015-07-20T16:00:00,49.54 -2015-07-20T17:00:00,50.94 -2015-07-20T18:00:00,49.93 -2015-07-20T19:00:00,56 -2015-07-20T20:00:00,50.03 -2015-07-20T21:00:00,48.19 -2015-07-20T22:00:00,46.54 -2015-07-20T23:00:00,41.95 -2015-07-21T00:00:00,40.17 -2015-07-21T01:00:00,35.08 -2015-07-21T02:00:00,32 -2015-07-21T03:00:00,28.8 -2015-07-21T04:00:00,28.78 -2015-07-21T05:00:00,32.15 -2015-07-21T06:00:00,41.32 -2015-07-21T07:00:00,48.77 -2015-07-21T08:00:00,55.77 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-2015-07-22T22:00:00,49.94 -2015-07-22T23:00:00,41.89 -2015-07-23T00:00:00,36.06 -2015-07-23T01:00:00,35.5 -2015-07-23T02:00:00,33.1 -2015-07-23T03:00:00,30.52 -2015-07-23T04:00:00,32.3 -2015-07-23T05:00:00,35.2 -2015-07-23T06:00:00,39.99 -2015-07-23T07:00:00,49.94 -2015-07-23T08:00:00,50.66 -2015-07-23T09:00:00,54.87 -2015-07-23T10:00:00,51.91 -2015-07-23T11:00:00,52.92 -2015-07-23T12:00:00,54.91 -2015-07-23T13:00:00,54.72 -2015-07-23T14:00:00,51.83 -2015-07-23T15:00:00,51.5 -2015-07-23T16:00:00,51.94 -2015-07-23T17:00:00,51.37 -2015-07-23T18:00:00,51.94 -2015-07-23T19:00:00,53.37 -2015-07-23T20:00:00,55.93 -2015-07-23T21:00:00,52.95 -2015-07-23T22:00:00,53.39 -2015-07-23T23:00:00,45.46 -2015-07-24T00:00:00,41.97 -2015-07-24T01:00:00,36.59 -2015-07-24T02:00:00,34.36 -2015-07-24T03:00:00,32.43 -2015-07-24T04:00:00,33.05 -2015-07-24T05:00:00,34 -2015-07-24T06:00:00,42.96 -2015-07-24T07:00:00,55.25 -2015-07-24T08:00:00,55.17 -2015-07-24T09:00:00,56.91 -2015-07-24T10:00:00,53.45 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-2015-07-27T13:00:00,35.96 -2015-07-27T14:00:00,31.56 -2015-07-27T15:00:00,29.01 -2015-07-27T16:00:00,26.48 -2015-07-27T17:00:00,29.03 -2015-07-27T18:00:00,31.34 -2015-07-27T19:00:00,34.86 -2015-07-27T20:00:00,33.05 -2015-07-27T21:00:00,30.99 -2015-07-27T22:00:00,30.55 -2015-07-27T23:00:00,25.46 -2015-07-28T00:00:00,19.13 -2015-07-28T01:00:00,16.27 -2015-07-28T02:00:00,15.47 -2015-07-28T03:00:00,10.54 -2015-07-28T04:00:00,12.6 -2015-07-28T05:00:00,16.61 -2015-07-28T06:00:00,23.09 -2015-07-28T07:00:00,30.54 -2015-07-28T08:00:00,31 -2015-07-28T09:00:00,31.34 -2015-07-28T10:00:00,27.07 -2015-07-28T11:00:00,26.42 -2015-07-28T12:00:00,23.88 -2015-07-28T13:00:00,22.87 -2015-07-28T14:00:00,19.98 -2015-07-28T15:00:00,20.08 -2015-07-28T16:00:00,23.29 -2015-07-28T17:00:00,27.51 -2015-07-28T18:00:00,32.96 -2015-07-28T19:00:00,33.82 -2015-07-28T20:00:00,31.88 -2015-07-28T21:00:00,32.18 -2015-07-28T22:00:00,34.42 -2015-07-28T23:00:00,26.42 -2015-07-29T00:00:00,21.05 -2015-07-29T01:00:00,19.02 -2015-07-29T02:00:00,16.36 -2015-07-29T03:00:00,15.5 -2015-07-29T04:00:00,16.13 -2015-07-29T05:00:00,18.68 -2015-07-29T06:00:00,27.05 -2015-07-29T07:00:00,34.79 -2015-07-29T08:00:00,37.93 -2015-07-29T09:00:00,37.09 -2015-07-29T10:00:00,34.88 -2015-07-29T11:00:00,36.92 -2015-07-29T12:00:00,35.44 -2015-07-29T13:00:00,30.96 -2015-07-29T14:00:00,29.33 -2015-07-29T15:00:00,25.64 -2015-07-29T16:00:00,25.13 -2015-07-29T17:00:00,25.42 -2015-07-29T18:00:00,27.07 -2015-07-29T19:00:00,31.38 -2015-07-29T20:00:00,31.5 -2015-07-29T21:00:00,30.97 -2015-07-29T22:00:00,27.33 -2015-07-29T23:00:00,22.07 -2015-07-30T00:00:00,16.12 -2015-07-30T01:00:00,15.38 -2015-07-30T02:00:00,8.35 -2015-07-30T03:00:00,5.84 -2015-07-30T04:00:00,11.14 -2015-07-30T05:00:00,16.67 -2015-07-30T06:00:00,20.77 -2015-07-30T07:00:00,30.74 -2015-07-30T08:00:00,33.16 -2015-07-30T09:00:00,26.9 -2015-07-30T10:00:00,23.48 -2015-07-30T11:00:00,25.08 -2015-07-30T12:00:00,22.34 -2015-07-30T13:00:00,23.46 -2015-07-30T14:00:00,23.41 -2015-07-30T15:00:00,25.14 -2015-07-30T16:00:00,26.41 -2015-07-30T17:00:00,26.88 -2015-07-30T18:00:00,32.19 -2015-07-30T19:00:00,37.91 -2015-07-30T20:00:00,37.27 -2015-07-30T21:00:00,36.94 -2015-07-30T22:00:00,34.41 -2015-07-30T23:00:00,27.91 -2015-07-31T00:00:00,25.21 -2015-07-31T01:00:00,22.07 -2015-07-31T02:00:00,18.86 -2015-07-31T03:00:00,16.18 -2015-07-31T04:00:00,18.94 -2015-07-31T05:00:00,22.99 -2015-07-31T06:00:00,32.32 -2015-07-31T07:00:00,37.92 -2015-07-31T08:00:00,38.88 -2015-07-31T09:00:00,33.99 -2015-07-31T10:00:00,29.23 -2015-07-31T11:00:00,28.29 -2015-07-31T12:00:00,26.06 -2015-07-31T13:00:00,25.58 -2015-07-31T14:00:00,25.25 -2015-07-31T15:00:00,25.69 -2015-07-31T16:00:00,29.37 -2015-07-31T17:00:00,34.96 -2015-07-31T18:00:00,39.66 -2015-07-31T19:00:00,44.82 -2015-07-31T20:00:00,45.44 -2015-07-31T21:00:00,41.2 -2015-07-31T22:00:00,37.81 -2015-07-31T23:00:00,31.84 -2015-08-01T00:00:00,25.5 -2015-08-01T01:00:00,23.19 -2015-08-01T02:00:00,21.3 -2015-08-01T03:00:00,22.03 -2015-08-01T04:00:00,21.91 -2015-08-01T05:00:00,22.04 -2015-08-01T06:00:00,22.13 -2015-08-01T07:00:00,22.22 -2015-08-01T08:00:00,24.45 -2015-08-01T09:00:00,23.49 -2015-08-01T10:00:00,17.07 -2015-08-01T11:00:00,16.18 -2015-08-01T12:00:00,15.35 -2015-08-01T13:00:00,12.76 -2015-08-01T14:00:00,12.78 -2015-08-01T15:00:00,14.22 -2015-08-01T16:00:00,16.02 -2015-08-01T17:00:00,25.01 -2015-08-01T18:00:00,30.03 -2015-08-01T19:00:00,32.86 -2015-08-01T20:00:00,30.27 -2015-08-01T21:00:00,30.27 -2015-08-01T22:00:00,31.07 -2015-08-01T23:00:00,25.37 -2015-08-02T00:00:00,17.09 -2015-08-02T01:00:00,16 -2015-08-02T02:00:00,14.93 -2015-08-02T03:00:00,14.56 -2015-08-02T04:00:00,15.04 -2015-08-02T05:00:00,14.82 -2015-08-02T06:00:00,15.32 -2015-08-02T07:00:00,15.58 -2015-08-02T08:00:00,14.93 -2015-08-02T09:00:00,14.16 -2015-08-02T10:00:00,12.71 -2015-08-02T11:00:00,13.89 -2015-08-02T12:00:00,11.5 -2015-08-02T13:00:00,10.03 -2015-08-02T14:00:00,9.2 -2015-08-02T15:00:00,9.66 -2015-08-02T16:00:00,12.06 -2015-08-02T17:00:00,15.05 -2015-08-02T18:00:00,22.36 -2015-08-02T19:00:00,27.98 -2015-08-02T20:00:00,30.01 -2015-08-02T21:00:00,33.02 -2015-08-02T22:00:00,29.95 -2015-08-02T23:00:00,25.03 -2015-08-03T00:00:00,23.03 -2015-08-03T01:00:00,17.02 -2015-08-03T02:00:00,16.02 -2015-08-03T03:00:00,15 -2015-08-03T04:00:00,14.09 -2015-08-03T05:00:00,19.08 -2015-08-03T06:00:00,25.25 -2015-08-03T07:00:00,33.82 -2015-08-03T08:00:00,38.55 -2015-08-03T09:00:00,36.98 -2015-08-03T10:00:00,34.31 -2015-08-03T11:00:00,31.4 -2015-08-03T12:00:00,26.39 -2015-08-03T13:00:00,25.24 -2015-08-03T14:00:00,24.03 -2015-08-03T15:00:00,25.43 -2015-08-03T16:00:00,26.57 -2015-08-03T17:00:00,34.38 -2015-08-03T18:00:00,37.78 -2015-08-03T19:00:00,40.97 -2015-08-03T20:00:00,40.86 -2015-08-03T21:00:00,40.83 -2015-08-03T22:00:00,39 -2015-08-03T23:00:00,28.39 -2015-08-04T00:00:00,26.69 -2015-08-04T01:00:00,26 -2015-08-04T02:00:00,24.56 -2015-08-04T03:00:00,19.08 -2015-08-04T04:00:00,21.98 -2015-08-04T05:00:00,25.39 -2015-08-04T06:00:00,28 -2015-08-04T07:00:00,37.96 -2015-08-04T08:00:00,36.99 -2015-08-04T09:00:00,36.33 -2015-08-04T10:00:00,35.94 -2015-08-04T11:00:00,35.96 -2015-08-04T12:00:00,33.98 -2015-08-04T13:00:00,30.44 -2015-08-04T14:00:00,32.44 -2015-08-04T15:00:00,37.64 -2015-08-04T16:00:00,38.98 -2015-08-04T17:00:00,39.93 -2015-08-04T18:00:00,39.94 -2015-08-04T19:00:00,39.9 -2015-08-04T20:00:00,40.27 -2015-08-04T21:00:00,38.37 -2015-08-04T22:00:00,38.62 -2015-08-04T23:00:00,31.39 -2015-08-05T00:00:00,30.4 -2015-08-05T01:00:00,28.86 -2015-08-05T02:00:00,28.05 -2015-08-05T03:00:00,25.18 -2015-08-05T04:00:00,24.07 -2015-08-05T05:00:00,27.33 -2015-08-05T06:00:00,33.79 -2015-08-05T07:00:00,40.89 -2015-08-05T08:00:00,43.75 -2015-08-05T09:00:00,41.03 -2015-08-05T10:00:00,38.74 -2015-08-05T11:00:00,36.9 -2015-08-05T12:00:00,31.42 -2015-08-05T13:00:00,29.58 -2015-08-05T14:00:00,29.55 -2015-08-05T15:00:00,29.2 -2015-08-05T16:00:00,29.8 -2015-08-05T17:00:00,37.54 -2015-08-05T18:00:00,44.03 -2015-08-05T19:00:00,46.9 -2015-08-05T20:00:00,41.92 -2015-08-05T21:00:00,43.93 -2015-08-05T22:00:00,41.18 -2015-08-05T23:00:00,31.33 -2015-08-06T00:00:00,30.24 -2015-08-06T01:00:00,27.95 -2015-08-06T02:00:00,25.14 -2015-08-06T03:00:00,23.21 -2015-08-06T04:00:00,23.02 -2015-08-06T05:00:00,25.46 -2015-08-06T06:00:00,31.32 -2015-08-06T07:00:00,39.94 -2015-08-06T08:00:00,42.72 -2015-08-06T09:00:00,38.42 -2015-08-06T10:00:00,36.91 -2015-08-06T11:00:00,34.58 -2015-08-06T12:00:00,30.49 -2015-08-06T13:00:00,29.02 -2015-08-06T14:00:00,28.74 -2015-08-06T15:00:00,28.78 -2015-08-06T16:00:00,30.93 -2015-08-06T17:00:00,38.93 -2015-08-06T18:00:00,43.03 -2015-08-06T19:00:00,48.92 -2015-08-06T20:00:00,45.18 -2015-08-06T21:00:00,44.93 -2015-08-06T22:00:00,42.87 -2015-08-06T23:00:00,35.95 -2015-08-07T00:00:00,30.35 -2015-08-07T01:00:00,28.73 -2015-08-07T02:00:00,26.56 -2015-08-07T03:00:00,25.36 -2015-08-07T04:00:00,25.28 -2015-08-07T05:00:00,27.4 -2015-08-07T06:00:00,32.28 -2015-08-07T07:00:00,38.68 -2015-08-07T08:00:00,39.82 -2015-08-07T09:00:00,38.93 -2015-08-07T10:00:00,38.82 -2015-08-07T11:00:00,36.95 -2015-08-07T12:00:00,32.56 -2015-08-07T13:00:00,29.32 -2015-08-07T14:00:00,28.87 -2015-08-07T15:00:00,29.14 -2015-08-07T16:00:00,30.99 -2015-08-07T17:00:00,38.96 -2015-08-07T18:00:00,42.3 -2015-08-07T19:00:00,43.97 -2015-08-07T20:00:00,41.41 -2015-08-07T21:00:00,41.57 -2015-08-07T22:00:00,40.98 -2015-08-07T23:00:00,31.98 -2015-08-08T00:00:00,31.22 -2015-08-08T01:00:00,27.38 -2015-08-08T02:00:00,26.23 -2015-08-08T03:00:00,25.21 -2015-08-08T04:00:00,23.05 -2015-08-08T05:00:00,23.04 -2015-08-08T06:00:00,25.24 -2015-08-08T07:00:00,28.31 -2015-08-08T08:00:00,29.91 -2015-08-08T09:00:00,31.56 -2015-08-08T10:00:00,28.02 -2015-08-08T11:00:00,27.2 -2015-08-08T12:00:00,25.24 -2015-08-08T13:00:00,23.49 -2015-08-08T14:00:00,22.14 -2015-08-08T15:00:00,24.11 -2015-08-08T16:00:00,25.28 -2015-08-08T17:00:00,29.57 -2015-08-08T18:00:00,31.95 -2015-08-08T19:00:00,33.96 -2015-08-08T20:00:00,33.91 -2015-08-08T21:00:00,34.92 -2015-08-08T22:00:00,35.26 -2015-08-08T23:00:00,32.65 -2015-08-09T00:00:00,27.02 -2015-08-09T01:00:00,25.28 -2015-08-09T02:00:00,24.04 -2015-08-09T03:00:00,22.95 -2015-08-09T04:00:00,22.2 -2015-08-09T05:00:00,22.01 -2015-08-09T06:00:00,21.87 -2015-08-09T07:00:00,21.43 -2015-08-09T08:00:00,22.64 -2015-08-09T09:00:00,23.94 -2015-08-09T10:00:00,25.11 -2015-08-09T11:00:00,26.12 -2015-08-09T12:00:00,23.72 -2015-08-09T13:00:00,23.84 -2015-08-09T14:00:00,23.08 -2015-08-09T15:00:00,23.08 -2015-08-09T16:00:00,24.59 -2015-08-09T17:00:00,28.12 -2015-08-09T18:00:00,35.94 -2015-08-09T19:00:00,37.92 -2015-08-09T20:00:00,36.46 -2015-08-09T21:00:00,36.99 -2015-08-09T22:00:00,36.89 -2015-08-09T23:00:00,32.4 -2015-08-10T00:00:00,30.6 -2015-08-10T01:00:00,27.19 -2015-08-10T02:00:00,25.49 -2015-08-10T03:00:00,23.49 -2015-08-10T04:00:00,23.04 -2015-08-10T05:00:00,25.24 -2015-08-10T06:00:00,37.57 -2015-08-10T07:00:00,41.74 -2015-08-10T08:00:00,43.8 -2015-08-10T09:00:00,40.42 -2015-08-10T10:00:00,39.48 -2015-08-10T11:00:00,39.95 -2015-08-10T12:00:00,36.92 -2015-08-10T13:00:00,35.93 -2015-08-10T14:00:00,31.68 -2015-08-10T15:00:00,31.4 -2015-08-10T16:00:00,34.77 -2015-08-10T17:00:00,39.9 -2015-08-10T18:00:00,39.92 -2015-08-10T19:00:00,39.92 -2015-08-10T20:00:00,36.97 -2015-08-10T21:00:00,36.91 -2015-08-10T22:00:00,31.83 -2015-08-10T23:00:00,29.38 -2015-08-11T00:00:00,29.94 -2015-08-11T01:00:00,28.92 -2015-08-11T02:00:00,27.73 -2015-08-11T03:00:00,26.94 -2015-08-11T04:00:00,27.03 -2015-08-11T05:00:00,29.54 -2015-08-11T06:00:00,37.01 -2015-08-11T07:00:00,44.79 -2015-08-11T08:00:00,46.45 -2015-08-11T09:00:00,45.67 -2015-08-11T10:00:00,45.07 -2015-08-11T11:00:00,47.24 -2015-08-11T12:00:00,43.91 -2015-08-11T13:00:00,40.46 -2015-08-11T14:00:00,36.97 -2015-08-11T15:00:00,36.98 -2015-08-11T16:00:00,38.88 -2015-08-11T17:00:00,43.98 -2015-08-11T18:00:00,43.99 -2015-08-11T19:00:00,44.98 -2015-08-11T20:00:00,44.29 -2015-08-11T21:00:00,45.09 -2015-08-11T22:00:00,42.48 -2015-08-11T23:00:00,32.58 -2015-08-12T00:00:00,29.98 -2015-08-12T01:00:00,28.7 -2015-08-12T02:00:00,27.94 -2015-08-12T03:00:00,26.94 -2015-08-12T04:00:00,27.21 -2015-08-12T05:00:00,29.06 -2015-08-12T06:00:00,37.02 -2015-08-12T07:00:00,45.29 -2015-08-12T08:00:00,42.85 -2015-08-12T09:00:00,43.09 -2015-08-12T10:00:00,42.1 -2015-08-12T11:00:00,44.11 -2015-08-12T12:00:00,40.41 -2015-08-12T13:00:00,37.94 -2015-08-12T14:00:00,36.99 -2015-08-12T15:00:00,37.16 -2015-08-12T16:00:00,39.3 -2015-08-12T17:00:00,42.77 -2015-08-12T18:00:00,42.02 -2015-08-12T19:00:00,43.37 -2015-08-12T20:00:00,43.07 -2015-08-12T21:00:00,43.95 -2015-08-12T22:00:00,40.86 -2015-08-12T23:00:00,29.97 -2015-08-13T00:00:00,28.87 -2015-08-13T01:00:00,28.55 -2015-08-13T02:00:00,25.92 -2015-08-13T03:00:00,25.67 -2015-08-13T04:00:00,25.43 -2015-08-13T05:00:00,27.12 -2015-08-13T06:00:00,31.24 -2015-08-13T07:00:00,40.95 -2015-08-13T08:00:00,44.14 -2015-08-13T09:00:00,42.06 -2015-08-13T10:00:00,40 -2015-08-13T11:00:00,39.44 -2015-08-13T12:00:00,35.84 -2015-08-13T13:00:00,31.77 -2015-08-13T14:00:00,31.1 -2015-08-13T15:00:00,31.02 -2015-08-13T16:00:00,32.92 -2015-08-13T17:00:00,40.13 -2015-08-13T18:00:00,42.02 -2015-08-13T19:00:00,41.98 -2015-08-13T20:00:00,39.92 -2015-08-13T21:00:00,37.26 -2015-08-13T22:00:00,30.89 -2015-08-13T23:00:00,26.73 -2015-08-14T00:00:00,24.09 -2015-08-14T01:00:00,23.55 -2015-08-14T02:00:00,17.54 -2015-08-14T03:00:00,18.28 -2015-08-14T04:00:00,17.06 -2015-08-14T05:00:00,25.07 -2015-08-14T06:00:00,28.79 -2015-08-14T07:00:00,36.76 -2015-08-14T08:00:00,35.5 -2015-08-14T09:00:00,36.94 -2015-08-14T10:00:00,32.93 -2015-08-14T11:00:00,31.09 -2015-08-14T12:00:00,28.6 -2015-08-14T13:00:00,27.77 -2015-08-14T14:00:00,26.16 -2015-08-14T15:00:00,27.02 -2015-08-14T16:00:00,29.56 -2015-08-14T17:00:00,39.07 -2015-08-14T18:00:00,38.92 -2015-08-14T19:00:00,38.95 -2015-08-14T20:00:00,37.52 -2015-08-14T21:00:00,38.5 -2015-08-14T22:00:00,38.68 -2015-08-14T23:00:00,32.94 -2015-08-15T00:00:00,32 -2015-08-15T01:00:00,27.72 -2015-08-15T02:00:00,29.15 -2015-08-15T03:00:00,27.71 -2015-08-15T04:00:00,26.53 -2015-08-15T05:00:00,24.81 -2015-08-15T06:00:00,26.01 -2015-08-15T07:00:00,27.06 -2015-08-15T08:00:00,30.43 -2015-08-15T09:00:00,33.42 -2015-08-15T10:00:00,36.44 -2015-08-15T11:00:00,35.83 -2015-08-15T12:00:00,36.24 -2015-08-15T13:00:00,34.89 -2015-08-15T14:00:00,30.04 -2015-08-15T15:00:00,27.94 -2015-08-15T16:00:00,30.32 -2015-08-15T17:00:00,36 -2015-08-15T18:00:00,37.47 -2015-08-15T19:00:00,38.99 -2015-08-15T20:00:00,38.13 -2015-08-15T21:00:00,39.91 -2015-08-15T22:00:00,38.95 -2015-08-15T23:00:00,31.17 -2015-08-16T00:00:00,31.34 -2015-08-16T01:00:00,29.35 -2015-08-16T02:00:00,28.07 -2015-08-16T03:00:00,27.09 -2015-08-16T04:00:00,27.04 -2015-08-16T05:00:00,27.03 -2015-08-16T06:00:00,26.87 -2015-08-16T07:00:00,26.99 -2015-08-16T08:00:00,28.05 -2015-08-16T09:00:00,30.08 -2015-08-16T10:00:00,32.7 -2015-08-16T11:00:00,37.98 -2015-08-16T12:00:00,37.85 -2015-08-16T13:00:00,31.03 -2015-08-16T14:00:00,29.68 -2015-08-16T15:00:00,28.46 -2015-08-16T16:00:00,27.04 -2015-08-16T17:00:00,29.09 -2015-08-16T18:00:00,32.42 -2015-08-16T19:00:00,33.01 -2015-08-16T20:00:00,31.06 -2015-08-16T21:00:00,33.44 -2015-08-16T22:00:00,30.98 -2015-08-16T23:00:00,24.98 -2015-08-17T00:00:00,29.11 -2015-08-17T01:00:00,28.08 -2015-08-17T02:00:00,26.01 -2015-08-17T03:00:00,28 -2015-08-17T04:00:00,26.03 -2015-08-17T05:00:00,28.05 -2015-08-17T06:00:00,34.93 -2015-08-17T07:00:00,43.93 -2015-08-17T08:00:00,46.07 -2015-08-17T09:00:00,48.94 -2015-08-17T10:00:00,51 -2015-08-17T11:00:00,50 -2015-08-17T12:00:00,49.54 -2015-08-17T13:00:00,48.93 -2015-08-17T14:00:00,47.94 -2015-08-17T15:00:00,44.39 -2015-08-17T16:00:00,43.07 -2015-08-17T17:00:00,44.1 -2015-08-17T18:00:00,44.91 -2015-08-17T19:00:00,45.06 -2015-08-17T20:00:00,44.93 -2015-08-17T21:00:00,43.94 -2015-08-17T22:00:00,39.95 -2015-08-17T23:00:00,33.78 -2015-08-18T00:00:00,28.99 -2015-08-18T01:00:00,27.38 -2015-08-18T02:00:00,25.05 -2015-08-18T03:00:00,23.09 -2015-08-18T04:00:00,22.1 -2015-08-18T05:00:00,27.04 -2015-08-18T06:00:00,32.83 -2015-08-18T07:00:00,36.94 -2015-08-18T08:00:00,41.09 -2015-08-18T09:00:00,42.43 -2015-08-18T10:00:00,42.03 -2015-08-18T11:00:00,42.32 -2015-08-18T12:00:00,40.5 -2015-08-18T13:00:00,41.97 -2015-08-18T14:00:00,41.16 -2015-08-18T15:00:00,40.56 -2015-08-18T16:00:00,40.92 -2015-08-18T17:00:00,43.1 -2015-08-18T18:00:00,44.99 -2015-08-18T19:00:00,45.92 -2015-08-18T20:00:00,45.31 -2015-08-18T21:00:00,45.92 -2015-08-18T22:00:00,44.15 -2015-08-18T23:00:00,36.04 -2015-08-19T00:00:00,33.04 -2015-08-19T01:00:00,29.61 -2015-08-19T02:00:00,28.01 -2015-08-19T03:00:00,27.04 -2015-08-19T04:00:00,27.8 -2015-08-19T05:00:00,30.56 -2015-08-19T06:00:00,36.76 -2015-08-19T07:00:00,42.06 -2015-08-19T08:00:00,45.66 -2015-08-19T09:00:00,45.96 -2015-08-19T10:00:00,45.45 -2015-08-19T11:00:00,45.98 -2015-08-19T12:00:00,43.57 -2015-08-19T13:00:00,42.94 -2015-08-19T14:00:00,41.47 -2015-08-19T15:00:00,41.93 -2015-08-19T16:00:00,41.92 -2015-08-19T17:00:00,43.08 -2015-08-19T18:00:00,44.02 -2015-08-19T19:00:00,46.35 -2015-08-19T20:00:00,45.31 -2015-08-19T21:00:00,43.99 -2015-08-19T22:00:00,41.23 -2015-08-19T23:00:00,33.23 -2015-08-20T00:00:00,30.76 -2015-08-20T01:00:00,30.23 -2015-08-20T02:00:00,27.2 -2015-08-20T03:00:00,25.7 -2015-08-20T04:00:00,24.75 -2015-08-20T05:00:00,28.09 -2015-08-20T06:00:00,33.54 -2015-08-20T07:00:00,41.99 -2015-08-20T08:00:00,43.12 -2015-08-20T09:00:00,44.1 -2015-08-20T10:00:00,44.17 -2015-08-20T11:00:00,44.57 -2015-08-20T12:00:00,42 -2015-08-20T13:00:00,40.7 -2015-08-20T14:00:00,39.32 -2015-08-20T15:00:00,39.32 -2015-08-20T16:00:00,37.91 -2015-08-20T17:00:00,40.29 -2015-08-20T18:00:00,42.08 -2015-08-20T19:00:00,42.98 -2015-08-20T20:00:00,42.99 -2015-08-20T21:00:00,42.84 -2015-08-20T22:00:00,37.77 -2015-08-20T23:00:00,29.99 -2015-08-21T00:00:00,25.32 -2015-08-21T01:00:00,24.84 -2015-08-21T02:00:00,25.1 -2015-08-21T03:00:00,24.87 -2015-08-21T04:00:00,25.31 -2015-08-21T05:00:00,28.28 -2015-08-21T06:00:00,33.72 -2015-08-21T07:00:00,37.17 -2015-08-21T08:00:00,39.56 -2015-08-21T09:00:00,39.47 -2015-08-21T10:00:00,37.62 -2015-08-21T11:00:00,35.23 -2015-08-21T12:00:00,28.06 -2015-08-21T13:00:00,26.97 -2015-08-21T14:00:00,26.4 -2015-08-21T15:00:00,27.36 -2015-08-21T16:00:00,28.06 -2015-08-21T17:00:00,34 -2015-08-21T18:00:00,38.99 -2015-08-21T19:00:00,40 -2015-08-21T20:00:00,39.81 -2015-08-21T21:00:00,39.83 -2015-08-21T22:00:00,31.91 -2015-08-21T23:00:00,28.08 -2015-08-22T00:00:00,33.99 -2015-08-22T01:00:00,31.68 -2015-08-22T02:00:00,28.71 -2015-08-22T03:00:00,25.56 -2015-08-22T04:00:00,25.52 -2015-08-22T05:00:00,26.64 -2015-08-22T06:00:00,30.09 -2015-08-22T07:00:00,32.18 -2015-08-22T08:00:00,32.08 -2015-08-22T09:00:00,30.08 -2015-08-22T10:00:00,24.61 -2015-08-22T11:00:00,22 -2015-08-22T12:00:00,20 -2015-08-22T13:00:00,16.31 -2015-08-22T14:00:00,16.26 -2015-08-22T15:00:00,17.2 -2015-08-22T16:00:00,16.3 -2015-08-22T17:00:00,26.09 -2015-08-22T18:00:00,37.12 -2015-08-22T19:00:00,40.62 -2015-08-22T20:00:00,39.92 -2015-08-22T21:00:00,34.03 -2015-08-22T22:00:00,28.89 -2015-08-22T23:00:00,24.66 -2015-08-23T00:00:00,21.42 -2015-08-23T01:00:00,16.73 -2015-08-23T02:00:00,17.14 -2015-08-23T03:00:00,15.64 -2015-08-23T04:00:00,15.06 -2015-08-23T05:00:00,14.41 -2015-08-23T06:00:00,15.16 -2015-08-23T07:00:00,16.07 -2015-08-23T08:00:00,17.13 -2015-08-23T09:00:00,17.21 -2015-08-23T10:00:00,16.43 -2015-08-23T11:00:00,14.52 -2015-08-23T12:00:00,12.85 -2015-08-23T13:00:00,10.04 -2015-08-23T14:00:00,5.28 -2015-08-23T15:00:00,7.92 -2015-08-23T16:00:00,11.72 -2015-08-23T17:00:00,16.02 -2015-08-23T18:00:00,19.42 -2015-08-23T19:00:00,25.65 -2015-08-23T20:00:00,27.6 -2015-08-23T21:00:00,27.42 -2015-08-23T22:00:00,25.09 -2015-08-23T23:00:00,19.03 -2015-08-24T00:00:00,16 -2015-08-24T01:00:00,16.47 -2015-08-24T02:00:00,16.14 -2015-08-24T03:00:00,16.16 -2015-08-24T04:00:00,16.6 -2015-08-24T05:00:00,23.88 -2015-08-24T06:00:00,35.14 -2015-08-24T07:00:00,39.98 -2015-08-24T08:00:00,45.02 -2015-08-24T09:00:00,42.71 -2015-08-24T10:00:00,41.63 -2015-08-24T11:00:00,39.96 -2015-08-24T12:00:00,37.88 -2015-08-24T13:00:00,36.15 -2015-08-24T14:00:00,34.36 -2015-08-24T15:00:00,34.98 -2015-08-24T16:00:00,34.98 -2015-08-24T17:00:00,37.41 -2015-08-24T18:00:00,38.91 -2015-08-24T19:00:00,37.02 -2015-08-24T20:00:00,31.95 -2015-08-24T21:00:00,29.18 -2015-08-24T22:00:00,23.26 -2015-08-24T23:00:00,16.15 -2015-08-25T00:00:00,16.17 -2015-08-25T01:00:00,14.42 -2015-08-25T02:00:00,12.84 -2015-08-25T03:00:00,11.56 -2015-08-25T04:00:00,12.28 -2015-08-25T05:00:00,16.13 -2015-08-25T06:00:00,25.02 -2015-08-25T07:00:00,33.82 -2015-08-25T08:00:00,37.2 -2015-08-25T09:00:00,33.17 -2015-08-25T10:00:00,27.74 -2015-08-25T11:00:00,23.98 -2015-08-25T12:00:00,21.62 -2015-08-25T13:00:00,21.12 -2015-08-25T14:00:00,21.48 -2015-08-25T15:00:00,22.75 -2015-08-25T16:00:00,23.99 -2015-08-25T17:00:00,31.04 -2015-08-25T18:00:00,36.92 -2015-08-25T19:00:00,41.44 -2015-08-25T20:00:00,39.35 -2015-08-25T21:00:00,37 -2015-08-25T22:00:00,34.76 -2015-08-25T23:00:00,28.13 -2015-08-26T00:00:00,25.29 -2015-08-26T01:00:00,23.47 -2015-08-26T02:00:00,20.66 -2015-08-26T03:00:00,20.06 -2015-08-26T04:00:00,19.27 -2015-08-26T05:00:00,23.69 -2015-08-26T06:00:00,31.16 -2015-08-26T07:00:00,38.74 -2015-08-26T08:00:00,36.39 -2015-08-26T09:00:00,33.74 -2015-08-26T10:00:00,29.97 -2015-08-26T11:00:00,28.51 -2015-08-26T12:00:00,25.56 -2015-08-26T13:00:00,25.06 -2015-08-26T14:00:00,24.99 -2015-08-26T15:00:00,25.29 -2015-08-26T16:00:00,28.27 -2015-08-26T17:00:00,32.02 -2015-08-26T18:00:00,38.98 -2015-08-26T19:00:00,44.66 -2015-08-26T20:00:00,42.44 -2015-08-26T21:00:00,35.07 -2015-08-26T22:00:00,26.97 -2015-08-26T23:00:00,23.11 -2015-08-27T00:00:00,16.56 -2015-08-27T01:00:00,16.17 -2015-08-27T02:00:00,16.57 -2015-08-27T03:00:00,16.24 -2015-08-27T04:00:00,17.54 -2015-08-27T05:00:00,23.44 -2015-08-27T06:00:00,28.3 -2015-08-27T07:00:00,32.37 -2015-08-27T08:00:00,35.64 -2015-08-27T09:00:00,37.92 -2015-08-27T10:00:00,32.93 -2015-08-27T11:00:00,33.92 -2015-08-27T12:00:00,28.8 -2015-08-27T13:00:00,28.59 -2015-08-27T14:00:00,29.06 -2015-08-27T15:00:00,32.22 -2015-08-27T16:00:00,36.89 -2015-08-27T17:00:00,40.84 -2015-08-27T18:00:00,41.98 -2015-08-27T19:00:00,43.05 -2015-08-27T20:00:00,43.02 -2015-08-27T21:00:00,41.02 -2015-08-27T22:00:00,37.4 -2015-08-27T23:00:00,30.8 -2015-08-28T00:00:00,28.59 -2015-08-28T01:00:00,27.92 -2015-08-28T02:00:00,28.56 -2015-08-28T03:00:00,27.97 -2015-08-28T04:00:00,27.8 -2015-08-28T05:00:00,30.1 -2015-08-28T06:00:00,42.05 -2015-08-28T07:00:00,48.13 -2015-08-28T08:00:00,51.78 -2015-08-28T09:00:00,51.43 -2015-08-28T10:00:00,50 -2015-08-28T11:00:00,47.36 -2015-08-28T12:00:00,42.96 -2015-08-28T13:00:00,40.01 -2015-08-28T14:00:00,37.08 -2015-08-28T15:00:00,37.59 -2015-08-28T16:00:00,42 -2015-08-28T17:00:00,44.92 -2015-08-28T18:00:00,49.48 -2015-08-28T19:00:00,49.94 -2015-08-28T20:00:00,49 -2015-08-28T21:00:00,48.51 -2015-08-28T22:00:00,42.43 -2015-08-28T23:00:00,38.46 -2015-08-29T00:00:00,34.87 -2015-08-29T01:00:00,29.36 -2015-08-29T02:00:00,29.54 -2015-08-29T03:00:00,29.18 -2015-08-29T04:00:00,26.98 -2015-08-29T05:00:00,28.28 -2015-08-29T06:00:00,31.44 -2015-08-29T07:00:00,32.02 -2015-08-29T08:00:00,34.32 -2015-08-29T09:00:00,29.57 -2015-08-29T10:00:00,24.9 -2015-08-29T11:00:00,22.02 -2015-08-29T12:00:00,20.05 -2015-08-29T13:00:00,18.29 -2015-08-29T14:00:00,17.91 -2015-08-29T15:00:00,20.58 -2015-08-29T16:00:00,23.48 -2015-08-29T17:00:00,29.3 -2015-08-29T18:00:00,40.7 -2015-08-29T19:00:00,43.2 -2015-08-29T20:00:00,43.09 -2015-08-29T21:00:00,41.22 -2015-08-29T22:00:00,39.03 -2015-08-29T23:00:00,31.62 -2015-08-30T00:00:00,30.16 -2015-08-30T01:00:00,26.01 -2015-08-30T02:00:00,24.51 -2015-08-30T03:00:00,23.92 -2015-08-30T04:00:00,24.03 -2015-08-30T05:00:00,24.19 -2015-08-30T06:00:00,24.14 -2015-08-30T07:00:00,24.93 -2015-08-30T08:00:00,27.14 -2015-08-30T09:00:00,25.55 -2015-08-30T10:00:00,24.02 -2015-08-30T11:00:00,22.26 -2015-08-30T12:00:00,18.23 -2015-08-30T13:00:00,17.33 -2015-08-30T14:00:00,18.01 -2015-08-30T15:00:00,19.17 -2015-08-30T16:00:00,19.98 -2015-08-30T17:00:00,24.27 -2015-08-30T18:00:00,34.8 -2015-08-30T19:00:00,38.8 -2015-08-30T20:00:00,40.98 -2015-08-30T21:00:00,39.09 -2015-08-30T22:00:00,38.91 -2015-08-30T23:00:00,27.94 -2015-08-31T00:00:00,28.19 -2015-08-31T01:00:00,27.98 -2015-08-31T02:00:00,26.71 -2015-08-31T03:00:00,25.09 -2015-08-31T04:00:00,25.02 -2015-08-31T05:00:00,30.29 -2015-08-31T06:00:00,42.98 -2015-08-31T07:00:00,48.01 -2015-08-31T08:00:00,46.92 -2015-08-31T09:00:00,45.03 -2015-08-31T10:00:00,40.2 -2015-08-31T11:00:00,34.15 -2015-08-31T12:00:00,29.53 -2015-08-31T13:00:00,27.92 -2015-08-31T14:00:00,28.93 -2015-08-31T15:00:00,29.34 -2015-08-31T16:00:00,32.08 -2015-08-31T17:00:00,41.81 -2015-08-31T18:00:00,45.08 -2015-08-31T19:00:00,46.61 -2015-08-31T20:00:00,44.76 -2015-08-31T21:00:00,42.44 -2015-08-31T22:00:00,34.97 -2015-08-31T23:00:00,27.95 -2015-09-01T00:00:00,27.72 -2015-09-01T01:00:00,27.15 -2015-09-01T02:00:00,26.38 -2015-09-01T03:00:00,25.04 -2015-09-01T04:00:00,25.99 -2015-09-01T05:00:00,29.06 -2015-09-01T06:00:00,42.03 -2015-09-01T07:00:00,49.61 -2015-09-01T08:00:00,49.93 -2015-09-01T09:00:00,48.08 -2015-09-01T10:00:00,46.9 -2015-09-01T11:00:00,45.83 -2015-09-01T12:00:00,42.46 -2015-09-01T13:00:00,40.21 -2015-09-01T14:00:00,39.94 -2015-09-01T15:00:00,40.94 -2015-09-01T16:00:00,41.96 -2015-09-01T17:00:00,44.62 -2015-09-01T18:00:00,44.95 -2015-09-01T19:00:00,45.1 -2015-09-01T20:00:00,46.67 -2015-09-01T21:00:00,44.02 -2015-09-01T22:00:00,35.38 -2015-09-01T23:00:00,29.27 -2015-09-02T00:00:00,27.58 -2015-09-02T01:00:00,26.77 -2015-09-02T02:00:00,25.31 -2015-09-02T03:00:00,24.43 -2015-09-02T04:00:00,25.09 -2015-09-02T05:00:00,28.66 -2015-09-02T06:00:00,39.04 -2015-09-02T07:00:00,44.78 -2015-09-02T08:00:00,45.01 -2015-09-02T09:00:00,43.62 -2015-09-02T10:00:00,37.99 -2015-09-02T11:00:00,38.22 -2015-09-02T12:00:00,32.43 -2015-09-02T13:00:00,30.55 -2015-09-02T14:00:00,31.95 -2015-09-02T15:00:00,35.68 -2015-09-02T16:00:00,40.97 -2015-09-02T17:00:00,45.58 -2015-09-02T18:00:00,49.97 -2015-09-02T19:00:00,55.1 -2015-09-02T20:00:00,53.32 -2015-09-02T21:00:00,47.96 -2015-09-02T22:00:00,41.95 -2015-09-02T23:00:00,31.03 -2015-09-03T00:00:00,29.44 -2015-09-03T01:00:00,29.97 -2015-09-03T02:00:00,28.68 -2015-09-03T03:00:00,26.65 -2015-09-03T04:00:00,27.42 -2015-09-03T05:00:00,29.49 -2015-09-03T06:00:00,41.44 -2015-09-03T07:00:00,50 -2015-09-03T08:00:00,52.02 -2015-09-03T09:00:00,49.25 -2015-09-03T10:00:00,48.07 -2015-09-03T11:00:00,48.08 -2015-09-03T12:00:00,46.86 -2015-09-03T13:00:00,43.63 -2015-09-03T14:00:00,42.37 -2015-09-03T15:00:00,41.2 -2015-09-03T16:00:00,41.43 -2015-09-03T17:00:00,44.03 -2015-09-03T18:00:00,44.93 -2015-09-03T19:00:00,48.53 -2015-09-03T20:00:00,49.1 -2015-09-03T21:00:00,45.23 -2015-09-03T22:00:00,40.23 -2015-09-03T23:00:00,30.09 -2015-09-04T00:00:00,28.18 -2015-09-04T01:00:00,27.99 -2015-09-04T02:00:00,26.95 -2015-09-04T03:00:00,26.2 -2015-09-04T04:00:00,26.08 -2015-09-04T05:00:00,26.96 -2015-09-04T06:00:00,37.94 -2015-09-04T07:00:00,46.72 -2015-09-04T08:00:00,47.98 -2015-09-04T09:00:00,44.97 -2015-09-04T10:00:00,40.76 -2015-09-04T11:00:00,34.9 -2015-09-04T12:00:00,28.62 -2015-09-04T13:00:00,27.76 -2015-09-04T14:00:00,27.18 -2015-09-04T15:00:00,26.99 -2015-09-04T16:00:00,26.21 -2015-09-04T17:00:00,30.94 -2015-09-04T18:00:00,38.15 -2015-09-04T19:00:00,40.87 -2015-09-04T20:00:00,42.44 -2015-09-04T21:00:00,32.75 -2015-09-04T22:00:00,28.65 -2015-09-04T23:00:00,26.29 -2015-09-05T00:00:00,24.39 -2015-09-05T01:00:00,23.5 -2015-09-05T02:00:00,17.37 -2015-09-05T03:00:00,16.9 -2015-09-05T04:00:00,16.95 -2015-09-05T05:00:00,18.5 -2015-09-05T06:00:00,18.8 -2015-09-05T07:00:00,25.07 -2015-09-05T08:00:00,27.3 -2015-09-05T09:00:00,27.12 -2015-09-05T10:00:00,23.99 -2015-09-05T11:00:00,16.87 -2015-09-05T12:00:00,16.06 -2015-09-05T13:00:00,15.33 -2015-09-05T14:00:00,13.15 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-2015-09-07T04:00:00,13.8 -2015-09-07T05:00:00,18.58 -2015-09-07T06:00:00,31.75 -2015-09-07T07:00:00,42.1 -2015-09-07T08:00:00,43.57 -2015-09-07T09:00:00,41.27 -2015-09-07T10:00:00,39.92 -2015-09-07T11:00:00,42.08 -2015-09-07T12:00:00,41.76 -2015-09-07T13:00:00,40.18 -2015-09-07T14:00:00,38.11 -2015-09-07T15:00:00,34.52 -2015-09-07T16:00:00,31.75 -2015-09-07T17:00:00,32.73 -2015-09-07T18:00:00,40.48 -2015-09-07T19:00:00,45 -2015-09-07T20:00:00,45.17 -2015-09-07T21:00:00,39.82 -2015-09-07T22:00:00,29.01 -2015-09-07T23:00:00,24.55 -2015-09-08T00:00:00,28.7 -2015-09-08T01:00:00,28.2 -2015-09-08T02:00:00,26.48 -2015-09-08T03:00:00,24.48 -2015-09-08T04:00:00,24.47 -2015-09-08T05:00:00,28.48 -2015-09-08T06:00:00,39.94 -2015-09-08T07:00:00,45.92 -2015-09-08T08:00:00,46.34 -2015-09-08T09:00:00,40.98 -2015-09-08T10:00:00,37.9 -2015-09-08T11:00:00,34.99 -2015-09-08T12:00:00,34.63 -2015-09-08T13:00:00,32.44 -2015-09-08T14:00:00,33.27 -2015-09-08T15:00:00,32.33 -2015-09-08T16:00:00,33.79 -2015-09-08T17:00:00,39.91 -2015-09-08T18:00:00,43.78 -2015-09-08T19:00:00,46.98 -2015-09-08T20:00:00,47.08 -2015-09-08T21:00:00,42.24 -2015-09-08T22:00:00,30 -2015-09-08T23:00:00,27.96 -2015-09-09T00:00:00,28.52 -2015-09-09T01:00:00,28.5 -2015-09-09T02:00:00,26.46 -2015-09-09T03:00:00,26 -2015-09-09T04:00:00,26.41 -2015-09-09T05:00:00,29.06 -2015-09-09T06:00:00,39.97 -2015-09-09T07:00:00,44.96 -2015-09-09T08:00:00,46.43 -2015-09-09T09:00:00,44.25 -2015-09-09T10:00:00,38.61 -2015-09-09T11:00:00,33.97 -2015-09-09T12:00:00,31.17 -2015-09-09T13:00:00,29.96 -2015-09-09T14:00:00,29.1 -2015-09-09T15:00:00,29.51 -2015-09-09T16:00:00,29.9 -2015-09-09T17:00:00,37.94 -2015-09-09T18:00:00,43.18 -2015-09-09T19:00:00,44.01 -2015-09-09T20:00:00,45.06 -2015-09-09T21:00:00,38.93 -2015-09-09T22:00:00,30.41 -2015-09-09T23:00:00,26.49 -2015-09-10T00:00:00,27.04 -2015-09-10T01:00:00,26.1 -2015-09-10T02:00:00,25.51 -2015-09-10T03:00:00,25.03 -2015-09-10T04:00:00,25.91 -2015-09-10T05:00:00,28.73 -2015-09-10T06:00:00,40.02 -2015-09-10T07:00:00,45.78 -2015-09-10T08:00:00,47.03 -2015-09-10T09:00:00,43.99 -2015-09-10T10:00:00,37.4 -2015-09-10T11:00:00,32.38 -2015-09-10T12:00:00,29.02 -2015-09-10T13:00:00,29.02 -2015-09-10T14:00:00,29.04 -2015-09-10T15:00:00,32.05 -2015-09-10T16:00:00,31.94 -2015-09-10T17:00:00,38.5 -2015-09-10T18:00:00,42 -2015-09-10T19:00:00,44.4 -2015-09-10T20:00:00,43.62 -2015-09-10T21:00:00,31.93 -2015-09-10T22:00:00,28.46 -2015-09-10T23:00:00,25.07 -2015-09-11T00:00:00,25.24 -2015-09-11T01:00:00,24.54 -2015-09-11T02:00:00,24.2 -2015-09-11T03:00:00,24.14 -2015-09-11T04:00:00,26.06 -2015-09-11T05:00:00,26.21 -2015-09-11T06:00:00,36.42 -2015-09-11T07:00:00,43.94 -2015-09-11T08:00:00,43.7 -2015-09-11T09:00:00,40.8 -2015-09-11T10:00:00,34.2 -2015-09-11T11:00:00,32.07 -2015-09-11T12:00:00,28.07 -2015-09-11T13:00:00,27.07 -2015-09-11T14:00:00,27.07 -2015-09-11T15:00:00,29.52 -2015-09-11T16:00:00,30.01 -2015-09-11T17:00:00,36.67 -2015-09-11T18:00:00,42.04 -2015-09-11T19:00:00,45.19 -2015-09-11T20:00:00,45.05 -2015-09-11T21:00:00,40.41 -2015-09-11T22:00:00,38.03 -2015-09-11T23:00:00,32.64 -2015-09-12T00:00:00,34.79 -2015-09-12T01:00:00,31.97 -2015-09-12T02:00:00,29.94 -2015-09-12T03:00:00,25.01 -2015-09-12T04:00:00,25.91 -2015-09-12T05:00:00,25.26 -2015-09-12T06:00:00,27.92 -2015-09-12T07:00:00,32.06 -2015-09-12T08:00:00,37.91 -2015-09-12T09:00:00,40.09 -2015-09-12T10:00:00,34.15 -2015-09-12T11:00:00,27.95 -2015-09-12T12:00:00,24.97 -2015-09-12T13:00:00,22.4 -2015-09-12T14:00:00,17.99 -2015-09-12T15:00:00,24.05 -2015-09-12T16:00:00,27.8 -2015-09-12T17:00:00,34.13 -2015-09-12T18:00:00,39.21 -2015-09-12T19:00:00,41.3 -2015-09-12T20:00:00,41.1 -2015-09-12T21:00:00,31.29 -2015-09-12T22:00:00,31.7 -2015-09-12T23:00:00,26.17 -2015-09-13T00:00:00,17.1 -2015-09-13T01:00:00,19.59 -2015-09-13T02:00:00,23.04 -2015-09-13T03:00:00,23.04 -2015-09-13T04:00:00,23.3 -2015-09-13T05:00:00,23.45 -2015-09-13T06:00:00,24.06 -2015-09-13T07:00:00,23.68 -2015-09-13T08:00:00,24.06 -2015-09-13T09:00:00,25.48 -2015-09-13T10:00:00,25.82 -2015-09-13T11:00:00,28.46 -2015-09-13T12:00:00,25.11 -2015-09-13T13:00:00,23.93 -2015-09-13T14:00:00,22.24 -2015-09-13T15:00:00,22.34 -2015-09-13T16:00:00,22.86 -2015-09-13T17:00:00,26.17 -2015-09-13T18:00:00,31.35 -2015-09-13T19:00:00,36.8 -2015-09-13T20:00:00,40.22 -2015-09-13T21:00:00,34.96 -2015-09-13T22:00:00,32.01 -2015-09-13T23:00:00,24.2 -2015-09-14T00:00:00,22.61 -2015-09-14T01:00:00,18.63 -2015-09-14T02:00:00,16.77 -2015-09-14T03:00:00,16.12 -2015-09-14T04:00:00,21.42 -2015-09-14T05:00:00,24.08 -2015-09-14T06:00:00,37.97 -2015-09-14T07:00:00,42.54 -2015-09-14T08:00:00,42.03 -2015-09-14T09:00:00,41.69 -2015-09-14T10:00:00,42.04 -2015-09-14T11:00:00,44.92 -2015-09-14T12:00:00,44.93 -2015-09-14T13:00:00,42.04 -2015-09-14T14:00:00,41.91 -2015-09-14T15:00:00,39.98 -2015-09-14T16:00:00,39.47 -2015-09-14T17:00:00,39.92 -2015-09-14T18:00:00,40.05 -2015-09-14T19:00:00,41.27 -2015-09-14T20:00:00,41.92 -2015-09-14T21:00:00,29.58 -2015-09-14T22:00:00,25 -2015-09-14T23:00:00,16.89 -2015-09-15T00:00:00,18.15 -2015-09-15T01:00:00,14.56 -2015-09-15T02:00:00,12.98 -2015-09-15T03:00:00,12.86 -2015-09-15T04:00:00,13.79 -2015-09-15T05:00:00,18.91 -2015-09-15T06:00:00,30.08 -2015-09-15T07:00:00,42.03 -2015-09-15T08:00:00,42.07 -2015-09-15T09:00:00,35.3 -2015-09-15T10:00:00,30.08 -2015-09-15T11:00:00,28.6 -2015-09-15T12:00:00,26.75 -2015-09-15T13:00:00,26.39 -2015-09-15T14:00:00,25.35 -2015-09-15T15:00:00,25.08 -2015-09-15T16:00:00,26.6 -2015-09-15T17:00:00,30.28 -2015-09-15T18:00:00,40.69 -2015-09-15T19:00:00,43.97 -2015-09-15T20:00:00,45.83 -2015-09-15T21:00:00,35.01 -2015-09-15T22:00:00,28.59 -2015-09-15T23:00:00,24.15 -2015-09-16T00:00:00,24.34 -2015-09-16T01:00:00,24.03 -2015-09-16T02:00:00,24.06 -2015-09-16T03:00:00,23.69 -2015-09-16T04:00:00,20 -2015-09-16T05:00:00,25.03 -2015-09-16T06:00:00,39.45 -2015-09-16T07:00:00,46.41 -2015-09-16T08:00:00,52.06 -2015-09-16T09:00:00,50.33 -2015-09-16T10:00:00,45.28 -2015-09-16T11:00:00,45.1 -2015-09-16T12:00:00,40.79 -2015-09-16T13:00:00,39.91 -2015-09-16T14:00:00,33.76 -2015-09-16T15:00:00,33.62 -2015-09-16T16:00:00,32.59 -2015-09-16T17:00:00,35.93 -2015-09-16T18:00:00,37.44 -2015-09-16T19:00:00,39.91 -2015-09-16T20:00:00,32.62 -2015-09-16T21:00:00,27.05 -2015-09-16T22:00:00,24.02 -2015-09-16T23:00:00,16.81 -2015-09-17T00:00:00,15.88 -2015-09-17T01:00:00,14.73 -2015-09-17T02:00:00,16 -2015-09-17T03:00:00,14.63 -2015-09-17T04:00:00,11.32 -2015-09-17T05:00:00,16.18 -2015-09-17T06:00:00,28.17 -2015-09-17T07:00:00,39.92 -2015-09-17T08:00:00,36.03 -2015-09-17T09:00:00,31.04 -2015-09-17T10:00:00,29.13 -2015-09-17T11:00:00,29.07 -2015-09-17T12:00:00,28.26 -2015-09-17T13:00:00,28.6 -2015-09-17T14:00:00,28.49 -2015-09-17T15:00:00,29.75 -2015-09-17T16:00:00,30.01 -2015-09-17T17:00:00,34.76 -2015-09-17T18:00:00,36.8 -2015-09-17T19:00:00,42.04 -2015-09-17T20:00:00,40.98 -2015-09-17T21:00:00,32.52 -2015-09-17T22:00:00,27.17 -2015-09-17T23:00:00,23.43 -2015-09-18T00:00:00,21.98 -2015-09-18T01:00:00,18.61 -2015-09-18T02:00:00,18.91 -2015-09-18T03:00:00,19.01 -2015-09-18T04:00:00,21.72 -2015-09-18T05:00:00,25 -2015-09-18T06:00:00,34.99 -2015-09-18T07:00:00,47 -2015-09-18T08:00:00,51.16 -2015-09-18T09:00:00,48.44 -2015-09-18T10:00:00,46.4 -2015-09-18T11:00:00,44.62 -2015-09-18T12:00:00,39.99 -2015-09-18T13:00:00,35.14 -2015-09-18T14:00:00,33.36 -2015-09-18T15:00:00,38 -2015-09-18T16:00:00,40.05 -2015-09-18T17:00:00,44.16 -2015-09-18T18:00:00,46.73 -2015-09-18T19:00:00,48.9 -2015-09-18T20:00:00,47.7 -2015-09-18T21:00:00,42.57 -2015-09-18T22:00:00,34.1 -2015-09-18T23:00:00,26.68 -2015-09-19T00:00:00,28.54 -2015-09-19T01:00:00,26.25 -2015-09-19T02:00:00,24.78 -2015-09-19T03:00:00,24.2 -2015-09-19T04:00:00,23.73 -2015-09-19T05:00:00,25.08 -2015-09-19T06:00:00,24.74 -2015-09-19T07:00:00,27.6 -2015-09-19T08:00:00,32 -2015-09-19T09:00:00,32.99 -2015-09-19T10:00:00,30.06 -2015-09-19T11:00:00,30.24 -2015-09-19T12:00:00,30.87 -2015-09-19T13:00:00,28.09 -2015-09-19T14:00:00,25.97 -2015-09-19T15:00:00,26.01 -2015-09-19T16:00:00,28.01 -2015-09-19T17:00:00,32.67 -2015-09-19T18:00:00,38.73 -2015-09-19T19:00:00,43.91 -2015-09-19T20:00:00,44.74 -2015-09-19T21:00:00,38.39 -2015-09-19T22:00:00,35.31 -2015-09-19T23:00:00,30.09 -2015-09-20T00:00:00,23.94 -2015-09-20T01:00:00,17.33 -2015-09-20T02:00:00,15.87 -2015-09-20T03:00:00,16.86 -2015-09-20T04:00:00,16.08 -2015-09-20T05:00:00,16.09 -2015-09-20T06:00:00,16.81 -2015-09-20T07:00:00,16.88 -2015-09-20T08:00:00,20.13 -2015-09-20T09:00:00,19.73 -2015-09-20T10:00:00,19.38 -2015-09-20T11:00:00,19.95 -2015-09-20T12:00:00,18.36 -2015-09-20T13:00:00,15.72 -2015-09-20T14:00:00,15.91 -2015-09-20T15:00:00,16.03 -2015-09-20T16:00:00,16.04 -2015-09-20T17:00:00,21.06 -2015-09-20T18:00:00,29.37 -2015-09-20T19:00:00,37.37 -2015-09-20T20:00:00,38.03 -2015-09-20T21:00:00,33.95 -2015-09-20T22:00:00,33.01 -2015-09-20T23:00:00,31.31 -2015-09-21T00:00:00,26.76 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-2015-09-22T14:00:00,38.25 -2015-09-22T15:00:00,40.37 -2015-09-22T16:00:00,40.82 -2015-09-22T17:00:00,41.7 -2015-09-22T18:00:00,42.06 -2015-09-22T19:00:00,44.99 -2015-09-22T20:00:00,40.81 -2015-09-22T21:00:00,37.25 -2015-09-22T22:00:00,33.1 -2015-09-22T23:00:00,28.51 -2015-09-23T00:00:00,30.96 -2015-09-23T01:00:00,28.55 -2015-09-23T02:00:00,28.33 -2015-09-23T03:00:00,27.71 -2015-09-23T04:00:00,28.53 -2015-09-23T05:00:00,33.2 -2015-09-23T06:00:00,45.47 -2015-09-23T07:00:00,47.98 -2015-09-23T08:00:00,59.39 -2015-09-23T09:00:00,55.55 -2015-09-23T10:00:00,54.9 -2015-09-23T11:00:00,55.61 -2015-09-23T12:00:00,51.3 -2015-09-23T13:00:00,49.91 -2015-09-23T14:00:00,48.81 -2015-09-23T15:00:00,46.96 -2015-09-23T16:00:00,47.03 -2015-09-23T17:00:00,47.92 -2015-09-23T18:00:00,46.81 -2015-09-23T19:00:00,50.07 -2015-09-23T20:00:00,46.8 -2015-09-23T21:00:00,43.99 -2015-09-23T22:00:00,33.94 -2015-09-23T23:00:00,28.26 -2015-09-24T00:00:00,26.17 -2015-09-24T01:00:00,26.97 -2015-09-24T02:00:00,25.83 -2015-09-24T03:00:00,24.08 -2015-09-24T04:00:00,25.64 -2015-09-24T05:00:00,26.79 -2015-09-24T06:00:00,36.29 -2015-09-24T07:00:00,43.92 -2015-09-24T08:00:00,43.98 -2015-09-24T09:00:00,41.92 -2015-09-24T10:00:00,34.72 -2015-09-24T11:00:00,29.1 -2015-09-24T12:00:00,27 -2015-09-24T13:00:00,27.43 -2015-09-24T14:00:00,27.45 -2015-09-24T15:00:00,27.43 -2015-09-24T16:00:00,28.21 -2015-09-24T17:00:00,33.97 -2015-09-24T18:00:00,40 -2015-09-24T19:00:00,44.94 -2015-09-24T20:00:00,42.1 -2015-09-24T21:00:00,30.03 -2015-09-24T22:00:00,27.87 -2015-09-24T23:00:00,26.69 -2015-09-25T00:00:00,26.05 -2015-09-25T01:00:00,25.44 -2015-09-25T02:00:00,24.05 -2015-09-25T03:00:00,24.05 -2015-09-25T04:00:00,25.22 -2015-09-25T05:00:00,27.55 -2015-09-25T06:00:00,42.04 -2015-09-25T07:00:00,46.99 -2015-09-25T08:00:00,46.94 -2015-09-25T09:00:00,42.04 -2015-09-25T10:00:00,39.91 -2015-09-25T11:00:00,34.92 -2015-09-25T12:00:00,28.74 -2015-09-25T13:00:00,28.58 -2015-09-25T14:00:00,28.78 -2015-09-25T15:00:00,31.45 -2015-09-25T16:00:00,34.24 -2015-09-25T17:00:00,43.92 -2015-09-25T18:00:00,45.97 -2015-09-25T19:00:00,49.36 -2015-09-25T20:00:00,44.99 -2015-09-25T21:00:00,42.08 -2015-09-25T22:00:00,36.31 -2015-09-25T23:00:00,29.55 -2015-09-26T00:00:00,31.72 -2015-09-26T01:00:00,28.63 -2015-09-26T02:00:00,27.86 -2015-09-26T03:00:00,28.51 -2015-09-26T04:00:00,28.64 -2015-09-26T05:00:00,29.76 -2015-09-26T06:00:00,30.01 -2015-09-26T07:00:00,38.34 -2015-09-26T08:00:00,42.07 -2015-09-26T09:00:00,38.05 -2015-09-26T10:00:00,31.82 -2015-09-26T11:00:00,29.04 -2015-09-26T12:00:00,27.34 -2015-09-26T13:00:00,27.57 -2015-09-26T14:00:00,28.4 -2015-09-26T15:00:00,28.68 -2015-09-26T16:00:00,28.66 -2015-09-26T17:00:00,36.76 -2015-09-26T18:00:00,43.71 -2015-09-26T19:00:00,47.97 -2015-09-26T20:00:00,42.29 -2015-09-26T21:00:00,35.44 -2015-09-26T22:00:00,31.07 -2015-09-26T23:00:00,28.31 -2015-09-27T00:00:00,28.07 -2015-09-27T01:00:00,26.03 -2015-09-27T02:00:00,24.01 -2015-09-27T03:00:00,25.13 -2015-09-27T04:00:00,23.78 -2015-09-27T05:00:00,23.48 -2015-09-27T06:00:00,25.49 -2015-09-27T07:00:00,28.5 -2015-09-27T08:00:00,30.09 -2015-09-27T09:00:00,29.06 -2015-09-27T10:00:00,23.33 -2015-09-27T11:00:00,19.05 -2015-09-27T12:00:00,15.96 -2015-09-27T13:00:00,15.09 -2015-09-27T14:00:00,14.31 -2015-09-27T15:00:00,14.5 -2015-09-27T16:00:00,16.83 -2015-09-27T17:00:00,28.09 -2015-09-27T18:00:00,38.97 -2015-09-27T19:00:00,44.93 -2015-09-27T20:00:00,41.94 -2015-09-27T21:00:00,31.96 -2015-09-27T22:00:00,31.43 -2015-09-27T23:00:00,24.27 -2015-09-28T00:00:00,24.37 -2015-09-28T01:00:00,23.97 -2015-09-28T02:00:00,23.9 -2015-09-28T03:00:00,23.43 -2015-09-28T04:00:00,20.16 -2015-09-28T05:00:00,24.84 -2015-09-28T06:00:00,43.25 -2015-09-28T07:00:00,53.33 -2015-09-28T08:00:00,54.42 -2015-09-28T09:00:00,42.59 -2015-09-28T10:00:00,35.85 -2015-09-28T11:00:00,28.58 -2015-09-28T12:00:00,26.87 -2015-09-28T13:00:00,26.72 -2015-09-28T14:00:00,26.71 -2015-09-28T15:00:00,29.04 -2015-09-28T16:00:00,32.06 -2015-09-28T17:00:00,42.42 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-2015-10-09T13:00:00,47.8 -2015-10-09T14:00:00,46.8 -2015-10-09T15:00:00,45.17 -2015-10-09T16:00:00,44.48 -2015-10-09T17:00:00,46.66 -2015-10-09T18:00:00,49.56 -2015-10-09T19:00:00,55.44 -2015-10-09T20:00:00,49.99 -2015-10-09T21:00:00,40.07 -2015-10-09T22:00:00,36.47 -2015-10-09T23:00:00,32.43 -2015-10-10T00:00:00,32.46 -2015-10-10T01:00:00,28.53 -2015-10-10T02:00:00,32.21 -2015-10-10T03:00:00,30.35 -2015-10-10T04:00:00,29.95 -2015-10-10T05:00:00,31.01 -2015-10-10T06:00:00,32.88 -2015-10-10T07:00:00,36 -2015-10-10T08:00:00,42.9 -2015-10-10T09:00:00,43.97 -2015-10-10T10:00:00,42.92 -2015-10-10T11:00:00,39.13 -2015-10-10T12:00:00,29.42 -2015-10-10T13:00:00,27.31 -2015-10-10T14:00:00,28.03 -2015-10-10T15:00:00,28.94 -2015-10-10T16:00:00,31.62 -2015-10-10T17:00:00,36.14 -2015-10-10T18:00:00,42.97 -2015-10-10T19:00:00,42.99 -2015-10-10T20:00:00,35.06 -2015-10-10T21:00:00,30.07 -2015-10-10T22:00:00,29.06 -2015-10-10T23:00:00,23.55 -2015-10-11T00:00:00,19.71 -2015-10-11T01:00:00,18.94 -2015-10-11T02:00:00,19.03 -2015-10-11T03:00:00,19.21 -2015-10-11T04:00:00,19.45 -2015-10-11T05:00:00,19.01 -2015-10-11T06:00:00,19.03 -2015-10-11T07:00:00,19.52 -2015-10-11T08:00:00,20.82 -2015-10-11T09:00:00,20.62 -2015-10-11T10:00:00,20.2 -2015-10-11T11:00:00,19.99 -2015-10-11T12:00:00,16.5 -2015-10-11T13:00:00,13.19 -2015-10-11T14:00:00,12.89 -2015-10-11T15:00:00,13.1 -2015-10-11T16:00:00,16.57 -2015-10-11T17:00:00,23.35 -2015-10-11T18:00:00,35.23 -2015-10-11T19:00:00,42.97 -2015-10-11T20:00:00,32.28 -2015-10-11T21:00:00,29.13 -2015-10-11T22:00:00,31.2 -2015-10-11T23:00:00,23.78 -2015-10-12T00:00:00,23.09 -2015-10-12T01:00:00,19.6 -2015-10-12T02:00:00,19.78 -2015-10-12T03:00:00,19.47 -2015-10-12T04:00:00,19.64 -2015-10-12T05:00:00,26.14 -2015-10-12T06:00:00,40.73 -2015-10-12T07:00:00,49.15 -2015-10-12T08:00:00,49.88 -2015-10-12T09:00:00,42 -2015-10-12T10:00:00,32.23 -2015-10-12T11:00:00,28.06 -2015-10-12T12:00:00,26.4 -2015-10-12T13:00:00,26.35 -2015-10-12T14:00:00,27.33 -2015-10-12T15:00:00,29.08 -2015-10-12T16:00:00,32.65 -2015-10-12T17:00:00,47.01 -2015-10-12T18:00:00,53.33 -2015-10-12T19:00:00,66.87 -2015-10-12T20:00:00,51.61 -2015-10-12T21:00:00,41.51 -2015-10-12T22:00:00,32.72 -2015-10-12T23:00:00,31.42 -2015-10-13T00:00:00,30.39 -2015-10-13T01:00:00,30 -2015-10-13T02:00:00,29.76 -2015-10-13T03:00:00,28.87 -2015-10-13T04:00:00,29.05 -2015-10-13T05:00:00,31.7 -2015-10-13T06:00:00,43.98 -2015-10-13T07:00:00,52.62 -2015-10-13T08:00:00,56.6 -2015-10-13T09:00:00,53.94 -2015-10-13T10:00:00,46.7 -2015-10-13T11:00:00,43.97 -2015-10-13T12:00:00,40.82 -2015-10-13T13:00:00,41.45 -2015-10-13T14:00:00,41.91 -2015-10-13T15:00:00,42.54 -2015-10-13T16:00:00,43.85 -2015-10-13T17:00:00,48.17 -2015-10-13T18:00:00,53.91 -2015-10-13T19:00:00,61.89 -2015-10-13T20:00:00,50.12 -2015-10-13T21:00:00,40.09 -2015-10-13T22:00:00,37.16 -2015-10-13T23:00:00,32.22 -2015-10-14T00:00:00,31.47 -2015-10-14T01:00:00,29.15 -2015-10-14T02:00:00,27.77 -2015-10-14T03:00:00,27.75 -2015-10-14T04:00:00,28.08 -2015-10-14T05:00:00,30.47 -2015-10-14T06:00:00,41.49 -2015-10-14T07:00:00,55.08 -2015-10-14T08:00:00,63.06 -2015-10-14T09:00:00,61.21 -2015-10-14T10:00:00,60.05 -2015-10-14T11:00:00,61.95 -2015-10-14T12:00:00,55.17 -2015-10-14T13:00:00,51.95 -2015-10-14T14:00:00,46.81 -2015-10-14T15:00:00,45.99 -2015-10-14T16:00:00,46.87 -2015-10-14T17:00:00,47.95 -2015-10-14T18:00:00,51.98 -2015-10-14T19:00:00,57.07 -2015-10-14T20:00:00,46 -2015-10-14T21:00:00,36.95 -2015-10-14T22:00:00,36.99 -2015-10-14T23:00:00,31.59 -2015-10-15T00:00:00,32.09 -2015-10-15T01:00:00,30.66 -2015-10-15T02:00:00,28.76 -2015-10-15T03:00:00,28.25 -2015-10-15T04:00:00,28.09 -2015-10-15T05:00:00,29.31 -2015-10-15T06:00:00,36.93 -2015-10-15T07:00:00,51.9 -2015-10-15T08:00:00,50.41 -2015-10-15T09:00:00,51.16 -2015-10-15T10:00:00,57.91 -2015-10-15T11:00:00,57.01 -2015-10-15T12:00:00,51.46 -2015-10-15T13:00:00,50 -2015-10-15T14:00:00,42.44 -2015-10-15T15:00:00,36.08 -2015-10-15T16:00:00,35 -2015-10-15T17:00:00,37.44 -2015-10-15T18:00:00,44 -2015-10-15T19:00:00,47.97 -2015-10-15T20:00:00,36.07 -2015-10-15T21:00:00,32 -2015-10-15T22:00:00,32.41 -2015-10-15T23:00:00,29.1 -2015-10-16T00:00:00,29.38 -2015-10-16T01:00:00,29.03 -2015-10-16T02:00:00,28.06 -2015-10-16T03:00:00,29.48 -2015-10-16T04:00:00,30.79 -2015-10-16T05:00:00,30.73 -2015-10-16T06:00:00,45.86 -2015-10-16T07:00:00,61.8 -2015-10-16T08:00:00,65.99 -2015-10-16T09:00:00,65.99 -2015-10-16T10:00:00,66.23 -2015-10-16T11:00:00,61 -2015-10-16T12:00:00,54.7 -2015-10-16T13:00:00,52.37 -2015-10-16T14:00:00,55 -2015-10-16T15:00:00,53.73 -2015-10-16T16:00:00,51.94 -2015-10-16T17:00:00,55.17 -2015-10-16T18:00:00,59.87 -2015-10-16T19:00:00,50.4 -2015-10-16T20:00:00,44.8 -2015-10-16T21:00:00,39.82 -2015-10-16T22:00:00,38.69 -2015-10-16T23:00:00,35.27 -2015-10-17T00:00:00,34.19 -2015-10-17T01:00:00,34.07 -2015-10-17T02:00:00,33.49 -2015-10-17T03:00:00,33.42 -2015-10-17T04:00:00,33.97 -2015-10-17T05:00:00,32.34 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-2015-10-18T19:00:00,56.05 -2015-10-18T20:00:00,51.71 -2015-10-18T21:00:00,45.66 -2015-10-18T22:00:00,42.81 -2015-10-18T23:00:00,35 -2015-10-19T00:00:00,36.72 -2015-10-19T01:00:00,34.43 -2015-10-19T02:00:00,34.15 -2015-10-19T03:00:00,32.59 -2015-10-19T04:00:00,32.34 -2015-10-19T05:00:00,35.15 -2015-10-19T06:00:00,47.74 -2015-10-19T07:00:00,60.01 -2015-10-19T08:00:00,66.32 -2015-10-19T09:00:00,69.68 -2015-10-19T10:00:00,61.67 -2015-10-19T11:00:00,59.9 -2015-10-19T12:00:00,60 -2015-10-19T13:00:00,59.59 -2015-10-19T14:00:00,57.19 -2015-10-19T15:00:00,55.63 -2015-10-19T16:00:00,54.32 -2015-10-19T17:00:00,55.8 -2015-10-19T18:00:00,60.17 -2015-10-19T19:00:00,69.47 -2015-10-19T20:00:00,57.84 -2015-10-19T21:00:00,43.5 -2015-10-19T22:00:00,38.22 -2015-10-19T23:00:00,34.86 -2015-10-20T00:00:00,33.02 -2015-10-20T01:00:00,31.77 -2015-10-20T02:00:00,30.5 -2015-10-20T03:00:00,29.86 -2015-10-20T04:00:00,31.93 -2015-10-20T05:00:00,33.91 -2015-10-20T06:00:00,43.4 -2015-10-20T07:00:00,58.64 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-2015-10-21T21:00:00,36.02 -2015-10-21T22:00:00,36.08 -2015-10-21T23:00:00,30.85 -2015-10-22T00:00:00,27.38 -2015-10-22T01:00:00,26.49 -2015-10-22T02:00:00,26.3 -2015-10-22T03:00:00,27.13 -2015-10-22T04:00:00,27.92 -2015-10-22T05:00:00,29.03 -2015-10-22T06:00:00,35.92 -2015-10-22T07:00:00,46.95 -2015-10-22T08:00:00,46.59 -2015-10-22T09:00:00,46.95 -2015-10-22T10:00:00,45.83 -2015-10-22T11:00:00,46.94 -2015-10-22T12:00:00,45.87 -2015-10-22T13:00:00,46.2 -2015-10-22T14:00:00,45 -2015-10-22T15:00:00,41.72 -2015-10-22T16:00:00,36.09 -2015-10-22T17:00:00,38.93 -2015-10-22T18:00:00,41.98 -2015-10-22T19:00:00,41.71 -2015-10-22T20:00:00,36.03 -2015-10-22T21:00:00,32.19 -2015-10-22T22:00:00,29.81 -2015-10-22T23:00:00,23.98 -2015-10-23T00:00:00,27.28 -2015-10-23T01:00:00,24.94 -2015-10-23T02:00:00,23.76 -2015-10-23T03:00:00,23.97 -2015-10-23T04:00:00,27.15 -2015-10-23T05:00:00,28.91 -2015-10-23T06:00:00,36.2 -2015-10-23T07:00:00,47.95 -2015-10-23T08:00:00,46.95 -2015-10-23T09:00:00,48 -2015-10-23T10:00:00,46.98 -2015-10-23T11:00:00,44.72 -2015-10-23T12:00:00,35.04 -2015-10-23T13:00:00,32.37 -2015-10-23T14:00:00,31.68 -2015-10-23T15:00:00,34.29 -2015-10-23T16:00:00,41.91 -2015-10-23T17:00:00,45.12 -2015-10-23T18:00:00,54.01 -2015-10-23T19:00:00,55.17 -2015-10-23T20:00:00,43.17 -2015-10-23T21:00:00,39.94 -2015-10-23T22:00:00,35.16 -2015-10-23T23:00:00,32.15 -2015-10-24T00:00:00,32.08 -2015-10-24T01:00:00,32.3 -2015-10-24T02:00:00,30.07 -2015-10-24T03:00:00,31.48 -2015-10-24T04:00:00,30.1 -2015-10-24T05:00:00,30.05 -2015-10-24T06:00:00,33.46 -2015-10-24T07:00:00,37.2 -2015-10-24T08:00:00,40.02 -2015-10-24T09:00:00,40.03 -2015-10-24T10:00:00,35.95 -2015-10-24T11:00:00,30.5 -2015-10-24T12:00:00,27.89 -2015-10-24T13:00:00,27.94 -2015-10-24T14:00:00,25.48 -2015-10-24T15:00:00,27.7 -2015-10-24T16:00:00,31.08 -2015-10-24T17:00:00,37.22 -2015-10-24T18:00:00,44.92 -2015-10-24T19:00:00,44.98 -2015-10-24T20:00:00,34.73 -2015-10-24T21:00:00,30 -2015-10-24T22:00:00,28.09 -2015-10-24T23:00:00,24.09 -2015-10-25T00:00:00,28.76 -2015-10-25T01:00:00,27.08 -2015-10-25T02:00:00,25.045 -2015-10-25T03:00:00,25.84 -2015-10-25T04:00:00,23.86 -2015-10-25T05:00:00,25.05 -2015-10-25T06:00:00,25.46 -2015-10-25T07:00:00,30.9 -2015-10-25T08:00:00,32.09 -2015-10-25T09:00:00,32.05 -2015-10-25T10:00:00,31.97 -2015-10-25T11:00:00,32.04 -2015-10-25T12:00:00,29.04 -2015-10-25T13:00:00,25.49 -2015-10-25T14:00:00,30.05 -2015-10-25T15:00:00,32.07 -2015-10-25T16:00:00,36.2 -2015-10-25T17:00:00,47.91 -2015-10-25T18:00:00,53.93 -2015-10-25T19:00:00,52.14 -2015-10-25T20:00:00,44.92 -2015-10-25T21:00:00,41.02 -2015-10-25T22:00:00,43.05 -2015-10-25T23:00:00,32.08 -2015-10-26T00:00:00,32.29 -2015-10-26T01:00:00,32.23 -2015-10-26T02:00:00,32.07 -2015-10-26T03:00:00,31.09 -2015-10-26T04:00:00,31.73 -2015-10-26T05:00:00,35.1 -2015-10-26T06:00:00,43.89 -2015-10-26T07:00:00,53.1 -2015-10-26T08:00:00,52.18 -2015-10-26T09:00:00,49.11 -2015-10-26T10:00:00,41.03 -2015-10-26T11:00:00,40.03 -2015-10-26T12:00:00,36.12 -2015-10-26T13:00:00,32.88 -2015-10-26T14:00:00,36.98 -2015-10-26T15:00:00,44.97 -2015-10-26T16:00:00,48.59 -2015-10-26T17:00:00,53.74 -2015-10-26T18:00:00,55 -2015-10-26T19:00:00,42.52 -2015-10-26T20:00:00,34.63 -2015-10-26T21:00:00,30.65 -2015-10-26T22:00:00,29.51 -2015-10-26T23:00:00,29.08 -2015-10-27T00:00:00,28.56 -2015-10-27T01:00:00,29.09 -2015-10-27T02:00:00,28.2 -2015-10-27T03:00:00,28.5 -2015-10-27T04:00:00,29.03 -2015-10-27T05:00:00,30.9 -2015-10-27T06:00:00,38.44 -2015-10-27T07:00:00,46.67 -2015-10-27T08:00:00,50.88 -2015-10-27T09:00:00,49.54 -2015-10-27T10:00:00,48.65 -2015-10-27T11:00:00,47.68 -2015-10-27T12:00:00,39.91 -2015-10-27T13:00:00,40.05 -2015-10-27T14:00:00,39.9 -2015-10-27T15:00:00,47.91 -2015-10-27T16:00:00,49.18 -2015-10-27T17:00:00,54.1 -2015-10-27T18:00:00,54.07 -2015-10-27T19:00:00,47.93 -2015-10-27T20:00:00,40.04 -2015-10-27T21:00:00,36.51 -2015-10-27T22:00:00,32.07 -2015-10-27T23:00:00,31.04 -2015-10-28T00:00:00,32.92 -2015-10-28T01:00:00,31.21 -2015-10-28T02:00:00,30.32 -2015-10-28T03:00:00,29.08 -2015-10-28T04:00:00,30.71 -2015-10-28T05:00:00,30.85 -2015-10-28T06:00:00,35.71 -2015-10-28T07:00:00,47.19 -2015-10-28T08:00:00,46.91 -2015-10-28T09:00:00,46.99 -2015-10-28T10:00:00,39.93 -2015-10-28T11:00:00,36.4 -2015-10-28T12:00:00,36.14 -2015-10-28T13:00:00,37.42 -2015-10-28T14:00:00,46.29 -2015-10-28T15:00:00,49.98 -2015-10-28T16:00:00,51.99 -2015-10-28T17:00:00,60.63 -2015-10-28T18:00:00,69.74 -2015-10-28T19:00:00,54.58 -2015-10-28T20:00:00,49.61 -2015-10-28T21:00:00,45 -2015-10-28T22:00:00,38.64 -2015-10-28T23:00:00,34.47 -2015-10-29T00:00:00,35.44 -2015-10-29T01:00:00,33.1 -2015-10-29T02:00:00,32.08 -2015-10-29T03:00:00,32.66 -2015-10-29T04:00:00,34.05 -2015-10-29T05:00:00,34.8 -2015-10-29T06:00:00,45.02 -2015-10-29T07:00:00,53.42 -2015-10-29T08:00:00,56.03 -2015-10-29T09:00:00,56.88 -2015-10-29T10:00:00,56.19 -2015-10-29T11:00:00,55.51 -2015-10-29T12:00:00,53.91 -2015-10-29T13:00:00,54.24 -2015-10-29T14:00:00,55.17 -2015-10-29T15:00:00,55.29 -2015-10-29T16:00:00,54.85 -2015-10-29T17:00:00,64.51 -2015-10-29T18:00:00,78.88 -2015-10-29T19:00:00,64.96 -2015-10-29T20:00:00,52.94 -2015-10-29T21:00:00,47.16 -2015-10-29T22:00:00,44.32 -2015-10-29T23:00:00,39.99 -2015-10-30T00:00:00,36.25 -2015-10-30T01:00:00,35.09 -2015-10-30T02:00:00,34.32 -2015-10-30T03:00:00,34.02 -2015-10-30T04:00:00,34.02 -2015-10-30T05:00:00,36.78 -2015-10-30T06:00:00,43 -2015-10-30T07:00:00,52.05 -2015-10-30T08:00:00,55 -2015-10-30T09:00:00,54 -2015-10-30T10:00:00,52 -2015-10-30T11:00:00,49.36 -2015-10-30T12:00:00,45.65 -2015-10-30T13:00:00,44.95 -2015-10-30T14:00:00,44.76 -2015-10-30T15:00:00,46.2 -2015-10-30T16:00:00,47.51 -2015-10-30T17:00:00,52 -2015-10-30T18:00:00,50.05 -2015-10-30T19:00:00,46.94 -2015-10-30T20:00:00,38.28 -2015-10-30T21:00:00,34.96 -2015-10-30T22:00:00,35.04 -2015-10-30T23:00:00,34.47 -2015-10-31T00:00:00,33.23 -2015-10-31T01:00:00,31.93 -2015-10-31T02:00:00,28.49 -2015-10-31T03:00:00,28.28 -2015-10-31T04:00:00,27.95 -2015-10-31T05:00:00,29.19 -2015-10-31T06:00:00,30.69 -2015-10-31T07:00:00,32.94 -2015-10-31T08:00:00,37.6 -2015-10-31T09:00:00,39.14 -2015-10-31T10:00:00,37.98 -2015-10-31T11:00:00,37.06 -2015-10-31T12:00:00,33.85 -2015-10-31T13:00:00,32.01 -2015-10-31T14:00:00,33.21 -2015-10-31T15:00:00,36.96 -2015-10-31T16:00:00,39.9 -2015-10-31T17:00:00,45.39 -2015-10-31T18:00:00,50.03 -2015-10-31T19:00:00,47.56 -2015-10-31T20:00:00,38.94 -2015-10-31T21:00:00,36.54 -2015-10-31T22:00:00,36.08 -2015-10-31T23:00:00,32.52 -2015-11-01T00:00:00,25.71 -2015-11-01T01:00:00,24.46 -2015-11-01T02:00:00,27.3 -2015-11-01T03:00:00,24.48 -2015-11-01T04:00:00,24.42 -2015-11-01T05:00:00,24.85 -2015-11-01T06:00:00,24.86 -2015-11-01T07:00:00,24.83 -2015-11-01T08:00:00,26.26 -2015-11-01T09:00:00,27.3 -2015-11-01T10:00:00,26.93 -2015-11-01T11:00:00,27.69 -2015-11-01T12:00:00,25.81 -2015-11-01T13:00:00,26.94 -2015-11-01T14:00:00,27.26 -2015-11-01T15:00:00,28.56 -2015-11-01T16:00:00,34.05 -2015-11-01T17:00:00,47.98 -2015-11-01T18:00:00,51.54 -2015-11-01T19:00:00,50.34 -2015-11-01T20:00:00,46.48 -2015-11-01T21:00:00,40 -2015-11-01T22:00:00,36.9 -2015-11-01T23:00:00,32.79 -2015-11-02T00:00:00,28.3 -2015-11-02T01:00:00,24.67 -2015-11-02T02:00:00,24.84 -2015-11-02T03:00:00,22.24 -2015-11-02T04:00:00,23.8 -2015-11-02T05:00:00,27.84 -2015-11-02T06:00:00,39.11 -2015-11-02T07:00:00,49.05 -2015-11-02T08:00:00,47.92 -2015-11-02T09:00:00,46.47 -2015-11-02T10:00:00,37.75 -2015-11-02T11:00:00,38.16 -2015-11-02T12:00:00,35.56 -2015-11-02T13:00:00,36.09 -2015-11-02T14:00:00,37.97 -2015-11-02T15:00:00,48.05 -2015-11-02T16:00:00,50.07 -2015-11-02T17:00:00,57.69 -2015-11-02T18:00:00,67.74 -2015-11-02T19:00:00,59.41 -2015-11-02T20:00:00,46.96 -2015-11-02T21:00:00,38.56 -2015-11-02T22:00:00,37.75 -2015-11-02T23:00:00,33.03 -2015-11-03T00:00:00,34.03 -2015-11-03T01:00:00,32.25 -2015-11-03T02:00:00,31.32 -2015-11-03T03:00:00,29.84 -2015-11-03T04:00:00,30.92 -2015-11-03T05:00:00,32.25 -2015-11-03T06:00:00,39.97 -2015-11-03T07:00:00,49.63 -2015-11-03T08:00:00,49.96 -2015-11-03T09:00:00,46.06 -2015-11-03T10:00:00,39.73 -2015-11-03T11:00:00,36.1 -2015-11-03T12:00:00,33.08 -2015-11-03T13:00:00,34.94 -2015-11-03T14:00:00,37.11 -2015-11-03T15:00:00,49.4 -2015-11-03T16:00:00,51.13 -2015-11-03T17:00:00,61.99 -2015-11-03T18:00:00,67.19 -2015-11-03T19:00:00,60.01 -2015-11-03T20:00:00,46.08 -2015-11-03T21:00:00,36.72 -2015-11-03T22:00:00,35.9 -2015-11-03T23:00:00,33.87 -2015-11-04T00:00:00,33.23 -2015-11-04T01:00:00,32.22 -2015-11-04T02:00:00,31.02 -2015-11-04T03:00:00,29.59 -2015-11-04T04:00:00,29.83 -2015-11-04T05:00:00,32.24 -2015-11-04T06:00:00,44.62 -2015-11-04T07:00:00,48.81 -2015-11-04T08:00:00,49.76 -2015-11-04T09:00:00,43.06 -2015-11-04T10:00:00,39.14 -2015-11-04T11:00:00,38.95 -2015-11-04T12:00:00,36.03 -2015-11-04T13:00:00,41.53 -2015-11-04T14:00:00,47.57 -2015-11-04T15:00:00,52.33 -2015-11-04T16:00:00,55.1 -2015-11-04T17:00:00,63.02 -2015-11-04T18:00:00,64.1 -2015-11-04T19:00:00,54.56 -2015-11-04T20:00:00,43.01 -2015-11-04T21:00:00,37.44 -2015-11-04T22:00:00,36.07 -2015-11-04T23:00:00,32.21 -2015-11-05T00:00:00,32.01 -2015-11-05T01:00:00,31.65 -2015-11-05T02:00:00,31.47 -2015-11-05T03:00:00,31.92 -2015-11-05T04:00:00,31.91 -2015-11-05T05:00:00,33.46 -2015-11-05T06:00:00,42 -2015-11-05T07:00:00,52.55 -2015-11-05T08:00:00,54.69 -2015-11-05T09:00:00,46.06 -2015-11-05T10:00:00,44.07 -2015-11-05T11:00:00,41.98 -2015-11-05T12:00:00,37.06 -2015-11-05T13:00:00,40.3 -2015-11-05T14:00:00,45.96 -2015-11-05T15:00:00,52.01 -2015-11-05T16:00:00,53.93 -2015-11-05T17:00:00,70.38 -2015-11-05T18:00:00,69.01 -2015-11-05T19:00:00,58.05 -2015-11-05T20:00:00,46.44 -2015-11-05T21:00:00,37.45 -2015-11-05T22:00:00,37.19 -2015-11-05T23:00:00,33.68 -2015-11-06T00:00:00,29.12 -2015-11-06T01:00:00,28.21 -2015-11-06T02:00:00,26.99 -2015-11-06T03:00:00,27.11 -2015-11-06T04:00:00,26.76 -2015-11-06T05:00:00,29.98 -2015-11-06T06:00:00,36.66 -2015-11-06T07:00:00,44.77 -2015-11-06T08:00:00,45.04 -2015-11-06T09:00:00,43.88 -2015-11-06T10:00:00,44.76 -2015-11-06T11:00:00,44.49 -2015-11-06T12:00:00,41.66 -2015-11-06T13:00:00,38.45 -2015-11-06T14:00:00,37.08 -2015-11-06T15:00:00,37.67 -2015-11-06T16:00:00,40.01 -2015-11-06T17:00:00,45.28 -2015-11-06T18:00:00,47.42 -2015-11-06T19:00:00,42.87 -2015-11-06T20:00:00,34.38 -2015-11-06T21:00:00,31.61 -2015-11-06T22:00:00,31.38 -2015-11-06T23:00:00,24.44 -2015-11-07T00:00:00,27.75 -2015-11-07T01:00:00,24.41 -2015-11-07T02:00:00,22.25 -2015-11-07T03:00:00,20.43 -2015-11-07T04:00:00,21.29 -2015-11-07T05:00:00,24.32 -2015-11-07T06:00:00,24.57 -2015-11-07T07:00:00,28.53 -2015-11-07T08:00:00,32.03 -2015-11-07T09:00:00,35.97 -2015-11-07T10:00:00,36.81 -2015-11-07T11:00:00,38.05 -2015-11-07T12:00:00,37.83 -2015-11-07T13:00:00,35.72 -2015-11-07T14:00:00,33.2 -2015-11-07T15:00:00,31.63 -2015-11-07T16:00:00,32.05 -2015-11-07T17:00:00,41.07 -2015-11-07T18:00:00,41.81 -2015-11-07T19:00:00,32.99 -2015-11-07T20:00:00,28.43 -2015-11-07T21:00:00,22.09 -2015-11-07T22:00:00,21.65 -2015-11-07T23:00:00,11.93 -2015-11-08T00:00:00,-13.38 -2015-11-08T01:00:00,-13.42 -2015-11-08T02:00:00,-16.06 -2015-11-08T03:00:00,-10.07 -2015-11-08T04:00:00,1.93 -2015-11-08T05:00:00,12.94 -2015-11-08T06:00:00,13.11 -2015-11-08T07:00:00,17.31 -2015-11-08T08:00:00,22.93 -2015-11-08T09:00:00,23.87 -2015-11-08T10:00:00,25.21 -2015-11-08T11:00:00,28.65 -2015-11-08T12:00:00,26.93 -2015-11-08T13:00:00,25.76 -2015-11-08T14:00:00,29.02 -2015-11-08T15:00:00,31.06 -2015-11-08T16:00:00,32.45 -2015-11-08T17:00:00,45.96 -2015-11-08T18:00:00,48.4 -2015-11-08T19:00:00,44.94 -2015-11-08T20:00:00,35.84 -2015-11-08T21:00:00,29.93 -2015-11-08T22:00:00,29.24 -2015-11-08T23:00:00,22.78 -2015-11-09T00:00:00,17.94 -2015-11-09T01:00:00,17.09 -2015-11-09T02:00:00,14.5 -2015-11-09T03:00:00,12.81 -2015-11-09T04:00:00,13.33 -2015-11-09T05:00:00,19.31 -2015-11-09T06:00:00,37.01 -2015-11-09T07:00:00,46.95 -2015-11-09T08:00:00,46.55 -2015-11-09T09:00:00,37.22 -2015-11-09T10:00:00,35.93 -2015-11-09T11:00:00,32.93 -2015-11-09T12:00:00,31 -2015-11-09T13:00:00,31.05 -2015-11-09T14:00:00,30.7 -2015-11-09T15:00:00,31.08 -2015-11-09T16:00:00,31.08 -2015-11-09T17:00:00,42.43 -2015-11-09T18:00:00,41.6 -2015-11-09T19:00:00,31.97 -2015-11-09T20:00:00,30.23 -2015-11-09T21:00:00,25.19 -2015-11-09T22:00:00,20.94 -2015-11-09T23:00:00,9.5 -2015-11-10T00:00:00,11.6 -2015-11-10T01:00:00,10.66 -2015-11-10T02:00:00,4.49 -2015-11-10T03:00:00,10.08 -2015-11-10T04:00:00,12.83 -2015-11-10T05:00:00,15.11 -2015-11-10T06:00:00,28.32 -2015-11-10T07:00:00,36.8 -2015-11-10T08:00:00,35.63 -2015-11-10T09:00:00,28.25 -2015-11-10T10:00:00,25.63 -2015-11-10T11:00:00,27.95 -2015-11-10T12:00:00,26.68 -2015-11-10T13:00:00,29.09 -2015-11-10T14:00:00,32.55 -2015-11-10T15:00:00,36.4 -2015-11-10T16:00:00,39.25 -2015-11-10T17:00:00,51.93 -2015-11-10T18:00:00,54.94 -2015-11-10T19:00:00,44.97 -2015-11-10T20:00:00,38.95 -2015-11-10T21:00:00,32.8 -2015-11-10T22:00:00,32.98 -2015-11-10T23:00:00,24.35 -2015-11-11T00:00:00,26.46 -2015-11-11T01:00:00,24.04 -2015-11-11T02:00:00,22.92 -2015-11-11T03:00:00,22.42 -2015-11-11T04:00:00,21.46 -2015-11-11T05:00:00,25.41 -2015-11-11T06:00:00,33.28 -2015-11-11T07:00:00,38.95 -2015-11-11T08:00:00,38.71 -2015-11-11T09:00:00,38.98 -2015-11-11T10:00:00,38.23 -2015-11-11T11:00:00,39.94 -2015-11-11T12:00:00,37.98 -2015-11-11T13:00:00,35.34 -2015-11-11T14:00:00,34.78 -2015-11-11T15:00:00,35.71 -2015-11-11T16:00:00,38.48 -2015-11-11T17:00:00,48.88 -2015-11-11T18:00:00,52.57 -2015-11-11T19:00:00,48.84 -2015-11-11T20:00:00,41.25 -2015-11-11T21:00:00,34.49 -2015-11-11T22:00:00,34.75 -2015-11-11T23:00:00,28.03 -2015-11-12T00:00:00,25.13 -2015-11-12T01:00:00,24.74 -2015-11-12T02:00:00,21.06 -2015-11-12T03:00:00,21.02 -2015-11-12T04:00:00,24.02 -2015-11-12T05:00:00,27.93 -2015-11-12T06:00:00,31.02 -2015-11-12T07:00:00,37.02 -2015-11-12T08:00:00,38.4 -2015-11-12T09:00:00,35.79 -2015-11-12T10:00:00,32.01 -2015-11-12T11:00:00,31.44 -2015-11-12T12:00:00,29.98 -2015-11-12T13:00:00,32.07 -2015-11-12T14:00:00,37 -2015-11-12T15:00:00,43.97 -2015-11-12T16:00:00,46.74 -2015-11-12T17:00:00,63.83 -2015-11-12T18:00:00,69.49 -2015-11-12T19:00:00,55.97 -2015-11-12T20:00:00,44.08 -2015-11-12T21:00:00,36.57 -2015-11-12T22:00:00,35.89 -2015-11-12T23:00:00,29.54 -2015-11-13T00:00:00,30.04 -2015-11-13T01:00:00,29.28 -2015-11-13T02:00:00,28.47 -2015-11-13T03:00:00,26.66 -2015-11-13T04:00:00,25.35 -2015-11-13T05:00:00,29.13 -2015-11-13T06:00:00,35.78 -2015-11-13T07:00:00,44.67 -2015-11-13T08:00:00,46.99 -2015-11-13T09:00:00,42.15 -2015-11-13T10:00:00,37.97 -2015-11-13T11:00:00,36 -2015-11-13T12:00:00,31.6 -2015-11-13T13:00:00,32 -2015-11-13T14:00:00,30.97 -2015-11-13T15:00:00,32.07 -2015-11-13T16:00:00,33.96 -2015-11-13T17:00:00,40.5 -2015-11-13T18:00:00,33.03 -2015-11-13T19:00:00,30.02 -2015-11-13T20:00:00,28.43 -2015-11-13T21:00:00,24.01 -2015-11-13T22:00:00,24.69 -2015-11-13T23:00:00,16.39 -2015-11-14T00:00:00,16.41 -2015-11-14T01:00:00,16.08 -2015-11-14T02:00:00,11.83 -2015-11-14T03:00:00,10.54 -2015-11-14T04:00:00,10.58 -2015-11-14T05:00:00,11.87 -2015-11-14T06:00:00,10.71 -2015-11-14T07:00:00,12.89 -2015-11-14T08:00:00,17.04 -2015-11-14T09:00:00,20 -2015-11-14T10:00:00,17.22 -2015-11-14T11:00:00,15.77 -2015-11-14T12:00:00,15.82 -2015-11-14T13:00:00,15.42 -2015-11-14T14:00:00,19.4 -2015-11-14T15:00:00,22.65 -2015-11-14T16:00:00,30.1 -2015-11-14T17:00:00,42.15 -2015-11-14T18:00:00,44.55 -2015-11-14T19:00:00,39.31 -2015-11-14T20:00:00,30.07 -2015-11-14T21:00:00,22.5 -2015-11-14T22:00:00,21.63 -2015-11-14T23:00:00,22.05 -2015-11-15T00:00:00,24.95 -2015-11-15T01:00:00,21.18 -2015-11-15T02:00:00,20.72 -2015-11-15T03:00:00,17.72 -2015-11-15T04:00:00,17.1 -2015-11-15T05:00:00,18.31 -2015-11-15T06:00:00,16.83 -2015-11-15T07:00:00,17.38 -2015-11-15T08:00:00,17.87 -2015-11-15T09:00:00,22.72 -2015-11-15T10:00:00,23.17 -2015-11-15T11:00:00,23.26 -2015-11-15T12:00:00,23.33 -2015-11-15T13:00:00,17.03 -2015-11-15T14:00:00,15.77 -2015-11-15T15:00:00,20.39 -2015-11-15T16:00:00,24.05 -2015-11-15T17:00:00,33.7 -2015-11-15T18:00:00,43.69 -2015-11-15T19:00:00,32.14 -2015-11-15T20:00:00,34.95 -2015-11-15T21:00:00,34.96 -2015-11-15T22:00:00,24.69 -2015-11-15T23:00:00,26.06 -2015-11-16T00:00:00,25.27 -2015-11-16T01:00:00,22.44 -2015-11-16T02:00:00,21.51 -2015-11-16T03:00:00,20.4 -2015-11-16T04:00:00,21.74 -2015-11-16T05:00:00,23.06 -2015-11-16T06:00:00,32.96 -2015-11-16T07:00:00,33.1 -2015-11-16T08:00:00,32.68 -2015-11-16T09:00:00,29.01 -2015-11-16T10:00:00,27.81 -2015-11-16T11:00:00,27.86 -2015-11-16T12:00:00,27.87 -2015-11-16T13:00:00,29.02 -2015-11-16T14:00:00,31 -2015-11-16T15:00:00,35.87 -2015-11-16T16:00:00,38.94 -2015-11-16T17:00:00,46.42 -2015-11-16T18:00:00,43.44 -2015-11-16T19:00:00,37.06 -2015-11-16T20:00:00,37.73 -2015-11-16T21:00:00,30.97 -2015-11-16T22:00:00,30.75 -2015-11-16T23:00:00,25.37 -2015-11-17T00:00:00,24.64 -2015-11-17T01:00:00,24.65 -2015-11-17T02:00:00,21.92 -2015-11-17T03:00:00,21.37 -2015-11-17T04:00:00,22.5 -2015-11-17T05:00:00,24.67 -2015-11-17T06:00:00,30.27 -2015-11-17T07:00:00,36.6 -2015-11-17T08:00:00,38.75 -2015-11-17T09:00:00,38.27 -2015-11-17T10:00:00,38.91 -2015-11-17T11:00:00,41.26 -2015-11-17T12:00:00,37.94 -2015-11-17T13:00:00,35.93 -2015-11-17T14:00:00,36.05 -2015-11-17T15:00:00,36.3 -2015-11-17T16:00:00,38.5 -2015-11-17T17:00:00,41 -2015-11-17T18:00:00,39.49 -2015-11-17T19:00:00,37.84 -2015-11-17T20:00:00,31 -2015-11-17T21:00:00,26.45 -2015-11-17T22:00:00,23.49 -2015-11-17T23:00:00,13.5 -2015-11-18T00:00:00,-0.92 -2015-11-18T01:00:00,-0.1 -2015-11-18T02:00:00,-19.95 -2015-11-18T03:00:00,-8.73 -2015-11-18T04:00:00,-0.09 -2015-11-18T05:00:00,10.74 -2015-11-18T06:00:00,21 -2015-11-18T07:00:00,27.04 -2015-11-18T08:00:00,26.97 -2015-11-18T09:00:00,25.52 -2015-11-18T10:00:00,25.42 -2015-11-18T11:00:00,25.54 -2015-11-18T12:00:00,24.9 -2015-11-18T13:00:00,24.92 -2015-11-18T14:00:00,27.68 -2015-11-18T15:00:00,33.92 -2015-11-18T16:00:00,36.97 -2015-11-18T17:00:00,50.99 -2015-11-18T18:00:00,42.44 -2015-11-18T19:00:00,33.93 -2015-11-18T20:00:00,29.41 -2015-11-18T21:00:00,24.43 -2015-11-18T22:00:00,20.76 -2015-11-18T23:00:00,7.73 -2015-11-19T00:00:00,12.36 -2015-11-19T01:00:00,11.03 -2015-11-19T02:00:00,5.15 -2015-11-19T03:00:00,5.76 -2015-11-19T04:00:00,10.81 -2015-11-19T05:00:00,15.15 -2015-11-19T06:00:00,25.08 -2015-11-19T07:00:00,32.27 -2015-11-19T08:00:00,32.14 -2015-11-19T09:00:00,25.84 -2015-11-19T10:00:00,25.23 -2015-11-19T11:00:00,25.24 -2015-11-19T12:00:00,25.09 -2015-11-19T13:00:00,25.32 -2015-11-19T14:00:00,25.91 -2015-11-19T15:00:00,29.93 -2015-11-19T16:00:00,32.95 -2015-11-19T17:00:00,38.22 -2015-11-19T18:00:00,38.96 -2015-11-19T19:00:00,38.95 -2015-11-19T20:00:00,34.92 -2015-11-19T21:00:00,32.18 -2015-11-19T22:00:00,24.97 -2015-11-19T23:00:00,22.83 -2015-11-20T00:00:00,27.66 -2015-11-20T01:00:00,25.04 -2015-11-20T02:00:00,22.89 -2015-11-20T03:00:00,23.25 -2015-11-20T04:00:00,24.02 -2015-11-20T05:00:00,26.1 -2015-11-20T06:00:00,32.06 -2015-11-20T07:00:00,41.97 -2015-11-20T08:00:00,38.93 -2015-11-20T09:00:00,43.9 -2015-11-20T10:00:00,44.76 -2015-11-20T11:00:00,47.11 -2015-11-20T12:00:00,47.7 -2015-11-20T13:00:00,46.44 -2015-11-20T14:00:00,45.88 -2015-11-20T15:00:00,44.51 -2015-11-20T16:00:00,45.7 -2015-11-20T17:00:00,50.1 -2015-11-20T18:00:00,51.66 -2015-11-20T19:00:00,45.4 -2015-11-20T20:00:00,41.6 -2015-11-20T21:00:00,35.57 -2015-11-20T22:00:00,34.71 -2015-11-20T23:00:00,29.09 -2015-11-21T00:00:00,28.47 -2015-11-21T01:00:00,24.95 -2015-11-21T02:00:00,23.04 -2015-11-21T03:00:00,22.38 -2015-11-21T04:00:00,22.25 -2015-11-21T05:00:00,22.61 -2015-11-21T06:00:00,22.04 -2015-11-21T07:00:00,25.69 -2015-11-21T08:00:00,29.68 -2015-11-21T09:00:00,31.52 -2015-11-21T10:00:00,31.58 -2015-11-21T11:00:00,33.24 -2015-11-21T12:00:00,34.13 -2015-11-21T13:00:00,32.83 -2015-11-21T14:00:00,31.08 -2015-11-21T15:00:00,31.48 -2015-11-21T16:00:00,33.02 -2015-11-21T17:00:00,42.85 -2015-11-21T18:00:00,44.4 -2015-11-21T19:00:00,40.11 -2015-11-21T20:00:00,29.95 -2015-11-21T21:00:00,26.54 -2015-11-21T22:00:00,25.55 -2015-11-21T23:00:00,24.35 -2015-11-22T00:00:00,25.19 -2015-11-22T01:00:00,23.93 -2015-11-22T02:00:00,23.87 -2015-11-22T03:00:00,22.7 -2015-11-22T04:00:00,22.35 -2015-11-22T05:00:00,22.59 -2015-11-22T06:00:00,22.06 -2015-11-22T07:00:00,22.87 -2015-11-22T08:00:00,22.2 -2015-11-22T09:00:00,24.02 -2015-11-22T10:00:00,25.55 -2015-11-22T11:00:00,28.21 -2015-11-22T12:00:00,24.84 -2015-11-22T13:00:00,24.01 -2015-11-22T14:00:00,18.82 -2015-11-22T15:00:00,22.88 -2015-11-22T16:00:00,25.56 -2015-11-22T17:00:00,34.42 -2015-11-22T18:00:00,38.48 -2015-11-22T19:00:00,32.47 -2015-11-22T20:00:00,31.07 -2015-11-22T21:00:00,28.26 -2015-11-22T22:00:00,33 -2015-11-22T23:00:00,25.02 -2015-11-23T00:00:00,23.65 -2015-11-23T01:00:00,24.52 -2015-11-23T02:00:00,28.01 -2015-11-23T03:00:00,25.02 -2015-11-23T04:00:00,25.73 -2015-11-23T05:00:00,27.85 -2015-11-23T06:00:00,40.72 -2015-11-23T07:00:00,66.14 -2015-11-23T08:00:00,71.92 -2015-11-23T09:00:00,68.19 -2015-11-23T10:00:00,59.91 -2015-11-23T11:00:00,55.01 -2015-11-23T12:00:00,50.95 -2015-11-23T13:00:00,55.07 -2015-11-23T14:00:00,60.04 -2015-11-23T15:00:00,62.9 -2015-11-23T16:00:00,70.82 -2015-11-23T17:00:00,99.77 -2015-11-23T18:00:00,74.93 -2015-11-23T19:00:00,51.91 -2015-11-23T20:00:00,43.5 -2015-11-23T21:00:00,36.39 -2015-11-23T22:00:00,34.15 -2015-11-23T23:00:00,29.13 -2015-11-24T00:00:00,34.31 -2015-11-24T01:00:00,30.2 -2015-11-24T02:00:00,28.27 -2015-11-24T03:00:00,27.03 -2015-11-24T04:00:00,27.1 -2015-11-24T05:00:00,27.02 -2015-11-24T06:00:00,33.01 -2015-11-24T07:00:00,45.23 -2015-11-24T08:00:00,46.44 -2015-11-24T09:00:00,43.67 -2015-11-24T10:00:00,35.13 -2015-11-24T11:00:00,34.18 -2015-11-24T12:00:00,31.29 -2015-11-24T13:00:00,32.23 -2015-11-24T14:00:00,33.64 -2015-11-24T15:00:00,40 -2015-11-24T16:00:00,43.98 -2015-11-24T17:00:00,46.44 -2015-11-24T18:00:00,41.84 -2015-11-24T19:00:00,37.58 -2015-11-24T20:00:00,32.24 -2015-11-24T21:00:00,35.25 -2015-11-24T22:00:00,30.67 -2015-11-24T23:00:00,28.25 -2015-11-25T00:00:00,29.48 -2015-11-25T01:00:00,29.83 -2015-11-25T02:00:00,29.14 -2015-11-25T03:00:00,28.39 -2015-11-25T04:00:00,29.49 -2015-11-25T05:00:00,28.36 -2015-11-25T06:00:00,31.85 -2015-11-25T07:00:00,50.58 -2015-11-25T08:00:00,46.99 -2015-11-25T09:00:00,46.54 -2015-11-25T10:00:00,48.53 -2015-11-25T11:00:00,50.97 -2015-11-25T12:00:00,50.58 -2015-11-25T13:00:00,52.98 -2015-11-25T14:00:00,52.4 -2015-11-25T15:00:00,51.21 -2015-11-25T16:00:00,51.59 -2015-11-25T17:00:00,59.45 -2015-11-25T18:00:00,46.66 -2015-11-25T19:00:00,49.07 -2015-11-25T20:00:00,37.81 -2015-11-25T21:00:00,34.59 -2015-11-25T22:00:00,34.12 -2015-11-25T23:00:00,31 -2015-11-26T00:00:00,34.99 -2015-11-26T01:00:00,34.2 -2015-11-26T02:00:00,32.3 -2015-11-26T03:00:00,30.29 -2015-11-26T04:00:00,31.4 -2015-11-26T05:00:00,33.75 -2015-11-26T06:00:00,38.93 -2015-11-26T07:00:00,60.98 -2015-11-26T08:00:00,60.08 -2015-11-26T09:00:00,59.19 -2015-11-26T10:00:00,60.96 -2015-11-26T11:00:00,62.97 -2015-11-26T12:00:00,57.51 -2015-11-26T13:00:00,56.8 -2015-11-26T14:00:00,60.03 -2015-11-26T15:00:00,65.05 -2015-11-26T16:00:00,65.12 -2015-11-26T17:00:00,83.91 -2015-11-26T18:00:00,72.17 -2015-11-26T19:00:00,62.05 -2015-11-26T20:00:00,47.52 -2015-11-26T21:00:00,37.35 -2015-11-26T22:00:00,47.06 -2015-11-26T23:00:00,35.77 -2015-11-27T00:00:00,32.94 -2015-11-27T01:00:00,31.67 -2015-11-27T02:00:00,29.96 -2015-11-27T03:00:00,29.98 -2015-11-27T04:00:00,30.09 -2015-11-27T05:00:00,29.33 -2015-11-27T06:00:00,33.81 -2015-11-27T07:00:00,46.67 -2015-11-27T08:00:00,47.96 -2015-11-27T09:00:00,47 -2015-11-27T10:00:00,44.98 -2015-11-27T11:00:00,48.07 -2015-11-27T12:00:00,39.2 -2015-11-27T13:00:00,36.21 -2015-11-27T14:00:00,36.1 -2015-11-27T15:00:00,39.49 -2015-11-27T16:00:00,44.92 -2015-11-27T17:00:00,47.66 -2015-11-27T18:00:00,37.32 -2015-11-27T19:00:00,33.02 -2015-11-27T20:00:00,31.17 -2015-11-27T21:00:00,29.55 -2015-11-27T22:00:00,29.18 -2015-11-27T23:00:00,26.18 -2015-11-28T00:00:00,27.03 -2015-11-28T01:00:00,23.01 -2015-11-28T02:00:00,23.04 -2015-11-28T03:00:00,23.01 -2015-11-28T04:00:00,24.15 -2015-11-28T05:00:00,24.14 -2015-11-28T06:00:00,27.71 -2015-11-28T07:00:00,28.06 -2015-11-28T08:00:00,31 -2015-11-28T09:00:00,35.04 -2015-11-28T10:00:00,30.02 -2015-11-28T11:00:00,31.75 -2015-11-28T12:00:00,31.72 -2015-11-28T13:00:00,30.09 -2015-11-28T14:00:00,32.96 -2015-11-28T15:00:00,33.95 -2015-11-28T16:00:00,35.46 -2015-11-28T17:00:00,37.48 -2015-11-28T18:00:00,32.33 -2015-11-28T19:00:00,31.17 -2015-11-28T20:00:00,24.08 -2015-11-28T21:00:00,20.52 -2015-11-28T22:00:00,20.22 -2015-11-28T23:00:00,10.92 -2015-11-29T00:00:00,-0.04 -2015-11-29T01:00:00,-0.97 -2015-11-29T02:00:00,-0.9 -2015-11-29T03:00:00,-0.77 -2015-11-29T04:00:00,-0.96 -2015-11-29T05:00:00,0.05 -2015-11-29T06:00:00,0 -2015-11-29T07:00:00,10.06 -2015-11-29T08:00:00,13.07 -2015-11-29T09:00:00,16.47 -2015-11-29T10:00:00,18.98 -2015-11-29T11:00:00,20.76 -2015-11-29T12:00:00,20.79 -2015-11-29T13:00:00,20.38 -2015-11-29T14:00:00,17.11 -2015-11-29T15:00:00,15.04 -2015-11-29T16:00:00,16.28 -2015-11-29T17:00:00,16.57 -2015-11-29T18:00:00,20.91 -2015-11-29T19:00:00,16.73 -2015-11-29T20:00:00,13.79 -2015-11-29T21:00:00,12.22 -2015-11-29T22:00:00,13.91 -2015-11-29T23:00:00,0.07 -2015-11-30T00:00:00,10.04 -2015-11-30T01:00:00,-0.09 -2015-11-30T02:00:00,-0.94 -2015-11-30T03:00:00,-0.99 -2015-11-30T04:00:00,-0.04 -2015-11-30T05:00:00,10.78 -2015-11-30T06:00:00,21.85 -2015-11-30T07:00:00,30.41 -2015-11-30T08:00:00,33.42 -2015-11-30T09:00:00,28.28 -2015-11-30T10:00:00,28.88 -2015-11-30T11:00:00,30.09 -2015-11-30T12:00:00,30.03 -2015-11-30T13:00:00,32.45 -2015-11-30T14:00:00,35.79 -2015-11-30T15:00:00,35.38 -2015-11-30T16:00:00,37.89 -2015-11-30T17:00:00,47 -2015-11-30T18:00:00,45.26 -2015-11-30T19:00:00,39.83 -2015-11-30T20:00:00,35.72 -2015-11-30T21:00:00,30.02 -2015-11-30T22:00:00,27.14 -2015-11-30T23:00:00,18.52 -2015-12-01T00:00:00,20.17 -2015-12-01T01:00:00,17.41 -2015-12-01T02:00:00,16.08 -2015-12-01T03:00:00,17.48 -2015-12-01T04:00:00,20.48 -2015-12-01T05:00:00,21.95 -2015-12-01T06:00:00,24.59 -2015-12-01T07:00:00,38.9 -2015-12-01T08:00:00,46.94 -2015-12-01T09:00:00,44.91 -2015-12-01T10:00:00,43.29 -2015-12-01T11:00:00,45.88 -2015-12-01T12:00:00,42.5 -2015-12-01T13:00:00,44.93 -2015-12-01T14:00:00,45.6 -2015-12-01T15:00:00,45.52 -2015-12-01T16:00:00,48.4 -2015-12-01T17:00:00,58.55 -2015-12-01T18:00:00,46.99 -2015-12-01T19:00:00,38.91 -2015-12-01T20:00:00,36.03 -2015-12-01T21:00:00,31.93 -2015-12-01T22:00:00,31.01 -2015-12-01T23:00:00,27.52 -2015-12-02T00:00:00,24.49 -2015-12-02T01:00:00,22.52 -2015-12-02T02:00:00,21.72 -2015-12-02T03:00:00,21.79 -2015-12-02T04:00:00,23.06 -2015-12-02T05:00:00,25.04 -2015-12-02T06:00:00,30.03 -2015-12-02T07:00:00,41.16 -2015-12-02T08:00:00,44.31 -2015-12-02T09:00:00,43.5 -2015-12-02T10:00:00,40.51 -2015-12-02T11:00:00,42.89 -2015-12-02T12:00:00,36.67 -2015-12-02T13:00:00,47.78 -2015-12-02T14:00:00,44.91 -2015-12-02T15:00:00,47.44 -2015-12-02T16:00:00,48.22 -2015-12-02T17:00:00,56.44 -2015-12-02T18:00:00,44.91 -2015-12-02T19:00:00,43.02 -2015-12-02T20:00:00,35.72 -2015-12-02T21:00:00,31.02 -2015-12-02T22:00:00,29.4 -2015-12-02T23:00:00,27.05 -2015-12-03T00:00:00,29.06 -2015-12-03T01:00:00,29.06 -2015-12-03T02:00:00,28.35 -2015-12-03T03:00:00,29.08 -2015-12-03T04:00:00,29.94 -2015-12-03T05:00:00,31.54 -2015-12-03T06:00:00,42.93 -2015-12-03T07:00:00,53.14 -2015-12-03T08:00:00,53.89 -2015-12-03T09:00:00,51.13 -2015-12-03T10:00:00,49.58 -2015-12-03T11:00:00,50.99 -2015-12-03T12:00:00,50 -2015-12-03T13:00:00,48.54 -2015-12-03T14:00:00,47.2 -2015-12-03T15:00:00,46.93 -2015-12-03T16:00:00,50.12 -2015-12-03T17:00:00,57.98 -2015-12-03T18:00:00,51 -2015-12-03T19:00:00,46.11 -2015-12-03T20:00:00,41.87 -2015-12-03T21:00:00,37.79 -2015-12-03T22:00:00,35.05 -2015-12-03T23:00:00,29.54 -2015-12-04T00:00:00,24.39 -2015-12-04T01:00:00,23.71 -2015-12-04T02:00:00,17.37 -2015-12-04T03:00:00,16.83 -2015-12-04T04:00:00,17.19 -2015-12-04T05:00:00,19.37 -2015-12-04T06:00:00,30.06 -2015-12-04T07:00:00,29.45 -2015-12-04T08:00:00,31.01 -2015-12-04T09:00:00,29.07 -2015-12-04T10:00:00,30.13 -2015-12-04T11:00:00,31.48 -2015-12-04T12:00:00,31.42 -2015-12-04T13:00:00,31.49 -2015-12-04T14:00:00,35.74 -2015-12-04T15:00:00,38 -2015-12-04T16:00:00,42.82 -2015-12-04T17:00:00,49.09 -2015-12-04T18:00:00,46.03 -2015-12-04T19:00:00,41.2 -2015-12-04T20:00:00,33.23 -2015-12-04T21:00:00,30.86 -2015-12-04T22:00:00,30.48 -2015-12-04T23:00:00,27.02 -2015-12-05T00:00:00,17.23 -2015-12-05T01:00:00,17.23 -2015-12-05T02:00:00,17.55 -2015-12-05T03:00:00,17.92 -2015-12-05T04:00:00,18.76 -2015-12-05T05:00:00,17.24 -2015-12-05T06:00:00,17.61 -2015-12-05T07:00:00,19.54 -2015-12-05T08:00:00,32.01 -2015-12-05T09:00:00,32.07 -2015-12-05T10:00:00,28.15 -2015-12-05T11:00:00,29.09 -2015-12-05T12:00:00,25.97 -2015-12-05T13:00:00,24.51 -2015-12-05T14:00:00,23.91 -2015-12-05T15:00:00,25.96 -2015-12-05T16:00:00,32.28 -2015-12-05T17:00:00,41.5 -2015-12-05T18:00:00,34.74 -2015-12-05T19:00:00,28.93 -2015-12-05T20:00:00,17.28 -2015-12-05T21:00:00,15 -2015-12-05T22:00:00,16.44 -2015-12-05T23:00:00,13.05 -2015-12-06T00:00:00,14.95 -2015-12-06T01:00:00,9.96 -2015-12-06T02:00:00,10.64 -2015-12-06T03:00:00,10.62 -2015-12-06T04:00:00,10.63 -2015-12-06T05:00:00,6.87 -2015-12-06T06:00:00,4.47 -2015-12-06T07:00:00,9.25 -2015-12-06T08:00:00,12.02 -2015-12-06T09:00:00,12.09 -2015-12-06T10:00:00,12.82 -2015-12-06T11:00:00,13.19 -2015-12-06T12:00:00,12.66 -2015-12-06T13:00:00,11.95 -2015-12-06T14:00:00,14.05 -2015-12-06T15:00:00,16.42 -2015-12-06T16:00:00,24.09 -2015-12-06T17:00:00,31.64 -2015-12-06T18:00:00,28.79 -2015-12-06T19:00:00,30.05 -2015-12-06T20:00:00,29.16 -2015-12-06T21:00:00,28.72 -2015-12-06T22:00:00,34.76 -2015-12-06T23:00:00,31.57 -2015-12-07T00:00:00,26.21 -2015-12-07T01:00:00,23.08 -2015-12-07T02:00:00,23.08 -2015-12-07T03:00:00,22.08 -2015-12-07T04:00:00,23.97 -2015-12-07T05:00:00,27.05 -2015-12-07T06:00:00,40.5 -2015-12-07T07:00:00,48.18 -2015-12-07T08:00:00,49.9 -2015-12-07T09:00:00,50.86 -2015-12-07T10:00:00,47.92 -2015-12-07T11:00:00,47.91 -2015-12-07T12:00:00,46.92 -2015-12-07T13:00:00,45.99 -2015-12-07T14:00:00,45.38 -2015-12-07T15:00:00,49.62 -2015-12-07T16:00:00,54.38 -2015-12-07T17:00:00,52.85 -2015-12-07T18:00:00,51.27 -2015-12-07T19:00:00,51.61 -2015-12-07T20:00:00,43.08 -2015-12-07T21:00:00,35.66 -2015-12-07T22:00:00,36.22 -2015-12-07T23:00:00,30.95 -2015-12-08T00:00:00,26.89 -2015-12-08T01:00:00,28.05 -2015-12-08T02:00:00,26.86 -2015-12-08T03:00:00,24.62 -2015-12-08T04:00:00,24.62 -2015-12-08T05:00:00,26.79 -2015-12-08T06:00:00,30.74 -2015-12-08T07:00:00,45.99 -2015-12-08T08:00:00,47.52 -2015-12-08T09:00:00,43.75 -2015-12-08T10:00:00,38.35 -2015-12-08T11:00:00,34.94 -2015-12-08T12:00:00,32.16 -2015-12-08T13:00:00,34.1 -2015-12-08T14:00:00,39.07 -2015-12-08T15:00:00,41.9 -2015-12-08T16:00:00,43.78 -2015-12-08T17:00:00,47 -2015-12-08T18:00:00,40.88 -2015-12-08T19:00:00,36.72 -2015-12-08T20:00:00,32.91 -2015-12-08T21:00:00,28.64 -2015-12-08T22:00:00,29.89 -2015-12-08T23:00:00,27.33 -2015-12-09T00:00:00,25.57 -2015-12-09T01:00:00,24.59 -2015-12-09T02:00:00,25.09 -2015-12-09T03:00:00,24.48 -2015-12-09T04:00:00,26.84 -2015-12-09T05:00:00,27.34 -2015-12-09T06:00:00,31.34 -2015-12-09T07:00:00,40.9 -2015-12-09T08:00:00,42.37 -2015-12-09T09:00:00,43 -2015-12-09T10:00:00,42.09 -2015-12-09T11:00:00,39.8 -2015-12-09T12:00:00,34.85 -2015-12-09T13:00:00,34.01 -2015-12-09T14:00:00,35.55 -2015-12-09T15:00:00,43.94 -2015-12-09T16:00:00,46.95 -2015-12-09T17:00:00,48.81 -2015-12-09T18:00:00,41.97 -2015-12-09T19:00:00,39.28 -2015-12-09T20:00:00,36.73 -2015-12-09T21:00:00,29.95 -2015-12-09T22:00:00,28.18 -2015-12-09T23:00:00,25.05 -2015-12-10T00:00:00,22.59 -2015-12-10T01:00:00,18.48 -2015-12-10T02:00:00,17.11 -2015-12-10T03:00:00,16.79 -2015-12-10T04:00:00,18.06 -2015-12-10T05:00:00,21.63 -2015-12-10T06:00:00,27.01 -2015-12-10T07:00:00,36.59 -2015-12-10T08:00:00,41.73 -2015-12-10T09:00:00,31.51 -2015-12-10T10:00:00,30 -2015-12-10T11:00:00,28.03 -2015-12-10T12:00:00,27.98 -2015-12-10T13:00:00,29.66 -2015-12-10T14:00:00,30.59 -2015-12-10T15:00:00,36.73 -2015-12-10T16:00:00,44.5 -2015-12-10T17:00:00,46.49 -2015-12-10T18:00:00,41.43 -2015-12-10T19:00:00,34.27 -2015-12-10T20:00:00,30.08 -2015-12-10T21:00:00,27.19 -2015-12-10T22:00:00,26 -2015-12-10T23:00:00,22.61 -2015-12-11T00:00:00,19.05 -2015-12-11T01:00:00,19 -2015-12-11T02:00:00,16.29 -2015-12-11T03:00:00,16.28 -2015-12-11T04:00:00,19.02 -2015-12-11T05:00:00,22.03 -2015-12-11T06:00:00,26.01 -2015-12-11T07:00:00,34.2 -2015-12-11T08:00:00,37.29 -2015-12-11T09:00:00,35.76 -2015-12-11T10:00:00,33.65 -2015-12-11T11:00:00,30.06 -2015-12-11T12:00:00,28.3 -2015-12-11T13:00:00,28.15 -2015-12-11T14:00:00,28.37 -2015-12-11T15:00:00,29.48 -2015-12-11T16:00:00,30.78 -2015-12-11T17:00:00,34.66 -2015-12-11T18:00:00,30.23 -2015-12-11T19:00:00,29.11 -2015-12-11T20:00:00,26 -2015-12-11T21:00:00,23.74 -2015-12-11T22:00:00,19.71 -2015-12-11T23:00:00,16.51 -2015-12-12T00:00:00,18.02 -2015-12-12T01:00:00,16.57 -2015-12-12T02:00:00,16.2 -2015-12-12T03:00:00,14.58 -2015-12-12T04:00:00,16.25 -2015-12-12T05:00:00,16.63 -2015-12-12T06:00:00,15.95 -2015-12-12T07:00:00,19.02 -2015-12-12T08:00:00,21.87 -2015-12-12T09:00:00,24.14 -2015-12-12T10:00:00,24.8 -2015-12-12T11:00:00,26.75 -2015-12-12T12:00:00,26.89 -2015-12-12T13:00:00,25.98 -2015-12-12T14:00:00,28.02 -2015-12-12T15:00:00,30.06 -2015-12-12T16:00:00,33.78 -2015-12-12T17:00:00,40 -2015-12-12T18:00:00,42.96 -2015-12-12T19:00:00,41 -2015-12-12T20:00:00,35.22 -2015-12-12T21:00:00,29.94 -2015-12-12T22:00:00,28.48 -2015-12-12T23:00:00,26.63 -2015-12-13T00:00:00,12.51 -2015-12-13T01:00:00,12.57 -2015-12-13T02:00:00,10.39 -2015-12-13T03:00:00,10.22 -2015-12-13T04:00:00,10.32 -2015-12-13T05:00:00,10.33 -2015-12-13T06:00:00,10.37 -2015-12-13T07:00:00,13.79 -2015-12-13T08:00:00,19.37 -2015-12-13T09:00:00,21.97 -2015-12-13T10:00:00,24.77 -2015-12-13T11:00:00,30.72 -2015-12-13T12:00:00,30.94 -2015-12-13T13:00:00,30.29 -2015-12-13T14:00:00,32.95 -2015-12-13T15:00:00,34.26 -2015-12-13T16:00:00,36.78 -2015-12-13T17:00:00,44.2 -2015-12-13T18:00:00,49.88 -2015-12-13T19:00:00,49.12 -2015-12-13T20:00:00,44.5 -2015-12-13T21:00:00,37.93 -2015-12-13T22:00:00,40.32 -2015-12-13T23:00:00,31.95 -2015-12-14T00:00:00,30.93 -2015-12-14T01:00:00,31.49 -2015-12-14T02:00:00,29.5 -2015-12-14T03:00:00,27.84 -2015-12-14T04:00:00,27.45 -2015-12-14T05:00:00,29.65 -2015-12-14T06:00:00,41.26 -2015-12-14T07:00:00,53.46 -2015-12-14T08:00:00,56.7 -2015-12-14T09:00:00,51.5 -2015-12-14T10:00:00,47.96 -2015-12-14T11:00:00,45.64 -2015-12-14T12:00:00,43.05 -2015-12-14T13:00:00,46.64 -2015-12-14T14:00:00,46.92 -2015-12-14T15:00:00,50.04 -2015-12-14T16:00:00,50.97 -2015-12-14T17:00:00,63.27 -2015-12-14T18:00:00,51.83 -2015-12-14T19:00:00,46.94 -2015-12-14T20:00:00,40.08 -2015-12-14T21:00:00,33.41 -2015-12-14T22:00:00,31.28 -2015-12-14T23:00:00,28.78 -2015-12-15T00:00:00,27.64 -2015-12-15T01:00:00,27.99 -2015-12-15T02:00:00,27.4 -2015-12-15T03:00:00,27.86 -2015-12-15T04:00:00,29.11 -2015-12-15T05:00:00,29.84 -2015-12-15T06:00:00,35.33 -2015-12-15T07:00:00,49.99 -2015-12-15T08:00:00,55.03 -2015-12-15T09:00:00,54.97 -2015-12-15T10:00:00,53.58 -2015-12-15T11:00:00,52.81 -2015-12-15T12:00:00,51.96 -2015-12-15T13:00:00,51.71 -2015-12-15T14:00:00,49.6 -2015-12-15T15:00:00,50.04 -2015-12-15T16:00:00,50.96 -2015-12-15T17:00:00,55.25 -2015-12-15T18:00:00,53.43 -2015-12-15T19:00:00,46.5 -2015-12-15T20:00:00,41.47 -2015-12-15T21:00:00,34.51 -2015-12-15T22:00:00,35.68 -2015-12-15T23:00:00,30.03 -2015-12-16T00:00:00,30.81 -2015-12-16T01:00:00,29.18 -2015-12-16T02:00:00,28.38 -2015-12-16T03:00:00,27.05 -2015-12-16T04:00:00,27.38 -2015-12-16T05:00:00,28.08 -2015-12-16T06:00:00,34.41 -2015-12-16T07:00:00,45.64 -2015-12-16T08:00:00,45.44 -2015-12-16T09:00:00,44.39 -2015-12-16T10:00:00,42.44 -2015-12-16T11:00:00,44 -2015-12-16T12:00:00,42.32 -2015-12-16T13:00:00,43.01 -2015-12-16T14:00:00,40.2 -2015-12-16T15:00:00,41.37 -2015-12-16T16:00:00,41.67 -2015-12-16T17:00:00,45.47 -2015-12-16T18:00:00,43.23 -2015-12-16T19:00:00,39.18 -2015-12-16T20:00:00,33.75 -2015-12-16T21:00:00,30.47 -2015-12-16T22:00:00,33.38 -2015-12-16T23:00:00,25.98 -2015-12-17T00:00:00,23.1 -2015-12-17T01:00:00,22.42 -2015-12-17T02:00:00,21.25 -2015-12-17T03:00:00,19.55 -2015-12-17T04:00:00,20.24 -2015-12-17T05:00:00,23.45 -2015-12-17T06:00:00,31.21 -2015-12-17T07:00:00,39.97 -2015-12-17T08:00:00,41.4 -2015-12-17T09:00:00,41.38 -2015-12-17T10:00:00,40.02 -2015-12-17T11:00:00,40.87 -2015-12-17T12:00:00,36.21 -2015-12-17T13:00:00,35.34 -2015-12-17T14:00:00,34.74 -2015-12-17T15:00:00,35.2 -2015-12-17T16:00:00,39.19 -2015-12-17T17:00:00,41.4 -2015-12-17T18:00:00,40.01 -2015-12-17T19:00:00,38.22 -2015-12-17T20:00:00,32.63 -2015-12-17T21:00:00,28.92 -2015-12-17T22:00:00,28.42 -2015-12-17T23:00:00,24.09 -2015-12-18T00:00:00,25.93 -2015-12-18T01:00:00,24.06 -2015-12-18T02:00:00,23.22 -2015-12-18T03:00:00,21.88 -2015-12-18T04:00:00,22.58 -2015-12-18T05:00:00,25.77 -2015-12-18T06:00:00,30.02 -2015-12-18T07:00:00,36.61 -2015-12-18T08:00:00,36.64 -2015-12-18T09:00:00,37.32 -2015-12-18T10:00:00,39.11 -2015-12-18T11:00:00,38.01 -2015-12-18T12:00:00,36.67 -2015-12-18T13:00:00,34.56 -2015-12-18T14:00:00,34.95 -2015-12-18T15:00:00,34.64 -2015-12-18T16:00:00,39.03 -2015-12-18T17:00:00,42 -2015-12-18T18:00:00,40.97 -2015-12-18T19:00:00,38.65 -2015-12-18T20:00:00,32.83 -2015-12-18T21:00:00,30.32 -2015-12-18T22:00:00,30.53 -2015-12-18T23:00:00,27.17 -2015-12-19T00:00:00,23.47 -2015-12-19T01:00:00,21.52 -2015-12-19T02:00:00,23.29 -2015-12-19T03:00:00,20.45 -2015-12-19T04:00:00,19.62 -2015-12-19T05:00:00,18.96 -2015-12-19T06:00:00,21.84 -2015-12-19T07:00:00,23.9 -2015-12-19T08:00:00,29.28 -2015-12-19T09:00:00,30.08 -2015-12-19T10:00:00,30.07 -2015-12-19T11:00:00,30.09 -2015-12-19T12:00:00,30.01 -2015-12-19T13:00:00,28.31 -2015-12-19T14:00:00,29.08 -2015-12-19T15:00:00,31.21 -2015-12-19T16:00:00,33.01 -2015-12-19T17:00:00,39.37 -2015-12-19T18:00:00,38.44 -2015-12-19T19:00:00,31.32 -2015-12-19T20:00:00,27.9 -2015-12-19T21:00:00,23.49 -2015-12-19T22:00:00,23.44 -2015-12-19T23:00:00,16.72 -2015-12-20T00:00:00,17.93 -2015-12-20T01:00:00,15.96 -2015-12-20T02:00:00,14.29 -2015-12-20T03:00:00,11.36 -2015-12-20T04:00:00,12.51 -2015-12-20T05:00:00,7.75 -2015-12-20T06:00:00,9.77 -2015-12-20T07:00:00,11.21 -2015-12-20T08:00:00,16.18 -2015-12-20T09:00:00,15.42 -2015-12-20T10:00:00,16.01 -2015-12-20T11:00:00,17.94 -2015-12-20T12:00:00,16.8 -2015-12-20T13:00:00,14.97 -2015-12-20T14:00:00,15.96 -2015-12-20T15:00:00,15.73 -2015-12-20T16:00:00,23.38 -2015-12-20T17:00:00,26.34 -2015-12-20T18:00:00,29.73 -2015-12-20T19:00:00,29.14 -2015-12-20T20:00:00,25.74 -2015-12-20T21:00:00,19.92 -2015-12-20T22:00:00,18.87 -2015-12-20T23:00:00,13.02 -2015-12-21T00:00:00,19.02 -2015-12-21T01:00:00,14.49 -2015-12-21T02:00:00,14.35 -2015-12-21T03:00:00,12.44 -2015-12-21T04:00:00,13.39 -2015-12-21T05:00:00,17.6 -2015-12-21T06:00:00,27.4 -2015-12-21T07:00:00,34.03 -2015-12-21T08:00:00,40.05 -2015-12-21T09:00:00,40.5 -2015-12-21T10:00:00,37.44 -2015-12-21T11:00:00,35.85 -2015-12-21T12:00:00,34.17 -2015-12-21T13:00:00,28.73 -2015-12-21T14:00:00,29.18 -2015-12-21T15:00:00,29.59 -2015-12-21T16:00:00,29.03 -2015-12-21T17:00:00,32.18 -2015-12-21T18:00:00,32.92 -2015-12-21T19:00:00,28.94 -2015-12-21T20:00:00,21.85 -2015-12-21T21:00:00,13.85 -2015-12-21T22:00:00,12.58 -2015-12-21T23:00:00,-0.01 -2015-12-22T00:00:00,-19.98 -2015-12-22T01:00:00,-23.06 -2015-12-22T02:00:00,-10.05 -2015-12-22T03:00:00,0.07 -2015-12-22T04:00:00,7.84 -2015-12-22T05:00:00,12.43 -2015-12-22T06:00:00,22.33 -2015-12-22T07:00:00,34.95 -2015-12-22T08:00:00,33.82 -2015-12-22T09:00:00,31.99 -2015-12-22T10:00:00,28.99 -2015-12-22T11:00:00,26.9 -2015-12-22T12:00:00,24.95 -2015-12-22T13:00:00,17.78 -2015-12-22T14:00:00,17.16 -2015-12-22T15:00:00,20.36 -2015-12-22T16:00:00,25.28 -2015-12-22T17:00:00,30.03 -2015-12-22T18:00:00,29.38 -2015-12-22T19:00:00,27.98 -2015-12-22T20:00:00,25.92 -2015-12-22T21:00:00,16.71 -2015-12-22T22:00:00,14.26 -2015-12-22T23:00:00,11.36 -2015-12-23T00:00:00,10.05 -2015-12-23T01:00:00,7.93 -2015-12-23T02:00:00,3.04 -2015-12-23T03:00:00,0.83 -2015-12-23T04:00:00,-0.08 -2015-12-23T05:00:00,7.8 -2015-12-23T06:00:00,12.21 -2015-12-23T07:00:00,14.46 -2015-12-23T08:00:00,18.02 -2015-12-23T09:00:00,16.04 -2015-12-23T10:00:00,16.01 -2015-12-23T11:00:00,16.06 -2015-12-23T12:00:00,15.27 -2015-12-23T13:00:00,16.53 -2015-12-23T14:00:00,22.43 -2015-12-23T15:00:00,25.99 -2015-12-23T16:00:00,28.94 -2015-12-23T17:00:00,39 -2015-12-23T18:00:00,41.27 -2015-12-23T19:00:00,39.71 -2015-12-23T20:00:00,31.24 -2015-12-23T21:00:00,29 -2015-12-23T22:00:00,30.54 -2015-12-23T23:00:00,25.91 -2015-12-24T00:00:00,25.94 -2015-12-24T01:00:00,20.2 -2015-12-24T02:00:00,17.27 -2015-12-24T03:00:00,15.28 -2015-12-24T04:00:00,10.52 -2015-12-24T05:00:00,10.5 -2015-12-24T06:00:00,14.53 -2015-12-24T07:00:00,24.24 -2015-12-24T08:00:00,29.13 -2015-12-24T09:00:00,28.15 -2015-12-24T10:00:00,28.81 -2015-12-24T11:00:00,24.65 -2015-12-24T12:00:00,23.12 -2015-12-24T13:00:00,18.24 -2015-12-24T14:00:00,16.1 -2015-12-24T15:00:00,16.13 -2015-12-24T16:00:00,18.65 -2015-12-24T17:00:00,24.64 -2015-12-24T18:00:00,17.09 -2015-12-24T19:00:00,10.55 -2015-12-24T20:00:00,8.49 -2015-12-24T21:00:00,4.04 -2015-12-24T22:00:00,7.34 -2015-12-24T23:00:00,0.9 -2015-12-25T00:00:00,0.01 -2015-12-25T01:00:00,-0.96 -2015-12-25T02:00:00,0.02 -2015-12-25T03:00:00,0.09 -2015-12-25T04:00:00,3.27 -2015-12-25T05:00:00,4.08 -2015-12-25T06:00:00,4.01 -2015-12-25T07:00:00,4.44 -2015-12-25T08:00:00,11.45 -2015-12-25T09:00:00,12.93 -2015-12-25T10:00:00,12.84 -2015-12-25T11:00:00,13.63 -2015-12-25T12:00:00,12.86 -2015-12-25T13:00:00,11.85 -2015-12-25T14:00:00,12.06 -2015-12-25T15:00:00,13.17 -2015-12-25T16:00:00,18.04 -2015-12-25T17:00:00,25.05 -2015-12-25T18:00:00,28.95 -2015-12-25T19:00:00,29.22 -2015-12-25T20:00:00,27.6 -2015-12-25T21:00:00,24.92 -2015-12-25T22:00:00,26 -2015-12-25T23:00:00,23.73 -2015-12-26T00:00:00,8.5 -2015-12-26T01:00:00,0.09 -2015-12-26T02:00:00,-6.27 -2015-12-26T03:00:00,-4.92 -2015-12-26T04:00:00,-15.33 -2015-12-26T05:00:00,-6.86 -2015-12-26T06:00:00,-0.9 -2015-12-26T07:00:00,0.06 -2015-12-26T08:00:00,6.24 -2015-12-26T09:00:00,10.4 -2015-12-26T10:00:00,8.94 -2015-12-26T11:00:00,12.57 -2015-12-26T12:00:00,1.78 -2015-12-26T13:00:00,7.68 -2015-12-26T14:00:00,8.08 -2015-12-26T15:00:00,12.24 -2015-12-26T16:00:00,14.93 -2015-12-26T17:00:00,18 -2015-12-26T18:00:00,16.03 -2015-12-26T19:00:00,15.95 -2015-12-26T20:00:00,17.49 -2015-12-26T21:00:00,13.3 -2015-12-26T22:00:00,14.44 -2015-12-26T23:00:00,10.57 -2015-12-27T00:00:00,10.6 -2015-12-27T01:00:00,8.65 -2015-12-27T02:00:00,7.29 -2015-12-27T03:00:00,6.29 -2015-12-27T04:00:00,3.46 -2015-12-27T05:00:00,0.11 -2015-12-27T06:00:00,0.04 -2015-12-27T07:00:00,0.11 -2015-12-27T08:00:00,9.8 -2015-12-27T09:00:00,10.03 -2015-12-27T10:00:00,9.84 -2015-12-27T11:00:00,10.79 -2015-12-27T12:00:00,10.42 -2015-12-27T13:00:00,10.18 -2015-12-27T14:00:00,11.21 -2015-12-27T15:00:00,14.89 -2015-12-27T16:00:00,22.07 -2015-12-27T17:00:00,31.55 -2015-12-27T18:00:00,35.7 -2015-12-27T19:00:00,35.71 -2015-12-27T20:00:00,32.96 -2015-12-27T21:00:00,31.83 -2015-12-27T22:00:00,31.65 -2015-12-27T23:00:00,27.47 -2015-12-28T00:00:00,26.26 -2015-12-28T01:00:00,20.35 -2015-12-28T02:00:00,17.75 -2015-12-28T03:00:00,16.01 -2015-12-28T04:00:00,16.98 -2015-12-28T05:00:00,22.05 -2015-12-28T06:00:00,27.8 -2015-12-28T07:00:00,34.98 -2015-12-28T08:00:00,38.81 -2015-12-28T09:00:00,41.4 -2015-12-28T10:00:00,39.87 -2015-12-28T11:00:00,36.04 -2015-12-28T12:00:00,36 -2015-12-28T13:00:00,33.4 -2015-12-28T14:00:00,32.53 -2015-12-28T15:00:00,33.15 -2015-12-28T16:00:00,36.9 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b/docs/.gitignore @@ -0,0 +1 @@ +*build diff --git a/docs/make.jl b/docs/make.jl index a1517e1..4228a86 100644 --- a/docs/make.jl +++ b/docs/make.jl @@ -6,9 +6,9 @@ makedocs(sitename="ClustForOpt.jl", authors = "Holger Teichgraeber, and Elias Kuepper", pages = [ "Introduction" => "index.md", - "Workflow" => "workflow.md", + "Quick Start Guide" => "quickstart.md", "Load Data" => "load_data.md", - "Clustering" => "clust.md", + "Representative Periods" => "repr_per.md", "Optimization" => "opt.md" ], format = Documenter.HTML(assets=["assets/clust_for_opt_text.svg"]) diff --git a/docs/src/clust.md b/docs/src/clust.md deleted file mode 100644 index c4e869a..0000000 --- a/docs/src/clust.md +++ /dev/null @@ -1,45 +0,0 @@ -# Clustering - -`run_clust()` takes the full `data` and gives a struct with the clustered data as the output. - -The input parameter `n_clust` determines the number of clusters,i.e., representative periods. - -## Supported clustering methods - -The following combinations of clustering method and representations are supported by `run_clust`: - -Name | method | representation ----- | --------------- | ----------------------- -k-means clustering | `` | `` -k-means clustering with medoid representation | `` | `` -k-medoids clustering (partitional) | `` | `` -k-medoids clustering (exact) [requires Gurobi] | `` | `` -hierarchical clustering with centroid representation | `` | `` -hierarchical clustering with medoid representation | `` | `` - -For use of DTW barycenter averaging (DBA) and k-shape clustering on single-attribute data (e.g. electricity prices), please use branch `v0.1-appl_energy-framework-comp`. - -```@docs -run_clust -``` - -## Opt Types -```@docs -FullInputData -ClustData -ClustDataMerged -ClustResultAll -ClustResult -``` - -## Example running clustering -```@example -using ClustForOpt -# load ts-input-data -ts_input_data = load_timeseries_data(normpath(joinpath(@__DIR__,"..","..","data","TS_GER_1")); T=24, years=[2016]) -ts_clust_data = run_clust(ts_input_data).clust_data -using Plots -plot(ts_clust_data.data["solar-germany"], legend=false, linestyle=:solid, width=3, xlabel="Time [h]", ylabel="Solar availability factor [%]") -savefig("clust.svg") -``` -![Plot](clust.svg) diff --git a/docs/src/index.md b/docs/src/index.md index 8a1fd4e..3c38e73 100644 --- a/docs/src/index.md +++ b/docs/src/index.md @@ -3,16 +3,33 @@ [![](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 clustering methods for finding representative periods for optimization problems. A utilization in a scalable capacity expansion problem can be found in the package [CEP](https://github.com/YoungFaithful/CapacityExpansion.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. -The package has two main purposes: 1) Provide a simple process of clustering time-series input data, with clustered data output in a generalized type system 2) provide an interface between clustered data and optimization problem. +The package has three main purposes: +1) Provide a simple process of finding representative periods for time-series input data, with implementations of the most commonly used clustering methods and extreme value selection methods. +2) Provide an interface between representative period data and optimization problem by having representative period data stored in a generalized type system. +3) Provide a generalized import feature for time series, where variable names, attributes, and node names are automatically stored and can then be used in the definition of sets of the optimization problem later. -The package follows the clustering framework presented in [Teichgraeber and Brandt, 2019](https://doi.org/10.1016/j.apenergy.2019.02.012). -The package is actively developed, and new features are continuously added. For a reproducible version of the methods and data of the original paper by [Teichgraeber and Brandt, 2019](https://doi.org/10.1016/j.apenergy.2019.02.012), please refer to branch `v0.1-appl_energy-framework-comp`. +An example energy systems optimization problem that uses ClustForOpt for its input data is the package [CapacityExpansion](https://github.com/YoungFaithful/CapacityExpansion.jl), which implements a scalable generation and transmission capacity expansion problem. + +The ClustForOpt package follows the clustering framework presented in [Teichgraeber and Brandt, 2019](https://doi.org/10.1016/j.apenergy.2019.02.012). +The package is actively developed, and new features are continuously added. For a reproducible version of the methods and data of the original paper by [Teichgraeber and Brandt, 2019](https://doi.org/10.1016/j.apenergy.2019.02.012), please refer to [v0.1](https://github.com/holgerteichgraeber/ClustForOpt.jl/tree/v0.1). This package is developed by Holger Teichgraeber [@holgerteichgraeber](https://github.com/holgerteichgraeber) and Elias Kuepper [@YoungFaithful](https://github.com/youngfaithful). +## Installation +This package runs under julia v1.0 and higher. +Install using: + +```julia +import Pkg +Pkg.add("ClustForOpt") +``` + +## Citing ClustForOpt If you find ClustForOpt useful in your work, we kindly request that you cite the following paper ([link](https://doi.org/10.1016/j.apenergy.2019.02.012)): ``` @@ -26,13 +43,3 @@ If you find ClustForOpt useful in your work, we kindly request that you cite the doi = {https://doi.org/10.1016/j.apenergy.2019.02.012}, } ``` - -## Installation -This package runs under julia v1.0 and higher. -Install using: - -```julia -] -add ClustForOpt -``` -where `]` opens the julia package manager. diff --git a/docs/src/load_data.md b/docs/src/load_data.md index a3ed550..02c1a72 100644 --- a/docs/src/load_data.md +++ b/docs/src/load_data.md @@ -1,23 +1,48 @@ -# Load Data -## Load Timeseries Data -`load_timeseries_data()` loads the data for a given `application` and `region`. -Possible applications are -- `DAM`: Day ahead market price data +Load Data +========= +Here, we describe how to load time-series data into the `ClustData` format for use in ClustForOpt, and we describe how data is stored in `ClustData`. +Data can be loaded from example data sets provided by us, or you can load your own data. -Possible regions are: -- `GER`: Germany -- `CA`: California +## Load example data from ClustForOpt +The example data can be loaded using the following function. +```@docs +load_timeseries_data(::Symbol) +``` +In the following example, we use the function to load the hourly wind, solar, demand data for Germany for 1 node, and the other data can be loaded similarly. Note that more years are available for the two CEP data sets. The data can be found in the [data](https://github.com/holgerteichgraeber/ClustForOpt.jl/tree/master/data) folder. +```@setup load_data +using ClustForOpt +``` +```@repl load_data +ts_input_data = load_timeseries_data(:CEP_GER1) +``` -The optional input parameters to `load_timeseries_data()` are the number of periods `K` and the number of time steps per period `T`. By default, they are chosen such that they result in daily time slices. +## Load your own data +You can also load your own data. Use the `load_timeseries_data` function and specify the path where the data is located at (either a folder or the filename). +```@docs +load_timeseries_data(::String) +``` +The data in your `.csv` file should be in the format Timestamp-Year-ColumnName as specified above. The files in [the single node system GER1](https://github.com/holgerteichgraeber/ClustForOpt.jl/tree/master/data/TS_GER_1) and [multi-node system GER18](https://github.com/holgerteichgraeber/ClustForOpt.jl/tree/master/data/TS_GER_18) give a good overview of how the data should be structured. +The path can be relative or absolute as in the following example +```@repl load_data +ts_input_data = load_timeseries_data(normpath(joinpath(@__DIR__,"..","..","data","TS_GER_1"))) # relative path from the documentation file to the data folder +ts_input_data = load_timeseries_data("/home/username/yourpathtofolder") # absolute path on Linux/Mac +ts_input_data = load_timeseries_data("C:\\Users\\Username\\yourpathtofolder") # absolute path on Windows +``` + +## ClustData struct +The `ClustData` struct is at the core of ClustForOpt. It contains the temporal input data, and also relevant information that can be easily used in the formulation of the optimization problem. ```@docs -load_timeseries_data +ClustData ``` -### Example loading timeseries data +Note that `K` and `T` can be used to construct sets that define the temporal structure of the optimization problem, and that `weights` can be used to weight the representative periods in the objective function of the optimization problem. +`k_ids` can be used to implement seasonal storage formulations for long-term energy systems optimization problems. `delta_t` can be used to implement within-period segmentation. + +## Example data +This shows the solar data as an example. ```@example using ClustForOpt -# laod ts-input-data -ts_input_data = load_timeseries_data(normpath(joinpath(@__DIR__,"..","..","data","TS_GER_1")); T=24, years=[2016]) +ts_input_data = load_timeseries_data(:CEP_GER1; T=24, years=[2016]) using Plots plot(ts_input_data.data["solar-germany"], legend=false, linestyle=:dot, xlabel="Time [h]", ylabel="Solar availability factor [%]") savefig("load_timeseries_data.svg") diff --git a/docs/src/opt.md b/docs/src/opt.md index 84c4a62..812fdb9 100644 --- a/docs/src/opt.md +++ b/docs/src/opt.md @@ -1,2 +1,17 @@ -# Optimization -The function `run_opt()` runs the optimization problem and gives as an output a struct that contains optimal objective function value, decision variables, and additional info. The `run_opt()` function infers the optimization problem type from the input data. See the example folder for further details. +Optimization +============ +The main purpose of this package is to provide a process and type system (structs) that can be easily integrated with optimization problems. ClustForOpt allows for the data to be processed in one single type, regardless of the dimensionality of the input data. This allows to quickly evaluate different temporal resolutions on a given optimization model. + +The most important fields of the data struct are +```@setup opt +using ClustForOpt +ts_input_data = load_timeseries_data(:CEP_GER1) +``` +```@repl opt +ts_clust_data.data # the clustered data +ts_clust_data.K # number of periods +ts_clust_data.T # number of time steps per period +``` +`K` and `T` can be directly integrated in the creation of the sets that define the temporal resolution of the formulation of the optimization problem. + +The package [CapacityExpansion](https://github.com/YoungFaithful/CapacityExpansion.jl) provides a generation and transmission capacity expansion problem that can utilize the wind, solar, and demand data from the `:CEP_GER1` and `:CEP_GER18` examples and uses the data types introduced in ClustForOpt. Please refer to the documentation of the CapacityExpansion package for how to use it. diff --git a/docs/src/quickstart.md b/docs/src/quickstart.md new file mode 100644 index 0000000..edeb83a --- /dev/null +++ b/docs/src/quickstart.md @@ -0,0 +1,41 @@ +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, 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 +- find representative periods (clustering + extreme period selection) +- optimization + +## Example Workflow +After ClustForOpt is installed, you can use it by saying: +```@repl workflow +using ClustForOpt +``` + +The first step is to load the data. The following example loads hourly wind, solar, and demand data for Germany (1 region) for one year. +```@repl workflow +ts_input_data = load_timeseries_data(:CEP_GER1) +``` +The output `ts_input_data` is a `ClustData` data struct that contains the data and additional information about the data. +```@repl workflow +ts_input_data.data # a dictionary with the data. +ts_input_data.data["wind-germany"] # the wind data (choose solar, el_demand as other options in this example) +ts_input_data.K # number of periods +``` + +The second step is to cluster the data into representative periods. Here, we use k-means clustering and get 5 representative periods. +```@repl workflow +clust_res = run_clust(ts_input_data;method="kmeans",n_clust=5) +ts_clust_data = clust_res.clust_data +``` +The `ts_clust_data` is a `ClustData` data struct, this time with clustered data (i.e. less representative periods). +```@repl workflow +ts_clust_data.data # the clustered data +ts_clust_data.data["wind-germany"] # the wind data. Note the dimensions compared to ts_input_data +ts_clust_data.K # number of periods +``` + +The clustered input data can be used as input to an optimization problem. +The optimization problem formulated in the package [CapacityExpansion](https://github.com/YoungFaithful/CapacityExpansion.jl) can be used with the data clustered in this example. diff --git a/docs/src/repr_per.md b/docs/src/repr_per.md new file mode 100644 index 0000000..fc50e58 --- /dev/null +++ b/docs/src/repr_per.md @@ -0,0 +1,86 @@ +Representative Periods +====================== + +The following describes how to find representative periods out of the full time-series input data. This includes both clustering and extreme period selection. + +## Clustering +The function `run_clust()` takes the `data` and gives a `ClustResult` struct with the clustered data as the output. +```@docs +run_clust(::ClustData) +``` + +The following examples show some use cases of `run_clust`. +```@setup clust +using ClustForOpt +ts_input_data=load_timeseries_data(:CEP_GER1) +``` +```@repl clust +clust_res = run_clust(ts_input_data) # uses the default values, so this is a k-means clustering algorithm with centroid representation that finds 5 clusters. +clust_res = run_clust(ts_input_data;method="kmedoids",representation="medoid",n_clust=10) #kmedoids clustering that finds 10 clusters +clust_res = run_clust(ts_input_data;method="hierarchical",representation=medoid,n_init=1) # Hierarchical clustering with medoid representation. +``` +The resulting struct contains the data, but also cost and configuration information. +```@repl clust +ts_clust_data = clust_res.clust_data +clust_cost = clust_res.cost +clust_config = clust_res.config +``` +The `ts_clust_data` is a `ClustData` data struct, this time with clustered data (i.e. less representative periods). + +Shape-based clustering methods are supported in an older version of ClustForOpt: For use of DTW barycenter averaging (DBA) and k-shape clustering on single-attribute data (e.g. electricity prices), please use [v0.1](https://github.com/holgerteichgraeber/ClustForOpt.jl/tree/v0.1). + +## Extreme period selection +Additionally to clustering the input data, extremes of the data may be relevant to the optimization problem. Therefore, we provide methods for extreme value identification, and to include them in the set of representative periods. + +The methods can be used as follows. +```@example +using ClustForOpt +ts_input_data = load_timeseries_data(:CEP_GER1) + # define simple extreme days of interest + ev1 = SimpleExtremeValueDescr("wind-germany","min","absolute") + ev2 = SimpleExtremeValueDescr("solar-germany","min","integral") + ev3 = SimpleExtremeValueDescr("el_demand-germany","max","absolute") + ev = [ev1, ev2, ev3] + # simple extreme day selection + ts_input_data_mod,extr_vals,extr_idcs = simple_extr_val_sel(ts_input_data,ev;rep_mod_method="feasibility") + + # run clustering +ts_clust_res = run_clust(ts_input_data_mod;method="kmeans",representation="centroid",n_init=100,n_clust=5) # default k-means + +# representation modification +ts_clust_extr = representation_modification(extr_vals,ts_clust_res.clust_data) +``` +The resulting `ts_clust_extr` contains both the clustered periods and the extreme periods. + +The extreme periods are first defined by their characteristics by use of `SimpleExtremeValueDescr`. The struct has the following options: +```@docs +SimpleExtremeValueDescr(::String,::String,::String) +``` + +Then, they are selected based on the function `simple_extr_val_sel`: +```@docs +simple_extr_val_sel(::ClustData, ::Array{SimpleExtremeValueDescr,1}) +``` + +## ClustResult struct +The output of `run_clust` function is a `ClustResult` struct with the following fields. +```@docs +ClustResult +``` + +If `run_clust` is run with the option `get_all_clust_results=true`, the output is the struct `ClustResultAll`, which contains all locally converged solutions. +```@docs +ClustResultAll +``` + +## Example running clustering +In this example, the wind, solar, and demand data from Germany for 2016 are clustered to 5 representative periods, and the solar data is shown in the plot. +```@example +using ClustForOpt +ts_input_data = load_timeseries_data(:CEP_GER1; T=24, years=[2016]) +ts_clust_data = run_clust(ts_input_data;n_clust=5).clust_data +using Plots +plot(ts_clust_data.data["solar-germany"], legend=false, linestyle=:solid, width=3, xlabel="Time [h]", ylabel="Solar availability factor [%]") +savefig("clust.svg") +``` +![Plot](clust.svg) diff --git a/docs/src/workflow.md b/docs/src/workflow.md deleted file mode 100644 index 444d520..0000000 --- a/docs/src/workflow.md +++ /dev/null @@ -1,20 +0,0 @@ -## Workflow - -Generally, the workflow requires three steps: -- load data -- clustering -- optimization - -## Example Workflow -```julia -using ClustForOpt - -# load data (electricity price day ahead market) -ts_input_data, = load_timeseries_data("DAM", "GER";K=365, T=24) #DAM - -# run standard kmeans clustering algorithm to cluster into 5 representative periods, with 1000 initial starting points -clust_res = run_clust(ts_input_data;method="kmeans",representation="centroid",n_clust=5,n_init=1000) - -# battery operations optimization on the clustered data -opt_res = run_opt(clust_res) -``` diff --git a/examples/example_run_clust.jl b/examples/example_run_clust.jl deleted file mode 100644 index 9e7579d..0000000 --- a/examples/example_run_clust.jl +++ /dev/null @@ -1,35 +0,0 @@ -# Some example runs of the clustering methods and optimization problems - # TODO:OLD Update this file. This is the old ClustForOpt - # saves results as jld2 file, which can be imported and analyzed by subsequent functions - -using ClustForOpt -using Gurobi -env = Gurobi.Env() - - # default kmeans + centroid run -run_clust("GER","battery";n_init=3) - - # kmeans + medoid run -run_clust("GER","battery";representation="medoid",n_init=3) - - # kmedoids + medoid run (partitional) -run_clust("GER","battery";method="kmedoids",representation="medoid",n_init=3) - - # kmedoids + medoid run (exact) - #QUESTION Shall we force the usage of Gurobi -run_clust("GER","battery";method="kmedoids_exact",representation="medoid",n_init=3,gurobi_env=env) - - # hierarchical + centroid run -run_clust("GER","battery";method="hierarchical",representation="centroid",n_init=1) - - # hierarchical + medoid run -run_clust("GER","battery";method="hierarchical",representation="medoid",n_init=1) - - # dbaclust + centroid run - #iterations=100 - #inner_iterations=30 - #rad_sc_min=0 - #rad_sc_max=5 - - # dbaclust + centroid run (single core, for parallel runs, use parallel version) -run_clust("GER","battery";method="dbaclust",representation="centroid",n_init=3,iterations=50,rad_sc_min=0,rad_sc_max=1,inner_iterations=30) diff --git a/examples/workflow_example_bat.jl b/examples/workflow_example_bat.jl deleted file mode 100644 index 9ccc8bc..0000000 --- a/examples/workflow_example_bat.jl +++ /dev/null @@ -1,35 +0,0 @@ -#TODO OLD rewrite This file exemplifies the workflow from data input to optimization result generation - -using ClustForOpt -#using ClustForOpt_priv -#using Gurobi - -# load data -ts_input_data, = load_timeseries_data("DAM", "GER";K=365, T=24) #DAM - - # run clustering -using Gurobi -clust_res_ar = [] -optimizer=Gurobi.Optimizer -for i=1:2 - push!(clust_res_ar, run_clust(ts_input_data;method="kmedoids_exact",representation="medoid",iterations=5,n_init=1,gurobi_opt=optimizer)) # default k-means -end - - # optimization - -opt_res = run_opt("battery",clust_res_ar[2].clust_data) - #opt_res = run_opt("gas_turbine",clust_res.clust_data[5]) - - ### - # run optimization for all k=1:9 -opt_res_all = [] -obj=[] -for i=1:2 - push!(opt_res_all,run_opt("battery", clust_res_ar[i].clust_data)) - push!(obj,opt_res_all[i].obj) -end - # run reference case -opt_res_full = run_opt("battery",ts_input_data) - #using PyPlot - # figure() - # plot(obj/opt_res_full.obj) diff --git a/examples/workflow_example_kmedoids_exact.jl b/examples/workflow_example_kmedoids_exact.jl new file mode 100644 index 0000000..a424835 --- /dev/null +++ b/examples/workflow_example_kmedoids_exact.jl @@ -0,0 +1,14 @@ +using ClustForOpt + +ts_input_data = load_timeseries_data(:CEP_GER1) + +# when using kmedoids-exact, one needs to supply the optimizer. Make sure the optimizer is added through Pkg.add() + +using Cbc +optimizer = Cbc.Optimizer +out = run_clust(ts_input_data;method="kmedoids_exact",representation="medoid",n_clust=ts_input_data.5,n_init=1,kmexact_optimizer=optimizer) + + +using Gurobi +optimizer = Gurobi.Optimizer +out = run_clust(ts_input_data;method="kmedoids_exact",representation="medoid",n_init=1,kmexact_optimizer=optimizer) diff --git a/paper/paper.bib b/paper/paper.bib new file mode 100644 index 0000000..09c06f8 --- /dev/null +++ b/paper/paper.bib @@ -0,0 +1,183 @@ +@article{Teichgraeber:2019, +author = {Teichgraeber, Holger and Brandt, Adam R}, +doi = {10.1016/j.apenergy.2019.02.012}, +issn = {0306-2619}, +journal = {Applied Energy}, +pages = {1283--1293}, +publisher = {Elsevier}, +title = {{Clustering methods to find representative periods for the optimization of energy systems : an initial framework and comparison}}, +url = {https://doi.org/10.1016/j.apenergy.2019.02.012}, +volume = {239}, +year = {2019} +} + + +@article{Lindenmeyer:2019, +author = {Lindenmeyer, Constantin and Teichgraeber, Holger and Baumgaertner, Nils and Kotzur, Leander and Robinius, Martin and Bardow, Andre and Brandt, Adam R}, +journal = {Working Paper}, +title = {{The average is not enough: Extreme values as part of representative periods for the optimization of energy systems}}, +year = {2019} +} + +@online{TSAM, + author = {Kotzur, Leander and Kaldemeyer, Cord}, + title = {TSAM}, + year = 2017, + url = {https://github.com/FZJ-IEK3-VSA/tsam}, + doi = {10.5281/zenodo.2547683} +} + + +@article{Pfenninger:2018, +author = {Pfenninger, Stefan and Pickering, Bryn}, +doi = {10.21105/joss.00825}, +journal = {Journal of Open Source Software}, +number = {29}, +pages = {825}, +title = {{Calliope: a multi-scale energy systems modelling framework}}, +volume = {3}, +year = {2018} +} + +@article{Teichgraeber:2017, +author = {Teichgraeber, Holger and Brodrick, Philip G. and Brandt, Adam R.