###########################################
# Project:      CMSIS DSP Library
# Title:        node.py
# Description:  Node class for description of dataflow graph
# 
# $Date:        29 July 2021
# $Revision:    V1.10.0
# 
# Target Processor: Cortex-M and Cortex-A cores
# -------------------------------------------------------------------- */
# 
# Copyright (C) 2010-2021 ARM Limited or its affiliates. All rights reserved.
# 
# SPDX-License-Identifier: Apache-2.0
# 
# Licensed under the Apache License, Version 2.0 (the License); you may
# not use this file except in compliance with the License.
# You may obtain a copy of the License at
# 
# www.apache.org/licenses/LICENSE-2.0
# 
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an AS IS BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
############################################
"""Description of the basic types used to build a dataflow graph"""
from jinja2 import Environment, FileSystemLoader, PackageLoader,select_autoescape
import pathlib
import os.path
import numpy as np

from sympy.core.numbers import ilcm


class NoFunctionArrayInPython(Exception):
    pass


def camelCase(st):
    output = ''.join(x for x in st.title() if x.isalnum())
    return output[0].lower() + output[1:]

def joinit(iterable, delimiter):
    it = iter(iterable)
    yield next(it)
    for x in it:
        yield delimiter
        yield x

### Definition of the IOs

class IO:
    """Class of input / outputs"""
    def __init__(self,owner,name,theType,nbSamples):
        self._theType = theType
        # For cycle static scheduling it may also be
        # a list of samples for each iteration of a cycle
        self._nbSamples = nbSamples 
        self._owner = owner
        self._name = name
        self._fifo = [] 
        self.constantNode = None

        # For cyclo static scheduling nbSamples is a list
        # and when simulating the schedule we need to know
        # where we currently are in the list
        self._cyclePosition = 0 

    # For cyclo static scheduling we advance the position in the
    # cycle
    def advanceCycle(self):
        if isinstance(self.nbSamples,int):
            pass 
        else:
            self._cyclePosition = self._cyclePosition + 1 
            if self._cyclePosition == len(self.nbSamples):
               self._cyclePosition = 0

    # For cyclo static scheduling, get the value in the current
    # cycle position
    @property
    def cycleValue(self):
        if isinstance(self.nbSamples,int):
            return(self.nbSamples)
        else:
            return(self.nbSamples[self._cyclePosition])

    # For cyclo static scheduling: we need
    # the length of the cycle 
    @property
    def cyclePeriod(self):
        if isinstance(self.nbSamples,int):
            return(1)
        else:
            return(len(self.nbSamples))

    # For cyclo static scheduling we need the total
    # data generated by a run of the cycle
    @property
    def cycleTotal(self):
        if isinstance(self.nbSamples,int):
            return(self.nbSamples)
        else:
            return(np.sum(self.nbSamples))

    # For cyclo static we need the max in a period top use
    # as a normalization factor to identify the most filled
    # FIFO 
    @property
    def cycleMax(self):
        if isinstance(self.nbSamples,int):
            return(self.nbSamples)
        else:
            return(np.max(self.nbSamples))
    
    


    @property
    def fifo(self):
        return self._fifo

    ## the attribute name and the method name must be same which is used to set the value for the attribute
    @fifo.setter
    def fifo(self, var):
        self._fifo = var

   

    def compatible(self,other):
        return(self._theType == other._theType)

    @property
    def owner(self):
        return self._owner

    @property
    def name(self):
        return self._name

    @property
    def ctype(self):
        """ctype string """
        return self._theType.ctype

    @property
    def nptype(self):
        """ctype string """
        return self._theType.nptype

    @property
    def theType(self):
        return self._theType

    @property
    def dspExtension(self):
        return self._theType.dspExtension

    @property
    def graphViztype(self):
        return self._theType.graphViztype

    @property
    def nbSamples(self):
        return self._nbSamples
    
    

class Input(IO):
     """Node input"""
     pass

class Output(IO):
     """Node output"""
     pass

### Definition of the nodes types

class Constant:
    """ Represent a constant object.

        A constant object is ignored for the scheduling.
        But it can be connected to CMSIS-DSP inputs.
        It is generated as DEFINE
    """
    def __init__(self,name):
        self._name = name 

    @property
    def name(self):
        return self._name

    @property
    def isConstantNode(self):
        return True
    
    
class SchedArg:
    """Class for arguments of the scheduler functions.
       They can either be a literal arg like string, boolean
       or number or they can be a variable name"""
    
    def __init__(self,name):
          self._name=name 

class ArgLiteral(SchedArg):
     def __init__(self,name):
        super().__init__(name)

     @property
     def arg(self):
        if isinstance(self._name,str):
             return("\"%s\"" % self._name) 
        else:
             return(str(self._name))

class VarLiteral(SchedArg):
     def __init__(self,name):
        super().__init__(name)

     @property 
     def arg(self):
        return(self._name)

class BaseNode:
    """Root class for all Nodes of a dataflow graph.
       To define a new kind of node, inherit from this class"""

    def __init__(self,name):
        """Create a new kind of Node.

        name :: The name of the node which is used as
        a C variable in final code"""
        self._nodeName = name
        self._nodeID = name
        self._inputs={}
        self._outputs={}
        # For code generations
        # The fifo args
        self._args=""
        # Literal arguments
        self.schedArgs=None 

        # For cyclo static scheduling
        # It is the LCM of the cycles of all
        # connected edges.
        # After this period, the node and its edges
        # should be back to the original state
        self.cyclePeriod = 1

        self._positionInCycle = 0

        self.sortedNodeID = 0
        # Updated during toplogical sorts to find the current
        # sinks of the truncated graph
        self.nbOutputsForTopologicalSort = 0

    def __getattr__(self,name):
        """Present inputs / outputs as attributes"""
        if name in self._inputs:
           return(self._inputs[name])
        if name in self._outputs:
           return(self._outputs[name])
        raise AttributeError

    def __getitem__(self,name):
        """Present inputs / outputs as keys"""
        if name in self._inputs:
           return(self._inputs[name])
        if name in self._outputs:
           return(self._outputs[name])
        raise IndexError 

    # For cyclo static scheduling we need to compute
    # the cycle period for this node based upon the
    # period of the IOs 
    def computeCyclePeriod(self):
        allLengths = []
        for k in self._inputs:
            allLengths.append(self._inputs[k].cyclePeriod)
        for k in self._outputs:
            allLengths.append(self._outputs[k].cyclePeriod)
        if len(allLengths)>1:
           self.cyclePeriod = ilcm(*allLengths)
        else:
           self.cyclePeriod = allLengths[0]

    # For cyclo static scheduling, we need to track the number
    # of period execution 
    # We return 1 when a period has been executed 
    # and this is used to decrease the period count in
    # the null vector of the toplogy matrix
    def executeNode(self):
        self._positionInCycle = self._positionInCycle + 1 
        if (self._positionInCycle == self.cyclePeriod):
            self._positionInCycle = 0 
            return(1)
        else:
            return(0)

    def addLiteralArg(self,*ls):
        for l in ls:
            if self.schedArgs:
                self.schedArgs.append(ArgLiteral(l))
            else:
                self.schedArgs=[ArgLiteral(l)]

    def addVariableArg(self,*ls):
        for l in ls:
            if self.schedArgs:
                self.schedArgs.append(VarLiteral(l))
            else:
                self.schedArgs=[VarLiteral(l)]

    @property
    def isConstantNode(self):
        return False

    @property 
    def isDuplicateNode(self):
        return False

    @property
    def hasState(self):
        """False if the node is a pure functiom with no state
           and no associated C++ object
        """
        return(True)

    @property
    def typeName(self):
        return "void"
    
    
    @property
    def nodeID(self):
        """Node ID to uniquely identify a node"""
        return self._nodeID

    @property
    def nodeName(self):
        """Node name displayed in graph

           It could be the same for different nodes if the
           node is just a function with no state.
        """
        return self._nodeName

    # For code generation



    def allIOs(self):
        """Get list of IO objects for inputs and outputs"""
        ins=[] 
        outs=[]
        # Use orderd io names
        for io in self.inputNames:
            x = self._inputs[io]
            ins.append(x)

        for io in self.outputNames:
            x = self._outputs[io]
            outs.append(x)

        
        return(ins,outs)



    

    def ioTemplate(self):
        """Template arguments for C
           input type, input size ...
           output type, output size ...

