Downsampling

basic.downsampling.run(data, src_freq, tgt_freq)

Downsample a signal (data) from src_freq to tgt_freq.

Parameters:

• data (np.ndarray, shape(duration * src_freq,)) – Data to be downsampled.

• src_freq (int) – The original frequency of the signal.

• tgt_freq (int) – The new frequency of the signal.

Returns:

Downsampled data

Return type:

np.ndarray, shape(duration * tgt_freq,)

The following code example shows how to apply downsampling.

def main():
   #Configure sample data
   channel_count = 1
   frequency = [random.randint(10, 25) for _ in range(channel_count)]
   data_range = np.arange(0, 1000)
   frequency_sampling = 10000
   frequency_downsampled = 1000

   #Generate some sample data
   raw_data = [None for _ in range(channel_count)]
   for idx in range(channel_count):
     genuine_signal = np.sin(2 * np.pi * frequency[idx] * data_range / frequency_sampling)

     raw_data[idx] = genuine_signal
   raw_data = np.asarray(raw_data)

   ds_data = ds.run(raw_data[0], frequency_sampling, frequency_downsampled)

   #visualize result
   plt.figure()

   plt.plot(np.arange(0, len(data_range), 1), raw_data[0], color = "red")
   plt.scatter(np.arange(0, len(data_range), frequency_sampling/frequency_downsampled), ds_data, color = "blue")
   plt.show(block = True)

main()

Applying downsampling reduced the high-density red line to the data points identified by the blue dots: