[update] down sample

src/controllers/analysis/spikeDetect.py

@@ -1,5 +1,6 @@
 import sys
 import ast
+import time
 import psycopg2
 import numpy as np
 import pandas as pd
@@ -8,6 +9,21 @@ from math import ceil, floor
 from json import loads, dumps
 from scipy.fft import fft, fftfreq, rfft, rfftfreq, irfft, ifft
 
+import lttb
+from lttb.validators import has_two_columns, x_is_regular
+
+def get_downsampled_signal(ori_signal, output_length):
+  signal_length = len(ori_signal)
+  signal = np.array([range(signal_length), ori_signal]).T
+  assert signal.shape == (signal_length, 2)
+
+  # Downsampling
+  downsampled_signal = lttb.downsample(signal, n_out=output_length, validators=[has_two_columns, x_is_regular])
+  assert downsampled_signal.shape == (output_length, 2)
+  downsampled_signal_index = downsampled_signal[:, 0]
+  downsampled_signal_value = downsampled_signal[:, 1]
+  return downsampled_signal_index, downsampled_signal_value
+
 if __name__ == '__main__':
 
   params = {}
@@ -20,7 +36,7 @@ if __name__ == '__main__':
   cut_off_freq = int(params['cut_off_freq'])
   threshold = ast.literal_eval('[' + params['threshold'] + ']')
   waveform = ast.literal_eval('[' + params['waveform'] + ']')
-  SAMPLE_RATE = int(params['sample_rate'])
+  SAMPLE_RATE = int(float(params['sample_rate']))
   DURATION = ceil(float(params['time_duration']))
   DELTA_TIME = 1e6 / SAMPLE_RATE
   N = SAMPLE_RATE * DURATION
@@ -39,30 +55,22 @@ if __name__ == '__main__':
     print(e)
     exit
   finally:
-      time = np.array([], dtype=int)
-      data = np.array([])
+      timeArray = np.array([], dtype=int)
+      dataArray = np.array([])
 
       for sub_data in ret:
         for split_data in sub_data[0].split('"***"')[:-1]:
           value = np.array(split_data.split(' '))
-          time = np.append(time, value[::2].astype(np.int))
-          data = np.append(data, value[1::2].astype(np.int))
-
-      # print(len(time), len(data))
-      
+          timeArray = np.append(timeArray, value[::2].astype(np.int32))
+          dataArray = np.append(dataArray, value[1::2].astype(np.int32))
       xf = rfftfreq(N, 1 / SAMPLE_RATE)
       points_per_freq = len(xf) / (SAMPLE_RATE / 2)
       target_idx = int(points_per_freq * cut_off_freq)
 
       # print(xf[target_idx])
-
-      yf = rfft(data)
+      yf = rfft(dataArray)
       yf[:target_idx] = 0
-      new_sig = np.round(irfft(yf, len(data)),3)
-
-      # print(time[-1])
-      # print(len(datas))
-      # print(len(yf),yf)
+      new_sig = np.round(irfft(yf, len(dataArray)),3)
 
       if threshold[0] == 'below':
         th = new_sig < threshold[1]
@@ -77,23 +85,26 @@ if __name__ == '__main__':
       filter_array = []
       for idx, point in enumerate(thresholded_edge_indices):
         if idx > 0:
-          if time[thresholded_edge_indices[idx]] - int(waveform[2]) >= time[thresholded_edge_indices[idx-1]]:
+          if timeArray[thresholded_edge_indices[idx]] - int(waveform[2]) >= timeArray[thresholded_edge_indices[idx-1]]:
             filter_array.append(True)
-          elif time[thresholded_edge_indices[idx]] - int(waveform[1]) >= time[thresholded_edge_indices[idx-1]]:
+          elif timeArray[thresholded_edge_indices[idx]] - int(waveform[1]) >= timeArray[thresholded_edge_indices[idx-1]]:
             filter_array.append(True)
           else:
             filter_array.append(False)
         else:
-          if time[thresholded_edge_indices[idx]] - int(waveform[1]) < 0:
+          if timeArray[thresholded_edge_indices[idx]] - int(waveform[1]) < 0:
             filter_array.append(False)
           else:
             filter_array.append(True)
       
       timemark_list = thresholded_edge_indices[filter_array]
 
+      OUTPUT_SIGNAL_SIZE = 2000
+      downsampled_signal_index, downsampled_signal_value = get_downsampled_signal(new_sig, OUTPUT_SIGNAL_SIZE)
       if ret_data_type == 'all':
-        print(' '.join(str(x) for x in time))
-        print(' '.join(str(x) for x in new_sig))
+        print(' '.join(str(x) for x in timeArray))
+        print(' '.join(str(x) for x in downsampled_signal_index))
+        print(' '.join(str(x) for x in downsampled_signal_value))
         print(' '.join(str(x) for x in timemark_list))
       elif ret_data_type == 'partial':
         return_sub_signal_lists = {}