From 97d02ac0a57e32c033812cb3c720035fe8ef5777 Mon Sep 17 00:00:00 2001 From: rodo Date: Sun, 11 Feb 2018 13:11:03 +0100 Subject: [PATCH 1/9] change setup to setuptool, using external requirements file --- requirements.txt | 3 +++ setup.py | 23 +++++++++++------------ 2 files changed, 14 insertions(+), 12 deletions(-) create mode 100644 requirements.txt diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..fe81435 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,3 @@ +sklearn>=0.0 +pandas>=0.18.1 +numpy>=1.11.0 diff --git a/setup.py b/setup.py index ace841e..ce6498b 100644 --- a/setup.py +++ b/setup.py @@ -1,17 +1,16 @@ -from distutils.core import setup +import os, setuptools +dir_path = os.path.dirname(os.path.realpath(__file__)) +with open( os.path.join(dir_path,'requirements.txt') ) as f: + required_packages = f.read().splitlines() -setup( +setuptools.setup( name='tsam', - version='0.9.2', + version='0.9.4', author='Leander Kotzur', url='', - packages = ['tsam', - 'tsam.utils',], - install_requires = [ - "sklearn>=0.0", - "pandas>=0.18.1", - "numpy>=1.11.0", - "pyomo>=5.3" - ] + include_package_data=True, # includes all files in sdist that are tracked in git, additionall using the MANIFEST.in to exclude some of them + packages = setuptools.find_packages(), + py_modules = [], + install_requires = required_packages, + setup_requires = ['setuptools-git'] ) - From 0ede758dc2d11f25737c63a1049bab6e7ca72def Mon Sep 17 00:00:00 2001 From: l-kotzur Date: Fri, 23 Mar 2018 09:51:46 +0100 Subject: [PATCH 2/9] correct requirements.txt --- requirements.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/requirements.txt b/requirements.txt index fe81435..dbbe8eb 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,3 +1,4 @@ sklearn>=0.0 pandas>=0.18.1 numpy>=1.11.0 +pyomo>=5.3 \ No newline at end of file From 93777483687fb58e1d9906f7537488374e5148f1 Mon Sep 17 00:00:00 2001 From: l-kotzur Date: Fri, 23 Mar 2018 09:57:27 +0100 Subject: [PATCH 3/9] ignore eggs --- .gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitignore b/.gitignore index 5bc7a4a..b3bb719 100644 --- a/.gitignore +++ b/.gitignore @@ -2,6 +2,7 @@ __pycache__/ trash/ example/.ipynb_checkpoints/ +*.eggs/ # file types to ignore *.pyc From e2d5b89459c6ed86a8760bf0b39f83c4bc0ccb10 Mon Sep 17 00:00:00 2001 From: l-kotzur Date: Fri, 23 Mar 2018 10:29:51 +0100 Subject: [PATCH 4/9] doc correction --- tsam/timeseriesaggregation.py | 5 ----- 1 file changed, 5 deletions(-) diff --git a/tsam/timeseriesaggregation.py b/tsam/timeseriesaggregation.py index 0bd075a..eddfd86 100644 --- a/tsam/timeseriesaggregation.py +++ b/tsam/timeseriesaggregation.py @@ -757,11 +757,6 @@ def createTypicalPeriods(self): ------- self.clusterPeriods All typical Periods in normalized form. - self.clusterOrder - The order of the cluster Periods related to the original profile. - self.clusterPeriodDict - Cluster Periods as dictionary with the profile names as keys in - original scaling. ''' self._preProcessTimeSeries() From 64b221bda4b6969118705684dbbdb5447254d3d0 Mon Sep 17 00:00:00 2001 From: l-kotzur Date: Fri, 23 Mar 2018 13:24:06 +0100 Subject: [PATCH 5/9] add relevant aggregation attributes as property and add example --- example/aggregation_example.ipynb | 110 +++---- example/aggregation_optiinput.ipynb | 480 ++++++++++++++++++++++++++++ tsam/timeseriesaggregation.py | 187 ++++++++--- 3 files changed, 668 insertions(+), 109 deletions(-) create mode 100644 example/aggregation_optiinput.ipynb diff --git a/example/aggregation_example.ipynb b/example/aggregation_example.ipynb index 59c151e..d2d3610 100644 --- a/example/aggregation_example.ipynb +++ b/example/aggregation_example.