Пример #1
0
    def run(self):
        """
        Starts writing a waveform continuously while reading 
        the buffer periodically
        """

        #DAQ
        with nidaqmx.Task() as slave_Task3, nidaqmx.Task() as master_Task:
            #slave_Task3 = nidaqmx.Task()
            slave_Task3.ao_channels.add_ao_voltage_chan("/Dev1/ao0:1")
            master_Task.ai_channels.add_ai_voltage_chan("/Dev1/ai0")

            slave_Task3.timing.cfg_samp_clk_timing(
                rate=self.sampleRate,
                source='ai/SampleClock',
                sample_mode=nidaqmx.constants.AcquisitionType.CONTINUOUS)

            # Analoginput
            master_Task.timing.cfg_samp_clk_timing(
                rate=self.sampleRate,
                sample_mode=nidaqmx.constants.AcquisitionType.CONTINUOUS,
                samps_per_chan=self.readNumber)

            reader = AnalogSingleChannelReader(master_Task.in_stream)
            writer = AnalogMultiChannelWriter(slave_Task3.out_stream)

            reader.auto_start = False
            writer.auto_start = False

            writer.write_many_sample(self.wave)
            """Reading data from the buffer in a loop. 
            The idea is to let the task read more than could be loaded in the buffer for each iteration.
            This way the task will have to wait slightly longer for incoming samples. And leaves the buffer
            entirely clean. This way we always know the correct numpy size and are always left with an empty
            buffer (and the buffer will not slowly fill up)."""
            output = np.zeros(self.readNumber)
            slave_Task3.start(
            )  #Will wait for the readtask to start so it can use its clock
            master_Task.start()
            while not self.isInterruptionRequested():
                reader.read_many_sample(
                    data=output, number_of_samples_per_channel=self.readNumber)

                #Emiting the data just received as a signal

                Dataholder_average = np.mean(output.reshape(
                    self.averagenumber, -1),
                                             axis=0)

                self.data_PMT = np.reshape(
                    Dataholder_average,
                    (self.ypixelnumber, self.ScanArrayXnum))

                if self.ypixelnumber == 500:
                    self.data_PMT = self.data_PMT[:, 50:550] * -1
                elif self.ypixelnumber == 256:
                    self.data_PMT = self.data_PMT[:, 70:326] * -1

                self.measurement.emit(self.data_PMT)
    def run(self):
        """
        Starts writing a waveform continuously while reading 
        the buffer periodically
        """

        with nidaqmx.Task() as slave_Task, nidaqmx.Task() as master_Task:

            slave_Task.ao_channels.add_ao_voltage_chan("/Dev1/ao0:1")
            master_Task.ai_channels.add_ai_voltage_chan("/Dev1/ai0")

            if self.flag_continuous == False:
                # Timing of analog output channels
                slave_Task.timing.cfg_samp_clk_timing(
                    rate=self.Daq_sample_rate,
                    source='ai/SampleClock',
                    sample_mode=AcquisitionType.FINITE,
                    samps_per_chan=self.Totalscansamples)

                # Timing of recording channels
                master_Task.timing.cfg_samp_clk_timing(
                    rate=self.Daq_sample_rate,
                    sample_mode=AcquisitionType.FINITE,
                    samps_per_chan=self.Totalscansamples)
            else:
                # Timing of analog output channels
                slave_Task.timing.cfg_samp_clk_timing(
                    rate=self.Daq_sample_rate,
                    source='ai/SampleClock',
                    sample_mode=AcquisitionType.CONTINUOUS)

                # Timing of recording channels
                master_Task.timing.cfg_samp_clk_timing(
                    rate=self.Daq_sample_rate,
                    sample_mode=AcquisitionType.CONTINUOUS,
                    samps_per_chan=self.Totalscansamples)

            reader = AnalogSingleChannelReader(master_Task.in_stream)
            writer = AnalogMultiChannelWriter(slave_Task.out_stream)

            reader.auto_start = False
            writer.auto_start = False

            writer.write_many_sample(self.Galvo_samples)
            """Reading data from the buffer in a loop. 
            The idea is to let the task read more than could be loaded in the buffer for each iteration.
            This way the task will have to wait slightly longer for incoming samples. And leaves the buffer
            entirely clean. This way we always know the correct numpy size and are always left with an empty
            buffer (and the buffer will not slowly fill up)."""
            output = np.zeros(self.Totalscansamples)
            slave_Task.start(
            )  #Will wait for the readtask to start so it can use its clock
            master_Task.start()

            # while not self.isInterruptionRequested():
            reader.read_many_sample(
                data=output,
                number_of_samples_per_channel=self.Totalscansamples)

            Dataholder_average = np.mean(output.reshape(self.averagenum, -1),
                                         axis=0)

            if self.flag_return_image == True:
                # Calculate the mean of average frames.
                self.data_PMT = np.reshape(
                    Dataholder_average,
                    (self.pixel_number, self.total_X_sample_number))

                # Cut off the flying back part.
                if self.pixel_number == 500:
                    self.image_PMT = self.data_PMT[:, 50:550] * -1
                elif self.pixel_number == 256:
                    self.image_PMT = self.data_PMT[:, 70:326] * -1

                return self.image_PMT