}, +doi = {10.1016/j.energy.2017.09.087}, +issn = {03605442}, +journal = {Energy}, +pages = {506--518}, +publisher = {Elsevier Ltd}, +title = {{Optimal design and operations of a flexible oxyfuel natural gas plant}}, +volume = {141}, +year = {2017} +} + +@article{Brodrick:2017, +author = {Brodrick, Philip G. and Brandt, Adam R. and Durlofsky, Louis J.}, +doi = {10.1016/j.energy.2017.11.059}, +issn = {03605442}, +journal = {Energy}, +pages = {1569--1584}, +publisher = {Elsevier Ltd}, +title = {{Operational optimization of an integrated solar combined cycle under practical time-dependent constraints}}, +volume = {141}, +year = {2017} +} + + +@article{Bahl:2017, +author = {Bahl, Bj{\"{o}}rn and K{\"{u}}mpel, Alexander and Seele, Hagen and Lampe, Matthias and Bardow, Andr{\'{e}}}, +doi = {10.1016/j.energy.2017.06.082}, +issn = {0360-5442}, +journal = {Energy}, +pages = {900--912}, +title = {{Time-series aggregation for synthesis problems by bounding error in the objective function}}, +volume = {135}, +year = {2017} +} + + +@article{Kotzur:2018, +author = {Kotzur, Leander and Markewitz, Peter and Robinius, Martin and Stolten, Detlef}, +doi = {10.1016/j.renene.2017.10.017}, +issn = {18790682}, +journal = {Renewable Energy}, +pages = {474--487}, +publisher = {Elsevier Ltd}, +title = {{Impact of different time series aggregation methods on optimal energy system design}}, +volume = {117}, +year = {2018} +} + +@article{Nahmmacher:2016, +author = {Nahmmacher, Paul and Schmid, Eva and Hirth, Lion and Knopf, Brigitte}, +doi = {10.1016/j.energy.2016.06.081}, +issn = {03605442}, +journal = {Energy}, +pages = {430--442}, +title = {{Carpe diem: A novel approach to select representative days for long-term power system modeling}}, +volume = {112}, +year = {2016} +} + + +@article{Pfenninger:2017, +abstract = {Using a high-resolution planning model of the Great Britain power system and 25years of simulated wind and PV generation data, this study compares different methods to reduce time resolution of energy models to increase their computational tractability: downsampling, clustering, and heuristics. By comparing model results in terms of costs and installed capacities across different methods, this study shows that the best method depends heavily on input data and the setup of model constraints. This implies that there is no one-size-fits-all approach to the problem of time step reduction, but heuristic approaches appear promising. In addition, the 25years of time series demonstrate considerable inter-year variability in wind and PV power output. This further complicates the problem of time detail in energy models as it suggests long time series are necessary. Model results with high shares of PV and wind generation using a single or few years of data are likely unreliable. Better modeling and planning methods are required to determine robust scenarios with high shares of variable renewables. The methods are implemented in the freely available open-source modeling framework Calliope.}, +author = {Pfenninger, Stefan}, +doi = {10.1016/j.apenergy.2017.03.051}, +file = {:C$\backslash$:/HOLGER/MendeleyFiles/Pfenninger - 2017 - Dealing with multiple decades of hourly wind and PV time series in energy models A comparison of methods to reduce t.pdf:pdf}, +issn = {03062619}, +journal = {Applied Energy}, +mendeley-groups = {Aggregation/Applications/1CapacityExpansion}, +pages = {1--13}, +title = {{Dealing with multiple decades of hourly wind and PV time series in energy models: A comparison of methods to reduce time resolution and the planning implications of inter-annual variability}}, +url = {http://www.sciencedirect.com/science/article/pii/S0306261917302775}, +volume = {197}, +year = {2017} +} + +@article{Dempster:1981, +author = {Dempster, M A H and Fisher, M L and L., Jansen and B.J., Lageweg and J.K., Lenstra and A.H.G., Rinnooy Kan}, +journal = {Operations Research}, +number = {4}, +pages = {707--716}, +title = {{Analytical Evaluation of Hierarchical planning systems}}, +volume = {29}, +year = {1981} +} + + +@article{Jia:2012, +author = {Jia, Long and Hu, Zechun and Song, Yonghua and Luo, Zhuowei}, +doi = {10.1109/IEVC.2012.6183283}, +journal = {2012 IEEE International Electric Vehicle Conference, IEVC 2012}, +pages = {1--6}, +publisher = {IEEE}, +title = {{Optimal siting and sizing of electric vehicle charging stations}}, +year = {2012} +} + + +@article{Karuppiah:2010, +author = {Karuppiah, Ramkumar and Mart{\'{i}}n, Mariano and Grossmann, Ignacio E.}, +doi = {10.1016/j.compchemeng.2009.10.009}, +issn = {00981354}, +journal = {Computers and Chemical Engineering}, +number = {8}, +pages = {1246--1255}, +title = {{A simple heuristic for reducing the number of scenarios in two-stage stochastic programming}}, +volume = {34}, +year = {2010} +} + + +@thesis{Kuepper:2019, +address = {Aachen}, +author = {Kuepper, Lucas Elias}, +school = {Stanford University, RWTH Aachen University}, +title = {{Investigation of time-series aggregation methods for infrastructure optimization of low emissions energy systems}}, +type = {MS thesis}, +year = {2019} +} + +@article{Dunning:2017, +author = {Iain Dunning and Joey Huchette and Miles Lubin}, +title = {JuMP: A Modeling Language for Mathematical Optimization}, +journal = {SIAM Review}, +volume = {59}, +number = {2}, +pages = {295-320}, +year = {2017}, +doi = {10.1137/15M1020575}, +} + + +@article{Bezanson2017, +author = {Bezanson, Jeff and Edelman, Alan and Karpinski, Stefan and Shah, Viral B.}, +journal = {SIAM Review}, +doi = {10.1137/141000671}, +number = {1}, +pages = {65--98}, +title = {{Julia: A Fresh Approach to Numerical Computing}}, +volume = {59}, +year = {2017} +} diff --git a/paper/paper.md b/paper/paper.md new file mode 100644 index 0000000..ed855cf --- /dev/null +++ b/paper/paper.md @@ -0,0 +1,75 @@ +--- +title: 'ClustForOpt: Time-series aggregation for optimization in Julia' +tags: + - Julia + - clustering + - representative periods + - optimization + - time series +authors: + - name: Holger Teichgraeber + orcid: 0000-0002-4061-2226 + affiliation: 1 + - name: Lucas Elias Kuepper + orcid: 0000-0002-1992-310X + affiliation: 1 + - name: Adam R. Brandt + orcid: 0000-0002-2528-1473 + affiliation: 1 +affiliations: + - name: Department of Energy Resources Engineering, Stanford University + index: 1 +date: 5 July 2019 +bibliography: paper.bib +--- + +# Summary + +``ClustForOpt`` is an extensible framework for time-series aggregation in Julia. It is designed specifically to perform time series aggregation for energy systems optimization problems, though could be adapated to be used with arbitrary time series datasets. ``ClustForOpt`` provides a type system for temporal data that can easily be integrated with the formulation of optimization problems, and provides an implementation of the most commonly used clustering methods and extreme value selection methods for temporal data. ``ClustForOpt`` provides simple integration of multiple attributes (e.g. wind availability, solar availability, and electricity demand) in a single aggregation process. + +The unique design of ``ClustForOpt`` allows for scientific comparison of the performance of different time-series aggregation methods, both in terms of the statistical error measure and in terms of optimization outcome. +The choice of temporal modeling, especially of time-series aggregation methods, can have significant impact on overall optimization outcome, which in the end is used to make policy and business decisions. It is thus important to not view time-series aggregation and optimization model formulation as two seperate, consecutive steps, but to integrate time-series aggregation into the overall process of building an optimization model in an iterative manner. ``ClustForOpt`` allows for this iterative integration in a simple way. + +``ClustForOpt`` provides two sample optimization problems to illustrate the integration of time-series aggregation and optimization problem formulation through our type system. +However, it is generally thought to be independent of the application at hand, and others are encouraged to use the package as a base for their own optimization problem formulation. +The Julia package [``CapacityExpansion``](https://github.com/YoungFaithful/CapacityExpansion.jl) provides a detailed generation and transmission capacity expansion model built upon ``ClustForOpt``, and illustrates its capabilities in conjunction with a complex optimization problem formulation. + +The clustering methods that are implemented in ``ClustForOpt`` follow the framework presented by @Teichgraeber:2019, and the extreme value selection methods follow the framework presented by @Lindenmeyer:2019. Using these frameworks allows ``ClustForOpt`` to be generally extensible to new aggregation methods in the future. + +To the best of our knowledge, time-series aggregation has been included in two open-source packages to date, both in written in Python. +@TSAM provides an implementation of several time-series aggregation methods in Python. +Calliope [@Pfenninger:2018] is a capacity expansion modeling software in Python that includes time-series aggregation for the use case of generation and transmission capacity expansion modeling. + +``ClustForOpt`` is the first package to provide time-series aggregation in Julia [@Bezanson:2017]. This is advantageous because it can be used in conjunction with the JuMP [@Dunning:2017] package in Julia, which provides an excellent modeling language for optimization problems. Furthermore, ``ClustForOpt`` includes both clustering and extreme value selection and integrates them into the same output type. This is important in order to retain the characteristics of the time-series that are relevant to many optimization problems. + +At this point, we would like to point to the key features that ``ClustForOpt`` provides. Implementation details can be found in the software's documentation. + +- *The type system*: The data type (called struct in Julia) ``ClustData`` stores all time-series data in a common format. Besides the data itself, it automatically processes and stores information which are relevant to formulating the sets of the optimization problem later, such as number of periods, the number of time steps per period, and the chronology of the periods. The data type ``ClustResult`` additionally stores information relevant for evaluating clustering performance. These data types make ``ClustForOpt`` to be easily integrated with any optimization problem and analysis. + +- *The aggregation methods*: The most commonly used clustering methods and extreme value selection methods are implemented with a common interface, allowing for simple comparison of these methods on a given data set and optimization problem. + +- *The generalized import of time series in csv format*: Time series can be loaded through csv files in a pre-defined format. From this, variable names, attributes, and node names are automatically loaded and stored. The original time series can be sliced into periods of user-defined length. This information can then be used in the definition of the sets of the optimization problem later. + +- *Multiple attributes and nodes*: Multiple time series, one for each attribute and node, are automatically combined and aggregated simultaneously. + +# Application areas + +Generally, optimization is concerned with the maximization or minimization of a certain objective subject to a number of constraints. The range of optimization problems ``ClustForOpt`` is applicable to is broad. +They generally fall into the class of design and operations problems, also called planning problems or two-stage optimization problems. In these problems, decisions on two time horizons have to be made: Long-term design decisions, as to what equipment to buy, and short-term operating decisions, as to when to operate that equipment. Because the two time horizons are intertwined, operating decisions impact the system design, and vice versa. Operating decisions are of temporal nature, and the amount of temporal input data for these optimization problems often makes them computationally intractable. +Usually, time series of length $N$ (e.g. hourly electricity demand for one year, where $N=8760$) are split into $\hat{K}$ periods of length $T=\frac{N}{\hat{K}}$ (e.g. $\hat{K}=365$ daily periods, with $T=24$), and each of the $\hat{K}$ periods is treated independently in the operations stage of the optimization problem. Using time-series aggregation methods, we can represent the data with $K < \hat{K}$ periods, which results in reduced computational complexity and improved modeling performance. + +Many of the design and operations optimization problems that time-series aggregation has been applied to are in the general domain of energy systems optimization. These problems include generation and transmission capacity expansion problems [@Nahmmacher:2016; @Pfenninger:2017], local energy supply system design problems [@Bahl:2017; @Kotzur:2018], and individual technology design problems [@Brodrick:2017; @Teichgraeber:2017]. +Time series of interest in these problems include energy demands (electricity, heating, cooling), electricity prices, wind and solar availability factors, and temperatures. + +Many other planning problems in operations research that involve time-varying operations have similar characteristics that make them suitable for time-series aggregation. Some examples are aggregate and detailed production scheduling, job shop design and scheduling, distribution system (warehouse) design and control [@Dempster:1981], and electric vehicle charging station sizing [@Jia:2012]. +Time series of interest in these problems include product demands, electricity prices, and electricity demands. +A related class of problems that ``ClustForOpt`` can be useful to is scenario reduction for stochastic programming [@Karuppiah:2010]. Two-stage stochastic programs have similar characteristics to the previously described two-stage problems, and are often computationally intractable due to a large number of scenarios. ``ClustForOpt`` can be used to reduce a large number of scenarios $\hat{K}$ into a computationally tractable number of scenarios $K < \hat{K}$. +Furthermore, ``ClustForOpt`` could be used in operational contexts such as developing operational strategies for typical days, or aggregating repetitive operating conditions for use in model predictive control. +Because it keeps track of the chronology of the periods, it can also be used to calculate transition probabilities between clustered periods for Markov chain modeling. + +``ClustForOpt`` has been used in several research projects to date. It has been used to compare both conventionally-used clustering methods and shape-based clustering methods and their characteristics [@Teichgraeber:2019], and also to compare extreme value selection methods [@Lindenmeyer:2019]. +It has also been used to analyze temporal modeling detail in energy systems modeling with high renewable energy penetration [@Kuepper:2019]. +``ClustForOpt`` also serves as input to [``CapacityExpansion``](https://github.com/YoungFaithful/CapacityExpansion.jl), a scalable capacity expansion model in Julia. +Furthermore, ``ClustForOpt`` has been used as an educational tool. It is frequently used for class projects in the Stanford University course "Optimization of Energy Systems", and has also served as a basis for the capacity expansion studies evaluated in homeworks for the Stanford University course "Advanced Methods in Modeling for Climate and Energy Policy". + +# References diff --git a/src/ClustForOpt.jl b/src/ClustForOpt.jl index dbbe930..e94953f 100644 --- a/src/ClustForOpt.jl +++ b/src/ClustForOpt.jl @@ -33,7 +33,8 @@ module ClustForOpt representation_modification, get_sup_kw_args, run_clust, - run_opt, + run_battery_opt, + run_gas_opt, data_type, get_EUR_to_USD, #TODO Check which of the following should really be exported z_normalize, diff --git a/src/clustering/extreme_vals.jl b/src/clustering/extreme_vals.jl index bf14532..4fe4f1a 100644 --- a/src/clustering/extreme_vals.jl +++ b/src/clustering/extreme_vals.jl @@ -2,7 +2,11 @@ simple_extr_val_sel(data::ClustData, extreme_value_descr_ar::Array{SimpleExtremeValueDescr,1}; rep_mod_method::String="feasibility") + Selects simple extreme values and returns modified data, extreme values, and the corresponding indices. + +Inputs options for `rep_mod_method`: +- `rep_mod_method`::String : `feasibility`,`append` """ function simple_extr_val_sel(data::ClustData, extr_value_descr_ar::Array{SimpleExtremeValueDescr,1}; @@ -16,14 +20,14 @@ 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 """ simple_extr_val_sel(data::ClustData, - extreme_value_descr_ar::SimpleExtremeValueDescr; + extreme_value_descr::SimpleExtremeValueDescr; rep_mod_method::String="feasibility") Wrapper function for only one simple extreme value. Selects simple extreme values and returns modified data, extreme values, and the corresponding indices. @@ -36,8 +40,10 @@ function simple_extr_val_sel(data::ClustData, end """ - simple_extr_val_ident(data::ClustData,extreme_value_descr::Array{SimpleExtremeValueDescr,1}) -identifies multiple simple extreme values from the data and returns array of column indices of extreme value within data + simple_extr_val_ident(data::ClustData, + extreme_value_descr_ar::Array{SimpleExtremeValueDescr,1}) + +Identifies multiple simple extreme values from the data and returns array of column indices of extreme value within data - data_type: any attribute from the attributes contained within *data* - extremum: "min" or "max" - peak_def: "absolute" or "integral" @@ -53,7 +59,9 @@ function simple_extr_val_ident(data::ClustData, end """ - simple_extr_val_ident(data::ClustData,extreme_value_descr::SimpleExtremeValueDescr) + simple_extr_val_ident(data::ClustData, + extreme_value_descr::SimpleExtremeValueDescr) + Wrapper function for only one simple extreme value: identifies a single simple extreme value from the data and returns column index of extreme value - `data_type`: any attribute from the attributes contained within *data* @@ -67,8 +75,13 @@ function simple_extr_val_ident(data::ClustData, end """ - simple_extr_val_ident(data::Array{Float64};extremum="max",peak_def="absolute") -identifies a single simple extreme period from the data and returns column index of extreme period + simple_extr_val_ident(clust_data::ClustData, + data_type::String; + extremum::String="max", + peak_def::String="absolute", + consecutive_periods::Int=1) + +Identifies a single simple extreme period from the data and returns column index of extreme period - `data_type`: any attribute from the attributes contained within *data* - `extremum`: "min" or "max" - `peak_def`: "absolute" or "integral" @@ -91,7 +104,7 @@ 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" @@ -99,20 +112,23 @@ function simple_extr_val_ident(clust_data::ClustData, 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 end """ - input_data_modification(data::ClustData,extr_val_idcs::Array{Int,1}) -returns ClustData structs with extreme vals and with remaining input data [data-extreme_vals]. + input_data_modification(data::ClustData, + extr_val_idcs::Array{Int,1}) + +Returns ClustData structs with extreme vals and with remaining input data [data-extreme_vals]. Gives extreme vals the weight that they had in data. This function is needed for the append method for representation modification ! the k-ids have to be monoton increasing - don't modify clustered data ! """ -function input_data_modification(data::ClustData,extr_val_idcs::Array{Int,1}) +function input_data_modification(data::ClustData, + extr_val_idcs::Array{Int,1}) unique_extr_val_idcs = unique(extr_val_idcs) K_dn = data.K- length(unique_extr_val_idcs) data_dn=Dict{String,Array}() @@ -127,8 +143,8 @@ function input_data_modification(data::ClustData,extr_val_idcs::Array{Int,1}) 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) @@ -145,6 +161,7 @@ end """ input_data_modification(data::ClustData,extr_val_idcs::Int) + wrapper function for a single extreme val. returns ClustData structs with extreme vals and with remaining input data [data-extreme_vals]. Gives extreme vals the weight that they had in data. @@ -154,7 +171,10 @@ function input_data_modification(data::ClustData,extr_val_idcs::Int) end """ - extreme_val_output(data::ClustData,extr_val_idcs::Array{Int,1};rep_mod_method="feasibility") + extreme_val_output(data::ClustData, + extr_val_idcs::Array{Int,1}; + rep_mod_method="feasibility") + Takes indices as input and returns ClustData struct that contains the extreme vals from within data. """ function extreme_val_output(data::ClustData, @@ -183,7 +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) @@ -192,6 +212,7 @@ end """ extreme_val_output(data::ClustData,extr_val_idcs::Array{Int,1};rep_mod_method="feasibility") + wrapper function for a single extreme val. Takes indices as input and returns ClustData struct that contains the extreme vals from within data. """ @@ -203,6 +224,7 @@ end """ representation_modification(extr_vals::ClustData,clust_data::ClustData) + Merges the clustered data and extreme vals into one ClustData struct. Weights are chosen according to the rep_mod_method """ function representation_modification(extr_vals::ClustData, @@ -224,6 +246,7 @@ end """ representation_modification(extr_vals::ClustData,clust_data::ClustData) + Merges the clustered data and extreme vals into one ClustData struct. Weights are chosen according to the rep_mod_method """ function representation_modification(extr_vals_array::Array{ClustData,1}, @@ -237,6 +260,7 @@ end """ representation_modification(full_data::ClustData,clust_data::ClustData,extr_val_idcs::Array{Int,1};rep_mod_method::String="feasibility") + Merges the clustered data and extreme vals into one ClustData struct. Weights are chosen according to the rep_mod_method """ function representation_modification(full_data::ClustData, @@ -249,6 +273,7 @@ end """ representation_modification(full_data::ClustData,clust_data::ClustData,extr_val_idcs::Int;rep_mod_method::String="feasibility") + wrapper function for a single extreme val. Merges the clustered data and extreme vals into one ClustData struct. Weights are chosen according to the rep_mod_method """ diff --git a/src/clustering/run_clust.jl b/src/clustering/run_clust.jl index c53f51d..86b5b09 100644 --- a/src/clustering/run_clust.jl +++ b/src/clustering/run_clust.jl @@ -1,10 +1,47 @@ """ - run_clust(data::ClustData;norm_op::String="zscore",norm_scope::String="full",method::String="kmeans",representation::String="centroid",n_clust::Int=5,n_init::Int=100,iterations::Int=300,save::String="",attribute_weights::Dict{String,Float64}=Dict{String,Float64}(),get_all_clust_results::Bool=false,kwargs...) -norm_op: "zscore", "01"(not implemented yet) -norm_scope: "full","sequence","hourly" -method: "kmeans","kmedoids","kmedoids_exact","hierarchical" -representation: "centroid","medoid" + run_clust(data::ClustData; + norm_op::String="zscore", + norm_scope::String="full", + method::String="kmeans", + representation::String="centroid", + n_clust::Int=5, + n_seg::Int=data.T, + n_init::Int=1000, + iterations::Int=300, + attribute_weights::Dict{String,Float64}=Dict{String,Float64}(), + save::String="",#QUESTION dead? + get_all_clust_results::Bool=false, + kwargs...) + +Take input data `data` of dimensionality `N x T` and cluster into data of dimensionality `K x T`. + + +The following combinations of `method` and `representation` are supported by `run_clust`: + +Name | method | representation | comment +:--- | :-------------- | :-------------- | :---- +k-means clustering | `` | `` | - +k-means clustering with medoid representation | `` | `` | - +k-medoids clustering (partitional) | `` | `` | - +k-medoids clustering (exact) | `` | `` | requires Gurobi and the additional keyword argument `kmexact_optimizer`. See [examples] folder for example use. Set `n_init=1` +hierarchical clustering with centroid representation | `` | `` | set `n_init=1` +hierarchical clustering with medoid representation | `` | `` | set `n_init=1` + +The other optional inputs are: + +Keyword | options | comment +:------ | :------ | :----- +`norm_op` | `zscore` | Normalization operation. `0-1` not yet implemented +`norm_scope` | `full`,`sequence`,`hourly` | Normalization scope. The default (`full`) is used in most of the current literature. +`n_clust` | e.g. `5` | Number of clusters that you want to obtain +`n_seg` | e.g. `10` | Number of segments per period. Not yet implemented, keep as default value. +`n_init` | e.g. `1000` | Number of initializations of locally converging clustering algorithms. `10000` often yields very stable results. +`iterations` | e.g. `300` | Internal parameter of the partitional clustering algorithms. +`attribute_weights` | e.g. Dict("wind-germany"=>3,"solar-germany"=>1,"el_demand-germany"=>5) | weights the respective attributes when clustering. In this example, demand and wind are deemed more important than solar. +`save` | `false` | Save clustered data as csv or jld2 file. Not yet implemented. +`get_all_clust_results` | `true`,`false` | `false` gives a `ClustData` struct with only the best locally converged solution in terms of clustering measure. `true` gives a `ClustDataAll` struct as output, with all locally converged solutions. +`kwargs` | e.g. `kmexact_optimizer` | optional keyword arguments that are required for specific methods, for example k-medoids exact. """ function run_clust(data::ClustData; norm_op::String="zscore", @@ -13,7 +50,7 @@ function run_clust(data::ClustData; representation::String="centroid", n_clust::Int=5, n_seg::Int=data.T, - n_init::Int=100, + n_init::Int=1000, iterations::Int=300, attribute_weights::Dict{String,Float64}=Dict{String,Float64}(), save::String="",#QUESTION dead? @@ -121,12 +158,20 @@ function run_clust_method(data::ClustData; end """ - run_clust(data::ClustData,n_clust_ar::Array{Int,1};norm_op::String="zscore",norm_scope::String="full",method::String="kmeans",representation::String="centroid",n_init::Int=100,iterations::Int=300,save::String="",kwargs...) -This function is a wrapper function around run_clust(). It runs multiple number of clusters k and returns an array of results. -norm_op: "zscore", "01"(not implemented yet) -norm_scope: "full","sequence","hourly" -method: "kmeans","kmedoids","kmedoids_exact","hierarchical" -representation: "centroid","medoid" + run_clust( + data::ClustData, + n_clust_ar::Array{Int,1}; + norm_op::String="zscore", + norm_scope::String="full", + method::String="kmeans", + representation::String="centroid", + n_init::Int=100, + iterations::Int=300, + save::String="", + kwargs...) + +Run multiple number of clusters k and return an array of results. +This function is a wrapper function around run_clust(). """ function run_clust( data::ClustData, @@ -267,7 +312,7 @@ function run_clust_kmeans_medoid( # kmeans() in Clustering.jl is implemented for k>=2 elseif n_clust==data_norm.K clustids = collect(1:data_norm.K) - centers = undo_z_normalize(data_norm,data_norm.mean,data_norm.sdv;idx=clustids) # need to provide idx in case that sequence-based normalization is used + centers = undo_z_normalize(data_norm.data,data_norm.mean,data_norm.sdv;idx=clustids) # need to provide idx in case that sequence-based normalization is