           Some nodes may customize it
        """
        ios=[] 
        # Use ordered io names
        for io in self.inputNames:
            x = self._inputs[io]
            # For cyclo static scheduling, we may have a list
            # of samples
            if isinstance(x.nbSamples,int):
               ios.append("%s,%d" % (x.ctype,x.nbSamples))
            else:
                # In case of a list of samples
                # thne templaet is receiving only the
                # max value
                m = np.max(x.nbSamples)
                ios.append("%s,%d" % (x.ctype,m))

        for io in self.outputNames:
            x = self._outputs[io]
            if isinstance(x.nbSamples,int):
               ios.append("%s,%d" % (x.ctype,x.nbSamples))
            else:
                m = np.max(x.nbSamples)
                ios.append("%s,%d" % (x.ctype,m))

        
        return("".join(joinit(ios,",")))

    def pythonIoTemplate(self):
        """Template arguments for Python
           input type, input size ...
           output type, output size ...

           Some nodes may customize it
        """
        ios=[] 
        # Use ordered io names
        # For cyclo static scheduling, the nbSamples
        # may be a list and we use the max value
        # for the code generayion
        for io in self.inputNames:
            x = self._inputs[io]
            if isinstance(x.nbSamples,int):
               ios.append("%d" % x.nbSamples)
            else:
                m = np.max(x.nbSamples)
                ios.append("%d" % m)

        for io in self.outputNames:
            x = self._outputs[io]
            if isinstance(x.nbSamples,int):
               ios.append("%d" % x.nbSamples)
            else:
                m = np.max(x.nbSamples)
                ios.append("%d" % m)

        
        return("".join(joinit(ios,",")))

    def cRun(self,ctemplate=True):
        """Run function

           Some nodes may customize it
        """
        if ctemplate:
           return ("cgStaticError = %s.run();" % self.nodeName)
        else:
           return ("cgStaticError = %s.run()" % self.nodeName)

    def cFunc(self,ctemplate=True):
        """Function call for code array scheduler

           Some nodes may customize it
        """
        if ctemplate:
           return ("(runNode)&%s<%s>::run" % (self.typeName,self.ioTemplate()))
        else:
           raise NoFunctionArrayInPython
    
    
    @property
    def listOfargs(self):
        """List of fifos args for object initialization"""
        return self._args


    @property
    def args(self):
        """String of fifo args for object initialization
            with literal argument and variable arguments"""
        allArgs=self.listOfargs
        # Add specific argrs after FIFOs
        if self.schedArgs:
            for lit in self.schedArgs:
                allArgs.append(lit.arg)
        return "".join(joinit(allArgs,","))
    
    @args.setter
    def args(self,fifoIDs):
       res=[]
       # Template args is used only for code array
       # scheduler when we create on the fly a new class
       # for a function.
       # In this case, the arguments of the template must only be
       # fifos and not constant.
       templateargs=[]
       for x in fifoIDs:
         # If args is a FIFO we generate a name using fifo ids
         if isinstance(x,int):
            res.append("fifo%d" % x)
            templateargs.append("fifo%d" % x)
         # If args is a constant node, we just use the constant node name
         # (Defined in C code)
         else:
            res.append(x)
       self._args=res
       self._templateargs=templateargs

    # For graphviz generation



    @property
    def graphvizName(self):
        """Name for graph vizualization"""
        return ("%s<BR/>(%s)" % (self.nodeName,self.typeName))

    @property
    def inputNames(self):
        return sorted(list(self._inputs.keys()))


    @property
    def outputNames(self):
        return sorted(list(self._outputs.keys()))

    @property
    def hasManyInputs(self):
        return len(self._inputs.keys())>1

    @property
    def hasManyOutputs(self):
        return len(self._outputs.keys())>1

    @property
    def hasManyIOs(self):
        return (self.hasManyInputs or self.hasManyOutputs)

    @property
    def nbInputs(self):
        return(len(self._inputs.keys()))

    @property
    def nbOutputs(self):
        return(len(self._outputs.keys()))

    @property
    def nbEmptyInputs(self):
        return (self.maxNbIOs - len(self._inputs.keys()))
    
    @property
    def nbEmptyOutputs(self):
        return (self.maxNbIOs - len(self._outputs.keys()))

    @property
    def maxNbIOs(self):
        return max(len(self._inputs.keys()),len(self._outputs.keys()))
    
    

class GenericSink(BaseNode):
    """A sink in the dataflow graph""" 

    def __init__(self,name):
        BaseNode.__init__(self,name)

    @property
    def typeName(self):
        return "void"

    def addInput(self,name,theType,theLength):
        self._inputs[name]=Input(self,name,theType,theLength)


class GenericSource(BaseNode):
    """A source in the dataflow graph""" 

    def __init__(self,name):
        BaseNode.__init__(self,name)

    @property
    def typeName(self):
        return "void"

    def addOutput(self,name,theType,theLength):
        self._outputs[name]=Output(self,name,theType,theLength)

class GenericNode(BaseNode):
    """A source in the dataflow graph""" 

    def __init__(self,name):
        BaseNode.__init__(self,name)
    
    @property
    def typeName(self):
        return "void"

    def addInput(self,name,theType,theLength):
        self._inputs[name]=Input(self,name,theType,theLength)

    def addOutput(self,name,theType,theLength):
        self._outputs[name]=Output(self,name,theType,theLength)

class SlidingBuffer(GenericNode):

    def __init__(self,name,theType,length,overlap):
        GenericNode.__init__(self,name)
        self._length = length 
        self._overlap = overlap 
        self.addInput("i",theType,length-overlap)
        self.addOutput("o",theType,length)
    
    def ioTemplate(self):
        """ioTemplate is different for window
        """
        theType=self._inputs[self.inputNames[0]].ctype  
        ios="%s,%d,%d" % (theType,self._length,self._overlap)
        return(ios)

    def pythonIoTemplate(self):
        """ioTemplate is different for window
        """
        theType=self._inputs[self.inputNames[0]].ctype  
        ios="%d,%d" % (self._length,self._overlap)
        return(ios)
        

    @property
    def typeName(self):
        return "SlidingBuffer"

class OverlapAdd(GenericNode):

    def __init__(self,name,theType,length,overlap):
        GenericNode.__init__(self,name)
        self._length = length 
        self._overlap = overlap 
        self.addInput("i",theType,length)
        self.addOutput("o",theType,length-overlap)
    
    def ioTemplate(self):
        """ioTemplate is different for window
        """
        theType=self._inputs[self.inputNames[0]].ctype  
        ios="%s,%d,%d" % (theType,self._length,self._overlap)
        return(ios)

    def pythonIoTemplate(self):
        """ioTemplate is different for window
        """
        theType=self._inputs[self.inputNames[0]].ctype  
        ios="%d,%d" % (self._length,self._overlap)
        return(ios)

    
        

    @property
    def typeName(self):
        return "OverlapAdd"




# Pure compute functions
# It is supporting unary function (src,dst,blockize)
# and binary functions (sraa,srcb, dst, blocksize)
# For cmsis, the prefix arm and the type suffix are not needed
# if class Dsp is used
class GenericFunction(GenericNode):
    # Number of function node of each category
    # Used to generate unique ID and names when
    # unique names are required
    # like for creating the graph where each call to
    # the same function must be identified as a
    # separate node
    NODEID={}
    PUREID=1


    ENV = Environment(
       loader=PackageLoader("cmsisdsp.cg.static.scheduler"),
       autoescape=select_autoescape(),
       lstrip_blocks=True,
       trim_blocks=True
    )
    
    CTEMPLATE = ENV.get_template("cmsis.cpp")
    CNODETEMPLATE = ENV.get_template("cmsisNode.cpp")

    PYTEMPLATE = ENV.get_template("cmsis.py")

    def __init__(self,funcname,theType,length):
        if not (funcname in GenericFunction.NODEID):
            GenericFunction.NODEID[funcname]=1 

        self._pureNodeID = GenericFunction.PUREID
        GenericFunction.PUREID = GenericFunction.PUREID + 1
        GenericNode.__init__(self,"%s%d" % (funcname,GenericFunction.NODEID[funcname]))

        self._hasState = False
        self._length = length 
        self._nodeName = funcname

        GenericFunction.NODEID[funcname]=GenericFunction.NODEID[funcname]+1


    # For class generated on the fly to contain a function call
    # Analyze which args are constant instead of being FIFOs
    def analyzeArgs(self):
        inputid=0 
        outputid=0
        # Arguments to use when calling the constructor
        ios=[]
        # Template params
        temptypes=[]
        specializedtemptypes=[]
        # template args
        tempargs=[]
        # Template params for the generic node
        tempgen=[]
        # Datatypes for the constructor
        constructortypes=[]
        # Args for the generic constructor
        genericconstructorargs=[]

        typenameID = 1
        # Use ordered io names
        for io in self.inputNames:
            x = self._inputs[io]
            if not x.constantNode:
               inputid = inputid + 1
               temptypes.append("typename T%d, int input%dSize" % (typenameID,inputid))
               specializedtemptypes.append("int input%dSize" % (inputid))
               tempargs.append("%s,input%dSize" % (x.ctype,inputid))
               tempgen.append("%s,input%dSize" % (x.ctype,inputid))
               constructortypes.append("FIFOBase<%s> &src%d" % (x.ctype,inputid))
               genericconstructorargs.append("src%d" % inputid)
               # For cyclo static scheduling, nbSamples may be a list
               if isinstance(x.nbSamples,int):
                  ios.append("%s,%d" % (x.ctype,x.nbSamples))
               else:
                  m = np.max(x.nbSamples)
                  ios.append("%s,%d" % (x.ctype,m))
               typenameID = typenameID + 1

        for io in self.outputNames:
            x = self._outputs[io]
            if not x.constantNode:
               outputid = outputid + 1
               temptypes.append("typename T%d,int output%dSize" % (typenameID,outputid))
               specializedtemptypes.append("int output%dSize" % (outputid))
               tempargs.append("%s,output%dSize" % (x.ctype,outputid))
               tempgen.append("%s,output%dSize" % (x.ctype,outputid))
               constructortypes.append("FIFOBase<%s> &dst%d" % (x.ctype,outputid))
               genericconstructorargs.append("dst%d" % outputid)
               if isinstance(x.nbSamples,int):
                  ios.append("%s,%d" % (x.ctype,x.nbSamples))
               else:
                  m = np.max(x.nbSamples)
                  ios.append("%s,%d" % (x.ctype,m))
               typenameID = typenameID + 1

        self._realInputs = inputid
        self._realOutputs = outputid
        # Arguments to use when calling the constructor
        self._constructorTypes = "".join(joinit(ios,","))
        # Argument 
        self._constructorArguments = "".join(joinit(self._templateargs,",")) 
        # Template parameters to use when defining the template
        self._templateParameters = "".join(joinit(temptypes,","))
        # Template parameters to use when defining the template
        self._specializedTemplateParameters = "".join(joinit(specializedtemptypes,","))
        # Template parameters to use when defining the template
        self._templateArguments = "".join(joinit(tempargs,","))
        # Template parameters to use when defining the template
        self._templateParametersForGeneric = "".join(joinit(tempgen,","))
        # Datatypes for the constructors 
        self._datatypeForConstructor = "".join(joinit(constructortypes,","))
        # Args for the generic constructor
        self._genericConstructorArgs = "".join(joinit(genericconstructorargs,","))

    @property
    def pureNodeID(self):
        return self._pureNodeID
    

    @property
    def realInputs(self):
        return self._realInputs
    
    @property
    def realOutputs(self):
        return self._realOutputs
    
    @property
    def constructorTypes(self):
        return self._constructorTypes

    @property
    def constructorArguments(self):
        return self._constructorArguments
    
    @property
    def templateParameters(self):
        return self._templateParameters

    @property
    def specializedTemplateParameters(self):
        return self._specializedTemplateParameters
    

    @property
    def templateArguments(self):
        return self._templateArguments
    

    @property
    def templateParametersForGeneric(self):
        return self._templateParametersForGeneric

    @property
    def datatypeForConstructor(self):
        return self._datatypeForConstructor

    @property
    def genericConstructorArgs(self):
        return self._genericConstructorArgs
    
    
    
    
    @property 
    def nodeKind(self):
        if (self._realInputs + self._realOutputs) == 2:
           return "GenericNode"
        else:
           return "GenericNode21"


    @property
    def hasState(self):
        return self._hasState
    

    @property
    def typeName(self):
        return "Function"

