public static async Task <BRAZILmodelDataSource> CreateAsync(IStorageContext dataContext)
{
var storageDefinition = dataContext.StorageDefinition;
var latIntegratorTask = LinearIntegratorsFactory.SmartConstructAsync(dataContext, IntegratorsFactoryHelpers.AutodetectLatName(storageDefinition));
var lonIntegratorTask = LinearIntegratorsFactory.SmartConstructAsync(dataContext, IntegratorsFactoryHelpers.AutodetectLonName(storageDefinition));
var timeAxis = Enumerable.Range(0, 47).ToArray();
var timeIntegrator = new TimeAxisAvgProcessing.TimeAxisAvgFacade(
timeAxis,
new TimeAxisProjections.ContinousYears(referenceYear),
new WeightProviders.StepFunctionInterpolation(),
new DataCoverageEvaluators.ContinousMeansCoverageEvaluator());
var latIntegrator = await latIntegratorTask;
var lonIntegrator = await lonIntegratorTask;
var baseNodeUncertainty = new NoBaseUncertaintyProvider();
var temporalVarianceCalculaator = new LinearCombination1DVarianceCalc(new StorageContextMetadataTimeVarianceExtractor(storageDefinition), timeIntegrator);
var spatialVarianceCalculator = new LinearCombinationOnSphereVarianceCalculator(new StorageContextMetadataSpatialVarianceExtractor(storageDefinition), latIntegrator, lonIntegrator);
var gaussianFieldUncertaintyEvaluator = new SequentialTimeSpatialUncertaintyEvaluatorFacade(timeIntegrator, latIntegrator, lonIntegrator, temporalVarianceCalculaator, spatialVarianceCalculator, baseNodeUncertainty);
var coverageCheckUncertaintyEvaluator = new GridUncertaintyConventionsDecorator(gaussianFieldUncertaintyEvaluator, latIntegrator, lonIntegrator, timeIntegrator);
var variablePresenceCheckEvaluator = new VariablePresenceCheckDecorator(dataContext.StorageDefinition, coverageCheckUncertaintyEvaluator);
var gridAggregator = new GridMeanAggregator(dataContext, timeIntegrator, latIntegrator, lonIntegrator, true);
var clusteringAggregator = new GridClusteringDecorator(dataContext.StorageDefinition, gridAggregator, timeIntegrator, latIntegrator, lonIntegrator);
var scaledAggregator = new Microsoft.Research.Science.FetchClimate2.ValueAggregators.LinearTransformDecorator(dataContext, clusteringAggregator);
gridAggregator.SetMissingValue("RoadDensityData", -9999.0);
gridAggregator.SetMissingValue("Deforestation", -9999.0);
return(new BRAZILmodelDataSource(dataContext, variablePresenceCheckEvaluator, scaledAggregator));
}
public static async Task <CpcDataHandler> CreateAsync(IStorageContext dataContext)
{
var storageDefinition = dataContext.StorageDefinition;
var timeAxis = await dataContext.GetDataAsync("time");
var timeIntegrator = new TimeAxisAvgProcessing.TimeAxisAvgFacade(
timeAxis,
new TimeAxisProjections.ContinuousDays(new DateTime(1948, 1, 1)),
new WeightProviders.StepFunctionInterpolation(),
new DataCoverageEvaluators.ContinousMeansCoverageEvaluator()
);
var latIntegratorTask = LinearIntegratorsFactory.SmartConstructAsync(dataContext, IntegratorsFactoryHelpers.AutodetectLatName(storageDefinition));
var lonIntegratorTask = LinearIntegratorsFactory.SmartConstructAsync(dataContext, IntegratorsFactoryHelpers.AutodetectLonName(storageDefinition));
var latIntegrator = await latIntegratorTask;
var lonIntegrator = await lonIntegratorTask;
var baseNodeUncertainty = new CpcBaseUnceratinty();
var temporalVarianceCalculaator = new LinearCombination1DVarianceCalc(new StorageContextMetadataTimeVarianceExtractor(storageDefinition), timeIntegrator);