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# TSAM-Example\n", + "# tsam - 1. Example\n", "Example usage of the time series aggregation module (tsam)\n", "Date: 08.05.2017\n", "\n", @@ -21,9 +21,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "%load_ext autoreload\n", @@ -70,14 +68,25 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", + "\n", "\n", " \n", " \n", @@ -157,7 +166,6 @@ "cell_type": "code", "execution_count": 4, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -186,14 +194,12 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "def plotTS(data, periodlength, vmin, vmax):\n", " fig, axes = plt.subplots(figsize = [6, 2], dpi = 100, nrows = 1, ncols = 1)\n", - " stacked = tsam.groupToPeriods(copy.deepcopy(data), periodlength)\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", @@ -215,7 +221,6 @@ "cell_type": "code", "execution_count": 6, "metadata": { - "collapsed": false, "scrolled": true }, "outputs": [ @@ -223,7 +228,7 @@ "data": { "image/png": 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HlHbIScZ26u+i4BStfClz6RxOiw0QwOZ6oG6TacWze7dpso9V40O21snWP5WW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\nEZFvFZHPishSRH5MRL7gzeZ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m7e8ZETm4yPaXfQT/B4HftxNy80dE5GdJ7yn8T5dM1zAM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\ncdx3QF8AvBV4Xy7L9wPvEZEvUdW9vM93Aa8CvhX4OPBvgHf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CDRWSy6za4n2T05cinnFR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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -251,9 +256,7 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "aggregation = tsam.TimeSeriesAggregation(raw, noTypicalPeriods = 8, hoursPerPeriod = 24, \n", @@ -270,9 +273,7 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "typPeriods = aggregation.createTypicalPeriods()" @@ -288,9 +289,7 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -317,9 +316,7 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "typPeriods.to_csv('testperiods.csv')" @@ -335,9 +332,7 @@ { "cell_type": "code", "execution_count": 11, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "predictedPeriods = aggregation.predictOriginalData()" @@ -353,15 +348,13 @@ { "cell_type": "code", "execution_count": 12, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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X9bknXfWIbY8R5mXaosteKJfSH8qGbZarc+q6yOmbRS4+b2bRF8aqj93UtTYp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\nQb4Bl1H+V36Kcobh9y/a712Iw0WUg6t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Ob8i21Vd6J7Wx6cs5P8Tsh/Q5rU7Xrt6xQtbFaW3v6h2r6HM6XV3IN+tHyKe6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\nqwBuAnCOK8tvE52D8vMmC4eIHIfsC30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/SJtWTBD/FgC/0sHVMwD8lbft3t18B4DLkc/avwbgrQAeAeBvAFzadQ5QQggh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\ndd4iZ8wa8n3g5ZH8Nu3wZgA/BODHAVw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YdAZW9ltJgHtA2ZNOJ46ET8lNO8rpPBvpuUXIrGPE5ibgRZ6r2AnpT6r05X/O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\n", 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fXwW7Fc4mXPa6XxyteNU6FIBjMphCSmaW9XI1cEOIF5H1PYisb3b+DKtnxmSo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\nICIiEmUtiSSzV1Tx0qJNzP1gGwvWpfOAJP0r1sA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oWagrJ2HD3QwOG8zoyNF0DLUVlcs7CVVlUlj4J8disTB//nxWrFhBVlYW7dq1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\nAAAASUVORK5CYII=\n", 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B9Yi8mDGkLTOGtKVTuA/Pjoqptk2UCx1RhRCYhczeGe5djQkmbp3UfCgauO0D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\n240moCUUpbaPIkfrYkyLGcOn/7BQcCjoThz+erLM0jfN1sw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+IQBhoxVElNU+8zNoGJgQ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VSga7kh66Jnj69g6FECTwoHOpxuVyx+DJb+DQ5ugjeugYL9fJKaAcCgdoM81QW3KhG+iMqb9ufd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\n7GXx7sWUV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LFQOQztGerQ4wcwxiw1xjwBxAB/N8asNMbkGmNexP6G2nn1QcsN1AFxl0Qg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\niKIoiqIoIef/AyKDuRpHbBeDAAAAAElFTkSuQmCC\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -660,7 +656,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.4.3" + "version": "3.6.1" } }, "nbformat": 4, diff --git a/example/aggregation_optiinput.ipynb b/example/aggregation_optiinput.ipynb new file mode 100644 index 0000000..fddbb5a --- /dev/null +++ b/example/aggregation_optiinput.ipynb @@ -0,0 +1,480 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# tsam - 2. Example\n", + "Usage of the time series aggregation module (tsam) to output the relevant parameters for a design optimization.\n", + "
Date: 23.03.2018\n", + "\n", + "Author: Leander Kotzur" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Import pandas and the relevant time series aggregation class" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\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": { + "collapsed": true + }, + "outputs": [], + "source": [ + "raw = pd.read_csv('testdata.csv', index_col = 0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Transform the index to a datetime index" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "raw.index = pd.to_datetime(raw.index)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot raw data" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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/MQLw9AEatANuHwbWTwZm7QGCh9h/zczbdBRfnGV5jOiBO8bOXFsiclxgAOaO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\nK5VVzzO2Vy9FhrW6bolA9VnxbSrXNOlzPlbIfWSOZXi+wt1QWfYGin4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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "axes = raw.plot(sharex = True, subplots = True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Aggregate the data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Initialize an aggregation to typical weeks which integrates the day with the minimal temperature and the day with the maximal load as periods." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "aggregation = tsam.TimeSeriesAggregation(raw, noTypicalPeriods = 5, hoursPerPeriod = 24*7, \n", + " clusterMethod = 'hierarchical', \n", + " extremePeriodMethod = 'new_cluster_center',\n", + " addPeakMin = ['T'], addPeakMax = ['Load'] )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create the typical periods" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "typPeriods = aggregation.createTypicalPeriods()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Show the resulting order of aggregated periods" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Calculates how the original index is represented by the old index" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "indexMatching = aggregation.indexMatching()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot the appearance of the 5+2 aggregated periods in the original timeframe" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "visDF = pd.DataFrame(0, index = indexMatching.index,\n", + " columns = aggregation.clusterPeriodIdx)\n", + "for col in visDF.columns:\n", + " visDF.loc[indexMatching['PeriodNum']==col,col] = 1" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "visDF.plot(kind = 'area', cmap = 'viridis', lw = 0, ylim = [0,1])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Get input for potential energy system optimization" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**i. clusterPeriodNoOccur**
The number of occurance of each typical period to evaluate it correctly e.g. in the objective function\n", + "
Note: Period three is only partially evaluated since its appearence in the end of the year exceeds the original time series" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{0: 10, 1: 17, 2: 7, 3: 4.1428571428571423, 4: 8, 5: 3, 6: 3}" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "aggregation.clusterPeriodNoOccur" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[,\n", + " ]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax = pd.Series(aggregation.clusterPeriodNoOccur).plot(kind='bar')\n", + "ax.set(ylabel = 'Number of occurence', xlabel = 'Period index')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**ii. clusterPeriodDict**\n", + "
