peterlu14
110 lines
3.7 KiB
Python
110 lines
3.7 KiB
Python
import sys
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import ast
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import psycopg2
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import numpy as np
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import pandas as pd
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from math import ceil, floor
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from json import loads, dumps
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from scipy.fft import fft, fftfreq, rfft, rfftfreq, irfft, ifft
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if __name__ == '__main__':
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params = {}
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for input in sys.argv[1:]: #Now we're going to iterate over argv[1:] (argv[0] is the program name)
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param = input.split("=") #Get what's left of the '='
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params[param[0]] = param[1]
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meta_id = params['meta_id']
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channel = params['channel']
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cut_off_freq = int(params['cut_off_freq'])
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threshold = ast.literal_eval('[' + params['threshold'] + ']')
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waveform = ast.literal_eval('[' + params['waveform'] + ']')
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SAMPLE_RATE = int(params['sample_rate'])
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DURATION = ceil(float(params['time_duration']))
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DELTA_TIME = 1e6 / SAMPLE_RATE
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N = SAMPLE_RATE * DURATION
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ret_data_type = params['ret_data_type']
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# print(meta_id, channel, cut_off_freq, threshold, waveForm, SAMPLE_RATE, DURATION, N)
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conn = psycopg2.connect(database="postgres", user="biopro", password="BioProControlBox", host="127.0.0.1", port="5432")
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cur = conn.cursor()
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sql_str = 'SELECT data FROM "public"."' + str(channel) + '_recording_data_raws" WHERE parent = ' + meta_id + 'ORDER BY id ASC'
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cur.execute(sql_str)
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ret = None
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try:
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ret = cur.fetchall()
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except BaseException as e:
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print(e)
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exit
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finally:
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time = np.array([], dtype=int)
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data = np.array([])
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for sub_data in ret:
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for split_data in sub_data[0].split('"***"')[:-1]:
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value = np.array(split_data.split(' '))
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time = np.append(time, value[::2].astype(np.int))
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data = np.append(data, value[1::2].astype(np.int))
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# print(len(time), len(data))
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xf = rfftfreq(N, 1 / SAMPLE_RATE)
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points_per_freq = len(xf) / (SAMPLE_RATE / 2)
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target_idx = int(points_per_freq * cut_off_freq)
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# print(xf[target_idx])
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yf = rfft(data)
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yf[:target_idx] = 0
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new_sig = np.round(irfft(yf, len(data)),3)
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# print(time[-1])
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# print(len(datas))
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# print(len(yf),yf)
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if threshold[0] == 'below':
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th = new_sig < threshold[1]
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elif threshold[0] == 'over':
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th = new_sig > threshold[1]
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elif threshold[0] == 'auto':
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th = new_sig
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threshold_edges = np.convolve([1, -1], th, mode='same')
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thresholded_edge_indices = np.where(threshold_edges==1)[0]
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filter_array = []
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for idx, point in enumerate(thresholded_edge_indices):
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if idx > 0:
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if time[thresholded_edge_indices[idx]] - int(waveform[2]) >= time[thresholded_edge_indices[idx-1]]:
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filter_array.append(True)
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elif time[thresholded_edge_indices[idx]] - int(waveform[1]) >= time[thresholded_edge_indices[idx-1]]:
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filter_array.append(True)
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else:
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filter_array.append(False)
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else:
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if time[thresholded_edge_indices[idx]] - int(waveform[1]) < 0:
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filter_array.append(False)
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else:
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filter_array.append(True)
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timemark_list = thresholded_edge_indices[filter_array]
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if ret_data_type == 'all':
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print(' '.join(str(x) for x in time))
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print(' '.join(str(x) for x in new_sig))
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print(' '.join(str(x) for x in timemark_list))
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elif ret_data_type == 'partial':
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return_sub_signal_lists = {}
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for timemark in timemark_list:
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start_index = timemark - floor(int(waveform[1] / DELTA_TIME))
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end_index = timemark + ceil(int(waveform[2] / DELTA_TIME))
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subSignal = new_sig[start_index: end_index + 2]
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return_sub_signal_lists[str(timemark)] = ' '.join(str(x) for x in subSignal)
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print(dumps(return_sub_signal_lists))
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conn.commit()
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conn.close()
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exit |