import sys
import serial
import numpy as np
import matplotlib.pyplot as plt
from collections import deque
port = "COM11"
baud = 9600
timeout=1
ser = serial.Serial()
ser.port = port
ser.baudrate = baud
ser.timeout = timeout
a1 = deque([0.0]*100)
#ax = plt.axes(xlim=(0, 100), ylim=(0, 1000))
line, = plt.plot(a1)
plt.ion()
plt.ylim([0,1000])
try:
ser.open()
except:
sys.stderr.write("Error opening serial port %s\n" % (ser.portstr) )
sys.exit(1)
#ser.setRtsCts(0)
while 1:
# Read from serial port, blocking
data = ser.read(1)
# If there is more than 1 byte, read the rest
n = ser.inWaiting()
data = data + ser.read(n)
#sys.stdout.write(data)
print(a1)
a1.appendleft((data))
datatoplot = a1.pop()
line.set_ydata(a1)
plt.draw()
I am getting a plot between serial port values and sample points. I want to plot serial plot values vs time. Is there a way to convert sample points to time values, something like how to we convert sample point to frequency values using freqs = scipy.fftpack.fftfreq(n, d)
Thanks
If you want to plot the data against time from the start of the program, then:
import time
t0 = time.time()
tlist = deque([np.nan] * 100)
while 1:
# read the serial data ...
# when you have read a sample, capture the time difference
# and put it into a queue (similarly to the data values)
deltat = time.time() - t0
dlist.appendleft((deltat))
# remember to pop the data, as well
dlist.pop()
a1.pop()
# set the x and y data
line.set_xdata(tlist)
line.set_ydata(a1)
# draw it
plt.draw()
Now you have the number of seconds from the start of the program on the X axis.
If you want to have the real time shown, then use datetime.datetime objects:
import datetime
dlist = deque([datetime.datetime.now()] * 100)
while 1:
# capture the serial data ...
dilst.appendleft((datetime.datetime.now()))
# everything else as above
This should give you a plot with real time on the X axis.
import sys
import serial
import numpy as np
import matplotlib.pyplot as plt
import time
from collections import deque
from scipy import arange
port = "COM13"
baud = 9600
timeout=1
ser = serial.Serial()
ser.port = port
ser.baudrate = baud
ser.timeout = timeout
t0=time.time()
tlist = deque([np.nan]*10)
a1 = deque([0.0]*10)
#ax = plt.axes(xlim=(0, 100), ylim=(0, 1000))
line, = plt.plot(a1)
plt.ion()
plt.ylim([-100,100])
plt.grid(b=True,which= 'major' , color= 'g' , linestyle= '--')
#plt.grid(b=True,which= 'minor' , color= '-m' , linestyle= '--')
try:
ser.open()
except:
sys.stderr.write("Error opening serial port %s\n" % (ser.portstr) )
sys.exit(1)
#ser.setRtsCts(0)
while 1:
# Read from serial port, blocking
data = ser.read(1)
# If there is more than 1 byte, read the rest
n = ser.inWaiting()
data = data + ser.read(n)
#sys.stdout.write(data)
#print(a1)
#data1=int(data)-128
deltat = time.time() - t0
tlist.appendleft((deltat1))
datatoplot = tlist.pop()
a1.appendleft((data))
datatoplot = a1.pop()
line.set_xdata(tlist)
line.set_ydata(a1)
plt.hold(False)
plt.draw()
This is the complete code I used, and yes I had already changed that line.pop . But as I explained earlier in the comment I am not able to get the time values in x axis
Related
I have programmed Raspberry Pi 3 to plot ECG SIgnal. I have used matplotlib library in Python2.7. Arduino board has been used to acquire the analog inputs.
the GUI created contains 2 subplots that reads signal from 2 analog pins of the arduino.
Now i need to detect the R peaks from the live ECG signal.
I have tried several codes to detect but i am unable to detect it in the same plot.
Please suggest how do i add the r-peak detection code in the code below of the real-time ECG signal plotting.
