Method noiseContribs

SLiCAP/SLiCAPrst.py:292–338

Creates a table with results of noise analysis stored in *resultObject*. If no label AND no caption are given this method returns a LaTeX tabular snippet. Else it returns a table snippet. :param resultObject: SLiCAP circuit object that comprises the circuit data to

(self, resultObject, label="", caption="")

        return Snippet(RST, self.format)

    def noiseContribs(self, resultObject, label="", caption=""):
        """
        Creates a table with results of noise analysis stored in *resultObject*.
        If no label AND no caption are given this method returns a LaTeX
        tabular snippet. Else it returns a table snippet.

        :param resultObject: SLiCAP circuit object that comprises the circuit data to be listed.
        :type resultObject: SLiCAP.SLiCAPinstruction.instruction
        
        :param label: Reference label for the table. Defaults to an empty string.
        :type label: str
        
        :param caption: Text that will used as table caption(s).
        :type caption: str
        
        :return: SLiCAP Snippet object
        :rtype: SLiCAP.SLiCAPprotos.Snippet
        """
        if resultObject.dataType == 'noise' and resultObject.step == False:
            detunits = resultObject.detUnits + '**2/Hz'
            if resultObject.srcUnits != None:
                srcunits = resultObject.srcUnits + '**2/Hz'
            # Add a table with noise contributions
            linesList = []
            headerList = ['', 'Value' , 'Units']
            for src in resultObject.onoiseTerms.keys():
                if src[0].upper() == 'I':
                    units = 'A**2/Hz'
                elif src[0].upper() == 'V':
                    units = 'V**2/Hz'
                #units = sp.sympify(units)
                line = [src + ': Source value', resultObject.snoiseTerms[src],
                        units]
                linesList.append(line)
                if resultObject.srcUnits != None:
                    line = [src + ': Source-referred', 
                            resultObject.inoiseTerms[src], srcunits]
                    linesList.append(line)
                line = [src + ': Detector-referred', 
                        resultObject.onoiseTerms[src], detunits]
                linesList.append(line)
            RST = _RSTcreateCSVtable(caption, headerList, linesList, unitpos=2, 
                                     label=label)
        else:
            RST = ''
            print('noise2RST: Error: wrong data type, or stepped analysis.')
        return Snippet(RST, self.format)

    def dcvarContribs(self, resultObject, label="", caption=""):
        """