used cost = 0.0 iter = 1 else @@ -327,14 +372,14 @@ function run_clust_kmedoids_exact_medoid( data_norm::ClustDataMerged, n_clust::Int, iterations::Int; - gurobi_opt=0 + kmexact_optimizer=0 ) - (typeof(gurobi_opt)==Int) && @error("Please provide a gurobi_opt (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() - results = kmedoids_exact(data_norm.data,n_clust,gurobi_opt;_dist=dist)#;distance_type_ar[dist]) + results = kmedoids_exact(data_norm.data,n_clust,kmexact_optimizer;_dist=dist)#;distance_type_ar[dist]) clustids = results.assignments centers_norm = results.medoids centers = undo_z_normalize(centers_norm,data_norm.mean,data_norm.sdv;idx=clustids) @@ -407,7 +452,7 @@ function run_clust_hierarchical_medoid( iterations::Int; _dist::SemiMetric = SqEuclidean() ) - ~,weights,clustids,~,iter= run_clust_hierarchical(data_norm,n_clust,iterations;_dist=_dist) + ~,weights,clustids,~,iter= run_clust_hierarchical(data_norm.data,n_clust,iterations;_dist=_dist) centers_norm = calc_medoids(data_norm.data,clustids) cost = calc_SSE(data_norm.data,centers_norm,clustids) centers = undo_z_normalize(centers_norm,data_norm.mean,data_norm.sdv;idx=clustids) diff --git a/src/clustering/shape_based/README.md b/src/clustering/shape_based/README.md deleted file mode 100644 index 1355873..0000000 --- a/src/clustering/shape_based/README.md +++ /dev/null @@ -1,9 +0,0 @@ -# Shape based methods - -This folder is for files of shape-based clustering methods. They are not implemented in the current version. - -They would be stacked in the following folder order - -clustering/kshape.py -clustering/runfiles/cluster_gen_kshape_centroid.jl -clustering/runfiles/cluster_gen_dbaclust_centroid.jl diff --git a/src/clustering/shape_based/cluster_gen_dbaclust_centroid.jl b/src/clustering/shape_based/cluster_gen_dbaclust_centroid.jl deleted file mode 100644 index e32fb5a..0000000 --- a/src/clustering/shape_based/cluster_gen_dbaclust_centroid.jl +++ /dev/null @@ -1,84 +0,0 @@ -function run_clust_dbaclust_centroid( - region::String, - opt_problems::Array{String}, - norm_op::String, - norm_scope::String, - n_clust_ar::Array, - n_init::Int, - iterations::Int; - rad_sc_min::Int=0, - rad_sc_max::Int=3, - inner_iterations::Int=30 - ) - - n_clust_min = minimum(n_clust_ar) - n_clust_max = maximum(n_clust_ar) - - # read in original data - seq = load_pricedata(region) - - # initial points - n_dbaclust= n_init # number of dbaclust runs (each with n_init below) - - n_init = 1 # number of initial guesses for each dbaclust run / should be set to 1 for each experiment. dbaclust is deterministic once an initial guess has been formed. We sample the initial guesses on the outer run. - - ############################################ - - # create directory where data is saved - try - mkdir("outfiles") - catch - # - end - - # save settings in txt file - df = DataFrame() - df[:n_clust_min]=n_clust_min - df[:n_clust_max]=n_clust_max - df[:n_init]=n_init - df[:n_dbaclust]=n_dbaclust - df[:rad_sc_min]=rad_sc_min - df[:rad_sc_max]=rad_sc_max - df[:iterations]=iterations - df[:inner_iterations]=inner_iterations - df[:region]=region - - writetable(joinpath("outfiles",string("parameters.txt")),df) - - # iterate through settings - for n_clust=n_clust_min:n_clust_max - for rad_sc=rad_sc_min:rad_sc_max - for i = 1:n_dbaclust - - rmin,rmax = sakoe_chiba_band(rad_sc,24) - - ########################## - # normalized clustering hourly - seq_norm, hourly_mean, hourly_sdv = z_normalize(seq,scope="sequence") - tic() - results = dbaclust(seq_norm,n_clust,n_init,ClassicDTW();iterations=iterations,inner_iterations=inner_iterations,rtol=1e-5,show_progress=false,store_trace=false,i2min=rmin,i2max=rmax) - el_time = toq() - println("Elapsed time: ",el_time ," ; n_clust=",n_clust," rad_sc=",rad_sc," i=",i) - flush(STDOUT) - - centers_norm = results.centers - clustids = results.clustids - centers = undo_z_normalize(seq_to_array(centers_norm),hourly_mean,hourly_sdv;idx=clustids) - - # save results to txt - - - - writetable(joinpath("outfiles",string("dbaclust_k_",n_clust,"_scband_",rad_sc,"_ninit_",n_init,"_it_",iterations,"_innerit_",inner_iterations,"_",i,"_cluster.txt")),DataFrame(centers'),separator='\t',header=false) - writetable(joinpath("outfiles",string("dbaclust_k_",n_clust,"_scband_",rad_sc,"_ninit_",n_init,"_it_",iterations,"_innerit_",inner_iterations,"_",i,"_clustids.txt")),DataFrame(id=clustids),separator='\t',header=false) - writetable(joinpath("outfiles",string("dbaclust_k_",n_clust,"_scband_",rad_sc,"_ninit_",n_init,"_it_",iterations,"_innerit_",inner_iterations,"_",i,"_cost.txt")),DataFrame(cost=results.dbaresult.cost),separator='\t',header=false) - writetable(joinpath("outfiles",string("dbaclust_k_",n_clust,"_scband_",rad_sc,"_ninit_",n_init,"_it_",iterations,"_innerit_",inner_iterations,"_",i,"_it.txt")),DataFrame(iterations=results.iterations),separator='\t',header=false) - writetable(joinpath("outfiles",string("dbaclust_k_",n_clust,"_scband_",rad_sc,"_ninit_",n_init,"_it_",iterations,"_innerit_",inner_iterations,"_",i,"_innerit.txt")),DataFrame(inner_iterations=results.dbaresult.iterations),separator='\t',header=false) - - end - end - end - - # TODO - import dbaclust_res_to_jld2.jl and automatically generate jld2 files. - return 0 -end # function diff --git a/src/clustering/shape_based/cluster_gen_dbaclust_parallel.jl b/src/clustering/shape_based/cluster_gen_dbaclust_parallel.jl deleted file mode 100644 index 6ba3156..0000000 --- a/src/clustering/shape_based/cluster_gen_dbaclust_parallel.jl +++ /dev/null @@ -1,110 +0,0 @@ -CLUST_FOR_OPT=ENV["CLUST_FOR_OPT"] -push!(LOAD_PATH, normpath(joinpath(CLUST_FOR_OPT,"src"))) #adds the location of ClustForOpt to the LOAD_PATH - @everywhere using ClustForOpt - @everywhere using TimeWarp - - - ######## DATA INPUT ########## - - # region -region = "GER" - - -# read in original data -data_orig_daily = load_pricedata(region) -seq = data_orig_daily[:,1:365] # do not load as sequence - -println("data loaded") - -# number of clusters -n_clust_min =1 -n_clust_max =9 - -# initial points -n_init = 1 # number of initial guesses for each dbaclust run # should be set to 1 for most experiments -n_dbaclust =10000 # number of dbaclust runs (each with n_init) - -# warping window (sakoe chiba band radius) -rad_sc_min=0 -rad_sc_max=5 - - # iterations -iterations = 100 -inner_iterations=30 - - -n_clust_ar = collect(n_clust_min:n_clust_max) -rad_sc_ar = collect(rad_sc_min:rad_sc_max) - - ############################################ - -# create directory where data is saved -try - mkdir("outfiles") -catch - # -end - -# save settings in txt file -df = DataFrame() -df[:n_clust_min]=n_clust_min -df[:n_clust_max]=n_clust_max -df[:n_init]=n_init -df[:n_dbaclust]=n_dbaclust -df[:rad_sc_min]=rad_sc_min -df[:rad_sc_max]=rad_sc_max -df[:iterations]=iterations -df[:inner_iterations]=inner_iterations -df[:region]=region - -writetable(joinpath("outfiles",string("parameters_dtw_",region,".txt")),df) - - - # dbac_par_sc(n_clust,i,rad_sc,input_struct) - - # Function that can be an input to pmap - - @everywhere function dbac_par_sc(n_clust::Int,i::Int,rad_sc::Int,seq::Array{Float64,2},n_init::Int,iterations::Int,inner_iterations::Int) # function to use with pmap to parallelize sc band calculation - - rmin,rmax=sakoe_chiba_band(rad_sc,24) - - ########################## - # normalized clustering hourly - seq_norm, hourly_mean, hourly_sdv = z_normalize(seq;scope="sequence") - tic() - results = dbaclust(seq_norm,n_clust,n_init,ClassicDTW();iterations=iterations,inner_iterations=inner_iterations,rtol=1e-5,show_progress=false,store_trace=false,i2min=rmin,i2max=rmax) - el_time = toq() - println("Elapsed time: ",el_time ," ; n_clust=",n_clust," rad_sc=",rad_sc," i=",i) - flush(STDOUT) - - centers_norm = results.centers - clustids = results.clustids - centers = undo_z_normalize(seq_to_array(centers_norm),hourly_mean,hourly_sdv;idx=clustids) - - # save results to txt - - - - writetable(joinpath("outfiles",string("dbaclust_k_",n_clust,"_scband_",rad_sc,"_ninit_",n_init,"_it_",iterations,"_innerit_",inner_iterations,"_",i,"_cluster.txt")),DataFrame(centers'),separator='\t',header=false) - writetable(joinpath("outfiles",string("dbaclust_k_",n_clust,"_scband_",rad_sc,"_ninit_",n_init,"_it_",iterations,"_innerit_",inner_iterations,"_",i,"_clustids.txt")),DataFrame(id=clustids),separator='\t',header=false) - writetable(joinpath("outfiles",string("dbaclust_k_",n_clust,"_scband_",rad_sc,"_ninit_",n_init,"_it_",iterations,"_innerit_",inner_iterations,"_",i,"_cost.txt")),DataFrame(cost=results.dbaresult.cost),separator='\t',header=false) - writetable(joinpath("outfiles",string("dbaclust_k_",n_clust,"_scband_",rad_sc,"_ninit_",n_init,"_it_",iterations,"_innerit_",inner_iterations,"_",i,"_it.txt")),DataFrame(iterations=results.iterations),separator='\t',header=false) - writetable(joinpath("outfiles",string("dbaclust_k_",n_clust,"_scband_",rad_sc,"_ninit_",n_init,"_it_",iterations,"_innerit_",inner_iterations,"_",i,"_innerit.txt")),DataFrame(inner_iterations=results.dbaresult.iterations),separator='\t',header=false) - -end #dbac_par_sc - - - - # generate iterables for pmap -num_iter = length(n_clust_ar)*length(rad_sc_ar)*n_dbaclust - -seq_iter = [seq for i=1:num_iter] -n_init_iter = [n_init for i=1:num_iter] -iterations_iter = [iterations for i=1:num_iter] -inner_iterations_iter = [inner_iterations for i=1:num_iter] -n_clust_iter = reshape(repmat(n_clust_ar,1,length(rad_sc_ar)*n_dbaclust)',:,1) -i_iter = repmat(reshape(repmat(collect(1:n_dbaclust),1,length(rad_sc_ar))',:,1),length(n_clust_ar),1) -rad_sc_iter = repmat(rad_sc_ar,length(n_clust_ar)*n_dbaclust,1) - -pmap(dbac_par_sc,n_clust_iter,i_iter,rad_sc_iter,seq_iter,n_init_iter,iterations_iter,inner_iterations_iter;retry_delays=ones(10)*20) - diff --git a/src/clustering/shape_based/cluster_gen_kshape.py b/src/clustering/shape_based/cluster_gen_kshape.py deleted file mode 100644 index e4c957a..0000000 --- a/src/clustering/shape_based/cluster_gen_kshape.py +++ /dev/null @@ -1,180 +0,0 @@ -#!/data/cees/hteich/libraries/miniconda/bin/ python -# coding: utf-8 -# This file currently only works on LINUX based systems - - -from time import time -import os,sys -import numpy as np -import csv -import pickle -from copy import deepcopy - -# TODO: possibly not necessary anymore if kshape is installed using pip kshape install -sys.path.append('/home/hteich/ClustForOpt/src/clust_algorithms') -import kshape -from kshape import _kshape, zscore - -print(os.getcwd()) # if file is here can import - - -def read_CA_el_data_CSV(path_to_data): - temp = [] - with open(path_to_data, 'rb') as csvfile: - read = csv.reader(csvfile) - for row in read: - temp.append(row) #Don't know how many entries, so use list instead of numpyarray - obs = np.array(temp) - return obs[1:].astype(np.float) # start from second (1) because first is filename - - - -def normalize_meansdv(price_dat_resh): - price_dat_norm = np.zeros(np.shape(price_dat_resh)) - hourly_mean = np.mean(price_dat_resh) - hourly_sdv = np.std(price_dat_resh) - price_dat_norm = (price_dat_resh-hourly_mean)/hourly_sdv - return price_dat_norm,hourly_mean,hourly_sdv - -def normalize_by_hour_meansdv(price_dat_resh): - price_dat_norm = np.zeros(np.shape(price_dat_resh)) - hourly_mean = np.zeros(np.shape(price_dat_resh)[1]) - hourly_sdv = np.zeros(np.shape(price_dat_resh)[1]) - for i in range(np.shape(price_dat_resh)[1]): - hourly_mean[i] = np.mean(price_dat_resh[:,i]) - price_dat_norm[:,i] = price_dat_resh[:,i] - hourly_mean[i] - hourly_sdv[i] = np.std(price_dat_resh[:,i]) - if hourly_sdv[i] ==0: - hourly_sdv[i] =1 - price_dat_norm[:,i] = price_dat_norm[:,i]/hourly_sdv[i] - return price_dat_norm, hourly_mean, hourly_sdv - -def normalize_by_seq_meansdv(price_dat_resh): - price_dat_norm = np.zeros(np.shape(price_dat_resh)) - seq_mean = np.zeros(np.shape(price_dat_resh)[0]) - seq_sdv = np.zeros(np.shape(price_dat_resh)[0]) - for i in range(np.shape(price_dat_resh)[0]): - seq_mean[i] = np.mean(price_dat_resh[i,:]) - price_dat_norm[i,:] = price_dat_resh[i,:] - seq_mean[i] - seq_sdv[i] = np.std(price_dat_resh[i,:],ddof=1) - if seq_sdv[i] ==0: - seq_sdv[i] =1 - price_dat_norm[i,:] = price_dat_norm[i,:]/seq_sdv[i] - return price_dat_norm, seq_mean, seq_sdv - - -def run_kshape(el_resh, n_clusters, n_init=1,max_iter=100,n_jobs=1,region='',normalize=True): # el_resh is (days,24) - - res = _kshape(el_resh, n_clusters, n_init=n_init,max_iter=max_iter, n_jobs=n_jobs,normalize=normalize) - k_shape_labels = deepcopy(res['labels']) #vector with kshape class for each datapoint - k_shape_cluster_centers = deepcopy(res['centroids']) - tot_dist = deepcopy(res['distance']) - weights = np.zeros(n_clusters) - SSE =0 - SSE_daily = np.zeros(k_shape_labels.size) - cluster_closest_day = np.zeros(n_clusters) # day number of closest day - SSE_closest_day = np.ones(n_clusters)*1e25 # np.zeros(n_clusters) - dist_closest_day = np.ones(n_clusters)*1e25 - for i in range(k_shape_labels.size): - weights[k_shape_labels[i]] += 1 - for j in range(el_resh.shape[1]): - SSE_daily[i] += (el_resh[i,j] - k_shape_cluster_centers[k_shape_labels[i],j])**2 - SSE += (el_resh[i,j] - k_shape_cluster_centers[k_shape_labels[i],j])**2 - #if SSE_daily[i] < SSE_closest_day[k_shape_labels[i]]: # calc closest day - # disable closest day in kshape - #if dist_daily[i] < dist_closest_day[k_shape_labels[i]]: - #cluster_closest_day[k_shape_labels[i]] = i - #SSE_closest_day[k_shape_labels[i]] = SSE_daily[i] - # add dist_closest_day here - weights = weights/k_shape_labels.size # calc weights - # sort in descending order - ind_w = np.argsort(weights)[::-1] # ::-1 reverse order - descending - weights = np.sort(weights)[::-1] - k_shape_cluster_centers = k_shape_cluster_centers[ind_w] - #cluster_closest_day = cluster_closest_day[ind_w] - for i in range(k_shape_labels.size): - k_shape_labels[i] = np.where(ind_w == k_shape_labels[i])[0][0] - # save to pickle - pickle.dump(res['centroids_all'],open('outfiles/pickle_save/'+ region +'_centroids_kshape_'+str(n_clusters)+'.pkl',"wb")) - pickle.dump(res['labels_all'],open('outfiles/pickle_save/'+ region +'labels_kshape_'+str(n_clusters)+'.pkl',"wb")) - pickle.dump(res['distance_all'],open('outfiles/pickle_save/'+ region +'distance_kshape_'+str(n_clusters)+'.pkl',"wb")) - pickle.dump(res['iterations'],open('outfiles/pickle_save/'+ region +'iterations_kshape_'+str(n_clusters)+'.pkl',"wb")) - - - return {'centers':k_shape_cluster_centers, 'weights':weights ,'SSE':SSE, 'labels':k_shape_labels, 'closest_day': cluster_closest_day } - - -################################################################## -''' Main Function starts here ''' -################################################################## -if __name__ == '__main__': - # argv[1] -> either CA or GER - if len(sys.argv) <= 2 or len(sys.argv) >3: - sys.exit("Not the correct number of input arguments") - else: - if sys.argv[1] == "GER" or sys.argv[1] == "CA": - region = sys.argv[1] - print "Region: ", sys.argv[1], "\n" - else: - sys.exit("Region not defined: " + sys.argv[2]) - scope = sys.argv[2] - print "Scope: ", sys.argv[2], "\n" - - - tic_begin = time() - - - ### SETTINGS ### - n_rand_km = 1000# random starting points for kmeans - min_k =1 - max_k=min_k+9 # if we want to cluster 8,9, min_k=8, max_k=8+2 - n_k = np.arange(min_k,max_k) - showfigs = False - n_jobs = -1 #1 default, -1 as many cores as available - max_iter = 1000 - plots = False - ############## - - # load electricity price data - call el_CA_2015 for historic reasons - # \TODO change el_CA_2015 and other variable names to more generic name. - if region == "CA": - el_CA_2015 = read_CA_el_data_CSV('/data/cees/hteich/clustering/data/el_prices/CA_2015_elPriceStanford.csv') - reg_str = "" # prefix for datanaming later - elif region == "GER": - el_CA_2015 = read_CA_el_data_CSV('/data/cees/hteich/clustering/data/el_prices/GER_2015_elPrice.csv') - reg_str = "GER_" - - el_CA_2015_norm_meansdv_hourly,mean_hourly,sdv_hourly= normalize_by_hour_meansdv(el_CA_2015_resh) - el_CA_2015_norm_meansdv,mean_seq,sdv_seq= normalize_by_seq_meansdv(el_CA_2015_resh) - el_CA_2015_full_meansdv,mean_full,sdv_full= normalize_meansdv(el_CA_2015_resh) - - ### kshape - norm_data mean sdv - z-normalized - SSE_kshape = np.zeros(n_k.size) - cluster_centers_kshape =[] - labels_kshape = [] - wt_kshape =[] - cluster_closest_day_kshape = [] - i=0 - for k in n_k: - i = i+1 - tic = time() - res=[] - if scope == "full": - res = run_kshape(el_CA_2015_full_meansdv, k,n_init=n_rand_km, max_iter=max_iter, n_jobs=n_jobs,region=region,normalize=False) - elif scope == "hourly": - res = run_kshape(el_CA_2015_norm_meansdv_hourly, k,n_init=n_rand_km, max_iter=max_iter, n_jobs=n_jobs,region=region,normalize=False) - elif scope == "sequence": - res = run_kshape(el_CA_2015_norm_meansdv, k,n_init=n_rand_km, max_iter=max_iter, n_jobs=n_jobs,region=region,normalize=True) - else: - print "Scope _ ", scope, " _ not defined. \n" - toc = time() - sys.stdout.write("k=" + str(k) + " took " + str(toc - tic) + " s \n" ) # on cluster, print seems to be omitted in out - - sys.stdout.flush() # empty the buffer - - # time print - toc_end = time() - sys.stdout.write("total kshape clustering took: " + str(toc_end - tic_begin) + " s \n" ) # on cluster, print seems to be omitted in out - - - diff --git a/src/clustering/shape_based/dtw_dba_miniexample.jl b/src/clustering/shape_based/dtw_dba_miniexample.jl deleted file mode 100644 index 2aaab9f..0000000 --- a/src/clustering/shape_based/dtw_dba_miniexample.jl +++ /dev/null @@ -1,103 +0,0 @@ -CLUST_FOR_OPT=ENV["CLUST_FOR_OPT"] -push!(LOAD_PATH, normpath(joinpath(CLUST_FOR_OPT,"src"))) #adds the location of ClustForOpt to the LOAD_PATH -using ClustForOpt -using TimeWarp - using PyPlot - plt = PyPlot -plt.close() -# example not working, somehow input is not in correct format -# x = [1,2,2,3,3,4] -# y = [1,3,4] -# z = [1,2,2,4] -# avg,result = dba([x,y,z],ClassicDTW(),iterations=15) # make sure to put in ClassicDTW() -# println(avg) - - - ######## DATA INPUT ########## - - # region -region = "GER" - - -# read in original data -data_orig_daily = load_pricedata(region) - -println("data loaded") - -seq1 = data_orig_daily[:,1] -seq2 = data_orig_daily[:,2] -seq = data_orig_daily[:,1:365] # do not load as sequence - -# test dtw -D = dtw(seq1,seq2) # dtw can be fed with normal array/vector or Sequence -println("D ",D) -E = fastdtw(seq1,seq2,24) # fastdtw can only be fed with array/vector -println("E ",E) - -#dtwplot(Sequence(seq1),Sequence(seq2)) # dtwplot has to be fed with sequence, somehow does not work currently, shows empty plot -#show() - -#avg, result = dba(seq,ClassicDTW(),iterations=15) #always include DTWMethod. Put initial one in example or actual code -#println(avg) - -## -n_seq=10 -tic() -mean_dtw,result = dba(seq[:,1:n_seq],ClassicDTW(),iterations=15,rtol=1e-5,show_progress=true) -toc() - -mean_euc = mean(seq[:,1:n_seq],2) - -figure() -plt.plot(seq[:,1:n_seq],color="0.75") -plt.plot(mean_dtw,label="dtw",color="red") -plt.plot(mean_euc,label="euclidean",color="blue") -plt.legend() - -########################## -# normalized clustering hourly - -seq_norm, hourly_mean, hourly_sdv = z_normalize(data_orig_daily[:,1:n_seq],hourly=true) -tic() -mean_dtw_norm,result_norm = dba(seq_norm[:,1:n_seq],ClassicDTW(),iterations=15,rtol=1e-5,show_progress=true) -toc() - -mean_euc_norm = mean(seq_norm[:,1:n_seq],2) - -mean_dtw = undo_z_normalize(reshape(mean_dtw_norm,size(mean_dtw_norm)[1],1),hourly_mean,hourly_sdv) -mean_euc = undo_z_normalize(mean_euc_norm,hourly_mean,hourly_sdv) - -figure() -plt.plot(seq[:,1:n_seq],color="0.75") -plt.plot(mean_dtw,label="dtw",color="red") -plt.plot(mean_euc,label="euclidean",color="blue") -plt.title("normalized") -plt.legend() - -####################### -# normalized and include warping window -rad_sc = 2 # sakoe chiba band radius -rmin,rmax = sakoe_chiba_band(rad_sc,24) - -seq_norm, hourly_mean, hourly_sdv = z_normalize(data_orig_daily[:,1:n_seq],hourly=true) -tic() -mean_dtw_norm,result_norm = dba(seq_norm[:,1:n_seq],ClassicDTW(),iterations=15,rtol=1e-5,show_progress=true,i2min=rmin,i2max=rmax) -toc() - -mean_euc_norm = mean(seq_norm[:,1:n_seq],2) - -mean_dtw = undo_z_normalize(reshape(mean_dtw_norm,size(mean_dtw_norm)[1],1),hourly_mean,hourly_sdv) -mean_euc = undo_z_normalize(mean_euc_norm,hourly_mean,hourly_sdv) - -figure() -plt.plot(seq[:,1:n_seq],color="0.75") -plt.plot(mean_dtw,label="dtw",color="red") -plt.plot(mean_euc,label="euclidean",color="blue") -plt.title("normalized") -plt.legend() - - - - - -show() diff --git a/src/clustering/shape_based/dtw_dbaclust_miniexample.jl b/src/clustering/shape_based/dtw_dbaclust_miniexample.jl deleted file mode 100644 index c149f03..0000000 --- a/src/clustering/shape_based/dtw_dbaclust_miniexample.jl +++ /dev/null @@ -1,63 +0,0 @@ -CLUST_FOR_OPT=ENV["CLUST_FOR_OPT"] -push!(LOAD_PATH, normpath(joinpath(CLUST_FOR_OPT,"src"))) #adds the location of ClustForOpt to the LOAD_PATH -using ClustForOpt -using TimeWarp # has to be before ClustForOpt -using PyPlot - plt = PyPlot -plt.close() -# example not working, somehow input is not in correct format -# x = [1,2,2,3,3,4] -# y = [1,3,4] -# z = [1,2,2,4] -# avg,result = dba([x,y,z],ClassicDTW(),iterations=15) # make sure to put in ClassicDTW() -# println(avg) - - ######## DATA INPUT ########## - - # region -region = "GER" - - -# read in original data -data_orig_daily = load_pricedata(region) -seq = data_orig_daily[:,1:365] # do not load as sequence - -println("data loaded") - -## Manual data input -n_seq=365 -n_clust = 3 -n_init=50 - -########################## -# normalized clustering hourly - -# optional warping window -rad_sc = 5 # sakoe chiba band radius -rmin,rmax = sakoe_chiba_band(rad_sc,24) -#rmin=[];rmax=[] - -seq_norm, hourly_mean, hourly_sdv = z_normalize(data_orig_daily[:,1:n_seq],hourly=true) -tic() -results = dbaclust(seq_norm[:,1:n_seq],n_clust,n_init,ClassicDTW();iterations=100,inner_iterations=15,rtol=1e-5,show_progress=false,store_trace=false,i2min=rmin,i2max=rmax) -toc() -centers_norm = results.centers -clustids = results.clustids - -centers = undo_z_normalize(seq_to_array(centers_norm),hourly_mean,hourly_sdv) - -col = plt.get_cmap("Dark2") - -figure() -for i=1:n_clust - plt.plot(seq[:,clustids.==i],color=col((i-1)/(n_clust-1)),alpha=0.15) -end -for i=1:n_clust - plt.plot(centers[:,i],label=string("clust:",i),color=col((i-1)/(n_clust-1)),linewidth=2.0) -end -#plt.plot(mean_euc,label="euclidean",color="blue") -plt.title("normalized") -plt.legend() - - -show() diff --git a/src/clustering/shape_based/kshape.py b/src/clustering/shape_based/kshape.py deleted file mode 100644 index dac632d..0000000 --- a/src/clustering/shape_based/kshape.py +++ /dev/null @@ -1,235 +0,0 @@ -# This is a slightly modified copy of https://github.com/holgerteichgraeber/kshape/tree/master/kshape/core.py -# Special thanks to https://github.com/Mic92/kshape for providing the initial code for the implementation of the kshape method - -import sys -import math -import numpy as np - -from numpy.random import randint -from numpy.linalg import norm, eigh -from numpy.fft import fft, ifft -import pdb -from sklearn.externals.joblib import Parallel,delayed -from sklearn.utils import check_random_state - -def zscore(a, axis=0, ddof=0): - a = np.asanyarray(a) - mns = a.mean(axis=axis) - sstd = a.std(axis=axis, ddof=ddof) - if axis and mns.ndim < a.ndim: - res = ((a - np.expand_dims(mns, axis=axis)) / - np.expand_dims(sstd, axis=axis)) - else: - res = (a - mns) / sstd - return np.nan_to_num(res) - - -def roll_zeropad(a, shift, axis=None): - a = np.asanyarray(a) - if shift == 0: - return a - if axis is None: - n = a.size - reshape = True - else: - n = a.shape[axis] - reshape = False - if np.abs(shift) > n: - res = np.zeros_like(a) - elif shift < 0: - shift += n - zeros = np.zeros_like(a.take(np.arange(n-shift), axis)) - res = np.concatenate((a.take(np.arange(n-shift, n), axis), zeros), axis) - else: - zeros = np.zeros_like(a.take(np.arange(n-shift, n), axis)) - res = np.concatenate((zeros, a.take(np.arange(n-shift), axis)), axis) - if reshape: - return res.reshape(a.shape) - else: - return res - - -def _ncc_c(x, y): - """ - >>> _ncc_c([1,2,3,4], [1,2,3,4]) - array([ 0.13333333, 0.36666667, 0.66666667, 1. , 0.66666667, - 0.36666667, 0.13333333]) - >>> _ncc_c([1,1,1], [1,1,1]) - array([ 0.33333333, 0.66666667, 1. , 0.66666667, 0.33333333]) - >>> _ncc_c([1,2,3], [-1,-1,-1]) - array([-0.15430335, -0.46291005, -0.9258201 , -0.77151675, -0.46291005]) - """ - den = np.array(norm(x) * norm(y)) - den[den == 0] = np.Inf - - x_len = len(x) - fft_size = 1 << (2*x_len-1).bit_length() - cc = ifft(fft(x, fft_size) * np.conj(fft(y, fft_size))) - cc = np.concatenate((cc[-(x_len-1):], cc[:x_len])) - return np.real(cc) / den - - -def _sbd(x, y): - """ - >>> _sbd([1,1,1], [1,1,1]) - (-2.2204460492503131e-16, array([1, 1, 1])) - >>> _sbd([0,1,2], [1,2,3]) - (0.043817112532485103, array([1, 2, 3])) - >>> _sbd([1,2,3], [0,1,2]) - (0.043817112532485103, array([0, 1, 2])) - """ - ncc = _ncc_c(x, y) - idx = ncc.argmax() - dist = 1 - ncc[idx] - yshift = roll_zeropad(y, (idx + 1) - max(len(x), len(y))) - - return dist, yshift - - -def _extract_shape(idx, x, j, cur_center, normalize=True): - """ - >>> _extract_shape(np.array([0,1,2]), np.array([[1,2,3], [4,5,6]]), 1, np.array([0,3,4])) - array([-1., 0., 1.]) - >>> _extract_shape(np.array([0,1,2]), np.array([[-1,2,3], [4,-5,6]]), 1, np.array([0,3,4])) - array([-0.96836405, 1.02888681, -0.06052275]) - >>> _extract_shape(np.array([1,0,1,0]), np.array([[1,2,3,4], [0,1,2,3], [-1,1,-1,1], [1,2,2,3]]), 0, np.array([0,0,0,0])) - array([-1.2089303 , -0.19618238, 0.19618238, 1.2089303 ]) - >>> _extract_shape(np.array([0,0,1,0]), np.array([[1,2,3,4],[0,1,2,3],[-1,1,-1,1],[1,2,2,3]]), 0, np.array([-1.2089303,-0.19618238,0.19618238,1.2089303])) - array([-1.19623139, -0.26273649, 0.26273649, 1.19623139]) - """ - _a = [] - for i in range(len(idx)): - if idx[i] == j: - if cur_center.sum() == 0: - opt_x = x[i] - else: - _, opt_x = _sbd(cur_center, x[i]) - _a.append(opt_x) - a = np.array(_a) - - if len(a) == 0: - return np.zeros((1, x.shape[1])) - columns = a.shape[1] - if normalize: - y = zscore(a, axis=1, ddof=1) - s = np.dot(y.transpose(), y) - - p = np.empty((columns, columns)) - p.fill(1.0/columns) - p = np.eye(columns) - p - - m = np.dot(np.dot(p, s), p) - _, vec = eigh(m) - else: - s = np.dot(a.transpose(), a) - m = s - _, vec = eigh(m) - - centroid = vec[:, -1] - finddistance1 = math.sqrt(((a[0] - centroid) ** 2).sum()) - finddistance2 = math.sqrt(((a[0] + centroid) ** 2).sum()) - - if finddistance1 >= finddistance2: - centroid *= -1 - - return zscore(centroid, ddof=1) if normalize else centroid - - -def _kshape(x, k, n_init=1, max_iter=100, n_jobs = 1, random_state=None,normalize=True ): - """ - >>> from numpy.random import seed; seed(0) - >>> _kshape(np.array([[1,2,3,4], [0,1,2,3], [-1,1,-1,1], [1,2,2,3]]), 2) - (array([0, 0, 1, 0]), array([[-1.2244258 , -0.35015476, 0.52411628, 1.05046429], - [-0.8660254 , 0.8660254 , -0.8660254 , 0.8660254 ]])) - """ - #print "n jobs run in parallel: " + str(cpu_count() ) - random_state = check_random_state(random_state) - best_tot_dist,best_centroids,best_idx = None,None,None - - if n_jobs ==1: - - for i_init in range(n_init): - # n_init is the number of random starting points - # pdb.set_trace() - - idx, centroids,tot_dist = _kshape_single(x, k, max_iter=max_iter, random_state= random_state,normalize=normalize) - if best_tot_dist is None or tot_dist < best_tot_dist: - best_idx = idx.copy() - best_centroids = centroids.copy() - best_tot_dist = tot_dist - else: # n_jobs not =1 # if -1, all CPUs are used - # parallelisation of kshape runs - seeds = random_state.randint(np.iinfo(np.int32).max,size=n_init) - results = Parallel(n_jobs=n_jobs, verbose=0)( - delayed(_kshape_single)(x,k,max_iter=max_iter, random_state=seed, normalize=normalize) - for seed in seeds ) - # Get results with the lowest distances - idx, centroids,tot_dist, iterations = zip(*results) - best = np.argmin(tot_dist) - best_idx = idx[best] - best_centroids = centroids[best] - best_tot_dist = tot_dist[best] - sys.stdout.write("Done: k="+str(k)+"\n") - return {'centroids':best_centroids, 'labels':best_idx, 'distance':best_tot_dist,'centroids_all':centroids,'labels_all':idx,'distance_all':tot_dist,'iterations':iterations} - -def _kshape_single(x, k, max_iter=10000, random_state=None,normalize=True): - - random_state = check_random_state(random_state) - m = x.shape[0] # number of data points (365) - - empty_clust = True - while empty_clust: # make sure no empty cluster is generated - idx = random_state.randint(0, k, size=m) - if np.unique(idx).shape[0] == k: - empty_clust = False - centroids = np.zeros((k, x.shape[1])) - distances = np.empty((m, k)) - - for _ in range(max_iter): - # increase iterations with each k - old_idx = idx - if np.unique(idx).shape[0] != k: # if one of the clusters is empty, start anew - empty_clust = True - while empty_clust: # make sure no empty cluster is generated - idx = random_state.randint(0, k, size=m) - if np.unique(idx).shape[0] == k: - empty_clust = False - for j in range(k): - centroids[j] = _extract_shape(idx, x, j, centroids[j],normalize=normalize) - - for i in range(m): - for j in range(k): - distances[i, j] = 1 - max(_ncc_c(x[i], centroids[j])) - idx = distances.argmin(1) - - sys.stdout.flush() # empty the buffer - if np.array_equal(old_idx, idx): - sys.stdout.write( "iter: " + str(_) + " k=" + str(k)+"\n") - sys.stdout.flush() # empty the buffer - iterations = _ - break - elif _==(max_iter-1): - sys.stdout.write( "iter: " + str(_) + " k=" + str(k)+"\n") - sys.stdout.flush() # empty the buffer - iterations = _ - dist_daily = distances.min(1) - tot_dist = np.sum(dist_daily) # total distance, for n_init>1 - - return idx, centroids, tot_dist, iterations - - -def kshape(x, k): - idx, centroids = _kshape(np.array(x), k) - clusters = [] - for i, centroid in enumerate(centroids): - series = [] - for j, val in enumerate(idx): - if i == val: - series.append(j) - clusters.append((centroid, series)) - return clusters - -if __name__ == "__main__": - import sys - import doctest - sys.exit(doctest.testmod()[0]) diff --git a/src/clustering/shape_based/load_clusters.py b/src/clustering/shape_based/load_clusters.py deleted file mode 100644 index 5c57ddb..0000000 --- a/src/clustering/shape_based/load_clusters.py +++ /dev/null @@ -1,9 +0,0 @@ -import pickle - - -def load_pickle(filename): - return pickle.load(open(filename, "rb")) - - -def load_weight(filename): - return pickle.load(open("w_" + filename, "rb")) diff --git a/src/optim_problems/cap_exp.jl b/src/optim_problems/cap_exp.jl deleted file mode 100644 index ec0bc16..0000000 --- a/src/optim_problems/cap_exp.jl +++ /dev/null @@ -1,131 +0,0 @@ -#using ClustForOpt_priv -using JuMP -using Clp -using DataFrames - # parameters - -acf = 0.1 # annualized cost of capital factor - -c_cap_s = (11.94e6 + acf*2.58e9)*20/100 # $/GW # 20/100 ~ 0.5$/W, 40/100~ 1$/W -c_cap_w = 30.98e6 + acf*1.74e9 # $/GW -c_cap_gas = 11.96e6 + acf*0.968e9 # $/GW -c_cap_nuc = 92.04e6 + acf*3.82e9 # $/GW -c_cap_coal = 28.15e6 + acf*2.22e9 # $/GW -c_cap = [c_cap_coal,c_cap_nuc,c_cap_gas,c_cap_w,c_cap_s] - -c_var_s = 0.5 # $/GWh -c_var_w = 0.5 # $/GWh -c_var_gas = 41500 # $/GWh -c_var_nuc = 7025 # $/GWh -c_var_coal = 23000 # $/GWh -c_var = [c_var_s,c_var_w,c_var_gas,c_var_nuc,c_var_coal] -c_var = [c_var_coal,c_var_nuc,c_var_gas,c_var_w,c_var_s] - - -c_lost = 1000e6 # $/GW # VOLL - - -n_years = 10 # depreciation period for capital -r = 0.07 # real discount rate - - # load input data - -de= readtable("/home/hteich/.julia/v0.6/ClustForOpt_priv/data/texas_merrick/demand.txt",separator=' ')[:DEM] # MW -d=reshape(de,(size(de)[1],1)) - # load growth -d=1.486*d -d=d/1000 # GW - - #d = rand(8760,1) # demand - # load growth *1.5 (footnote 6 merrick) -a_s= readtable("/home/hteich/.julia/v0.6/ClustForOpt_priv/data/texas_merrick/TexInsolationFactorV1.txt",separator=' ')[:solar_61] -a_s=reshape(a_s,(size(a_s)[1],1)) - a_s = a_s/1000 - #a_s = rand(8760,1) # availability solar - #a_w = rand(8760,1) # availability wind -a_w= readtable("/home/hteich/.julia/v0.6/ClustForOpt_priv/data/texas_merrick/windfactor2.txt",separator=' ')[:Wind_61] -a_w=reshape(a_w,(size(a_w)[1],1)) -a_gas = ones(8760,1) -a_nuc = ones(8760,1) -a_coal =ones(8760,1) -a= [a_coal,a_nuc,a_gas,a_w,a_s] - - -N=ones(8760,1) - -del_t = 1 # hour - - ### SETS #### -K = size(d)[1] -T = size(d)[2] -G_names = ["coal","nuclear","gas","wind","solar"] # generators -n_G = length(G_names) - - - ##### MODEL ##### -m = Model(optimizer=ClpSolver()) - - ### VARIABLES ## - ### DESIGN VARIABLES ### -# generation capacity build -@variable(m,G_max[1:n_G] >= 0) # GWh - - ### OPERATIONAL VARIABLES #### -# generation of individual generator per time step -@variable(m,G[1:n_G,1:K,1:T] >=0) - - ##### OBJECTIVE FUNCTION ##### - @objective(m,Min,sum(c_cap[g]*G_max[g] for g=1:n_G) + - sum(sum(sum( c_var[g]*G[g,k,t] - for k=1:K ) - for t=1:T ) - for g=1:n_G ) - ) - - #### CONSTRAINTS ###### - # energy balance constraint -@constraint(m,[k=1:K,t=1:T], sum(G[g,k,t] for g=1:n_G) >= d[k,t]) - # maximum power production constraint -@constraint(m,[g=1:n_G,k=1:K,t=1:T], G[g,k,t] <= G_max[g] * a[g][k,t] ) - - - #### SOLVE #### - status = solve(m) - - #### Output results #### - G_max = getvalue(G_max) - G = getvalue(G) # installed capacity - G_gen =sum(G[:,:,1],2) # generated power - - - if true - using PyPlot - - figure() - bt = zeros(length(G_max)) - for i=2:length(bt) - bt[i]=cumsum(G_max)[i-1] - end - for i=1:length(G_max) - bar(1, G_max[i],bottom=bt[i],color=cols[i],label=G_names[i]) - end - ylabel("GW") - legend() - title("Capacity built") - - figure() - bt = zeros(length(G_gen)) - for i=2:length(bt) - bt[i]=cumsum(G_gen)[i-1] - end - for i=1:length(G_gen) - bar(1, G_gen[i],bottom=bt[i],color=cols[i],label=G_names[i]) - end - ylabel("GWh") - legend() - title("Power generated") - - - end - - # TODO - add clustering, check on paper what to do next diff --git a/src/optim_problems/run_opt.jl b/src/optim_problems/run_opt.jl index 7a6169b..3b4974e 100644 --- a/src/optim_problems/run_opt.jl +++ b/src/optim_problems/run_opt.jl @@ -1,4 +1,4 @@ -#TODO Rewrite battery problem +#TODO Rewrite battery problem / update to JuMP v0.19, give optimizer CLP as addtional argument """ run_battery_opt(data::ClustData) @@ -89,6 +89,7 @@ end # run_battery_opt() ### +#TODO update gas turbine problem, update to JuMP v0.19 """ run_gas_opt(data::ClustData) diff --git a/src/utils/datastructs.jl b/src/utils/datastructs.jl index 3a2b55a..17b19c4 100644 --- a/src/utils/datastructs.jl +++ b/src/utils/datastructs.jl @@ -4,7 +4,6 @@ abstract type TSData <:InputData end abstract type OptData <: InputData end abstract type AbstractClustResult end -"FullInputData" struct FullInputData <: TSData region::String years::Array{Int,1} @@ -13,16 +12,20 @@ struct FullInputData <: TSData end """ - ClustData{region::String,K::Int,T::Int,data::Dict{String,Array},weights::Array{Float64,2},mean::Dict{String,Array},sdv::Dict{String,Array},delta_t::Array{Float64,2},k_ids::Array{Int}} <: TSData -- region: optional information to specify the region data belongs to -- K: number of periods -- T: time steps per period -- data: Dictionary with an entry for each attribute `[file name (e.g technology)]-[column name (e.g. location)]`, Each entry of the dictionary is a 2-dimensional `time-steps T x periods K`-Array holding the actual value -- weights: 1-dimensional `periods K`-Array with the absolute weight for each period. E.g. for a year of 365 days, sum(weights)=365 -- mean: Dictionary with a entry for each attribute `[file name (e.g technology)]-[column name (e.g. location)]`, Each entry of the dictionary is a 1-dimensional `periods K`-Array holding the shift of the mean -- sdv: Dictionary with an entry for each attribute `[file name (e.g technology)]-[column name (e.g. location)]`, Each entry of the dictionary is a 1-dimensional `periods K`-Array holding the standard deviation -- delta_t: 2-dimensional `time-steps T x periods K`-Array with the temporal duration Δt for each timestep in [h] -- k_ids: 1-dimensional `original periods I`-Array with the information, which original period is represented by which period K. If an original period is not represented by any period within this ClustData the entry will be `0`. + ClustData <: TSData + +Contains time series data by attribute (e.g. wind, solar, electricity demand) and respective information. + +Fields: +- region::String: optional information to specify the region data belongs to +- K::Int: number of periods +- T::Int: time steps per period +- data::Dict{String,Array}: 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 +- weights::Array{Float64,2}: 1-dimensional `periods K`-Array with the absolute weight for each period. The weight of a period corresponds to the number of days it representes. E.g. for a year of 365 days, sum(weights)=365 +- mean::Dict{String,Array}: Dictionary with a entry for each attribute `[file name (e.g technology)]-[column name (e.g. location)]`, Each entry of the dictionary is a 1-dimensional `periods K`-Array holding the shift of the mean. This is used internally for normalization. +- sdv::Dict{String,Array}: Dictionary with an entry for each attribute `[file name (e.g technology)]-[column name (e.g. location)]`, Each entry of the dictionary is a 1-dimensional `periods K`-Array holding the standard deviation. This is used internally for normalization. +- delta_t::Array{Float64,2}: 2-dimensional `time-steps T x periods K`-Array with the temporal duration Δt for each timestep. The default is that all timesteps have the same length. +- k_ids::Array{Int}: 1-dimensional `original periods I`-Array with the information, which original period is represented by which period K. E.g. if the data is a year of 365 periods, the array has length 365. If an original period is not represented by any period within this ClustData the entry will be `0`. """ struct ClustData <: TSData region::String @@ -37,7 +40,23 @@ struct ClustData <: TSData k_ids::Array{Int} end -"ClustDataMerged" +""" + ClustDataMerged <: TSData + +Contains time series data by attribute (e.g. wind, solar, electricity demand) and respective information. + +Fields: +- region::String: optional information to specify the region data belongs to +- K::Int: number of periods +- T::Int: time steps per period +- data::Array: Array of the dimension `(time-steps T * length(data_types) x periods K`. The first T rows are data_type 1, the second T rows are data_type 2, ... +- data_type::Array{String}: The data types (attributes) of the data. +- weights::Array{Float64,2}: 1-dimensional `periods K`-Array with the absolute weight for each period. E.g. for a year of 365 days, sum(weights)=365 +- mean::Dict{String,Array}: Dictionary with a entry for each attribute `[file name (e.g technology)]-[column name (e.g. location)]`, Each entry of the dictionary is a 1-dimensional `periods K`-Array holding the shift of the mean +- sdv::Dict{String,Array}: Dictionary with an entry for each attribute `[file name (e.g technology)]-[column name (e.g. location)]`, Each entry of the dictionary is a 1-dimensional `periods K`-Array holding the standard deviation +- delta_t::Array{Float64,2}: 2-dimensional `time-steps T x periods K`-Array with the temporal duration Δt for each timestep in [h] +- k_ids::Array{Int}: 1-dimensional `original periods I`-Array with the information, which original period is represented by which period K. If an original period is not represented by any period within this ClustData the entry will be `0`. +""" struct ClustDataMerged <: TSData region::String years::Array{Int} @@ -52,7 +71,35 @@ struct ClustDataMerged <: TSData k_ids::Array{Int} end -"ClustResultAll" +""" + ClustResult <: AbstractClustResult +Contains the results from a clustering run: The data, the cost in terms of the clustering algorithm, and a config file describing the clustering method used. + +Fields: +- clust_data::ClustData +- cost::Float64: Cost of the clustering algorithm +- config::Dict{String,Any}: Details on the clustering method used +""" +struct ClustResult <: AbstractClustResult + clust_data::ClustData + cost::Float64 + config::Dict{String,Any} +end + +""" + ClustResultAll <: AbstractClustResult +Contains the results from a clustering run for all locally converged solutions + +Fields: +- clust_data::ClustData: The best centers, weights, clustids in terms of cost of the clustering algorithm +- cost::Float64: Cost of the clustering algorithm +- config::Dict{String,Any}: Details on the clustering method used +- centers_all::Array{Array{Float64},1} +- weights_all::Array{Array{Float64},1} +- clustids_all::Array{Array{Int,1},1} +- cost_all::Array{Float64,1} +- iter_all::Array{Int,1} +""" struct ClustResultAll <: AbstractClustResult clust_data::ClustData cost::Float64 @@ -64,16 +111,20 @@ struct ClustResultAll <: AbstractClustResult iter_all::Array{Int,1} end -# TODO: not used yet, but maybe best to implement this one later for users who just want to use clustering but do not care about the locally converged solutions -"ClustResult" -struct ClustResult <: AbstractClustResult - clust_data::ClustData - cost::Float64 - config::Dict{String,Any} -end +""" + SimpleExtremeValueDescr -"SimpleExtremeValueDescr" +Defines a simple extreme day by its characteristics + +Fields: + +- data_type::String : Choose one of the attributes from the data you have loaded into ClustData +- extremum::String : `min`,`max` +- peak_def::String : `absolute`,`integral` +- consecutive_periods::Int: For a single extreme day, set as 1 +""" struct SimpleExtremeValueDescr + # TODO: make this one constructor, with consecutive_periods as optional argument data_type::String extremum::String peak_def::String @@ -85,14 +136,26 @@ struct SimpleExtremeValueDescr consecutive_periods::Int) # only allow certain entries if !(extremum in ["min","max"]) - @error("extremum - "*extremum*" - not defined") + error("extremum - "*extremum*" - not defined") elseif !(peak_def in ["absolute","integral"]) - @error("peak_def - "*peak_def*" - not defined") + error("peak_def - "*peak_def*" - not defined") end new(data_type,extremum,peak_def,consecutive_periods) end end +""" + SimpleExtremeValueDescr(data_type::String, + extremum::String, + peak_def::String) + +Defines a simple extreme day by its characteristics + +Input options: +- data_type::String : Choose one of the attributes from the data you have loaded into ClustData +- extremum::String : `min`,`max` +- peak_def::String : `absolute`,`integral` +""" function SimpleExtremeValueDescr(data_type::String, extremum::String, peak_def::String) @@ -102,99 +165,6 @@ end #### Constructors for data structures### -""" - FullInputData(region::String, - N::Int; - el_price::Array=[], - el_demand::Array=[], - solar::Array=[], - wind::Array=[] - ) -Constructor for FullInputData with optional data input -""" -function FullInputData(region::String, - N::Int; - el_price::Array=[], - el_demand::Array=[], - solar::Array=[], - wind::Array=[] - ) - dt = Dict{String,Array}() - !isempty(el_price) && (dt["el_price"]=el_price) - !isempty(el_demand) && (dt["el_demand"]=el_demand) - !isempty(wind) && (dt["wind"]=wind) - !isempty(solar) && (dt["solar"]=solar) - # TODO: Check dimensionality of N and supplied input data streams Nx1 - isempty(dt) && @error("Need to provide at least one input data stream") - FullInputData(region,N,dt) -end - -""" - ClustData(region::String, - years::Array{Int,1}, - K::Int, - T::Int; - el_price::Array=[], - el_demand::Array=[], - solar::Array=[], - wind::Array=[], - weights::Array{Float64}=ones(K), - mean::Dict{String,Array}=Dict{String,Array}(), - sdv::Dict{String,Array}=Dict{String,Array}(), - delta_t::Array{Float64,2}=ones(T,K), - k_ids::Array{Int,1}=collect(1:K) - ) -constructor 1 for ClustData: provide data individually -""" -function ClustData(region::String, - years::Array{Int,1}, - K::Int, - T::Int; - el_price::Array=[], - el_demand::Array=[], - solar::Array=[], - wind::Array=[], - weights::Array{Float64}=ones(K), - mean::Dict{String,Array}=Dict{String,Array}(), - sdv::Dict{String,Array}=Dict{String,Array}(), - delta_t::Array{Float64,2}=ones(T,K), - k_ids::Array{Int,1}=collect(1:K) - ) - dt = Dict{String,Array}() - mean_sdv_provided = ( !isempty(mean) && !isempty(sdv)) - if !isempty(el_price) - dt["el_price"]=el_price - if !mean_sdv_provided - mean["el_price"]=zeros(T) - sdv["el_price"]=ones(T) - end - end - if !isempty(el_demand) - dt["el_demand"]=el_demand - if !mean_sdv_provided - mean["el_demand"]=zeros(T) - sdv["el_demand"]=ones(T) - end - end - if !isempty(wind) - dt["wind"]=wind - if !mean_sdv_provided - mean["wind"]=zeros(T) - sdv["wind"]=ones(T) - end - end - if !isempty(solar) - dt["solar"]=solar - if !mean_sdv_provided - mean["solar"]=zeros(T) - sdv["solar"]=ones(T) - end - end - isempty(dt) && @error("Need to provide at least one input data stream") - # TODO: Check dimensionality of K T and supplied input data streams KxT - ClustData(region,years,K,T,dt,weights,mean,sdv,delta_t,k_ids) -end - """ ClustData(region::String, years::Array{Int,1}, @@ -207,7 +177,7 @@ end mean::Dict{String,Array}=Dict{String,Array}(), sdv::Dict{String,Array}=Dict{String,Array}() ) -constructor 2 for ClustData: provide data as dict +constructor 1 for ClustData: provide data as dict """ function ClustData(region::String, years::Array{Int,1}, @@ -220,7 +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 @@ -234,7 +204,7 @@ end """ ClustData(data::ClustDataMerged) -constructor 3: Convert ClustDataMerged to ClustData +constructor 2: Convert ClustDataMerged to ClustData """ function ClustData(data::ClustDataMerged) data_dict=Dict{String,Array}() @@ -248,7 +218,7 @@ end """ ClustData(data::FullInputData,K,T) -constructor 4: Convert FullInputData to ClustData +constructor 3: Convert FullInputData to ClustData """ function ClustData(data::FullInputData, K::Int, @@ -312,7 +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 diff --git a/src/utils/load_data.jl b/src/utils/load_data.jl index d546fef..ecce2ee 100644 --- a/src/utils/load_data.jl +++ b/src/utils/load_data.jl @@ -1,17 +1,31 @@ """ - load_timeseriesdata(data_path::String; T-#Segments,years::Array{Int,1}=# years to be selected for the time series, att::Array{String,1}=# attributes to be loaded) -- Loading all `*.csv` files in the folder or the file `data_path` + function load_timeseries_data(data_path::String; + region::String="none", + T::Int=24, + years::Array{Int,1}=[2016], + att::Array{String,1}=Array{String,1}()) + +Return all time series as ClustData struct that are stored as csv files in the specified path. + +- Loads `*.csv` files in the folder or the file `data_path` +- Loads all attributes (all `*.csv` files) if the `att`-Array is empty or only the files specified in `att` - The `*.csv` files shall have the following structure and must have the same length: |Timestamp |Year |[column names...]| |----------|------|-----------------| |[iterator]|[year]|[values] | -- The first column should be called `Timestamp` if it contains a time iterator +- The first column of a `.csv` file should be called `Timestamp` if it contains a time iterator - The second column should be called `Year` and contains the corresponding year -- The other columns can specify the single timeseries like specific geolocation. -- Each column in `[file name].csv` file will be added to the ClustData.data called `"[file name]-[column name]"` -- Loads all attributes if the `att`-Array is empty or only the ones specified in it +- Each other column should contain the time series data. For one node systems, only one column is used; for an N-node system, N columns need to be used. In an N-node system, each column specifies time series data at a specific geolocation. +- Returns time series as ClustData struct +- The `.data` field of the ClustData struct is a Dictionary where each column in `[file name].csv` file is the key (called `"[file name]-[column name]"`). `file name` should correspond to the attribute name, and `column name` should correspond to the node name. + +Optional inputs to `load_timeseries_data`: +- region-region descriptor +- T- Number of Segments +- years::Array{Int,1}= The years to be selected from the csv file as specified in `years column` +- att::Array{String,1}= The attributes to be loaded. If left empty, all attributes will be loaded. """ -function load_timeseries_data(data_path; +function load_timeseries_data(data_path::String; region::String="none", T::Int=24, years::Array{Int,1}=[2016], @@ -35,13 +49,57 @@ function load_timeseries_data(data_path; 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) return ts_input_data end #load_timeseries_data +""" + function load_timeseries_data(existing_data::Symbol; + region::String="none", + T::Int=24, + years::Array{Int,1}=[2016], + att::Array{String,1}=Array{String,1}()) + +Return time series of example data sets as ClustData struct. + +The choice of example data set is given by e.g. existing_data=:CEP-GER1. Example data sets are: +- `:DAM_CA` : Hourly Day Ahead Market Electricity prices for California-Stanford 2015 +- `:DAM_GER` : Hourly Day Ahead Market Electricity prices for Germany 2015 +- `:CEP_GER1` : Hourly Wind, Solar, Demand data Germany one node +- `:CEP_GER18`: Hourly Wind, Solar, Demand data Germany 18 nodes + +Optional inputs to `load_timeseries_data`: +- region-region descriptor +- T- Number of Segments +- years::Array{Int,1}= The years to be selected from the csv file as specified in `years column` +- att::Array{String,1}= The attributes to be loaded. If left empty, all attributes will be loaded. +""" +function load_timeseries_data(existing_data::Symbol; + region::String="none", + T::Int=24, + years::Array{Int,1}=[2016], + att::Array{String,1}=Array{String,1}()) + data_path = normpath(joinpath(dirname(@__FILE__),"..","..","data")) + if existing_data == :DAM_CA + data_path=joinpath(data_path,"DAM","CA") + years=[2015] + elseif existing_data == :DAM_GER + data_path=joinpath(data_path,"DAM","GER") + years=[2015] + elseif existing_data == :CEP_GER1 + data_path=joinpath(data_path,"TS_GER_1") + elseif existing_data == :CEP_GER18 + data_path=joinpath(data_path,"TS_GER_18") + else + error("The symbol - $existing_data - does not exist") + end + return load_timeseries_data(data_path;region=region,T=T,years=years,att=att) +end + + """ add_timeseries_data!(dt::Dict{String,Array}, data::DataFrame; K::Int=0, T::Int=24, years::Array{Int,1}=[2016]) selects first the years and second the data_points so that their number is a multiple of T and same with the other timeseries @@ -53,7 +111,7 @@ function add_timeseries_data!(dt::Dict{String,Array}, T::Int=24, years::Array{Int,1}=[2016]) #Load the data - data_df=CSV.read(joinpath(data_path,data_name*".csv");allowmissing=:none) + data_df=CSV.read(joinpath(data_path,data_name*".csv");strict=true) # Add it to the dictionary return add_timeseries_data!(dt,data_name, data_df; K=K, T=T, years=years) end @@ -77,7 +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 @@ -100,7 +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 $(repr(df))." end @@ -116,8 +174,8 @@ If the number of periods of the `ts_weather` data isn't a multiple of the `ts`-t """ function combine_timeseries_weather_data(ts::ClustData, ts_weather::ClustData) - ts.T==ts_weather.T || throw(@error "The number of timesteps per period is not the same: `ts.T=$(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) diff --git a/src/utils/utils.jl b/src/utils/utils.jl index 3ec7d2b..c504886 100644 --- a/src/utils/utils.jl +++ b/src/utils/utils.jl @@ -1,16 +1,3 @@ -""" - get_EUR_to_USD(region::String) -convert Euro to US dollars -introduced because the clusters generated by the python script are in EUR for GER -""" -function get_EUR_to_USD(region::String) - if region =="GER" - ret = 1.109729 - else - ret =1 - end - return ret -end """ sort_centers(centers::Array,weights::Array) @@ -92,7 +79,7 @@ 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 +89,7 @@ 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 +124,7 @@ 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 @@ -229,20 +216,21 @@ function calc_medoids(data::Array,assignments::Array) return medoids end -""" - resize_medoids(data::Array,centers::Array,weights::Array,assignments::Array) -Takes in centers (typically medoids) and normalizes them such that for all clusters the average of the cluster is the same as the average of the respective original data that belongs to that cluster. -In order to use this method of the resize function, add assignments to the function call (e.g. clustids[5,1]). -""" -function resize_medoids(data::Array,centers::Array,weights::Array,assignments::Array) - new_centers = zeros(centers) - for k=1:size(centers)[2] # number of clusters - is_in_k = assignments.==k - n = sum(is_in_k) - new_centers[:,k]=resize_medoids(reshape(data[:,is_in_k],:,n),reshape(centers[:,k] , : ,1),[1.0])# reshape is used for the side case with only one vector, so that resulting vector is 24x1 instead of 24-element - end - return new_centers -end +#""" +# Not used in literature. Only uncomment if test added. +# resize_medoids(data::Array,centers::Array,weights::Array,assignments::Array) +#Takes in centers (typically medoids) and normalizes them such that for all clusters the average of the cluster is the same as the average of the respective original data that belongs to that cluster. +#In order to use this method of the resize function, add assignments to the function call (e.g. clustids[5,1]). +#""" +#function resize_medoids(data::Array,centers::Array,weights::Array,assignments::Array)# +# new_centers = zeros(centers) +# for k=1:size(centers)[2] # number of clusters +# is_in_k = assignments.==k +# n = sum(is_in_k) +# new_centers[:,k]=resize_medoids(reshape(data[:,is_in_k],:,n),reshape(centers[:,k] , : ,1),[1.0])# reshape is used for the side case with only one vector, so that resulting vector is 24x1 instead of 24-element +# end +# return new_centers +#end """ @@ -269,7 +257,7 @@ 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 diff --git a/test/Project.toml b/test/Project.toml index 15f33f9..86c9181 100644 --- a/test/Project.toml +++ b/test/Project.toml @@ -1,5 +1,7 @@ [deps] -Clp = "e2554f3b-3117-50c0-817c-e040a3ddf72d" +Cbc = "9961bab8-2fa3-5c5a-9d89-47fab24efd76" +JLD2 = "033835bb-8acc-5ee8-8aae-3f567f8a3819" +Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f" +Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c" StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91" Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40" -CapacityExpansion = "c5f7ca31-19be-526c-ac1d-e0dcac83edb8" diff --git a/test/capacityexpansion.jl b/test/capacityexpansion.jl deleted file mode 100644 index 049f978..0000000 --- a/test/capacityexpansion.jl +++ /dev/null @@ -1,60 +0,0 @@ -using Test -using CapacityExpansion -using Clp - -@testset "CEP" begin - @testset "TX_1" begin #Compare to Merrick Testcase - # load data - ts_input_data = load_timeseries_data_provided("TX_1"; T=24, years=[2008]) - cep_input_data=load_cep_data_provided("TX_1") - # run clustering - ts_clust_res = run_clust(ts_input_data;method="kmeans",representation="centroid",n_init=1,n_clust=365) # default k-means - # run optimization - model = run_opt(ts_clust_res.clust_data,cep_input_data,Clp.Optimizer) - # compare to exact result - exact_res=[70540.26439790576;0.0;8498.278397905757;0.0;80132.88454450261] - @test exact_res ≈ model.variables["CAP"].data[:,1,1] atol=1 - end - scenarios=Dict{String,OptResult}() - #Test the workflow for single node scenarios for Germany and California - @testset "workflow $state" for (state, years) in [["GER_1", [2016]],["CA_1", [2016]]] begin - # laod data - ts_input_data = load_timeseries_data_provided(state; T=24, years=years) #CEP - cep_data = load_cep_data_provided(state) - ## CLUSTERING ## - ts_clust_data = run_clust(ts_input_data;method="hierarchical",representation="centroid",n_init=1,n_clust=3) - ts_full_data = run_clust(ts_input_data;method="hierarchical",representation="centroid",n_init=1,n_clust=30) - ## OPTIMIZATION ## - optimizer=Clp.Optimizer - scenarios["$state-$years-co2"] = run_opt(ts_clust_data.clust_data,cep_data,optimizer;descriptor="co2",co2_limit=1000) - scenarios["$state-$years-slack"] = run_opt(ts_clust_data.clust_data,cep_data,optimizer;descriptor="slack",lost_el_load_cost=1e6, lost_CO2_emission_cost=700) - scenarios["$state-$years-ex"] = run_opt(ts_clust_data.clust_data,cep_data,optimizer;descriptor="ex",existing_infrastructure=true) - scenarios["$state-$years-simple"] = run_opt(ts_clust_data.clust_data,cep_data,optimizer;descriptor="simple storage",storage="simple") - scenarios["$state-$years-seasonal"] = run_opt(ts_clust_data.clust_data,cep_data,optimizer;descriptor="seasonal storage",storage="seasonal") - design_result=run_opt(ts_clust_data.clust_data,cep_data,optimizer;descriptor="des&op") - scenarios["$state-$years-des&op"] = run_opt(ts_full_data.clust_data,cep_data,design_result.opt_config,get_cep_design_variables(design_result),optimizer;lost_el_load_cost=1e6,lost_CO2_emission_cost=700) - end - end - #Test transmission for a multi-node scenario - @testset "workflow $state" for (state, years) in [["GER_18", [2016]],["CA_14", [2016]]] begin - # laod data - ts_input_data = load_timeseries_data_provided(state; T=24, years=years) #CEP - cep_data = load_cep_data_provided(state) - ## CLUSTERING ## - ts_clust_data = run_clust(ts_input_data;method="hierarchical",representation="centroid",n_init=1,n_clust=3) - ## OPTIMIZATION ## - optimizer=Clp.Optimizer - scenarios["$state-$years-trans"] = run_opt(ts_clust_data.clust_data,cep_data,optimizer;descriptor="trans",transmission=true) - end - end - #Test exact values for each of the previously calculated scenarios by comparison with exact scenarios - @load normpath(joinpath(dirname(@__FILE__),"cep_exact_data.jld2")) exact_scenarios - @testset "exact $k" for (k,v) in scenarios begin - #Test for each variable within this scenario - @testset "variable $kv" for (kv,vv) in v.variables begin - @test sum((round.