问对对数函数的串行数据过滤

EN

import serial
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from scipy import stats


#initialize serial port
ser = serial.Serial()
ser.port = '/dev/cu.usbmodem14101' #Arduino serial port
ser.baudrate = 9600
ser.timeout = 10 #specify timeout when using readline()
ser.open()
if ser.is_open==True:
    print("\nAll right, serial port now open. Configuration:\n")
    print(ser, "\n") #print serial parameters

fig1 = plt.figure(figsize=(10,7))
ax1 = fig1.add_subplot(2, 1, 1)
xs = [] #store trials here (n)
ys = [] #store relative frequency here

# This function is called periodically from FuncAnimation
def animate(i, xs, ys):

        #Acquire and parse data from serial port
        line=ser.readline()      #ascii
        line_as_list = line.split(b',')
        i = float(line_as_list[0])
        Y = line_as_list[1]
        Y_as_list = Y.split(b'\n')
        Y_float = float(Y_as_list[0])
        cc=str(ser.readline())
        print(cc[2:][:-5])
        xs.append(i)
        ys.append(Y_float)
K = 0
def filter_where(xs, K):
        return xs[np.where(ys > K)]
print(xs)

print(ys)
def filter_where(ys, K):
        return ys[np.where(ys > K)]
print(ys)
#adding regression - commented out b/c it generates error that it is empty



#slope, intercept, r, p, std_err = stats.linregress(xs, ys)

#def myfunc(xs):
#  return slope * xs + intercept

#mymodel = list(map(myfunc, xs))

plt.scatter(xs, ys)
#plt.plot(xs, mymodel)
# Draw x and y lists
ax1.clear()
ax1.plot(xs, ys)


ani = animation.FuncAnimation(fig1, animate, fargs=(xs, ys), interval=10)
plt.show()

All right, serial port now open. Configuration:

Serial<id=0x109132c50, open=True>(port='/dev/cu.usbmodem14101', baudrate=9600, bytesize=8, parity='N', stopbits=1, timeout=10, xonxoff=False, rtscts=False, dsrdtr=False) 

[]
[]
[]
0.30,0.011
0.70,0.011
1.11,0.013
1.51,0.005
1.91,0.006
2.31,0.012
2.71,0.004
3.12,0.005
3.52,0.004
3.92,0.014
4.32,0.005
4.72,-0.001
5.13,0.008
5.53,0.009
5.93,0.010
6.33,0.013
6.73,0.000
7.13,0.004