    # To clean
    def cRun(self,ctemplate=True,codeArray=False):
       if ctemplate:
         theType=self._inputs[self.inputNames[0]].ctype
       else:
         theType=self._inputs[self.inputNames[0]].nptype
       # For cyclo static scheduling, nbSamples may be a list
       # and in this case we are uding the mac value
       nbSamples = self._inputs[self.inputNames[0]].nbSamples
       if isinstance(nbSamples,int):
          theLen = self._inputs[self.inputNames[0]].nbSamples
       else:
          theLen = np.max(nbSamples)
       theId = 0
       # List of buffer and corresponding fifo to initialize buffers
       inputs=[]
       outputs=[]
       # List of buffers variable to declare
       ptrs=[]

       # Argument names (buffer or constant node)
       args=[]
       inargs=[]
       outargs=[]

       argsStr=""
       inArgsStr=""
       outArgsStr=""
       inputId=1
       outputId=1
       for io in self.inputNames:
            ioObj = self._inputs[io] 
            if ioObj.constantNode:
               # Argument is name of constant Node
               args.append(ioObj.constantNode.name)
               inargs.append(ioObj.constantNode.name)
            else:
               # Argument is a buffer created from FIFO
               buf = "i%d" % theId
               ptrs.append(buf)
               args.append(buf)
               inargs.append(buf)
               if self.realInputs == 1:
                  readFuncName="getReadBuffer"
               else:
                  readFuncName="getReadBuffer%d"%inputId
               # Buffer and fifo
               inputs.append((buf,self.listOfargs[theId],readFuncName))
               inputId = inputId + 1
            theId = theId + 1
       for io in self.outputNames:
            buf = "o%d" % theId
            ptrs.append(buf)
            args.append(buf)
            outargs.append(buf)
            writeFuncName="getWriteBuffer"

            outputs.append((buf,self.listOfargs[theId],writeFuncName))
            outputId = outputId + 1
            theId = theId + 1

       argsStr="".join(joinit(args,","))
       inArgsStr="".join(joinit(inargs,","))
       outArgsStr="".join(joinit(outargs,","))

       if ctemplate:
           if codeArray:
              result=Dsp.CNODETEMPLATE.render(func=self._nodeName,
               theType = theType,
               nb = theLen,
               ptrs = ptrs,
               args = argsStr,
               inputs=inputs, 
               outputs=outputs,
               node=self
               )
           else:
              result=Dsp.CTEMPLATE.render(func=self._nodeName,
               theType = theType,
               nb = theLen,
               ptrs = ptrs,
               args = argsStr,
               inputs=inputs, 
               outputs=outputs,
               node=self
               )
       else:
           result=Dsp.PYTEMPLATE.render(func=self._nodeName,
            theType = theType,
            nb = theLen,
            ptrs = ptrs,
            args = argsStr,
            inArgs= inArgsStr, 
            outArgs= outArgsStr, 
            inputs=inputs, 
            outputs=outputs,
            node=self
            )
       return(result)

    def codeArrayRun(self):
        return(self.cRun(codeArray=True))


class Unary(GenericFunction):
    def __init__(self,funcname,theType,length):
        GenericFunction.__init__(self,funcname,theType,length)

        self.addInput("i",theType,length)
        self.addOutput("o",theType,length)


class Binary(GenericFunction):
    def __init__(self,funcname,theType,length):
        GenericFunction.__init__(self,funcname,theType,length)

        self.addInput("ia",theType,length)
        self.addInput("ib",theType,length)
        
        self.addOutput("o",theType,length)


    


BINARYOP=["scale","add","and","mult","not","or","sub","xor","cmplx_mult_cmplx","cmplx_mult_real"
]

class Dsp(GenericFunction):

    def __init__(self,name,theType,length):
        # Some different graph functions correspond to the same
        # DSP function like IFFT
        # So we rename the cmsis function to call the same function
        
        cmsisname = "arm_%s_%s" % (name,theType.dspExtension)
        GenericFunction.__init__(self, cmsisname,theType,length)
        self._binary=True
        
        if name in BINARYOP:
            self.addInput("ia",theType,length)
            self.addInput("ib",theType,length)
            self._binary=True
        else:
           self.addInput("i",theType,length)
           self._binary=False
        
        self.addOutput("o",theType,length)

    

    @property
    def typeName(self):
        return "CMSIS-DSP"