From 7d717b6a04f71bcf444fb31b343ebe208b39e8c4 Mon Sep 17 00:00:00 2001 From: "j.schoenau" Date: Fri, 16 Jul 2021 14:57:39 +0200 Subject: [PATCH 01/12] GitLab version. --- .gitlab-ci.yml | 4 +- README.md | 4 +- docs/source/aboutUsDoc.rst | 33 + docs/source/conf.py | 4 +- docs/source/gettingStartedDoc.rst | 38 - docs/source/index.rst | 1 + docs/source/periodAggregationDoc.rst | 15 + docs/source/representationsDoc.rst | 15 + docs/source/structureOfTsamDoc.rst | 2 + docs/source/timeseriesaggregationDoc.rst | 2 +- examples/aggregation_representation.ipynb | 810 +++++++ examples/results/testperiods_kmedoids.csv | 2690 ++++++++++----------- requirements.txt | 4 +- setup.py | 2 +- test/test_accuracyIndicators.py | 36 + test/test_adjacent_periods.py | 30 + test/test_assert_raises.py | 146 ++ test/test_averaging.py | 40 + test/test_durationCurve.py | 53 + test/test_extremePeriods.py | 57 + test/test_minmaxRepresentation.py | 48 + test/test_properties.py | 135 ++ test/test_subhourlyResolution.py | 45 + test/test_subhourly_periods.py | 31 + test/test_weightingFactors.py | 45 + tsam/periodAggregation.py | 94 + tsam/representations.py | 102 + tsam/timeseriesaggregation.py | 238 +- tsam/utils/k_medoids_exact.py | 48 - tsam/utils/segmentation.py | 14 +- 30 files changed, 3180 insertions(+), 1606 deletions(-) create mode 100644 docs/source/aboutUsDoc.rst create mode 100644 docs/source/periodAggregationDoc.rst create mode 100644 docs/source/representationsDoc.rst create mode 100644 examples/aggregation_representation.ipynb create mode 100644 test/test_accuracyIndicators.py create mode 100644 test/test_adjacent_periods.py create mode 100644 test/test_assert_raises.py create mode 100644 test/test_averaging.py create mode 100644 test/test_durationCurve.py create mode 100644 test/test_extremePeriods.py create mode 100644 test/test_minmaxRepresentation.py create mode 100644 test/test_properties.py create mode 100644 test/test_subhourlyResolution.py create mode 100644 test/test_subhourly_periods.py create mode 100644 test/test_weightingFactors.py create mode 100644 tsam/periodAggregation.py create mode 100644 tsam/representations.py diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml index ef2ff4d..73fe053 100644 --- a/.gitlab-ci.yml +++ b/.gitlab-ci.yml @@ -7,5 +7,5 @@ conda: - conda env update -q --file=requirements.yml - conda env update -q --file=requirements_dev.yml - source activate tsam - - pip install --no-cache-dir . - - pytest --cov tsam --cov-report term-missing \ No newline at end of file + - pip install -e . + - pytest --cov=tsam test/ diff --git a/README.md b/README.md index 35b76fc..a48d811 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ [![Build Status](https://travis-ci.com/FZJ-IEK3-VSA/tsam.svg?branch=master)](https://travis-ci.com/FZJ-IEK3-VSA/tsam) [![Version](https://img.shields.io/pypi/v/tsam.svg)](https://pypi.python.org/pypi/tsam) [![Documentation Status](https://readthedocs.org/projects/tsam/badge/?version=latest)](https://tsam.readthedocs.io/en/latest/) [![PyPI - License](https://img.shields.io/pypi/l/tsam)]((https://github.com/FZJ-IEK3-VSA/tsam/blob/master/LICENSE.txt)) [![codecov](https://codecov.io/gh/FZJ-IEK3-VSA/tsam/branch/master/graph/badge.svg)](https://codecov.io/gh/FZJ-IEK3-VSA/tsam) -Forschungszentrum Juelich Logo +Forschungszentrum Juelich Logo # tsam - Time Series Aggregation Module tsam is a python package which uses different machine learning algorithms for the aggregation of time series. The data aggregation can be performed in two freely combinable dimensions: By representing the time series by a user-defined number of typical periods or by decreasing the temporal resolution. @@ -91,7 +91,7 @@ You should have received a copy of the MIT License along with this program. If not, see https://opensource.org/licenses/MIT ## About Us -Abteilung VSA +Abteilung VSA We are the [Techno-Economic Energy Systems Analysis](https://www.fz-juelich.de/iek/iek-3/EN/Forschung/_Process-and-System-Analysis/_node.html) department at the [Institute of Energy and Climate Research: Electrochemical Process Engineering (IEK-3)](https://www.fz-juelich.de/iek/iek-3/EN/Home/home_node.html) belonging to the [Forschungszentrum Jülich](https://www.fz-juelich.de/). Our interdisciplinary department's research is focusing on energy-related process and systems analyses. Data searches and system simulations are used to determine energy and mass balances, as well as to evaluate performance, emissions and costs of energy systems. The results are used for performing comparative assessment studies between the various systems. Our current priorities include the development of energy strategies, in accordance with the German Federal Government’s greenhouse gas reduction targets, by designing new infrastructures for sustainable and secure energy supply chains and by conducting cost analysis studies for integrating new technologies into future energy market frameworks. diff --git a/docs/source/aboutUsDoc.rst b/docs/source/aboutUsDoc.rst new file mode 100644 index 0000000..2843c75 --- /dev/null +++ b/docs/source/aboutUsDoc.rst @@ -0,0 +1,33 @@ +######## +About Us +######## + +.. image:: https://www.fz-juelich.de/iek/iek-3/DE/_Documents/Pictures/IEK-3Team_2019-02-04.jpg?__blob=poster + :target: https://www.fz-juelich.de/iek/iek-3/EN/Home/home_node.html + :alt: Abteilung TSA + :align: center + +We are the `Institute of Energy and Climate Research: Techno-Economic Energy Systems Analysis (IEK-3) +`_ belonging to the Forschungszentrum Jülich. +Our interdisciplinary department's research is focusing on energy-related process and systems analyses. Data searches and +system simulations are used to determine energy and mass balances, as well as to evaluate performance, emissions and +costs of energy systems. The results are used for performing comparative assessment studies between the various systems. +Our current priorities include the development of energy strategies, in accordance with the German Federal Government’s +greenhouse gas reduction targets, by designing new infrastructures for sustainable and secure energy supply chains and +by conducting cost analysis studies for integrating new technologies into future energy market frameworks. + +**Contributions and Users** + +Within the BMWi funded project `METIS `_ we develop together with the RWTH-Aachen +`(Prof. Aaron Praktiknjo) `_, +the EDOM Team at FAU `(PD Lars Schewe) `_ and the +`Jülich Supercomputing Centre (JSC) `_ energy system models +on both, global and national scales, and new methods for effectively solving them. + +.. image:: http://www.metis-platform.net/metis-platform/DE/_Documents/Pictures/projectTeamAtKickOffMeeting_640x338.jpg?__blob=normal + :target: http://www.metis-platform.net + :alt: METIS Team + :align: center + +Dr. Martin Robinius is teaching a `course `_ +at TU Berlin in which he is introducing tsam to students. \ No newline at end of file diff --git a/docs/source/conf.py b/docs/source/conf.py index 50243ee..0a74885 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -66,9 +66,9 @@ # built documents. # # The short X.Y version. -version = '1.1.0' +version = '1.1.1' # The full version, including alpha/beta/rc tags. -release = '1.1.0' +release = '1.1.1' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. diff --git a/docs/source/gettingStartedDoc.rst b/docs/source/gettingStartedDoc.rst index d36f380..211c97a 100644 --- a/docs/source/gettingStartedDoc.rst +++ b/docs/source/gettingStartedDoc.rst @@ -4,10 +4,6 @@ Getting started ############### -************ -Installation -************ - In the following, instructions for installing and using the tsam package on Windows are given. The installation instructions for installing and using tsam on Linux/macOS systems are however quite similar and can be, hopefully easily, derived from the instructions below. @@ -41,37 +37,3 @@ or install directly via python as **Installation of an optimization solver** Some clustering algorithms implemented in tsam are based on Mixed-Integer Linear Programming. Accordingy, an appropriate solver for using these functionalities is required that can be accessed by `Pyomo `_. In theory many solvers can be used (e.g. `GUROBI `_ or `GLPK `_). For the installation of GUROBI, follow the instructions on the solver's website. For installation of GLPK, move the downloaded folder to a desired location. Then, manually append the Environment Variable *Path* with the absolute path leading to the folder in which the glpsol.exe is located (c.f. w32/w64 folder, depending on operating system type). - -******** -About Us -******** - -.. image:: https://www.fz-juelich.de/iek/iek-3/DE/_Documents/Pictures/IEK-3Team_2019-02-04.jpg?__blob=poster - :target: https://www.fz-juelich.de/iek/iek-3/EN/Home/home_node.html - :alt: Abteilung TSA - :align: center - -We are the `Institute of Energy and Climate Research: Techno-Economic Energy Systems Analysis (IEK-3) -`_ belonging to the Forschungszentrum Jülich. -Our interdisciplinary department's research is focusing on energy-related process and systems analyses. Data searches and -system simulations are used to determine energy and mass balances, as well as to evaluate performance, emissions and -costs of energy systems. The results are used for performing comparative assessment studies between the various systems. -Our current priorities include the development of energy strategies, in accordance with the German Federal Government’s -greenhouse gas reduction targets, by designing new infrastructures for sustainable and secure energy supply chains and -by conducting cost analysis studies for integrating new technologies into future energy market frameworks. - -**Contributions and Users** - -Within the BMWi funded project `METIS `_ we develop together with the RWTH-Aachen -`(Prof. Aaron Praktiknjo) `_, -the EDOM Team at FAU `(PD Lars Schewe) `_ and the -`Jülich Supercomputing Centre (JSC) `_ energy system models -on both, global and national scales, and new methods for effectively solving them. - -.. image:: http://www.metis-platform.net/metis-platform/DE/_Documents/Pictures/projectTeamAtKickOffMeeting_640x338.jpg?__blob=normal - :target: http://www.metis-platform.net - :alt: METIS Team - :align: center - -Dr. Martin Robinius is teaching a `course `_ -at TU Berlin in which he is introducing tsam to students. \ No newline at end of file diff --git a/docs/source/index.rst b/docs/source/index.rst index f739812..24faf80 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -38,6 +38,7 @@ The authors of both, the program and the publications, **kindly request you to c newsDoc integratedSoftwareDoc legalNoticeDoc + aboutUsDoc furtherReadingDoc * :ref:`genindex` diff --git a/docs/source/periodAggregationDoc.rst b/docs/source/periodAggregationDoc.rst new file mode 100644 index 0000000..7efb71c --- /dev/null +++ b/docs/source/periodAggregationDoc.rst @@ -0,0 +1,15 @@ +############################# +The Aggregation Functionality +############################# + +.. |br| raw:: html + +
+ +Descriptions of the basic functions are given below. + +**Function descriptions:** + +.. automodule:: periodAggregation + :members: + :member-order: bysource diff --git a/docs/source/representationsDoc.rst b/docs/source/representationsDoc.rst new file mode 100644 index 0000000..4eedd8c --- /dev/null +++ b/docs/source/representationsDoc.rst @@ -0,0 +1,15 @@ +################################ +The Representation Functionality +################################ + +.. |br| raw:: html + +
+ +Descriptions of the basic functions are given below. + +**Function descriptions:** + +.. automodule:: representations + :members: + :member-order: bysource diff --git a/docs/source/structureOfTsamDoc.rst b/docs/source/structureOfTsamDoc.rst index bf7a219..9f80cac 100644 --- a/docs/source/structureOfTsamDoc.rst +++ b/docs/source/structureOfTsamDoc.rst @@ -15,6 +15,8 @@ clustering of adjacent time steps called segmentation. :maxdepth: 2 timeseriesaggregationDoc + periodAggregationDoc + representationsDoc **Submodules including exact k-medoids and segmentation** diff --git a/docs/source/timeseriesaggregationDoc.rst b/docs/source/timeseriesaggregationDoc.rst index 4c422ad..2ac3312 100644 --- a/docs/source/timeseriesaggregationDoc.rst +++ b/docs/source/timeseriesaggregationDoc.rst @@ -11,7 +11,7 @@ Descriptions of the basic functions are given below. **Function descriptions:** .. automodule:: timeseriesaggregation - :members: unstackToPeriods, aggregatePeriods, medoidRepresentation, meanRepresentation + :members: unstackToPeriods :member-order: bysource .. autoclass:: TimeSeriesAggregation diff --git a/examples/aggregation_representation.ipynb b/examples/aggregation_representation.ipynb new file mode 100644 index 0000000..c70d9a5 --- /dev/null +++ b/examples/aggregation_representation.ipynb @@ -0,0 +1,810 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# tsam - Regmentation\n", + "Example usage of the time series aggregation module (tsam)\n", + "Date: 29.04.2019\n", + "\n", + "Author: Maximilian Hoffmann" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Import pandas and the relevant time series aggregation class" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "import copy\n", + "import os\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import tsam.timeseriesaggregation as tsam\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Input data " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Read in time series from testdata.csv with pandas" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "raw = pd.read_csv('testdata.csv', index_col = 0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create a plot function for the temperature for a visual comparison of the time series" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def plotTS(data, periodlength, vmin, vmax, label = 'T [°C]'):\n", + " fig, axes = plt.subplots(figsize = [6, 2], dpi = 100, nrows = 1, ncols = 1)\n", + " stacked, timeindex = tsam.unstackToPeriods(copy.deepcopy(data), periodlength)\n", + " cax = axes.imshow(stacked.values.T, interpolation = 'nearest', vmin = vmin, vmax = vmax)\n", + " axes.set_aspect('auto') \n", + " axes.set_ylabel('Hour')\n", + " plt.xlabel('Day')\n", + "\n", + " fig.subplots_adjust(right = 1.2)\n", + " cbar=plt.colorbar(cax) \n", + " cbar.set_label(label)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Hierarchical aggregation with medoid representation at hourly resolution" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Initialize an aggregation class object with hierarchical as method for eight typical days" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "aggregationMedoid = tsam.TimeSeriesAggregation(raw, noTypicalPeriods = 8, hoursPerPeriod = 24, \n", + " clusterMethod = 'hierarchical')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create the typical periods" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "typPeriodsMedoid = aggregationMedoid.createTypicalPeriods()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Predict original data" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "predictedPeriodsMedoid = aggregationMedoid.predictOriginalData()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Get accuracy indicators" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " RMSE RMSE_duration MAE\n", + "GHI 0.072693 0.019636 0.037938\n", + "Load 0.064350 0.015139 0.045835\n", + "T 0.069056 0.020487 0.054453\n", + "Wind 0.107620 0.049146 0.083569" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "aggregationMean.accuracyIndicators()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Hierarchical aggregation with minmax representation and 10 typical days at hourly resolution" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "aggregationMinmax = tsam.TimeSeriesAggregation(raw, noTypicalPeriods = 20, hoursPerPeriod = 24, \n", + " clusterMethod = 'hierarchical',\n", + " representationMethod='minmaxRepresentation',\n", + " rescaleClusterPeriods=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create the typical periods" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "typPeriodsMinmax = aggregationMinmax.createTypicalPeriods()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Predict original data" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "predictedPeriodsMinmax = aggregationMinmax.predictOriginalData()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Get accuracy indicators" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " RMSE RMSE_duration MAE\n", + "GHI 0.168564 0.143625 0.097259\n", + "Load 0.144795 0.113654 0.112219\n", + "T 0.155706 0.134596 0.133953\n", + "Wind 0.264593 0.227514 0.224926" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "aggregationMinmax.accuracyIndicators()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Comparison of the aggregations \n", + "It was shown for the temperature, but both times all four time series have been aggregated. Therefore, we compare here also the duration curves of the electrical load for the original time series, the aggregation with k-mean, and the hierarchical aggregation including peak periods." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Duration Load [MW]')" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(figsize = [6, 2], dpi = 100, nrows = 1, ncols = 1)\n", + "raw['Load'].sort_values(ascending=False).reset_index(drop=True).plot(label = 'Original')\n", + "predictedPeriodsMedoid['Load'].sort_values(ascending=False).reset_index(drop=True).plot(label = '10 Medoids')\n", + "predictedPeriodsMean['Load'].sort_values(ascending=False).reset_index(drop=True).plot(label = '10 Centroids')\n", + "predictedPeriodsMinmax['Load'].sort_values(ascending=False).reset_index(drop=True).plot(label = '10 Maxoid')\n", + "plt.legend()\n", + "plt.xlabel('Hours [h]')\n", + "plt.ylabel('Duration Load [MW]')" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "param = 'GHI'" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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WhFqioZAGKhmrAF/YaogUUkxcXt8ndD9qFeP1cweQKVfBg8WtvjPE1jTp/VaBnt+aYWZHhO6n7HFsyTJ5oW2f7N4wjYlLIgzWOoLtlBe2Z6EvQqphcubj7ziL3t1WeZ4AJl4WBz/6wEOloM2nLhim86B1e4gddThxaTq4/sUQHJMdSVHocelKnmHjHs+Re7l1e+i+Z4TZ8+013JUvkD+rj3jOtnHk+HToIDGWozbYhevautUGu4L058+y8Tv2WteBuR0dZPf562d2Ep23bZN8XDC9XYivnF/c1knqoD25ha1diGdIHPLdCYyBnO9i0NURrFd2bSG65wjSkQ3i1Z8v0pHBzM7h7dgclLlRpe/NzeOkfZeB3i7MoaWTCUoyAV0diO+CgDGYGb8c0Rje3FzgIGCKJZy0vQ+9fAFJ2D6HxKJ4vkMBEDqRhAFL8g3qrM4CLyhqRpC24qfgR6r1Ivt94KY2SZ0B/AbwSeCPgJcDnxKRkjHm89hOKdg3pI2MA1v99SGgbIzJtYkzxGlAO6aKoiiKoihrRH2mp3bhPpuA+YZdpSWRLQ7wY2PMjf72Q76w6TeAzzfEa/2vRdqEtXIycZ4X9FO+oiiKoijKGnESn/LnjTFzDctyHdNRoNWpaA9QNyce8/+2vvkcIHyLOgbEfCHVcnHWFO2YKoqiKIqirBE14yy7rJK7gJ0tYWcDda/3g9iO53X1nSISA64B7vaDHsCq+hvjDAPnN8RZU/RTvqIoiqIoyhpxEp/yT5Y/B+4WkRuBf8GOMf01f8EYY0TkZuBGEdkH7ANuBBaBL/pxZkXkb4E/E5EpYBr4U+Ax4LbV1+7Zox1TRVEURVGUNaK2jF1UbZV2UcaY+0XkzcDHgd/FviH9gDHmCw3R/gRIAp8BuoF7gdcZYxrHsH4QqGI7t0nge8C7jDGNsyyvGdoxhUDdLK4bzMeM4wYqSIm0zO3dqB4XwSuVcOJWWemVK8tn4zT/N+RkrULUm59vUrlLLBqWox43aZXJplgKVO7e2ARSV4sWiuF870H6ofq9XlYA78zNEPHV9q4EqnAcX7nu+vPQOxIo0ONHZyA311T3xjbwV2z6gz04J0J/30DR3ZPBHZ+hOmyHskTGZ5rL3LBe3tRDzHcEOHFJip6465fDYeKSOJnj9tzMb3HYcjxULY9dESc96qtfBea3+XVxDcaBeM5ud+7Jkt9s227qQhPMJx/NOyCC15Xx0xCmLrN59T6SbipnZL7MzEW+0nw+xvQFdnf37hjRuRS5S+w57LsnQvFSew5idxmqXSlmX29V895t6Sa3gGpXkvyQTXP6khrmEb/ekwkW/PDEiXC+cYDB8yZYPGiHEI2/3CF5Is6Ji2ycLbtGEF+GfrQyTOawbdOFrR6p8Thn7rLzyE8upMnnfKeA8Tj5IYf+R4oAjP5kZ7AOMHEFbP+aVX9XO+Kkj9k0F4cgPRa6mhgXdu0MxaV7i3bY0+Lg0ofvlp22HEdrQ1Q74gw8FOYdJgibr7IODNU7+tl+3giOX7cDo30wYfNe2OJBf4n6zOjVh+NBngtbPbx+uyedLZI/1EVywt5byYkyvWfZZ3X5W/1kbgiHVxkgt8V3VkgVyR8eoGfXiWD/2FX9S+qU8NNdGLbnrfexAtUuG5YfcqlcOc/Cgr3Opi+qIRlb52pXw/m9YJ6FkdDpIJZLMX2uLUfuAoMX7afnCXstzV9UIp6z1/6R1/Xj9RepX1wHt0fov8e2gVOB8cttOTbOp5m5sOGZZQSp2GMSOYdyp5Df6Jd54zCLAzaNzq5MoAL3urNU0w7VlC13djET3FuZUgUpVvG6fFeEUo38mfZ+nbxQwEBk0e6busAlUrTrkxe4dByyeSWOd1DYHN7juZ1RSl29AJQzQrlDyHbb9l/Y5LDpP2y88qYuYv4xhcE4sZEwjfKmHmL+fVze2E1MBC9mz5O3uY/8JlvnzNECkVGX6qC1g5g9O03v7EJDe9k2qA10M78jEzg+iAeZ/bYN8MB1HarbBgFwD44FhzvRGNWzNwJQTbokHj7UXl2/kkLfM0g2gzcX9jOcXvuMNYVi4AbgTeeWpO34LgWe7yBQdwjAM3ilcFhjPQ18VwhT/51rcQqQSATTmEdLuU2js8syLgMSi1kXmCU7HPAajm/4jbbpLaPTafpdlNMk52nG85y2nVBv9T6mGGNuAW5ZYb/BKvpvWiFOEXi/v5x2tGOqKIqiKIqyRhja96PXQZ95XaAdU0VRFEVRlDWiZhxo62OqenTQjqmiKIqiKMqaUfMEvKXD4mptwl6KaMdUURRFURRljfCWET+dyhjTFyPaMVUURVEURVkjPCPIc2MX9aJEO6aKoiiKoihrhOeBtPls77U3KnjJ8ZLpmDrpFI7E2u6TjLUBkmQCbyq39NhMGm+gYbauBvsJKVdxj43hnW2tcNz9R1sSX+bVvCOYjdb2yTnebLdEdycyM98UvXLuJhu3VGOxz7cyKZbw+my5nCnfnslZmp/T2dGUfu6CjmDcddMYbGneNiLB3GC9tU5intfW3sk0lh0oDKdJlSpBnKkLrSVJ6kSNVLFKYYO1lUkvVsK5x1osp05ckmRowVqGLGwxJKasZVd0NsnCWRUQu50/u4zXkfTLC6VeD1NvA4HaWdamyXE9BIjfZs91LZvg2OusBrJjaA7XsU+EE+UeEEiN+rYpDgyfaW2BqtmuIF2AExcn2HbGcQCm9m6klrRWJoV+l0J/lsx+G2/y5VWuPeNpAA5n7SQdl22y24/07WLxDNtWqQNRjl+TCtrx9Zc9yp6vWg+qSjZKyTrkUMlEgjIAvHHjY/xDr7WLiuSFSiZCyTo/8aaND+P6Ws+/ONHN4MWTAFzVcYLHbr+IXxh+MEjn5kfeaNNPRyhckaey37ZBsddQSYePCndokSO/7jfFvjQ7rz4AwN47twfxxq5wiczDzw+F6X9ivM+mN5+mlct6jwCQubjEvvltDXtCnaoYeO3AkwB8Iz3M9YN76HTt+a0MRfjLL18PQG1HgQ1fjJH8kD03xfQGin7bve7qh/n+Ny4F4Cevf4Lv916K/I49v8d+uJkNcds+h64o8AsbHwvynq0mGe+y99H21CRf6H01r9uwD4CsW+SLva9eUqdK1l6juZfb6zj2MwvM3WXv+cKZZV63dT8/6r0EgFdf+gQPn9gAwPFr+hh40B7zqm2Pc9cbttP7Gdtm1WyUYp89+dvOP87EyCYqGZvP+duPc/DxMwAYPH+c4fRc8AZmZKGTxacHgnZc3GCv99GrOjjjzGOB7VbVczg8Yhur0JdAqlDorz8QQHy3nmo2zmK/tVcauS6Nmymx/TN2Xy2bYNE/JjmeaJYfG1jss8dVtxZxXEPiVluW0rYoh31LNEkVMYd9C75MghMXR0kft+kUBgyFfluvSo9/zw3b/NLHwMvY58HiQIxozn82uILXkWJ+h30WZffN4WXtvuJAnMhciuJAaHVW6rLplTrTDMyXKPXbZ64XEY6/eSsAG247EdghlQZTzOxwcerOWwLRuXTQ3vFiiVrS3htuNo002CgV++3vUqHbJbm/5d5osV6qDdnnvTvWbP1U6+/CbThs8RxrTZV65CiVLfa+i1YqmN4umAx/42qb/d+gmt8j6u4I8nVys2EZuq1tm9eVxp2ax8zM0Q7pyDRbRLVIz02DpVWjrZSTCevt9XYhh48vTTuVxCzkw+2NQzDbkF7DviXH1tOPGDi2bLQ1Q9+YrsxLpmOqKIqiKIpyujFGMG06oe3CXopox1RRFEVRFGWt8ATTToGvqnxAO6aKoiiKoihrhreMXZSnHVNAO6aKoiiKoihrhxFo99leP+UDofTkpBGRiIi8U0SGnm3mInK1iHxdREZExIjIm1r2i4jc5O8viMgdInLes81XURRFURTldGC85RflFN6YGmOqIvJXwK7nIP808Ajw98CX2+z/CPAh4F3AU8BvA98VkZ3GmPk28ZdFkinEaaPK7+uisMUqDpMPH0E2+f3t6dkgSv7SzVah7mNcQlW0gXTEoZbwFZfJZFtlPEBtqBt3fCbY9mL2GCflK7EdX2nalyVaqTYdO32OVYX27C4wt8Uel3kiSaXPHhsrlvyKhuUsbrUKzsSRGQrbu0kesnnPbw7V9kYI1x1jFfkSbtfXMyNRps4dZuiu3JJ8bDrC7DlZfx8Ue6yyt2vvAlOX2LvNuc8lkYoycpXN8MyxWNPx9WMBir3gpazaOLJ9gdKBjG3DZJS+jbPM5Gz6g8Mz1FLZII3B8ycYm7Tnk8k4O4cnbPs4NfaMDVLqtBlUky6XnHsQgM5YMSjHHaMd4MDIVcmgPNf07gFgT6K36R/acha2ZacBGMtuxOmxSurKdJLaWYuIr3SO7U9x923nAzAUr2EceGjEuizIZXOk/HjSCz2fTzN2pdXXFmpRqsnwWqpkjd8GzdfXazO7+bfLrbo792QP1YRD72NWrXztO/cG8W4eez0XnmPVrpelD/Fw4mJenX4KABfDPz5qz1MtIfR3z1N9n73FqnsGqCVsxUd/QmAsdA4gY5gu2O1q1lBN2nh9j3qM/YRwbcpaE0QFei9bAODDY+8AYPgce25Gnxwg6Vo584Wdx5m5PMnf7fo/ALz+Kx9uquurMk8A8DeveQ2vyjxBVuxxMfG4OfN628Y9c1TfBxd127reldxEJWvr9ou9d/Pt7MUA/ETHPr6TuZiDD2+0iWc98mV7Tb5p1yMMR3Nsidrz+9WZS8lG7XUyUc5y3Rvv5zXZ3QD0unn+oeNVAPyXG/6Dv9l/lW3H3V2MXekQsaJ/zts1xp0d/QBEJqKUvQiVDntOf7b3Qe4dsWrvi96wh2N7zgJgMDZHpeo2XQe9T9hnw/HLOjGXLXDMmgxwtpEgvbdtfgBHPDzfYuPxxAbu7LAK7PKOApsH7H08Pr2Bq/qfxvHdD/YuDHLeuWMA/Lh/M/M/Gggbv0FgPXJVEv+y5YrznmaqmGb0JzYDMPBAic6n7b1QSzRqxW0aZf8e3DI0TdytUk1YBfnWjZPkvm7PRdcbJpjpsC4FtVSE2CunKH+rN0gkdo5VhafdGp4RIt+0jhnTF1UZvNc+H6fPdShnO4N8a8ko6WMFALxklNw56WBfKhGl1OEG29MX2Ovl7M/nyV0UOrK4JUOqaCvuJWKIrywvd7gUBzwieVu3WhwqHbYci/0OA+Nxyp12OzYRC5oyv70Tp2K38hsEk4w3qdoL22y9kgft+ap02OvTmYkFivfy5m4wkN9u27/j4XHmttq8Unvj4TMrEUfyBUgm/HoajGt3SiphVf+d9l6eOTtN7w9KQTmKW6zNR7kzQsfoNJLyn48t/kaFnYPERxZoRYyxbgD1vGkyFsF0ZcO8NmWtm0ML3uYBnMPjYbxt3UjNtk/86EyT08ESOm365Z7oulDlG9N+jKmKnyyn+in/XuBi4PCzydwY803gmwDS0tERG/AB4A+NMV/xw94JjANvBz77bPJWFEVRFEVZa1SVvzKn2jH9DPBJEdkMPAA0GYgZYx59tgUDtgNDwHca0i2JyA+AV7JMx1RE4kC8ISjbLp6iKIqiKMqao2NMV+RUO6Zf8v9+qiHMYN/OG8BdcsTqqY9hHW8JHwe2rnDcx4Dfew7yVxRFURRFeW7x/KVd+AsEEXmIpgE+7THGXLratE+1Y7r9FI87FVorLm3CGvk48MmG7SzrYlSJoiiKoigvdcwyPqZtvU3XL199vhI+pY6pMeZZjS09Scb8v0PAaEP4AEvfogYYY0pAMGq7deyqoiiKoijKacPQ/vXaM75/XD8YY37/+Ur7lDqmIvLLK+03xnz+1IrTxEFs5/Q64CE/3xhwDfDR1SYmsQjiRPH6u3BOhMr4WiJGNe3PzxyJUEtZ1aM7Hw3iFLvcJgmhcaRpKEjl/C6yB63ak3gb5b8tPLVkFDcaCbbdaateNPFoU9RaMkKkIR5AqdtXRV+VCueyj0eZvMCqFzdMRJviA0G9TCxK4vgCJm7TLHcZcIxfF5pV+C3bQz+0mVWSQn6zwcTCS6beBrlzMxiByVdYtXDvvRF6nrBzmHsxN1D9V1MweUkGxwp28SJOs7q/QWxe3l7Ei9qA3uwc80k7VHji8hRbs2NMpa1SdlN2huloZzcOBXgAACAASURBVHDc7O1DpF9hFaxutsC2jFVVx5wq+x45k77HrYrbizqck7X/36TcMvM1245Oqoo40O+Pkh57eZStiSkAdkek6Trof6TKlp+36d+VMDhH/Pm044ZEsszLho8C8KMj55M5x5bJe9CWtXLIugy84dX3c8sdLwOgb9ckXiRDLW7PzVMz/YxdbTMcutswcL/9zuO5NLXb1kiFt2x9CIDPHnw1xhVGf9Km0eNW6HTq14Hhy3e/HIAPvfGHfMoVtkdC9asX8a+xqz2+tPNL3HToZwH41Js+xVvi7wNg+E5h9GqPj157KwCfuOMGuhL22j8e9zBueBJ3XXyYbRGr8nXF4b0HftJvHw8MvGPLvTaNAzfw8Ix1KXjDwKN0JTbyoYNvsWlccpjdT2wO0rwk5gThdj0cTr7rUvv/8u9v/RoAmyP2XP8wciVewrbdg4XtGL99z4xOMHA/dPyaPU9P7dnExEGr/P7g+f/Au/e9jZ8atC4AW+LTvCx1AIB33frr/N31/5tL4uHQ+s/+3N8A8J5bfzUIS0aEWsILbpQtyWk8P++