The dictionary which describes the aggregated time series by an index touple (period number, time step number) the resulting aggregated time series value for each time series, e.g. 'GHI' " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "132.86908868164946" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "aggregation.clusterPeriodDict['GHI'][(aggregation.clusterPeriodIdx[3], aggregation.stepIdx[12])]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Alternatively this is given as data frame" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " GHI T Wind Load\n", + " TimeStep \n", + "0 0 0.0 0.220495 0.307872 416.965693\n", + " 1 0.0 0.008529 0.000000 409.436001\n", + " 2 0.0 -0.203436 0.000000 403.636735\n", + " 3 0.0 -0.097453 0.205248 404.487067\n", + " 4 0.0 -0.203436 0.307872 410.103512" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "aggregation.typicalPeriods.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**iii. clusterOrder**\n", + "
The order of the typical periods to represent the original time series e.g. to model seasonal storage" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([3, 2, 5, 6, 6, 2, 5, 5, 3, 0, 0, 0, 4, 0, 0, 4, 1, 4, 4, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 4, 1, 4, 4, 0, 0, 3, 0, 0, 0, 2,\n", + " 2, 2, 2, 6, 2, 3, 3], dtype=int64)" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "aggregation.clusterOrder" + ] + } + ], + "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": "3.6.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tsam/timeseriesaggregation.py b/tsam/timeseriesaggregation.py index eddfd86..8986dce 100644 --- a/tsam/timeseriesaggregation.py +++ b/tsam/timeseriesaggregation.py @@ -10,11 +10,12 @@ import numpy as np import copy import time +import warnings from sklearn.metrics import mean_squared_error, mean_absolute_error -def groupToPeriods(timeSeries, periodStepNumber): +def unstackToPeriods(timeSeries, timeStepsPerPeriod): ''' Extend the timeseries to an integer multiple of the period length and groups the time series to the periods. @@ -23,37 +24,57 @@ def groupToPeriods(timeSeries, periodStepNumber): ----------- timeSeries, required pandas.DataFrame() - periodStepNumber: integer, required + timeStepsPerPeriod: integer, required The number of discrete timesteps which describe one period. Returns ------- - timeseries + unstackedTimeSeries pandas.DataFrame() which is stacked such that each row represents a candidate period + timeIndex + pandas.Series.index which is the modification of the original + timeseriesindex in case an integer multiple was created ''' - group_index = [] - column_index = [] - if len(timeSeries) % periodStepNumber == 0: + # init new grouped timeindex + unstackedTimeSeries = timeSeries.copy() + + # initalize new indices + periodIndex = [] + stepIndex = [] + + # extend to inger multiple of period length + if len(timeSeries) % timeStepsPerPeriod == 0: attached_timesteps = 0 else: -# raise ValueError() - attached_timesteps = periodStepNumber - \ - len(timeSeries) % periodStepNumber - rep_data = timeSeries.head(attached_timesteps) - timeSeries = timeSeries.append(rep_data, ignore_index=True) - - for ii in range(0, len(timeSeries)): - group_index.append(int(ii / periodStepNumber)) - column_index.append( - ii - int(ii / periodStepNumber) * periodStepNumber) - - timeSeries.index = pd.MultiIndex.from_arrays([column_index, - group_index], - names=['TimeStep', - 'PeriodNum']) - timeSeries = timeSeries.unstack(level='TimeStep') - return timeSeries + # calculate number of timesteps which get attached + attached_timesteps = timeStepsPerPeriod - \ + len(timeSeries) % timeStepsPerPeriod + + # take these from the head of the original time series + rep_data = unstackedTimeSeries.head(attached_timesteps) + + # append them at the end of the time series + unstackedTimeSeries = unstackedTimeSeries.append(rep_data, + ignore_index=False) + + # create period and step index + for ii in range(0, len(unstackedTimeSeries)): + periodIndex.append(int(ii / timeStepsPerPeriod)) + stepIndex.append( + ii - int(ii / timeStepsPerPeriod) * timeStepsPerPeriod) + + # save old index + timeIndex = copy.deepcopy(unstackedTimeSeries.index) + + # create new double index and unstack the time series + unstackedTimeSeries.index = pd.MultiIndex.from_arrays([stepIndex, + periodIndex], + names=['TimeStep', + 'PeriodNum']) + unstackedTimeSeries = unstackedTimeSeries.unstack(level='TimeStep') + + return unstackedTimeSeries, timeIndex @@ -427,7 +448,7 @@ def _preProcessTimeSeries(self): self.normalizedTimeSeries[column] = self.normalizedTimeSeries[ column] * self.weightDict[column] - self.normalizedPeriodlyProfiles = groupToPeriods( + self.normalizedPeriodlyProfiles, self.timeIndex = unstackToPeriods( self.normalizedTimeSeries, self.timeStepsPerPeriod) # check if no NaN is in the resulting profiles @@ -628,7 +649,7 @@ def _rescaleClusterPeriods( series in the typical Periods fits the mean value of the original time series, without changing the values of the extremePeriods. ''' - weightingVec = pd.Series(self.clusterPeriodNoOccur).values + weightingVec = pd.Series(self._clusterPeriodNoOccur).values typicalPeriods = pd.DataFrame( clusterPeriods, columns=self.normalizedPeriodlyProfiles.columns) idx_wo_peak = np.delete(typicalPeriods.index, extremeClusterIdx) @@ -756,7 +777,7 @@ def createTypicalPeriods(self): Returns ------- self.clusterPeriods - All typical Periods in normalized form. + All typical Periods in scaled form. ''' self._preProcessTimeSeries() @@ -779,11 +800,11 @@ def createTypicalPeriods(self): cluster_duration = time.time() if not self.sortValues: # cluster the data - self.clusterCenters, self.clusterOrder = aggregatePeriods( + self.clusterCenters, self._clusterOrder = aggregatePeriods( candidates, n_clusters=self.noTypicalPeriods, n_iter=100, clusterMethod=self.clusterMethod) else: - self.clusterCenters, self.clusterOrder = self._clusterSortedPeriods( + self.clusterCenters, self._clusterOrder = self._clusterSortedPeriods( candidates) self.clusteringDuration = time.time() - cluster_duration @@ -794,10 +815,10 @@ def createTypicalPeriods(self): if not self.extremePeriodMethod == 'None': # overwrite clusterPeriods and clusterOrder - self.clusterPeriods, self.clusterOrder, self.extremeClusterIdx = \ + self.clusterPeriods, self._clusterOrder, self.extremeClusterIdx = \ self._addExtremePeriods(self.normalizedPeriodlyProfiles, self.clusterPeriods, - self.clusterOrder, + self._clusterOrder, extremePeriodMethod = self.extremePeriodMethod, addPeakMin = self.addPeakMin, addPeakMax = self.addPeakMax, @@ -807,17 +828,17 @@ def createTypicalPeriods(self): self.extremeClusterIdx = [] # get number of appearance of the the typical periods - nums,counts = np.unique(self.clusterOrder, return_counts=True) - self.clusterPeriodNoOccur = {num: counts[ii] for ii,num in enumerate(nums)} + nums,counts = np.unique(self._clusterOrder, return_counts=True) + self._clusterPeriodNoOccur = {num: counts[ii] for ii,num in enumerate(nums)} if self.rescaleClusterPeriods: self.clusterPeriods = self._rescaleClusterPeriods( - self.clusterOrder, self.clusterPeriods, self.extremeClusterIdx) + self._clusterOrder, self.clusterPeriods, self.extremeClusterIdx) # if additional time steps have been added, reduce the number of occurance of the typical period which is related to these time steps if not len(self.timeSeries) % self.timeStepsPerPeriod == 0: - self.clusterPeriodNoOccur[self.clusterOrder[-1]] -= (1 - float(len(self.timeSeries) % self.timeStepsPerPeriod)/self.timeStepsPerPeriod) + self._clusterPeriodNoOccur[self._clusterOrder[-1]] -= (1 - float(len(self.timeSeries) % self.timeStepsPerPeriod)/self.timeStepsPerPeriod) # put the clustered data in pandas format and scale back clustered_data_raw = pd.DataFrame( @@ -834,24 +855,57 @@ def prepareEnersysInput(self): Creates all dictionaries and lists which are required for the energysystem optimization input. ''' + warnings.warn('"prepareEnersysInput" is deprecated, since the created attributes can be directly accessed as properties', + DeprecationWarning) + return + + @property + def stepIdx(self): + ''' + Index inside a single cluster + ''' + return [ix for ix in range(0,self.timeStepsPerPeriod)] + + @property + def clusterPeriodIdx(self): + ''' + Index of the clustered periods + ''' if not hasattr(self, 'clusterOrder'): self.createTypicalPeriods() - - self.clusterPeriodIdx = [i for i in range(len(self.clusterPeriods))] - - # how many Periods represents the typical period/ or how often does the - # typical period occur while the observation period - self.clusterPeriodNoOccur = {} - for typPeriod in self.clusterPeriodIdx: - self.clusterPeriodNoOccur[typPeriod] = 0 - for daynum in self.clusterOrder: - self.clusterPeriodNoOccur[daynum] += 1 - - self.clusterPeriodDict = {} - for column in self.typicalPeriods: - self.clusterPeriodDict[column] = self.typicalPeriods[column].to_dict() - - return + return np.sort(np.unique(self._clusterOrder)) + + @property + def clusterOrder(self): + ''' + How often does an typical period occure in the original time series + ''' + if not hasattr(self, '_clusterOrder'): + self.createTypicalPeriods() + return self._clusterOrder + + @property + def clusterPeriodNoOccur(self): + ''' + How often does an typical period occure in the original time series + ''' + if not hasattr(self, 'clusterOrder'): + self.createTypicalPeriods() + return self._clusterPeriodNoOccur + + @property + def clusterPeriodDict(self): + ''' + Time series data for each period index as dictionary + ''' + if not hasattr(self, '_clusterOrder'): + self.createTypicalPeriods() + if not hasattr(self, '_clusterPeriodDict'): + self._clusterPeriodDict = {} + for column in self.typicalPeriods: + self._clusterPeriodDict[column] = self.typicalPeriods[column].to_dict() + return self._clusterPeriodDict + def predictOriginalData(self): ''' @@ -863,11 +917,11 @@ def predictOriginalData(self): pandas.DataFrame DataFrame which has the same shape as the original one. ''' - if not hasattr(self, 'clusterOrder'): + if not hasattr(self, '_clusterOrder'): self.createTypicalPeriods() new_data = [] - for label in self.clusterOrder: + for label in self._clusterOrder: new_data.append(self.clusterPeriods[label]) # back in matrix @@ -886,6 +940,35 @@ def predictOriginalData(self): self.normalizedPredictedData) return self.predictedData + + def indexMatching(self): + ''' + Relates the index of the original time series with the indices + represented by the clusters + + Returns + ------- + pandas.DataFrame + DataFrame which has the same shape as the original one. + ''' + if not hasattr(self, '_clusterOrder'): + self.createTypicalPeriods() + + # create aggregated period and timestep index lists + periodIndex = [] + stepIndex = [] + for label in self._clusterOrder: + for step in range(self.timeStepsPerPeriod): + periodIndex.append(label) + stepIndex.append(step) + + # create a dataframe + timeStepMatching = \ + pd.DataFrame([periodIndex,stepIndex], + index=['PeriodNum','TimeStep'], + columns=self.timeIndex).T + + return timeStepMatching def accuracyIndicators(self): ''' From 2869b5cfbfbb44c169655ca9b2b7d67529835579 Mon Sep 17 00:00:00 2001 From: l-kotzur Date: Fri, 23 Mar 2018 13:30:43 +0100 Subject: [PATCH 6/9] update readme with second example and publication --- README.md | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 8fd4e81..88ac15a 100644 --- a/README.md +++ b/README.md @@ -6,6 +6,8 @@ tsam is a python package which