This is my code below:-
import serial
import numpy as np
from matplotlib import pyplot as plt
from matplotlib import animation
ser = serial.Serial('/dev/ttyACM0', 115200)
n = 200
fig = plt.figure(figsize=(12,9))
ax1 = fig.add_subplot(2,1,1)
ax2 = fig.add_subplot(2,1,2)
ch1, = ax1.plot([], [], 'b', label = 'Channel 1')
ch2, = ax2.plot([], [], 'r', label = 'Channel 2')
axes = [ax1, ax2]
for ax in axes:
ax.set_xlim(0, n+1)
ax.set_ylim(0, n-1)
ax.set_ylabel('values')
ax.legend(loc='upper right')
ax.grid(True)
ax1.set_title('Real-time ECG plot')
ax2.set_xlabel('Values')
t = list(range(0,n))
channel1 = [0] * n
channel2 = [0] * n
def init():
ch1.set_data([], [])
ch2.set_data([], [])
return ch1, ch2,
def animate(i):
while (ser.inWaiting() == 0):
pass
arduinoString = ser.readline()
dataArray = arduinoString.split(',')
channel1.append(float(dataArray[0]))
channel2.append(float(dataArray[1]))
channel1.pop(0)
channel2.pop(0)
ch1.set_data(t, channel1)
ch2.set_data(t, channel2)
return ch1, ch2, ch3,
delay = 0
anim = animation.FuncAnimation(fig, animate, init_func=init,interval=delay, blit=True)
plt.show()
ser.close()
I am trying to read-out a serial port.
The problem is that the script works but the read command seems to not respect the argument (2 bytes to read).
The typical output of the script (the ans variable in return function "readPosValue") is:
print(currTime,readPosValue(serPort))
(1517909247.176, '0b11000010110111001110011')
They are clearly more than 16 bits.
The used script:
import time
import struct
import binascii
import serial
ser = serial.Serial(
port='COM2',
baudrate=9600,
parity=serial.PARITY_NONE,
stopbits=serial.STOPBITS_ONE,
bytesize=serial.EIGHTBITS
)
def inficonAcquisition(serPort):
try:
while True:
position = readPosValue(serPort)
currTime = time.time()
print(currTime,position)
except KeyboardInterrupt:
serPort.close()
exit()
def readPosValue(serPort):
ans = ''
while serPort.inWaiting() > 0:
ans += serPort.read(2)
return bin(int(binascii.hexlify('ans'), 16))
The problem was in the inWaiting() function.
It was not required in this kind of readout.
I am using windows 10 and I am reading ECG data using Arduino micro from pin A1. I used the serial plotter without any problems. The Arduino is connected to COM4
However, I want to read that ECG data using python and plot it there (interface). I came over a useful class called: deque. Link: http://electronut.in/plotting-real-time-data-from-arduino-using-python/
I am calling the function from another python file and I am getting this error:
usage: mac1.py [-h] --port PORT
mac1.py: error: argument --port is required
Code:
import serial
from Arduino import Arduino
import time
import Idr
import matplotlib.pyplot as plt
import matplotlib.animation as animation
if __name__ == '__main__':
Idr.main()
Idr.py function:
import sys, serial, argparse
import numpy as np
from time import sleep
from collections import deque
import matplotlib.pyplot as plt
import matplotlib.animation as animation
# plot class
class AnalogPlot:
# constr
def __init__(self, strPort, maxLen):
# open serial port
self.ser = serial.Serial(strPort, 9600)
self.ax = deque([0.0] * maxLen)
self.ay = deque([0.0] * maxLen)
self.maxLen = maxLen
# add to buffer
def addToBuf(self, buf, val):
if len(buf) < self.maxLen:
buf.append(val)
else:
buf.pop()
buf.appendleft(val)
# add data
def add(self, data):
assert (len(data) == 2)
self.addToBuf(self.ax, data[0])
self.addToBuf(self.ay, data[1])
# update plot
def update(self, frameNum, a1):
try:
line = self.ser.readline()
data = [float(val) for val in line.split()]
# print data
if (len(data) == 2):
self.add(data)
a1.set_data(range(self.maxLen), self.ay)
except KeyboardInterrupt:
print('exiting')
return a1,
# clean up
def close(self):
# close serial
self.ser.flush()
self.ser.close()
# main() function
def main():
# create parser
parser = argparse.ArgumentParser(description="LDR serial")
# add expected arguments
parser.add_argument('--port', dest='port', required=True)
# parse args
args = parser.parse_args()
# strPort = '/dev/tty.usbserial-A7006Yqh'
strPort = args.port
print('reading from serial port %s...' % strPort)
# plot parameters
analogPlot = AnalogPlot(strPort, 100)
print('plotting data...')