Nu8bZN9MfnOtPPP3TFA7ba/xgsoeJq209dyTGySRLeJGWOdSB6uEMHTung+3js53U/Ob41C3X89dv+Rtq/sPi01+/ntiF1mnE3dvBRMJ3uUgYasbh7OQIAJ2RxUDJHx+scGuin8H7rMPDeb/3KLff2vBVzv8R3ZUZ43sLO/H8R9/YlfFg3+ADFSYujTLwQDk4rF7GwdQ8abfMwYgdtXVo/yCcY58hO5ILTPnxPNdhIZ8g6W/XkoaulL3m4pEqi5Uo46+07yKS6TInLrXzvZc2lUmO20KJr0A3/g08fV6S3sdtGtPnJjERh7ntdl/6mEH8N1cm4lCLhfeaeDDtGxZ2PO2Q2xkq+72kBwv2Xqula9T859fikEDEIX1oIUizjhcLxxpWMwbjusFAODEmeAYSsYHxkblw21eh12IOYmB+k42TfdzFi9o0Zy4fJnnCtn1pex+xoznKm7qD9INPx44DDkxelAnqU88ToJaw5TBuczheWJfaUDeFvgix6YZuhe8cYAA3snx3Y+Z8q/qPLnhUMg6pWMNvoq/8l0oNs6EPZ8I+S2txh9QR6xxikjGc+eVHEC6eYdMvRsvLxllLxJPgGmsNV079U/5ftGxHgRRQBhaBk+qYikgG2NEQtF1ELgamjTFHRORm4EYR2QfsA2700//iKZZbURRFURTl9LHMzE8vpClJ190YU2NMd2uYiJwF/BXwP1eR1MuA2xu262NDP4f1Lv0TIIl1AejG2lS9brUepoqiKIqiKOuCF8GnfJrHmApWeP7XwHT76CfPczYlqTFmn4j8V+AfgXNO8pg7aPbZbd1vgJv8RVEURVEU5YXNi0CV3zrGVEQ+DPyFMebAs037OeuY+tSADc9xmoqiKIqiKC8KdIzpypyq+OmNrUHAMPA+4K5nWyhFURRFUZQXJS+OT/nPG6f6xrTVv8oAJ4DvAx9eGn39UNiQJt2gyseB2IxVgtaGuptV4v66cZrncTfSvN6o2myi1arKlaVptolrlfLN5agr8TNHDfNbw33lrmXSEAnyWDirk8zTs2HergkU8EYIFPo4vjK/noyYIN+FjQ7VbK0hfavGr693P7mIcfw51AX2/7q9tHb8TRXpqCshExiBzNE27ePnc+IiOwdzpiMHYtfT0TL1QcVGoFSNBGWcWMwGF/GJS+P17Jfg+crX8cus2nPgwQqOhE+BiVI4QZhZxcNh99xw2/BLhpqtc3/v3FsA+K0rf4mhe8LvNdd3PsItxqryJ/f0MWQMA+fYydXHnhjgn978aQB+gfczdHdDwRoK6QEVE87zDTD8Q1vfa/kwj7451Cq+79rvAlAJjm34duQfK0a46dDP8qSvhv8d3rSkfp+444awDRpU83VGr/boATw/0Ua97PAPHEav9prSqFPDaZtenTfvewMArx94nFmvSJ9rVdEVUwuOu2fgTAC2Zp9sm8aQ3zbO9bbuT+3ZFOw7e5c9b/82fz6vH3o8CP/U7T/FnW/8M7shcEZ0Dte/8irG4z3f+NVgXyvD/r/qd5x7VuBk8Zqhvfxy13289sn/suSwD555Gx978hcAcPG4Zng///7KK2zZG66B7l1TXDF4GM8/+p7RrUFCqbPtM84VW8d333Abf3vrawH4+M9/gccLts5feOoaNsXaDwnbOz/YNryVf/7ytZTOLMKZVhmf2B86JUxcGmXgwUpT/PptN1VMk0xXgvt28xmjHNs/EMZruDRr48ng+swcdIieZ59FNc+hWnMD/8d4tBp8Du16IJzLHhPmW097elcy2De9KxXkJx4kJuwDKbczjXjQvccq6nPnZKgl6mmasIwGpCokJm05Kp0SzN0uVRu3fs8u7Ogk89Ssn5cJ77uqr5RvuLeD+d+NCVT4AX685MEcpc1dSP3x7HnBdZacKBM/Ep7f8sZupBo2bNAmflqO/SkkNVFtKkd8smjjd8eby9EQxx2dRs5I447a/GoDoRRFnuGh2vHUXLBe3dERKPFbqSvybd4rJtlEpGArJu766PkJzddjY/gppynyMeCPsJ/TP+CHCXbCoV8j1On8f8aY3Q3HxYE/BX4Bq+n5HvBeY8xp838/VfGT88yxFEVRFEVRlCaeY1W+iFyO7Xy2Tgf/EeBDWDH5U8BvA98VkZ0NIvKbgZ8B3gZMAX8G3CIilxljaiyDiPxmS1AEeJeITDYGGmM+xSp51mNM/R55XaikKIqiKIqiLIN4zV8EGsNXnZa13fwC8B5sx7MeLsAHgD80xnzFD3sndoKitwOfFZFO4N3ALxljbvPjvAM4CrwW+PYKWX+wZXsM+KWWMEPz1PUnxSm/+RSRXxaRx4ACUBCRR0WktVCKoiiKoihKHbPCYsmKSEfDEm+bjuUvgVvrHcsGtmNnzvxOkK2dGfMHwCv9oMuwPvSNcUaAxxvitK+CMdtPYjljpTSW41TFTx8C/gD4X1ixkwA/Afy1iPQZY/78VNJVFEVRFEV5MXMSb0xbx3f+Pm1sM0XkbdjO5cvaZDPk/22dwn0c2NoQp2yMybWJM8QKiEgCeK0x5hZ/++M0TsUHVeB3jTHFldJpx6l+yn8/8BstU49+TUR2YxtPO6aKoiiKoiitPPMY001A40RCpdaoIrIZOwvn656h89c6zFLahC1J/iTivBN4A3CLv/0+YDf2KzpYP/sxwomTTppT/ZQ/DNzdJvxuf5+iKIqiKIrSgpjlF595Y8xcw7KkY4p9UzoAPCAiVRGpAtcAv+mv19+Utr75HGjYNwbERKR1Ns/GOMvxi8DftYS93RjzKmPMq4DfAv6vZ0ijLaf6xnQ/8H9jrQkaeSt2Tvt1hylXMY5D6uAMploNwp1CBa/P2oY4x05An39+GuLEFuz79cCLQAjsOACiix5O0Y9frVnbKcAdnwnXx3I4pRr5nf02zZky0dGWt+e+fZJb8pBqgzWTI0QW7Xrvf45R6Ld9/9HX9NO53wvybbRfEkdwS3Zf6tBsU/qRvIR2UY61lrIN0GwfZQTckl3vPOhR6nHAtxoRB9xyGG/ujCSlXgnSdCdiNl61jJe3b/edsm277j2LDXW2f6bPSdHzRCHIbyGfQCo2rxP5NE4pjH9kuht30VagWI3QUfWtTkrgvHoa744eW8/XTDKy2AlAoRpl6N4Spd+ybe7c282BfB8AcbdKoWZtpLxFa0UlfppuCb585GIA0tWwvvWi7/7u2TZeQ10QGF3s5OiPrH3R62+4n//6T3b4tSPgVAzuljwAv/ng23BLEqTrVD2m8/Z63Hinx6euvM6u/yD8xmNabMierKT53LdeFZTDqXos/oqt5/VDh9lbsW3lFIV/OmS/+PTsWMCpGvZW7HXmGcHx6/cHr/03/urGtwQzZZz3ilH2FrfYNKoGp+jwllfdA8C/3X4lA7usvZX7t33U/8ne+H3hvFeM8lDZlrvXKbDnIfv1aLhqcErN/xNf2HkcgE9++4Zl/1sevsuQO8O2zVBklh8Uhrk0PgJA0ThBmp/89g0M7Zqg/4y5oMwbv2/b7J/PuIxx67zEB/e+FadqkLLd95Zr7+HOMWs1NVru4pLUYR7Ib7NplITD1VSwfqiaYcaxLykeLm7hja98AIBbbg+/qEnN1tPx75m3bvwxX/gra3d1zRue5JaF83D8c/+WzQ/ylw+/HoBj5V5S22zZazh889C5vOu6OwD4zg+vZuzlvvnWo72M/eQ0j915FgC7rjrAnrK99ouPdfH0zsHAJu3vv3Id3ln2vvvMkWs5IzsFgFsSni4OkHKtpdttozu5atBO3LL74AZSxfC6OLbYFdyHi9sqpA/aewYDWzdMcWi/tZdyG97diAGnHF67kxfFg/3j8xmqnoPrv1/xjNC5x9btyLbuIF5uZ4zOJ6HwGvsCKXVbhvFZa+9mjJD+dobERlvP+XiN7oIJ8p58lS1w3/fjOOUax15tj8sc9xrs0Wwdogs2jeiioWu/3ZkfdEiP1ZBy1d/n0bXHXmdSrhJdtHVb7HeJ5oTYvD0uPu0SXbQ2WQMPeRQ2ZEgdsPdkJF9DynZfJemQPGHX47kIlMqUN9vfjNjRHNG8zbfSlyF2rMHWq0Vr7C5WSY/bn3OpVInPGD+vCrUea+vnjuVwF8uBnVaThVOlCp5HYsa34Yo7UCoHu6tZ+zyPzlUw5TC8lfRICePXzVls7kuZcgW6O+xGbg5Mg21VoRzUKz5TxVSaLcYAZLFE+exhoruP2LLk7W86gJMvNh8jzU+R6Ji9djK1dv2708Ayn/JXOfPT94ALWsL+HngS+ARwANvxvA54CEBEYtjO60f9+A9g3QOvA/7FjzMMnI9V9K/E2Vilf51iSw3uw45/XTWn2jH9PeBLInI1doypAa4CXoPtsCqKoiiKoiitPAdTkvp2T483holIHpgyxjzub98M3Cgi+7AvDW8EFoEv+mnMisjfAn8mIlPYee7/FHgMaBVTtdKJHUdaL09/y36H5jGnJ82p+ph+WUSuwNoFvAn7rugJ4OXGmIdOJU1FURRFUZQXOy2f7ZvCn2P+BGua/xlCg/3XNXiYgu3HVbFvTOsG++9aycPU5xj2zereZfZfyFIR10mxqo6piHQ0bO4D3tsujjFmrjVcURRFURTlJc/zNCWpMebalm2DFaTftMIxRayg/f2rzO4bwH8XkVtbxVciksR+Wb91lWkCq39jOsPJNZ37zFEURVEURVFeWohZxi7qhTVN0R9hh27uFZH/hR1varBq/Pdh+5etOqSTYrUd01c1rAu2x/yrwPFTyVxRFEVRFOUlxfP0xnQtMcaMi8grgb8C/phQ/muA7wLvNcY8k7K/LavqmBpjftC4LSI14B5jzIFTyXwtkXQCceJ4x8aadywWSNbVidk0MmOHXpjFgpVQA+mjeYwjwbYRaVBgC+5sMVS5Fwo4BwtBPGfGqhlNsYT79CipHqsSxxFMIXz7LQ1K68hsAer7HAcB0qO+Gr5YputpO964nHHo3G/V3VJXUPrpVDZ0k35ivCms/jc1LoHDgFXhh+vGCeMZB2JzNt1Cf4TYjOAUQ0Vm98OhSjR/ZhczZ9uEovMwfLcdnuIUK8THrRI2PlcjP+TiFpYqLmNzBmexQv+PfbVqphO3aEeE5EY7GJyz9Z8+XzBPZ4jN+nX6lz7cxQWb/mwc89Xu4N/O6Zk0c/lEkMfWQpXJ+6xyeEuhwEMjm2ze0SqLi3aMdnTaqvIji7b9y52G8m6r3u8sFJco4jf+0NbzxEWJ4JowAocnekj4A1pu+c9LSc+FUn63UCMS8dvn3s7Q4UEgUvAoTFnl+dSuCEd3W8X1toLX4LrQ/PS6dfZion76Aw/Y9st+xl5n33/ZZfS+xbZPdM4hHbPn7wczO4kUPL45b0WdNeMwdb790LGnsIGxnysT3WfL8e97LiY6b89tpFAhOh9lvmrbVYaK5Pw25QIYui88t4/kNgZuB2clJ4jO+64TRS9ID6C2o8C/7rnUlnFeqO0oIA2vDpx9qeC4cT+v/RsHGS13MlWziuNcNc3PXP1jAL7x1HmcyGX53X9+u03gQhi8z7b3aC7Lpu/Zayl9cZEDFzpE/dFWX9lzcZDn8JYZ/uF4w8Qnw0VuXzjXtsGCwx8efAOvH7K6g8/+6/VsuMoOpYouOMHpiRQqRBdc3KLN7965M4L12xd2sW9hIGiTfz16aaAK//R9rw6yvXP2bHb0TvLvhy8EoKPgEZ23j+3F7RViTi1I45E9W8nMhdfnHdM78fyLcuOdJQ722Gs8MujxyKT1XIjNwaO5jUG8sROd3OtsA0Acw4a7CkF6T433UzrTXj9dD8ZCVbeBQ08P0vOwvX5yF9bY8QV7/+x7T4Shu0OHk6H/rJA7Jw1A/r5uDu9M0j9r0zl+uJfBGds+48e62HmfvYGeelea7NMu5eNpPxGhfCwTpJmIC72P2/O7OJHEs49c0uM1Yodtnft+bJ9VvbttWcSY8DYyUOiL4PkmA4lcFTy7c3EgTmKqiuRtfToeKZLYZFXzzkKRuH+vFrpdMkchmrfHZY5AbNq2ldQ8Ig0OLPGpIuI/X+JzNeKTto17n3CQfIH4k36bG4PnWpeF+OQCJhmHqRm7z2t4BhiPyGSEtH8+zEKejkM2fSlUcPL+71F+EefAItJh267W1xE+6/N56O8lddj+nrgTOby8dXFwMmnix6yzi8zO4y2G1wRe82u/6FPHMYv2uFaXTpPPI74S3zSm0RI3sVhszqMhTqxSDfbFHjuMZNJ+2oUwTWnN2brU2PIu7yiwljyXU5KeTowxB4GfFpEeYIcfvN8YM73CYc/IqaryFUVRFEVRlFXyYumY1vE7ovc9V+lpx1RRFEVRFGWNeLF1TJ9rnouO6QtoVISiKIqiKMpp5EUwxvT5ZLV2UV9pCUoAf+2bugYYY37u2RZMURRFURTlxYa+MV2Z5Wb/W47ZluUfgZE24SeFiFwtIl8XkRERMSLyppb9/+CHNy73rLLMiqIoiqIo64J6x7Tdoqxelf8rz3H+aeAR7PyuX14mzreAxnxPSVZXOz6GSBRTWzqZgew/bFeMF8yxa2o1xLUqU9l7GEdCVX7TPLx1db2vBPTKlTAe4PjzBtfnF5Zqtfk4HwNBGmLMElVi516rUDVz86SftuupZBR3YsYPt8pr6bJzIET2Hg2/CnR3wsxcUO6ufeUm5b1xQsW4ccO52I0DiXGrsHRLCdxSFJlvnOc+rGdiPE7n01btGSl4JMbtnMTO3CKZo1bF2vngGMlNPThzYRr1vFMTCZz5RUpbrAo1c8zDmbNtkHm6g577JwBInuhi5sxYMCd1crKKO2/bMXUiQX4gQmrCnuO5g4nwXy8P3LkckbxVcbrzRWpP23Ll3dA/Ln1cMAKReVv+1EiS9Jh9WkxcmmT4zvD/rtmd2UBRnzzhNajyhcLhFMkTNtHEpEM4MTdEFyqkbvHnqmhQn9t8KyRGbRm9OEjU5h2dr7ZVmwJ86/AuEpPGj9fseBCfinLLkfP8ckBu0aqIZwsb6Zuv8tVjVu3tiCFhp0/ne6NnE0+UifqK40o6QWLS7ovka1TTLj88dgYAmXtSLGy1ZcwcdojkQ5cCM9fByJyt5wOJzcSn/LnIF6rEp8JHz8IZQub+ZLidSZA5Yk9c/yNFDr7bnt+Z6SRxX+t568j5AOyODAfHHXh0Y1CORvofCe+z5L1por7Ceu/xQTLTYZsujCVgwJ73r49eyORCOthnasL3xnfadpyCpx/exNcutM+HjXcUOZSxKvfMFPQ/Gt67ialoMN/5w//nAthuwz9//yvoejAWzNd3IpclM2XPYTUVJWOnA+fu7u3EvtXJ9MW2XXsXyiQnrXy8lohyX2Yr0XoiUS+4DnKvKXLfoW3BlOo75sv0/djK1Z/KDBKZsOs9k4anDg6BZ9sh81SUwzts+tmnIrhz4fVeO9CBG/WV327oFIKB9HeE+nyKCzmH4oAtVPbxCJG5BfLbrUtE+sAM9rFvHQHMUwlSE/aa3fYVgxjbVpl9MZxZe0EO35kheaKI1HwniCokTvjz1RtD72OL5Hba66fv4UXmzrTrybEiw3fZespcHoyhkrH3fMfeeaqdvhNHrkB0Pk7Cd1BJPnoU09tl27tqiI3Owax9vtLdQey4/8ydncM3AKC72gmOEMn5ivR8IVTOG4OZm8f4J8OtVvEW7EfG1N4YZtQ+21KTXXizc0jS1tPkF4nvtsdILIaZncMrNcz13qDMl8UCjm/a6JXLRB+zRjmmWKJWV+tXfEcCP285MRWo6mulEtLwe1StVIO57E2hgBk/4dfFI7io2pFv+Hi6uLg07uIibVkuvDXO5FTz9pT/QFipTBC43NRqxZXjrRX6KX9FTqv4yRjzTeCb0GyX1ELJGDO23E5FURRFUZQXCms4JekLktV+yj8dXCsiEyLylIj8bxEZWCmyiMRFpKO+ANk1KqeiKIqiKMqK6Kf8lVnvHdNvAr8IvBr4MHA58H0Ria9wzMdoHu967PkupKIoiqIoyklhVliU9e1jaoz5UsPm4yLyY+AwcAPQ6hBQ5+PAJxu2s2jnVFEURVGUdYCYZVT52jEF1nnHtBVjzKiIHAbOWiFOCQhGiK8wdlVRFEVRFGVNUbuolXlBdUxFpBfYDIye7rIoiqIoiqKsFu2Yrsxp7ZiKSAbY0RC0XUQuBqb95SasjdQosA34I2AS+PfV5mWq1cAGacm+SnsHKuPVVtx/MtQa7T0AiidhV5HPLw17dK/969WQJ621hqnVqNatq+pWV7NzSw6VlrDkA6EtlrhOaEPk+Ouun6YIZt7apMTHY8SOp/ByM0vL5jg4M7P0HbU2MKYrC8fH7boj9D3sWyN5HrGjU5jZef+48HzEAGbmiM/Ysrpnb4KcXe97pNvaXQFS62TgwYXgXLrzJcSPlzzqkjwWptmX6gzsnMSAibt0HrDn1Mkt0POYtYQxrgRje5KTFRBwZmy9u57OEM/Z8999Xy5oF4BIIUM812DP5OcVO5qj0D9M5rg996bxsnMEdzpPuctaa/XsCY83ApGZReJTth2rKYgfsMOpy90OqYOzTfnXKe7uYXDM1qvUFyO9f46Fs6w9T2a0xtjuXgCGxmqM7+kKypoe8Dixuz/YHvLTmP7RIABpfzvd4IkRnSmSOZJgsWrT7x/zgv0Lw3a/zTfG2MFOnLIta2VR6PZttxYHY4EFF8DiSIL0uN3ODztE552gLRcHY8io3Sh1Q7nblml+r6+BdP1EajAYzNTc/HQ/dEOMHV+w11xqPEZkxp4XGe0gPeaRH7bX++B9MHGZteo5ODOMuxgOwXcMHJ4ZAiArMHAfHI7a7Z0z8wzeVzcO8ojMhve8zc+2SXo8Flxn6W+6iAmt6yoPpxn8obW+yezoCMInH+yikoHkMfuojszMkh61eWVGDNwXZeZMGzd7KEpqwloCLe5ONo1Xc2fGyRy3xxV2xxl4yC+jgeLueNBk/Y8UiRRsG/Q9WsD17wOA/oezVFL2XHTvySO+RU+5K05q9yi1QXttbZqNk9hrL4rYzCAyM0/qqP+8yc3RtS/lH2dtqZKHG54pvgVSv9cJuVm/DTuJHc3RX7Hpi2dC97WaR2Q0R/+CbTPnxAw90zZ9mZ0nErd19qZzYAzd99k8zewcscDOySOWTAbWT7WpHI5v6xcbmcAUGqz7isXA+g/PYKbtM8E5HgPPUKvbEZqGa7DFysibnw83Gta9+nO/0TrpZH4vWPobVZtZ3k58ud810/pbVQ+vWxyulmeycHouONk8vKU2kacVtYtakdP9xvRlwO0N2/WxoZ8DfgO4APhloAvbOb0deKsxZh5FURRFUZQXGPrGdGVOt4/pHQTvRtryU2tUFEVRFEVRlOcd8Yx9898mXDn9b0wVRVEURVFeOuin/BXRjqmiKIqiKMoaoZ/yV0Y7poqiKIqiKGuEdkxXRjumLxQaVIVNKskGZW/TegOm5WKvTU49lyULCNSmo2PNOx6wqvmqMVZRXldSNqrLp+uKd6vedR/MUytbxXri7iKer4xN/LiEKZasgwBgKlXqtZb8op+sTbdraq7BcUCgVKZr2qqMvalpeupKapEwnq+q9aZseTK7Bfxy1B0KpMPOcpu9/Umk06qBzdwC0mnDzew8g/d1EhuxamPjCLU+u8+dWoDcLMN3WuWzM18M3Afo64LpWQbvTwJQ7ozhRW09k4fnken2qvz+B7tIHwrV005ujviMVSZH5kr0x2ze6cML9CbseudTNn5vKpyxN3PYqoLTR2imwT3BS0To2leha7/djuVCZW/6aOhmkD4WpXt3KnjQGjFkjhaayl/qjvllcckcterj7ntzjL9mA9G88dMp0p1M+jkYzLFQKW+c0PHAqRG4U2eOFCn1xohP2bJVMslAXZ45Gg3We3Z3EM17DN4XKp+rCZuXF3FwKuA1PiH9a05qhsyRAj0Z28aFDWmiC7WgPZxcqM3MHHZxpuf9do0EBtrl7jix6TDfWjSDiVuLgdTh0JWjO5ohuuAFxxlXSB3xrxcPyoNpNnzP3s+FTVmSR+2+fjp9E2//wOkZEv59N1jrInbUXt8mFWfg/hpSs/vcQoWBB+zzRaoeVKqYOVv+7vsEL+NftxM5jJ9eKhbDFEu4o9ZVwDlYxPPvxdjCArVCEce/d2qlEpGqbasIgFfDq7uGuC4Ss9dE/NgotYJtH/eeBWq1Gu6RcJ4U06CorxoDR4MmWRHvwKH2O1pU7Ms+I5dRyS+naFeUdohpb6avBvsW7ZgqiqIoiqKsFcvM/KRjTC3aMVUURVEURVkjVJW/MtoxVRRFURRFWStUlb8izjNHURRFURRFUZ4LpLb8sqp0RD4mIveLyLyITIjIV0VkZ0scEZGbRGRERAoicoeInNcSJy4inxaRSRHJi8h/iMimZ1/TU0M7poqiKIqiKGtEI8Jv1AAAED1JREFU/VN+u2WVXAP8JXAlcB32K/h3RCTdEOcjwIeA9wGXA2PAd0Uk2xDnZuDNwNuAq4AMcIuIuJwGxKzFfLanERHpAGav5WeJSPR0F0d5MVNXy4uDuC7GV/iLI0jEjpox1SrGMzgxf87umtcUDwjiSjoVpGkKRYzvDgA0KeWdbCZwDsARTLGEM9hvt2vhCHtTKAQuAt7YBJJKQk9XmGbdHaCefk+nXZ+eDZwOlq2zj7dgFeVORxavv6tpn3MinBPdzC/AYJ89piOJM+cr9scn8c7ajDvuOxrMzeOdtXlJXu74TDA3e1CUou9WEbH/bztTtj61gU6cp62iW7Lhs7g20BnkA2CScUwqbo+dnMXr70LKNk0TC0c9SamCzMzjDXTb7UoN8dvO9HRijoyEcVNJzEKosg8zM7b9/eevpFLhrsb4xkMymSBebXIqUK7X06fBpcP4SnaJx60dh/9DV1vII679jZFoJJzvXZrfTdSvxYD1Nse4ojwLqqbCHXwNoNMYM/dM8Z9r6v2RK97wB0SiiaXlqxS595bfOeXyiUg/MAFcY4y5U+yDewS42RjzCT9OHBgHPmrM/9/e/cdKVtZ3HH9/WFykuCKR4kIQpYmxKhDN2l9UQWi2VZuKYhusxh/YpFas1tJECzHtNg3aqLU0oNZU2rVtDKbV2EpE158oAanSYEFtoc1KWZddqlhYkf3Bvd/+cc5lZ+fO3L0L956ZnfN+JU9m55xn5j7nu8+d+d7nOec59aEkxwL/C7y6qj7W1jmJZq2LF1fVZx/JsT4ajphKkiR1ZGG5qFGltS7J4wfKUct863Y0gXvbx1OB9cCWhQpVtQe4Djiz3bQBeMxQne3AbQN1OmViKkmS1JFlTOVvA+4bKJcc9D2b0dH3AddX1W3t5vXt486h6jsH9q0H9lbVD5eo0ymvypckSerIMu78dDKwa2DXcu7gcCVwBs05osOGz9nMiG2LmrOMOqvCEVNJkqSuzNf40thVVfcPlCUT0yRXAC8BzqmqbQO7Fm7DODzyeQL7R1F3AGuTHLdEnU6ZmEqSJHUktX/U9IByiOOT7VJQVwLnA+dW1dahKltpEs+NA69ZS3M1/w3tppuBfUN1TgROG6jTKafyJUmSOrKCd356P/BK4DxgV5KFkdH7qurBqqoklwOXJrkDuAO4FPgx8FGAqrovyVXAnyf5Ac2FU+8FbgU+f8gHtwJcLkrSyjtixPJ31Z5AVXXA0k9Zs4Za+ECu+cXPj1z8e1tzcw8vfzRq36Ackf3bcsTDy3LVfO1v08HaMXwcQ8fAjH+OSrNgWpaLet45mzjyyBHLRT20m+u/tAmW2b5k7BjrhVW1ua0T4I+BNwDHATcBbxq4QIokjwXeQ5PkHg18Abioqu5a9sGtIEdMJUmSOpK5YlROmblD+wO3qsYsMH1AnQI2tWVcnd3Am9sycSamkiRJHVnBqfyZZGIqSZLUERPTpZmYSpIkdaVq9HnpnqsOTHi5qCSXJPl6kl1J7knyySRPH6qTJJuSbE/yYJIvJ3nWpNosSZL0SGWuxhZNfsT0bJrlDr7etuUyYEuSZ1bVA22dtwEXA68DbgfeAXwuydOratfit5Q0cfNzS+8fGBmohx46cNfw8317R7/FwX7Gw/UGn8wd+Hypnzv0fPEL/BKRdOicyl/aRBPTqnrh4PMkFwL3ABuAr7TLHLwVuKyqPtHWeS3N3QheCXyo2xZLkiQ9Ck7lL2na7vx0bPt4b/t4Ks2ttLYsVGhvzXUdcOaoN0hyVJLHLxRg3Sq2V5IkadkyP2Yq3xFTYIoS03Z09H3A9QMLvy7cxWD4fq07WXzv1wWXAPcNlG1j6kmSJHVrvmB+fkQxMYUpSkyBK4EzgN8csW/4fysjti14F83I60I5eaUaKEmS9KjML1E08YufAEhyBfAS4KyqGhzh3NE+rgfuHth+AotHUYGHp/r3DLz3yjZWkiTpEcr8PMniLDTzZqYw+eWikuRK4Hzg3KraOlRlK01yunHgNWtprua/obOGSpIkrYSR0/ht0cRHTN9Pc3X9ecCuJAvnjd5XVQ9WVSW5HLg0yR3AHcClwI+Bj06kxZIkSY/UPM0JiaO2a+KJ6Rvbxy8Pbb8Q2Nz++93A0cAHgOOAm4Bfdg1TSZJ0uHEqf2mTXsf0oCeAVlUBm9oiSZJ0+Jobc6XTnIkpTH7EVJIkqT9qzPmk425L1zMmppIkSV2ZL0aueOk6poCJqSRJUnfm54C5MdtlYipJktSVufnR0/Ze/ASYmEqSJHWngBoxbe9MPmBiKkmS1J25OSin8scxMZUkSerK/JjlopzKB0xMJUmSuuNV+UsyMZUkSepIzc9RI6byR23rIxNTSZKkrszNQUYkoSamgImpJElSd2rMVP6oK/V76IhJN0CSJKkvam5ubDlUSS5KsjXJ7iQ3J3n+KjS5UyamkiRJXZmbb6bzF5VDuyo/yQXA5cBlwHOArwLXJjll5RvdHRNTSZKkjqzgiOnFwFVV9eGq+k5VvRW4C3jjije6Q705x/Qh9nlXBUmSeuoh9k26CQDsm99LjUhIBtq3Lsngrj1VtWdwQ5K1wAbgz4beZgtw5oo1dgL6kJiuA7ieT0+6HZIkafLWAfdP4OfuBXZcX9esX6LOj4BtQ9v+BNg0tO14YA2wc2j7TmCp9596fUhMtwM/DfwHcDKwa7LNmSrraH4BjMtixmY8YzOacRnP2IxnbMZbjdiso8kLOldVu5OcCqw9xJfuWWLf8NBrRmw7rMx8YlpVleTu9umuqprEX0lTaWCqwLgMMTbjGZvRjMt4xmY8YzPeKsVmojGuqt3A7hV4q+8DcyweHT2BxaOohxUvfpIkSTqMVNVe4GZg49CujcAN3bdo5cz8iKkkSdIMeh/w90m+AdwI/DZwCvBXE23Vo9SXxHQPzcnDS52n0UfGZTxjM56xGc24jGdsxjM24xmbJVTVx5I8Efgj4ETgNuDFVXXnZFv26KS8BZYkSZKmgOeYSpIkaSqYmEqSJGkqmJhKkiRpKpiYSpIkaSrMfGKa5KIkW5PsTnJzkudPuk1dS7IpSQ2VHQP709bZnuTBJF9O8qxJtnk1JDkryafa46wkLx3af9A4JDkqyRVJvp/kgST/kuTkbo9k5S0jNptH9KGvDdWZudgkuSTJ15PsSnJPkk8mefpQnV72m2XGpq/95o1J/j3J/W25McmLBvb3tc8cLC697C860EwnpkkuAC4HLgOeA3wVuDbJKRNt2GR8i2Y5iYVy+sC+twEXA78L/AywA/hcknVdN3KVHQN8k+Y4R1lOHC4HXga8Ange8DjgmiRrVqvRHTlYbAA+w4F96MVD+2cxNmcD7wd+nmbh6iOBLUmOGajT136znNhAP/vNNuAPgee25YvAPw8kn33tMweLC/Szv2hQVc1sAW4CPji07TvAuybdto7jsAm4Zcy+AHcDbx/YdhTwf8AbJt32VYxJAS89lDgAxwJ7gQsG6pxEc1u4X5n0Ma1WbNptm4FPLvGavsTmJ9v4nGW/WTo29ptFx3ov8Fv2mdFxsb9YFsrMjpgmWQtsALYM7doCnNl9iybuae200dYkVyf5qXb7qTT32n04TlW1B7iOfsVpOXHYADxmqM52mkWN+xCrF7RTtrcn+eskJwzs60tsjm0f720f7Tf7DcdmQa/7TZI1SV5BMytxI/YZYGRcFvS6v2i27/x0PLAG2Dm0fSfNh0Kf3AS8BrgdeBLwDuCGdvpkIRaj4vSUzlo4ecuJw3pgb1X9cESdWe9T1wL/CNxJ88X6p8AXk2xov1RnPjZJQnMLwOur6rZ2s/2GsbGBHvebJKfTJFyPBX4EvKyqvp1kIYHqZZ8ZF5d2d2/7i/ab5cR0wfCtrTJi20yrqmsHnt6a5Ebgv4HXAgsnlvc+Tq1HEoeZj1VVfWzg6W1p7s18J/CrwCeWeOksxeZK4Aya89qG9b3fjIxNz/vNfwLPBp4AvBz4SJKzB/b3tc+MjEtVfbvn/UWtmZ3KB75Pc97J8F9RJ7D4L9VeqaoHgFuBp9GcdA/GaTlx2AGsTXLcEnV6oarupvnCeFq7aaZjk+QK4CXAOVW1bWBX7/vNErFZpE/9pqr2VtV/VdU3quoSmosLf4+e95kl4jKqbm/6i/ab2cS0qvYCN9NcLTpoI3BD9y2aHkmOAp5BcwL+Vppf9o0D+9fSXHHbpzgtJw43A/uG6pwInEa/YkWSJwJPpulDMKOxaZf1uRI4Hzi3qrYOVeltv1lGbEa9phf9ZozQXOTU2z4zxkJcFu/od3/pr0lffbWaBbiA5gq+19MkYn9Bc07LUybdto7j8F6aD71TgZ8DPgXcvxAH4O00V4S+jOYX/KPAdmDdpNu+wnF4HM0U0rNppn1+v/33KcuNA/BB4C7gl2iWIPsCcAuwZtLHt1qxafe9F/gF4KnAC2i+BLbNemyAD7R94myaEa6FcvRAnV72m4PFpuf95p3A89vjPp1mycI5YGPP+8zYuPS5v1iG+smkG7DqBwgXAd8F9tD8tXXWpNs0gRhc3X7o7QW+B3wceObA/tAsKXU3sJvm6tDTJt3uVYjDC2iSruGyeblxoDlh/wrgB8CPaZL8J0/62FYzNsDRwGeBe9o+dGe7/clD7zFzsRkTkwJeN1Cnl/3mYLHpeb+5auB75x7g87RJac/7zNi49Lm/WA4saf+jJUmSpIma2XNMJUmSdHgxMZUkSdJUMDGVJEnSVDAxlSRJ0lQwMZUkSdJUMDGVJEnSVDAxlSRJ0lQwMZUkSdJUMDGVJEnSVDAxlXRYSrI5SbVlX5KdST6X5PVJ/GyTpMOQH96SDmefAU4Engq8CPgS8JfANUmOnGC7JEmPgImppMPZnqraUVXfq6p/q6p3AufRJKmvA0hycZJbkzyQ5K4kH0jyuHbfMUnuT/Lrg2+a5Nfa+uu6PiBJ6jMTU0kzpaq+CHwTOL/dNA+8BTgNeC1wLvDutu4DwNXAhUNvcyHwT1W1q4s2S5IaqapJt0GSDlmSzcATquqlI/ZdDZxRVc8cse83gA9W1fHt858FbgBOqartSY4HtgMbq+q61TwGSdKBHDGVNIsCFECSc9qLor6XZBfwd8ATkxwDUFX/CnwLeE372lcD/wN8pftmS1K/mZhKmkXPALYmeQrwaeA24OXABuBNbZ3HDNT/MPun8y8E/racTpKkzpmYSpopSc4FTgc+DjwXOBL4g6r6WlXdDpw04mX/AJyS5C3As4CPdNVeSdJ+Lqci6XB2VJL1wBrgScALgUuAa2im7E+n+Zx7c5JPAb8I/M7wm1TVD5N8AngPsKWqtnXUfknSAEdMJR3OXgjcDXyXZk3Tc2iuwD+vquaq6hbgYuDtNNP5r6JJXEe5ClgL/M0qt1mSNIZX5UsSkORVNIvzn1RVeyfdHknqI6fyJfVakp8ATqUZSf2QSakkTY5T+ZL67m3ALcBO4F0Tbosk9ZpT+ZIkSZoKjphKkiRpKpiYSpIkaSqYmEqSJGkqmJhKkiRpKpiYSpIkaSqYmEqSJGkqmJhKkiRpKpiYSpIkaSr8PwPSV+2Z69EBAAAAAElFTkSuQmCC\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plotTS(raw[param], 24, vmin = raw[param].min(), vmax = raw[param].max(), label = param)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plotTS(predictedPeriodsMedoid[param], 24, vmin = raw[param].min(), vmax = raw[param].max(), label = param)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plotTS(predictedPeriodsMean[param], 24, vmin = raw[param].min(), vmax = raw[param].max(), label = param)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plotTS(predictedPeriodsMinmax[param], 24, vmin = raw[param].min(), vmax = raw[param].max(), label = param)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Load [MW]')" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(figsize = [6, 2], dpi = 100, nrows = 1, ncols = 1)\n", + "raw['Load']['20100210':'20100218'].plot(label = 'Original')\n", + "predictedPeriodsMedoid['Load']['20100210':'20100218'].plot(label = '10 Medoids')\n", + "predictedPeriodsMean['Load']['20100210':'20100218'].plot(label = '10 Centroids')\n", + "predictedPeriodsMinmax['Load']['20100210':'20100218'].plot(label = '10 Maxoids')\n", + "plt.legend()\n", + "plt.ylabel('Load [MW]')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Validation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Check that the means of the original time series and the predicted ones are the same." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "GHI 110.990183\n", + "Load 450.260335\n", + "T 7.790616\n", + "Wind 3.057306\n", + "dtype: float64" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw.mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "GHI 110.990183\n", + "Load 450.260335\n", + "T 7.790616\n", + "Wind 3.057306\n", + "dtype: float64" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "predictedPeriodsMedoid.mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "GHI 110.990183\n", + "Load 450.260335\n", + "T 7.790616\n", + "Wind 3.057306\n", + "dtype: float64" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "predictedPeriodsMean.mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "GHI 193.174201\n", + "Load 491.386678\n", + "T 12.934406\n", + "Wind 5.756416\n", + "dtype: float64" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "predictedPeriodsMinmax.mean()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.10" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/results/testperiods_kmedoids.csv b/examples/results/testperiods_kmedoids.csv index 4484ba9..39f2a66 100644 --- a/examples/results/testperiods_kmedoids.csv +++ b/examples/results/testperiods_kmedoids.csv @@ -1,1345 +1,1345 @@ 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-scikit-learn>=0.0 +sklearn>=0.0 pandas>=0.18.1 numpy>=1.11.0 -pyomo>=5.3 +pyomo>=5.3 \ No newline at end of file diff --git a/setup.py b/setup.py index d0c6dfc..21de831 100644 --- a/setup.py +++ b/setup.py @@ -7,7 +7,7 @@ setuptools.setup( name='tsam', - version='1.1.0', + version='1.1.1', author='Leander Kotzur, Maximilian Hoffmann', author_email='l.kotzur@fz-juelich.de, max.hoffmann@fz-juelich.de', description='Time series aggregation module (tsam) to create typical periods', diff --git a/test/test_accuracyIndicators.py b/test/test_accuracyIndicators.py new file mode 100644 index 0000000..89b91fa --- /dev/null +++ b/test/test_accuracyIndicators.py @@ -0,0 +1,36 @@ +import os +import time + +import pandas as pd +import numpy as np + +import tsam.timeseriesaggregation as tsam + + + +def test_accuracyIndicators(): + + hoursPerPeriod = 24 + + noTypicalPeriods = 8 + + raw = pd.read_csv(os.path.join(os.path.dirname(__file__),'..','examples','testdata.csv'), index_col = 0) + + aggregation1 = tsam.TimeSeriesAggregation(raw, noTypicalPeriods=noTypicalPeriods, hoursPerPeriod=hoursPerPeriod, + clusterMethod='hierarchical') + + aggregation2 = tsam.TimeSeriesAggregation(raw, noTypicalPeriods=noTypicalPeriods, hoursPerPeriod=hoursPerPeriod, + clusterMethod='hierarchical', sortValues=True) + + # make sure that the sum of the attribute specific RMSEs is smaller for the normal time series clustering than for + # the duration curve clustering + np.testing.assert_array_less(aggregation1.accuracyIndicators().loc[:, 'RMSE'].sum(), + aggregation2.accuracyIndicators().loc[:, 'RMSE'].sum()) + + # make sure that the sum of the attribute specific duration curve RMSEs is smaller for the duration curve + # clustering than for the normal time series clustering + np.testing.assert_array_less(aggregation2.accuracyIndicators().loc[:, 'RMSE_duration'].sum(), + aggregation1.accuracyIndicators().loc[:, 'RMSE_duration'].sum()) + +if __name__ == "__main__": + test_accuracyIndicators() \ No newline at end of file diff --git a/test/test_adjacent_periods.py b/test/test_adjacent_periods.py new file mode 100644 index 0000000..35a7b81 --- /dev/null +++ b/test/test_adjacent_periods.py @@ -0,0 +1,30 @@ +import os +import time + +import pandas as pd +import numpy as np + +import tsam.timeseriesaggregation as tsam + +def test_adjacent_periods(): + + raw = pd.read_csv(os.path.join(os.path.dirname(__file__), '..', 'examples', 'testdata.csv'), index_col=0) + + noTypicalPeriods = 8 + + starttime = time.time() + + aggregation = tsam.TimeSeriesAggregation(raw, noTypicalPeriods = noTypicalPeriods, hoursPerPeriod = 24, + clusterMethod = 'adjacent_periods', + representationMethod='meanRepresentation') + + typPeriods = aggregation.createTypicalPeriods() + + print('Clustering took ' + str(time.time() - starttime)) + + # check whether the clusterOrder consists of noTypicalPeriods blocks of the same number + np.testing.assert_array_almost_equal(np.size(np.where(np.diff(aggregation.clusterOrder) != 0)), noTypicalPeriods-1, + decimal=4) + +if __name__ == "__main__": + test_adjacent_periods() \ No newline at end of file diff --git a/test/test_assert_raises.py b/test/test_assert_raises.py new file mode 100644 index 0000000..474720a --- /dev/null +++ b/test/test_assert_raises.py @@ -0,0 +1,146 @@ +import os +import copy + +import pandas as pd +import numpy as np + +import tsam.timeseriesaggregation as tsam + +def test_assert_raises(): + # important: special signs such as brackets must be marked with '\' when matching error message + + raw = pd.read_csv(os.path.join(os.path.dirname(__file__), '..', 'examples', 'testdata.csv'), index_col=0) + + # check error message for wrong time series + np.testing.assert_raises_regex(ValueError, + 'timeSeries has to be of type pandas.DataFrame\(\) or of type np.array\(\) in ' + 'initialization of object of class TimeSeriesAggregation', + tsam.TimeSeriesAggregation, timeSeries='erroneousTimeSeries') + + # check error messages for wrong attribute names added for extreme period methods + np.testing.assert_raises_regex(ValueError, + 'erroneousAttribute listed in "addPeakMin" does not occur as timeSeries column', + tsam.TimeSeriesAggregation, timeSeries=raw, addPeakMin = ['erroneousAttribute']) + np.testing.assert_raises_regex(ValueError, + 'erroneousAttribute listed in "addPeakMax" does not occur as timeSeries column', + tsam.TimeSeriesAggregation, timeSeries=raw, addPeakMax = ['erroneousAttribute']) + np.testing.assert_raises_regex(ValueError, + 'erroneousAttribute listed in "addMeanMin" does not occur as timeSeries column', + tsam.TimeSeriesAggregation, timeSeries=raw, addMeanMin = ['erroneousAttribute']) + np.testing.assert_raises_regex(ValueError, + 'erroneousAttribute listed in "addMeanMax" does not occur as timeSeries column', + tsam.TimeSeriesAggregation, timeSeries=raw, addMeanMax = ['erroneousAttribute']) + + # check error message for missing datetime index and missing resolution argument + np.testing.assert_raises_regex(ValueError, + '\'resolution\' argument has to be nonnegative float or int or the given ' \ + 'timeseries needs a datetime index', + tsam.TimeSeriesAggregation, timeSeries=raw.reset_index()) + # overwrite one of the datetime-like string indices in the raw data to an index that cannot be converted + rawErrInd = copy.deepcopy(raw) + as_list = rawErrInd.index.tolist() + idx = as_list.index('2010-01-01 00:30:00') + as_list[idx] = 'erroneousDatetimeIndex' + rawErrInd.index = as_list + # check error message for erroneous datetime index and missing resolution argument + np.testing.assert_raises_regex(ValueError, + '\'resolution\' argument has to be nonnegative float or int or the given ' \ + 'timeseries needs a datetime index', + tsam.TimeSeriesAggregation, timeSeries=rawErrInd) + # check erroneous resolution argument + np.testing.assert_raises_regex(ValueError, + 'resolution has to be nonnegative float or int', + tsam.TimeSeriesAggregation, timeSeries=raw, resolution='erroneousResolution') + + # check erroneous hoursPerPeriod argument + np.testing.assert_raises_regex(ValueError, + 'hoursPerPeriod has to be nonnegative