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)