Channel restoration
==========================


.. currentmodule:: cleansing.channel_restoration
.. autofunction:: run

The following code example shows how to apply bad channel identification & subsequent restoration.

.. code:: python
    
   import numpy as np
   import random

   import matplotlib
   matplotlib.use("Qt5agg")
   import matplotlib.pyplot as plt

   import finn.cleansing.bad_channel_identification as bci
   import finn.cleansing.channel_restoration as cr

   def main():
       #Configure sample data
       channel_count = 64
       frequency = [random.randint(5, 50) for _ in range(channel_count)]
       data_range = np.arange(0, 10000)
       frequency_sampling = 200
       ch_names = ['O1', 'Oz', 'O2', 'PO9', 'PO7', 'PO3', 'POz', 'PO4', 'PO8', 'PO10', 'P9', 'P7', 'P5', 'P3', 'P1', 'Pz', 'P2', 'P4', 'P6', 'P8', 'P10',
                  'TP9', 'TP7', 'CP5', 'CP3', 'CP1', 'CPz', 'CP2', 'CP4', 'CP6', 'TP8', 'TP10', 'T9', 'T7', 'C5', 'C3', 'C1', 'Cz', 'C2', 'C4', 'C6', 'T8', 'T10',
                  'FT9', 'FT7', 'FC5', 'FC3', 'FC1', 'FCz', 'FC2', 'FC4', 'FC6', 'FT8', 'FT10', 'F9', 'F7', 'F5', 'F3', 'F1', 'Fz', 'F2', 'F4', 'F6', 'F8', 'F10',
                  'AF9', 'AF7', 'AF3', 'AFz', 'AF4', 'AF8', 'AF10', 'Fp1', 'Fpz', 'Fp2']
       
       #Configure noise data
       frequency_noise = 50
       shared_noise_strength = 1
       random_noise_strength = 1
       
       #Configure bad channel
       bad_channel_idx = 1
       bad_channel_signal_power = 1.1
       
       #Generate some sample data
       raw_data = [None for _ in range(channel_count)]
       for channel_idx in range(channel_count):
           genuine_signal = np.sin(2 * np.pi * frequency[channel_idx] * data_range / frequency_sampling)
           shared_noise_signal = np.sin(2 * np.pi * frequency_noise * data_range / frequency_sampling) * shared_noise_strength
           random_noise_signal = np.random.random(len(data_range)) * random_noise_strength
           
           raw_data[channel_idx] = genuine_signal + shared_noise_signal + random_noise_signal
       
       raw_data[bad_channel_idx] = np.random.random(len(data_range)) * bad_channel_signal_power
       #raw_data = np.asarray(raw_data)
       
       #Faulty channel gets identified
       (_, invalid_list, _) = bci.run(raw_data, ch_names, [frequency_sampling for _ in range(channel_count)], [[60, 100]], broadness = 3, visual_inspection = True)
       #Faulty channel gets substituted via neighbors
       rest_data = cr.run(raw_data, ch_names, invalid_list)
       
       #visualization
       channels_to_plot = 3
       (_, axes) = plt.subplots(channels_to_plot, 2)
       for channel_idx in range(channels_to_plot):
           axes[channel_idx, 0].plot(raw_data[channel_idx][:200])
           axes[channel_idx, 1].plot(rest_data[channel_idx][:200])
           
       axes[0, 0].set_title("before correction")
       axes[0, 1].set_title("after correction")
       
       axes[0, 0].set_ylabel("Channel #0\n"); axes[0, 0].set_yticks([-2, 0, 2])
       axes[1, 0].set_ylabel("Channel #1\n(faulty channel)"); axes[1, 0].set_yticks([-2, 0, 2])
       axes[2, 0].set_ylabel("Channel #2\n"); axes[2, 0].set_yticks([-2, 0, 2])
       
       plt.show()

   main()

Having identified bad channels, those got restored by averaging activity from their neighbors.
    
.. image:: img/channel_restoration.png