float or int', + tsam.TimeSeriesAggregation, timeSeries=raw, hoursPerPeriod=None) + + # check erroneous noTypicalPeriods argument + np.testing.assert_raises_regex(ValueError, + 'noTypicalPeriods has to be nonnegative integer', + tsam.TimeSeriesAggregation, timeSeries=raw, noTypicalPeriods=None) + + # check non-integer time step number per typical period + np.testing.assert_raises_regex(ValueError, + 'The combination of hoursPerPeriod and the resulution does not result in an integer ' + 'number of time steps per period', + tsam.TimeSeriesAggregation, timeSeries=raw, hoursPerPeriod=23, resolution=2) + + # check warning when number of segments per period is higher than the number of time steps per period + np.testing.assert_warns(Warning, tsam.TimeSeriesAggregation, timeSeries=raw, segmentation=True, noSegments=25) + + # check erroneous clusterMethod argument + np.testing.assert_raises_regex(ValueError, + 'clusterMethod needs to be one of the following: \[\'averaging\', \'k_medoids\', ' + '\'k_means\', \'hierarchical\', \'adjacent_periods\'\]', + tsam.TimeSeriesAggregation, timeSeries=raw, clusterMethod='erroneousClusterMethod') + + # check erroneous representationMethod argument + np.testing.assert_raises_regex(ValueError, + 'If specified, representationMethod needs to be one of the following: ' + '\[\'meanRepresentation\', \'medoidRepresentation\', \'minmaxmeanRepresentation\'\]', + tsam.TimeSeriesAggregation, timeSeries=raw, + representationMethod='erroneousRepresentationMethod') + + # check erroneous extremePeriodMethod argument + np.testing.assert_raises_regex(ValueError, + 'extremePeriodMethod needs to be one of the following: \[\'None\', \'append\', ' + '\'new_cluster_center\', \'replace_cluster_center\'\]', + tsam.TimeSeriesAggregation, timeSeries=raw, + extremePeriodMethod='erroneousExtremePeriodMethod') + + # check erroneous evalSumPeriods argument + np.testing.assert_raises_regex(ValueError, + 'evalSumPeriods has to be boolean', + tsam.TimeSeriesAggregation, timeSeries=raw, evalSumPeriods='erroneousEvalSumPeriods') + + # check erroneous sortValues argument + np.testing.assert_raises_regex(ValueError, + 'sortValues has to be boolean', + tsam.TimeSeriesAggregation, timeSeries=raw, sortValues='erroneousSortValues') + + # check erroneous sameMean argument + np.testing.assert_raises_regex(ValueError, + 'sameMean has to be boolean', + tsam.TimeSeriesAggregation, timeSeries=raw, sameMean='erroneousSameMean') + + # check erroneous rescaleClusterPeriods argument + np.testing.assert_raises_regex(ValueError, + 'rescaleClusterPeriods has to be boolean', + tsam.TimeSeriesAggregation, timeSeries=raw, + rescaleClusterPeriods='erroneousrescaleClusterPeriods') + + # check erroneous predefClusterOrder argument + np.testing.assert_raises_regex(ValueError, + 'predefClusterOrder has to be an array or list', + tsam.TimeSeriesAggregation, timeSeries=raw, + predefClusterOrder='erroneousPredefClusterOrder') + + # get a cluster order from a preceding clustering run + aggregation = tsam.TimeSeriesAggregation(timeSeries=raw) + periodOrder = aggregation.clusterOrder + # check erroneous predefClusterCenterIndices argument + np.testing.assert_raises_regex(ValueError, + 'predefClusterCenterIndices has to be an array or list', + tsam.TimeSeriesAggregation, timeSeries=raw, predefClusterOrder=periodOrder, + predefClusterCenterIndices='erroneousPredefClusterCenterIndices') + + # check error, when predefClusterCenterIndices are defined but not predefClusterOrder + np.testing.assert_raises_regex(ValueError, + 'If "predefClusterCenterIndices" is defined, "predefClusterOrder" needs to be ' + 'defined as well', + tsam.TimeSeriesAggregation, timeSeries=raw, + predefClusterCenterIndices='erroneousPredefClusterCenterIndices') + + # check erroneous dataframe containing NaN values + rawNan = copy.deepcopy((raw)) + rawNan.iloc[10, :] = np.NaN + aggregation = tsam.TimeSeriesAggregation(timeSeries=rawNan) + np.testing.assert_raises_regex(ValueError, + 'Pre processed data includes NaN. Please check the timeSeries input data.', + aggregation.createTypicalPeriods) + +if __name__ == "__main__": + test_assert_raises() \ No newline at end of file diff --git a/test/test_averaging.py b/test/test_averaging.py new file mode 100644 index 0000000..59677fd --- /dev/null +++ b/test/test_averaging.py @@ -0,0 +1,40 @@ +import os +import time + +import pandas as pd +import numpy as np + +import tsam.timeseriesaggregation as tsam + +def test_averaging(): + + raw = pd.read_csv(os.path.join(os.path.dirname(__file__), '..', 'examples', 'testdata.csv'), index_col=0) + + noTypicalPeriods = 8 + + hoursPerPeriod = 24 + + starttime = time.time() + + aggregation = tsam.TimeSeriesAggregation(raw, noTypicalPeriods = noTypicalPeriods, hoursPerPeriod = hoursPerPeriod, + clusterMethod = 'averaging', + representationMethod='meanRepresentation') + + typPeriods = aggregation.createTypicalPeriods() + + print('Clustering took ' + str(time.time() - starttime)) + + # check whether the clusterOrder consists of noTypicalPeriods blocks of the same number + np.testing.assert_array_almost_equal(np.size(np.where(np.diff(aggregation.clusterOrder) != 0)), noTypicalPeriods-1, + decimal=4) + + # check whether the cluster centers are in line with the average of the candidates assigned to the different + # clusters + for i in range(noTypicalPeriods): + calc = tsam.unstackToPeriods(raw, hoursPerPeriod)[0].loc[np.where(aggregation.clusterOrder == i)].mean(axis=0)\ + .to_frame().values + orig = tsam.unstackToPeriods(typPeriods.loc[i], hoursPerPeriod)[0].T.values + np.testing.assert_array_almost_equal(calc, orig, decimal=4) + +if __name__ == "__main__": + test_averaging() \ No newline at end of file diff --git a/test/test_durationCurve.py b/test/test_durationCurve.py new file mode 100644 index 0000000..9a25782 --- /dev/null +++ b/test/test_durationCurve.py @@ -0,0 +1,53 @@ +import os +import time +import copy + +import pandas as pd +import numpy as np + +import tsam.timeseriesaggregation as tsam + + + +def test_durationCurve(): + + # do everything for one attribute only to make sure that scaling does not play a role + raw = pd.read_csv(os.path.join(os.path.dirname(__file__), '..', 'examples', 'testdata.csv'), index_col=0)['GHI']\ + .to_frame() + + noTypicalPeriods = 8 + + hoursPerPeriod = 24 + + starttime = time.time() + + aggregation = tsam.TimeSeriesAggregation(raw, noTypicalPeriods = noTypicalPeriods, hoursPerPeriod = hoursPerPeriod, + clusterMethod = 'hierarchical', sortValues=True, + rescaleClusterPeriods=False) + + typPeriods = aggregation.createTypicalPeriods() + + print('Clustering took ' + str(time.time() - starttime)) + + # sort every attribute in every period in descending order for both, the found typical period and the days + # that belong to the corresponding cluster + for i in range(noTypicalPeriods): + calculated = tsam.unstackToPeriods(raw, hoursPerPeriod)[0].loc[ + np.where(aggregation.clusterOrder == i)[0], :] + calculatedSorted = copy.deepcopy(calculated) + algorithmResult = tsam.unstackToPeriods(typPeriods.loc[i], hoursPerPeriod)[0] + for j in raw.columns: + dfR = algorithmResult[j] + dfR[dfR.columns] = np.sort(dfR)[:, ::-1] + algorithmResult[j] = dfR + df = calculatedSorted[j] + df[df.columns] = np.sort(df)[:, ::-1] + calculatedSorted[j] = df + + # make sure that the found typical period is always the one that is closest to the clusters centroid + currentMeant = calculatedSorted.mean(axis=0) + minIdx = np.square(calculatedSorted - currentMeant).sum(axis=1).idxmin() + np.testing.assert_array_almost_equal(calculatedSorted.loc[minIdx], algorithmResult.iloc[0], decimal=4) + +if __name__ == "__main__": + test_durationCurve() \ No newline at end of file diff --git a/test/test_extremePeriods.py b/test/test_extremePeriods.py new file mode 100644 index 0000000..c5d969d --- /dev/null +++ b/test/test_extremePeriods.py @@ -0,0 +1,57 @@ +import os +import time + +import pandas as pd +import numpy as np + +import tsam.timeseriesaggregation as tsam + + + +def test_extremePeriods(): + + hoursPerPeriod = 24 + + noTypicalPeriods = 8 + + raw = pd.read_csv(os.path.join(os.path.dirname(__file__),'..','examples','testdata.csv'), index_col = 0) + + aggregation1 = tsam.TimeSeriesAggregation(raw, noTypicalPeriods=noTypicalPeriods, hoursPerPeriod=hoursPerPeriod, + clusterMethod='hierarchical', rescaleClusterPeriods=False, + extremePeriodMethod='new_cluster_center', addPeakMax=['GHI']) + + aggregation2 = tsam.TimeSeriesAggregation(raw, noTypicalPeriods=noTypicalPeriods, hoursPerPeriod=hoursPerPeriod, + clusterMethod='hierarchical', rescaleClusterPeriods=False, + extremePeriodMethod='append', addPeakMax=['GHI']) + + aggregation3 = tsam.TimeSeriesAggregation(raw, noTypicalPeriods=noTypicalPeriods, hoursPerPeriod=hoursPerPeriod, + clusterMethod='hierarchical', rescaleClusterPeriods=False, + extremePeriodMethod='replace_cluster_center', addPeakMax=['GHI']) + + # make sure that the RMSE for new cluster centers (reassigning points to the exxtreme point if the distance to it is + # smaller)is bigger than for appending just one extreme period + np.testing.assert_array_less(aggregation1.accuracyIndicators().loc['GHI', 'RMSE'], + aggregation2.accuracyIndicators().loc['GHI', 'RMSE']) + + # make sure that the RMSE for appending the extreme period is smaller than for replacing the cluster center by the + # extreme period (conservative assumption) + np.testing.assert_array_less(aggregation2.accuracyIndicators().loc['GHI', 'RMSE'], + aggregation3.accuracyIndicators().loc['GHI', 'RMSE']) + + # check if addMeanMax and addMeanMin are working + aggregation4 = tsam.TimeSeriesAggregation(raw, noTypicalPeriods=noTypicalPeriods, hoursPerPeriod=hoursPerPeriod, + clusterMethod='hierarchical', rescaleClusterPeriods=False, + extremePeriodMethod='append', addMeanMax=['GHI'], addMeanMin=['GHI']) + + origData = aggregation4.predictOriginalData() + + np.testing.assert_array_almost_equal(raw.groupby(np.arange(len(raw)) // 24).mean().max().loc['GHI'], + origData.groupby(np.arange(len(origData)) // 24).mean().max().loc['GHI'], + decimal=6) + + np.testing.assert_array_almost_equal(raw.groupby(np.arange(len(raw)) // 24).mean().min().loc['GHI'], + origData.groupby(np.arange(len(origData)) // 24).mean().min().loc['GHI'], + decimal=6) + +if __name__ == "__main__": + test_extremePeriods() \ No newline at end of file diff --git a/test/test_minmaxRepresentation.py b/test/test_minmaxRepresentation.py new file mode 100644 index 0000000..dafc7a9 --- /dev/null +++ b/test/test_minmaxRepresentation.py @@ -0,0 +1,48 @@ +import os +import time + +import pandas as pd +import numpy as np + +import tsam.timeseriesaggregation as tsam + +def test_minmaxRepresentation(): + + raw = pd.read_csv(os.path.join(os.path.dirname(__file__), '..', 'examples', 'testdata.csv'), index_col=0) + + noTypicalPeriods = 8 + + hoursPerPeriod = 24 + + representationDict = {'GHI': 'max', 'T': 'min', 'Wind': 'mean', 'Load': 'min'} + + starttime = time.time() + + print(raw.columns) + + aggregation = tsam.TimeSeriesAggregation(raw, noTypicalPeriods = noTypicalPeriods, hoursPerPeriod = hoursPerPeriod, + clusterMethod = 'hierarchical', rescaleClusterPeriods=False, + representationMethod='minmaxmeanRepresentation', + representationDict=representationDict) + + typPeriods = aggregation.createTypicalPeriods() + + print('Clustering took ' + str(time.time() - starttime)) + + for i in range(noTypicalPeriods): + for j in representationDict: + if representationDict[j] == 'min': + calculated = tsam.unstackToPeriods(raw, hoursPerPeriod)[0].loc[ + np.where(aggregation.clusterOrder == i)[0], j].min().values + elif representationDict[j] == 'max': + calculated = tsam.unstackToPeriods(raw, hoursPerPeriod)[0].loc[ + np.where(aggregation.clusterOrder == i)[0], j].max().values + elif representationDict[j] == 'mean': + calculated = tsam.unstackToPeriods(raw, hoursPerPeriod)[0].loc[ + np.where(aggregation.clusterOrder == i)[0], j].mean().values + algorithmResult = typPeriods.loc[i, :].loc[:, j].values + # print(calculated,algorithmResult) + np.testing.assert_array_almost_equal(calculated, algorithmResult, decimal=4) + +if __name__ == "__main__": + test_minmaxRepresentation() \ No newline at end of file diff --git a/test/test_properties.py b/test/test_properties.py new file mode 100644 index 0000000..a6ea1d4 --- /dev/null +++ b/test/test_properties.py @@ -0,0 +1,135 @@ +import os +import time + +import pandas as pd +import numpy as np + +import tsam.timeseriesaggregation as tsam + + + +def test_properties(): + + hoursPerPeriod = 24 + + noSegments = 8 + + noTypicalPeriods = 8 + + raw = pd.read_csv(os.path.join(os.path.dirname(__file__),'..','examples','testdata.csv'), index_col = 0) + + + + starttime = time.time() + + aggregation1 = tsam.TimeSeriesAggregation(raw, noTypicalPeriods=noTypicalPeriods, hoursPerPeriod=hoursPerPeriod, + clusterMethod='hierarchical', segmentation=True, noSegments=noSegments) + + print('Clustering took ' + str(time.time() - starttime)) + + np.testing.assert_array_almost_equal(aggregation1.stepIdx, np.arange(noSegments), decimal=4) + + + + starttime = time.time() + + aggregation2 = tsam.TimeSeriesAggregation(raw, noTypicalPeriods=noTypicalPeriods, hoursPerPeriod=hoursPerPeriod, + clusterMethod='hierarchical') + + print('Clustering took ' + str(time.time() - starttime)) + + np.testing.assert_array_almost_equal(aggregation2.stepIdx, np.arange(hoursPerPeriod), decimal=4) + + + + starttime = time.time() + + aggregation3 = tsam.TimeSeriesAggregation(raw, noTypicalPeriods=noTypicalPeriods, hoursPerPeriod=hoursPerPeriod, + clusterMethod='hierarchical') + + print('Clustering took ' + str(time.time() - starttime)) + + np.testing.assert_array_almost_equal(aggregation3.clusterPeriodIdx, np.arange(noTypicalPeriods), decimal=4) + + + + starttime = time.time() + + aggregation4 = tsam.TimeSeriesAggregation(raw, noTypicalPeriods=noTypicalPeriods, hoursPerPeriod=hoursPerPeriod, + clusterMethod='hierarchical', segmentation=True, noSegments=noSegments) + + print('Clustering took ' + str(time.time() - starttime)) + + appearances = np.unique(aggregation4.clusterOrder, return_counts=True)[1].tolist() + + occurrenceDict = {i: j for i, j in enumerate(appearances)} + + # make sure that the clusterPeriodNoOccur equals the number of appearances in the clusterOrder + np.testing.assert_array_almost_equal(list(aggregation4.clusterPeriodNoOccur.values()), + list(occurrenceDict.values()), decimal=4) + + + + starttime = time.time() + + aggregation5 = tsam.TimeSeriesAggregation(raw, noTypicalPeriods=noTypicalPeriods, hoursPerPeriod=hoursPerPeriod, + clusterMethod='hierarchical', segmentation=True, noSegments=noSegments) + + print('Clustering took ' + str(time.time() - starttime)) + + # make sure that the values of the clusterPeriodDict equal those from the typicalPeriods-dataframe + np.testing.assert_array_almost_equal(pd.DataFrame.from_dict(data=aggregation5.clusterPeriodDict).values, + aggregation5.createTypicalPeriods().values, decimal=4) + + + + starttime = time.time() + + aggregation6 = tsam.TimeSeriesAggregation(raw, noTypicalPeriods=noTypicalPeriods, hoursPerPeriod=hoursPerPeriod, + clusterMethod='hierarchical') + + print('Clustering took ' + str(time.time() - starttime)) + + # make sure that the sum of all segment durations in each period equals the hours per period + for i in range(noTypicalPeriods): + np.testing.assert_array_almost_equal(pd.DataFrame.from_dict(aggregation6.segmentDurationDict).loc[i].sum()[0], + hoursPerPeriod, decimal=4) + + + + starttime = time.time() + + aggregation7 = tsam.TimeSeriesAggregation(raw, noTypicalPeriods=noTypicalPeriods, hoursPerPeriod=hoursPerPeriod, + clusterMethod='hierarchical', segmentation=True, noSegments=noSegments) + + print('Clustering took ' + str(time.time() - starttime)) + + # make sure that the sum of all segment durations in each period equals the hours per period + for i in range(noTypicalPeriods): + np.testing.assert_array_almost_equal(pd.DataFrame.from_dict(aggregation7.segmentDurationDict).loc[i].sum()[0], + hoursPerPeriod, decimal=4) + + + + starttime = time.time() + + aggregation8 = tsam.TimeSeriesAggregation(raw, noTypicalPeriods=noTypicalPeriods, hoursPerPeriod=hoursPerPeriod, + clusterMethod='hierarchical', segmentation=True, noSegments=noSegments) + + print('Clustering took ' + str(time.time() - starttime)) + + indexTable = aggregation8.indexMatching() + + # make sure that the PeriodNum column equals the clusterOrder + np.testing.assert_array_almost_equal(indexTable.loc[::24,'PeriodNum'].values, aggregation8.clusterOrder, decimal=4) + + # make sure that the TimeStep indices equal the number of hoursPerPeriod arranged as array + np.testing.assert_array_almost_equal(pd.unique(indexTable.loc[:, 'TimeStep']), + np.arange(hoursPerPeriod, dtype='int64'), decimal=4) + + # make sure that the SegmentIndex indices equal the number of noSegments arranged as array + np.testing.assert_array_almost_equal(pd.unique(indexTable.loc[:, 'SegmentIndex']), + np.arange(noSegments, dtype='int64'), decimal=4) + +if __name__ == "__main__": + test_properties() \ No newline at end of file diff --git a/test/test_subhourlyResolution.py b/test/test_subhourlyResolution.py new file mode 100644 index 0000000..06c0af4 --- /dev/null +++ b/test/test_subhourlyResolution.py @@ -0,0 +1,45 @@ +import os +import time + +import pandas as pd +import numpy as np +import copy + +import tsam.timeseriesaggregation as tsam + + + +def test_subhourlyResolution(): + + raw = pd.read_csv(os.path.join(os.path.dirname(__file__), '..', 'examples', 'testdata.csv'), index_col=0) + + rawSubhourlyInndex = copy.deepcopy(raw) + + # reset index of new dataframe to 15 min. intervals + rawSubhourlyInndex.index = pd.date_range('2050-01-01 00:30:00', periods=8760, freq=(str(0.25) + 'H'), + tz='Europe/Berlin') + + starttime = time.time() + + aggregation1 = tsam.TimeSeriesAggregation(raw, noTypicalPeriods=8, hoursPerPeriod=24, + clusterMethod='hierarchical') + + typPeriods1 = aggregation1.createTypicalPeriods() + + print('Clustering took ' + str(time.time() - starttime)) + + starttime = time.time() + + # cluster dataframe with 15 min. intervals to six hours per period, which equals 24 time steps per period + aggregation2 = tsam.TimeSeriesAggregation(rawSubhourlyInndex, noTypicalPeriods=8, hoursPerPeriod=6, + clusterMethod='hierarchical') + + typPeriods2 = aggregation2.createTypicalPeriods() + + print('Clustering took ' + str(time.time() - starttime)) + + # check that the results from both aggregations are the same with respect to the clustered values + np.testing.assert_almost_equal(typPeriods1.values, typPeriods2.values, decimal=6) + +if __name__ == "__main__": + test_hierarchical() \ No newline at end of file diff --git a/test/test_subhourly_periods.py b/test/test_subhourly_periods.py new file mode 100644 index 0000000..5682bb8 --- /dev/null +++ b/test/test_subhourly_periods.py @@ -0,0 +1,31 @@ +import os +import time + +import pandas as pd +import numpy as np + +import tsam.timeseriesaggregation as tsam + + + +def test_subhourly_periods(): + + # Create linearly growing test data for two hours. + testData = pd.DataFrame(np.arange(1, 121), columns = ['testdata']) + testData.index = pd.date_range('2050-01-01 00:30:00', periods=120, freq=('1Min'), tz='Europe/Berlin') + + # Aggregate every quarter hour to one time step with mean representation. + starttime = time.time() + + aggregation = tsam.TimeSeriesAggregation(testData, noTypicalPeriods = 8, hoursPerPeriod=0.25, + clusterMethod = 'hierarchical', segmentation=True, noSegments=1) + + results = aggregation.predictOriginalData() + + print('Clustering took ' + str(time.time() - starttime)) + + # Compare it to the expected result of eight linearly growing mean values. + np.testing.assert_array_almost_equal(np.unique(results.values), np.arange(8, 114, 15), decimal=4) + +if __name__ == "__main__": + test_subhourly_periods() \ No newline at end of file diff --git a/test/test_weightingFactors.py b/test/test_weightingFactors.py new file mode 100644 index 0000000..5daf4ab --- /dev/null +++ b/test/test_weightingFactors.py @@ -0,0 +1,45 @@ +import os +import time + +import pandas as pd +import numpy as np + +import tsam.timeseriesaggregation as tsam + + + +def test_weightingFactors(): + + hoursPerPeriod = 24 + + noTypicalPeriods = 8 + + weightDict1 = {'GHI': 1, 'T': 1, 'Wind': 1, 'Load': 1} + + weightDict2 = {'GHI': 2, 'T': 2, 'Wind': 2, 'Load': 2} + + weightDict3 = {'GHI': 2, 'T': 1, 'Wind': 1, 'Load': 1} + + raw = pd.read_csv(os.path.join(os.path.dirname(__file__),'..','examples','testdata.csv'), index_col = 0) + + aggregation1 = tsam.TimeSeriesAggregation(raw, noTypicalPeriods=noTypicalPeriods, hoursPerPeriod=hoursPerPeriod, + clusterMethod='hierarchical', weightDict=weightDict1) + + aggregation2 = tsam.TimeSeriesAggregation(raw, noTypicalPeriods=noTypicalPeriods, hoursPerPeriod=hoursPerPeriod, + clusterMethod='hierarchical', weightDict=weightDict2) + + aggregation3 = tsam.TimeSeriesAggregation(raw, noTypicalPeriods=noTypicalPeriods, hoursPerPeriod=hoursPerPeriod, + clusterMethod='hierarchical', weightDict=weightDict3) + + # make sure that the accuracy indicators stay the same when the different attributes are equally overweighted + np.testing.assert_almost_equal(aggregation1.accuracyIndicators().values, aggregation2.accuracyIndicators().values, + decimal=6) + + # make sure that the RMSE of GHI is less while the other RMSEs are bigger, when GHI is overweighted + np.testing.assert_array_less(aggregation3.accuracyIndicators().loc['GHI', 'RMSE'], + aggregation1.accuracyIndicators().loc['GHI', 'RMSE']) + np.testing.assert_array_less(aggregation1.accuracyIndicators().loc[['Load', 'T', 'Wind'], 'RMSE'], + aggregation3.accuracyIndicators().loc[['Load', 'T', 'Wind'], 'RMSE']) + +if __name__ == "__main__": + test_weightingFactors() \ No newline at end of file diff --git a/tsam/periodAggregation.py b/tsam/periodAggregation.py new file mode 100644 index 0000000..6820e35 --- /dev/null +++ b/tsam/periodAggregation.py @@ -0,0 +1,94 @@ +# -*- coding: utf-8 -*- + +import numpy as np +from tsam.representations import representations + +def aggregatePeriods(candidates, n_clusters=8, n_iter=100, clusterMethod='k_means', solver='glpk', + representationMethod=None, representationDict=None, timeStepsPerPeriod=None): + ''' + Clusters the data based on one of the cluster methods: + 'averaging', 'k_means', 'exact k_medoid' or 'hierarchical' + + :param candidates: Dissimilarity matrix where each row represents a candidate. required + :type candidates: np.ndarray + + :param n_clusters: Number of aggregated cluster. optional (default: 8) + :type n_clusters: integer + + :param n_iter: Only required for the number of starts of the k-mean algorithm. optional (default: 10) + :type n_iter: integer + + :param clusterMethod: Chosen clustering algorithm. Possible values are + 'averaging','k_means','exact k_medoid' or 'hierarchical'. optional (default: 'k_means') + :type clusterMethod: string + ''' + + # cluster the data + if clusterMethod == 'averaging': + n_sets = len(candidates) + if n_sets % n_clusters == 0: + cluster_size = int(n_sets / n_clusters) + clusterOrder = [ + [n_cluster] * + cluster_size for n_cluster in range(n_clusters)] + else: + cluster_size = int(n_sets / n_clusters) + clusterOrder = [ + [n_cluster] * + cluster_size for n_cluster in range(n_clusters)] + clusterOrder.append([n_clusters - 1] * + int(n_sets - cluster_size * n_clusters)) + clusterOrder = np.hstack(np.array(clusterOrder)) + clusterCenters, clusterCenterIndices = representations(candidates, clusterOrder, default='meanRepresentation', + representationMethod=representationMethod, + representationDict=representationDict, + timeStepsPerPeriod=timeStepsPerPeriod) + + if clusterMethod == 'k_means': + from sklearn.cluster import KMeans + k_means = KMeans( + n_clusters=n_clusters, + max_iter=1000, + n_init=n_iter, + tol=1e-4) + + clusterOrder = k_means.fit_predict(candidates) + # get with own mean representation to avoid numerical trouble caused by sklearn + clusterCenters, clusterCenterIndices = representations(candidates, clusterOrder, default='meanRepresentation', + representationMethod=representationMethod, + representationDict=representationDict, + timeStepsPerPeriod=timeStepsPerPeriod) + + if clusterMethod == 'k_medoids': + from tsam.utils.k_medoids_exact import KMedoids + k_medoid = KMedoids(n_clusters=n_clusters, solver=solver) + + clusterOrder = k_medoid.fit_predict(candidates) + clusterCenters, clusterCenterIndices = representations(candidates, clusterOrder, default='medoidRepresentation', + representationMethod=representationMethod, + representationDict=representationDict, + timeStepsPerPeriod=timeStepsPerPeriod) + + if