# set up animation
fig = plt.figure()
ax = plt.axes(xlim=(0, 100), ylim=(0, 1023))
a1, = ax.plot([], [])
anim = animation.FuncAnimation(fig, analogPlot.update,
fargs=(a1),
interval=50)
# show plot
plt.show()
# clean up
analogPlot.close()
print('exiting.')
So I have created this code for my research, but I want to use it for plenty of data files, I do not want to do it manually, which means retyping some lines in my code to use desired file. How to use input command in python (I work with python 2.7 on Windows OS) to use it faster, just by typing name of desired datafile. My code so far:
import iodata as io
import matplotlib.pyplot as plt
import numpy as np
import time
from scipy.signal import welch
from scipy import signal
testInstance = io.InputConverter()
start = time.time()
conversionError = io.ConversionError()
#data = testInstance.convert(r"S:\Doktorat\Python\", 1", conversionError)
data = testInstance.convert(r"/Users/PycharmProjects/Hugo/20160401", "201604010000", conversionError)
end = time.time()
print("time elapsed " + str(end - start))
if(conversionError.conversionSucces):
print("Conversion succesful")
if(conversionError.conversionSucces == False):
print("Conversion failed: " + conversionError.conversionErrorLog)
print "Done!"
# Create a new subplot for two cannals 1 & 3
a = np.amin(data.data)
Bx = data.data[0,]
By = data.data[1,]
dt = float(300)/266350
Fs = 1/dt
t = np.arange(0,300,dt*1e3)
N = len(Bx)
M = len(By)
time = np.linspace(0,300,N)
time2 = np.linspace(0,300,M)
filename = 'C:/Users/PycharmProjects/Hugo/20160401/201604010000.dat'
d = open(filename,'rb')
degree = u"\u00b0"
headersize = 64
header = d.read(headersize)
ax1 = plt.subplot(211)
ax1.set_title(header[:16] + ', ' + # station name
'Canals: '+header[32:33]+' and '+header[34:35]+ ', ' # canals
+'Temp'+header[38:43]+degree+'C' # temperature
+', '+'Time:'+header[26:32]+', '+'Date'+' '+header[16:26]) # date
plt.ylabel('Pico Tesle [pT]')
plt.xlabel('Time [ms]')
plt.grid()
plt.plot(time[51:-14], Bx[51:-14], label='Canal 1', color='r', linewidth=0.1, linestyle="-")
plt.plot(time2[1:-14], By[1:-14], label='Canal 3', color='b', linewidth=0.1, linestyle="-")
plt.legend(loc='upper right', frameon=False, )
# Create a new subplot for FFT
plt.subplot(212)
plt.title('Fast Fourier Transform')
plt.ylabel('Power [a.u.]')
plt.xlabel('Frequency Hz')
xaxis2 = np.arange(0,470,10)
plt.xticks(xaxis2)
fft1 = (Bx[51:-14])
fft2 = (By[1:-14])
plt.grid()
# Loop for FFT data
for dataset in [fft1]:
dataset = np.asarray(dataset)
freqs, psd = welch(dataset, fs=266336/300, window='hamming', nperseg=8192)
plt.semilogy(freqs, psd/dataset.size**0, color='r')
for dataset2 in [fft2]:
dataset2 = np.asarray(dataset2)
freqs2, psd2 = welch(dataset2, fs=266336/300, window='hamming', nperseg=8192)
plt.semilogy(freqs2, psd2/dataset2.size**0, color='b')
plt.show()
As you can see there are some places where it would be better to put input and when I run the code I can write names of filenames etc. to python instead of creating every single pythonfile, with specified info in the code.