var spatialVarianceCalculator = new LinearCombinationOnSphereVarianceCalculator(new StorageContextMetadataSpatialVarianceExtractor(storageDefinition), latIntegrator, lonIntegrator);
var gaussianFieldUncertaintyEvaluator = new SequentialTimeSpatialUncertaintyEvaluatorFacade(timeIntegrator, latIntegrator, lonIntegrator, temporalVarianceCalculaator, spatialVarianceCalculator, baseNodeUncertainty);
var coverageCheckUncertaintyEvaluator = new GridUncertaintyConventionsDecorator(gaussianFieldUncertaintyEvaluator, latIntegrator, lonIntegrator, timeIntegrator);
var variablePresenceCheckEvaluator = new VariablePresenceCheckDecorator(dataContext.StorageDefinition, coverageCheckUncertaintyEvaluator);
var gridAggregator = new GridMeanAggregator(dataContext, timeIntegrator, latIntegrator, lonIntegrator, true);
var clusteringAggregator = new GridClusteringDecorator(dataContext.StorageDefinition, gridAggregator, timeIntegrator, latIntegrator, lonIntegrator);
var scaledAggregator = new Microsoft.Research.Science.FetchClimate2.ValueAggregators.LinearTransformDecorator(dataContext, clusteringAggregator);
return(new CpcDataHandler(dataContext, variablePresenceCheckEvaluator, scaledAggregator));
}
public static async Task <DataHandler> CreateAsync(IStorageContext dataContext)
{
IScatteredPointContextBasedLinearWeightProviderOnSphere <IDelaunay_Voronoi> weightsProvider = new AlexNNIAdapter();
IAsyncMap <INodes, IDelaunay_Voronoi> interpolationContextFactory = new AlexNNIContextProvider();
//NOTE: interpolation context is cached in the produced interpolators (e.g. Delaunay triangulation is computed only once for each nodes set)
//We can use one cache for all of the requests (cache is not cleaned) as we use AllStationsStationLocator which returns all stations for any request
IScatteredPointsLinearInterpolatorOnSphereFactory pointsInterpolatorOnSphereFactory = new CachingLinearWeightsProviderFactory2 <IDelaunay_Voronoi>(weightsProvider, interpolationContextFactory);
int stationsCount = dataContext.StorageDefinition.DimensionsLengths["stations"];
var timeAxis = await dataContext.GetDataAsync("time");
ITimeAxisAvgProcessing timeAxisIntegrator = new TimeAxisAvgProcessing.TimeAxisAvgFacade(
timeAxis,
new TimeAxisProjections.DateTimeMoments(),
new WeightProviders.StepFunctionInterpolation(),
new DataCoverageEvaluators.ContinousMeansCoverageEvaluator());
IStationLocator stationLocator = new AllStationsStationLocator(stationsCount);
ICellRequestMapFactory <RealValueNodes> timeSeriesAveragerFactory = (await TimeIntegratorBasedAveragerFactory.CreateAsync(dataContext, timeAxisIntegrator, stationLocator));
ICellRequestMap <RealValueNodes> timeSeriesAverager = await timeSeriesAveragerFactory.CreateAsync();
//NOTE: Here we engage caching of timeseries. Timeseries is cached for all cells having the same time segment. It is acheived by HashBasedTimeSegmentOnlyConverter
ICellRequestMapFactory <RealValueNodes> cachingTimeSeriesAveragerFactory = new CellRequestMapCachingFactory <RealValueNodes>(timeSeriesAverager, new HashBasedTimeSegmentOnlyConverter());
ILinearCombintaionContextFactory compContextFactory = new LinearWeightsContextFactoryFacade <RealValueNodes>(pointsInterpolatorOnSphereFactory, cachingTimeSeriesAveragerFactory);
//NOTE: Hege caching is engaged as well. We cache the variogram for the cells with the same corresponding node set.
