#!/usr/bin/env python
# -*- coding: utf-8 -*-
# 
# Copyright 2018 <+YOU OR YOUR COMPANY+>.
# 
# This is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 3, or (at your option)
# any later version.
# 
# This software is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
# 
# You should have received a copy of the GNU General Public License
# along with this software; see the file COPYING.  If not, write to
# the Free Software Foundation, Inc., 51 Franklin Street,
# Boston, MA 02110-1301, USA.
# 

import numpy
from gnuradio import gr

class vave(gr.decim_block):
    """
    docstring for block vave
    """
    def __init__(self, vsize, vdecimate):
        gr.decim_block.__init__(self,
            name="vave",
            in_sig=[(numpy.float32, int(vsize))],
            out_sig=[(numpy.float32, int(vsize))], 
            decim=int(vdecimate))
        self.vsize = int(vsize)
        self.vdecimate = int(vdecimate)
        self.sum = numpy.zeros(self.vsize)
        self.count = 0
        self.oneovern = 1./float(self.vdecimate)

    def forecast( self, noutput_items, ninput_items):
        return self.vdecimate
    
    def work(self, input_items, output_items):
        """
        Work averages all input vectors and outputs one vector for all inputs
        """
        out = output_items[0]
        # get the number of input vectors
        n = len( input_items)
        print 'Number of work input vectors: ',n

        for j in range(n):
            # get the lenght of one input
            in0 = input_items[j]
            print 'Lenght of input ', len(in0),' for vectors: ',j

            # indicate consumption of a vector from input
            self.consume(0,len(in0))

            # now save this vector until all are received
            self.sum = self.sum + in0
            self.count = self.count + 1

            if self.count >= self.vdecimate:
                # normalize output average
                out[:] = self.oneovern * self.sum[:]
                # now reset the count and restart the sum
                self.count = 0
                self.sum = numpy.zeros( self.vdecimate)
                return len(output_items[0])
        # end for all input vectors
        # if here, then not enough vectors input to produce an output
        return 0
    # end vave()