Question

我有一个程序从串行读取数据，在数组中搜索大于阈值的位置，然后使用这些数组进行后续处理。当我使用我创建的数组运行模拟时（减去连续部分，我可以使用此行（其中sens1是我的数组）

i1 = np.where(sens1 >= threshold)
sens1hits = np.concatenate(i1).tolist()

读取超过阈值的值以进行处理。但是，当我使用串行读取时，会抛出此错误。

TOAsens1 = timepoints.item(s1)

AttributeError: 'list' object has no attribute 'item'

这是该行

TOAsens1 = timepoints.item(s1)

我不确定为何在与串行阅读配对时不起作用。据我所知，它正在抛出错误，因为数组是空的，因为我在没有传感器设备的情况下进行测试。以下是代码的完整上下文：

import numpy as np
import math
import serial
import xlsxwriter
from time import time

# initialize serial
ser = serial.Serial('/dev/cu.usbmodem1411', 1000000) 

# set up arrays for sensor readings
start_time = time()
timepoints = []
sens1 = []
sens2 = []
sens3 = []
sens4 = []
duration = 5 # total seconds to collect data
threshold = 4 #threshold for sampling

# set up arrays to write hit times into
sens1hits = np.array([])
sens2hits = np.array([])
sens3hits = np.array([])
sens4hits = np.array([])
TOA1 = []
TOA2 = []
TOA3 = []
TOA4 = []
xcord =[]
ycord =[]

# flush any junk left in the serial buffer
ser.flushInput()
ser.reset_input_buffer() # for pyserial 3.0+
run = True

# collect the data
while run:
    ser.reset_input_buffer()
    data = ser.readline().split('\t')

    # sometimes the incoming data is garbage, so just 'try' to do this
    try:
        # store the entire dataset for sensor 1 for later
        sens1.append(float(data[0])*5.0/1024)
        timepoints.append(time()-start_time)
        current_time = timepoints[-1]

        # store the entire dataset for sensor 2 for later
        sens2.append(float(data[1])*5.0/1024)

        # store the entire dataset for sensor 3 for later
        sens3.append(float(data[2])*5.0/1024)

        # store the entire dataset for sensor 2 for later
        sens4.append(float(data[3])*5.0/1024)

        # when time's up, kill the collect loop
        if timepoints[-1] > duration: run=False

    # if the try statement throws an error, just do nothing
    except: pass

# close serial port
ser.close()

# find positions of target hits in string and print locations in array
print('Sensor 1 Hits:')
i1 = np.where(sens1 == threshold)
sens1hits = np.concatenate(i1).tolist()
print (sens1hits)

print('\nSensor 2 Hits:')
i2 = np.where(sens2 == threshold)
sens2hits = np.concatenate(i2).tolist()
print (sens2hits)

print('\nSensor 3 Hits:')
i3 = np.where(sens3 == threshold)
sens3hits = np.concatenate(i3).tolist()
print (sens3hits)

print('\nSensor 4 Hits:')
i4 = np.where(sens4 == threshold)
sens4hits = np.concatenate(i4).tolist()
print (sens4hits)

print('\nNumber of Hits Detected:')
n = len(sens1hits)
print n
print ('\n')

# set counter variable
f = 0

# begin array TOA array creation for each sensor
for x in xrange(0,n):
    # sensor 1
    s1 = sens1hits[f]
    TOAsens1 = timepoints.item(s1)
    TOA1.append(TOAsens1)

    # sensor 2
    s2 = sens2hits[f]
    TOAsens2 = timepoints.item(s2)
    TOA2.append(TOAsens2)

    # sensor 3
    s3 = sens3hits[f]
    TOAsens3 = timepoints.item(s3)
    TOA3.append(TOAsens3)

    # sensor 4
    s4 = sens4hits[f]
    TOAsens4 = timepoints.item(s4)
    TOA4.append(TOAsens4)

    #increase counter
    f += 1

numpy.where（大于阈值）不适用于串行数据采集

0 个答案: