Exemplo n.º 1
0
 def create_covariance(self, time_series):
     """
     :returns: a stored DataType Covariance.
     """
     operation, _, storage_path = self.__create_operation()
     covariance = Covariance(storage_path=storage_path, source=time_series)
     covariance.write_data_slice(numpy.random.random((10, 10, 10)))
     adapter_instance = StoreAdapter([covariance])
     OperationService().initiate_prelaunch(operation, adapter_instance, {})
     return covariance
Exemplo n.º 2
0
 def create_covariance(self, time_series):
     """
     :returns: a stored DataType Covariance.
     """
     operation, _, storage_path = self.__create_operation()
     covariance = Covariance(storage_path=storage_path, source=time_series)
     covariance.write_data_slice(numpy.random.random((10, 10, 10)))
     adapter_instance = StoreAdapter([covariance])
     OperationService().initiate_prelaunch(operation, adapter_instance, {})
     return covariance
Exemplo n.º 3
0
    def build():
        ts_index = time_series_factory()

        ts_h5 = h5_file_for_index(ts_index)
        ts = TimeSeries()
        ts_h5.load_into(ts)
        ts_h5.close()

        data_shape = ts.data.shape

        result_shape = (data_shape[2], data_shape[2], data_shape[1],
                        data_shape[3])
        result = numpy.zeros(result_shape)

        for mode in range(data_shape[3]):
            for var in range(data_shape[1]):
                data = ts_h5.data[:, var, :, mode]
                data = data - data.mean(axis=0)[numpy.newaxis, 0]
                result[:, :, var, mode] = numpy.cov(data.T)

        covariance = Covariance(source=ts, array_data=result)

        op = operation_factory()

        covariance_db = CovarianceIndex()
        covariance_db.fk_from_operation = op.id
        covariance_db.fill_from_has_traits(covariance)

        covariance_h5_path = h5.path_for_stored_index(covariance_db)
        with TimeSeriesH5(covariance_h5_path) as f:
            f.store(ts)

        session.add(covariance_db)
        session.commit()
        return covariance_db
    def _compute_node_covariance(self, small_ts, input_ts_h5):
        """
        Compute the temporal covariance between nodes in a TimeSeries dataType.
        A nodes x nodes matrix is returned for each (state-variable, mode).
        """
        data_shape = small_ts.data.shape

        # (nodes, nodes, state-variables, modes)
        result_shape = (data_shape[2], data_shape[2], data_shape[1],
                        data_shape[3])
        self.log.info("result shape will be: %s" % str(result_shape))

        result = numpy.zeros(result_shape)

        # One inter-node temporal covariance matrix for each state-var & mode.
        for mode in range(data_shape[3]):
            for var in range(data_shape[1]):
                data = input_ts_h5.data[:, var, :, mode]
                data = data - data.mean(axis=0)[numpy.newaxis, 0]
                result[:, :, var, mode] = numpy.cov(data.T)

        self.log.debug("result")
        self.log.debug(narray_describe(result))

        covariance = Covariance(source=small_ts, array_data=result)
        return covariance
Exemplo n.º 5
0
    def launch(self, time_series):
        """ 
        Launch algorithm and build results.

        :returns: the `Covariance` built with the given timeseries as source
        """

        #Create a FourierSpectrum dataType object.
        covariance = Covariance(source=time_series,
                                storage_path=self.storage_path)

        #NOTE: Assumes 4D, Simulator timeSeries.
        node_slice = [
            slice(self.input_shape[0]), None,
            slice(self.input_shape[2]), None
        ]

        for mode in range(self.input_shape[3]):
            for var in range(self.input_shape[1]):
                small_ts = TimeSeries(use_storage=False)
                node_slice[1] = slice(var, var + 1)
                node_slice[3] = slice(mode, mode + 1)
                small_ts.data = time_series.read_data_slice(tuple(node_slice))
                self.algorithm.time_series = small_ts
                partial_cov = self.algorithm.evaluate()
                covariance.write_data_slice(partial_cov.array_data)
        covariance.close_file()
        return covariance
    def launch(self, time_series):
        """ 
        Launch algorithm and build results.

        :returns: the `Covariance` built with the given timeseries as source
        """
        
        #Create a FourierSpectrum dataType object.
        covariance = Covariance(source=time_series, storage_path=self.storage_path)
        
        #NOTE: Assumes 4D, Simulator timeSeries.
        node_slice = [slice(self.input_shape[0]), None, slice(self.input_shape[2]), None]
        
        for mode in range(self.input_shape[3]):
            for var in range(self.input_shape[1]):
                small_ts = TimeSeries(use_storage=False)
                node_slice[1] = slice(var, var + 1)
                node_slice[3] = slice(mode, mode + 1)
                small_ts.data = time_series.read_data_slice(tuple(node_slice))
                self.algorithm.time_series = small_ts 
                partial_cov = self.algorithm.evaluate()
                covariance.write_data_slice(partial_cov.array_data)
        covariance.close_file()
        return covariance