uses different machine learning algorithms for th A publication which validates the methods and describes their cababilites is found [`here`](https://www.sciencedirect.com/science/article/pii/S0960148117309783). The manuscript is found [`here`](https://arxiv.org/abs/1708.00420). If you want to use tsam in a published work, please kindly cite that publication. +A [second publication](https://www.sciencedirect.com/science/article/pii/S0306261918300242) introduces a method how to model states (e.g. state of charge of storage) between the aggregated typical periods. + [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.597956.svg)](https://doi.org/10.5281/zenodo.597956) ## Features @@ -59,7 +61,9 @@ Store the results as .csv file typPeriods.to_csv('typperiods.csv') -A more detailed example showing the capabilites of tsam is found [`here`](https://github.com/FZJ-IEK3-VSA/tsam/blob/master/example/aggregation_example.ipynb) as jupyter notebook. +A [first example](example/aggregation_example.ipynb) shows the capabilites of tsam as jupyter notebook. + +A [second example](example/aggregation_example.ipynb) shows in more detail how to access the relevant aggregation results required for paramtrizing e.g. an optimization. The example time series are based on a department [publication](http://www.mdpi.com/1996-1073/10/3/361) and the [test reference years of the DWD](http://www.dwd.de/DE/leistungen/testreferenzjahre/testreferenzjahre.html). From b4dd48da180166c4ca47662f63314c07f7393aac Mon Sep 17 00:00:00 2001 From: l-kotzur Date: Fri, 23 Mar 2018 13:33:23 +0100 Subject: [PATCH 7/9] minor formatting --- README.md | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 88ac15a..ac1fe3b 100644 --- a/README.md +++ b/README.md @@ -6,7 +6,7 @@ tsam is a python package which uses different machine learning algorithms for th A publication which validates the methods and describes their cababilites is found [`here`](https://www.sciencedirect.com/science/article/pii/S0960148117309783). The manuscript is found [`here`](https://arxiv.org/abs/1708.00420). If you want to use tsam in a published work, please kindly cite that publication. -A [second publication](https://www.sciencedirect.com/science/article/pii/S0306261918300242) introduces a method how to model states (e.g. state of charge of storage) between the aggregated typical periods. +A ['second publication'](https://www.sciencedirect.com/science/article/pii/S0306261918300242) introduces a method how to model states (e.g. state of charge of storage) between the aggregated typical periods. [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.597956.svg)](https://doi.org/10.5281/zenodo.597956) @@ -37,6 +37,8 @@ In order to use the k-medoids clustering, make sure that you have installed a MI ## Examples +### Basic workflow + A small example how tsam can be used is decribed as follows import pandas as pd @@ -61,9 +63,11 @@ Store the results as .csv file typPeriods.to_csv('typperiods.csv') -A [first example](example/aggregation_example.ipynb) shows the capabilites of tsam as jupyter notebook. +### Detailed examples + +A ['first example'](example/aggregation_example.ipynb) shows the capabilites of tsam as jupyter notebook. -A [second example](example/aggregation_example.ipynb) shows in more detail how to access the relevant aggregation results required for paramtrizing e.g. an optimization. +A ['second example'](example/aggregation_example.ipynb) shows in more detail how to access the relevant aggregation results required for paramtrizing e.g. an optimization. The example time series are based on a department [publication](http://www.mdpi.com/1996-1073/10/3/361) and the [test reference years of the DWD](http://www.dwd.de/DE/leistungen/testreferenzjahre/testreferenzjahre.html). From 635bb47364e7ce19cdbdb84321ffc16d5a427be1 Mon Sep 17 00:00:00 2001 From: l-kotzur Date: Fri, 23 Mar 2018 13:33:59 +0100 Subject: [PATCH 