Python NP.min方法代码示例

# 需要导入模块: from augustus.core.NumpyInterface import NP [as 别名]
# 或者: from augustus.core.NumpyInterface.NP import min [as 别名]
    def prepare(self, state, dataTable, functionTable, performanceTable, plotRange):
        """Prepare a plot element for drawing.

        This stage consists of calculating all quantities and
        determing the bounds of the data.  These bounds may be unioned
        with bounds from other plot elements that overlay this plot
        element, so the drawing (which requires a finalized coordinate
        system) cannot begin yet.

        This method modifies C{plotRange}.

        @type state: ad-hoc Python object
        @param state: State information that persists long enough to use quantities computed in C{prepare} in the C{draw} stage.  This is a work-around of lxml's refusal to let its Python instances maintain C{self} and it is unrelated to DataTableState.
        @type dataTable: DataTable
        @param dataTable: Contains the data to plot.
        @type functionTable: FunctionTable
        @param functionTable: Defines functions that may be used to transform data for plotting.
        @type performanceTable: PerformanceTable
        @param performanceTable: Measures and records performance (time and memory consumption) of the drawing process.
        @type plotRange: PlotRange
        @param plotRange: The bounding box of plot coordinates that this function will expand.
        """

        self.checkRoles(["y(x)", "dy/dx", "x(t)", "y(t)", "dx/dt", "dy/dt", "x", "y", "dx", "dy"])

        performanceTable.begin("PlotCurve prepare")
        self._saveContext(dataTable)
        
        yofx = self.xpath("pmml:PlotFormula[@role='y(x)']")
        dydx = self.xpath("pmml:PlotFormula[@role='dy/dx']")

        xoft = self.xpath("pmml:PlotFormula[@role='x(t)']")
        yoft = self.xpath("pmml:PlotFormula[@role='y(t)']")
        dxdt = self.xpath("pmml:PlotFormula[@role='dx/dt']")
        dydt = self.xpath("pmml:PlotFormula[@role='dy/dt']")

        nx = self.xpath("pmml:PlotNumericExpression[@role='x']")
        ny = self.xpath("pmml:PlotNumericExpression[@role='y']")
        ndx = self.xpath("pmml:PlotNumericExpression[@role='dx']")
        ndy = self.xpath("pmml:PlotNumericExpression[@role='dy']")
        cutExpression = self.xpath("pmml:PlotSelection")

        if len(yofx) + len(dydx) + len(xoft) + len(yoft) + len(dxdt) + len(dydt) > 0:
            if len(yofx) == 1 and len(dydx) == 0 and len(xoft) == 0 and len(yoft) == 0 and len(dxdt) == 0 and len(dydt) == 0:
                expression = (yofx[0].text,)
                derivative = (None,)

            elif len(yofx) == 1 and len(dydx) == 1 and len(xoft) == 0 and len(yoft) == 0 and len(dxdt) == 0 and len(dydt) == 0:
                expression = (yofx[0].text,)
                derivative = (dydx[0].text,)

            elif len(yofx) == 0 and len(dydx) == 0 and len(xoft) == 1 and len(yoft) == 1 and len(dxdt) == 0 and len(dydt) == 0:
                expression = xoft[0].text, yoft[0].text
                derivative = None, None

            elif len(yofx) == 0 and len(dydx) == 0 and len(xoft) == 1 and len(yoft) == 1 and len(dxdt) == 1 and len(dydt) == 1:
                expression = xoft[0].text, yoft[0].text
                derivative = dxdt[0].text, dydt[0].text

            else:
                raise defs.PmmlValidationError("The only allowed combinations of PlotFormulae are: \"y(x)\", \"y(x) dy/dx\", \"x(t) y(t)\", and \"x(t) y(t) dx/dt dy/dt\"")

            low = self.get("low", convertType=True)
            high = self.get("high", convertType=True)
            if low is None or high is None:
                raise defs.PmmlValidationError("The \"low\" and \"high\" attributes are required for PlotCurves defined by formulae")

            samples = self.generateSamples(low, high)

            loop = self.get("loop", defaultFromXsd=True, convertType=True)
            state.x, state.y, state.dx, state.dy, xfieldType, yfieldType = self.expressionsToPoints(expression, derivative, samples, loop, functionTable, performanceTable)

        else:
            performanceTable.pause("PlotCurve prepare")
            if len(ndx) == 1:
                dxdataColumn = ndx[0].evaluate(dataTable, functionTable, performanceTable)
            else:
                dxdataColumn = None
            if len(ndy) == 1:
                dydataColumn = ndy[0].evaluate(dataTable, functionTable, performanceTable)
            else:
                dydataColumn = None
            performanceTable.unpause("PlotCurve prepare")

            if len(nx) == 0 and len(ny) == 1:
                performanceTable.pause("PlotCurve prepare")
                ydataColumn = ny[0].evaluate(dataTable, functionTable, performanceTable)
                performanceTable.unpause("PlotCurve prepare")

                if len(cutExpression) == 1:
                    performanceTable.pause("PlotCurve prepare")
                    selection = cutExpression[0].select(dataTable, functionTable, performanceTable)
                    performanceTable.unpause("PlotCurve prepare")
                else:
                    selection = NP("ones", len(ydataColumn.data), NP.dtype(bool))

                if ydataColumn.mask is not None:
                    selection = NP("logical_and", selection, NP(ydataColumn.mask == defs.VALID), selection)
                if dxdataColumn is not None and dxdataColumn.mask is not None:
                    selection = NP("logical_and", selection, NP(dxdataColumn.mask == defs.VALID), selection)
#.........这里部分代码省略.........