IVariogramProvider lmVariogramFitter = new LmDotNetVariogramProvider();
IVariogramProviderFactory variogramProviderFactory = new VariogramProviderCachingFactory(lmVariogramFitter);
IUncertaintyEvaluatorOfLinearCombination uncertatintyEvaluator = new PointsGausianFieldUncertaintyEvaluator(variogramProviderFactory);
return(new DataHandler(dataContext, compContextFactory, uncertatintyEvaluator));
}
public static async Task <GenericLinearGridDateTimeMomentsDataHandler> CreateAsync(IStorageContext dataContext)
{
var timeIntegrator = new TimeAxisAvgProcessing.TimeAxisAvgFacade(
await TimeAxisAutodetection.GetTimeAxisAsync(dataContext),
new TimeAxisProjections.DateTimeMoments(),
new WeightProviders.StepFunctionInterpolation(),
new DataCoverageEvaluators.IndividualObsCoverageEvaluator());
var a = await GenericLinearGridDataHandlerHelper.EasyConstructAsync(dataContext, timeIntegrator);
var uncertatintyEvaluator = a.Item1;
var valuesAggregator = a.Item2;
return(new GenericLinearGridDateTimeMomentsDataHandler(dataContext, uncertatintyEvaluator, valuesAggregator));
}
public static async Task <CESM1BGCDataHandler> CreateAsync(IStorageContext dataContext)
{
var storageDefinition = dataContext.StorageDefinition;
var timeAxis = await dataContext.GetDataAsync("time");
var timeIntegrator = new TimeAxisAvgProcessing.TimeAxisAvgFacade(
timeAxis,
new TimeAxisProjections.ContinuousDays(new DateTime(2005, 1, 1)),
new WeightProviders.StepFunctionInterpolation(),
new DataCoverageEvaluators.ContinousMeansCoverageEvaluator()
);
var latIntegratorTask = LinearIntegratorsFactory.SmartConstructAsync(dataContext, IntegratorsFactoryHelpers.AutodetectLatName(storageDefinition));
var lonIntegratorTask = LinearIntegratorsFactory.SmartConstructAsync(dataContext, IntegratorsFactoryHelpers.AutodetectLonName(storageDefinition));
var latIntegrator = await latIntegratorTask;
var lonIntegrator = await lonIntegratorTask;
var thinningTimeIntegrator = new TimeAxisIntegratorThinningDecorator(timeIntegrator);
var thinningLatIntegrator = new SpatGridIntegatorThinningDecorator(latIntegrator);
var thinningLonIntegrator = new SpatGridIntegatorThinningDecorator(lonIntegrator);
var baseNodeUncertainty = new NoBaseUncertaintyProvider();
var temporalVarianceCalculaator = new LinearCombination1DVarianceCalc(new StorageContextMetadataTimeVarianceExtractor(storageDefinition), timeIntegrator);
var spatialVarianceCalculator = new LinearCombinationOnSphereVarianceCalculator(new StorageContextMetadataSpatialVarianceExtractor(storageDefinition), latIntegrator, lonIntegrator);
var gaussianFieldUncertaintyEvaluator = new SequentialTimeSpatialUncertaintyEvaluatorFacade(thinningTimeIntegrator, thinningLatIntegrator, thinningLonIntegrator, temporalVarianceCalculaator, spatialVarianceCalculator, baseNodeUncertainty);
var coverageCheckUncertaintyEvaluator = new GridUncertaintyConventionsDecorator(gaussianFieldUncertaintyEvaluator, latIntegrator, lonIntegrator, timeIntegrator);
var scaledUncertaintyEvaluator = new Microsoft.Research.Science.FetchClimate2.UncertaintyEvaluators.LinearTransformDecorator(coverageCheckUncertaintyEvaluator);
var variablePresenceCheckEvaluator = new VariablePresenceCheckDecorator(dataContext.StorageDefinition, scaledUncertaintyEvaluator);
//we do not need DegK to DegC in uncertainty as uncertainty is based on defference and does not depend on constant ofsets