clusterMethod == 'hierarchical' or clusterMethod == 'adjacent_periods': + if n_clusters==1: + clusterOrder=np.asarray([0]*len(candidates)) + else: + from sklearn.cluster import AgglomerativeClustering + if clusterMethod == 'hierarchical': + clustering = AgglomerativeClustering( + n_clusters=n_clusters, linkage='ward') + elif clusterMethod == 'adjacent_periods': + adjacencyMatrix = np.eye(len(candidates), k=1) + np.eye(len(candidates), k=-1) + clustering = AgglomerativeClustering( + n_clusters=n_clusters, linkage='ward', connectivity=adjacencyMatrix) + clusterOrder = clustering.fit_predict(candidates) + # represent hierarchical aggregation with medoid + clusterCenters, clusterCenterIndices = representations(candidates, clusterOrder, default='medoidRepresentation', + representationMethod=representationMethod, + representationDict=representationDict, + timeStepsPerPeriod=timeStepsPerPeriod) + + return clusterCenters, clusterCenterIndices, clusterOrder + + + diff --git a/tsam/representations.py b/tsam/representations.py new file mode 100644 index 0000000..2c69347 --- /dev/null +++ b/tsam/representations.py @@ -0,0 +1,102 @@ +# -*- coding: utf-8 -*- + +import numpy as np +from sklearn.metrics.pairwise import euclidean_distances + +def representations(candidates, clusterOrder, default, representationMethod=None, representationDict=None, + timeStepsPerPeriod=None): + clusterCenterIndices = None + if representationMethod is None: + representationMethod = default + if representationMethod == 'meanRepresentation': + clusterCenters = meanRepresentation(candidates, clusterOrder) + elif representationMethod == 'medoidRepresentation': + clusterCenters, clusterCenterIndices = medoidRepresentation(candidates, clusterOrder) + elif representationMethod == 'minmaxmeanRepresentation': + clusterCenters = minmaxmeanRepresentation(candidates, clusterOrder, representationDict, timeStepsPerPeriod) + return clusterCenters, clusterCenterIndices + +def medoidRepresentation(candidates, clusterOrder): + ''' + Represents the candidates of a given cluster group (clusterOrder) + by its medoid, measured with the euclidean distance. + + :param candidates: Dissimilarity matrix where each row represents a candidate. required + :type candidates: np.ndarray + + :param clusterOrder: Integer array where the index refers to the candidate and the + Integer entry to the group. required + :type clusterOrder: np.array + ''' + # set cluster center as medoid + clusterCenters = [] + clusterCenterIndices = [] + for clusterNum in np.unique(clusterOrder): + indice = np.where(clusterOrder == clusterNum) + innerDistMatrix = euclidean_distances(candidates[indice]) + mindistIdx = np.argmin(innerDistMatrix.sum(axis=0)) + clusterCenters.append(candidates[indice][mindistIdx]) + clusterCenterIndices.append(indice[0][mindistIdx]) + + return clusterCenters, clusterCenterIndices + + +def meanRepresentation(candidates, clusterOrder): + ''' + Represents the candidates of a given cluster group (clusterOrder) + by its mean. + + :param candidates: Dissimilarity matrix where each row represents a candidate. required + :type candidates: np.ndarray + + :param clusterOrder: Integer array where the index refers to the candidate and the + Integer entry to the group. required + :type clusterOrder: np.array + ''' + # set cluster centers as means of the group candidates + clusterCenters = [] + for clusterNum in np.unique(clusterOrder): + indice = np.where(clusterOrder == clusterNum) + currentMean = candidates[indice].mean(axis=0) + clusterCenters.append(currentMean) + return clusterCenters + +def minmaxmeanRepresentation(candidates, clusterOrder, representationDict, timeStepsPerPeriod): + ''' + Represents the candidates of a given cluster group (clusterOrder) + by either the minimum, the maximum or the mean values of each time step for + all periods in that cluster depending on the command for each attribute. + + :param candidates: Dissimilarity matrix where each row represents a candidate. required + :type candidates: np.ndarray + + :param clusterOrder: Integer array where the index refers to the candidate and the + Integer entry to the group. required + :type clusterOrder: np.array + + :param representationDict: A dictionary which defines for each attribute whether the typical + period should be represented by the minimum or maximum values within each cluster. + optional (default: None) + :type representationDict: dictionary + + :param timeStepsPerPeriod: The number of discrete timesteps which describe one period. required + :type timeStepsPerPeriod: integer + ''' + # set cluster center depending of the representationDict + clusterCenters = [] + for clusterNum in np.unique(clusterOrder): + indice = np.where(clusterOrder == clusterNum) + currentClusterCenter = np.zeros(len(representationDict) * timeStepsPerPeriod) + for attributeNum in range(len(representationDict)): + startIdx = attributeNum * timeStepsPerPeriod + endIdx = (attributeNum + 1) * timeStepsPerPeriod + if list(representationDict.values())[attributeNum] == 'min': + currentClusterCenter[startIdx:endIdx] = candidates[indice, startIdx:endIdx].min(axis=1) + elif list(representationDict.values())[attributeNum] == 'max': + currentClusterCenter[startIdx:endIdx] = candidates[indice, startIdx:endIdx].max(axis=1) + elif list(representationDict.values())[attributeNum] == 'mean': + currentClusterCenter[startIdx:endIdx] = candidates[indice, startIdx:endIdx].mean(axis=1) + else: + raise ValueError('At least one value in the representationDict is neither "min", "max" nor "mean".') + clusterCenters.append(currentClusterCenter) + return clusterCenters diff --git a/tsam/timeseriesaggregation.py b/tsam/timeseriesaggregation.py index 83e06ed..b4bf67f 100644 --- a/tsam/timeseriesaggregation.py +++ b/tsam/timeseriesaggregation.py @@ -1,9 +1,4 @@ # -*- coding: utf-8 -*- -""" -Created on Tue Nov 22 23:25:37 2016 - -@author: Leander Kotzur, Maximilian Hoffmann -""" import copy import time @@ -15,7 +10,9 @@ from sklearn.metrics import mean_squared_error, mean_absolute_error from sklearn.metrics.pairwise import euclidean_distances from sklearn import preprocessing - + +from tsam.periodAggregation import aggregatePeriods +from tsam.representations import representations pd.set_option('mode.chained_assignment', None) @@ -85,131 +82,13 @@ def unstackToPeriods(timeSeries, timeStepsPerPeriod): return unstackedTimeSeries, timeIndex - -def aggregatePeriods(candidates, n_clusters=8, - n_iter=100, clusterMethod='k_means', solver='glpk', ): - ''' - Clusters the data based on one of the cluster methods: - 'averaging','k_means','exact k_medoid' or 'hierarchical' - - :param candidates: Dissimilarity matrix where each row represents a candidate. required - :type candidates: np.ndarray - - :param n_clusters: Number of aggregated cluster. optional (default: 8) - :type n_clusters: integer - - :param n_iter: Only required for the number of starts of the k-mean algorithm. optional (default: 10) - :type n_iter: integer - - :param clusterMethod: Chosen clustering algorithm. Possible values are - 'averaging','k_means','exact k_medoid' or 'hierarchical'. optional (default: 'k_means') - :type clusterMethod: string - ''' - - clusterCenterIndices = None - - # cluster the data - if clusterMethod == 'averaging': - n_sets = len(candidates) - if n_sets % n_clusters == 0: - cluster_size = int(n_sets / n_clusters) - clusterOrder = [ - [n_cluster] * - cluster_size for n_cluster in range(n_clusters)] - else: - cluster_size = int(n_sets / n_clusters) - clusterOrder = [ - [n_cluster] * - cluster_size for n_cluster in range(n_clusters)] - clusterOrder.append([n_clusters - 1] * - int(n_sets - cluster_size * n_clusters)) - clusterOrder = np.hstack(np.array(clusterOrder)) - clusterCenters = meanRepresentation(candidates, clusterOrder) - - if clusterMethod == 'k_means': - from sklearn.cluster import KMeans - k_means = KMeans( - n_clusters=n_clusters, - max_iter=1000, - n_init=n_iter, - tol=1e-4) - - clusterOrder = k_means.fit_predict(candidates) - # get with own mean representation to avoid numerical trouble caused by sklearn - clusterCenters = meanRepresentation(candidates, clusterOrder) - - elif clusterMethod == 'k_medoids': - from tsam.utils.k_medoids_exact import KMedoids - k_medoid = KMedoids(n_clusters=n_clusters, solver=solver) - - clusterOrder = k_medoid.fit_predict(candidates) - clusterCenters = k_medoid.cluster_centers_ - - elif clusterMethod == 'hierarchical': - if n_clusters==1: - clusterOrder=np.asarray([0]*len(candidates)) - else: - from sklearn.cluster import AgglomerativeClustering - clustering = AgglomerativeClustering( - n_clusters=n_clusters, linkage='ward') - clusterOrder = clustering.fit_predict(candidates) - # represent hierarchical aggregation with medoid - clusterCenters, clusterCenterIndices = medoidRepresentation(candidates, clusterOrder) - - return clusterCenters, clusterCenterIndices, clusterOrder - - -def medoidRepresentation(candidates, clusterOrder): - ''' - Represents the candidates of a given cluster group (clusterOrder) - by its medoid, measured with the euclidean distance. - - :param candidates: Dissimilarity matrix where each row represents a candidate. required - :type candidates: np.ndarray - - :param clusterOrder: Integer array where the index refers to the candidate and the - Integer entry to the group. required - :type clusterOrder: np.array - ''' - # set cluster center as medoid - clusterCenters = [] - clusterCenterIndices = [] - for clusterNum in np.unique(clusterOrder): - indice = np.where(clusterOrder == clusterNum) - innerDistMatrix = euclidean_distances(candidates[indice]) - mindistIdx = np.argmin(innerDistMatrix.sum(axis=0)) - clusterCenters.append(candidates[indice][mindistIdx]) - clusterCenterIndices.append(indice[0][mindistIdx]) - - return clusterCenters, clusterCenterIndices - - -def meanRepresentation(candidates, clusterOrder): - ''' - Represents the candidates of a given cluster group (clusterOrder) - by its mean. - - :param candidates: Dissimilarity matrix where each row represents a candidate. required - :type candidates: np.ndarray - - :param clusterOrder: Integer array where the index refers to the candidate and the - Integer entry to the group. required - :type clusterOrder: np.array - ''' - # set cluster centers as means of the group candidates - clusterCenters = [] - for clusterNum in np.unique(clusterOrder): - indice = np.where(clusterOrder == clusterNum) - currentMean = candidates[indice].mean(axis=0) - clusterCenters.append(currentMean) - return clusterCenters - - class TimeSeriesAggregation(object): ''' Clusters time series data to typical periods. ''' - CLUSTER_METHODS = ['averaging', 'k_medoids', 'k_means', 'hierarchical'] + CLUSTER_METHODS = ['averaging', 'k_medoids', 'k_means', 'hierarchical', 'adjacent_periods'] + + REPRESENTATION_METHODS = ['meanRepresentation', 'medoidRepresentation', 'minmaxmeanRepresentation'] EXTREME_PERIOD_METHODS = [ 'None', @@ -221,8 +100,8 @@ def __init__(self, timeSeries, resolution=None, noTypicalPeriods=10, noSegments=10, hoursPerPeriod=24, clusterMethod='hierarchical', evalSumPeriods=False, sortValues=False, sameMean=False, rescaleClusterPeriods=True, weightDict=None, segmentation = False, - extremePeriodMethod='None', predefClusterOrder=None, - predefClusterCenterIndices=None, solver='glpk', + extremePeriodMethod='None', representationMethod=None, representationDict=None, + predefClusterOrder=None, predefClusterCenterIndices=None, solver='glpk', roundOutput = None, addPeakMin=None, addPeakMax=None, @@ -257,6 +136,7 @@ def __init__(self, timeSeries, resolution=None, noTypicalPeriods=10, * 'k_means' * 'k_medoids' * 'hierarchical' + * 'adjacent_periods' :type clusterMethod: string :param evalSumPeriods: Boolean if in the clustering process also the averaged periodly values @@ -295,6 +175,21 @@ def __init__(self, timeSeries, resolution=None, noTypicalPeriods=10, case system design) :type extremePeriodMethod: string + :param representationMethod: Chosen representation. If specified, the clusters are represented in the chosen + way. Otherwise, each clusterMethod has its own commonly used default representation method. + |br| Options are: + + * 'meanRepresentation' (default of 'averaging' and 'k_means') + * 'medoidRepresentation' (default of 'k_medoids', 'hierarchical' and 'adjacent_periods') + * 'minmaxmeanRepresentation' + :type representationMethod: string + + :param representationDict: Dictionary which states for each attribute whether the profiles in each cluster + should be represented by the minimum value or maximum value of each time step. This enables estimations + to the safe side. This dictionary is needed when 'minmaxmeanRepresentation' is chosen. If not specified, the + dictionary is set to containing 'mean' values only. + :type representationDict: dict + :param predefClusterOrder: Instead of aggregating a time series, a predefined grouping is taken which is given by this list. optional (default: None) :type predefClusterOrder: list or array @@ -359,6 +254,10 @@ def __init__(self, timeSeries, resolution=None, noTypicalPeriods=10, self.weightDict = weightDict + self.representationMethod = representationMethod + + self.representationDict = representationDict + self.predefClusterOrder = predefClusterOrder self.predefClusterCenterIndices = predefClusterCenterIndices @@ -402,41 +301,43 @@ def _check_init_args(self): for peak in self.addPeakMin: if peak not in self.timeSeries.columns: raise ValueError(peak + ' listed in "addPeakMin"' + - ' does not occure as timeSeries column') + ' does not occur as timeSeries column') for peak in self.addPeakMax: if peak not in self.timeSeries.columns: raise ValueError(peak + ' listed in "addPeakMax"' + - ' does not occure as timeSeries column') + ' does not occur as timeSeries column') for peak in self.addMeanMin: if peak not in self.timeSeries.columns: raise ValueError(peak + ' listed in "addMeanMin"' + - ' does not occure as timeSeries column') + ' does not occur as timeSeries column') for peak in self.addMeanMax: if peak not in self.timeSeries.columns: raise ValueError(peak + ' listed in "addMeanMax"' + - ' does not occure as timeSeries column') + ' does not occur as timeSeries column') # derive resolution from date time index if not provided if self.resolution is None: try: timedelta = self.timeSeries.index[1] - self.timeSeries.index[0] self.resolution = float(timedelta.total_seconds()) / 3600 + except AttributeError: + raise ValueError("'resolution' argument has to be nonnegative float or int" + + " or the given timeseries needs a datetime index") except TypeError: try: self.timeSeries.index = pd.to_datetime(self.timeSeries.index) timedelta = self.timeSeries.index[1] - self.timeSeries.index[0] self.resolution = float(timedelta.total_seconds()) / 3600 except: - ValueError("'resolution' argument has to be nonnegative float or int" + + raise ValueError("'resolution' argument has to be nonnegative float or int" + " or the given timeseries needs a datetime index") if not (isinstance(self.resolution, int) or isinstance(self.resolution, float)): raise ValueError("resolution has to be nonnegative float or int") # check hoursPerPeriod - if self.hoursPerPeriod is None or self.hoursPerPeriod <= 0 or \ - not isinstance(self.hoursPerPeriod, int): - raise ValueError("hoursPerPeriod has to be nonnegative integer") + if self.hoursPerPeriod is None or self.hoursPerPeriod <= 0: + raise ValueError("hoursPerPeriod has to be nonnegative float or int") # check typical Periods if self.noTypicalPeriods is None or self.noTypicalPeriods <= 0 or \ @@ -459,6 +360,19 @@ def _check_init_args(self): "the following: " + "{}".format(self.CLUSTER_METHODS)) + # check representationMethod + if self.representationMethod is not None and self.representationMethod not in self.REPRESENTATION_METHODS: + raise ValueError("If specified, representationMethod needs to be one of " + + "the following: " + + "{}".format(self.REPRESENTATION_METHODS)) + + # if representationDict None, represent by maximum time steps in each cluster + if self.representationDict is None: + self.representationDict = {i: 'mean' for i in list(self.timeSeries.columns)} + # sort representationDict alphabetically to make sure that the min, max or mean function is applied to the right + # column + self.representationDict = pd.Series(self.representationDict).sort_index(axis=0).to_dict() + # check extremePeriods if self.extremePeriodMethod not in self.EXTREME_PERIOD_METHODS: raise ValueError("extremePeriodMethod needs to be one of " + @@ -569,13 +483,14 @@ def _preProcessTimeSeries(self): raise ValueError( 'Pre processed data includes NaN. Please check the timeSeries input data.') - def _postProcessTimeSeries(self, normalizedTimeSeries): + def _postProcessTimeSeries(self, normalizedTimeSeries, applyWeighting=True): ''' Neutralizes the weighting the time series back and unnormalizes them. ''' - for column in self.weightDict: - normalizedTimeSeries[column] = normalizedTimeSeries[ - column] / self.weightDict[column] + if applyWeighting: + for column in self.weightDict: + normalizedTimeSeries[column] = normalizedTimeSeries[ + column] / self.weightDict[column] unnormalizedTimeSeries = self._unnormalizeTimeSeries( normalizedTimeSeries, sameMean=self.sameMean) @@ -616,14 +531,6 @@ def _addExtremePeriods(self, groupedSeries, clusterCenters, ''' # init required dicts and lists - if addPeakMin is None: - addPeakMin = [] - if addPeakMax is None: - addPeakMax = [] - if addMeanMin is None: - addMeanMin = [] - if addMeanMax is None: - addMeanMax = [] self.extremePeriods = {} extremePeriodNo = [] @@ -747,11 +654,6 @@ def _addExtremePeriods(self, groupedSeries, clusterCenters, extremeClusterIdx.append( self.extremePeriods[periodType]['clusterNo']) - else: - raise NotImplementedError('Chosen "extremePeriodMethod": ' + - str(extremePeriodMethod) + ' is ' + - 'not implemented.') - return newClusterCenters, newClusterOrder, extremeClusterIdx def _append_col_with(self, column, append_with=' max.'): @@ -853,7 +755,10 @@ def _clusterSortedPeriods(self, candidates, n_init=20): aggregatePeriods( sortedClusterValues, n_clusters=self.noTypicalPeriods, n_iter=30, solver=self.solver, - clusterMethod=self.clusterMethod)) + clusterMethod=self.clusterMethod, + representationMethod=self.representationMethod, + representationDict=self.representationDict, + timeStepsPerPeriod=self.timeStepsPerPeriod)) clusterCenters_C = [] @@ -917,7 +822,11 @@ def createTypicalPeriods(self): self.clusterCenters = candidates[self.predefClusterCenterIndices] else: # otherwise take the medoids - self.clusterCenters, self.clusterCenterIndices = medoidRepresentation(candidates, self._clusterOrder) + self.clusterCenters, self.clusterCenterIndices =\ + representations(candidates, self._clusterOrder, default='medoidRepresentation', + representationMethod=self.representationMethod, + representationDict=self.representationDict, + timeStepsPerPeriod=self.timeStepsPerPeriod) else: cluster_duration = time.time() if not self.sortValues: @@ -925,7 +834,10 @@ def createTypicalPeriods(self): self.clusterCenters, self.clusterCenterIndices, \ self._clusterOrder = aggregatePeriods( candidates, n_clusters=self.noTypicalPeriods, n_iter=100, - solver=self.solver, clusterMethod=self.clusterMethod) + solver=self.solver, clusterMethod=self.clusterMethod, + representationMethod=self.representationMethod, + representationDict=self.representationDict, + timeStepsPerPeriod=self.timeStepsPerPeriod) else: self.clusterCenters, self._clusterOrder = self._clusterSortedPeriods( candidates) @@ -972,7 +884,10 @@ def createTypicalPeriods(self): if self.segmentation: from tsam.utils.segmentation import segmentation - self.segmentedNormalizedTypicalPeriods, self.predictedSegmentedNormalizedTypicalPeriods = segmentation(self.normalizedTypicalPeriods, self.noSegments, self.timeStepsPerPeriod) + self.segmentedNormalizedTypicalPeriods, self.predictedSegmentedNormalizedTypicalPeriods =\ + segmentation(self.normalizedTypicalPeriods, self.noSegments, self.timeStepsPerPeriod, + representationMethod=self.representationMethod, + representationDict=self.representationDict) self.normalizedTypicalPeriods = self.segmentedNormalizedTypicalPeriods.reset_index(level=3, drop=True) self.typicalPeriods = self._postProcessTimeSeries(self.normalizedTypicalPeriods) @@ -1101,7 +1016,7 @@ def predictOriginalData(self): if self.sameMean: self.normalizedPredictedData /= self._normalizedMean self.predictedData = self._postProcessTimeSeries( - self.normalizedPredictedData) + self.normalizedPredictedData, applyWeighting=False) return self.predictedData @@ -1158,7 +1073,10 @@ def accuracyIndicators(self): 'MAE': {}} # 'Silhouette score':{}, for column in self.normalizedTimeSeries.columns: - origTS = self.normalizedTimeSeries[column] + if self.weightDict: + origTS = self.normalizedTimeSeries[column] / self.weightDict[column] + else: + origTS = self.normalizedTimeSeries[column] predTS = self.normalizedPredictedData[column] indicatorRaw['RMSE'][column] = np.sqrt( mean_squared_error(origTS, predTS)) diff --git a/tsam/utils/k_medoids_exact.py b/tsam/utils/k_medoids_exact.py index 5e35662..a3d8c26 100644 --- a/tsam/utils/k_medoids_exact.py +++ b/tsam/utils/k_medoids_exact.py @@ -1,6 +1,4 @@ # -*- coding: utf-8 -*- -"""Exact K-medoids clustering""" - import numpy as np @@ -122,52 +120,6 @@ def _check_array(self, X): return X - def transform(self, X): - """Transforms X to cluster-distance space. - - :param X: Data to transform. shape=(n_samples, n_features) - :type X: array-like or sparse matrix, - - Returns - ------- - :returns: **X_new** (array) -- X transformed in the new space. shape=(n_samples, n_clusters) - """ - - check_is_fitted(self, "cluster_centers_") - - # Apply distance metric wrt. cluster centers (medoids), - # and return these distances - return self.distance_func(X, Y=self.cluster_centers_) - - def predict(self, X): - - check_is_fitted(self, "cluster_centers_") - - # Check that the array is good and attempt to convert it to - # Numpy array if possible - X = check_array(X) - - # Apply distance metric wrt. cluster centers (medoids) - D = self.distance_func(X, Y=self.cluster_centers_) - - # Assign data points to clusters based on - # which cluster assignment yields - # the smallest distance - labels = np.argmin(D, axis=1) - - return labels - - def inertia(self, X): - - # Map the original X to the distance-space - Xt = self.transform(X) - - # Define inertia as the sum of the sample-distances - # to closest cluster centers - inertia = np.sum(np.min(Xt, axis=1)) - - return inertia - def _k_medoids_exact(self, distances, n_clusters): """ Parameters diff --git a/tsam/utils/segmentation.py b/tsam/utils/segmentation.py index 468c37d..e355083 100644 --- a/tsam/utils/segmentation.py +++ b/tsam/utils/segmentation.py @@ -1,14 +1,13 @@ # -*- coding: utf-8 -*- -"""Further irregular feature-based segmentation of typical periods""" - import numpy as np import pandas as pd from sklearn.cluster import AgglomerativeClustering -from tsam.timeseriesaggregation import meanRepresentation +from tsam.representations import representations -def segmentation(normalizedTypicalPeriods, noSegments, timeStepsPerPeriod): +def segmentation(normalizedTypicalPeriods, noSegments, timeStepsPerPeriod, representationMethod=None, + representationDict=None): ''' Agglomerative clustering of adjacent time steps within a set of typical periods in order to further reduce the temporal resolution within typical periods and to further reduce complexity of input data. @@ -51,7 +50,12 @@ def segmentation(normalizedTypicalPeriods, noSegments, timeStepsPerPeriod): segNo, indices, segmentNoOccur = np.unique(clusterOrder, return_index=True, return_counts=True) clusterOrderUnique = [clusterOrder[index] for index in sorted(indices)] # determine the segments' values - clusterCenters = meanRepresentation(segmentationCandidates, clusterOrder) + clusterCenters, clusterCenterIndices = representations(segmentationCandidates, clusterOrder, + default='meanRepresentation', + representationMethod=representationMethod, + representationDict=representationDict, + timeStepsPerPeriod=1) + #clusterCenters = meanRepresentation(segmentationCandidates, clusterOrder) # predict each time step of the period by representing it with the corresponding segment's values predictedSegmentedNormalizedTypicalPeriods = pd.DataFrame( clusterCenters, From 9ea1797d141dec7f6b172a489023cb757ac4e1b5 Mon Sep 17 00:00:00 2001 From: "j.schoenau" Date: Fri, 16 Jul 2021 16:09:54 +0200 Subject: [PATCH 02/12] Updated change log --- docs/source/newsDoc.rst | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/docs/source/newsDoc.rst b/docs/source/newsDoc.rst index d3d184d..b6808c1 100644 --- a/docs/source/newsDoc.rst +++ b/docs/source/newsDoc.rst @@ -2,8 +2,7 @@ tsam's Change Log ################# -In tsam's latest release (1.1.0) the following functionalities were included: +In tsam's latest release (1.1.1) the following functionalities were included: -* Segmentation - the clustering of adjacent time steps - according to `Pineda et al. (2018) `_ -* k-MILP - an extension of the MILP-based k-medoids clustering that allows automatic identification of extreme periods according to `Zatti et al. (2019) `_ -* The option to dynamically choose whether to clusters found should be represented by their centroid or medoid. \ No newline at end of file +* Significantly increased test coverage +* Separation between clustering and representation, i.e. for clustering algorithms like Ward’s hierarchical clustering algorithm the representation by medoids or centroids can now freely be chosen \ No newline at end of file From c9e80f5f02f02822a99d66d5d9fab91f228ada51 Mon Sep 17 00:00:00 2001 From: "j.schoenau" Date: Fri, 16 Jul 2021 16:15:17 +0200 Subject: [PATCH 03/12] Redo copy --- docs/source/newsDoc.