8/9] add new release --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index ce6498b..37c1e97 100644 --- a/setup.py +++ b/setup.py @@ -5,7 +5,7 @@ setuptools.setup( name='tsam', - version='0.9.4', + version='0.9.5', author='Leander Kotzur', url='', include_package_data=True, # includes all files in sdist that are tracked in git, additionall using the MANIFEST.in to exclude some of them From 2d593f637f80d797fcd2041337af5194afc440e6 Mon Sep 17 00:00:00 2001 From: l-kotzur Date: Fri, 23 Mar 2018 13:38:38 +0100 Subject: [PATCH 9/9] formatting --- README.md | 23 +++++++++++++++-------- 1 file changed, 15 insertions(+), 8 deletions(-) diff --git a/README.md b/README.md index ac1fe3b..4cd0ff9 100644 --- a/README.md +++ b/README.md @@ -4,9 +4,9 @@ tsam is a python package which uses different machine learning algorithms for the aggregation of typical periods. It is applicable for all type of time series, eather weather data, load data or both simultaneously. The module is able to significantly reduce input time series for energy system models, and therefore the model's complexity and computational time. -A publication which validates the methods and describes their cababilites is found [`here`](https://www.sciencedirect.com/science/article/pii/S0960148117309783). The manuscript is found [`here`](https://arxiv.org/abs/1708.00420). If you want to use tsam in a published work, please kindly cite that publication. +A publication which validates the methods and describes their cababilites is found [**here**](https://www.sciencedirect.com/science/article/pii/S0960148117309783). The manuscript is found [`here`](https://arxiv.org/abs/1708.00420). If you want to use tsam in a published work, please kindly cite that publication. -A ['second publication'](https://www.sciencedirect.com/science/article/pii/S0306261918300242) introduces a method how to model states (e.g. state of charge of storage) between the aggregated typical periods. +A [**second publication**](https://www.sciencedirect.com/science/article/pii/S0306261918300242) introduces a method how to model states (e.g. state of charge of storage) between the aggregated typical periods. [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.597956.svg)](https://doi.org/10.5281/zenodo.597956) @@ -40,34 +40,41 @@ In order to use the k-medoids clustering, make sure that you have installed a MI ### Basic workflow A small example how tsam can be used is decribed as follows - +```python import pandas as pd import tsam.timeseriesaggregation as tsam +``` + Read in the time series data set with pandas - +```python raw = pd.read_csv('testdata.csv', index_col = 0) +``` Initialize an aggregation object and define the number of typical periods, the length of a single period and the aggregation method - +```python aggregation = tsam.TimeSeriesAggregation(raw, noTypicalPeriods = 8, hoursPerPeriod = 24, clusterMethod = 'hierarchical') +``` Run the aggregation to typical periods - +```python typPeriods = aggregation.createTypicalPeriods() +``` Store the results as .csv file +```python typPeriods.to_csv('typperiods.csv') +``` ### Detailed examples -A ['first example'](example/aggregation_example.ipynb) shows the capabilites of tsam as jupyter notebook. +A [**first example**](example/aggregation_example.ipynb) shows the capabilites of tsam as jupyter notebook. -A ['second example'](example/aggregation_example.ipynb) shows in more detail how to access the relevant aggregation results required for paramtrizing e.g. an optimization. +A [**second example**](example/aggregation_example.ipynb) shows in more detail how to access the relevant aggregation results required for paramtrizing e.g. an optimization. The example time series are based on a department [publication](http://www.mdpi.com/1996-1073/10/3/361) and the [test reference years of the DWD](http://www.dwd.de/DE/leistungen/testreferenzjahre/testreferenzjahre.html).