def processVariable( self, request: TaskRequest, node: OpNode, variable: EDASArray ) -> EDASArray: variable.persist() axisIndex = variable.getAxisIndex( node.axes, 0, 0 ) dim = variable.xr.dims[axisIndex] window_size = node.getParm("wsize", variable.xr.shape[axisIndex]//8 ) lowpass_args = { dim:int(window_size), "center":True, "min_periods": 1 } lowpass = variable.xr.rolling(**lowpass_args).mean() return EDASArray( variable.name, variable.domId, lowpass )
def processVariable( self, request: TaskRequest, node: OpNode, variable: EDASArray ) -> EDASArray: variable.persist() parms = self.getParameters( node, [ Param("lat"), Param("lon")]) aIndex = variable.xr.get_axis_num('t') center: xa.DataArray = variable.selectPoint( float(parms["lon"]), float(parms["lat"]) ).xr cmean, data_mean = center.mean(axis=aIndex), variable.xr.mean(axis=aIndex) cstd, data_std = center.std(axis=aIndex), variable.xr.std(axis=aIndex) cov = np.sum((variable.xr - data_mean) * (center - cmean), axis=aIndex) / variable.xr.shape[aIndex] cor = cov / (cstd * data_std) return EDASArray( variable.name, variable.domId, cor )
def processVariable( self, request: TaskRequest, node: OpNode, variable: EDASArray ) -> EDASArray: data = variable.persist() norm = bool(node.getParm("norm", False)) grouping = node.getParm("groupby", 't.month') climatology = data.groupby(grouping).mean('t') anomalies = data.groupby(grouping) - climatology if norm: anomalies = anomalies.groupby(grouping) / data.groupby(grouping).std('t') return variable.updateXa( anomalies, "decycle" )
def processVariable( self, request: TaskRequest, node: OpNode, variable: EDASArray ) -> EDASArray: data = variable.persist() axisIndex = variable.getAxisIndex( node.axes, 0, 0 ) dim = data.dims[axisIndex] window_size = node.getParm("wsize", data.shape[axisIndex] // 8) detrend_args = {dim: int(window_size), "center": True, "min_periods": 1} trend = data.rolling(**detrend_args).mean() detrend: EDASArray = variable - variable.updateXa(trend, "trend") return detrend
def processVariable(self, request: TaskRequest, node: OpNode, variable: EDASArray) -> EDASArray: variable.persist() return variable - variable.ave(node.axes)
def setResult(self, key: str, value: EDASArray): self.logger.info(f"Computed value for WorldClim field bio-{key}") value.persist() self.results[key] = value
def processVariables(self, request: TaskRequest, node: OpNode, variable: EDASArray) -> List[EDASArray]: variable.persist() freq = node.getParm("freq", 'month') operation = str(node.getParm("op", 'mean')).lower() return variable.timeResample(freq, operation)
def processVariables(self, request: TaskRequest, node: OpNode, variable: EDASArray) -> List[EDASArray]: variable.persist() period = node.getParm("period", 'month') operation = str(node.getParm("op", 'mean')).lower() return variable.timeAgg(period, operation)
def processVariable(self, request: TaskRequest, node: OpNode, variable: EDASArray) -> EDASArray: variable.persist() centered_result = variable - variable.ave(node.axes) return centered_result / centered_result.std(node.axes)