Mercurial > dive4elements > river
view flys-artifacts/src/main/java/de/intevation/flys/artifacts/model/fixings/FixCalculation.java @ 4357:11cf29eb002c
FacetTypes: New type: exreme_wq_curve_base (for the Stuetz-Q-Poits).
author | Felix Wolfsteller <felix.wolfsteller@intevation.de> |
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date | Fri, 02 Nov 2012 11:58:11 +0100 |
parents | 3cda41b5eb23 |
children | 345f3bba6f15 |
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package de.intevation.flys.artifacts.model.fixings; import de.intevation.artifacts.common.utils.StringUtils; import de.intevation.flys.artifacts.access.FixAccess; import de.intevation.flys.artifacts.math.fitting.Function; import de.intevation.flys.artifacts.math.fitting.FunctionFactory; import de.intevation.flys.artifacts.model.Calculation; import de.intevation.flys.artifacts.model.CalculationResult; import de.intevation.flys.artifacts.model.FixingsColumn; import de.intevation.flys.artifacts.model.FixingsColumnFactory; import de.intevation.flys.artifacts.model.FixingsOverview.Fixing.Filter; import de.intevation.flys.artifacts.model.FixingsOverview.Fixing; import de.intevation.flys.artifacts.model.FixingsOverview.IdsFilter; import de.intevation.flys.artifacts.model.FixingsOverview; import de.intevation.flys.artifacts.model.FixingsOverviewFactory; import de.intevation.flys.artifacts.model.Parameters; import de.intevation.flys.utils.DoubleUtil; import de.intevation.flys.utils.KMIndex; import java.util.ArrayList; import java.util.Date; import java.util.HashMap; import java.util.List; import java.util.Map; import org.apache.log4j.Logger; public abstract class FixCalculation extends Calculation { private static Logger log = Logger.getLogger(FixCalculation.class); public static final double EPSILON = 1e-4; public static final String [] STANDARD_COLUMNS = { "km", "chi_sqr", "max_q", "std-dev" }; protected static class FitResult { protected Parameters parameters; protected KMIndex<QWD []> referenced; protected KMIndex<QWI []> outliers; public FitResult() { } public FitResult( Parameters parameters, KMIndex<QWD []> referenced, KMIndex<QWI []> outliers ) { this.parameters = parameters; this.referenced = referenced; this.outliers = outliers; } public Parameters getParameters() { return parameters; } public KMIndex<QWD []> getReferenced() { return referenced; } public KMIndex<QWI []> getOutliers() { return outliers; } } // class FitResult /** Helper class to bundle the meta information of a column * and the real data. */ protected static class Column { protected Fixing.Column meta; protected FixingsColumn data; protected int index; public Column() { } public Column(Fixing.Column meta, FixingsColumn data, int index) { this.meta = meta; this.data = data; this.index = index; } public Date getDate() { return meta.getStartTime(); } public String getDescription() { return meta.getDescription(); } public int getIndex() { return index; } public boolean getQW( double km, double [] qs, double [] ws, int index ) { qs[index] = data.getQ(km); return data.getW(km, ws, index); } public boolean getQW(double km, double [] wq) { data.getW(km, wq, 0); if (Double.isNaN(wq[0])) return false; wq[1] = data.getQ(km); return !Double.isNaN(wq[1]); } } // class Column /** * Helper class to find the data belonging to meta info more quickly. */ protected static class ColumnCache { protected Map<Integer, Column> columns; public ColumnCache() { columns = new HashMap<Integer, Column>(); } public Column getColumn(Fixing.Column meta) { Integer key = meta.getId(); Column column = columns.get(key); if (column == null) { FixingsColumn data = FixingsColumnFactory .getInstance() .getColumnData(meta); if (data != null) { column = new Column(meta, data, columns.size()); columns.put(key, column); } } return column; } } // class ColumnCache protected String river; protected double from; protected double to; protected double step; protected boolean preprocessing; protected String function; protected int [] events; protected int qSectorStart; protected int qSectorEnd; public FixCalculation() { } public FixCalculation(FixAccess access) { String river = access.getRiver(); Double from = access.getFrom(); Double to = access.getTo(); Double step = access.getStep(); String function = access.getFunction(); int [] events = access.getEvents(); Integer qSectorStart = access.getQSectorStart(); Integer qSectorEnd = access.getQSectorEnd(); Boolean preprocessing = access.getPreprocessing(); if (river == null) { addProblem("fix.missing.river"); } if (from == null) { addProblem("fix.missing.from"); } if (to == null) { addProblem("fix.missing.to"); } if (step == null) { addProblem("fix.missing.step"); } if (function == null) { addProblem("fix.missing.function"); } if (events == null || events.length < 1) { addProblem("fix.missing.events"); } if (qSectorStart == null) { addProblem("fix.missing.qstart.sector"); } if (qSectorEnd == null) { addProblem("fix.missing.qend.sector"); } if (preprocessing == null) { addProblem("fix.missing.preprocessing"); } if (!hasProblems()) { this.river = river; this.from = from; this.to = to; this.step = step; this.function = function; this.events = events; this.qSectorStart = qSectorStart; this.qSectorEnd = qSectorEnd; this.preprocessing = preprocessing; } } protected static String toString( String [] parameterNames, double [] values ) { StringBuilder sb = new StringBuilder(); for (int i = 0; i < parameterNames.length; ++i) { if (i > 0) sb.append(", "); sb.append(parameterNames[i]).append(": ").append(values[i]); } return sb.toString(); } protected Filter createFilter() { return new IdsFilter(events); } protected List<Column> getEventColumns( FixingsOverview overview, ColumnCache cc ) { FixingsColumnFactory fcf = FixingsColumnFactory.getInstance(); Filter filter = createFilter(); List<Fixing.Column> metas = overview.filter(null, filter); List<Column> columns = new ArrayList<Column>(metas.size()); for (Fixing.Column meta: metas) { Column data = cc.getColumn(meta); if (data == null) { addProblem("fix.cannot.load.data"); } else { columns.add(data); } } return columns; } protected FitResult doFitting( FixingsOverview overview, ColumnCache cc, Function func ) { boolean debug = log.isDebugEnabled(); final List<Column> eventColumns = getEventColumns(overview, cc); if (eventColumns.size() < 2) { addProblem("fix.too.less.data.columns"); return null; } final double [] qs = new double[eventColumns.size()]; final double [] ws = new double[qs.length]; final boolean [] interpolated = new boolean[ws.length]; Fitting.QWDFactory qwdFactory = new Fitting.QWDFactory() { @Override public QWD create(double q, double w) { // Check all the event columns for close match // and take the description and the date from meta. for (int i = 0; i < qs.length; ++i) { if (Math.abs(qs[i]-q) < EPSILON && Math.abs(ws[i]-w) < EPSILON) { Column column = eventColumns.get(i); return new QWD( qs[i], ws[i], column.getDescription(), column.getDate(), interpolated[i], 0d, column.getIndex()); } } log.warn("cannot find column for (" + q + ", " + w + ")"); return new QWD(q, w); } }; Fitting fitting = new Fitting(func, qwdFactory, preprocessing); String [] parameterNames = func.getParameterNames(); Parameters results = new Parameters( StringUtils.join(STANDARD_COLUMNS, parameterNames)); boolean invalid = false; double [] kms = DoubleUtil.explode(from, to, step / 1000.0); if (debug) { log.debug("number of kms: " + kms.length); } KMIndex<QWI []> outliers = new KMIndex<QWI []>(); KMIndex<QWD []> referenced = new KMIndex<QWD []>(kms.length); int kmIndex = results.columnIndex("km"); int chiSqrIndex = results.columnIndex("chi_sqr"); int maxQIndex = results.columnIndex("max_q"); int stdDevIndex = results.columnIndex("std-dev"); int [] parameterIndices = results.columnIndices(parameterNames); int numFailed = 0; for (int i = 0; i < kms.length; ++i) { double km = kms[i]; // Fill Qs and Ws from event columns. for (int j = 0; j < ws.length; ++j) { interpolated[j] = !eventColumns.get(j).getQW(km, qs, ws, j); } fitting.reset(); if (!fitting.fit(qs, ws)) { ++numFailed; addProblem(km, "fix.fitting.failed"); continue; } referenced.add(km, fitting.referencedToArray()); if (fitting.hasOutliers()) { outliers.add(km, fitting.outliersToArray()); } int row = results.newRow(); double [] values = fitting.getParameters(); results.set(row, kmIndex, km); results.set(row, chiSqrIndex, fitting.getChiSquare()); results.set(row, stdDevIndex, fitting.getStandardDeviation()); results.set(row, maxQIndex, fitting.getMaxQ()); invalid |= results.set(row, parameterIndices, values); if (debug) { log.debug("km: "+km+" " + toString(parameterNames, values)); } } if (debug) { log.debug("success: " + (kms.length - numFailed)); log.debug("failed: " + numFailed); } if (invalid) { addProblem("fix.invalid.values"); results.removeNaNs(); } outliers.sort(); referenced.sort(); return new FitResult( results, referenced, outliers); } public CalculationResult calculate() { FixingsOverview overview = FixingsOverviewFactory.getOverview(river); if (overview == null) { addProblem("fix.no.overview.available"); } Function func = FunctionFactory.getInstance() .getFunction(function); if (func == null) { addProblem("fix.invalid.function.name"); } if (hasProblems()) { return new CalculationResult(this); } return innerCalculate(overview, func); } protected abstract CalculationResult innerCalculate( FixingsOverview overview, Function function ); } // vim:set ts=4 sw=4 si et sta sts=4 fenc=utf8 :