scaledUncertaintyEvaluator.SetTranform("pr", new Func <double, double>(v => v * 2592000.0)); //kg/m^2/s to mm/month (assuming month is 30 days)
var gridAggregator = new GridMeanAggregator(dataContext, timeIntegrator, latIntegrator, lonIntegrator, false);
var clusteringAggregator = new GridClusteringDecorator(dataContext.StorageDefinition, gridAggregator, timeIntegrator, latIntegrator, lonIntegrator);
var scaledAggregator = new Microsoft.Research.Science.FetchClimate2.ValueAggregators.LinearTransformDecorator(dataContext, clusteringAggregator);
scaledAggregator.SetAdditionalTranform("tas", new Func <double, double>(v => v - 273.15)); // DegK to DegC
scaledAggregator.SetAdditionalTranform("pr", new Func <double, double>(v => v * 2592000.0)); //kg/m^2/s to mm/month (assuming month is 30 days)
return(new CESM1BGCDataHandler(dataContext, variablePresenceCheckEvaluator, scaledAggregator));
}
public static async Task <GFDLDataHandler> CreateAsync(IStorageContext dataContext)
{
var storageDefinition = dataContext.StorageDefinition;
var latIntegratorTask = LinearIntegratorsFactory.SmartConstructAsync(dataContext, IntegratorsFactoryHelpers.AutodetectLatName(storageDefinition));
var lonIntegratorTask = LinearIntegratorsFactory.SmartConstructAsync(dataContext, IntegratorsFactoryHelpers.AutodetectLonName(storageDefinition));
var latIntegrator = await latIntegratorTask;
var lonIntegrator = await lonIntegratorTask;
var timeAxis = ((double[])(dataContext.GetDataAsync(timeAxisName).Result)).Select(elem => (elem - 15.5) * 0.9863013698630137).ToArray(); // Shifting middle of the interval to the beginning of the interval and convert from 365 to 360 years day
int startYear = GetStartYear(dataContext, timeAxisName);
int startDay = GetStartDay(dataContext, timeAxisName);
var timeIntegrator = new TimeAxisAvgProcessing.TimeAxisAvgFacade(
timeAxis,
new TimeAxisProjections.ContinuousDays360(startYear, startDay),
new WeightProviders.StepFunctionInterpolation(),
new DataCoverageEvaluators.ContinousMeansCoverageEvaluator())
; // units = days since 2001-01-01 00:00:00
var baseNodeUncertainty = new NoBaseUncertaintyProvider();
var temporalVarianceCalculaator = new LinearCombination1DVarianceCalc(new StorageContextMetadataTimeVarianceExtractor(storageDefinition), timeIntegrator);
var spatialVarianceCalculator = new LinearCombinationOnSphereVarianceCalculator(new StorageContextMetadataSpatialVarianceExtractor(storageDefinition), latIntegrator, lonIntegrator);
var gaussianFieldUncertaintyEvaluator = new SequentialTimeSpatialUncertaintyEvaluatorFacade(timeIntegrator, latIntegrator, lonIntegrator, temporalVarianceCalculaator, spatialVarianceCalculator, baseNodeUncertainty);
var coverageCheckUncertaintyEvaluator = new GridUncertaintyConventionsDecorator(gaussianFieldUncertaintyEvaluator, latIntegrator, lonIntegrator, timeIntegrator);
var scaledUncertaintyEvaluator = new Microsoft.Research.Science.FetchClimate2.UncertaintyEvaluators.LinearTransformDecorator(coverageCheckUncertaintyEvaluator);
var variablePresenceCheckEvaluator = new VariablePresenceCheckDecorator(dataContext.StorageDefinition, scaledUncertaintyEvaluator);
scaledUncertaintyEvaluator.SetTranform(PRECIPITATION, v => v * 2592000.0);// overriding variable metadata to convert kg/m^2/s to mm/month (assuming month is 30 days)
//we do not need DegK to DegC in uncertainty as uncertainty is based on defference and does not depend on constant ofsets
var gridAggregator = new GridMeanAggregator(dataContext, timeIntegrator, latIntegrator, lonIntegrator, true);
