如何在python中计算传感器数据的移动平均值？

如何解决如何在python中计算传感器数据的移动平均值？

我正在尝试创建一个简单的数组来收集传感器数据并在 python 中计算移动平均值。最终结果将存储在我可以发布到 MQTT 的变量中。我有收集传感器数据并将其发布到 MQTT 的代码，但我还没有弄清楚数组和移动平均线的计算。

我搜索了有关创建数组和计算移动平均数的任何信息，但没有找到任何从变量收集数据、将该信息输入数组并在收集 40 个值后计算移动平均数并存储的示例该信息位于不同的变量中。

感谢任何建议！

请温柔，因为我是一个新手python程序员...

  GNU nano 3.2                                                                                                                                                                                                                                                                                                mqtt.ph.py                                                                                                                                                                                                                                                                                                           

#!/usr/bin/env python3



import math

import sys

import time

from grove.adc import ADC

import paho.mqtt.client as mqtt # Import the MQTT library

# Our "on message" event

def messageFunction (client,userdata,message):
    topic = str(message.topic)
    message = str(message.payload.decode("utf-8"))
    print(topic + message)

ourClient = mqtt.Client("openhab_mqtt") # Create a MQTT client object
ourClient.username_pw_set(username="AAAAA",password="BBBBB")
ourClient.connect("192.168.1.XXX",XXXX) # Connect to the test MQTT broker
ourClient.subscribe("openhab") # Subscribe to the topic TDS
ourClient.on_message = messageFunction # Attach the messageFunction to subscription
ourClient.loop_start() # Start the MQTT client

# CONSTANTS

Vref = 4.95   # volts

Vcal_mv = 1903  # milli-volts (using raw value 539 * 4.95/1023 * 1000)  note 10-bit A2D

mv_per_ph = 59.6

ph_per_mv = 1.0 / mv_per_ph





class GrovePH:



    def __init__(self,channel):

        self.channel = channel

        self.adc = ADC()





    @property

    def PH(self):

        value = self.adc.read(self.channel)

        if value != 0:

            voltage_mv = value * Vref / 1023.0 * 1000

            PHValue = 7 - ((voltage_mv - Vcal_mv) * ph_per_mv)

            return PHValue

        else:

            return     


def main():



    if len(sys.argv) < 2:

        print('Usage: {} adc_channel'.format(sys.argv[0]))

        sys.exit(1)



    sensor = GrovePH(int(sys.argv[1]))

    print('Detecting PH...')



    while True:

        try:
             ourClient.publish("openhab/pH",'{:2.1f}'.format(sensor.PH)) # Publish message to MQTT broker
             time.sleep(1) # Sleep for a second

#            print('PH Value: {:2.1f}'.format(sensor.PH))

#            time.sleep(1)

        except KeyboardInterrupt:

            print("\nExiting...")

            sys.exit(0)



if __name__ == '__main__':

    main()

解决方法

我发现使用包含最大条目数的 deque 计算样本的移动平均值很容易。然后你可以继续添加样本，长度自行处理：

#!/usr/bin/env python3

import collections
import random

# Ensure repeatable randomness ;-)
random.seed(42)

# Define a deque with max of 40 samples
samples = collections.deque(maxlen=40)

# Add 100 random samples to deque,each one between 10..20
for i in range(100):
    # Generate random sample
    value = random.randint(10,20)
    # Append to deque
    samples.append(value)
    # Show sample,number of samples,total and moving average
    N = len(samples)
    total = sum(samples)
    movingAvg = total/N
    print(f'value: {value},num samples: {N},total: {total},moving average: {movingAvg}')

检查下面输出中的样本数并观察它上升到 40 但并没有增加：

样本输出

value: 20,num samples: 1,total: 20,moving average: 20.0
value: 11,num samples: 2,total: 31,moving average: 15.5
value: 10,num samples: 3,total: 41,moving average: 13.666666666666666
value: 14,num samples: 4,total: 55,moving average: 13.75
value: 13,num samples: 5,total: 68,moving average: 13.6
value: 13,num samples: 6,total: 81,moving average: 13.5
value: 12,num samples: 7,total: 93,moving average: 13.285714285714286
value: 11,num samples: 8,total: 104,moving average: 13.0
value: 20,num samples: 9,total: 124,moving average: 13.777777777777779
value: 18,num samples: 10,total: 142,moving average: 14.2
value: 11,num samples: 11,total: 153,moving average: 13.909090909090908
value: 19,num samples: 12,total: 172,moving average: 14.333333333333334
value: 16,num samples: 13,total: 188,moving average: 14.461538461538462
value: 10,num samples: 14,total: 198,moving average: 14.142857142857142
value: 10,num samples: 15,total: 208,moving average: 13.866666666666667
value: 11,num samples: 16,total: 219,moving average: 13.6875
value: 13,num samples: 17,total: 232,moving average: 13.647058823529411
value: 13,num samples: 18,total: 245,moving average: 13.61111111111111
value: 18,num samples: 19,total: 263,moving average: 13.842105263157896
value: 19,num samples: 20,total: 282,moving average: 14.1
value: 10,num samples: 21,total: 292,moving average: 13.904761904761905
value: 18,num samples: 22,total: 310,moving average: 14.090909090909092
value: 13,num samples: 23,total: 323,moving average: 14.043478260869565
value: 20,num samples: 24,total: 343,moving average: 14.291666666666666
value: 18,num samples: 25,total: 361,moving average: 14.44
value: 16,num samples: 26,total: 377,moving average: 14.5
value: 13,num samples: 27,total: 390,moving average: 14.444444444444445
value: 17,num samples: 28,total: 407,moving average: 14.535714285714286
value: 19,num samples: 29,total: 426,moving average: 14.689655172413794
value: 14,num samples: 30,total: 440,moving average: 14.666666666666666
value: 10,num samples: 31,total: 450,moving average: 14.516129032258064
value: 12,num samples: 32,total: 462,moving average: 14.4375
value: 16,num samples: 33,total: 478,moving average: 14.484848484848484
value: 15,num samples: 34,total: 493,moving average: 14.5
value: 14,num samples: 35,total: 507,moving average: 14.485714285714286
value: 12,num samples: 36,total: 519,moving average: 14.416666666666666
value: 13,num samples: 37,total: 532,moving average: 14.378378378378379
value: 15,num samples: 38,total: 547,moving average: 14.394736842105264
value: 11,num samples: 39,total: 558,moving average: 14.307692307692308
value: 11,num samples: 40,total: 569,moving average: 14.225
value: 16,total: 565,moving average: 14.125
value: 11,moving average: 14.125
value: 15,total: 570,moving average: 14.25
value: 15,total: 571,moving average: 14.275

对于纯粹主义者，我同意将我的变量 total 保持为最后 N 个样本的运行总和，并从总数中减去第 41 个最旧的样本可能更有效留下有趣样本的窗口。这样您就可以避免在每次迭代时都运行 sum(samples)。但是对于小窗口大小和非性能关键用途，建议的方法绝对具有简单性的优点。

关键字：Python、移动平均、duque、固定长度、最大长度、窗口、窗口。

如何在python中计算传感器数据的移动平均值？

如何解决如何在python中计算传感器数据的移动平均值？

解决方法

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