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/newsDoc.rst b/docs/source/newsDoc.rst index b6808c1..189b5d6 100644 --- a/docs/source/newsDoc.rst +++ b/docs/source/newsDoc.rst @@ -5,4 +5,4 @@ tsam's Change Log In tsam's latest release (1.1.1) the following functionalities were included: * Significantly increased test coverage -* Separation between clustering and representation, i.e. for clustering algorithms like Ward’s hierarchical clustering algorithm the representation by medoids or centroids can now freely be chosen \ No newline at end of file +* Separation between clustering and representation, i.e. for clustering algorithms like Ward’s hierarchical clustering algorithm the representation by medoids or centroids can now freely be chosen. 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+7,14,341.9384386783649,516.9620847668075,13.20973065243004,2.993004179611542 +7,15,303.46128952772085,505.7492719105137,14.925503835879567,2.993004179611542 +7,16,194.56369759193586,495.238752888846,14.258258708982531,2.993004179611542 +7,17,82.7621698711966,487.92438710147707,13.400372117257763,1.995336119741028 +7,18,0.7259839462385667,481.78528843294487,11.684598933808235,0.997668059870514 +7,19,0.0,474.9964485862585,9.873505017944838,0.997668059870514 +7,20,0.0,471.94362061796505,9.206259891047797,0.997668059870514 +7,21,0.0,460.8406903997377,9.30158062346166,0.997668059870514 +7,22,0.0,425.89321451228415,9.01561842622007,1.3967352838187195 +7,23,0.0,396.3777659256018,8.729656228978484,1.1972016718446168 From fbfeb421b37fcc7421695a154134721f4ff3ae61 Mon Sep 17 00:00:00 2001 From: "j.schoenau" Date: Tue, 10 Aug 2021 12:53:54 +0200 Subject: [PATCH 10/12] Copy of develop branch on jugit: Fix cluster-order test --- docs/source/conf.py | 4 +- docs/source/newsDoc.rst | 7 +- examples/predefined_sequence_example.ipynb | 656 ++++-------------- .../testperiods_predefClusterOrder.csv | 384 +++++----- ...ds_predefClusterOrderAndClusterCenters.csv | 384 +++++----- setup.py | 2 +- test/test_cluster_order.py | 4 +- 7 files changed, 526 insertions(+), 915 deletions(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index 0a74885..50243ee 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -66,9 +66,9 @@ # built documents. # # The short X.Y version. -version = '1.1.1' +version = '1.1.0' # The full version, including alpha/beta/rc tags. -release = '1.1.1' +release = '1.1.0' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. diff --git a/docs/source/newsDoc.rst b/docs/source/newsDoc.rst index 189b5d6..d3d184d 100644 --- a/docs/source/newsDoc.rst +++ b/docs/source/newsDoc.rst @@ -2,7 +2,8 @@ tsam's Change Log ################# -In tsam's latest release (1.1.1) the following functionalities were included: +In tsam's latest release (1.1.0) the following functionalities were included: -* Significantly increased test coverage -* Separation between clustering and representation, i.e. for clustering algorithms like Ward’s hierarchical clustering algorithm the representation by medoids or centroids can now freely be chosen. +* Segmentation - the clustering of adjacent time steps - according to `Pineda et al. (2018) `_ +* k-MILP - an extension of the MILP-based k-medoids clustering that allows automatic identification of extreme periods according to `Zatti et al. (2019) `_ +* The option to dynamically choose whether to clusters found should be represented by their centroid or medoid. \ No newline at end of file diff --git a/examples/predefined_sequence_example.ipynb b/examples/predefined_sequence_example.ipynb index 559b0a7..94ffce4 100644 --- a/examples/predefined_sequence_example.ipynb +++ b/examples/predefined_sequence_example.ipynb @@ -221,12 +221,14 @@ "outputs": [ { "data": { - "image/png": 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U1k3Z1kfyPHnE5GXic11UCDqy+rEWJeZ8zfCcsdLtlHOUphfJfzS2C1kdjZbjPHeBYH/RraHMege9Yu1B9qMDs060jd+uJ6xCTehVVyLtjnZuJSoE7phJWJ5YW5Gl26cSViTlephpTZKmMnvBWSN1xrqYBbhirL1Mu3JdYn+xod2xtZHrXLmsPZVbt47YAirf1qhOtC51OWkTFXRC1Kox6gkHFLqQx65bz7knTdkxfadTHnBxhnmFaq9lljlhi3TyVMICKm1f44drVQNn22XPBRh9lIQtjH89pS1fWpMlZymli/F1thEbWR+V51d6LHiyLJ+sLU3inKjcWXVjx5uN+kp+rsnqjFnnPNfuuLHh2Cdu4eCkTtgiWfut1qsPUz55CjLKlLby8ses8vxKbFe1cJ6wPIhaz/gp7HadHY5dPrJbMv1lfSi2TCrXQzpT4xQbOi63F02rGjAejT/W8iaNtV3aCGeTdGZxQzsqe8349VJsaAaXuqxFSyXmWuu0Js24vfyGZS7cv4/xL3RcWcN2tP78clL0EQ5oqjcby7f6QxU3BrcmNWP3JcdnWx+2fsC0Q2F/ctweq0W2QtNhT7n8OEpns63/TPqlvscuhWAdViZM/oe/3CaYOkhnzqzZrjqapYaxAZy84SD55VUGlqMleIN4TfRcZI3nfhcGQy4cND2h0FTOGmrxpjKVj5+FxjmXfj66NIKOZnB+ifUDxk4p/2SBC1OmfgpLYwzf8xmT1i3XoAc0duDeV2obS0Jg5VDajGx36PS509mRO52CIAiCIAjCdrGuc6zrXMb+p/69Tpl0CoIgCIIg7BBdFN0Me6SsfU81ZNIpCIIgCIKwQ/S/07kLmdlhZNIpCIIgCIKwQ6zrPGuZk0650ykIgiAIgiBsEyEBYYZ6PeSpf6vzaTPpLDVC8gMh7UqON13/IADv/uhrqE4s06pWAShPjTslaKs6RHM6574Xa4rFaaOwq8yuAEYR2JosUa6HCSWrjaPWvNYpRWduOsEJnu3yE5xvc/6ZRoVXnVjg2MNH3LExfDW6dukAjPxrK6E+tWrMcn2KVjVwKkCI1Y1O/RuphfNtzchLjIK/eHfVpeHKE+W/XQ2TavYgICyYc0986yDVnFEmEkKp0aE9Hv3nFioWnm3Un3qg49Svq1/ejxoYJLewDMDAvkGC9fi/vdyaplMdAaBxa56pjxg1qwq1U5rn2iHdT+xnZdzkY/B8yPL15r/DyscDp44EjHK9m1TqxqprXJwA3cHec8CoVbuDpi0GlzX5uSYjLzF1slSb2lB1bOu9jNevovq1x1qTJcrEStoeZbGn+Pa/tyvKbXdLOHU5ofliXQBMeUz95Ntx3XSHQsKBeNAL2h2nVg8HA3RhAB3FpVZWXF11C4puIXBx+k4I0F/N7+cdjEq6n1q8XA/juvPrd2DA9fc8pj/bOPy48nNNWjORehvI3XRjZjpZ4bL2p/dlqdj7lcVXlKeV5L5TRlrNn2a9bNqqO5hUIo8en0vmyyrlsxTqHnZsGz2eHLMAl1914zPoFnLkz/eKG/RqbGOdW4scEYqXdpfGKchvn6JcD728dKjMXrikOFydHj5EZyp2Mhg510K1jJLb74NpVxHAuVAUG5r8hQ7j/2rKpC60ybeNIrv622Uat8Lwg2bV5TOvvZGRKPy1n0yq7YN15VxI6pURt7883wVKbkw3Y3T0vZJLODVYZw/ovWZ8OlPjibrSgXL9wFerj86RGHv69VW7L/HbEu0bfFIzPG9U3+qxebo3HoRIvb74zEHUgKnv3Mkn6B6+hpVrovHmcYUeMG2bW9MMLCsKS1FfKoaEBXOsM2zqDiKHjVab9Wnj1jHwWInz15k8TX5sBbW6hrr/ywCsv/g5rEyZ+Mr3rhBUK4AZk1ShQ35lAIBK6TyzJ01d5kb6q/x3kjWdI59xp3PtqT/nfPpMOgVBEARBEHabdZ3v807nU//xeu/9XUEQBEEQBOGK0CFwYiL/09nklEwpdVQp9SGl1GmllFZKvS51XCml3h4dX1FKfVgpdcu2FmaTyKRTEARBEARhh+jqoO9nkwwBnwF+sM/xHwV+ODr+AuAM8PdKqZE+519x5PG6IAiCIAjCDrHe553Odb25lzq11vcA9wAolXw0r8yOtwA/o7V+f7TvO4CzwLcAv7GFrF82cqdTEARBEARhh8h6tJ7y7hxRSo16n/7rWvfnBuAA8Hd2h9Z6FfgI8OLLLsQWedrc6TzzVYqgqCjW4VPLRhnXroaMgFMB9lP2zXz35/jkB5/jtluTsRKxMnuB1mTJKVFHj8+57/WHyhSnzXmf/OBzYBq3znA4WnaqwieBYq2UUJ47ZWVd0apqpyrUpex1pFvVIAoT/6e0OG3X5sWtQw2QvoPvlKuVHDDolK7F+nDiPLW6ThhdE8NfyvNoZz8Aq+PAYzDymFEGrt3qKWzzoVMwdifWUbmAcLTsDq9MGVXkoXtXuTBVZKhulO359pBb/3jk4SfpjJrvnaEcF27oUKqbONv7AwasiDMIyLU7FJaMarHbWCC33+RRhZqB8+uAqb+BlqZwztRP0InXZFch0brRvf9x6sAoOusL5uQjnuLXVxHbbV+d7OM7BDRmhrBOCKq78RrcTp3qrVcNsDqmGT4ZVcG588kwS0tOte7HrToKHZh6AQgWlgj3m/YO8wqdU5Az5dTtNoWl0KXdLQYuzrWR/i+++4rszLXXoz6ZvubSddCvTjZcK91T4apuGKveDx9y1+7o8WbP+vB23XGXP0/xnFaAZyrfvfOdcjoqt7/qc36uSZnxhNrfxZuxZrryxOg2/2O1jesgf/iQSzN93qF7z/dNyxGGBJ2QYGHJ5OGGEddfuufOufXVCwurqKULBJ2xvnlJ5wtM/TRmhhJ92Xfa2GjN8Cw3BJtnW9b0NVmeX8lsi3ZFofMBrooffoTcYTNuhAOKQ//wJJ0zRpUedGInhPWRPDaH7WpIvpV9LTRmhijXQzemj9WgbYTWRjnv9at8q5PYtmGotDd0QtCBcm2Tpl89+s4Z+blmsg9610V42zCD5yJl//AQ3dIA+aIZj1Wo0StmGqH3jUT74jTW95nzRh+5wOJNI2CE59x4uM7jdZNeONlGL8bjsm6tJMpi67VbzBOceQJC01+Wry8ycsJcC+vDA+RWIteV/QHPveEkDz10k4sjbJg5W5e9sfZ6SEA30zLJ7TuVOvRTwNs3mcyB6O/Z1P6zwPWbjGvbeNpMOgVBEARBEHabdZ0jt/Hj9UPAsndotefkSyf934jK2LdjyKRTEARBEARhh1jXQZ9Jp7tNvKy1XrrMZM5Efw8Anhss19B793PHkHc6BUEQBEEQdohQB30/28iXMRPPV9odSqlB4KXAx7czoc0gdzoFQRAEQRB2iE5SNOTtDzPO7o9Sahg44u26QSn1XKCptX5cKfVO4G1KqYeBh4G3AS3gj7aa98tFJp2CIAiCIAg7xHqYIwgzHq+Hm5t0ArcD/+RtvyP6+3vAdwK/iFGqvgvYDzwAfI3W2n9fdEfZ05NOpdTbgZ9M7T6rtT6QcfqGHPiYJj+gaU4n99cXRthH79rC7YpyavJjn7gFpmMF7emjBcZq5rtTW1ol7slTlCNl4VhtiHLdU2ZWA085u8T5SC04Pf5lai/Brem7Xqu6ddP9sADl+TzlDzwAJNWpYNda99f3jcOWPeVjvj3FkWiN4FmqrgzViQVgwik+/TwYlesM4ZRRqIdLJf688QIASk9ouoMB3apRCPrqxWA579SvweKoWR832l6/YYTySXPhLdyao7QQ0p0wCurRR+N6C86dZzBSZberUwQriuK5WE09sGzyqVpt2F/uUQ1b8nNNmBly4awytDK7Qqs6lF3f9bBXPR0pIfNzp5zis3z/KXcxdabGe5TrVglaxqwT7dYWr4euPdXROzZcx9ti8h0watN7RpsD/28dMCroxHrcflnmk0rWcj1MKGXD8mB03kpSqd1PKQxAHF+rGuepVQ36rqPdOXkKbp9KrBWd6NOp8jeiNjuQWhs9rSZ3bWEVuTbf6/FVYlTsh+M8ewp7/3pKrw2fTsvf15kaTzgL2LW0fYV+Tx1soNjPHz7EUmrt+ZWJuD/6WGU0kFi73bZF5hr2wKm7zHX2jLlD0K/PdcOEwtmWCaBMvH67K281fjSYTs+psD1ak6We8lg6U+PxWuUZYX03gnS+/Ha5FIoNTa4dq8Y7QL617uLtXDdM81UvdudahwO/bfd/XtGuKGrNawGoTixTX/DXX1/Bv1YWp02ZxmvJMXbAy3d+rumU/adTv1uJ63h+haDTpx6ja81te+vUJ85L1ZV/XQzPdxk4ueCOBZ3QrXO+Oq4IVkw7dR86Qev1L3TjcW5Nu3rUOUXpCU3hnNHDPLk2yOiJKMITRdfG5yc1xXPnyM9F43EQMLBsjuXaHYJqxb0TuO9LLRrPM04oBz6xjiqZcpUWQtrdAff7tW+wTelslMfVgcx62mnWdY5g43c6Lwmt9YcxwqB+xzVG9f72TUV8BdnTk86IzwOv8LZ7Z4iCIAiCIAhXASGBb4+U2P9U52qYdHa01mcufpohMlH1jVR3bbknQRAEQRAEn/UwIAh7J5jrGfuealwNJbwpWqz+y0qpP1FK3XiR89+K8Vu3n7TJqiAIgiAIwq7Q6bMaUSfjkftTjb0+6XwA+HbgVcD3YPymPq6UmtggzM8B+7zPoQ3OFQRBEARB2DFCDaFWGZ/dztmVZ08/Xo8Ws7d8Vin1CeBLwHcQq7TSYVbx3PvNmveCIAiCIAi7T6ePkOjpcKdzT08602itLyilPgvcdNGTBUEQBEEQ9hjrYQ6VaZkkk849RSQSmgbu22zYlUpAbjCgPb0S21rc3KC+MBLbbdx/Cjy7oHbVhB2rKaDIwOuMLc1yrZSwQmpO5xg9br4nbFfqYY/th7UyqszGliC3jTzOiV94NuvfE9tS+DYh1jYDjI1GJ7LswLNxaVdUwuKoWA8Stkn5uaazvinPr3DsYeMne2T2git/fWGEI2l7IBvelqsRa7RsPdo4rbVNu6JcntuVXGzHUVlNWN00p3OJPA+c77q8tKpBXzuVYj3AmhiU66GzbknbBUFsrcLJUwnrH98Sy7cQSafrW784e5rKqkvPP+7SitJPHEtbkvj2MolyZZNo24ZO9I/qxHKcdsqqKW0RFNu8TG2YP39ffq7pndtrt5IVpjJ7wVh0He59uyVtZVWeX3E2NLZP277k4oritgPWo3cNc+jebCujrPyn07THnZ2YPc+z22lNlly5O77lWIYVV9oiy0+jXx79/b41UX6u6dpp6fYpRud6rZI2Kq+PH4/f3v6YshHlDzxA43tfZL73yYNvr+PvA9O2rcmS67+Z51pLr5OnnOUWQCUVl09WewIXtRzzr1F7/RcbKln/kLCsaswMufpqVQNnN5XV/3ybJEu5bqyn7Djlj8vgtdvUOHh90KaXRY+NG722bNC/nmwcifxPZvdhiO3IRo/PJcL4Nn0u3npsZ1ee792f3rb14u/r14bp66nQjNP2x8BrS8uZ6zwWF/bG8+sQ8zg9a/9TnT096VRK/U/gQ8DjmPVCfxwYxRifCoIgCIIgXFV0dUAnY8nL7vYug7kn2dOTTowI6I8x//TWgfuBO7TWj+1qrgRBEARBELZAp8/j9c4eeLyulHr/FoJ9n9b6iUs5cU9POrXW37TbeRAEQRAEQdguQlTmo/Q98nj9dcCfAtnv2vXyLcAwcPVPOgVBEARBEJ5KdMIAlWEE39k75vA/dKl3LpVS37CZiGXSKQiCIAiCsEPs8Unny4H+Cs1eXg3MXfSsiKfNpPPJmzVB0SjXpseNrq3WvDZSY0eKtjtmYtVlPaRdse9XGLXwYqRMLOIpLuu9SsSEirAaKbojxaJRwpMI+8LyCd55VEEU/8FE2iYtX8Hrq+2tOrHY0JTrIYvTJv6DxzRNclE63US+WpMlaJjzGjPKxV2daBFrqY3C20+rdfuUU24Xa8We82x52pWcqx9fJVudWCY/1yaLdkVRWsgxNLcSlXko87zy/ArN6eFE3fjKblc+6FHJ+hQbmvFaHM5XXCa2J0ux4ntmyKRXs8rW87HamVgp2iGpDE20WUp16qvQLTZcP0UAf/RDAAAgAElEQVS0DWPVmvWH7mDicDsRv6uDw4fc+Y2ZIQ5soPZO4/JMrCRNK1J95wb/WmhNlijfMeOcHPIk1eTp8tqyWuWtzfN4rZuou47nLpGlhLblHFyO2zateM2q8yys8hqMareTOu4rbLPU/VkK43R4P19WsZ9uI6uyh2QdL05r41IRjRWjx2Pldrui+vY7XxFt1eJ+X/Vpvf6FffPvX3e+S0K7oijXk+fGdR67Bdj6tWXKHz6U2UdsH+t3DflspFy3aYC5RmO3CFOOfvUPxM4id1fdWJ7ux8WGpm3dPSqrVCeWo/yWaU2WGHmJuXHUfqjipd3fRaAzNd63r/rj0sXKnB4T0mlcyrmu/0V91o497cp1veNvdK79XQFTV827rnNjR/2hCgfpVZKbtj7Uc63ZOBLpeK4IG1FrXuvOG2xc3AViJ+hqhcoUEu3+43Wt9Uc2ef5HN3P+nphWC4IgCIIgPB3IXo0o20ZpN1FK3aaUeo63/X8qpe5WSv2sUmpwK3HKpFMQBEEQBGGH6IRB389mUErllVL/XSn1ZaXUilLqEaXUTyiltmtu9xvAs6K0bgT+BGgB3wj84lYifNo8XhcEQRAEQdhtujro83h903PFHwO+D7M0+OeB24HfAZ4EfvnycgmYCee/RN+/ETimtf4WpdRXYSagb9lshDLpFARBEARB2CH6PUrfwuP1FwF/obX+q2j7UaXUN2Mmn9uBIn4i/grgL6PvJ4kXDdsU8nhdEARBEARhh+iGQd9PxIhSatT7FPpE9VHgq5VS9hH4vwFeAvz1NmX1OPDjSqlvA14K2MntDZC50uhFedrc6bzxfS3y+dAoFG9KHnPKvPlsVfrinW1oFBi7zyi221uZ31dWKdZKibA2rQdaRxirKRbvjPdbtV27ohIKxtZkyYUb9dbVXZzWlOuxIvP00ZBipCBNqz3L8ysQKV7blY3X0rZhrerTKjLXKSbO8xX2vvLcT7u+MMK+qXLmWtTjtS75VlLJ6VS20ZrqYBSmvmJx8c62a5dWNehZe9xfezitBvdVvH4d+8pbf23uxWmdcB9oTZbcesXlaN1ku7+fwtYeS6jzo3XHiw3do4i26zy79KLzFqe1K9tYTcXqb0/hbcscr/UcuGPlDzwQr3d+kTz72HWk7bl+W6SV7b4qfXQu6Srgp5VeF9yP11eQQ6wwLtfDnrL65Ns6U/Wen2u6+kgrdP247Brefr6c8nmD9dRNn0oq2P1+566nqG76OSx0PIV3fq5JO+oHft8s1pPODUu3TyX6dGNmKL4mU+3rHBAy6s8vZ6samPEPKL9/kLXhaHwk6fgweviQN2YGl9SXLG48S5XftvV4zbhv2DW6/T5my5YYw1PrpkO2Ij1Ne8K7tr342hXFTOR4MkvVnbPkjUtgxgfr7lGdWHYuKcfeeAtH3tviST8te03W+jsH2LIDnJ422/545q8jvxHOtWTmMH7fTDhzAExOJa4Jfy1zP67O1DjYY7bcXPy2Vz+Ve/qczsnIKSVFZ2oc7p9NjFuJsqTWmbf98d9d/yDvnH41AAMjObOw9i4TJieYif0Rp1KHfgp4e0ZUvwDsA76glOoCOeC/aK3/eJuy+hbgPRjD+J/RWp+I9n8D8PGtRPi0mXQKgiAIgiDsNhrQGW5P3q5DwLJ3aLVPVG8EvhWzKtDngecC71RKndZa/95W86eUepbW+ota61ngORmn/Agb+X1tgEw6BUEQBEEQdoiuDmBjIdGy1nrpEqL6H8DPa63/JNr+rFLqeuCtwJYnncCnlVKPAx8E7tZaf8I/qLXONty+BOSdTkEQBEEQhB2iG6q+n01SBtKO910uf243Afxo9PcDSqmzSqnfUkq9VilVvEjYDZFJpyAIgiAIwg4RhkHfzyb5EPBflFKvUUo9Qyn1euCHgQ9cTv601m2t9Ye01v8BmAReD9SBnwcWlFJ/oZT6bqXUNZuNWyadgiAIgiAIO8Q2rkj0ZuDPgXcBNeB/Ygzd/+t25VUbPq61/s9a62dj3hs9BnwncFIp9QObiU/e6RQEQRAEQdghwhBUxqP0cJNLw2utlzEK802btG8VrfXDwC8Bv6SUmgA2tk9I8bSZdLYOlMgPmFcRas1rAXjT9Q/yp7/5tTSnzQ3f/FzT2T60Jku0pyPbj4axyLIWDIvTsV+qtTDxLVksvtUJGDsI35rH8ub9j/HOae3S8dNqV3IUG0n7F2u341u3FOsB5fnznH5DLo64XnJlAZw9S2NmCCL7ivFa1x2vP1RmYFI7uxP/mA1rbUBO1CdoftTcWW9XQ8Zrse6uXVE99hgAR286wWluSFiD2Pqw9ejKGVnipEnH/Zbb7uVP3/+1ADSnc5TrSQuWfpY6ftrFhnYWJtZyyrfOsnkaq6lE+r6dT2uyRJnY5gZI2BhZ0pY6ftva/Pi2QH5ZbTynj5adNYvFWp+ky2vSi9va0vjeF5k+NtmbR2eLYs/1rHf8tADadw3H9j710PXHcmocStsPpfM4etyEs/ZJto/7Vj/5uSbMxFZTvuVLutxpq6VLIV3udP7zniXW6FzS6sZPy/adyuwFc85ktv2LXx/l+RWa08M959lzfWw/MX02SLSrpdjQib6b7nc2TLrMfj+29V2s2XBdOvbrHTOunO2KcvZKkLxmTFte2gM13zqr4bdzlHf/Wkjk2Yvft0/yy5uuQ9N2qfpWcT7S4/Sxh48AcLAesnjnWrQ3Xnq6XA9pV3JuzK1PG4s4iMcUO3bWKyOJsf5SMLZwF1w5WtWAcnQsy+7MtcXJU85iqFwPk9Z2k6XE79ZG14yz2Dp5ijzQsfuroStf+tx03/LHBygnLLbi34HeWZf7Hb7XWEZ1vGP2/N4xFmexBXEbDCxkSMZ3gVAr1PaYw19xlFJTwFcB15B8Oq611r8KLGwmvqfNpFMQBEEQBGG30VqhMyaYWft2E6XUdwHvBtYwk0t/1q6BX91snDLpFARBEARB2ClChc5Sqm9evX6l+W/R5+e01pt8+J+NTDoFQRAEQRB2iDBUmRPMcO9NOsvAn2zXhBNEvS4IgiAIgrBzaNX/s7f4LeAbtzPCTd/pVErlgTcBf6u1PnM5iSuljmKWU3o+kReU1vpu77gCfhL4XmA/8ADwA1rrz19OuoIgCIIgCLuBDs0na/8e463AXyqlvhb4LLDuH9Ra//BmI9z0pFNr3VFK/W9gerNhMxgCPgP8DvC+jOM/ijE6/U7gi8CPA3+vlLo5sgrYElZF+MLyCX63Gjjlsq9CbU7nEgrDsZrCvkM7VlMs3mnUcq2GUcRbVZ6vwMzCpuXzq+euj+I0K0u1KyXsTWhfKQ2Rqj5DAd+eXqFVK1GdMEKy+kMVF7ZVDRKqwHI9jBT4Jt5TTmEfJsqSVt22qgG3jTwOwGz1Vhd/u2qOWcVxlnIdjGvAgKc4blfLUb1C6WybxkyZobMmD0Z5GCtgE2WtxO4Bn1q+Lt5fDWmSc+nrcjGpMPZUrn4djNe0UZ565XQK4UrOKX3bFWXUmg1zbCN1dFrx7Sua+4WzSna/rQZeZ2T963dX3T7TV/omTX6uSWvmcFxOT23rq4L7KdbtdZClOrZhslTe5fmVxH5foe0rbJ1C3XMq8PH7eLnuORp4ivjy/AqNmSFGvTC+mnpDIuV/eb7r+myadB0cuOdknL9qQPkSXBFsPL4iuzyfzO8Bz+3CD9MTpx03aslytCtALVZR+2n7Tg6+G0SrOuSUveV5o8rPclroTI1vWJfOzePONVq1uN/6Cul0ni6G3z8Xp811fPCY7nE0cHmYX+lxtPAVzf3aF+I6tc4bdnv0D+Y4ffRQMj/Rb8Hpo1CdWI7KWXX9rzmdS4zt1Yllp1638ZyIFPA0Cp77xGDinMr9s3EGvX5g69ONzRnq/tHYVCJTBW77QmL8sekdNuX1++potA/iMT3v7TMZW3WK/TSdk6doRf1qdC5yqcjIjx9/Zhp+nKlrI1aoJ8vvx+n/RuwVtM5+p3OvCYmAtwGvAh6KttNCok2z1Xc6H8AYhD62xfAAaK3vAe4BMDc1Y6K7nG8BfkZr/f5o33cAZzGL2//G5aQtCIIgCIKw01wt6nXMTb/v1lr/7nZFuNVJ57uAdyilDgP/DFzwD2qtZzNDbY4bgAPA33nxriqlPgK8mD6TTqVUAfBN0EayzhMEQRAEQdhx+r2/ufcmnavAx7Yzwq1OOt8b/f0Vb5/G2OtqINcTYvMciP6eTe0/C1y/Qbi3Yt4DFQRBEARB2FuE2LfZevfvLX4Zs9TmD21XhFuddN6wXRm4BNLvDaiMfT4/B7zD2x4BTm13pgRBEARBEDaL7uPTmendubt8JXCXUurfAp+nV0j0hs1GuKVJp9b6st7lvESsMv4AMO/tv4beu58OrfUq5pYw0PuuqCAIgiAIwq6hyb51tjdW6fRZBN6/nRFuadKplPr2jY5rrX9/a9lJ8GXMxPOVwKejdAeBlwI/ttnIymdWyOc1zelht/b6AyNHjLKyYlTjCQWsp0Qs1gOjEqW/OtaqP9M4hXejYNTpkQrSX0f6U8vX9cTpK0ETitTUGs1OzdcoAJo3Xf8gAO/+6Gvo14Nb1SBRZkdlFRh0ZfFVqGkl6+K0jtfcrbSBYnZaXp0O0DLfvbqyCtXRxwYY8N4MTqgZPWW1U/NH9XPsE7cwVjX5GqsRtZMhHC6g95ddHL7iszmdo1i3W90e9atVtvvrChcbmvb0KovTpg0rsyuJta3L/r9GqTpw7Zmh2LZq/ua3XUe5nnQqWI7Wt8crVxZuTebJpIrTV4j6CtuDx3RCUb7RGvVpJazv0NCuhgmHgI2wSuAsBX/P+uJ2veXaYOba6zZ8em1nMI4Dvgp69HhqTXXnStHqyWOvC0Wcjj3mK7MvBZvXxWnNgXuSzhAXqzOLdXloVWOl9sjEcuRq4I1VfZwjmtM51z/9/r1RftOuEc3p2BkiP9ekeZdRBVcnlinPl/uv210NXf5tWMguu78ueOwo0aU1WXJjRfoasWWCpFNHa7KUdJDwro3O1LiXJx1dg3GbxNe9SfOPX2dW+/vmu9/s3E9miR0l2tWQ6s0N6g+ZC7W+MMLRm04AcKxxC8WGcqr35VopMU759eArt5e8+rTl9K9te267oqjMriTGgMQ5GYr3dBwX64du7XXvOgB61pDPcrzw43DjeD1gcdq0k983bBq+64JzC5icoDy/4tI3bhZxX/XLcNvI43yy8pz4O+Z7e2Jv3IRSoUJl3NXM2rebaK2/a7vj3Orj9V9ObQ9gnOvXMCP5JU06lVLDwBFv1w1KqecCTa3140qpdwJvU0o9DDyMke+3gD/aYr4FQRAEQRB2jz4rEu3BZTC3nS2tSKS13p/6DAM3Ax8FvnkTUd2OuYv56Wj7HdH3/xZt/yLwToxa/jjmX9GvuRyPTkEQBEEQhF1Db/DZJEqpKaXUHyqlFpRSLaXUvyilnr/VrCmlPqWU2r+J8z+qlMp+1JvBtq29rrV+WCn1n4E/BL7iEsN8GCMM6ndcA2+PPoIgCIIgCFc326RejyaHHwP+CXg18ATwTMy7mFvlucC/UUr1rlzR//zCRc+K2LZJZ0QXOLjNcQqCIAiCIDwl2MZ3On8MOJl69/LRrefM8Y9scEMwxabuz25VSPTa9C7M2uk/yDYbiQqCIAiCIDxluLh6fSTlvLMaOfOkeS3wt0qpP8OIrOeAd2mtf/MycrcVS8xLtqXc6p3Ou1PbGqgD9wL/9xbjvKI88vVlgmKRsVqypYsNTTvj/LGaitffTqkviw1NO1LtuTWiPTWlr9K1x8dqsHhnrIb21e5WTe8rAdMqQF9RatcCz+I9j73A5dGqOJvTOcr1vkFiIgW8pVwPE+rstDLWKoyLKTWmf56vJj29MMKYF95Xhj95w4Bbdz2NX1e2Pg/cY2Scp99QxVfO+2l3SwMEneSa87Zeiw3t1LDUkupXX91PPV4rvvXqw4l8JddX71Wa9lvD3qZj8+SXz1+TfCMlaD9sP/TXn168M+fKHL/G3e1RJ6fz79eJrxJN46tG0+ti+9/9dbDL8yucusu4MGT1Z6tYhYmEcjdrTWmLbSerQrb1t5Ey11fSl+8/5dS8rh495byNx37PUmGX66Gnuk6SVun2K4uflsV3jvD7+OK0pjKbVHLbuNN1bvPbvOs659zgr7uejiMrPT/Pdn/9oQr7PCeAjdqoXA83KFvsAOE7LbQaZgyx40WrqvH74+k35BLrz9t2TyvX033Xjd315BiTn2vSrpZdnvzy+2NWoi7qAXXfYqJR4FhCI0vkNEDCicK/tsCsV77R2uM+Z6LxyHda2Qh/TLiUc50avhq4azShXI/ouy67R+fkKYqNw65/tadXGLvPjNvtiur7m9aqBixGjgAH0ZnXRhafWr7Oxfmp5etcHps37A33dQWojEmnN0KkJ3E/RfZrhjcC34/Rw/wsxlfzV5RSq1t1EbrSlphb9enckgBJEARBEAThac3F1euHAF8wnXWXE8xdhONa67dF259WSt2CmYhuh3XltnPZ73Sq6B5wJPoRBEEQBEEQ+qBC88naH7GstV66hKjmgX9N7asBX38Z2buibPmOpVLq25VSnwVWgBWl1KxS6tu2L2uCIAiCIAhPMbbPMuljGLtKn2cBO7Fq5JbYqpDoh4GfBn4NU2gFfBXwbqVURWv9v7Yvi4IgCIIgCE8NLuFO56Xyv4CPK6XeBvwp5p3O740+e5KtPl5/M/D9qRdV/0Ip9XnMy64y6RQEQRAEQUizTSsSaa0fVEq9Hvg54Ccwy4e/RWv9nm3I5RVhq5POSeDjGfs/Hh0TBEEQBEEQUijdR72+BWWM1vovgb+87EzZPCh1jkt80K+1vriVQIqtTjpPAP8OI9H3eSNmjfQ9R6ERkCsE+HVp7YWsVVF5voW1r/AtgtqR04W12Cg2dMI6I2EpkrIpscesPY+1tyjfmzwPgIoRqI3cvOzsNbIsWKwlyOjx2D7C5KdLPbKXSFgTNTStasCozWM9ZMCzpEmX00/H2WzUQ5rTubjOKrGYzll2RPvajYKzh/EtaWw81rKjOT3s9g9cSNoF+eX047D2VdbipDqxwEB90J3v22/4dkmdqXFjvzGdZX/STVgY+VZF7emVvhYdvqUUxHYio3PGXilhRZOKP8tWJm190q4ot8+321mc1p6lkIkzy74HiGyJfBuluGxZNkAuTq8t/DazZbF99+CxOM9puyQ/ztZkqcfCxrZTqxpb5VhsPz4y30rsT9rhlFy5y4y7+j59VCXqqzVZ8uyU4OCx5HjqynbHDJ1UWfxy27SWbp+iPL+SqLsy5ru1S7P7+/addF/PsCrysfZkY/cVXR+uAlRWE3ntGVM8/Lxcis1Ofq7JeK3U17rL9uHFO9dMGbzzEu1ZWaVcjy2NErY7np2Yfz359l7tClRmey23wNbbWub1m67T1mSJ0bgb9FjAuXE1Vc52RfFDtezVndPtW725AUB9YcRdo/VGgXI95PQbTD0Va6WEPV6CO2bAqx/bb8+8+nBizFic1q4fL05v7u5YVt+GpH1WwibNSzdt6WTG0LSlVtwPmtO5KK2ZRNtUJ5bduL04rWg1bFsrRo83KWfYxrWqAeVUfdv4R497fWpmiNtGHme2eisA33/tvXzz9C3mWJY/4m7Q5/H6ZlckukK8xfs+Afw48LfAJ6J9LwJehXnFctNsddL5k8B7lVJHMe90auAlwFdjJqOCIAiCIAhCmm1aBvNKoLX+PftdKfU+4Ce01r/mnfIrSqkfBF7BFl6l3JJ6XWv9PuCFQAN4HfCG6PtXaq0/sJU4BUEQBEEQnurYx+tZnz3Gq4C/ydj/t5hJ56bZ1J1OpZT/5OFh4D9lnXOJ/lKCIAiCIAhPLy6+DOZeYQF4PfA/UvtfFx3bNJt9vL7IpVVL9jpwgiAIgiAIT2OU7mOZtPcmnT8J/JZS6mXE73TeAXwt8B+2EuFmJ50v974r4K+jhOeyTxcEQRAEQRAcV8mdTq317yqlasAPYV6jVJgVkL5Ka/3AVuLc1KRTa/0Rf1sp1QXu11o/spXEd4v6wghgFHTtiqJY71X/WaU6EB3XCbVjWvmYFbY1WYoV73UF9ZIL5ysr6wsjjDU07UjRWG8UEurzNFZNmFajlu9dAcobhtn4mFEiOmVodch9z881aU5fx/T4WZdnq8C0KvC2ERxTvbnBeq3qwjmFaqNAeb7VV6mbVrlbEnGklLD1hRGOePm1Sn2AfGuQwSfX4jhmhpyq2W9fU8Zk2ovR92KtBJxP7PdV77Z+GjND4KnV43hxeTN/gyjtWIVtFc9jNVMHNlx53qrPAbSrn7Gaol4ZYWIDKaZVyo7dV3RnJdwIvHYFMpXrVjnbOXnKqGqj88teHk/8QI6x+4Zc/bjyTpYoM+7y7KthR4/PUWY8ofSO68nk8ch7Y9W6rYNn/EFzw35sGasp57Zg8hUf60yN96h2s0g7DPj1M3p8zinY7TFf6W/b1obJcmuwfdqGM3U9lTyOUcqPHp+jOlEEYLlSwv4y1RdGKNZKtKo6Sifu15XZlUT9FhvaU43H+dgIm3/ftcM/llAWb3RNN1TkDGK2fdW/7xKSdsuwrh3jtS6tyZJT7fvuA61qQLFWcmOPr3JPXH9R/mzaft2knQQ6U+MJdxKI3RTG0uP+/bOmjt54B1RWE78tLkxN0ZwO4vHSU+b74629PpZS44ifT4t1K4m/96dz8pT5EsXrK/9HU7eL0nmxOGW/p1yPy5I8z/YXX+Wen2vSmokV+Ot+HPWAyqwZY0/dNdzjCGDLV653E84HS7dPufQT41c9jF1WgP999i4XR2uEPcE2msNfcaLJ5Zu2K77LXntdEARBEARBuDSupkmnUioAjgDXkBKfa62PbTY+mXQKgiAIgiDsEFfLpFMpdQfwR8D1mEfrPpot6He2Y9K5x95CEARBEARB2KNcJe90Au8GjgOvAebZhhxu1jLp/aldReDdSqkL/k6t9RsuN2OCIAiCIAhPNa6WO53ATcA3aK1PbFeEm73T+WRq+w+3KyOCIAiCIAhPda6iSecDmPc5d2fSqbX+ru1KGCBaRvNHgOcDk8DrtdZ3e8d/F/iOVLAHtNZ3bDat1UpIUAwpNpRbE/dN1z/Iuz/6mouGHa91nRLWYlVzG62hXb53xSlWD917vket69YuzliH1yoAfeWtC+epD+13q86LlYy9qsJRL/yT3vf0mtlZ64n75QKy1w626nug2KMOByq5i64x7efXhffVjJEC3q/LdJy2Xq1yHYy60Ves2zXc+6Xt1k2u9lkjOcKt/Z1RLn+N42Q/ict24o1lt365WYM8VqieumvY1XmxoRLK0oGJ5b7renemxjn4fqvwj5X4Ng2Tp5TqOqXoNv3dqF1HgSV7Hsk0x+4rJtZU98vur0lenk+tuc3GjgpWsX7o3vNujfZ0/LasYNTIvvIZcOHS66SfefVhwDgH+OtN+3WQzpu/jrpVwDvl/8lT8PoXenH0rvkNZszw8wEk1oT31fA+nalx1u+O+nvFc4ugQLGhXf1nXVv9FPi+4tm/5kePz7nz7PiStZ67dTEAaEcKepuPxsxQ3NaTU1BZ7Qlr85NwPPD6/6m7hl37taoBldkLFOvD0bZ2a21XonPteGxdKnrqISqjf72m15T3XUfS+A4dxx4+AsCR+RWn0B6rKQZet+zU6/WFkR5XD3+82cj9xB/fnfIc02ftdnu6CjVzjY/Xun3Xs+9Mjff9kU9f8+l+569n785Jrb3erqjM6zPO63Uubt9R4ivHzzJbrfbJWVJFb10MWtWAsqsP+7uQMe5WAwa87dtGHudY1ay9Xji1pUUYt5+r5/H6rwK/pJQ6AHyWpPEAWuvZzUa420KiIeAzwO8A7+tzzt8A/mR3rc95giAIgiAIe5p+S17uQXN4Oy/7bW+fxoiKdk1ItGW01vcA9wAolX3XCVjVWp+51DiVUgXAvz21R5y5BEEQBEF4unMVPV6/Ybsj3CP3mjfkZUqpJ5RSX1RK/aZS6pqLnP9WzLun9nNq49MFQRAEQRB2CL3BZ4sopd6qlNJKqXduRxYBtNaPbfTZSpy7/Xj9YtwD/BnwGGbG/dPAvUqp52utV/uE+TngHd72CDLxFARBEARhD7Dda68rpV4AfC+w6XcsM+J6LXCP1no9+t4XrfUHNxv/np50aq3f621+Til1HDMBfQ2Qtm+yYVYBNyHd4LG9IAiCIAjCjnIJj9dHUnOX1X432pRSw8B7gO8Bfnwbsnc3cAB4Ivrejy2903k1PF53aK3nMZPOm3Y7L4IgCIIgCJvFTjqzPhGnSL4m+NYNovt14K+01v+wHXnTWgda6ye87/0+m55wwh6/05lGKTWB8Y6Y32oc5XpI86PmtdBPjV9HsaETVjrWquH00bKzvEhba6QtIprTOWfh0Zkad+FOvLHMWM3cL0/bcLSqAYvT8b10YzdirD78/Vlk2dy0qyGtyRLf96a/AuDd73lNwm4pYU0yWaK+YPrLProJe4p0Pn2Ll2I9oNa8FjAWIrHth6lD38rFD+8sY2o58NLzrVjKT6wz+ORaX0ulOI9lY+USladdKzF6/HETX2SF49IuD6Cjpsu3OqhufFHrvKZbNAdX9xdYL5s+MNDSdIuKztpAFE4R5s151u7Dt+LJymN+rgm3TyUteDwbFJ+xmqI834q2kseKDU07chUp18OesL4tTdruxObVt2Yp18OevuzneSMLJr8svq3O4p1tDtxj8rHkldnaFPn2OOn4s+rEtqtvgWNtl/zwaQucA17+L2bL5V/v4F/fpdjCaHIqsvDpDe9blQEJC5k0fv6hlLQL8uI48+rDseVTRr+xdkTWSsovi7NfmoNWNb4Gets7tm6ydWCvT9+6yU/bv9Z8a6XOyVPOyuz00TLFhorHpXro2qI8v0KxNgyc7ylbT3reGFVsaK8vmTr3+7K1DmrdPtUzHmVbNRcAACAASURBVB+452R8XqrPJMqWYQUFpn83p6+L8+Hlqzmdc3ZqUE7k/fRCrFmtTixTn47jHLuvyOKdbfc9q62zsH3Ltr/f1+w13q6GlD+QtDLyrdH6xp2yMEuPAfF1EvRaiEVpFRvapTVKL9byrTVZojmdc21Ya14bWwZOJ63s/Hy1ZpJjul/GpX6/FfUw4evzqeXrMs/bVS5umXQIWPaO9LvL+U3AbcALtjF3KKWObKchvM+uTjqj28JHvF03KKWeCzSjz9sxkv154BnAzwIN4AM7mlFBEARBEIRt4BIery9rrZc2jEOpw8AvA1+jtW5vcxa/qJSaA/7JfrTWj25HxLt9p/N2TIEsVgD0e8D3A88Bvh0Yw0w8/wl4o9ba/w9AEARBEAThqkCFGhX23urM2rcBzweuAf7Ze/8zBxxVSv0gUNBaZzv3X5yXRp+XAb8GFJVSjwP3Ek9Cex+PXAK77dP5YYzJaD9etUNZEQRBEARBuPJsz4pE/4i5MefzO8AXgF+4jAknWuv7gPuA/66UGgBehJmAvgz4ZqCglDqhtb55s3Hv9p1OQRAEQRCEpw3bYQ4fPfH9XCK8UheABa3157JDbR6t9TpwTCn1IPAJzM3A7yH5auQlI5NOQRAEQRCEHeJqWJFIKVUEXgy8HHOH8wXAl4GPYF5//MhW4n36TDrHV6GsKM93Of2GpNLfV0VaDh7TnD5qe0BAuxo6dXYWCdVfpNgr1gMW74wUmO8fNApvLy2n9q6GNGaGevb3I0uZWKwHlOfP857HjIjNV3S2K4rKbEol2TAK89ZknJ9WNXAqSbPdq+6sW4VmNaTYMG9GGBVirHBsNeJyc6+XR0+R6ucNYDS1tkFaLemXE5JPDZxSNqXWzbfW6ZQH4u2V5EWt4uZ134OORoVwoWXqp7Ck0JF6/dC95zlRLWOfgZx4Y5kjnpNsQnXpKZ/zc02Wbp9K5NnWc2uy5BTJaRVuuR7Srpi+Wp4/7/Y3ZoZYfKjCvpP3mzhefTjRF1qTJQ7de959X2xYNwHdt17T+KrUfgpuw1rm/vL8So8a1q8ff79Jx1Owb6AsTuO7K/hspApOqKCnxhN9PqF29hwDyownVOJLt08xGr3R1Dl5CqL27ef+kJWnxswQleh7ZfZCQtlusXUYl2/Y5X+8ZsYo/3y/LGl81wjb16y63q/HRF1Oxv02ocS/Y8aVdaymKNfDhBuBPWZU8cOZjhvpuvIV9BWSfcmE6+9KcDEVeJoshwN7zUDcR9yY5o0rdgycmKs7Fb1tfzuu1huxg0exHtCuxMfS+dgo7y7+yGWgfL/ZLtauc214uqp6HBRsvtPXhX+tjXpv5LUmS5QZT7SbVfD75A8fivMELE4fYqzmqc9TzhD2t6w8fz5xTdcXRjiYOsd+T/b3uC0qsxfonDyVqeAvf2AW7phxZRzw8jJVXHTnh3tkxnOl1l7XWr/s8mKI8qHURzCTzC8Bx4BfBT6itT57uXHvkSYQBEEQBEF4GtBnRaLLWQZzm3kxsXj7w8AxrXVjOyK+qszhBUEQBEEQrmasej3rs0cYwyyr2QJ+DJhTSn1WKfVrSqlvUEpVtxqx3OkUBEEQBEHYKbZHvX7F0FpfAP4m+qCUGgFegnm/80eB9yilHtZa37rZuGXSKQiCIAiCsEOoLqiM58xqyyZHV5wLxIv2nAM6wPSGIfogk05BEARBEIQdYpvM4a8YSqkAs3jPyzB3N78Ks063XaXoB0gu7HPJPG0mnYUvlsgVirQmuxCprm+77XFmSd4dtuq9VjWgWI+VeNWbG9St1rSWUmF6qrz8XBMixbG/BnmrGingG8l1ZgGorALFHuWfDZeVv35Mjxtx2Wy12qNUtqrD5rddB5hjzemcU0Ha9K1ysH3XcI+itlgz6benV4BiIo+JvDqlZitzrfV++GuBn4nWUi/Pk1DvWmW0YSoVNl5HOr3Wd7CmCTrxtuqYC1wHyXpXHU2nZS6N4eX4eGuyFLXVIABH3tty+YjX147yUk+uIewre9PrqPtKcx9TjlgV7dS9M0MU64FTcSba+f5ZeP0LE+mO1Yai73F+H/226xJq3URd3T8LMy/KVEK78kblGbtvyCnzN1Lh+gr+rHW+7b4zKSV+j/p9sr9K3DpAWCV4lnK+MzXeV9nbOXnKKWBtefpdb+m117PWkbdp+Pmw12AeYObSFPpZSl6w11vyRyqhGveU5746vuwpptNK4UQeo/7lr62dpbB364LbeCanEvFD/77Rr47zc02nrj7z6sNUZi+4PpJ2gkiOB73x2DDpfmfHOzfmT8aqbt9doh+dqfHED2h1Ypm6t+2PlcVayVNpZ08smtO5xFjXmjnsVndPOAdEcZ6uRmNsZdXlByLnjKiP97onxOXx2962g99uvmrctXtKPe4r102/SSre7W9cfq5JO3LpAKLfB504x5Kfa3oK+cOJ30VfPV+eGnflyR8+hB3a02uvj+cvEKxHv22bMzm4YlwFlkmLmEnmPEZI9MOYVYi+dLkRP20mnYIgCIIgCLvNlbJM2kZ+BDPJ/OJ2RyyTTkEQBEEQhB1irz9e11r/xpWKWyadgiAIgiAIO8RV8Hj9iiGTTkEQBEEQhJ0i1OaTtf8pjkw6BUEQBEEQdgjVZ0WiPfRO5xVDJp2CIAiCIAg7xF5/p/NK8rSZdK5WQoJiCDWcdcV7HntBwm7Gt4xoVYcS9iT1hRFnTdGqFhO2LpCyX2nEVhZHbzoBwOx9t1KsB6yPmEP7HumyeOeaC7I4rZ1Fk59uuR6yOK3ofDmyfGjrHiskP8yxh48AZg2rNMHQkDu3XY3D+VZHrcmSs1oZr3Wd5Ub+zCLFxlBsX9EosDht0jx4TCdsOlrVwNVVmA8IOqEri28h066oRFkBZ3tzYK6ZsHBK2x/5ViyJuvdsYrrFfOIizq1BfiXeTtgnhfavsVVSF8ylUXgy7BkI+lky9SNhTcJ4ojytyRKrEwVXFsBYFgEcPpRIy4ZxfWV9PRnOx8Zxx0yiHtcPT2SeF8x8BcE5Y6HUIWn5lLbV8bcHbygnbK78co3O9doSbVRHlvxcM2EnpNZjx+SNbJlc/4vSDlrm+gpnvwCezUvfOFJ2Sf7fSy3HhvHbNiG2bvKtaGwe/Xpcun2K0c+cpfPIo1HIw4nrv1xP5s3a6nSmxjMtcvrhrvO5Jvmpg4myZFmd+WOlDceAuWZa1cCNj/m5Ju3pHNwbbzs7nMj6KNeO7X2yLJnS9j/l+RXXVuX5FU7dNcz172sBoFrtRFk2KrMde3JrId3BlFO3bavIfsxeQ8/4g8f5ws/2rgAYdDTT42fN7wRw9KYTzN5n7PjajQLtasjRF30egE9+8DmUPW8la8s0uJSRyaicndQ2xL9jXLeWCJL1+wBJyzG7beunMTNEZfZCwnIrvG04PmYDnTzVY5vkU2xoVvdH483hQy6P1uYqHu8DwFzXYzXl2n3g/DBp0pZKltZkKWHnZdGpptyXaxFEHkoD5/fIpG6Pr0h0JXnaTDoFQRAEQRB2G9XVqIxn6ar71J91ZizEJAiCIAiCIFwJ7OP1rM+m4lHqrUqpB5VSy0qpJ5RSdyulbr5C2d4WZNIpCIIgCIKwQ2zXpBN4KfDrwB3AKzFPr/9OKXVpy53tAvJ4XRAEQRAEYafQ2nyy9m8qGv21/rZS6ruAJ4DnA8e2nsErx67e6byUW8NKqYJS6leVUg2l1AWl1AeVUtlvMQuCIAiCIOxhVFf3/USMKKVGvU/hEqPeF/29dOXjDrPbdzrtreEHo7z8DObW8LO11heic94JfB3wTcAC8EvAXyqlnq+17mbEeVHa00Z1+ZXjZ5mtVvuq/Xx1drsWq0DblVhxWJ5f6Q0fKbeLtRLcFO8uNjRr+1RPOtWJZerA+LFBAE4fVYzVes8DEkrz0eNNpwrMIlaXlsjPNbmUxQ5Gj8+5OH0VKu1Vp6QHTzkJtKpJRb1TuAM6H4BVr8+vJNSkvnJ9fTjH4JPZeVq6fcqVJa1ktEple56PDhTBWtxF8iuafDtO0yrWw7yKH2tEK0XkW1E5FzvoQLn8F2vDCXVsFp2pcVN3GyhnnUr/npO0nn0gLsvxOadi9svN/bNOuWr6hk7EVZk1l0s+UrxbtavJo1GEdk6eQkV5OnSvUarbOLWnEM8fPkT5Aw+4fLQmS04Vzf2z4JWrsNh11wKTUz2qb6tUzntKfNtO7lxPxW1V67Y8kFSvZ6mbbR7TqNWOS9sP74f1FdPp9kyr9tNkKaPL8yuJsSHBHTNx/tNxWEV3Fq2VhGrZjgHleujqtB+++4HNV1pd7h8rM07+zKIJ8+jjtDYYX/w+DMB6x+XLj69YKwHn4/J6ZW1Nlsi3sofxrDbNYryWHb5z8pTLf6saJBTO5fkVTh8tA1A8F9DeHyTGsLQ62447pu/GanHbfp2iota81u0/9vARiH4/ivWAdjXk2CduAWDMcwzx01wfSo5nkPydSTqkHHLlXn/JauLcVjWI2wTvesNT/2fQ40gQjZWtA9m/RWB+I91vVdRHfVcQH3+897+3Kyqux4wm939rOl7fSbsz+P2lvjBCMfo+kT+P6pg85pJC/13jEiyT0gPCTwFv3zBOpRTwDuCjWuvPXX4urwy7Oum82K1hpdQ+4N8D36a1/ofonG8FTgKvAP52Z3MsCIIgCIJwGVz88foh/v/27j9IkrO+7/j7OzP7e/d+r3Q/JYitUs5IihzZIDASIJcccCoG4aQgwTbgVNmBxE6sVEFIpVy4UjapBDskAhMXJpGD4xLYpuxAgiPbGIEjiUSi4CQkix8lyN3enXSnvTvt7e786O5v/ni6e3p6Zvb27J2du5nPq2rqbvrn099+ZubZ7v4+D6wU5jQ2sdUPA7cAr/4rlm6ghn2ls6x8afg2YAJ4MFvA3U+a2ZPAq+jR6EwvQxcvRS8MpqgiIiIil8cS79k9UuFK54q79+q5tff2zO4Dfgy40903uG0yfFdM9nqfS8P7gaa7nyst/lw6r5f3ARcKryv6BIiIiMgYSRySpMfrsrtMMjP7MPBm4C53f3Yg5d1CV0yjk/al4b+/iWU7H2rr9AHCFdPspaQjERERuTIkG7wuz0eAnwD+AbBiZvvT1+YeiB6CK+L2+gaXhk8Dk2a2u3S18xrg4V7bcvcGhecfwgVUERERkeGzJMGsu4VpyWW3Ot+V/vuF0vR3Avdf7sa2w1Abnekt9fuAe4DX9rg0/DjQInR6+ql0nQPATcB7LmdfO58xqpPpBdJ0bPQvnn0ZB4vjGBcyTyFkrUPIBqwfXefgp0N2+fLRanuM9nT812Jm5OLe8PzvCt1/bCRpxNcWK+0x2tPll49W0/3GrC0WMqbPzJPUOsdj7yVkN4Zxgev7LM8uBZg9tIfqxdV8Xnah+PxRz48TKkSH9nDyzqyh7uxLhyBOVi6ytljJx4evH11Ps1Lbipn12bFVmqXxmenOMgdozYZ1s6zl6NCejgzH7Lz0y+ovliHLGk8mjNpaISO17vl5s2iW6XPteVnG5dS5BvF0hbWVcC6mXmgQzU507iMbn3ipdwZzOeu2OJ79jseWurKns+z45aNVdjzWztCc7cgqLmSynklYW6zgje5UzOjQnq4xvnO339KVWZ29nzux1l4n3X8+jnqxF4NNysdeL2QBZ+c9j0FpfOvy+pmFpwpl2+S42kC4XVXSkan96Ak4tKedEVvIEO+1Xlbe2pHDHfvfqCzR8RMd2f6ZrKeCfBt9xrKePbUeznMWq1temfcOse/Yet/xyrs8eoy1n3ll/rZfVn5taRmfnuzYf5YhfKlsfqqVfJ0sxsXeE/L3fY61fAzFzPuNlLOx8zIeOdzRg0dZlhWd1JKu79TTbzgChO+jE3fNc/CLIVN8bbGSf7+HT2XQmq3wwgsLhXnQ+lLIn67vC/vKvs+yz29ZlG6unaF9JP9N2PFY/7jvnKp3xKjYe0h0/EQ+xn1tabndC0VJ+fjD91LfXRbKeLhr3Wycc2hnqZ+80zj4xd7HXdSa7397ee3ADDuOHO7IYC+Ovd7xnXa2ndZxvLmXanoZqth7yVAlSbv7lPL0y+DuV91VtWFf6fwI4bLwG0kvDafTL7j7urtfMLOPA79qZi8QEow+CDwB/MlQSiwiIiLyl5UQHhLsNX3EDbvRuZlLw79A6NbuU4Q/V/8UeMdfto9OERERkWHZwtvrV51h99N5yUvD7l4Hfi59iYiIiFy94j5ZQ7EanSIiIiKyVTzp/fymq9EpIiIiIlslcXr2+niZ/XRejcam0XnhRqcy7ew7ts7ijSEbduXPrwHiQkZdZ4Zjlj158t7ObMvps96RBby22M4Sjw7t4W3XPwTApz72ep5+9bUd62Zjv+47tsr5O0Jm4pk067F1djpfLstAXDsww/RZpxJllbHziYRidmZ0aE9H1l6+TDq2te/bHba9mOTjwwMsH53Jp+871s7qrC+2x8RO4pjpcwlJLZS5NREzezqUKZ6Exo5KngFuMXiabW+JU18MxzV/ugL1BhMrYcHK7klWD4bj2fnthHi6RrUe5q0emmH+VHhstzlfYf54HYCLB2Zp7G0f49yJtY7xj8P+w76rjSTPDPeKkdSgsbu9bms2XT6BajOs09g9RXO+kn8fJLUK8UyIR20tnPt8XPFiJnRhXPnZU+vseKwzozkfa7mwDoSM0gvfE+rX9FnvGJu6mG1azG7OMopt50Jaxs46Ud29G+I0dtO1PJu0NT8Rjg2o7p1i8lwzr1deNZK9oQ+G6vpe/OJqPh57NGd5Bv/0oYM0pmvY/lCXKq2EeLo7I3X21DrR8RN5uaNDezrHdV5aJipki5czlIuZqLZW71omy6QuxjzrrSHLprU+t6q6sp2zMhamFccOz97vKJyDflnjtaVl1m45ku6n5+7zMpSzwXtl0a8dmGHhW5WO85+NA55lwBf1G/e9X3Z8VoeLy2dxq8zN0ZydyLOYvWL4bPgsJ7V28q3vmIMoxqd6/5wUe6HI9tlLuRz9egfo1XOANbrHQy9n2hfjXezZI540ohlj7lSoAX5xlWv/7Lnw/5lJojmnvjsEYf+DS0z9RChjY6nO+o3h+725M3wGzzyzL2x0XyMfe33X02FSO6u8c5z3/JhvDVn+vc51ub5Pn6mwfDQss3x+J4fp8VtA5w98+VwXe1ZYWyyNPb+0TPTyUJ5orn3+KnNzJKurVOZC/LzmzH/7Qojj/FQ6Fnu6cLVCfW865nkdVo5UqK2Fba0vGgvHk/QYLf9e9lpan1KtBcv339hRIVk+h02E3hVOv3yePU+F37Hztx9i5kz4oqvvrpAstFibS39Dk0laC9k2rpBk7yQGeqSlJKOfqjI2jU4RERGRoYuT3rfSlUgkIiIiIlvGAe9xK330766r0SkiIiKybeIYevX6qNvrIiIiIrJlkj5dJun2uoiIiIhsGWWvi4iIiMigeRLTa1DFcRhocWwanfv/t1ObcM7eMsf5Z2YB2JV2f5N1bVLsymb2TMJ3fvI6AKbPAGdm8i4qgnY3S/uOrXZ06fGVlbDe8tEqC6VyTKx0/yWzuHcl32dZ1iVT1qVPucukrMxri5W066bu7WddZCS75tLjqVBPe/bY9aVpzh9N8um1pWXqi7Nd27CZGS4erubvbzp4im/uvQGA/Y+s8fwPzpKktckS8Im0y6RWTGs2jVWlArMzTFwMXVvMLxnLN4eV4uNGpRl3dGn04vVh3s5vt1i5Pu1OqgLRdIXJC6F7k9bCJMlEiEno/sewRth+tWp5l0kQuhbytHuhSsPyWMWTlsc3n5/2n5NMVmnsCMc99UJXWLq6psn06xamvc5MvlxjZ4hPc1dpG4UuTYrO3jLH7JmEeG+7dmXdRAHY1CS+lp7zmlFt945FK+2NZPJi2GelmeTrJ9Mh3rWZGYgTLO2+qtKCeCo9h1OTJDUjngoxaXfl1am2tAyFrl/K3d7kywAcOJRPmz2TdB93q9VzH13dLKWfn/ycXOJWVe3IYV48MMOOQq9D/boc2sz+y9NqS8tEdH6vFPdV7vqnV9dNADY/t+FIJcV95uV+9Bhr97yiY3/5MqWucThwKC9jcXmbmcZKV16s1S5dpXBafGaSaD50ZVOJusta7L6oGA8IXZsBTF5odnRD1atMvbYH9I1Pttwse3rWwcz0uST/7NvunTSvDd2HtXZOEE85e54K3exdvHk/T/1FiMnRHS+yeiAcc3MHobu6Qld03/vJsM6Ju+aZPut590fhtyPtQq1wLrJ4Zl1nnb9jkemn0+/3Uj2dPuv5ukvXLgBrPY+r3FVZtq0Qk7Z9x1a7zkuUvp14sdBl2sJ86DJpIXSnxI4W64fC99DcM2eYfn6O2nqIT/PwHmqhtzMaN9WZ+OY0UdpNXTIB528I20ymI2rrcbqvCeyF81T37QWgtupUG9k6DnGMTYb1LCL/LalETqUZthFPwtw3J1k7GOIzYTHV9bT7pGuvkC6T4jj0LVimRqeIiIiIbBnvc3u9V0b7iOnu1VlEREREBsLjuO/rcpnZu83sWTOrm9njZnbHAIq8ZdToFBEREdkucRJusXe9Li973czeAnwI+GXg+4EvAZ8zs+u2vtBbQ41OERERkW2yhVc67wU+7u6/6e5Pu/s/A44D79ryQm+RsXmmM26FJ5rjZoWkniaQNI0oqufLRK3Oh4zjRpV+4jTxJGolHdsAaF5spuvXiVcb6fKdy0RRnWQtbD+eDstYq71Mtu8oqhM3q0StJC9/9v8oaRAVjgtoH1uj0i5jVIekQRynyzYmSNay9TrXiZIGSb0QkySUzb1J3GhPb62230dRnbhR6ejXNlkPCQdR3CBqTXRsK4tXFCUk9TSRKD0X+bG1KsTNNAatVv7/cPwxlShs3xJIzNLlqmF+HPaTRJ1jr8ct8u0n9WoenySxfLq3nLhZJU4fXo+iBnE2Bnl2Lkrn+1KiluXnJz/2luXv42Yag0Y63wsZGtm+Suc6aiX5ccbNOlHcLpMnTdyb7e03snrQIG5m8Q31NorCsVXjBu6W78u9GQqUbT9N5omSBlFUx9P1nHA+8uIWytsVhz7zola9Iy6Rt/LjjlpGlDQ71++jGFOSBlj6N3UhoahjG2lMi/WyYxulbUeFcm9UlrzeJg0ib+XnLZtWLGOvmJS/k6KkkR9D3Kznn9eo5fn56JLut1jm7DsoaiWdx1IuX/pB9qSZxqRd/y0rf6vertNxA69UidIEokqUtOOYNEL96bM/gGoU6pZHUR6HjnNRPsZy/cnOdzl+5WmF7RQ/43HTiVpJnhRX3F/Uiknq1cJ7SNJEmShu5DGNG0ZST2CtuM80saVRI2567+/mwu9H3KgRteJQ/4Fkrd7+HmrFHeUq/iYk692/Y/3qZ/E3rvg561ovaXR830dRel6SZlgv/Uwm63WiVru+x416x+9O3Ki0j6UJcZqsFTfyrxeS9Ygo/T6PG2EfnqTvm3U8ScvcdCJvglfzeXk8ohqVLN7NSjgf6+nY7hej/FiS1sbfIdullTTxHs90RuTf/QtmHe2Rhrt3VHwzmwRuA/5NaTMPAq/assJuMfMRf3DVzA4BJ4ZdDhEREbkiHHb3Hv0iDJaZTQPPAvs3WOwiMF+a9kvu/v7Stg4CS8APufvDhen/Eni7u9+4JYXeYuNwpfMk8NeBvwAOAyvDLc4VZYHQIFdcuik2/Sk2vSku/Sk2/Sk2/Q0iNguEdsG2c/e6mb0UmLzMVbtvHRU2W3pvPaZdMUa+0enubmZpT5ysuPuLQy3QFaRw+V5xKVFs+lNselNc+lNs+lNs+htQbIYaY3evA1txn/8sENN91fQa4Lkt2P5AKJFIRERE5Cri4cH9x4G7S7PuBh7uXuPKMPJXOkVERERG0K8BnzCzx4BHgJ8BrgP+01BLtYFxaXQ2gF9i4+cixpHi0p9i059i05vi0p9i059i059iswF3/6SZ7QV+ETgAPAn8qLt/d7gl62/ks9dFREREZPj0TKeIiIiIDJwanSIiIiIycGp0ioiIiMjAqdEpIiIiIgM38o1OM3u3mT1rZnUze9zM7hh2mbabmb3fzLz0Ol2Yb+kyJ81s3cy+YGYvG2aZB8HM7jSzz6TH6Wb2ptL8S8bBzHab2SfM7EL6+oSZ7dreI9l6m4jN/T3q0KOlZabM7D4zO2tmq2b2383s8PYeydYys/eZ2f81sxUze97M/sDMbiwtc8njNrPr0viupsv9x3Ts5KvWJmPzhR715oHSMiP3mTKzd5nZMTN7MX09YmZvKMwf1zpzqbiMZX0ZJyPd6DSztwAfAn4Z+H7gS8DnzOy6oRZsOL5O6FIhe91cmPce4F7gnwA/CJwG/tjMFra7kAM2B3yNcJy9bCYOvwPcCrw+fd0KfGJQBd5Gl4oNwB/RWYd+tDT/Q8A9wFuBVxPGD/6smVW3vLTb5zXAR4DbCZ0u14AHzWyusMyGx53++z8IMX51utyPA7+6TccwKJuJDcDH6Kw3P1uaP4qfqRPAvwB+IH19HvjDwh+x41pnLhUXGM/6Mj7cfWRfwJeBj5amPQ18YNhl2+Y4vB/4ap95BpwC3luYNgWcB3522GUfYEwceNPlxAE4mq73isIyt6fTbhz2MQ0qNum0+4E/2GCdnUATeEth2kHCMG1/a9jHtIWxWUzjc+dmjxt4Q/r+YGGZtxKGwtsx7GMaVGzSaV8APrTBOmPxmUqPaxn4h6ozveOi+jIer5G90pnehrgNeLA060HgVdtfoqG7Ib11+qyZPWBmfy2d/lLC2K15nNy9ATzEeMVpM3F4JXDB3b9cWOZR4ALjEavXprdRv2FmHzOzawrzbgMm6IzfSUJnxaMUm53pv8vpv5s57lcCT6bTM/+L8EfNbQMt7fYqxybztvT28NfN7IOlOwcj/5kys6qZvZVw1fIRVGeAnnHJjHV9GXWjPCLRPqBK98D3zxEaF+Pky8BPAd8ArgX+FfBwGP2j6wAABj5JREFUeksji0WvOF2/bSUcvs3EYT/wfI91n2f069TngN8FvktooP9r4PNmdlvaON8PNN39XGm9kfm8mZkRhp37c3d/Mp28mePeT6leufs5M2sy2rEB+G/As4RHVW4CPgD8DdrjRY/sZ8rMbiY0pqaBi8A97v6Umd3KGNeZfnFJZ49tfRkXo9zozJSHXLIe00aau3+u8PYJM3sE+DbwdiBLBhn7OKUuFYdeMRn5WLn7Jwtvn7Qw1u93gb8NfHqDVUcpNh8GbiE8Y3cp41ZvesbG3T9WePukmX0TeMzM/qa7fyVbrMf2RiE2zxCeN9xFeB7zt8zsNRssPy51pmdc3P2pMa8vY2Fkb68DZwnPxJT/+rmG7qtZY8XdV4EngBsIf1GC4rSZOJwmXCkuW2S8YoW7nyI0Om9IJ50GJs1sd2nRkahHZnYf8GPA69z9RGHWZo77NKV6lS4/wWjHppevAC06681Ifqbcvenu33L3x9z9fYREvX/KmNeZDeLSy9jUl3Exso1Od28Cj9O+LJ+5G3h4+0t05TCzKcID2ado38q4uzB/kpCZOk5x2kwcHgF2mtnLC8u8gvAs2zjFCjPbCxwh1CEIn7UWnfE7QLhFdtXGxoIPA28G7nL3Z0uLbOa4HwFuSqdnfgRopOtflTYRm15eRmg4ZfVmnD5TRngmc2zrTB9ZXHoZ5/oymoadyTTIF/AWQpbgTxMaWf+e8AzJ9cMu2zbH4YOExtNLgVcAnwFezOIAvJeQpX0P4Yvvd4CTwMKwy77FcZgn3Na5lXAr5hfS/1+32TgQnm38GiFj8nbgGPCZYR/bIGOTzvsg4SH+lwCvJXzBnyjF5qPAceCHCV2U/SnwVaA67OP7K8Tl19M68RrClafsNbPZ4yY8W/4E8Cfp/B9Ol79v2Mc3yNgA3wP8IqFrnJcQuth6mnD1qlrYzsh9poBfAe5Ij/tmQrd9MXD3mNeZvnEZ5/oyTq+hF2DgBwjvBr5D+y/EO4ddpiHE4AFC46kJLAG/D3xfYb4RulU6ReiS4yHgpmGXewBxeC2hQVV+3b/ZOAB7gN8mNNpfTP+/a9jHNsjYADOEzNnn0zr03XT6kdI2poH7gBeANcIfN0eGcTxbGJdeMXHgHZdz3ITG+2fT+S+ky08N+/gGGRvClfCH0uNtAN8C/gOwp7SdkftMAR8v/O48T2g83q060z8u41xfxull6UkUERERERmYkX2mU0RERESuHGp0ioiIiMjAqdEpIiIiIgOnRqeIiIiIDJwanSIiIiIycGp0ioiIiMjAqdEpIiIiIgOnRqeIiIiIDJwanSIiIiIycGp0ishVyczuNzNPXy0ze87M/tjMftrM9N0mInKF0ReziFzN/gg4ALwEeAPwZ4Txmj9rZrUhlktERErU6BSRq1nD3U+7+5K7f8XdfwV4I6EB+g4AM7vXzJ4ws1UzO25mv25m8+m8OTN70cz+bnGjZvZ30uUXtvuARERGlRqdIjJS3P3zwNeAN6eTEuDngZuAtwN3Af82XXYVeAB4Z2kz7wR+z91XtqPMIiLjwNx92GUQEblsZnY/sMvd39Rj3gPALe7+fT3m/T3go+6+L33/cuBh4Dp3P2lm+4CTwN3u/tAgj0FEZJzoSqeIjCIDHMDMXpcmGC2Z2QrwX4G9ZjYH4O7/B/g68FPpuj8J/D/gi9tfbBGR0aVGp4iMoqPAs2Z2PfA/gSeBHwduA/5xusxEYfnfpH2L/Z3Af3HdBhIR2VJqdIrISDGzu4Cbgd8HfgCoAf/c3R91928AB3us9tvAdWb288DLgN/arvKKiIwLdSkiIlezKTPbD1SBa4HXA+8DPku4jX4z4Xvu58zsM8APAf+ovBF3P2dmnwb+HfCgu5/YpvKLiIwNXekUkavZ64FTwHcIfXa+jpCp/kZ3j939q8C9wHsJt9jfRmiU9vJxYBL4zwMus4jIWFL2uogIYGZvI3Qsf9Ddm8Muj4jIqNHtdREZa2Y2C7yUcAX0N9TgFBEZDN1eF5Fx9x7gq8BzwAeGXBYRkZGl2+siIiIiMnC60ikiIiIiA6dGp4iIiIgMnBqdIiIiIjJwanSKiIiIyMCp0SkiIiIiA6dGp4iIiIgMnBqdIiIiIjJwanSKiIiIyMD9f8cpSugUV3RIAAAAAElFTkSuQmCC\n", + "image/png": 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qndU2drzZqK/k5xqsTk8AxrrKjg1H73s2ByZ0wp7K2qA1X3WI0slZyKhT2lLNH7NK8yuxbdjCecJSP2o94yu003G2RHab0k7R9Jf1wdi6qlQLaU+OUajruN5eMs1KwFg0/ljroTTW/mojnF3V6cUNbcHsPeO3S6Gu6V/qsBZtyZlrrtOcMOP28muXuXD/VYz9c9vVNWwZu8O+5aQYJuzTVG401nu142U3BjcnNKN3J8dn2x62fcBch4F9yXF7tBrZO02FXfXy0yieybZgNPkXe57bDME6rIyb8g893CKYPEB77hQAqq1Zqhs7xonrDpBfXqVvOdrqOYCBxy8AkIssCt33Qn/IhQOmJww0lLPoWryhRPneM1A/6/LPR7dG0Nb0zy+xvt/YWuXPDXBh0rTPwNIoQ3d+yeT17KvRfRo7cF9VbBlrSGDlYNoU7tLQ7vFktS1PVnvylJmsCoIgCIIgXGrWdY51ncs4Ls9WeyGTVUEQBEEQhF2ig6KTYVOVdUwwyGRVEARBEARhl+j9ZPUSFOYyQSargiAIgiAIu8S6zrOWOVmVJ6u9kMmqIAiCIAjCLhESEGa4AYTIo9VePGUmq8V6SL4vpFXO8cZrPw/AB+55NZXxZZqVCgClyTGnrG1WBmlM5dz7QlWxOGUUi+WZFcAoLJsTRUq1MKEMtmlUG9c45e30DSc4wbNceYLzLc5/k1E1VsYXOPrg9e7cKL66X7t8AIa/1kyoea26tVSbpFkJnKoSYrWoU1NH6ut8SzP8YuOIUPhYxeXh6hOVv1UJk+4AQUA4YMKeeFM/lZxRehJCsd6mNRb9pxgqFp5l1LS6r+3UxKsP70P19ZNbWAag76p+gvX4v8vcmqZdGQagflOeyc8adbAKtVPu51ohnfv2sTJmytF/PmT5WvPfaPnewKlNAeME0Ekqn2MVOy5NgE5/dxgw6t9Ov7kW/cua/FyD4RebNlmqTm6o4rbtXsLrV1H72nPNiSIlYmVyl1LbU9D771tl5T53iji1PqF5Y10VTH1M++Rbcdt0BkPCvniwDFptp/4P+wP0QB86SkutrLi26gwoOgOBS9N3loDe7gh+2cGoznup70u1MG47v337+lx/z2P6s03DTys/16A5HanhgdwNT8/MJyte1vH0sSxXgF518RX6aWW+7zySdkdIs14y16rTn1R2jxybS5bLOg9kKf497Ng2ciw5ZgGuvOrpT6MzkCN/vlv0oVdj+/LcWuQwUdjcUyGnyL95klIt9MrSpjxzYVNpuDY9dJD2ZOwMMXy2iWoaZbzfB9MuLYBz9SjUNfkLbca+ZuqkLrTIt4zCvfJ7Jeo3wdDnze7ep7/z6QxH8a/5XNK9IFhXztWlVh52x0vzHaDoxnQzRkfvy7mE84V1SoHue8anPTmWaCsdKNcPfPX/yByJsadXX7XHEt8t0bH+c5qheaOiV4/O03n6AYjcABa/qR/VZ9o7d/JxOoeuZuXqaLx5TKH7zLXNrWn6lhUDS1FfKoSEA+Zce8i0HUSOJc0W61PG/aTv0SLnD5syTfzDCmp1DXX/wwCsv+g5rEya9Ep3rRBUyoAZk9RAm/xKHwDl4nlmTpq2zA33dk3YTdZ0jnzGk9U1mav25CkzWRUEQRAEQbjUrOt8jzWrsgygF93PoQVBEARBEIQnhTaBE1n5r/YWp2RKqSNKqY8rpU4ppbRS6o7UeaWUeld0fkUp9Rml1LM3ke7rlFJfU0qtRn9fs8Uq7jgyWRUEQRAEQdglOjro+doig8CXgJ/ocf5ngbdH518AnAY+qZQa7hEepdStwIeAPwb+RfT3z5VSL9xq4XYSWQYgCIIgCIKwS6z3WLO6rre2aFVrfSdwJ4BSySUEyhx4G/ArWuuPRMd+ADgDvAH47R7Jvg34pNb63dHndyulXhId/74tFXAHkSergiAIgiAIu0TWEoCU9+qwUmrEe/XeP7031wH7gU/YA1rrVeCzwIs2iHerHyfi7y4S50nnKfNk9fS3KoKColCDLywbpWGrEjIMTlXZSyk5/W++wuf+6jnuc3MiVnaWZy7QnCg6Ze/IsTn3vna8RGHKhPvcXz0HpnD7YIcjJafSPAcUqsWEkt8pVWuKZkU7laYuZu9z3qwEUZz4P7PFKbt3NG6fdID0Lw1OCVzOAf1OOVyoDSXCqdV1wuheGvpGnkfa+wBYHQMeheFHjdJy7SZPsZwPnSK0M76OygWEIyV3emXSqEwP3rXKhckCgzXjFJBvDbr9uYcfPEd7xLxvD+a4cF2bYs2k2doX0GdFsUFArtVmYMmoQDv1BXL7TBlVqOk7vw6Y9utragbOmvYJ2mDHCBUS7Wve/R+uDoxCtrZgAl/vKah9Vbb97Ku9fXzHhfr0INZZQnU23iPeqX29/dQBVkc1QyejJjh7Phlnacm5APhpq7ZCB6ZdAIKFJcJ95nqHeYXOKciZeupWi4Gl0OXdKQQuzbXh3oIAX+HePjmbUDc3J4quT6bvuXQb9GqTXgp8SKqaVSeMXQQOHXT37sgxc787J4+Ts3DLdLJ8noI8rajPdBLwwjslelRvf1fy/FyDEmMJ9wSX7snZLucA5Yn7bflHqxu3Qf7QQZdnOtzBu873zMsRhgTtkGBhyZThumHXXzpnz0b3CQwsrKKWLhC0R3uWJV0uMO1Tnx5M9GXfuWSjPe2z3CVsmW1d0/dkaX4l81q0ygqdD3BN/OBD5A6ZcSPsUxz8+3O0TxuVf9COnSXWh/PYErYqIflm9r1Qnx6kVAvdmD5ahZYRrhsnAq9f5ZvtxGcbh3JrQ2cJHSh3bdL0akffiSQ/10j2Qe++CJ83RP/ZyClhaJBOsY98wYzHKtToFTON0FcNR8fiPNavMuFGHrrA4g3DYIT8PP1QjcdqJr9wooVejMdl3VxJ1MW2a6eQJzj9OISmvyxfW2D4hLkX1of6yK1ELjb7Ar75upMcP36DSyOsm7leJ3HlLx0hAZ1M6yp3bDZ16j8A79piNvujv2dSx88A114kXlac/Rlhd42nzGRVEARBEAThUrOuc+Q2XgZwEFj2Tq12Bd486f9iVMaxnYjzpCKTVUEQBEEQhF1iXQc9JqvusfSy1nrpCWZzOvq7H/DcfLma7ien6Xjpp6gXi/OkI2tWBUEQBEEQdolQBz1fO8jDmInnK+wBpVQ/8BLg3g3i3efHifi2i8R50pEnq4IgCIIgCLtEOymm8o6HGaF7o5QaAq73Dl2nlPpmoKG1fkwp9T7gnUqpB4EHgXcCTeBPvTT+CJjTWr8jOvTrwFGl1M8Bfwl8F/By4MVbKtwOs6cnq0qpdwG/lDp8Rmt9SRf6CoIgCIIgbIf1MEcQZiwDCLc2WQVuBj7tfX5v9PcPgR8E3oNR8P4WsA94APg2rbW/HvYwxPpCrfW9SqnvBf4T8MvAN4DXa60f2GrhdpI9PVmN+CpmVm/p3qh6E+z/B02+T9OYSh6vLQxzVUaSrbJy6vyj9z0bpmJF8qkjA4xWzXunXrXK5pOzlCKl5mh1kFLNU7pWAk+JvMT5SH05NfYw1Rfj9pxer1Yo1JNrmW280nye0kdNn/HVvkAUx99/Oo5b8pSk+dYk10d7WM9QcXWojC8A405B65fBqIanCSeN4j9cKvIX9RcAUHxc0+kP6FSM4tJXgwbLeacmDhZHzP7N0ef164YpnTQ37MJNOYoLIZ1xo0gfeSRut+DsefojlXurMkmwoiicjdXpfcumnKrZgn2lLhW2JT/XgOlBF88qbcszKzQrg9ntXQu71eiRsjQ/N+sUtKX7Z93N1J4c63ICsMraEmYfc9vmpVrorqc6csuG+8xbTLkDRmx+T2ux//+rAUZVntgv3q/LfFIZXKqFCeVxWOqPwq0kle+9lNcAxOk1K3GZmpWg5z7v7ZOzcPNkYi/zRJ9O1b8eXbP9Xpl8tbLFXQurcLblXo/vEuMKcCgus+dY4N9PpfmVbLW/h3MRmBxLODXYvd59x4OuNtjAASF/6CBLtn9Ex1fG4/7oY5XmgKuHLccI8fVO9+HZ28199rS5g9Crz3XChGLc1gmgBDSmclHacZ5Z9QFP1e7RnCh21cfSnhxz6WXF9d0d0uXyr8tmKNQ1uVaswm8D+ea6S7d9eIjGK1/kwlrHCP/a7vuqolVWVBvXAFAZX6a2EPuum/aI75XFKVOnsWpyjO3zyp2fazinhFOp763EfTy/QtDu0Y7RveY+T45lugOk28q/L4bmO/SdXHDngnZoxnFgdUwRrJjr1Dl+guZrXujG49yadu2oc4ri45qBs0YndG6tn5ETUYInCu4an5/QFM6eJT8XjcdBQN+yOZdrtQkqZbd28apvNKk/1zjL7L9vHVU09SouhLQ6fe7766r+FsUzURlX+zLbabdZ1zmCjdesbgqt9Wcw4qde5zXGReBdG4R5acaxvwD+YkuFeZK5HCarba316YsHEwRBEARB2NuEBL5NVeK4kM3lMFm9QSl1CmPd8ADwTq31Q70CR+a5voFuz23FBEEQBEEQdpP1MCAIuyem6xnHBMNeb5kHgDcDrwR+GGOncK9SanyDOO/A+OzbV9pcVxAEQRAE4ZLQ7rF7VTtjaYBg2NOTVa31nVrrD2utv6y1/nvg1dGpH9gg2ruBq7zXwQ3CCoIgCIIg7BqhhlCrjNelLtne5XJYBuDQWl9QSn0ZuGGDMKt4uz0o1Xs7SEEQBEEQhN2k3UNgJU9We3NZTVaj9ahTwN2XuiyCIAiCIAhbZT3MoTKtq2Sy2os9PVlVSv0a8HHgMcx2X78AjGA8xLbESjkg1x/QmlqJ7UVurFNbGI5tT+6fBc+2qVUxcUerCijQd4exB1quFhOWVI2pHCPHzPuE/U0t7LJfsZZS5ZnYmuV5w49x4r88i/Ufju1BfLsWa18Cxs6kHVmn4NnptMoqYTVVqAUJ+6r8XMNZEJXmVzj6oPERvn7mgqt/bWGY69M2TTa+rVc91q7ZdrRpWouhVlm5MrfKudgWpbyasBxqTOUSZe4733FlaVaCnrY2hVqAdTAr1UJnoZO2bYLY4oaTswkLJt+azLdySefrW/A4m6DyqsvPP+/yivJPnEtbw/g2P4l6ZZO4tnWd6B+V8eU475RlVtqqKbbbmdywfP6x/FzDC9tte5MVpzxzwVilHepehZO2FCvNrzg7INunbV9yaUVp2wHrkduHOHhXtqVUVvnTedrzztbNhvNsj5oTRVfvtm/9lmGJlrYq8/PoVUb/uG8RlZ9ruOu0dPMkI3PdllUb1dfHT8e/3v6YshGljz5A/UduNe97lMG3OfKPgbm2zYmi67+ZYa212slZZ30GUE6l5ZN1PYGLWr/596i9/wt1lWx/SFiH1acHXXs1K4Gz/crqf75dlaVUMxZgdpzyx2XwrtvkGHh90OaXRZedHt32eNC7nWwaifJPZPdhiG3hRo7NJeL4doku3VpsK1ia7z6e/mzbxT/W6xqm76eBRpy3PwZeU1zO3Bu0sLA3fmcPMT/7Zx0XstnTk1XMetM/w4xbNeB+4Bat9aOXtFSCIAiCIAjboKMD2hlbq3Z2drvVK4o9PVnVWn/vpS6DIAiCIAjCTtHusQygfZkuA1BKfWQb0X5Ua/34ZgPv6cmqIAiCIAjClUSIyvzJ/zJeBnAH8OdA9jrCbt4ADAEyWRUEQRAEQdhrtMMAlbEBQPvy3hTgpzb7pFQp9d1bTVwmq4IgCIIgCLvEFThZfRnQW+3azauAuYuG8njKTFbP3agJCkYJODVmdILVxjWRuj1SCN4yHatYayGtsl0/YtTXi5HSs4CnYK11KzsTqsxKpJCPFKDGWYBE3BeWTvC+Iwqi9A8k8jZ5+Ypo373Aqj0LdU2pFrI4ZdI/cFTTIBfl00mUqzlRhLoJV59WLu3KeJNYm24U835ezZsnnRK+UC10hbP1aZVzrn181XFlfJn8XIssWmVFcSHH4NxKVOfBzHCl+RUaU0OJtvGV8q5+0KU69inUNWPVOJ6vYE18nijGCvrpQZNf1SqFz8fqcWLlbZuk0jZxzVIqXl/Vb7HxeinMbRyrfq0dv4XxQ61E+q4NDh104evTg+zfQD2fxpWZWJmbVvj6Thj+vdCcKFK6Zdo5Y+RJqvPT9bV1tUpmW+axaifRdukEc/0AACAASURBVG3PrSNLWW7r2b8cX9u0gjirzbOwSnYwKuh26ryvWM5yS8hSbKfj++WyDgjpa2RdCyDZxotT2rh+RGPFyLFYCd8qq579zleYW/W931d9mq95Yc/y+/ed7zrRKitKtWTYuM1j9wXbvrZO+UMHM/uI7WO97iGfjZwAbB5g7tHYfcPUo1f7A7FTy8cqbixP9+NCXdOybinlVSrjy1F5SzQnigy/2Dx0ah0ve3n3dmVoT4717Kv+uHSxOqfHhHQemwnr+l/UZ+3Y0yof7h5/o7D2ewVMWzVuP+zGjtrxMgfoVuaba32w616zaSTy8VwmNqLauMaF669f3FVjN+hohcoUWF2eywC01p/dYvh7tprHZTuNFwRBEARBuNzI3r0q287qckMp9Tyl1HO8z9+llPqYUupXlVL9201XJquCIAiCIAi7RDsMer6uAH4beAaAUurpwP8AmsD3AO/ZbqJXRMsIgiAIgiBcDnR00PN1BfAM4J+i998DHNVavwH4QeB12030KbNmVRAEQRAE4VLT6yf/K2EZAKCIH4S+HPjr6P1J4o3ptoxMVgVBEARBEHaJTg83gM6VsQzgGPALSqm/B14CvDU6fh1k7oK7KZ4yk9Wnf7hJPh8axecNyXNO6TifrfJfvK0F9QFG7zYK+NZ2/jcor1KoFhNxbV4PNK9ntKpYvC0+btWLrbJKKEKbE0UXb8Tb93lxSlOqxQrXU0dCCpEiN62eLc2vQKQgbpU33uvdxrUqWqtwXaeQCOc7FvhKfj/v2sIwV02WMvdKH6t2yDeTylinWr550ilf69ODCQXo4m0td12alQBSCnp/b+y0ut5XRftt7CuZ/b3jF6d0ws2hOVF0+2mXon297fFeimV7LuF2cMs0YNTEaYW53Yfc5ReFW5zSrm6jVRWr6T3FvK1zvBd54M6VPvqAy/diZfax+5zbsP61SDsF+Cr/kbmkS4OfV3rfej9dX5EPsWK7VAu76uqTb+lMF4H8XMO1R1rx7Kdl95j3y+WU5CkVv4/pU0lHAL/fufspaptejhVtTzGfn2vQivqB3zcLtaQTxtLNk4k+XZ8ejO/J1PV1jhIZ7efXs1kJzPgHlD7Sz9pQND6SdNAYOXTQGzODTfUlixvPUvW313qsatxM7B7yfh+zdUuM4d5130jhn6Y17t3bXnqtsmI6cpCZoeLCLHnjEpjxwbqlVMaXnevM0dc/m+s/1OScn5e9J6u9nRhs3QFOTZnP/nhmFflczAHBjkvTh/D7ZsLpBGBiMnFPuPS9/mvvC+w5W28u/sisl2tAOkz7ZOQ8k6I9OQb3zyTGrURdvLaBuH/+62s/z/umXgVA33AOPn6Rgu4CYRhkTkzDK2Oy+jbgg5iNAn5Fa30iOv7dwL3bTfQpM1kVBEEQBEG41GhAZ7huXdyIa++ilHqG1vrrWusZ4DkZQX6GjbzaLsIVMY0XBEEQBEG4HNgpgZVS6hGllM54/WaP8C/tEf6ZO1CtLyqlqkqp/6KUujV9Umvd0lqvbzdxebIqCIIgCIKwS3RCBWG3mKqTcewivADIeZ9vAj4J/M+LxLsRWPI+13oF3ALjwCuA7wI+qpRSGHHVXwKf0Fpn7wi0SWSyKgiCIAiCsEuEPQRWW12zqrVOTDKVUj8PfAO42I5Sj2utF7eU2cXL0sKsCP54NFG9FfhO4D8DfxYJrv4S+Gut9eNbTV+WAQiCIAiCIOwSm9jBalgpNeK9Bi6WZrQ71JuA39M6a0Vsgi8qpeaVUp9SSr3sCVanC224V2v981rrZwHfDBzFeK2eVEr9+FbTlMmqIAiCIAjCLhGGEIYq4+WCzALnvNc7NpHsHcAo8AcbhJkHfgRjzv9a4DjwKaXUkW1VZJNorR/UWv9XrfUR4ADwia2m8ZRZBtDcXyTfZyyOqo1rAHjjtZ/nz3/n22lMmTl7fq7h7DeaE0VaU5H9St38U2OtMBanYs9bayXjW+NYfMsZMLYcvkWS5Sf3Pcr7prTLx8+rVc5RqCdteKztkW+hU6gFlObPc+q13vKVWtHVBXA2OfXpQYhsRMaqHXe+drxE34R2tjP+ORvX2rGcqI3TuOdqU8ZKyFg1/keuVVZdNiUAR244wSmuS1i02Paw7ejqGVkTpUmn/bbn3cWff+TbAWhM5SjVklY4vayN/LwLde2sZKz1l29hZss0WlWJ/H1bpeZEkRKx3RCQsJOypK2N/Gtry+PbM/l1temcOlJyFjkWa0GTrq/JL77WlvqP3Gr62ER3GZ09jQ3rWSD5eQG0bh+KbZZqoeuPti0saRuodBlHjpl41sbK9nHfcik/14Dp2PLLt95J1zttebUZ0vVOlz/vWZONzCUth/y8bN8pz1wwYSaybXj89ijNr9CYGuoKZ8P62H5i+myQuK6WQl0n+m6639k46Tr7/di2d6Fq43Vo27e3TLt6tsrK2VxB8p4x13Jzz0R8C7O6f52jsvv3QqLMXvq+jZVf33QbmmuXam8VlyM9Th998HoADtRCFm9bi47G25yXaiGtcs6NubUpY9UH8Zhix85aeTgx1m8GY893wdWjWQkoReeybOfctTg566yeSrUwaTE4UUx8b210zzirs5Oz5IG2PV4JXf3SYdN9yx8foJSwOou/B0LSuO/hu4x1V9s7Z8N3j7E4qzOIr0Hfwt7Q24daoTbeFOAgsOydWu0K3M0PAXdqrU/1CqC1Po6ZoFruU0odAn4a8+RzR1BKTQLfClxN8qGo1lq/H1jYappPmcmqIAiCIAjCpUZrhc6YrHrHlrXWS10BeqCUuhazW9Rrt1Gc+zHLB3YEpdRbgA8Aa5hJqf8fggbev510ZbIqCIIgCIKwW4QKnaX837obgOUtwOPA/9pG3OdilgfsFP8xer1ba939qHybyGRVEARBEARhlwh7WFeF25isKqUCzGT1D7XW7dS5dwOTWus3R5/fBjwCfBWzjuVNmPWrr9tyxr0pAf9jJyeqIAIrQRAEQRCE3UOr3q+t83LgMPB7GecmonOWfuDXgBngbuDFwKu11h/ZTsY9+F3ge3YwPWAbT1aVUnngjcDfaa1PP5HMIwXazwDPxzTqa7TWH/POK+CXMOq1fcADwI9rrb/6RPIVBEEQBEG4FOjQvLKObzktrT+Bkwd2nfvB1Of3AO/Zei5b4h3AXyulvh34MpDYtUpr/fbtJLrlyarWuq2U+u/A1HYyTDEIfAn4feDDGed/Fng7xpvr68AvAJ9USt2otV7OCL8prCrzhaUT/EElcEpwX9XbmMolFJujVYVdJzxaVSzeZtSHzbpxGLAqR1/RmoXNy+f9Z6+N0jQbPLTKRexDb195DpFLQYajQGtqhWa1SGXciOxqx8subrMSJFSWpVoYORqYdGedY0GYqEtaxdysBDxv+DEAZio3ufRbFXPOKriznADAuDD0eQruVqUUtSsUz7SoT5cYPGPKYJScsaI4Uddy7MbwheX4n8ZWJaRBzuWvS4WkYttTDfttMFbVRsnr1dMprss5p5xulZVRv9bNuY3U5mkFva8Q7xXPOgP416rvDmOTsP6xijtm+krPrMnPNWhOH4rr6amXfZV1LwcAex9kqbhtnCzVfGl+JXHcV7z7imWn+PecH3z8Pl6qeQ4RnsNAaX6F+vQgI14cX52+IZGTQmm+4/psmnQb7L/zZFy+SkBpEy4TNh1f4V6aT5Z3v+ce4sfpStOOG9VkPVploBqr0v28fWcM312jWRl0SunSvHE5yHKuaE+ObdiWzh3ltjWa1bjf+orzdJkuht8/F6fMfXzgqO5yiHBlmF/pcgjxFeK9ri/EbWqdTOznkT+e49SRg8nyRN8Fp45AZXw5qmfF9b/GVC4xtlfGl50bgE3nROQoQH3Ac/PoT4Qp3z8TF9DrB7Y93dic4ZYwEpt0ZKrqbV9IjD82v0Omvn5fHYmOQTym571jpmCrzgEhTfvkLM2oX43MRa4fGeXx08/Mw08zdW/Eiv9k/f00/e+IvYLW2WtWs0RXlyHvBF5J7DqQFlhti+2uWX0AY/L66HYzBtBa3wncCWAeosZET1XfBvyKfUStlPoB4AzwBuC3n0jegiAIgiAIu80m3AAuZ94O/But9R/sZKLbnaz+FvDeyJ/rH4EL/kmt9UxmrK1xHbAfzzxWa72qlPos8CJ6TFajnR58E7vhrHCCIAiCIAi7Tq/1qVfGZHUV+IedTnS7k9UPRX//m3dMY9ZNaCDXFWPr7I/+nkkdPwNcu0G8d2DWuQqCIAiCIOwtQuyqu+7jlz+/Dvwk8FM7meh2J6vX7WQhLkJ6jYPKOObzbuC93udhzNZlgiAIgiAIlxTdw2c103v18uNbgNuVUv8HxiIrLbDazsYF25usaq2f0FrVTWKdBvaTNKy9mu6nrQ6t9Sre1mTptbCCIAiCIAiXDE32I7e9sRvsE2UR2EkrLGCbk1Wl1Js3Oq+1/qPtFSfBw5gJ6yuAL0b59gMvAX5uq4mVTq+Qz2saU0NUG9cA8MDw9UapWjYq/ISi2FN2FmqBUd3SW21s1bRpnGK+PmDU/pGq1N/n/AvLh7vS9JW1CYVvag9xp46sDwCaN177eQA+cM+r6dXzm5UgUWdHeRXod3XxVb1pZfDilI73hC63gEJ2Xl6b9tE07722sorfkUf76PNWPifUoZ5S3bkjRO1z9L5nM1ox5RqtEl0nQzg0gN5Xcmn4CtrGVI5CzX7qdKmJrVOAv+91oa5pTa2yOGWuYXlmJbH3eqnHHiC+wj9LAW/dERrff5hSLen8sHzP1SaQV68s3J7hE0lVrK+49RXLB47qhEI/S4FuSSuLfceLViVMOC5shFVWZzkipOO6/cCr/S4v/xra+Om9x8E4OPiq8pFjyT3KY5ePZlcZu1094nzsOV/pvhlsWRenNPvvTDptXKzNLNY1o1mJle/D48uRS4Q3VvVw4mhM5Vz/9Pv3RuVNu3A0pmKnjfxcg8btRmVdGV+mNF/qva98JXTlt3Ehu+7+vvWxQ0eH5kTRjRXpe8TWCZLOJ82JYtKRw7s32pNjXpl0dA/G1yS+702ef3aH2SHy+z72k85NZobYoaNVCancWKd23NyotYVhjtxwAoCj9WdTqCvnIrBcLSbGKb8dfCX8kteetp7+vW3DtsqK8sxKYgxIhMlwEEincbF+aMd+/z4AEo45QKaDiJ+GG8drAYtT5jr5fcPm4btYOPeFiXFK8ysuf+MOEvdVvw7PG36Mz5WfE7/HvG+N742HVypUqIynqFnHLje01m95MtLd7jKAX0997sPsWrCG+QbY1GRVKTUEXO8duk4p9c1AQ2v9mFLqfcA7lVIPAg9iLBGawJ9us9yCIAiCIAiXjh47WD2B7VaveLa1g5XWel/qNQTcCNwDfN8WkroZ89T0i9Hn90bv/2P0+T3A+zDuA8cw//p+2xPxWBUEQRAEQbhk6A1elyFKqS8opfZtIfw9Sqnsn6N7sN0nq11orR9USv088CfAMzcZ5zP02HkhOq+Bd0UvQRAEQRCEy5srzw3gm4F/oZTq3uWkd/iBi4by2LHJakQHOLDDaQqCIAiCIFwRXKFrVj/FBg8fU2z5GfJ2BVbfmT4ETAA/wZNgBisIgiAIgnBFcOW5AWzHznRLlqLbfbL6sdRnDdSAu4D/a5tpPqk89LoSQaHAaDXZGwp1TSsj/GhVxfvDp9SshbqmFakg3R7mnjrVVz3b86NVWLwtVpf77gHWncBXVqZVlb5C1+5Vn8UHH32BK6NVxTamcpRqPaPERI4CllItTKjd00pjq9gupNStfjhfnXtqYZhRL76vtD93XR+DZ7J/A/Hbyrbn/juNLPbUayv4TgR+3p1iH0E7TrNUC127FuraqYupJtXEvlsCtaJT6zdfdShRrvr0oKc27lbuOtXsRPfSHF/t6tevNJ90GNgqth/6+6Mv3pZzdY6XqXe61N7p8vtt4qtu0/gq3PS+7f57f5/20vwKs7cbV4us/mwVwDCeUEJn7XlusdfJqrpt+22kdPadCUr3zzp1tGtHz4nApmPfZ6naS7XQU7EnSauee9XFz8viO3H4fXxxSlOeSSrjbdrpNrflbdx+2Dlh5Ocaif6XpehPO2Wkj9eOl7nKc1bY6BqVauEGdYsdNXznimbdjCF2vGhWNH5/PPXaHFT74zpE1z3tBJDuu27sriXHmPxcg1al5Mrk198fsxJtUQuo+ZYd9QGOJrTDRM4NJJw9/HsLoH1yNuEIsBGno/HId67ZCH9M2ExY5y5QCdw9mnACiHAOERuUu31ylkL9kOtfrakVRu8243arrHp+pzUrAYuRw8IBdOa9kcUXlg+7NL+wfNiVsXHd3vidXQEqY2J6uT5X3Q070+36rG5LmCUIgiAIgvCURtwAtswTXrOqItf9SAwlCIIgCIIg9ECF5pV1XMhm209IlVJvVkp9GVgBVpRSM0qp79+5ogmCIAiCIFxhXGHWVbvBdgVWbwd+GfgNjKBKAd8KfEApVdZa/787V0RBEARBEIQrA3myunW2uwzgJ4G3prZV/Uul1FcxnqgyWRUEQRAEQUgja1a3zHYnqxPAvRnH743OCYIgCIIgCCmU7uEGcJkuA1BKnWWTixi01he3c8hgu5PVE8C/Bn41dfz1wIPbTPNJZaAekBsI8NvT2jxZy6jSfBNrI+JbNbUixxFrdVKo64SFScLaJWUXY89ZmyRrM1K6KxkOgPIqAMM3LjubkywrHGvNMnIstvEw5elQi2w+EhZRdU2zEjBiy1gL6fOsgdL19PNxdie1kMZULm6zqKx+neyxVn3A2fT41kA2HWud0pgacsf7LiRtm/x6+mlYGzFrNVMZX6Cv1u/C+zYovm1Ve3LM2KBMZdnQdBJWUr5lVGtqpadVim/tBbGty8icsblKWAKl0s+y90lb0LTKyh3zbY8Wp7Rn7WTSzLJRAiJ7KN/OKq5blh2TS9O7Fv41s3WxfffA0bjMadsqP83mRLHLSshep2Yltiyy2H58/XwzcTxpS1R09S4x5tr71BGVaK/mRNGztYIDR5NjqqvbLdO0U3Xx623zWrp5ktL8SqLtSpj31rbOHu/Zd9J9PcMyysfaxI3eXXB9uAJQXk2UtWtM8fDLshm7o/xcg7FqsaeFmu3Di7etmTp44RLXs7xKqRZbSyXsjzxbN/9+8m3WWmUoz3Rbn4Ftt7XM+zfdps2JIiNxN+iy4nPjaqqerbLip6rZu4inr2/lxjoAtYVhd4/W6gOUaiGnXmvaqVAtJmwKE9wyDV772H57+lWHEmPG4pR2/XhxamtP47L6NiRtzBJ2dV6+aWstM4amrc3iftCYykV5TSeuTWV82Y3bi1OKZt1ea8XIsQalDPu+ZiWglGpvm/7IMa9PTQ/yvOHHmKncBMBbr7mL75t6tjmX5VN5KeixDOAy3sHqbd77ceAXgL8D7ouO3Qq8ErN8dFtsd7L6S8CHlFJHMGtWNfBi4F9iJrGCIAiCIAhCmitsu1Wt9R/a90qpDwO/qLX+DS/If1NK/QTwcra5THRbbgBa6w8DLwTqwB3Aa6P336K1/uh20hQEQRAEQbjSscsAsl5bSkepdymldOp1+iJxXqKU+kelVEsp9ZBS6kefSF0yeCXwtxnH/w4zWd0WW3qyqpTyfyF5EPixrDBa66XtFkgQBEEQBOGKZWe3W/0qyUlgzy0ulVLXAX8D/A7wJoyL028ppWrRQ8idYAF4DfD/pI7fEZ3bFltdBrDI5poze79BQRAEQRCEpzBK97Cu2t5kta213vBpqsePAo9pre0a06pS6mbgp4Gdmqz+EvC7SqmXEq9ZvQX4duDfbjfRrU5WX+a9V5gZ+r8F5rKDC4IgCIIgCI6LP1kdjjYHtaxqrVe7whtuUEqdAlaBB4B3aq0f6hH2VuATqWN/B/yQUqpPa72+idJviNb6D5RSVeCnMEtEFfA14Fu11g9sN90tTVa11p/1PyulOsD9GzTMnqS2MAwYRWKrrCjUutWUVvkPROd1Qj2aVpJmxW1OFGMHgZqCWtHF85WqtYVhRuuaVqQQrdUHEmr+NFadmVb3lu5aAUobxtn4nFF2OqVtZdC9z881aEwdZmrsjCuzVbRaVX3LCLip3FhnvVpx8Zzitz5Aab7ZU/mcdg2wJNJIKYtrC8Nc75XXOh8A5Jv99J9bi9OYHnQqcf/6mjom816M3heqReB84rjvImDbpz49CJ76P04XVzbzN4jyjlXtVkE+WjVtYOOV5q2aH0C79hmtKmrlYcY3kLZa5fHo3QUXKuHu4F1XINMJwCqR2ydnjUo5Cl/yynjix3OM3j3o2sfVd6JIiTFXZl9dPHJsjhJjCeV83E6mjNd/KHYBsG3wtD9ubNiPLaNV5dwrTLnic+3JsS4VdBZpxwa/fUaOzTlHAHvOd06w19bGyXK/sH3axjNtPZk8j3EeGDk2R2W8AMByuYj9RqstDFOoFmlWdJRP3K/LMyuJ9i3UtafCj8uxEbb8vguKfy6h1N7onq6ryGnFfPZdFHzXlbT7iHVBGat2aE4UnQuC7+bQrAQUqkU39viuAYn7Lyqfzdtvm7QzQ3tyLOH2ArE7xWh63L9/xrTR62+B8mriu8XFqSoaU0E8XnpOB/54a++PpdQ44pfTYt1f4ve9aZ+cNW+idH0nhZHUY6Z0WSzOKcFzAojrkgxn+4vvGpCfa9Ccjh0N/BlRoRZQnjFj7OztQ10OC7Z+pVon4SSxdPOkyz8xftXC2LUG+O9nbndpNIfZE2xiU4DZ1Kn/gPGwT/MA8Gbg68A1GBX+vUqpZ2uts35y3w+cSR07g5kLloH5rhjbIJqUvnEn0rJs1w1AEARBEARB2CKbmKweBJa9U5lPVbXWd3ofv6yUug/4BvADwHt7ZJ9+0qZ6HN82SqkAuB64mpSQX2t9dDtpymRVEARBEARhl9jEZHV5O0J1rfUFpdSXgRt6BDmNebrqczXQ5gmIn3yUUrcAfwpcSzwRdkVkm5qmbVlXZWQuCIIgCIIgXAy9wesJoJQaAKbo/XP+fcArUse+DTi2E+tVIz4AHANuAsaAfd5rW7tXwdatqz6SOlQAPqCUuuAf1Fq/drsFEgRBEARBuFLZxJPVzaWj1K8BHwcewzwh/QXMJmx/GJ1/NzCptX5zFOUDwE8opd6Lsa+6FfghIHuLtu1xA/DdWusTO5jmlp+snku9/gQ4lXF8UyiljiilPq6UOhWZ2d6ROv8HGYa392+xzIIgCIIgCHsCO1nNem2Rg8CfAceBjwBrwC1a60ej8xPAYRtYa/0w8K+AlwL/BPx74Kd20GMVjOjr+h1MD9i6G8Bbdjj/QeBLwO/T2+PrbwE/37XtZLRaDgkKIYW6cns2v/Haz/OBe1590bhj1Y5TFlusCnGjPd5Ld604BfDBu853qZ/d3toZ+0RbRaWvZHbxPDWnfW/VjrEytFulOeLFP+e9T+/pnrXfvV8vIHtva+tmABS61PZAOXfRPdD98rr4vjo0chTw2zKdpm1X6wQARi3qOwC0KmFi//h03m5f70qPPbwj3N70GfXy9+BO9pO4bideX+LA0dDl6yt+Z28fcm1eqKuEUrdvfLnnvvPtyTEOfMQ6JsTOBjYPU6aUij2lkDf93aiHR4AlGy71K87o3QXKM4kfVlzd83MNF740n9oTno0dKqwDwMG7zjNW7fa4zlJ3+0pywMUrza8kFOinX3UIME4M/n7ofhuky5afa7g0rKOAc1I4OQuveaGXRvee9GDGDL8cEO/97uefvq7tyTHWPxb197LnvsEAhbp27Z91b/VyNPAV5P49P3JszoWz44tV16fbw/b5VuRIYMtRnx6Mr/XEJJRXu+La8iQcJLz+P3v7kLt+zUpAeeYChdpQ9Fm7veDLUVg7HlvXj652iOro36/+GANJF5c0vuPJ0QfN9/D18ytO8T5aVfTdsezcAGoLw10uKf54s5GbjD++OyU/ps/az62pClTNPT5WTarknXMKpo17fcmn7/l0v3Njhd+HTs4mVP6tssq8P+OyHnZp+w4d3zJ2hplKpUfJkq4E1hWiWQkonYxF8uZ7IWPcrQT0eZ+fN/wYRyvPBmBgdidWPu4AO7QpgNb6ey9y/gczjn0WeN7WctoS7wf+q1JqP/BlkuYPaK1ntpPoJRVYRUq2OwFSnmI+q1swvBUEQRAEQdiz9NpadZubAuw17IPH3/OOaYzYatsCq8vBDeClSqnHMbtnfRb4v7XWj/cKHC0w9h+H7RFnNUEQBEEQnurs1JrVPcp1T0aie32yeifwP4FHMQ3wy8BdSqnnb7Cbwzsw230JgiAIgiDsLXZoGcBexFsvu6Ps6cmq1vpD3sevKKWOYSaur8YsJs7i3STNcIfp3g1CEARBEARh11G6x5PVy3SyqpT6TuBOrfV69L4nWuu/2k4ee3qymkZrPa+UepTehrdET1zdU9cN1sIKgiAIgiDsKlfgMoCPYTYbeDx634sres2qQyk1jpFF7sj+tYIgCIIgCLvJlTZZ1VoHWe93kks6WVVKDZH047pOKfXNQCN6vQujLJsHngb8KlAHPrrdPEu1kMY9VwPwhbHDFOo6YWlkLTNOHSk565G0xUnaqqMxlXNWKu3JMRfvxOtLjFbNc/20HUqzErA4FT/zN7YvxnLFP55Flt1QqxLSnCjyo2/8XwB84IOvTtheJSxiJorUFsw/N1fRSdiEpMvpW+0UagHVxjWAsXKJ7VdMG/qWOn58Z91TzYGXn2+JU3p8nf5zaz2treIyloylTlSfVrXIyLHHTHqRJZHLu9SHvW3yzTaqEw8GOq/pFMzJ1X0DrJdMH+hrajoFRXutL4qnCPMmnLVd8S2RssqYn2vAzZNJKyTPjsZntKoozTejT8lzhbqmFbm7lGphV1zfHihtO2PL6lvklGphV1/2y7yRFZZfF9/eaPG2FvvvNOVY8ups7aJ8m6J0+lltYq+rb0Vk7a/8+GkrC34+IwAAIABJREFUov1e+S9mj+bf7+Df38XYSmpiMrJS6o7vW8YBCSufNH75oZi0bfLSOP2qQ7H1Vka/sbZQ1tLLr4uzwZqDZiW+B7qvd2yhZdvA3p++hZaft3+v+RZX7ZOzzlLu1JEShbqKx6Va6K5FaX6FQnUION9Vt678vDGqUNdeXzJt7vdla+HUvHmyazzef+fJOFyqzyTqlmHJBaZ/N6YOx+XwytWYyjlbOyglyn5qIdbyVsaXqU3FaY7eXWDxtpZ7n3Wts7B9y15/v6/Ze7xVCSl9NGkp5VvU9Uw7ZSWXHgPi+yTotnKL8irUtctrhG6s9V5zokhjKueuYbVxTWzdOJW0FPTL1ZxOjul+HZd6fVfUwoRX0heWD2eGu6RcgWtWlVLX7/RGAD6X+snqzcCnvc92rekfAm8FngO8GRjFTFg/Dbxea72MIAiCIAjCZcaV9mQ14utKqTnMPO3TwKe11o/sVOKX2mf1MxjvrV68cpeKIgiCIAiC8KSjQo0Kux+jZh27jHhJ9Hop8BtAQSn1GHAX8eS1+yecTXKpn6wKgiAIgiA8dbgClwFore8G7gb+k1KqD7gVM3F9KfB9wIBS6oTW+sbtpC+TVUEQBEEQhF3iCl0G4NBarwNHlVKfB+7D/Er+wyQ1SltCJquCIAiCIAi7xJU6WVVKFYAXAS/DPFF9AfAwZvfRt0Z/t8VTZ7I6tgolRWm+w6nXJm2+fJWp5cBRzakjtucEtCqhU7tnkVBRRgrIQi1g8bZI0fqRfqOY9/Jy6vlKSH16sOt4L7KUnoVaQGn+PB989AWmup5CtlVWlGdSqtO6Uew3J+LyNCuBU52az91q2ZpVvFZCCnWz3NioOmPFaLMe15u7vDJ6Cl+/bAAjqT0v0upTv56QVP865XFK/ZxvrtMu9cWfV5KDgYovr3sftDUqhAtN0z4DSwoduQEcvOs8Jyol7G81J15f4npv24qEitVTkufnGizdPJkos23n5kTRKbzTquZSLaRVNn21NH/eHa9PD7J4vMxVJ+83abzqUKIvNCeKHLzrvHu/WLfuDLpnu6bxVb69FPGGtczjpfmVLnWx3z7+cZOP5wiwgVI7je9W4bORyjqhKp8cS/T5hHrcc2AoMZZQ3S/dPMlItPqqfXIWouvby00jq0z16UHK0fvyzIWEU4DFtmFcvyFX/rGqGaP88H5d0vguHLavWbcCvx0TbTkR99uEs8Et066uo1VFqRYm3B3sOeMyMJTpYJJuK9+RoEyyL5l4vV0eLqaqT5PlGGHvGYj7iBvTvHHFjoHjczXnSmCvvx1Xa/XYEaVQC2iV43PpcmxUdpd+5NpQut98LlQPu2t4qqK6HClsudP3hX+vjXirB5sTRUqMJa6bdUTwyR86GJcJWJw6yGjVU/OnnDbsd1lp/nzinq4tDHMgFca+T/b3+FqUZy7QPjmb6YhQ+ugM3DLt6tjnlWWysOjCh3tkxqN09gYAl+umAABKqc9iJqffAI4C7wc+q7U+sxPp75FLJwiCIAiC8BSgxw5Wl/OaVcwTVeva9BngqNa6vlOJPynmrYIgCIIgCEI31g0g63UZMwr8CNAEfg6YU0p9WSn1G0qp71ZKVZ5I4vJkVRAEQRAEYbe4Mt0ALgB/G71QSg0DL8asX/1Z4INKqQe11jdtJ32ZrAqCIAiCIOwSqgMq43dt1ek+dhlzgXg30rNAG5jaMMYGyGRVEARBEARhl7gSNwVQSgWYXUlfinma+q2YPeTtrlY/TnLH0i3xlJmsDny9SG6gQHOiA5GK/XnPe4wZkk+krRqyWQko1GJlY+XGOjWr3a2mVK2eyjE/14BIwd1iwAsTOQrUk/sgA1BeBQpdSkobL6t8vZgaM8K7mUqlS/ltVZyN7z8MmHONqZxTldr8rRKzdftQl0K5UDX5t6ZWgEKijImyOuVrM6FCvhj+XvWnX3UoOkZCDW2V5obJVNx4n/P0XvTBmiZox59V2wwMOki2u2pr2k1zawwtx+ebE8XoWvUDcP2Hmq4c8f7vUVlqyT2ufaW0rzIHEsp9H1OPWGXu1NLTgxRqgVPFJq7z/TPwmhcm8h2tDkbv4/I+8v2HE+rnRFvdPwPTt2Yqy119o/qM3j3onA42UjX7jghZ+9DbY6dTzgZdbgITvVX31lHDKuuznAjak2M9ldLtk7NOUWzr0+t+8+uaP3QwcX3T9fbLYe/BPMD05hwPspTRYO+35JdbQoXvKfl9t4GSp0BPK68TZYz6l7/3e5Zjgdu33qYzMZlIH3r3jV5tnJ9rOLX66VcdojxzwfWRtLNGcjzoTsfGSfc7O965MX8iVsn7bh29aE+OJb5AK+PL1LzP/lhZqBY91Xv2hKQxlUuMdc3pQ5S8evhjTGtqhVOVaIwtr7ryQOREEvXxbjeKuD7+tbfXwb9uvgrfXfeUGt93AjD9JukgYL/j8nMNWpHrCRB9P+hEGEt+ruE5DhxKfC/6bgSlyTFXn/yhg9ihvVQLWffSG8tfIFiPvtu2ZhrxpHGFWlctYian8xiB1dsxu1Z9YycSf8pMVgVBEARBEC41V6J1FfAzmMnp15+MxMUNQBAEQRAEYZfYKTcApdQ7lFKfV0otK6UeV0p9TCm14XamSqmXKqV0xuuZT6ROWuvffrImqiCTVUEQBEEQhF3DLgPIem2RlwC/CdwCvALza/knlFKbWWN0IzDhvR7ccu67iCwDEARBEARB2C1CbV5Zx7eA1vrb/c9KqbcAjwPPx+witRGPa60XLxJmzyBPVgVBEARBEHYJpXs8WY3nqsNKqRHv1b1XbzZXRX+zFYdJvqiUmldKfUop9bKt1mG3kcmqIAiCIAjCLrGJNauzwDnv9Y6LpqmUAt4L3KO1/soGQecxO029DngtcBz4lFLqyPZr9OTzlFkGsFoOCQohVHEWIh989AUJ2x/fuqNZGUzYxNQWhp1FSLNSSNjrQMoGpx5bihy54QQAM3ffRKEWsD5sTl31UIfF29ZclMUp7ayy/HxLtZDFKUX74ch6o6W7LKn8OEcfvB4w+56lCQYHXdhWJY7nW041J4rO8mas2nHWJ/nTixTqg7GNSH2AxSmT54GjOmGX0qwErq3CfEDQDl1dfCufVlkl6go4+6H9c42ElVbahsq3xEm0vWfX0ynkEwvWc2uQX4k/J2ysQvvX2FupC+bWGDgXdi1672WN1YuERQxjifo0J4qsjg+4ugDGOgrg0MFEXjaO6yvr68l4PjaNW6YT7bh+aDwzXDD9TIKzxsqqTdJ6K21v5H/uv66UsBvz6zUy120PtVEbWfJzjYStk1qPnbI3ssdy/S/KO2ia+yuc+Wfw7HZ6ppGyrfL/brYeG6ZvrwmxhZZvCWTL6Lfj0s2TjHzpDO2HHoliHkrc/6VasmzW3qg9OZZpVdQLd5/PNchPHkjUJctyzh8rbTz6zD3TrARufMzPNWhN5eCu+LOzJYosqHKt2GYpyxorbcNUml9x16o0v8Ls7UNc++EmAKrZStRlozrbsSe3FtLpTz23sdcqsoGz99DT/vgx/vlXu3eNDNqaqbEz5nsCOHLDCWbuNraIrfoArUrIkVu/CsDn/uo5lDyPK2uP1b+UUcionu3UZ4i/xzi8loiS9f0ASes3+9m2T316kPLMhYT1Wfi8oficjXRytsu+yqdQ16zui8abQwddGa3dWDzeB4C5r0eryl33vvNDpElbW1maE8WErZpFpy7lVbkmQeRl1Xd+j8jtL76D1UFg2TuzuolUfwOYxuwa1TtrrY9jJqiW+5RSh4Cf5uJLBy4ZT5nJqiAIgiAIwqVGdTQqw6dKddyxZa111r8v2ekp9X7gO4EjWuvZi4XP4H7gTduIt2vIZFUQBEEQBGGX2KkdrKKf/t8PvAZ4qdb64W0W6bmY5QF7FpmsCoIgCIIg7BI7uN3qbwJvAL4LWFZK7Y+On9NarwAopd4NTGqt3xx9fhvwCPBVzHaMb8KsX33dliuyi8hkVRAEQRAEYbfQ2ryyjm+Nt0Z/P5M6/hbgD6L3EyT3we0Hfg2zl/cKZtL6aq3132w1893kkk5WlVLvwKjRnolptHuBn4sWANswCvgljHptH/AA8ONa66/ufokFQRAEQRC2zybWrG4KrbXaRJgfTH1+D/CeLWW0B7jUT1bt7gufj8ryK5jdF56ltb4QhflZ4O3ADwJfB34B+KRS6kat9XJ3khenNWVUrN8ydoaZSqWnetJXu7eqsaq2VY4VnKX5le74kRK+UC3CDfHhQl2zdlV336qML1MDxo72A3DqiGK0mt0HfeX+yLGGU1lmEat1i+TnGmxmc4yRY3MuTV/VS2vVOROAp0QFmpWkQ4FzDAB0PgDrBjC/klDn+k4A60M5+s9ll2np5klXl7Qy1Cq/bTgfHSiCtVhJnl/R5FtxntYBIMyr+OeXyO8u34zqudhGB8qVv1AdSqiNs2hPjpm220CJ7FwP7jxJ81n747ocm3OqcL/e/O/27j9Ikru87/j7mZndnf11p7vble6nZCVWKWckFZRsDBgJUEoOkEoMJimcIjYGpxKME0KUlAlUKoUrBaQcypDggF0GR3alXGAHYhvK2HKCg7AlkUgOPrDOQiYH3O3dSXfaO+luZ+dHdz/549vd09Mzs7sn63bmdj6vqqm77Z/ffvo7Pd+d7efpR47lmcChb3jPtpaOhbdLLa0gkGUPhzaGDNvo5CksbdPhL4XM/2ybXsi4rx05zNx//2rejsaB2TzLnEeOQeG4Zi7G+XuBA4f6suizzO9aobJBdp7yZQtZ8VkVgOx4oLcawKBs8ayNZdaK8n0X1y+uW8xAL5/PchWEskGZ5nNn1nuuDT1edke3/eVtnNwgJ6Kx3pMFnl0D5s4leUyHKVaTyNpVztYvzptjL7WzoU549O3v0tjg+lLswwB0orxdxe3Vj88Cl7vHWzjWxoFZao3u+e057AHndJC9xwevH508lbe/sVzpyRifO7PO6bvnAKhfqNDcU+m5hpWz3bPrTui73ez77PxFdeP46g359Aef/F5IPz/q5yo0lxMefPhFAFxXqMBS3Gdnvvd6Br2fM70VZw7nx915Zatn2cZypXtOKLzfKFRTGKCvwkN6rWzsHz4eunjUu59VaR8tVlkpKl7vi/9vLlk3jgNOefGzJir0nXK1i2J/OffMIvX0//tql7EotLHaWzhhZF7A2wAmxkgHq5s9fSH9VvXdwAfc/XPpMm8FniLcp/HL29tiERERkb+CF+42gIkxbg8FKD994WZgP/BAtoC7t4AvA68YtAEzmyk++QFYvIrtFREREdkySzzcClB+6ZvVocZmsDrk6QtZZttTpcWfKswrey+9T354PjXHRERERF54iUOSDHhpsDrM2AxW6T594R8MmFc+gzZgWuZDhG9os9fgR22IiIiIbLdkg5cMNOoEK2DDpy+cTf/dT2/B2uvp/7YVyG8TyB9NFr6wFRERERk9SxLM+kemlmi0OsyoS1dt9vSFE4QB673A/03XmSZUEXjPlexr9xNGdTr9QvZ8eBb7g+dfxMHic7YLmbwQqgBAyK5sHl3n4OdCtv7q0WqeNZk9n7iYabq8LxQpuER/amOSRryxXMnbQbr86tFqut+YxnIhA/3cAkmt+0Xyhs9+Jjy3urlkebYuwNyhvVQvr+Xzsi+mLx71/DihQnRoL6fvzgb4zlL6iOzk0mUay5XwTHpCRYX68d7jK1YqyI6t0i49P5z+rH2AzlxYN8sCjw7t7ckYzc7LsCoJxTZkWfjJlFFrFDJ8m56fN4vmqF/ozssyWGcutIjrFRqXwrmYeaZFNDfVu4/s+dkrgzPCy1nMjQOz+TO5dz260peNnlUbWD1aZdej3YzXuZ4s7UJm8LmExnIFb/WntkaH9vY9gz73sjv6MtWzn+dPNbrrpPvP1u2pCrFFWVZzMas6O+95DErPXy+vn1l8vNC2LT73HQh/VivpyXx/5BQc2tvNMC5k3A9aL2tv7cjhnv1v1Jbo5Kme6gmZrPJDvo0hz1qfO7MeznMWqztenlfbWDq23lOxYMN4PHKMxj9+ef7jsCoHtZVVvD7ds/8s43qz6ghUK/k6WYyL1Sjyn4cca/kYipUMNlLObs/beORwT0WUsizLPKklfdfUs687AoTr0al7Fjj4YMi8byxX8ut7eFcGnbkKzzyzWJgHna+EfPTmUthXdj3L3r9lUbq5bsb7kfwzYdejw+O+e6bZE6NiNZbo5ClI33e1ldVuVY+S8vGH69LQXRbaeLhv3Uqn+//smE/fbRx8cPBxF3UWhv8ZvHFgll1HDvdUBMi2t4vSNS37bAVOtvdRTb++KlaDGakk6ZajKU+XgUb9zeqGT19wdzezjwLvM7MngSeB9wEN4DdG0mIRERGR5ysh3Mw4aLoMNOrB6laevvDzhF+LP073oQA//HxrrIqIiIiMim4DuHKjrrO6lacvOPD+9CUiIiJy7YqHZFPFGqwOM+pvVkVEREQmhyeD7091DVaH0WBVREREZLskzsDqm6qzOtTEDFafvdWp1J2lY+ss3xqyiy/98fVAXMhQ7M0YzbJRT9/Xm71aP+89WdWN5W7WfXRoL2+56csA/OavvJbjr7yhZ93s2cRLx9a4eFfI9DyXZpF2ztfz5bKMzsaBWernnUqUdeLeOyeK2a7Rob09WZD5Mumz131pT9j2cgJLeXUvVo/O5tOXjnWzZJvL3We2J3FM/UJCUgtt7kzFzJ0NbYqnobWrkmfUWwyeVi+wxGkuh+NaOFuBZoupS2HByp5p1g6G49n9rYS4XqPaDPPWDs2ycCZk4LYXKiycbAJw+cAcrX3dY5w/1eh5PnfYf9h3tZXkmfZeMZIatPZ01+3MpcsnUG2HdVp7ZmgvVPLrSFKrEM+GeNQa4dznz70vZpafWc/bMXdmnV2P9maI588CL6wDIUP32b8e+lf9vPc8O72YvVvMFs8ytG33YtrG3j5R3bMH4jR29VqendtZmArHBlT3zTB9oZ33K68ayb5Q06K6vg+/vIZ3wjaiecsrItQPHaRVr2H7Q1+qdBLien+G79yZdaKTp/J2R4f29j53fGWVqJB9X874Lmb2WqPZt0yWmV6MeVb9IstOtiF/UuvLHs/aWJhWfLZ99vOuwjkYloVfW1mlcceRdD8Dd5+3oZxdP6gqQePALIt/Wek5/9lz6rOKAkXlag/lYyzL+nBx+Sxulfl52nNTeVa4VwyfC+/lpNZNZvZd8xDF+Mzgj5NiVY9sn4OU2zGs2sKgSgxWqIxRnFfebzatWCklnjaiWWP+TOgBfnmNG/4oVEb02Wmieae5JwRh/wMrzPzD0MbWSpP1W8P1vb07vAfPPbEUNrrUgqPhuK87HiZ1s/QrPRn0+TG/OFRNGHSuy/29fq7C6tGwzOrF3RxmwGcBvR/w5XNdrFTRWO5tU21lleiloT3RfPf8VebnSdbWqMyH+HnNWfjWsyGOCzNcPOr58VKt0NwXjrnahEtHKtQaYVvry8biySQ9Rsuvy15L+1Oqs2j5/lu7KiSrF7CpUK3i7EsX2Pt4+By7+LJDzJ4LF7rmngrJYofGfPoZmkzTWcy2MSalLJMYiIdMl0EmZrAqIiIiMnJxMvhP/kqwGkqDVREREZHt4oAP+JO/7gIYSoNVERERke0Sx+C6DeBKaLAqIiIisl2SIaWrdBvAUBqsioiIiGwXVQO4YhqsioiIiGwTT2J8wG0Ag6ZJMDGD1f1/4tSmnPN3zHPxiTkArkvLEGUlZoolhebOJXz7x28EoH4OODeblwoJuuWulo6t9ZRW+dNLYb3Vo1UWS+2YutT/m9Pyvkv5Psuy0lhZaaVy6aqszY3lSlpCq3/7WamS5Lr59HgqNNMKK9d9pc7Fo0k+vbaySnN5rm8bNjvL5cPV/OfbDp7hyX23ALD/4QZP/8AcSdqbLAGfSktXdWI6c2msKhWYm2XqcigxsrBirN4eVopPGpV23FNa6rmbwrzd3+pw6aa0rFcFonqF6WdDmZnO4jTJVIhJKMNkWCtsv1q1vHQVhBJPnpZ5qrQsj1U8bXl88/lpHaNkukprVzjumWf6wtJXIigzrDxPd53ZfLnW7hCf9nWlbRRKyxSdv2OeuXMJ8b5u78rKdQHYzDTeSM95zah2q5TRSavCTF8O+6y0k3z9pB7iXZudhTjB0jJilQ7EM+k5nJkmqRnxTIhJt6Rar9rKKhRK8JTLD+XLABw4lE+bO5f0H3enM3AffeWu0vdPfk42+ZNa7chhnjswy65C9adhpZ+2sv/ytNrKKhG915XivsolmAaV0AKwhfkNn2xT3Gfe7keO0XjjD/bsL1+mVKKIA4fyNhaXt9k6Vvqmxzrd1lUKp8Vnp4kWQkmhStTf1mIZqWI8IJSYA5h+tt1TDmxQmwZtDxgan2y5OfYO7IOZ+oUkf+/bnt20bwhl3Dq7p4hnnL2Ph3KHl2/fz+N/EWJydNdzrB0Ix9zeRSgbWCgJ+L2fCeucumeB+nnPy1CFz460lF3hXGTxzEqYXbxrmfrx9Ppe6qf1856vu3LDItAYeFzlknHZtkJMupaOrfWdlyj9ceq5Qum6xYVQumoxlLViV4f1Q+E6NP/EOepPz1NbD/FpH95LLVSdo3Vbk6kn60RpucBkCi7eEraZ1CNq63G6rynsmYtUl/YBUFtzqq1sHYc4xqbDehaRf5ZUIqfSDtuIp2H+yWkaB0N8piymup6WsbphTEpXxXGo8VimwepQEzNYFRERERk5H3IbwKAKAQJ0vx4UERERkavM43jo60qZ2TvN7ISZNc3sMTO7a5PlX5Uu1zSz/2dm73jeB7KNNFgVERER2S5xEm4F6HtdWTUAM3sz8FHgA8BLgK8AXzSzG4csfzPwe+lyLwE+CPwnM3vTX+FotoUGqyIiIiLb5AX8ZvU+4FPu/kl3P+7u7wZOAj89ZPl3AN9193eny38S+FXgXz3vg9kmE3PPatwJd3rH7QpJM02saRtR1MyXiTq9N1/HrSrDxGlCTtRJerYB0L7cTtdvEq+10uV7l4miJkkjbD+uh2Ws010m23cUNYnbVaJOkrc/+3+UtIgKxwV0j61V6bYxakLSIo7TZVtTJI1svd51oqRF0izEJAltc28Tt7rTO2vdn6OoSdyq9NQzTtZDIkYUt4g6Uz3byuIVRQlJM02wSs9FfmydCnE7jUGnk/8/HH9MJQrbtwQSs3S5apgfh/0kUZInWHnFiDvk20+a1Tw+SWL5dO84cbtKnN7UH0Ut4k7cey5K53szUcfy85Mfe8fyn+N2GoNWOt8LmSvZvkrnOuok+XHG7SZR3G2TJ23c293tt7J+0CJuZ/EN/TaKwrFV4xbulu/LvR0alG0/TXKKkhZR1MTT9ZxwPvLmFtrbF4ch86JOsycukXfy4446RpS0e9cfohhTkhZY+rt4IdGqZxtpTIv9smcbpW1HhXZv1Ja83yYtIu/k5y2bVmzjoJiUr0lR0sqPIW438/dr1PH8fPRJ91tsc3YNijpJ77GU25e+kT1ppzHp9n/L2t9pdvt03MIrVaI0saoSJd04Jq3Qf4bsD6Aahb7lUZTHoedclI+x3H+y812OX3laYTvF93jcdqJOkicLFvcXdWKSZrXwMyRpAlEUt/KYxi0jaSbQKO4zTfhp1YjbPvjaXPj8iFs1ok4c+j+QNJrd61An7mlX8TMhWe//HBvWP4ufccX3Wd96Savneh9F6XlJ2mG99D2ZrDeJOt3+HreaPZ87cavSPZY2xGkSW9zKLy8k6xFRej2PW2EfnqQ/t5t4kra57UTeBq/m8/J4RDUqWbzblXA+1kN8mpej/FiSzsbXkO3SSdr4gHtWI/Jr/6JZz3ik5e49Hd/MpoE7gX9f2swDwCuG7Prl6fyiPwB+ysym3H1wNusYMN/hN/Sa2SHg1KjbISIiImPhsLsPqDNxdZlZHTgB7N9gscvAQmnaz7n7+0vbOgisAD/k7g8Vpr8PeKu73zpg/98E7nf3DxamvQL4E+Cgu5+5siPaPpPwzepp4G8AfwEcBi6NtjljZZEwkFdc+ik2wyk2gykuwyk2wyk2w12N2CwSxgXbzt2b6X2j01e4av+fqgqbLf1sA6Zttvyg6WNlxw9W3d3NLPtt4ZK7PzfSBo2Rwp8ZFJcSxWY4xWYwxWU4xWY4xWa4qxSbkcbY3ZvAC3E/wnkgpv9b2uuBp4asc3bI8hEwoJL4+FCClYiIiMg1xENiwmPAvaVZ9wIP9a8BwMMDlv9h4NFxvl8VNFgVERERuRb9AvCPzOztZnbUzD4C3Aj8EoCZfcjMfr2w/C8BN5nZL6TLvx34KeDD297yK7TjbwNItYCfY+P7PiaR4jKcYjOcYjOY4jKcYjOcYjOcYrMBd/+Mme0D/i1wAPgG8Hp3/066yAHC4DVb/oSZvR74CPAzhHt33+Xun93ell+5HV8NQERERESuXboNQERERETGlgarIiIiIjK2NFgVERERkbGlwaqIiIiIjK0dP1g1s3ea2Qkza5rZY2Z216jbtN3M7P1m5qXX2cJ8S5c5bWbrZva/zOxFo2zz1WBmd5vZ59PjdDN7Q2n+pnEwsxkz+5iZnTezNTP7XTM7vL1H8sLbQmzuH9CHHikts+NiY2bvNbP/Y2aXzOxpM/ttM7u1tMxE9pstxmZS+81Pm9kxM3sufT1sZq8rzJ/UPrNZXCayv8jmdvRg1czeDHwU+ADwEuArwBfN7MYNV9yZ/pxQxiJ73V6Y97PAfcA/BX6A8JSLPzSzxe1u5FU2D/wZ4TgH2UocPgq8Efgx4JWEZzh/wcyqV6vR22Sz2AD8Pr196PWl+TsxNq8C/jPwMkIx7RrwgJnNF5aZ1H6zldjAZPabU8C/Br4/fX0J+J3CgHRS+8xmcYHJ7C+yGXffsS/gq8AnStOOAx8addu2OQ7vB742ZJ4BZ4D3FKbNABeBfzLqtl/FmDjwhiuJA7AbaAPBYsaIAAAGUElEQVRvLixzkPDIu7816mO6WrFJp90P/PYG60xKbJbT+NytfrNxbNRv+o51lVCAXX1mQFzUX/Ta6LVjv1k1s2ngTuCB0qwHgFdsf4tG7pb0T04nzOzTZvbX0uk3E54VnMfJ3VvAl5msOG0lDncCU6VlThMKMU9CrF6d/rn3m2b2K2Z2fWHepMRmd/rvavqv+k1XOTaZie43ZlY1sx8j/PXiYdRngIFxyUx0f5HBdvITrJaAKvBUafpThAvFJPkq8BPAN4EbgH8DPJT+6SWLxaA43bRtLRy9rcRhP9B29wsDltnpfeqLwG8B3yF82P474Etmdmf6QbvjY2NmRni84R+7+zfSyeo3DI0NTHC/MbPbCYOwOnAZeKO7P25m2aBqIvvMsLiksye2v8jGdvJgNVN+RJcNmLajufsXCz9+3cweBr4FvBXIbl6f+Dilnk8cdnys3P0zhR+/YWaPEj5Q/jbwuQ1W3Umx+UXgDsJ9cmWT3m8GxmbC+80TwIuB64A3Ab9mZq8qzJ/UPjMwLu7++IT3F9nAjr0NADhPuI+l/NvW9fT/RjtR3H0N+DpwC+HGflCcthKHs8C0me3ZYJmJ4O5nCB8it6STdnRszOxjwN8FXuPupwqzJr7fbBCbPpPUb9y97e5/6e6Puvt7CQmM/5wJ7zMbxGXQshPTX2RjO3aw6u5t4DFClmrRvcBD29+i8WFmM8BRwk3+JwgXgHsL86cJmb6TFKetxOExoFNa5gBwG5MVK8xsH3CE0Idgh8YmLTH0i8CPAve4+4nSIhPbb7YQm0HrTES/GcIIiVQT22eGyOLSP2Oy+4sUjTrD62q+gDcTMgffThicfYRwj8xNo27bNsfhw4QL4c3ADwKfB57L4gC8h5CJ+kbCm/43gNPA4qjb/gLHYYHw56cXE/5k9C/S/9+41TgAnwBOAn+TUA7tfwJfA6qjPr6rFZt03oeBlwPfA7ya8MFwaqfHBvh42ideRfgmLHvNFpaZyH6zWWwmvN98ELgrPe7bCeUTY+DeCe8zQ+Myyf1Fry30nVE34KofILwT+DbQIvxWdveo2zSCGHw6vRC2gRXgs8D3FeYbobzVGaBJyEq9bdTtvgpxeDVhIFZ+3b/VOBCSAj4GPAM0CAP/I6M+tqsZG2AW+APg6bQPfSedfqS0jR0XmyExceAnC8tMZL/ZLDYT3m8+VfjceRr4H6QD1QnvM0PjMsn9Ra/NX5aefBERERGRsbNj71kVERERkWufBqsiIiIiMrY0WBURERGRsaXBqoiIiIiMLQ1WRURERGRsabAqIiIiImNLg1URERERGVsarIqIiIjI2NJgVURERETGlgarInJNMrP7zczTV8fMnjKzPzSzt5uZrm0iIjuELugici37feAA8D3A64A/Av4j8AUzq42wXSIi8gLRYFVErmUtdz/r7ivu/qfu/kHgRwgD158EMLP7zOzrZrZmZifN7ONmtpDOmzez58zs7xU3amZ/J11+cbsPSEREemmwKiI7irt/Cfgz4EfTSQnwLuA24K3APcDPp8uuAZ8G3lbazNuA/+bul7ajzSIiMpy5+6jbICJyxczsfuA6d3/DgHmfBu5w9+8bMO/vA59w96X055cCDwE3uvtpM1sCTgP3uvuXr+YxiIjI5vTNqojsRAY4gJm9Jk28WjGzS8CvA/vMbB7A3f838OfAT6Tr/jjwXeDB7W+2iIiUabAqIjvRUeCEmd0E/B7wDeBNwJ3Az6TLTBWW/yTdWwHeBvwX15+dRETGggarIrKjmNk9wO3AZ4HvB2rAv3T3R9z9m8DBAav9V+BGM3sX8CLg17arvSIisjGVdhGRa9mMme0HqsANwGuB9wJfIPy5/3bCde6fmdnngR8C3lHeiLtfMLPPAf8BeMDdT21T+0VEZBP6ZlVErmWvBc4A3ybUXH0NIfP/R9w9dvevAfcB7yHcCvAWwmB2kE8B08CvXuU2i4jIFVA1ABERwMzeQnigwEF3b4+6PSI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"text/plain": [ "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -255,7 +257,7 @@ "outputs": [], "source": [ "aggregation = tsam.TimeSeriesAggregation(raw, noTypicalPeriods = 8, hoursPerPeriod = 24, \n", - " clusterMethod = 'hierarchical')" + " clusterMethod = 'hierarchical', representationMethod='meanRepresentation')" ] }, { @@ -317,216 +319,40 @@ " \n", " \n", " \n", - " 0\n", + " 0\n", " 0\n", - " 0.000000\n", - " 404.749377\n", - " -0.662325\n", - " 3.499451\n", + " 0.0\n", + " 401.162755\n", + " -0.541667\n", + " 2.837500\n", " \n", " \n", " 1\n", - " 0.000000\n", - " 395.399863\n", - " -0.956596\n", - " 4.432638\n", + " 0.0\n", + " 390.314344\n", + " -0.875000\n", + " 2.816667\n", " \n", " \n", " 2\n", - " 0.000000\n", - " 390.974340\n", - " -1.054687\n", - " 3.032858\n", + " 0.0\n", + " 385.746997\n", + " -1.172917\n", + " 2.668750\n", " \n", " \n", " 3\n", - " 0.000000\n", - " 392.521756\n", - " -1.250867\n", - " 2.799561\n", + " 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" \n", - " 16\n", - " 0.000000\n", - " 519.596251\n", - " 0.710939\n", - " 1.166484\n", - " \n", - " \n", - " 17\n", - " 0.000000\n", - " 537.996198\n", - " 0.122397\n", - " 2.332967\n", - " \n", - " \n", - " 18\n", - " 0.000000\n", - " 537.835822\n", - " -0.073784\n", - " 3.499451\n", - " \n", - " \n", + " 7\n", " 19\n", - " 0.000000\n", - " 523.878736\n", - " -0.956596\n", - " 2.332967\n", + " 0.0\n", + " 536.348654\n", + " 3.112821\n", + " 1.971795\n", " \n", " \n", " 20\n", - " 0.000000\n", - " 498.981376\n", - " -1.250867\n", - " 3.499451\n", + " 0.0\n", + " 511.367680\n", + " 2.992308\n", + " 2.041026\n", " \n", " \n", " 21\n", - " 0.000000\n", - " 481.023548\n", - " -1.741319\n", - " 3.499451\n", + " 0.0\n", + " 491.284883\n", + " 2.851282\n", + " 1.897436\n", " \n", " \n", " 22\n", - " 0.000000\n", - " 456.516293\n", - " -1.839409\n", - " 3.499451\n", + " 0.0\n", + " 468.986028\n", + " 2.717949\n", + " 2.058974\n", " \n", " \n", " 23\n", - " 0.000000\n", - " 424.306634\n", - " -2.133680\n", - " 3.499451\n", + " 0.0\n", + " 438.652594\n", + " 2.638462\n", + " 2.261538\n", " \n", " \n", "\n", @@ -754,69 +404,19 @@ "" ], "text/plain": [ - " GHI Load T Wind\n", - " TimeStep \n", - "0 0 0.000000 404.749377 -0.662325 3.499451\n", - " 1 0.000000 395.399863 -0.956596 4.432638\n", - " 2 0.000000 390.974340 -1.054687 3.032858\n", - " 3 0.000000 392.521756 -1.250867 2.799561\n", - " 4 0.000000 398.377664 -1.447048 2.566264\n", - " 5 0.000000 414.254932 -1.152777 1.166484\n", - " 6 9.788716 455.350313 -0.858506 1.166484\n", - " 7 92.448984 482.055159 -0.171874 1.166484\n", - " 8 183.810333 495.591798 0.710939 1.166484\n", - " 9 259.944790 504.577214 1.201390 1.166484\n", - " 10 315.414180 512.331632 1.888022 1.166484\n", - " 11 344.780328 525.716563 3.163196 2.332967\n", - " 12 348.043234 526.219365 4.046008 3.499451\n", - " 13 323.027626 518.204877 4.830731 3.499451\n", - " 14 270.821141 504.061408 3.555557 3.499451\n", - " 15 104.412970 495.756509 1.691842 3.499451\n", - " 16 57.644661 487.187205 0.710939 2.332967\n", - " 17 10.876351 486.376654 0.122397 2.332967\n", - " 18 0.000000 491.916143 0.024307 1.166484\n", - " 19 0.000000 504.213116 0.318577 2.332967\n", - " 20 0.000000 482.362908 0.710939 2.332967\n", - " 21 0.000000 474.079682 1.005209 3.499451\n", - " 22 0.000000 465.237304 1.005209 2.566264\n", - " 23 0.000000 454.799832 1.201390 4.082693\n", - "1 0 0.000000 373.805391 11.108509 3.499451\n", - " 1 0.000000 361.187666 10.912329 4.665935\n", - " 2 0.000000 353.992398 10.519968 2.332967\n", - " 3 0.000000 354.040077 9.833336 1.166484\n", - " 4 4.350540 362.982148 10.029516 1.166484\n", - " 5 40.242499 379.344880 9.931426 1.166484\n", - "... ... ... ... ...\n", - "6 18 17.402162 455.749087 20.623267 1.166484\n", - " 19 0.000000 450.846769 19.151913 1.166484\n", - " 20 0.000000 440.933771 18.171010 2.332967\n", - " 21 0.000000 437.119412 17.288197 2.332967\n", - " 22 0.000000 412.612157 16.699656 3.499451\n", - " 23 0.000000 384.034808 15.914933 3.499451\n", - "7 0 0.000000 402.764176 1.005209 2.332967\n", - " 1 0.000000 390.961336 1.005209 2.332967\n", - " 2 0.000000 389.591635 1.103300 2.332967\n", - " 3 0.000000 396.743558 1.103300 2.332967\n", - " 4 0.000000 404.489307 1.103300 3.499451\n", - " 5 0.000000 425.082509 1.201390 3.499451\n", - " 6 0.000000 481.110238 1.201390 3.499451\n", - " 7 0.000000 519.817311 1.495661 3.499451\n", - " 8 22.840337 529.704302 1.691842 3.499451\n", - " 9 55.469390 528.442963 1.789932 2.332967\n", - " 10 77.222092 531.152025 2.084203 2.332967\n", - " 11 88.098444 531.520457 2.378474 2.332967\n", - " 12 83.747903 530.553864 2.967015 2.332967\n", - " 13 67.433377 528.282587 3.163196 1.166484\n", - " 14 97.887159 526.466432 2.967015 1.166484\n", - " 15 19.577432 519.652600 1.789932 2.332967\n", - " 16 0.000000 519.596251 0.710939 1.166484\n", - " 17 0.000000 537.996198 0.122397 2.332967\n", - " 18 0.000000 537.835822 -0.073784 3.499451\n", - " 19 0.000000 523.878736 -0.956596 2.332967\n", - " 20 0.000000 498.981376 -1.250867 3.499451\n", - " 21 0.000000 481.023548 -1.741319 3.499451\n", - " 22 0.000000 456.516293 -1.839409 3.499451\n", - " 23 0.000000 424.306634 -2.133680 3.499451\n", + " GHI Load T Wind\n", + " TimeStep \n", + "0 0 0.0 401.162755 -0.541667 2.837500\n", + " 1 0.0 390.314344 -0.875000 2.816667\n", + " 2 0.0 385.746997 -1.172917 2.668750\n", + " 3 0.0 385.847417 -1.360417 2.616667\n", + " 4 0.0 388.700611 -1.506250 2.556250\n", + "... ... ... ... ...\n", + "7 19 0.0 536.348654 3.112821 1.971795\n", + " 20 0.0 511.367680 2.992308 2.041026\n", + " 21 0.0 491.284883 2.851282 1.897436\n", + " 22 0.0 468.986028 2.717949 2.058974\n", + " 23 0.0 438.652594 2.638462 2.261538\n", "\n", "[192 rows x 4 columns]" ] @@ -887,12 +487,14 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -1008,7 +610,7 @@ "outputs": [], "source": [ "aggregation_wind = tsam.TimeSeriesAggregation(raw_wind, noTypicalPeriods = 8, hoursPerPeriod = 24, \n", - " clusterMethod = 'hierarchical')" + " clusterMethod = 'hierarchical', representationMethod='meanRepresentation')" ] }, { @@ -1072,12 +674,14 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -1116,7 +720,7 @@ " 2, 7, 7, 0, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 7, 1, 7, 7, 1, 1, 2, 7,\n", " 7, 2, 7, 2, 1, 1, 7, 1, 7, 7, 7, 0, 0, 7, 7, 7, 7, 7, 0, 0, 7, 7,\n", " 7, 7, 7, 0, 2, 7, 5, 5, 7, 5, 0, 2, 7, 2, 7, 2, 7, 2, 0, 7, 5, 2,\n", - " 7, 0, 0, 0, 2, 0, 0, 0, 2, 1, 2, 2, 2])" + " 7, 0, 0, 0, 2, 0, 0, 0, 2, 1, 2, 2, 2], dtype=int64)" ] }, "execution_count": 20, @@ -1136,23 +740,23 @@ { "data": { "text/plain": [ - "array([0, 1, 1, 0, 0, 1, 3, 2, 2, 3, 1, 1, 1, 1, 1, 1, 7, 3, 6, 6, 4, 1,\n", - " 4, 1, 1, 1, 5, 4, 0, 1, 0, 3, 1, 1, 4, 5, 2, 7, 6, 2, 1, 3, 0, 1,\n", - " 1, 5, 7, 6, 5, 0, 5, 5, 0, 2, 5, 1, 2, 6, 5, 0, 4, 0, 1, 3, 3, 1,\n", - " 4, 1, 2, 0, 2, 2, 1, 1, 5, 2, 2, 3, 5, 2, 2, 4, 1, 3, 4, 3, 7, 7,\n", - " 7, 2, 2, 2, 7, 3, 4, 1, 5, 1, 3, 5, 4, 3, 2, 3, 4, 1, 2, 2, 2, 6,\n", - " 2, 5, 2, 2, 2, 7, 7, 5, 3, 2, 1, 2, 6, 7, 3, 3, 2, 1, 4, 6, 1, 2,\n", - " 2, 6, 7, 2, 4, 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dtype=int64)" ] }, "execution_count": 21, @@ -1185,7 +789,7 @@ "outputs": [], "source": [ "aggregation_predefClusterOrder = tsam.TimeSeriesAggregation(raw, noTypicalPeriods = 8, hoursPerPeriod = 24, \n", - " clusterMethod = 'hierarchical', \n", + " clusterMethod = 'hierarchical', representationMethod='meanRepresentation', \n", " predefClusterOrder=aggregation_wind.clusterOrder)" ] }, @@ -1258,23 +862,23 @@ { "data": { "text/plain": [ - "array([0, 1, 1, 0, 0, 1, 3, 2, 2, 3, 1, 1, 1, 1, 1, 1, 7, 3, 6, 6, 4, 1,\n", - " 4, 1, 1, 1, 5, 4, 0, 1, 0, 3, 1, 1, 4, 5, 2, 7, 6, 2, 1, 3, 0, 1,\n", - " 1, 5, 7, 6, 5, 0, 5, 5, 0, 2, 5, 1, 2, 6, 5, 0, 4, 0, 1, 3, 3, 1,\n", - " 4, 1, 2, 0, 2, 2, 1, 1, 5, 2, 2, 3, 5, 2, 2, 4, 1, 3, 4, 3, 7, 7,\n", - " 7, 2, 2, 2, 7, 3, 4, 1, 5, 1, 3, 5, 4, 3, 2, 3, 4, 1, 2, 2, 2, 6,\n", - " 2, 5, 2, 2, 2, 7, 7, 5, 3, 2, 1, 2, 6, 7, 3, 3, 2, 1, 4, 6, 1, 2,\n", - " 2, 6, 7, 2, 4, 7, 7, 1, 2, 2, 2, 6, 2, 2, 7, 2, 2, 2, 2, 2, 3, 2,\n", - " 5, 2, 2, 2, 6, 6, 2, 6, 2, 7, 7, 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1, 0, 1, 1, 3, 2, 5, 2, 6, 5, 5, 1, 6,\n", - " 6, 4, 5, 4, 2, 2, 5, 7, 6, 2, 3, 7, 2, 6, 6, 2, 2, 2, 7, 5, 5, 7,\n", - " 6, 6, 6, 1, 1, 6, 5, 1, 2, 5, 5, 1, 5, 0, 1, 1, 1, 5, 6, 6, 5, 1,\n", - " 2, 6, 6, 2, 3, 2, 2, 5, 0, 1, 0, 1, 1])" + "array([7, 0, 0, 3, 3, 0, 2, 1, 2, 2, 0, 0, 0, 0, 0, 0, 2, 2, 5, 5, 4, 0,\n", + " 4, 0, 0, 0, 6, 4, 3, 0, 7, 2, 0, 0, 4, 6, 1, 2, 5, 1, 0, 2, 3, 0,\n", + " 0, 2, 2, 5, 6, 3, 6, 6, 7, 1, 6, 0, 1, 5, 4, 3, 4, 7, 0, 2, 2, 0,\n", + " 4, 0, 1, 7, 1, 1, 0, 0, 2, 1, 1, 2, 6, 1, 1, 4, 0, 2, 4, 2, 2, 2,\n", + " 2, 1, 1, 1, 2, 2, 4, 0, 2, 0, 2, 2, 4, 2, 1, 2, 4, 0, 1, 1, 1, 5,\n", + " 1, 2, 1, 1, 2, 2, 2, 2, 2, 1, 0, 1, 5, 1, 2, 2, 1, 0, 4, 5, 0, 1,\n", + " 1, 5, 1, 1, 4, 2, 2, 0, 1, 1, 1, 5, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1,\n", + " 2, 1, 1, 1, 5, 5, 1, 5, 1, 1, 2, 1, 2, 1, 2, 1, 1, 5, 1, 1, 2, 1,\n", + " 1, 1, 2, 1, 1, 5, 5, 5, 2, 5, 1, 5, 5, 1, 1, 1, 1, 1, 5, 5, 1, 1,\n", + " 1, 1, 2, 2, 1, 5, 1, 2, 1, 1, 2, 1, 1, 1, 5, 1, 1, 5, 2, 4, 0, 7,\n", + " 2, 1, 1, 2, 2, 1, 2, 1, 1, 2, 2, 5, 1, 1, 5, 5, 5, 2, 2, 2, 1, 1,\n", + " 2, 2, 1, 2, 2, 1, 1, 4, 2, 1, 5, 1, 2, 2, 1, 1, 1, 2, 2, 1, 2, 2,\n", + " 5, 1, 2, 1, 5, 1, 5, 1, 1, 1, 5, 1, 0, 0, 2, 2, 5, 2, 2, 0, 4, 2,\n", + " 4, 1, 5, 0, 3, 3, 3, 7, 4, 0, 3, 0, 0, 2, 1, 6, 1, 5, 6, 6, 0, 5,\n", + " 5, 4, 6, 4, 1, 1, 6, 2, 5, 1, 2, 2, 1, 5, 5, 1, 1, 1, 1, 6, 6, 2,\n", + " 5, 5, 5, 0, 0, 5, 4, 0, 1, 4, 6, 0, 6, 3, 0, 0, 0, 4, 5, 5, 6, 0,\n", + " 2, 5, 5, 1, 2, 1, 1, 2, 3, 0, 3, 0, 0], dtype=int64)" ] }, "execution_count": 28, @@ -1336,12 +940,14 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -1378,7 +984,7 @@ "source": [ "aggregation_predefClusterOrderAndClusterCenters = tsam.TimeSeriesAggregation(raw, \n", " noTypicalPeriods = 8, hoursPerPeriod = 24, \n", - " clusterMethod = 'hierarchical', \n", + " clusterMethod = 'hierarchical', representationMethod='meanRepresentation', \n", " predefClusterOrder=aggregation_wind.clusterOrder,\n", " predefClusterCenterIndices=aggregation_wind.clusterCenterIndices)" ] @@ -1444,12 +1050,14 @@ "outputs": [ { "data": { - "image/png": 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u9Pa1nTuSpZs/1JNYpPs6p5fCvn7wwlL5C/lF3iLfDQfO7Nob+jBV7kLGw077ep/82MYUYfjSgWa3rMV5Kt0gCjmF7uJ4kNwwX6yv8FERPqw94TUMr1OcP0wHTYrVfUMfF3bENmf0wuI8KfuHJUxfJTOMi+0bVvYo+apsSMlP2ThITl269doc54ntL+rfI087wg2cOVS7qaNO1zgy4vO4LQ2q38PIr2proe9Svg/zxf1PFWF/Vi5PryzLC1a6r4TccODMUnzYN88uel/7SdXX+P6xdKB3T8jsSfsy9kHVtR50neP7QJVdYVjqflr4+WH5PS8mdR1CeVU2x7pC+Y8MjtsrW2ObyTVv0qj/pnMo3P2TZBODquKdbNb75SMJ3kC29KAz5wbgu4Pz9rQMEUIIIYRYDx0a4fJIpfDtzskw6Gy5++HByTLyRVTDhVSntt2TEEIIIUTIWqdBo9M/wFxLhG03ToYSPjTfrP5mM3u/mX3LgPSXAd8IfvEiq0IIIYQQU6FVsRtRK/HKfbux1QednwVeDDwT+Amy9aauMbMzavK8CXhA8Dtno40UQgghhBiGjkPHLfGbtmUbz5Z+vZ5vZl9wnZl9BvhX4CX0ZmnFeVYIVu/P9rwXQgghhJg+rYqJRDvhSeeWHnTGuPuSmV0HPHTatgghhBBCjMpap4kll0zSoHNLkU8SegTwqVHzrpzRoTGbLUfw6Zt6a6naQofZxexp6Py1d3TDl559LssLI6+ZBfSWeMjyN4LjdLpP3/QQbKHTteu0vvSN7lIS89fewfGLz+rGFDYfuuQcZhetK8MG2F6kOycKe0Qgf3mhU/JJmG/vQllX4cNUuQsZhfyCQ5eUv3yI/Z8KP/qi81gO9IXLJ8XEtod+W16wko+L41Budu5dOVmd6OWL5Yd6j198VjI+tCG289Al51TKjNOFX8akrlOY5+iLzgPKy5EMyhPaWmfT0rPPLaUfxv7iOFWPCxnF9Y/lhed1Po7lD0ud/cOkS9XbKv+kzoexo6h/n77pIZwW2RTLSMkMbRykq4qU7Lo2m2qjqWsU1726tCm9dfbG53H/U0XYn80uerdvOHjlHaU04X0lZG/eZ6T65tlFG6r9xPePcPmh5QUr2VLl06o2lQobtu2k72u9/rHc5/bSFve8UXSkrmGqTMcvPqskPzzuLI93T580HbLX6anw7c6WHnSa2ZuBDwO3ku0X+nrgFLKFT4UQQgghTira3qCV2PKyPdltMLckW3rQSfYg7n8DC8DdwD8BT3T3W6ZqlRBCCCHEGLQqXq+3tsDrdTP70BjZftrda7ZY6LGlB53u/sJp2yCEEEIIMSk6WPJV+hZ5vf5c4E+BE0Om/xFgP3DyDzqFEEIIIbYTrU4DSywE39o6i8P/3LBPLs3sB0cRrEGnEEIIIcQmscUHnU8Hjo2Q/tnAoWET75hB54P+scPMrmzm2qFLdnfDz766w9KB9HcUZ189/Ey31tmnd4+LmZpnfLnD0gHrHscU6ZYXd3P21Z0+uwoKGSmdM4eOlcphi7srbQ9tLNJBJxHWb0ORt0gzu+gsB7pCH2bn/eWukz8ss4ue+7XZPR+HQk5syyC5cb6QwkfFNRmVwobwOlWlWzpg3XSxH+P8qbLU1Y9x7U8R2lLIDevtqLLGzVcXnrJxkJxh7RjH5jhPbH9x7cL+YlwG6ZoEcf2bRD2rkhHanypLmK+q/0n5O+yDw/KEbbDqehR9RhFf7tsT3/Ul7D776k7yHhMfp2RV+Xnc+hzbFcdV+TUMt/yeN66OQTaH8g9d0jturXW4tTLX5tF2w5ITiab/et3drxox/adHSb8lhtVCCCGEEDuB9G5E6WWUpomZPc7MHh2cf7+ZXWFmv2JmY/3Xq0GnEEIIIcQm0eo0Kn+jYGYzZvY/zOxmMzthZv/PzH7RzCY1tvs94GG5rm8B3g/cD/wQ8OvjCNwxr9eFEEIIIaZN2xsVr9dHHiu+Fvhpsq3BbwAeD/wB8A3gt9ZnJZANOL+QH/8QcLW7/4iZPZlsAPqqUQVq0CmEEEIIsUlUvUof4/X6dwB/6e5/nZ9/3cx+mGzwOQmM3hvx7wY+kh/fRrZ++sjo9boQQgghxCbR7jQqfznzZnZK8NtTIerTwHeZWfEK/FuBpwB/MyFTPwe83sxeBDwVKAa3FwB3jSNwxzzpvP/MJs3d2UxBX1jphi8d6F3LeNZc1az2kJl8+dTSTLrHzPXlj2XNHWl3j31hlaUDewK79lTqbp19OivzWdz8tce6NhfpfWG1r1xzR9qszDeZvza38eBZ3XRhFfCF1ZIPlg402RvOEi3l25PUlSp3ISMlP7Rx0CzjVL6Qwi8Fe6N8cXwhZ+5Iu/ZaV+mP5Yezs1P5Qhv2HO9d//Aa7g1mm648tLyndHyti3RLjzm3ZMvMoWPdvGF4WP7W3sH2F+njdDF1M4ZTM6NnDh0r1eNQx8p8s6Qvnq3aisqTsq0ID9tJibx9Fn4K4+tmbw87yzv0W2hzbG/qvLC3b5ZubjOEbX0F2FPSV7rGFT6Oy1Q3Iziug1WyYn9U+Sfsh+I0oT/ithqXJZYR+q6wOTwu9WN5vrj/Kajyd0w4M3zpMeeW7itlsv68iI/7y7j9dK/Bwd6+5UsHmuV6HNgXy4DquhT6uK4+r8w3S31UVVtaekyvfsxfm90jqvrHpQPNrp+Le17M3JF2uQ5G8qpsju0P5fvCSve4vTr9HX8AOuUBZik85/Yo6g3A5QlRvwY8ALjRzNpAE/iv7v6/J2Tqq4A/IVsw/o3u/rU8/AeBa8YRuGMGnUIIIYQQ08YBT6x0FQSdAxwPoqr+o3kB8GNkuwLdADwWeJuZ3eHu7x3XPjN7mLt/1d2/BDw6keTngXYifCAadAohhBBCbBJtb0D9RKLj7n7fEKJ+A/hVd39/fn6dmZ0PXAaMPegE/sXMbgX+CrjC3T8TRrr78riC9U2nEEIIIcQm0e5Y5W9E9hHu8JKLZ/1juzOA1+R//8LM7jKzd5vZc8xsdj2CNegUQgghhNgkOp1G5W9EPgz8VzP7PjN7sJk9D3g18Bfrsc/dl939w+7+n4AHAc8D7gZ+FThqZn9pZj9uZgdGla1BpxBCCCHEJjHBHYl+Fvhz4J3AV4A3ky3o/t8mZatnXOPuv+DuF5F9N3o18FLgNjP7mVHk6ZtOIYQQQohNotMBS7xK76S3o6/E3Y+TzTAfeZH2cXH3m4C3AG8xszOA+uVNInbMoHP5DKO5J7vIttjbMnR5wZhdTEwjy+MGUeRNLRESyo5lzR3pHdvibpYXrM+uWEdBay99OgtdhYww/9yRLE+YvqerE4W1kjYUeUMbQ12hjVXlrpMf+mMQVdes8EsVcXyhf+5I77iqLqTy9ckfsLRQaMOe4/1xhdzukksV5SnKX6SL7WmdfXoybxiWKkPfsjcD/DkMKZ/Ula9O57D2ddtHop3E8uJ2UcfQ6QK7Ssu67K1OF54PWn6ouHZF+yvpG6Cjz9ZBSx9NoA7E8iqvR005+uIr7AzrfqodxH1mQdj/pPxd1S+EbTDsG2PC+Lq+PaW/yDNs/YOaulTjwzhd2EdVyUvdI6r6x+UF6/q5uOfFzB0ZTscgQvnhcXtla+xt3nHDJrM4/IZjZmcDTwYOUH477u7+duDoKPJ2zKBTCCGEEGLauBueGGCmwqaJmb0MeBewSja4DP9LcuDto8rUoFMIIYQQYrPoGJ6aqT767PWN5r/nvze5+4gv/9No0CmEEEIIsUl0OpYcYHa23qBzH/D+SQ04QbPXhRBCCCE2D7fq39bi3cAPTVLgyE86zWwG+FHgo+5+eD3KzewSsu2ULiZfC8rdrwjiDfgl4CeB04DPAj/j7jesR68QQgghxDTwTvZLhW8xLgM+YmbPAq4D1sJId3/1qAJHHnS6e8vMfhd4xKh5E8wBXwT+APhgIv41ZAudvhT4KvB64O/M7OH5UgFDs3JGh8Zs/xVdXugwu1g1225wDUjlnTuSbUl674UNzvhyp3scsnSgWUoX6wrPz/hyh6UDTQD2Vgzz43KE+Yu85XNPxJXDYpvCfHNH2t0ypXRXlbuqjEsHmn1lS5W50FvomzvS7ivfMIT2Lx1odm2ZXbTStYllh/nqSJUnjo8Zpr4V6Qr/DpMvVZZhdY3CoDKL9RPW1WkzbLura6PjtN1JUddHxumK9hb2DVWyYuI+I0wb9ptx+6lrr6FPY3sm5dNh5KTuEVV+XV7oBOee9FnKB6PW9+I+FeYrjjvLW6PtuKe/6dxqE4mA1wHPBP5vfh5PJBqZcb/p/CzZAqG3jJkfAHe/ErgSIHuo2SN/yvkq4I3u/qE87CXAXWSb2//eenQLIYQQQmw2J8vsdbKHfj/u7u+ZlMBxB53vBN5qZucC1wJLYaS7f2m9hgEXAAeBjwVyV8zsKuBJVAw6zWwPsCcImp+ALUIIIYQQ66fq+82tN+hcAf5xkgLHHXR+IP/720GYA5b/ncTz/YP537ui8LuA82vyXUb2HagQQgghxNaiQ7gvSzl8a/FbZFtt/tykBI476LxgUgYMQfzdgCXCQt4EvDU4nwdun7RRQgghhBCj4hXrdCbX7pwu3wY8w8z+HXAD/ROJnj+qwLEGne6+rm85h6T4nPggcGcQfoD+p59d3H2F7JEw0P+tqBBCCCHE1HDSj87GmpqzodwLfGiSAscadJrZi+vi3f0PxzOnxM1kA89LgX/J9e4Gngq8dlRhe442aO7JZhCeuHC5G773xlmqrvTs4jDLmPbnPXrRTJ4fjl7U6B6HzB1pddP5wjJ7b5wN7NpT0n30ogZnfLlFHVl6xxdWgaJcPV3hLMK5I23ueUorP+5VgV5YNhtyeaFcPcJ8Ry+axReWA109P8wuNpLlLmwr29yzMSY1S/ToRTO5TA/OR2+pPTmZ7l5Znbkj7eAaemW+OlK2p+LD6zJcfev595zDw+VLlWVYXaMwqMxi/cwdaXfratZf7KnPsMG2wOBZznVtdNzVJyZB3P9U2VHuz7zbns45XE4T3ldCjl40y+xi774T9s1hvxm3n/A87ouPXjTTvSccvWimZMukfBrqr/NNnCe0NS5D0c/f85RW5IcifSs6b/eVfRi7Q/knLlzuHrdXtsbS5NYxLPFUMxU2Tdz9ZZOWOe7r9d+KzneRrVy/CtwPDDXoNLP9wEOCoAvM7LHAMXe/1czeBrzOzG4CbiKbvn8/8L4x7RZCCCGEmB4VOxJtwW0wJ85Yw353Py367QceDnwa+OERRD2e7Cnmv+Tnb82P/3t+/uvA28hmy38OOBv4nlHX6BRCCCGE2BJ4zW9EzOxsM/tjMztqZveb2RfM7OJxTTOzz5vZaSOk/7SZnT1s+ontve7uN5nZLwB/DFw4ZJ5Pkk0Mqop34PL8J4QQQghxcjOh2ev54PAfgU8AzwaOAP+G7FvMcXks8K1mdmyE9EN/5zOxQWdOGzhrwjKFEEIIIbYFE/ym87XAbdG3l18f37Iu/0DNA8GIkZ7PjjuR6DlxENne6a9kwguJCiGEEEJsGwbPXp+PVt5ZyVfmiXkO8FEz+zOySdaHgHe6+++vw7pxlsQcelnKcZ90XhGdO3A38HHgv4wpc0PZqL3Xw33RC4qZmvde6Jx6o3WPQ5YONLvpTiR0heen3milfcir9v6t23u9fyZiKzguKIel915vdct4okZ3Vbmryjjs3uuzi869F3pXXzgrdpgZ44XMQk6hJ957vWq2bZivjqp9yOtm/NbtrxynK/ybyhfqKWyOw4bZP37U2eij7r2ekj+Kzqq0RfgwsjZixv0gu4Y9T5HaizrMF9ev9ZavLv+wM6Tr2mi8qkbqeJiyVOWtyzPK3uthe0v1DXX3irjPmMTe67OLXurLQqr2h4fh60Pc91TlG3/v9dYG771elr/V9l43wBK3kWAkEg/i3kD6M8NvAV5ONh/mV8jW1fxtM1sZdxWhjV4Sc9x1OrfGugNCCCGEECcTg2evnwOEE6ZTTzkhmwz+OXd/XX7+L2b2SLKB6CSWrpw46/6m0/JnwPmkHyGEEEIIUYF1sl8qPOe4u983hKg7gS9HYV8BfmAd5m0oYz+xNLMXm9l1ZG+HT5jZl8zsRZMzTQghhBBimzG5JZP+kWy5ypDbUwL5AAAgAElEQVSHAZuxa+RYjDuR6NXALwPvICu0AU8G3mVmC+7+m5MzUQghhBBiezDEk85h+U3gGjN7HfCnZN90/mT+25KM+3r9Z4GXRx+q/qWZ3UD2sasGnUIIIYQQMRPakcjd/9nMnge8CfhFsu3DX+XufzIBKzeEcQedDwKuSYRfk8cJIYQQQogI84rZ62PMjHH3jwAfWbdRhQ1m9zDki353P31U+eMOOr8G/AeyKfohLyDbI33Lsedog+ae7BPW+Qt73+cev/EA916YPdNe+FIvfbYszzCfvHa490Lvywswu9joyu6X1VtG48DCfRy/8UDXrrsvXCilv/fC8pIdKQpdBxbu65Yr1BVTpFvjzChsX0lmVb67L1wo6SrKGdoSy6iXX7YxXG4qDg/lh8wd6QtK5u/X37vWha/DdMW1Da/rpAjrTkpuyo6i/HX5UjJC/0yqDHHdnxSFzSej7GHq4ThyIVs+rbh28xfex90XLiT11V2XVJ1ar20LX6qWG4ZX2VoVF8qv0z+Oz+P+J6wXcT+R6m9Cm2YXG6X7SkjRnxfxYd8ctuPhbe1fjm8YGRtRN1P9SFXfkoWX73n99N+r6vqqqrYcyp+/sHfcXtkiC+9UvF4fdUeiDeJVwfEZwOuBjwKfycO+A3gm2SeWIzPuoPOXgA+Y2SVk33Q68BTgu8gGo0IIIYQQImZC22BuBO7+3uLYzD4I/KK7vyNI8ttm9krguxnjU8qxhv3u/kHg24FF4LnA8/Pjb3P3vxhHphBCCCHEdqd4vZ76bTGeCfxtIvyjZIPOkRnpSaeZnRKc3gS8IpVmyPWlhBBCCCF2FoO3wdwqHAWeB/xGFP7cPG5kRn29fi/DuWW4/dGEEEIIIXYQ5hVLJm29QecvAe82s6fR+6bzicCzgP80jsBRB51PD44N+Jtc8aFxlAshhBBC7ChOkied7v4eM/sK8HNkn1Ea2Q5IT3b3z44jc6RBp7tfFZ6bWRv4J3f/f+Mo3yoUswBnDh0LwuZYXhg+f5h3dnF/cJz+bHbuSJvlhbT7T73RSrpnFxvMHWn1bDx4Vp+9sD8rx4VpXWH6uYOzrEZxAKt5mrmDswAsL8yUdIT54nKFM82XF8rxPRmzfTaHNsb+j+3O9O4v6VteMGYX0y21rAs4eFYpX6i7uBan3mil89lF78rJ6kQvX5/8gLmDcVl7NhQ6M5pBuv3lPIG9ZX37mV1sVOcL9MwcOhbUx/K/1nX2xzKqOPXGuVK9GigzIb+k4+BZzBw6xqk3zg22MU9bGV5hf0l2lYwxKWSn9PbpSpwn8wVy436jr37kMrp9UsLHtTbGBPlDwn6iqMNVcmcX95faaNynhC/GwnoQlrGqLMnyhH6srcPl/qewIy5L2J/NLnq3D6jqy2LqZl+H9464/fT8298Xzy7u794TUv10QcpPQ9X3g2eV7g0peRn7S3FzB2e79bNcRzI7C5mrpInvA7G8mFSZwvvUVmWCi8NvOPng8kcnJW/de68LIYQQQojhOJkGnWbWAB4CHCCafO7uV48qT4NOIYQQQohN4mQZdJrZE4H3AeeTvVoPccaYvzOJQecW+wpBCCGEEGKLcpJ80wm8C/gc8H3AnUzAwlGXTPpQFDQLvMvMlsJAd3/+eg0TQgghhNhunCxPOoGHAj/o7l+blMBRn3R+Izr/40kZIoQQQgix3TmJBp2fJfueczqDTnd/2aQUA+TbaP48cDHwIOB57n5FEP8e4CVRts+6+xNH1TV71GnuTu3r2puN2Dq7t3d93azomOUFi/JmNSfcwzveR3zpQLObbj63o0r3vRc6SweyTyf2BnpCnUU5dgXnoa65I+1u+qUDzW66Qi7ArlxmETa76CUdYb7lhQ7zke7QlrDcPRn399kcyt4b+b/4XGRv5Ntsdr91z4tvm+N9hUNdZbk9OYXuwpZCb3FtoNGVs7xgpXwp+VXlScUXMgs5sb9De0N9hX/r8oV5emXpUZcnJaOK0P5BZR5WR2bzYB/XxcfyYpvD8GH9MAzLC9athym5cdgwaQq5Gc2+viKlL+6TwvhUnRqVsJ8odFXJjdto3KcU+eK4YcpSxKd8UFcHoL//CetFVX8GjW57qurL+u3vlPr/+PpVtZ+wb47b6/JCJ/B/p8+nxSzxlJ+Gveah/pS8uCxFnSjCwjpSpC3Od5H2WcoHobyYVD9R3KdC+cVxe3WLvL8+eV6vvx14i5kdBK4D1sJId/9SMlcN055INAd8EfgD4IMVaf4WCAe7W301BCGEEEKIJFVbXm7BxeGLcdn/CsKcbFLR1CYSjY27XwlcCWAWT4zqsuLuh4eVaWZ7gD1B0HxVWiGEEEKIzeQker1+waQFVq9au3V4mpkdMbOvmtnvm9mBAekvI/v2tPjdvuEWCiGEEEIMg9f8xsTMLjMzN7O3TcJEAHe/pe43jsxpv14fxJXAnwG3kI24fxn4uJld7O4rFXneBLw1OJ9HA08hhBBCbAEmvfe6mT0B+Elg5G8sE7KeA1zp7mv5cSXu/lejyt/Sg053/0Bwer2ZfY5sAPp9QLx8U5FnBegOSGte2wshhBBCbCpDvF6fj8YuK1UP2sxsP/AnwE8Ar5+AeVcAB4Ej+XEVY33TeTK8Xu/i7neSDTofOm1bhBBCCCFGpRh0pn45t1P+TPCyGnG/A/y1u//9JGxz94a7HwmOq34jDzhhiz/pjDGzM4BzyVbGH4n2HoM92X8O9yye0g0/ZdZorPWnb6xBe3a4p6Rxfp/Jl2eYbeAzne5xTBF3ZPEUTpm1rl1zkW6f6STzh7TzchzJZZxSY3s70BWmu2fxFB5Aq7LcYT5mvKQr9EF71pLlPpLLD9NVUeV/n3Has42uPp/x5PWrkxvKqdJb2B/KbqxV54sZVHeK+NhvVfbGeQv7htFV1MdR7BuWQfZPQu6kORllF3LbUXudq9BXZ8ekbIzrcJXcujbaruh/Q9YbX0VcX+O2HqYL+4Oq9hTeV0rkfUaqz61rP3XtKeyHwr6gLt+ofhqmPQ+yuUrGPfk9b702VPWPofzwuL1V3nwOXjLpHOB4EFP1lPOFwOOAJ0zQOszsIZNcED5kqoPO/LHwQ4KgC8zsscCx/Hc52ZT9O4EHA78CLAJ/samGCiGEEEJMgCFerx939/tqZZidC/wW8D3uvjxhE79qZoeATxQ/d//6JARP+0nn48kKVFBMAHov8HLg0cCLgVPJBp6fAF7g7uF/AEIIIYQQJwXWcazT/6gzFVbDxcAB4Nrg+88mcImZvRLY4+7tMU18av57GvAOYNbMbgU+Tm8QemgcwdNep/OTZIuMVvHMTTJFCCGEEGLjmcyORP9A9mAu5A+AG4FfW8eAE3f/FPAp4H+Y2S7gO8gGoE8DfhjYY2Zfc/eHjyp72k86hRBCCCF2DJNYHD5/43t9Kb/ZEnDU3a9P5xodd18DrjazfwY+Q/Yw8Ccofxo5NBp0CiGEEEJsEifDjkRmNgs8CXg62RPOJwA3A1eRff541Thyd8ygM1yM1RZ398I7MHOiP30qLMXew8scP2d/WmcnfdyXbnF3Vglzu1K6w/x7D/d/M1yUo5AR6wvzhLpKNi7uBlqVtob5fMZLMqr8lZKfioup83+srzhO+SUkvlYp/bHelJ5hOoaqNIWNhR2h/NQ1S9WtOF1KV8oXYVhVnR0kI6bO/nHkF2HDtL8q+4rwqvhQ9jBlHIWZE4PtGvY8lhtji7v79MX1KyVz2L5tkE3F9S7k1cmtaqNhW06VA+rLUsiuyjuM/bGN8XFVmlhWeF8J8Rmv7HPr2s+g9lTXT1dd92Hre9z3VOUbZHOljfk9bxCD0lTdK0P5peMtsrf5Ru297u5PW5+E3A6zq8gGmf8KXA28HbjK3e9ar+wdM+gUQgghhJg6FTsSrWcbzAnzJHqTtz8JXO3ui5MQfFItDi+EEEIIcTJTzF5P/bYIp5Jtq3k/8FrgkJldZ2bvMLMfNLMzxxWsJ51CCCGEEJvFZGavbxjuvgT8bf7DzOaBp5B93/ka4E/M7CZ3f9SosjXoFEIIIYTYJKwNlnjPbGMvcrThLNHbtOcesskZjxhHkAadQgghhBCbxIQWh98wzKxBtnnP08iebj4ZmAOKXYp+hvLGPkOzYwad1nKskbjILafR6k/fXHY6Q3hn5tAxmo+aY+bQsSD07K7sUM8g++p0h/GhrtbZp3fjw3LE+sI89ti5WrtSYTOHjpXyxelD3aHtdeWui6vzf6yvuexdG0MK3xQU16pOf6NVtr+QHR5X2V3ob519+sA0hS9T1ywsR2hvVbr4uqTqRxyeupah/anzFKF/qupNbMvMoWO0zj69Ni6UG8qKy5OyLSW/ZHPu01R8XVmr/FqSvewlv4U2DrK/7roVNsdtIqWvqoyFzNC3dTbUEdbxog6nrlloZ8qesC3HvurmHXC9Qh/U2RvKSLXR0I6w3VX1B6G8qr6xoOpeUNf+69o1nF3Zx1jLK697lY9TfWWov8q/cZrY5nH6/7p+KrY5VecKuSmfD7oPbxYnwZJJ95INMu8km0j0arJdiP51vYJ3zKBTCCGEEGLabNSSSRPk58kGmV+dtGANOoUQQgghNomt/nrd3X9vo2Rr0CmEEEIIsUmcBK/XNwwNOoUQQgghNouOZ79U+DZHg04hhBBCiE0i3JY7Dt/uaNAphBBCCLFJbPVvOjeSHTPobLSgkViMtdGCXUv9F3rXkrM2Z0PJ3rXksKvfleFyGKllmarSpnQn8wc643LUpR9kV1XeqjLEukPb68pdF1fn/1hf6vrVya3TH+tN6am0O7oedRTxSXsKOWutZNnidH26ds3AWr0Bg+rTsAxb50bVk7ymY9iXyteVXfhpWLnj2D2OzXGeXTNduXGbqKr7dW2iFJfQVaKmHsV1eBydVf1vZd6EjD7Zhc2pdlDTRsN6UdWfVV3fUfr3uK/vC69r1xUyK8Or/JSQVytnGBsS94ix+v+6frSqLSfkpnzuA67TprHFdyTaSHbMoFMIIYQQYtpY27HEu3Rrb/9RZ+LZnxBCCCGE2AiK1+up30hyzC4zs382s+NmdsTMrjCzh2+Q2RNBg04hhBBCiE1iUoNO4KnA7wBPBC4le3v9MTOr3yJriuj1uhBCCCHEZuGe/VLhI4nxZ4XnZvYy4AhwMXD1+AZuHFN90jnMo2Ez22NmbzezRTNbMrO/MrNzpmWzEEIIIcS4WNsrfznzZnZK8NszpOgH5H+P1aaaItN+0lk8Gv7n3JY3kj0avsjdl/I0bwP+PfBC4CjwFuAjZnaxu7eHVWTt7JcK33Wif8GsXSc6tGabQ8nedaID959Iyk4dV9lXpzuZ//4TwCnd+KwcjQHpB9tVlbeqDLEPQ9vryl0XV+f/sj5LXr86uUW+lP5Ybyg7O07nA4I6cMrQ1zuU381TyNm1K1m2OF2frvtPwK5dQ+nvywcUdWoYkvYnZQZyg3o7jNyyrOFtS9sQyC78lLJxoJx0uj67x7E5znP/ia7cuE1U1f26NlGKS+gqJ66uR3EdHk0nFO1kUPutrguZjD7Zhc2pdlDTRsN6UdWfFX1ALG+U/j3u6/vC69p1hczK8OD6putmQX8dHdye+3XFecbq/wMd9T44pbZ/TPl80HXaLIZYMun2KOoNwOW1Ms0MeCvwaXe/fv1WbgxTHXQOejRsZg8A/iPwInf/+zzNjwG3Ad8NfHRzLRZCCCGEWAeDX6+fAxwPYlaGkPoO4DHAU9Zp3YYy7SedMfGj4YuBXcDHigTufoeZXQ88icSgM38MHT6Knt8YU4UQQgghRsM6pVfppfCc4+5+39DyzN4OPAe4xN3jp6Rbii0ze73i0fBBYNXd74mS35XHpbgM+Ebw29IXQAghhBA7iI5Dp5P4jbxkkpnZO4DnA89w95s3xN4JsmUGnfQeDf/wEGmN6rX730T2xLT4adKREEIIIbYGnZrfaPwO8GPAjwDHzexg/ts7OWMny5Z4vV7zaPgwsNvMTouedh4ArknJcvcVgu8fsgeoQgghhBDTxzodzBKToDojjzpfnv/9ZBT+MuA9owrbDKY66Mxfqb8deB7wtMSj4WuBNbJFT/80z/Mg4FHAa0bRNXtvm5ldxdS13gPevfe02Xt4uS99FjY7lOy9h5fxldXuubWsK9taje5xTBHXi28kdVurkcwf6uyVY1+lviJ9SVcpXbWtvrJayleUsay7YLa23GG+yrgK/1vLSvqstY+9h/tXDqiXm+dL6C/0du0PZO89vFyZD/qvR60deXzotyKskGO7dqXrZl6vytezbIcFs3bDa1VnX2j/sKTsHyRzGD2pco9jXypfIbvw07Byx7F7HJvjPL6yGsgN20Qj6aeUHVVxKV0hVjN7Pa7DVTqtZaV2lO63qhlUF+LyFDbH7SClO6Wn7G+I+wNr7euTF/aNMUWflepf69p/ql33yyzfR+J8VX6K41IM055jXX0+rewH0/e0WEedDyBdNwpf9/L2jltrW2T6eqcDiUEnIw463f2ke6o27Sedv0P2WPj7yR8N5+HfcPcT7v4NM3s38BYzO0o2wejNwHXA30/FYiGEEEKIcYlW3iqFb3OmPegc5tHwfwZaZE869wL/ALx0lDU6hRBCCCG2AhN8vX7SMe11Ogc+Gnb3ZeBn858QQgghxMlLu2LWUFuDTiGEEEIIMSm8k/5+0zXoFEIIIYQQk6LjJFd9HHGdzpORHTPoXNvXoLM7m+XnC70ZcKtzu2mclm1gtOee3k5TK6ftYXVu8DKmvm+WldP2sK/ZnzbMH8vyRm+pUV9YZXVud9eulO6V+Wz/372HM50AnOjNCF2da9A4bU9Xxurc7pKu/ftmu+lX5pv4wkp+3Nu8qReW6bKO013sq9ko5bPF3SVdhQ9T5S5khH6P03nD2Hs4KO9pe0pljvOF+lby4+Y99asNFNcq1u8N6+4E0ejq9a7sol4UeYt8fQuhBXVgZb7ZZ3dhQxEP0Fjpye/6KpcT2xumC/WFPi7Cu3UkoTuZJ7I/Tl9FyT+pMifaRVi+KhtXEm1yWPuK8FQ7CWXvK/x0YsjVD1JliVg5bU+5HgZ5Ynsrz2N7mo2uzSvzzW5d9YXVPn2h//bcs5L0UVin+vqtEepAXIdTdTXW2byn7O+V+SaNlf72G+pNliWUEfrgxImSH/vsb6b7ZG9Y1459gb/jdGF4KG91rtHXvxXY4u5SfNg3N1LtJ5dZ+BfynWqa6XtCPHhZmW9m/T1U+ymwPYXvmy3pT8nLyhLIyO8RRf3cmyhDdt/L7jWhH7p6G8b+qB8N5fX5IHXvzu9Thfzi/grQXt0iS5N32kBiWkpn+09V2TGDTiGEEEKIqdPupF+layKREEIIIYSYGA544lX69n+7rkGnEEIIIcSm0W5DatVHvV4XQgghhBATo1OxZJJerwshhBBCiImh2etCCCGEEGKj8U6b1KaKO2GjRQ06AZ/p3xgpFZbC1lr4jGH757phjdUir5eOY4q4uJrFunsyejoBCHQOstnWWn3pR8GivI1VK9ldbfN4/7lVlSWT6934xqp1j7t+qaC4VqGcKr2F/aEdPmOV+aDfR1U2pPQl5QT2xjRWrVKf7Z+DQE9RloH+ieQNSg+D20nKxiIsJb8Iq/XLAPuK8EHtpOunIdvFMNfXZ6xkV5gntrfyPC7n/rlKP/fpi/yX8lEoq8+ncRlHqAN1dbVkT9X1SJSjKj6UUfJBWPejdgD11zCsF1X9Wa8PGCyvnDfdZwxTz1PhoQ3x/QHSdamublbmr5E3rN2jUmvXCPe8LU27DZYYYGrQKYQQQgghJoZXvF5PzWjfZmyRlVKFEEIIIbY/3m5X/kbFzF5hZjeb2bKZXWtm37kBJk8MDTqFEEIIITaLdid7xd73G232upm9AHgb8Ebg3wKfAq40s/Mmb/Rk0KBTCCGEEGKTmOCTzlcD73b3/+nuX3H3VwG3AS+fuNETYsd809leW+4ed0709shtr3ZorWX/XbRavTStteH2aW11VmitLdPq9PZ+7Swv57KdzrJ1j0O8YXSWC3tWaK92una11mZKujvLRjs3udVahkJXp9O1ub3aoLXWoXOi1S1XqKvVWemuAdZeXaZzYqXPrs6JFVqtFu3V3t7rXZ90Vkr5bNkDGT0fFraE5S5kdE7M0Gq1SulKNvb5v9krc9cXzUxmrq+z3KS1ljXU8BrE+YoytPJ6UMgpdBf7+7bWOrRXm91r01prd+W01sr5UvKL8PZqsz8+SNNeLWS2y9cwvL40uvaG+gr/tgJZJV2dFYr/J1utZTrLzT7/9OWJ7E+eJ2ithXYlyhzoDOWWy9kf11qLwsN8VedxeNBOyjbnvi/8FMTXlTVpb0Rrrefnwoa+cteUp7C3L91az8dFXe2cWKW1NtOnryhjlY8L3xY+SNrQJd0HFnU8k9cuXbNQFyTaaKdTkhHGxXWvqiyhjNDnWXij7zhVp/v6n9COoN2F/Vlrrd1rT4FNWd9Ysff6smf3gjw+7Jtba53+9hP1EZD3xdE9prgndJbL/m6vNkv+CK97pY8T7TbU36poS3Ga9upyry8N6khRhmLv9eKeF9O9VwXyQ3mxzal+or3aLMnvnFjtHofjgGmy1lnFE990tug2onmz0iSpFXcvNU4z2w1cDPxqJOZjwJMmZuyEMd/mH66a2dnA7dO2QwghhBBbgnPc/dBmKzWzWeBm4GBNsm8C+6OwN7j75ZGss4BDwJPd/Zog/HXAS9z94RMxesLshCeddwAXAjcC5wDHp2vOlmKebEAuv/Qj31Qj36SRX6qRb6qRb6rZCN/Mk40LNh13XzazC4DdI2aNX0GUxEbn1Wt0bQG2/aDT3d3M7sxPj7v7fVM1aAsRPL6XXyLkm2rkmzTySzXyTTXyTTUb5Jup+tjdl4FJvOdfJFvmO35qegC4awLyNwRNJBJCCCGEOIlw91XgWuDSKOpS4Jr+HFuDbf+kUwghhBBiG/JW4I/M7HPAZ4CfBM4D3jVVq2rYKYPOFeAN1H8XsRORX6qRb6qRb9LIL9XIN9XIN9XINzW4+wfM7AzgF4EHAdcD3+vut0zXsmq2/ex1IYQQQggxffRNpxBCCCGE2HA06BRCCCGEEBuOBp1CCCGEEGLD0aBTCCGEEEJsONt+0GlmrzCzm81s2cyuNbPvnLZNm42ZXW5mHv0OB/GWp7nDzE6Y2SfN7JHTtHkjMLNLzOzDeTndzJ4bxQ/0g5mdZmZ/ZGbfyH9/ZGanbm5JJs8QvnlPog79U5Rmj5m93cwWzWzJzP7KzM7Z3JJMFjO7zMz+2cyOm9kRM7vCzB4epRlYbjM7L/fvUp7ut/O9k09ahvTNJxP15v1Rmm3Xpszs5Wb2JTO7L/99xsyeHcTv1DozyC87sr7sJLb1oNPMXgC8DXgj8G+BTwFXmtl5UzVsOtxAtqRC8Xt0EPca4NXAK4EnAIeBvzOz+c02coOZA75IVs4Uw/jhfcBjgWflv8cCf7RRBm8ig3wD8LeU69D3RvFvA54HvBB4Ctn+wR8xs+bErd08ngr8DvBEskWXZ4CPmdlckKa23Pnfvybz8VPydD8AvGWTyrBRDOMbgN+nXG9+Korfjm3qduAXgMfnv48Dfxn8E7tT68wgv8DOrC87B3fftj/gs8DvRmFfAd40bds22Q+XA1+oiDPgTuC1Qdge4F7gp6Zt+wb6xIHnjuIH4BF5vm8P0jwxD3v4tMu0Ub7Jw94DXFGT5wHAKvCCIOwssm3anjntMk3QN2fm/rlk2HIDz87PzwrSvJBsK7xTpl2mjfJNHvZJ4G01eXZEm8rLdQz4j6ozab+ovuyM37Z90pm/hrgY+FgU9THgSZtv0dR5aP7q9GYze7+ZfUsefgHZ3q1dP7n7CnAVO8tPw/jhO4BvuPtngzT/BHyDneGrp+WvUb9qZr9vZgeCuIuBXZT9dwfZYsXbyTcPyP8ey/8OU+7vAK7Pwws+SvZPzcUbau3mEvum4Efz18M3mNmbozcH275NmVnTzF5I9tTyM6jOAEm/FOzo+rLd2c47Ei0ATfo3vr+LbHCxk/gs8GLgq8ADgdcD1+SvNApfpPx0/qZZOH2G8cNB4Egi7xG2f526Evgz4BayAfovAx83s4vzwflBYNXd74nybZv2ZmZGtu3cp939+jx4mHIfJKpX7n6Pma2yvX0D8CfAzWSfqjwKeBPwrfT2i962bcrMHk02mJoFvgk8z92/bGaPZQfXmSq/5NE7tr7sFLbzoLMg3nLJEmHbGne/Mji9zsw+A/wr8BKgmAyy4/2UM8gPKZ9se1+5+weC0+st2+v3FuD7gA/VZN1OvnkH8Biyb+wGsdPqTdI37v77wen1ZnYT8Dkze5y7f75IlpC3HXzzf8m+NzyV7HvM95rZU2vS75Q6k/SLu395h9eXHcG2fb0OLJJ9ExP/93OA/qdZOwp3XwKuAx5K9h8lyE/D+OEw2ZPimDPZWb7C3e8kG3Q+NA86DOw2s9OipNuiHpnZ24HnAE9399uDqGHKfZioXuXpd7G9fZPi88Aa5XqzLduUu6+6+9fc/XPufhnZRL3/jx1eZ2r8kmLH1JedwrYddLr7KnAtvcfyBZcC12y+RVsHM9tD9kH2nfReZVwaxO8mm5m6k/w0jB8+AzzAzL4tSPPtZN+y7SRfYWZnAOeS1SHI2toaZf89iOwV2UnrG8t4B/B84BnufnOUZJhyfwZ4VB5e8D3ASp7/pGQI36R4JNnAqag3O6lNGdk3mTu2zlRQ+CXFTq4v25Npz2TayB/wArJZgj9ONsj6TbJvSM6ftm2b7Ic3kw2eLgC+HfgwcF/hB+C1ZLO0n0fW8b0PuAOYn7btE/bDfrLXOo8lexXzn/Pj84b1A9m3jV8kmzH5ROBLwIenXbaN9E0e92ayj/gfDDyNrIO/PfLN7wK3Ad9FtkTZPwBfAJrTLt86/PLOvE48lezJU/HbO2y5yb4tvw74+zz+uxHDqN4AAAPJSURBVPL0b592+TbSN8C/AX6RbGmcB5MtsfUVsqdXzUDOtmtTwK8A35mX+9Fky/a1gUt3eJ2p9MtOri876Td1Aza8gPAK4Ov0/kO8ZNo2TcEH7ycbPK0Ch4APAhcF8Ua2rNKdZEtyXAU8atp2b4AfnkY2oIp/7xnWD8DpwB+TDdrvy49PnXbZNtI3wF6ymbNH8jp0Sx5+biRjFng7cBS4n+yfm3OnUZ4J+iXlEwdeOkq5yQbvH8njj+bp90y7fBvpG7In4Vfl5V0Bvgb8FnB6JGfbtSng3cF95wjZ4PFS1Zlqv+zk+rKTfpZfRCGEEEIIITaMbftNpxBCCCGE2Dpo0CmEEEIIITYcDTqFEEIIIcSGo0GnEEIIIYTYcDToFEIIIYQQG44GnUIIIYQQYsPRoFMIIYQQQmw4GnQKIYQQQogNR4NOIYQQQgix4WjQKYQ4KTGz95iZ5781M7vLzP7OzH7czNS3CSHEFkMdsxDiZOZvgQcBDwaeDXyCbL/mj5jZzBTtEkIIEaFBpxDiZGbF3Q+7+yF3/7y7/wrw/WQD0JcCmNmrzew6M1sys9vM7J1mtj+PmzOz+8zsB0OhZvbv8/Tzm10gIYTYrmjQKYTYVrj7x4EvAs/PgzrAzwGPAl4CPAP49TztEvB+4GWRmJcBf+7uxzfDZiGE2AmYu0/bBiGEGBkzew9wqrs/NxH3fuAx7n5RIu6HgN9194X8/NuAa4Dz3P0OM1sA7gAudferNrIMQgixk9CTTiHEdsQABzCzp+cTjA6Z2XHgD4EzzGwOwN3/D3AD8OI874uAW4GrN99sIYTYvmjQKYTYjjwCuNnMzgf+Brge+AHgYuBn8jS7gvT/k94r9pcBf+B6DSSEEBNFg04hxLbCzJ4BPBr4IPB4YAb4L+7+T+7+VeCsRLY/Bs4zs58DHgm8d7PsFUKInYKWFBFCnMzsMbODQBN4IPAs4DLgI2Sv0R9N1s/9rJl9GHgy8NOxEHe/x8w+BPwG8DF3v32T7BdCiB2DnnQKIU5mngXcCXydbM3Op5PNVP9+d2+7+xeAVwOvJXvF/qNkg9IU7wZ2A/9rg20WQogdiWavCyEEYGY/Sraw/Fnuvjpte4QQYruh1+tCiB2Nme0DLiB7Avp7GnAKIcTGoNfrQoidzmuALwB3AW+asi1CCLFt0et1IYQQQgix4ehJpxBCCCGE2HA06BRCCCGEEBuOBp1CCCGEEGLD0aBTCCGEEEJsOBp0CiGEEEKIDUeDTiGEEEIIseFo0CmEEEIIITYcDTqFEEIIIcSG8/8DKQJ6WA7vVdEAAAAASUVORK5CYII=\n", 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -1488,7 +1096,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.6.10" }, "toc": { "base_numbering": 1, diff --git a/examples/results/testperiods_predefClusterOrder.csv b/examples/results/testperiods_predefClusterOrder.csv index 722c42d..dfab68f 100644 --- a/examples/results/testperiods_predefClusterOrder.csv +++ b/examples/results/testperiods_predefClusterOrder.csv @@ -1,193 +1,193 @@ ,TimeStep,GHI,Load,T,Wind -0,0,0.0,367.8357421347344,2.1232965918307456,6.8727431363851315 -0,1,0.0,363.32401619598926,2.4294423695875564,6.8727431363851315 -0,2,0.0,359.0490817955563,2.0212479992451433,5.8909226883301145 -0,3,0.0,359.75945668665145,2.225345184416348,6.8727431363851315 -0,4,0.0,368.43194960796075,2.0212479992451433,6.8727431363851315 -0,5,0.0,389.7981643552341,1.7151022214883325,6.8727431363851315 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+7,20,0.0,480.9583941714285,4.428571428571428,4.142857142857144 +7,21,0.0,468.22658981428566,4.257142857142857,3.4285714285714284 +7,22,0.0,453.61474111428566,4.1714285714285735,3.9857142857142858 +7,23,0.0,427.80793404285714,4.0428571428571445,3.414285714285714 diff --git a/setup.py b/setup.py index 21de831..d0c6dfc 100644 --- a/setup.py +++ b/setup.py @@ -7,7 +7,7 @@ setuptools.setup( name='tsam', - version='1.1.1', + version='1.1.0', author='Leander Kotzur, Maximilian Hoffmann', author_email='l.kotzur@fz-juelich.de, max.hoffmann@fz-juelich.de', description='Time series aggregation module (tsam) to create typical periods', diff --git a/test/test_cluster_order.py b/test/test_cluster_order.py index b0f71a1..f4c82d5 100644 --- a/test/test_cluster_order.py +++ b/test/test_cluster_order.py @@ -21,12 +21,13 @@ def test_cluster_order(): starttime = time.time() aggregation_wind = tsam.TimeSeriesAggregation(raw_wind, noTypicalPeriods = 8, hoursPerPeriod = 24, - clusterMethod = 'hierarchical') + clusterMethod = 'hierarchical', representationMethod='meanRepresentation') typPeriods_wind = aggregation_wind.createTypicalPeriods() aggregation_predefClusterOrder = tsam.TimeSeriesAggregation(raw, noTypicalPeriods=8, hoursPerPeriod=24, clusterMethod='hierarchical', + representationMethod='meanRepresentation', predefClusterOrder=aggregation_wind.clusterOrder) typPeriods_predefClusterOrder = aggregation_predefClusterOrder.createTypicalPeriods() @@ -34,6 +35,7 @@ def test_cluster_order(): aggregation_predefClusterOrderAndClusterCenters = tsam.TimeSeriesAggregation(raw, noTypicalPeriods=8, hoursPerPeriod=24, clusterMethod='hierarchical', + representationMethod='meanRepresentation', predefClusterOrder=aggregation_wind.clusterOrder, predefClusterCenterIndices=aggregation_wind.clusterCenterIndices) From 3406d5495f9ef960fff2d6c0fcb82a484bc56f9d Mon Sep 17 00:00:00 2001 From: "j.schoenau" Date: Tue, 10 Aug 2021 12:56:45 +0200 Subject: [PATCH 11/12] Update news documentation. --- docs/source/newsDoc.rst | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/docs/source/newsDoc.rst b/docs/source/newsDoc.rst index d3d184d..189b5d6 100644 --- a/docs/source/newsDoc.rst +++ b/docs/source/newsDoc.rst @@ -2,8 +2,7 @@ tsam's Change Log ################# -In tsam's latest release (1.1.0) the following functionalities were included: +In tsam's latest release (1.1.1) the following functionalities were included: -* Segmentation - the clustering of adjacent time steps - according to `Pineda et al. (2018) `_ -* k-MILP - an extension of the MILP-based k-medoids clustering that allows automatic identification of extreme periods according to `Zatti et al. (2019) `_ -* The option to dynamically choose whether to clusters found should be represented by their centroid or medoid. \ No newline at end of file +* Significantly increased test coverage +* Separation between clustering and representation, i.e. for clustering algorithms like Ward’s hierarchical clustering algorithm the representation by medoids or centroids can now freely be chosen. From bdda74853bd1d1dabf6389288054589283878cc2 Mon Sep 17 00:00:00 2001 From: "j.schoenau" Date: Tue, 10 Aug 2021 13:05:02 +0200 Subject: [PATCH 12/12] Update setup.py for PyPI and docs/source/conf.py for readTheDocs --- docs/source/conf.py | 4 ++-- setup.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index 50243ee..0a74885 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -66,9 +66,9 @@ # built documents. # # The short X.Y version. -version = '1.1.0' +version = '1.1.1' # The full version, including alpha/beta/rc tags. -release = '1.1.0' +release = '1.1.1' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. diff --git a/setup.py b/setup.py index d0c6dfc..21de831 100644 --- a/setup.py +++ b/setup.py @@ -7,7 +7,7 @@ setuptools.setup( name='tsam', - version='1.1.0', + version='1.1.1', author='Leander Kotzur, Maximilian Hoffmann', author_email='l.kotzur@fz-juelich.de, max.hoffmann@fz-juelich.de', description='Time series aggregation module (tsam) to create typical periods',