var clusteringAggregator = new GridClusteringDecorator(dataContext.StorageDefinition, gridAggregator, timeIntegrator, latIntegrator, lonIntegrator);
var scaledAggregator = new Microsoft.Research.Science.FetchClimate2.ValueAggregators.LinearTransformDecorator(dataContext, clusteringAggregator);
scaledAggregator.SetAdditionalTranform(PRECIPITATION, v => v * 2592000.0); // overriding variable metadata to convert kg/m^2/s to mm/month (assuming month is 30 days)
scaledAggregator.SetAdditionalTranform(TEMPERATURE, v => v - 273.15); // K to C
return(new GFDLDataHandler(dataContext, variablePresenceCheckEvaluator, scaledAggregator));
}
public static async Task <HADCM3DataHandler> CreateAsync(IStorageContext dataContext)
{
var storageDefinition = dataContext.StorageDefinition;
var latIntegratorTask = LinearIntegratorsFactory.SmartConstructAsync(dataContext, IntegratorsFactoryHelpers.AutodetectLatName(storageDefinition));
var lonIntegratorTask = LinearIntegratorsFactory.SmartConstructAsync(dataContext, IntegratorsFactoryHelpers.AutodetectLonName(storageDefinition));
var timeAxis = ((double[])(await dataContext.GetDataAsync("time"))).Select(elem => elem - 15).ToArray();//shifting middle of the interval (30 days length in total) to the beginning of the interval
var timeIntegrator = new TimeAxisAvgProcessing.TimeAxisAvgFacade(
timeAxis,
new TimeAxisProjections.ContinuousDays360(1960, 1),//0 index axis value is 14400 which is 1/1/2000 12:00:00 AM
new WeightProviders.StepFunctionInterpolation(),
new DataCoverageEvaluators.ContinousMeansCoverageEvaluator()
);
var latIntegrator = await latIntegratorTask;
var lonIntegrator = await lonIntegratorTask;
var baseNodeUncertainty = new NoBaseUncertaintyProvider();
var temporalVarianceCalculaator = new LinearCombination1DVarianceCalc(new StorageContextMetadataTimeVarianceExtractor(storageDefinition), timeIntegrator);
var spatialVarianceCalculator = new LinearCombinationOnSphereVarianceCalculator(new StorageContextMetadataSpatialVarianceExtractor(storageDefinition), latIntegrator, lonIntegrator);
var gaussianFieldUncertaintyEvaluator = new SequentialTimeSpatialUncertaintyEvaluatorFacade(timeIntegrator, latIntegrator, lonIntegrator, temporalVarianceCalculaator, spatialVarianceCalculator, baseNodeUncertainty);
var coverageCheckUncertaintyEvaluator = new GridUncertaintyConventionsDecorator(gaussianFieldUncertaintyEvaluator, latIntegrator, lonIntegrator, timeIntegrator);
var scaledUncertaintyEvaluator = new Microsoft.Research.Science.FetchClimate2.UncertaintyEvaluators.LinearTransformDecorator(coverageCheckUncertaintyEvaluator);
var variablePresenceCheckEvaluator = new VariablePresenceCheckDecorator(dataContext.StorageDefinition, scaledUncertaintyEvaluator);
scaledUncertaintyEvaluator.SetTranform("pr", p => p * 2592000);
//we do not need DegK to DegC in uncertainty as uncertainty is based on defference and does not depend on constant ofsets
var gridAggregator = new GridMeanAggregator(dataContext, timeIntegrator, latIntegrator, lonIntegrator, false);
var clusteringAggregator = new GridClusteringDecorator(dataContext.StorageDefinition, gridAggregator, timeIntegrator, latIntegrator, lonIntegrator);
var scaledAggregator = new Microsoft.Research.Science.FetchClimate2.ValueAggregators.LinearTransformDecorator(dataContext, clusteringAggregator);
scaledAggregator.SetAdditionalTranform("pr", p => p * 2592000);