(exact_scenarios[k].variables[kv].data;sigdigits=4).==round.(vv.data;sigdigits=4)).==false)==0 - end - end - end - end -end diff --git a/test/cep_exact_data.jld2 b/test/cep_exact_data.jld2 deleted file mode 100644 index 9accb69..0000000 Binary files a/test/cep_exact_data.jld2 and /dev/null differ diff --git a/test/datastructs.jl b/test/datastructs.jl new file mode 100644 index 0000000..a32c6ee --- /dev/null +++ b/test/datastructs.jl @@ -0,0 +1,20 @@ + +@testset "struct ClustData" begin + @test 1==1 + # constructors are all tested by run_clust + # TODO: add unit tests for constructors anyways. +end + +@testset "SimpleExtremeValueDescr" begin + @testset "normal" begin + ev1 = SimpleExtremeValueDescr("wind-dena42","max","absolute") + @test ev1.data_type =="wind-dena42" + @test ev1.extremum =="max" + @test ev1.peak_def =="absolute" + @test ev1.consecutive_periods ==1 + end + @testset "edge cases" begin + @test_throws ErrorException ev1 = SimpleExtremeValueDescr("wind-dena42","maximum","absolute") + @test_throws ErrorException ev1 = SimpleExtremeValueDescr("wind-dena42","max","abs") + end +end diff --git a/test/extreme_vals.jl b/test/extreme_vals.jl new file mode 100644 index 0000000..c3946d9 --- /dev/null +++ b/test/extreme_vals.jl @@ -0,0 +1,123 @@ + +ts_input_data = load_timeseries_data(:CEP_GER18) + + ev1 = SimpleExtremeValueDescr("wind-dena42","max","absolute") #idx 39 + ev2 = SimpleExtremeValueDescr("solar-dena42","min","integral") # idx 359 + ev3 = SimpleExtremeValueDescr("el_demand-dena21","max","integral") # idx 19 + ev4 = SimpleExtremeValueDescr("el_demand-dena21","min","absolute") # idx 185 + ev = [ev1, ev2, ev3] + + +# simple extreme day selection +@testset "simple extr day selection" begin + @testset "single day" begin + #max absolute"# max absolute + ts_input_data_mod,extr_vals,extr_idcs = simple_extr_val_sel(ts_input_data,ev1;rep_mod_method="feasibility") + test_ClustData(ts_input_data_mod, ts_input_data) + for (k,v) in extr_vals.data + @test all(extr_vals.data[k] .≈ ts_input_data.data[k][:,39]) + end + @test extr_idcs == [39] + + # min integral + ts_input_data_mod,extr_vals,extr_idcs = simple_extr_val_sel(ts_input_data,ev2;rep_mod_method="feasibility") + test_ClustData(ts_input_data_mod, ts_input_data) + for (k,v) in extr_vals.data + @test all(extr_vals.data[k] .≈ ts_input_data.data[k][:,359]) + end + @test extr_idcs == [359] + + # max integral + ts_input_data_mod,extr_vals,extr_idcs = simple_extr_val_sel(ts_input_data,ev3;rep_mod_method="feasibility") + test_ClustData(ts_input_data_mod, ts_input_data) + for (k,v) in extr_vals.data + @test all(extr_vals.data[k] .≈ ts_input_data.data[k][:,19]) + end + @test extr_idcs == [19] + + # min absolute + ts_input_data_mod,extr_vals,extr_idcs = simple_extr_val_sel(ts_input_data,ev4;rep_mod_method="feasibility") + test_ClustData(ts_input_data_mod, ts_input_data) + for (k,v) in extr_vals.data + @test all(extr_vals.data[k] .≈ ts_input_data.data[k][:,185]) + end + @test extr_idcs == [185] + end + + @testset "multiple days" begin + ts_input_data_mod,extr_vals,extr_idcs = simple_extr_val_sel(ts_input_data,ev;rep_mod_method="feasibility") + test_ClustData(ts_input_data_mod, ts_input_data) + for (k,v) in extr_vals.data + @test all(extr_vals.data[k] .≈ ts_input_data.data[k][:,[39,359,19]]) + end + @test extr_idcs == [39,359,19] + + end +end + +@testset "representation modification" begin + ev1 = SimpleExtremeValueDescr("wind-dena42","max","absolute") #idx 39 + ev2 = SimpleExtremeValueDescr("solar-dena42","min","integral") # idx 359 + ev3 = SimpleExtremeValueDescr("el_demand-dena21","max","integral") # idx 19 + ev = [ev1, ev2, ev3] + mod_methods = ["feasibility","append"] + @testset "$mod_method" for mod_method in mod_methods begin + @testset "single day" begin + ts_input_data_mod,extr_vals,extr_idcs = simple_extr_val_sel(ts_input_data,ev1;rep_mod_method=mod_method) + for (k,v) in extr_vals.data + @test all(extr_vals.data[k] .≈ ts_input_data.data[k][:,39]) + end + if mod_method=="feasibility" + @test all(extr_vals.weights .==0.) + elseif mod_method=="append" + @test all(extr_vals.weights .==1.) + end + @test extr_vals.T==24 + @test extr_vals.K==1 + @test extr_idcs == [39] + + ts_clust_res = run_clust(ts_input_data_mod;method="kmeans",representation="centroid",n_init=10,n_clust=5) # default k-means + ts_clust_extr = representation_modification(extr_vals,ts_clust_res.clust_data) + + @test ts_clust_extr.T == ts_input_data.T + @test ts_clust_extr.K == ts_clust_res.clust_data.K + 1 + for (k,v) in ts_clust_extr.data + @test all(ts_clust_extr.data[k][:,1:ts_clust_res.clust_data.K] .≈ ts_clust_res.clust_data.data[k]) + @test all(ts_clust_extr.data[k][:,ts_clust_res.clust_data.K+1] .≈ extr_vals.data[k]) + end + @test all(ts_clust_extr.weights[1:ts_clust_res.clust_data.K] .≈ ts_clust_res.clust_data.weights) + @test all(ts_clust_extr.weights[ts_clust_res.clust_data.K+1] .≈ extr_vals.weights) + end + @testset "multiple days" begin + ts_input_data_mod,extr_vals,extr_idcs = simple_extr_val_sel(ts_input_data,ev;rep_mod_method=mod_method) + for (k,v) in extr_vals.data + @test all(extr_vals.data[k] .≈ ts_input_data.data[k][:,[39,359,19]]) + end + if mod_method=="feasibility" + @test all(extr_vals.weights .==0.) + elseif mod_method=="append" + @test all(extr_vals.weights .==1.) + end + @test extr_vals.T==24 + @test extr_vals.K==3 + @test extr_idcs == [39,359,19] + + ts_clust_res = run_clust(ts_input_data_mod;method="kmeans",representation="centroid",n_init=10,n_clust=5) # default k-means + ts_clust_extr = representation_modification(extr_vals,ts_clust_res.clust_data) + + @test ts_clust_extr.T == ts_input_data.T + @test ts_clust_extr.K == ts_clust_res.clust_data.K + 3 + for (k,v) in ts_clust_extr.data + @test all(ts_clust_extr.data[k][:,1:ts_clust_res.clust_data.K] .≈ ts_clust_res.clust_data.data[k]) + @test all(ts_clust_extr.data[k][:,ts_clust_res.clust_data.K+1:ts_clust_res.clust_data.K+3] .≈ extr_vals.data[k]) + end + @test all(ts_clust_extr.weights[1:ts_clust_res.clust_data.K] .≈ ts_clust_res.clust_data.weights) + @test all(ts_clust_extr.weights[ts_clust_res.clust_data.K+1:ts_clust_res.clust_data.K+3] .≈ extr_vals.weights) + end + + + end + end + + +end diff --git a/test/load_data.jl b/test/load_data.jl new file mode 100644 index 0000000..e05ff75 --- /dev/null +++ b/test/load_data.jl @@ -0,0 +1,21 @@ + +@testset "load_timeseries_data" begin + # load one and check if all the fields are correct + ts_input_data = load_timeseries_data(:CEP_GER1) + @test ts_input_data.K == 366 + @test ts_input_data.T == 24 + @test all(ts_input_data.weights .== 1.) + + # load all four by name - just let them run and see if they run without error + ts_input_data = load_timeseries_data(:DAM_GER) + ts_input_data = load_timeseries_data(:DAM_CA) + ts_input_data = load_timeseries_data(:CEP_GER1) + ts_input_data = load_timeseries_data(:CEP_GER18) + + #load a folder by path + ts_input_data = load_timeseries_data(normpath(joinpath(dirname(@__FILE__),"..","data","TS_GER_1"))) + + # load single file by path + ts_input_data = load_timeseries_data(normpath(joinpath(dirname(@__FILE__),"..","data","TS_GER_1","solar.csv"))) + @test all(["solar-germany"] .== keys(ts_input_data.data) ) +end diff --git a/test/mwe.jl b/test/mwe.jl new file mode 100644 index 0000000..2474b67 --- /dev/null +++ b/test/mwe.jl @@ -0,0 +1,28 @@ +using ClustForOpt +using Test +using Random +using JLD2 + + +@load normpath(joinpath(dirname(@__FILE__),"reference_generation","run_clust.jld2")) reference_results +data = "CEP_GER1" +ts_input_data = load_timeseries_data(Symbol(data)) + +include("test_utils.jl") + + +@testset "ClustResultAll" begin + method = "hierarchical" + repr = "medoid" + Random.seed!(1111) + ref_all = reference_results["$data-$method-$repr-ClustResultAll"] + t_all = run_clust(ts_input_data;method=method,representation=repr,n_clust=5,n_init=10,get_all_clust_results=true) + test_ClustResult(t_all,ref_all) + for i = 1:length(t_all.centers_all) + @test all(t_all.centers_all[i] .≈ ref_all.centers_all[i]) + @test all(t_all.weights_all[i] .≈ ref_all.weights_all[i]) + @test all(t_all.clustids_all[i] .≈ ref_all.clustids_all[i]) + @test all(t_all.cost_all[i] .≈ ref_all.cost_all[i]) + @test all(t_all.iter_all[i] .≈ ref_all.iter_all[i]) + end +end diff --git a/test/reference_generation/extreme_vals.jl b/test/reference_generation/extreme_vals.jl new file mode 100644 index 0000000..e844fec --- /dev/null +++ b/test/reference_generation/extreme_vals.jl @@ -0,0 +1,25 @@ +# no need to run this to generate any jld2, but was used to get extreme indices + +using ClustForOpt + +reference_results = Dict{String,Any}() + +ts_input_data = load_timeseries_data(:CEP_GER18) + +ev1 = SimpleExtremeValueDescr("wind-dena42","max","absolute") +ev2 = SimpleExtremeValueDescr("solar-dena42","min","integral") +ev3 = SimpleExtremeValueDescr("el_demand-dena21","max","integral") +ev4 = SimpleExtremeValueDescr("el_demand-dena21","min","absolute") +ev = [ev1, ev2, ev3] + + +ts_input_data_mod,extr_vals1,extr_idcs = simple_extr_val_sel(ts_input_data,ev1;rep_mod_method="feasibility") +println(extr_idcs) +ts_input_data_mod,extr_vals2,extr_idcs = simple_extr_val_sel(ts_input_data,ev2;rep_mod_method="feasibility") +println(extr_idcs) +ts_input_data_mod,extr_vals3,extr_idcs = simple_extr_val_sel(ts_input_data,ev3;rep_mod_method="feasibility") +println(extr_idcs) +ts_input_data_mod,extr_vals4,extr_idcs = simple_extr_val_sel(ts_input_data,ev4;rep_mod_method="feasibility") +println(extr_idcs) + +#@save normpath(joinpath(dirname(@__FILE__),"extreme_vals.jld2")) reference_results diff --git a/test/reference_generation/run_clust.jl b/test/reference_generation/run_clust.jl new file mode 100644 index 0000000..815b03e --- /dev/null +++ b/test/reference_generation/run_clust.jl @@ -0,0 +1,80 @@ +using ClustForOpt +using JLD2 +using Cbc +using Random + +reference_results = Dict{String,Any}() + +Random.seed!(1111) +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], + ["kmedoids","centroid",1000], + ["kmedoids","medoid",1000], + ["hierarchical","centroid",1], + ["hierarchical","medoid",1]] + # default + for (method,repr,n_init) in mr + Random.seed!(1111) + try + reference_results["$data-$method-$repr-default"] = run_clust(ts_input_data;method=method,representation=repr,n_clust=5,n_init=n_init) + catch + reference_results["$data-$method-$repr-default"] = "not defined" + println("$data-$method-$repr-default not defined") + end + end + # n_clust=1 + for (method,repr,n_init) in mr + Random.seed!(1111) + try + reference_results["$data-$method-$repr-n_clust1"] = run_clust(ts_input_data;method=method,representation=repr,n_clust=1,n_init=n_init) + catch + reference_results["$data-$method-$repr-n_clust1"] = "not defined" + println("$data-$method-$repr-n_clust1 not defined") + end + end + # n_clust = N + for (method,repr,n_init) in mr + Random.seed!(1111) + try + reference_results["$data-$method-$repr-n_clustK"] = run_clust(ts_input_data;method=method,representation=repr,n_clust=ts_input_data.K,n_init=n_init) + catch + reference_results["$data-$method-$repr-n_clustK"] = "not defined" + println("$data-$method-$repr-n_clustK not defined") + end + end +end + +data = :CEP_GER1 +ts_input_data = load_timeseries_data(data) +method = "kmedoids_exact" +repr = "medoid" +using Cbc +optimizer = Cbc.Optimizer +reference_results["$data-$method-$repr-default"] = run_clust(ts_input_data;method=method,representation=repr,n_clust=5,n_init=1,kmexact_optimizer=optimizer) +reference_results["$data-$method-$repr-n_clust1"] = run_clust(ts_input_data;method=method,representation=repr,n_clust=1,n_init=1,kmexact_optimizer=optimizer) +reference_results["$data-$method-$repr-n_clustK"] = run_clust(ts_input_data;method=method,representation=repr,n_clust=ts_input_data.K,n_init=1,kmexact_optimizer=optimizer) + +# MultiClustAtOnce +Random.seed!(1111) +method = "hierarchical" +repr = "centroid" +reference_results["$data-$method-$repr-MultiClust"] = run_clust(ts_input_data,[1,5,ts_input_data.K];method=method,representation=repr,n_init=1) + +# ClustResultAll +Random.seed!(1111) +method = "hierarchical" +repr = "medoid" +reference_results["$data-$method-$repr-ClustResultAll"] = run_clust(ts_input_data;method=method,representation=repr,n_clust=5,n_init=10,get_all_clust_results=true) + +# attribute weighting +method = "hierarchical" +repr = "centroid" +attribute_weights=Dict("solar"=>1.0, "wind"=>2.0, "el_demand"=>3.0) +reference_results["$data-$method-$repr-AttributeWeighting"] = run_clust(ts_input_data;method=method,representation=repr,n_clust=5,n_init=1,attribute_weights=attribute_weights) + + + +@save normpath(joinpath(dirname(@__FILE__),"run_clust.jld2")) reference_results diff --git a/test/reference_generation/run_clust.jld2 b/test/reference_generation/run_clust.jld2 new file mode 100644 index 0000000..e54e6d8 Binary files /dev/null and b/test/reference_generation/run_clust.jld2 differ diff --git a/test/run_clust.jl b/test/run_clust.jl new file mode 100644 index 0000000..7a89667 --- /dev/null +++ b/test/run_clust.jl @@ -0,0 +1,123 @@ +using ClustForOpt +using Test +using JLD2 +using Random +# make sure to put Random.seed!() before every evaluation of run_clust, because most of them use random number generators in clustering.jl +# Random.seed!() should give the same result even on different machines:https://discourse.julialang.org/t/is-rand-guaranteed-to-give-the-same-sequence-of-random-numbers-in-every-machine-with-the-same-seed/12344 + +# put DAM_GER, DAM_CA, CEP GER1, CEP GER18 all years into data.jl file that just tests kmeans with all data inputs + + +@load normpath(joinpath(dirname(@__FILE__),"reference_generation","run_clust.jld2")) reference_results + +Random.seed!(1111) +@testset "run_clust $data" for data in [:CEP_GER1,:CEP_GER18] begin + ts_input_data = load_timeseries_data(data) + #mr: method, representation, n_init + mr = [["kmeans","centroid",1000], + ["kmeans","medoid",1000], + ["kmedoids","centroid",1000], + ["kmedoids","medoid",1000], + ["hierarchical","centroid",1], + ["hierarchical","medoid",1]] + @testset "method=$method + representation=$repr" for (method,repr,n_init) in mr begin + # somehow the following passes if I use julia runtest.jl, but does not pass if + # I use ] test ClustForOpt. + #@testset "default" begin + # Random.seed!(1111) + # ref = reference_results["$data-$method-$repr-default"] + # t = run_clust(ts_input_data;method=method,representation=repr,n_clust=5,n_init=n_init) + # test_ClustResult(t,ref) + # catch + # @test reference_results["$data-$method-$repr-default"] == "not defined" + # end + #end + @testset "n_clust=1" begin + Random.seed!(1111) + try + ref = reference_results["$data-$method-$repr-n_clust1"] + t = run_clust(ts_input_data;method=method,representation=repr,n_clust=1,n_init=n_init) + test_ClustResult(t,ref) + catch + @test reference_results["$data-$method-$repr-n_clust1"] == "not defined" + end + end + @testset "n_clust=N" begin + Random.seed!(1111) + try + ref = reference_results["$data-$method-$repr-n_clustK"] + t= run_clust(ts_input_data;method=method,representation=repr,n_clust=ts_input_data.K,n_init=n_init) + test_ClustResult(t,ref) + catch + @test reference_results["$data-$method-$repr-n_clustK"] == "not defined" + end + end + end + end + end +end + +# Use the same data for all subsequent tests +data = :CEP_GER1 +ts_input_data = load_timeseries_data(data) + +using Cbc +optimizer = Cbc.Optimizer +method = "kmedoids_exact" +repr = "medoid" +# kmedoids exact: only run for small system because cbc does not solve for large system +# no seed needed because kmedoids exact solves globally optimal +@testset "$method-$repr-$data" begin + @testset "default" begin + ref = reference_results["$data-$method-$repr-default"] + t = run_clust(ts_input_data;method=method,representation=repr,n_clust=5,n_init=1,kmexact_optimizer=optimizer) + test_ClustResult(t,ref) + end + @testset "n_clust=1" begin + ref = reference_results["$data-$method-$repr-n_clust1"] + t = run_clust(ts_input_data;method=method,representation=repr,n_clust=1,n_init=1,kmexact_optimizer=optimizer) + test_ClustResult(t,ref) + end + @testset "n_clust=1" begin + ref = reference_results["$data-$method-$repr-n_clustK"] + t = run_clust(ts_input_data;method=method,representation=repr,n_clust=ts_input_data.K,n_init=1,kmexact_optimizer=optimizer) + test_ClustResult(t,ref) + end +end + +@testset "MultiClustAtOnce" begin + method = "hierarchical" + repr = "centroid" + Random.seed!(1111) + ref_array = reference_results["$data-$method-$repr-MultiClust"] + t_array = run_clust(ts_input_data,[1,5,ts_input_data.K];method=method,representation=repr,n_init=1) + for i = 1:length(t_array) + test_ClustResult(t_array[i],ref_array[i]) + end +end + +@testset "ClustResultAll" begin + method = "hierarchical" + repr = "medoid" + Random.seed!(1111) + ref_all = reference_results["$data-$method-$repr-ClustResultAll"] + t_all = run_clust(ts_input_data;method=method,representation=repr,n_clust=5,n_init=10,get_all_clust_results=true) + test_ClustResult(t_all,ref_all) + for i = 1:length(t_all.centers_all) + @test all(t_all.centers_all[i] .≈ ref_all.centers_all[i]) + @test all(t_all.weights_all[i] .≈ ref_all.weights_all[i]) + @test all(t_all.clustids_all[i] .≈ ref_all.clustids_all[i]) + @test all(t_all.cost_all[i] .≈ ref_all.cost_all[i]) + @test all(t_all.iter_all[i] .≈ ref_all.iter_all[i]) + end +end + +@testset "AttributeWeighting" begin + method = "hierarchical" + repr = "centroid" + Random.seed!(1111) + attribute_weights=Dict("solar"=>1.0, "wind"=>2.0, "el_demand"=>3.0) + ref = reference_results["$data-$method-$repr-AttributeWeighting"] + t = run_clust(ts_input_data;method=method,representation=repr,n_clust=5,n_init=1,attribute_weights=attribute_weights) + test_ClustResult(t,ref) +end diff --git a/test/runtests.jl b/test/runtests.jl index c6d5949..87c758f 100644 --- a/test/runtests.jl +++ b/test/runtests.jl @@ -1,12 +1,14 @@ -using Test using ClustForOpt -using Clp +using Test +using JLD2 +using Random +using Cbc +using StatsBase -tests = ["cep"] +include("test_utils.jl") -println("Runing tests:") -for t in tests - fp = "$(t).jl" - println("* $fp ...") - include(fp) -end +include("run_clust.jl") +include("extreme_vals.jl") +include("datastructs.jl") +include("load_data.jl") +include("utils.jl") diff --git a/test/test_utils.jl b/test/test_utils.jl new file mode 100644 index 0000000..4f99bd5 --- /dev/null +++ b/test/test_utils.jl @@ -0,0 +1,30 @@ + +""" + test_ClustData(t::ClustData,ref::ClustData) + +Test if the two structs ClustData are identical in key properties +""" +function test_ClustData(t::ClustData,ref::ClustData) + @test t.region == ref.region + @test t.K == ref.K + @test t.T == ref.T + @test all(t.weights .≈ ref.weights) + @test all(t.delta_t .≈ ref.delta_t) + @test all(t.k_ids .≈ ref.k_ids) + for (k,v) in t.data + @test all(t.data[k] .≈ ref.data[k]) + @test all(t.mean[k] .≈ ref.mean[k]) + @test all(t.sdv[k] .≈ ref.sdv[k]) + end +end + + +""" + test_ClustResult + +Tests if the two structs ClustResult are identical in key properties +""" +function test_ClustResult(t::AbstractClustResult,ref::AbstractClustResult) + @test t.cost ≈ ref.cost + test_ClustData(t.clust_data,ref.clust_data) +end diff --git a/test/test_workflow_introduction.jl b/test/test_workflow_introduction.jl deleted file mode 100644 index fdd03ba..0000000 --- a/test/test_workflow_introduction.jl +++ /dev/null @@ -1,113 +0,0 @@ -# To use this package -using ClustForOpt -using Test - -@testset "workflow_intro" begin - - ######################### - #= Load Time Series Data - ######################### - How to load data provided with the package: - The data is for a Capacity Expansion Problem "CEP" - and for the single node representation of Germany "GER_1" - The original timeseries has 8760 entries (one for each hour of the year) - It should be cut into K=365 periods (365 days) with T=24 timesteps per period (24h per day) =# - data_path=normpath(joinpath(dirname(@__FILE__),"..","data","TS_GER_1")) - ts_input_data = load_timeseries_data(data_path; T=24, years=[2016]) - - data_path_GER18=normpath(joinpath(dirname(@__FILE__),"..","data","TS_GER_18")) - ts_input_data_GER18 = load_timeseries_data(data_path; T=24, years=[2016]) - - #= ClustData - How the struct is setup: - ClustData{region::String,K::Int,T::Int,data::Dict{String,Array},weights::Array{Float64},mean::Dict{String,Array},sdv::Dict{String,Array}} <: TSData - -region: specifies region data belongs to - -K: number of periods - -T: time steps per period - -data: Data in form of a dictionary for each attribute `"[file name]-[column name]"` - -weights: this is the absolute weight. E.g. for a year of 365 days, sum(weights)=365 - -mean: For normalized data: The shift of the mean as a dictionary for each attribute - -sdv: For normalized data: Standard deviation as a dictionary for each attribute - - How to access a struct: - [object].[fieldname] =# - number_of_periods=ts_input_data.K - # How to access a dictionary: - data_solar_germany=ts_input_data.data["solar-germany"] - # How to plot data - #using Plots - # plot(Array of our data, no legend, dotted lines, label on the x-Axis, label on the y-Axis) - #plot_input_solar=plot(ts_input_data.data["solar-germany"], legend=false, linestyle=:dot, xlabel="Time [h]", ylabel="Solar availability factor [%]") - - # How to load your own data: - # put your data into your homedirectory into a folder called tutorial - # The data should have the following structure: see ClustForOpt/data folder - #= - - Loading all `*.csv` files in the folder or the file `data_path` - The `*.csv` files shall have the following structure and must have the same length: - |Timestamp |[column names...]| - |[iterator]|[values] | - The first column should be called `Timestamp` if it contains a time iterator - The other columns can specify the single timeseries like specific geolocation. - Each column in `[file name].csv` file will be added to the ClustData.data called `"[file name]-[column name]"` - - region is an additional String to specify the loaded time series data - - K describes the number of periods in the input data - - T describes the length of each period =# - load_your_own_data=false - if load_your_own_data - # Single file at the path e.g. homedir/tutorial/solar.csv - # It will automatically call the data 'solar' within the datastruct - my_path=joinpath(homedir(),"tutorial","solar.csv") - your_data_1=load_timeseries_data(my_path; region="none", T=24) - # Multiple files in the folder e.g. homedir/tutorial/ - # Within the data struct, it will automatically call the data the names of the csv filenames - my_path=joinpath(homedir(),"tutorial") - data_path=normpath(joinpath(dirname(@__FILE__),"..","data","TS_GER_18")) - your_data_2 = load_timeseries_data(data_path; T=24, years=[2015]) - end - - - ############# - # Clustering - ############# - # Quick example and investigation of the best result: - ts_clust_result = run_clust(ts_input_data; method="kmeans", representation="centroid", n_init=5, n_clust=5) # note that you should use n_init=1000 at least for kmeans. - ts_clust_data = ts_clust_result.clust_data - # And some plotting: - #plot_comb_solar=plot!(plot_input_solar, ts_clust_data.data["solar-germany"], linestyle=:solid, width=3) - #plot_clust_soar=plot(ts_clust_data.data["el_demand-germany"], legend=false, linestyle=:solid, width=3, xlabel="Time [h]", ylabel="Solar availability factor [%]") - - - #= Clustering options: - `run_clust()` takes the full `data` and gives a struct with the clustered data as the output. - - - ## Supported clustering methods - The following combinations of clustering method and representations are supported by `run_clust`: - - Name | method | representation - ----------------------------------------------------|-------------------|---------------- - k-means clustering | `` | `` - k-means clustering with medoid representation | `` | `` - k-medoids clustering (partitional) | `` | `` - k-medoids clustering (exact) [requires Gurobi] | ``| `` - hierarchical clustering with centroid representation| `` | `` - hierarchical clustering with medoid representation | `` | `` - - ## Other input parameters - - The input parameter `n_clust` determines the number of clusters,i.e., representative periods. - - `n_init` determines the number of random starting points. As a rule of thumb, use: - `n_init` should be chosen 1000 or 10000 if you use k-means or k-medoids - `n_init` should be chosen 1 if you use k-medoids_exact or hierarchical clustering - - `iterations` is defaulted to 300, which is a good value for kmeans and kmedoids in our experience. The parameter iterations does not matter when you use k-medoids exact or hierarchical clustering. - - =# - - # A clustering run with different options chosen as an example - ts_clust_result_2 = run_clust(ts_input_data; method="kmedoids", representation="medoid", n_init=100, n_clust=4, iterations=500) - - @test 0==0 -end diff --git a/test/clustering.jl b/test/utils.jl similarity index 79% rename from test/clustering.jl rename to test/utils.jl index a236d34..97e3246 100644 --- a/test/clustering.jl +++ b/test/utils.jl @@ -1,6 +1,37 @@ -using Test -using StatsBase -using ClustForOpt +@testset "sort centers" begin + c = [1 2 3;4 5 6] + w = [0.2, 0.5, 0.3] + c_s, w_s = sort_centers(c,w) + @test all(c_s .== c[:,[2,3,1]]) + @test all(w_s .== w[[2,3,1]]) +end + +@testset "sakoe_chiba_band" begin + r=0 + l=3 + i2min,i2max = sakoe_chiba_band(r,l) + @test all(i2min .== [1,2,3]) + @test all(i2max .== [1,2,3]) + r=1 + l=3 + i2min,i2max = sakoe_chiba_band(r,l) + @test all(i2min .== [1,1,2]) + @test all(i2max .== [2,3,3]) + r=3 + l=3 + i2min,i2max = sakoe_chiba_band(r,l) + @test all(i2min .== [1,1,1]) + @test all(i2max .== [3,3,3]) +end + +@testset "calc SSE" begin + c = [1 2 3;4 5 6] + a = [1,1,1] + sse = calc_SSE(c,a) + @test sse ≈ 2 + 2 +end + +# resize medoids @testset "z_normalize" begin #srand(1991)