Пример #1
0
    def publishBuffer(self):
        rospy.loginfo("Publishing buffer dishes...")
        loop_rate = rospy.Rate(self.loop_rate)

        for current_dish in range(self.buffer_size):
            # Initialize a new dish state
            d = dish_state()

            # For each channel in the dish state
            for channel in range(60):
                d.samples[channel] = 0.0
                # print 'Channel', channel, ':'

                # If there are no recorded neurons on this channel, then the voltage is 0.0
                # Otherwise, get the weighted average of the volts of the neurons on this channel
                if self.channels[channel] != None:
                    for neuron in self.channels[channel].neurons:
                        weight = neuron.weight / self.channels[channel].total_weight
                        weighted = self.M[neuron.data][current_dish] * weight
                        d.samples[channel] += weighted
                        # print '    Neuron: ', neuron.data, 'Weight:', weight, 'State:', M[neuron.data][current_dish]
                        # print '    Weighted State: ', weighted, 'Total:', d.samples[channel]

            # If we are publishing to burst_calc, then publish the dish_state now
            # Otherwise, do nothing with it
            if self.do_burst_calc == True:
                self.dish_pub_burst.publish(d)
                loop_rate.sleep()
Пример #2
0
    def publish(self):
        rospy.loginfo("Publishing dishes...")
        loop_rate = rospy.Rate(self.loop_rate)
        current_dish = self.buffer_size

        while current_dish < len(self.M[self.recorded_neurons[0]]) and not rospy.is_shutdown():
            # Initialize a new dish state
            d = dish_state()

            # For each channel in the dish state
            for channel in range(60):
                d.samples[channel] = 0.0
                # print 'Channel', channel, ':'

                # If there are no recorded neurons on this channel, then the voltage is 0.0
                # Otherwise, get the weighted average of the volts of the neurons on this channel
                if self.channels[channel] != None:
                    for neuron in self.channels[channel].neurons:
                        weight = neuron.weight / self.channels[channel].total_weight
                        weighted = self.M[neuron.data][current_dish] * weight
                        d.samples[channel] += weighted
                        # print '    Neuron: ', neuron.data, 'Weight:', weight, 'State:', M[neuron.data][current_dish]
                        # print '    Weighted State: ', weighted, 'Total:', d.samples[channel]

            # Add the time stamp
            d.header.stamp = rospy.Time.now() - self.offset

            # Publish the dish state
            if self.do_volt_distr == True:
                self.dish_pub_volt.publish(d)
            if self.do_burst_calc == True:
                self.dish_pub_burst.publish(d)
                self.dish_pub_viz.publish(d)

            # Sleep and move on to the next dish
            current_dish += 1
            loop_rate.sleep()

        # Publish a final dish to let the nodes know to finish up
        end = dish_state()
        end.last_dish = True
        if self.do_volt_distr == True:
            self.dish_pub_volt.publish(end)
        if self.do_burst_calc == True:
            self.dish_pub_burst.publish(end)

        rospy.loginfo("Publishing finished")