scaledAggregator.SetAdditionalTranform("tas", t => t - 273.15);
return(new HADCM3DataHandler(dataContext, variablePresenceCheckEvaluator, scaledAggregator));
}
public void TestProjections()
{
int[] data = new int[] { 0, 1, 2, 3, 4, 5, 6, 7 };
DateTime baseTime = new DateTime(2009, 1, 1);
var handler = new TimeAxisAvgProcessing.TimeAxisAvgFacade(
data,
new TimeAxisProjections.ContinousYears(baseTime.Year),
new WeightProviders.StepFunctionInterpolation(),
new DataCoverageEvaluators.ContinousMeansCoverageEvaluator());
var res1 = handler.GetTempIPs(new TimeSegment(2009, 2009, 1, 365, 0, 24));
Assert.AreEqual(1, res1.Weights.Length);
Assert.AreEqual(1.0, res1.Weights[0]);
Assert.AreEqual(0, res1.Indices[0]);
res1 = handler.GetTempIPs(new TimeSegment(2015, 2015, 1, 365, 0, 24));
Assert.AreEqual(1, res1.Weights.Length);
Assert.AreEqual(1.0, res1.Weights[0]);
Assert.AreEqual(6, res1.Indices[0]);
res1 = handler.GetTempIPs(new TimeSegment(2016, 2016, 1, 365, 0, 24));
Assert.AreEqual(0, res1.Weights.Length);
}
public static async Task <MLDDataHandler> CreateAsync(IStorageContext dataContext)
{
var storageDefinition = dataContext.StorageDefinition;
var latIntegratorTask = LinearIntegratorsFactory.SmartConstructAsync(dataContext, IntegratorsFactoryHelpers.AutodetectLatName(storageDefinition));
var lonIntegratorTask = LinearIntegratorsFactory.SmartConstructAsync(dataContext, IntegratorsFactoryHelpers.AutodetectLonName(storageDefinition));
var timeAxis = await dataContext.GetDataAsync("days since 1998-1-1 0:0:0");
var timeIntegrator = new TimeAxisAvgProcessing.TimeAxisAvgFacade(
timeAxis,
new TimeAxisProjections.ContinuousDays(new DateTime(1998, 1, 1)),
new WeightProviders.StepFunctionInterpolation(),
new DataCoverageEvaluators.ContinousMeansCoverageEvaluator());
var latIntegrator = await latIntegratorTask;
var lonIntegrator = await lonIntegratorTask;
var thinningLatIntegrator = new SpatGridIntegatorThinningDecorator(latIntegrator);
var thinningLonIntegrator = new SpatGridIntegatorThinningDecorator(lonIntegrator);
var thinningTimeIntegrator = new TimeAxisIntegratorThinningDecorator(timeIntegrator);
var baseNodeUncertainty = new NoBaseUncertaintyProvider();
var temporalVarianceCalculaator = new LinearCombination1DVarianceCalc(new StorageContextMetadataTimeVarianceExtractor(storageDefinition), timeIntegrator);
var spatialVarianceCalculator = new LinearCombinationOnSphereVarianceCalculator(new StorageContextMetadataSpatialVarianceExtractor(storageDefinition), latIntegrator, lonIntegrator);
var gaussianFieldUncertaintyEvaluator = new SequentialTimeSpatialUncertaintyEvaluatorFacade(thinningTimeIntegrator, thinningLatIntegrator, thinningLonIntegrator, temporalVarianceCalculaator, spatialVarianceCalculator, baseNodeUncertainty);
var coverageCheckUncertaintyEvaluator = new GridUncertaintyConventionsDecorator(gaussianFieldUncertaintyEvaluator, latIntegrator, lonIntegrator, timeIntegrator);
var variablePresenceCheckEvaluator = new VariablePresenceCheckDecorator(dataContext.StorageDefinition, coverageCheckUncertaintyEvaluator);
var gridAggregator = new GridMeanAggregator(dataContext, timeIntegrator, latIntegrator, lonIntegrator, true);
var clusteringAggregator = new GridClusteringDecorator(dataContext.StorageDefinition, gridAggregator, timeIntegrator, latIntegrator, lonIntegrator);
gridAggregator.SetMissingValue("data", -9999);
return(new MLDDataHandler(dataContext, variablePresenceCheckEvaluator, clusteringAggregator));
}