Method noiseContribs

SLiCAP/SLiCAPlatex.py:381–434

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="", color="myyellow")

        return Snippet(TEX, self.format)

    def noiseContribs(self, resultObject, label="", caption="", color="myyellow"):
        """
        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
                
        :param color: Alternate row color name, should be defined in 
                     'preambuleSLiCAP.tex' defaults to 'myyellow'. Use None for
                     no background color.
        :type color: str
        
        :return: SLiCAP Snippet object
        :rtype: SLiCAP.SLiCAPprotos.Snippet
        """
        TEX = ''
        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 for source contributions
            linesList = []
            alignstring = '[c]{lll}'
            headerList = ['Name', 'Value' , 'Units']
            linesList = []
            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 + ': 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)
            TEX += _TEXcreateCSVtable(headerList, linesList, alignstring, 
                                      unitpos=2, caption=caption, 
                                      label=label, color=color)
        else:
            print('noiseContribs2TEX: Error: wrong data type, or stepped analysis.')
        return Snippet(TEX, self.format)

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