Btw. I use Pycharm to my python.
If all you are trying to do is get rid of the hardcoded pathname, you should be able to format your name string with input variables
name = raw_input("Name: ")
measurement = raw_input("Measurement: ")
filename = "C:/Users/PycharmProjects/{0}/{1}".format(name, measurement)
see raw_input and string formatting
I seem to have some problems starting my learning... I am not sure why..
the network is multi input (72 1d arrays) and output is a 1d array length 24. the 1d array output consist of numbers related to 145 different classes.
So: 72 inputs => 24 outputs
Minimal working example - without the input/output being set.
import keras
from keras.utils import np_utils
from keras import metrics
from keras.models import Sequential
from keras.layers.core import Dense, Activation, Lambda, Reshape,Flatten
from keras.layers import Conv1D,Conv2D, MaxPooling2D, MaxPooling1D, Reshape, ZeroPadding2D
from keras.utils import np_utils
from keras.layers.advanced_activations import LeakyReLU, PReLU
from keras.layers.advanced_activations import ELU
from keras.models import Model
from keras.layers import Input, Dense
from keras.layers import Dropout
from keras import backend as K
from keras.callbacks import ReduceLROnPlateau
from keras.callbacks import CSVLogger
from keras.callbacks import EarlyStopping
from keras.models import load_model
from keras.layers.merge import Concatenate
import numpy as np
def chunks(l, n):
"""Yield successive n-sized chunks from l."""
for i in range(0, len(l), n):
yield l[i:i + n]
nano_train_input = []
nano_train_output = []
nano_test_input = []
nano_test_output = []
## Creating train input:
for i in range(974):
nano_train_input.append(np.random.random((78,684,4)))
nano_train_output.append(np.random.randint(145,size=(228)).tolist())
## Creating test input:
for i in range(104):
nano_test_input.append(np.random.random((78,684,4)))
nano_test_output.append(np.random.randint(145,size=(228)).tolist())
def model(train_input, train_output, test_input, test_output, names=0):
# Paper uses dimension (40 x 45 =(15 * 3))
# Filter size 5
# Pooling size
# I use dimension (78 x 72 = (24 * 3)
# Filter size 9
print "In model"
i = 0
print_once = True
data_test_output = []
data_test_input = []
for matrix in test_input:
row,col,channel = matrix.shape
remove_output = (col/3)%24
remove_input = col%72
if remove_output > 0 :
test_output[i] = test_output[i][:-(remove_output)]
for split in chunks(test_output[i],24):
data_test_output.append(np.array(split))
if remove_input > 0:
out = np.split(matrix[:,:-(remove_input),:-1],matrix[:,:-(remove_input),:-1].shape[1]/72,axis=1)
else:
out = np.split(matrix[:,:,:-1],matrix[:,:,:-1].shape[1]/72,axis=1)
data_test_input.extend(out)
del out
i=i+1 # Increment
i=0
data_train_output = []
data_train_input = []
for matrix in train_input:
row,col,channel = matrix.shape
remove_output = (col/3)%24
remove_input = col%72
if remove_output > 0 :
train_output[i] = train_output[i][:-(remove_output)]
for split in chunks(train_output[i],24):
data_train_output.append(np.array(split))
if remove_input > 0:
out = np.split(matrix[:,:-(remove_input),:-1],matrix[:,:-(remove_input),:-1].shape[1]/72,axis=1)
else:
out = np.split(matrix[:,:,:-1],matrix[:,:,:-1].shape[1]/72,axis=1)
data_train_input.extend(out)
del out
i=i+1 # Increment
print
print "Len:"
print len(data_train_input)
print len(data_train_output)
print len(data_test_input)
print len(data_test_output)
print
print "Type[0]:"
print type(data_train_input[0])
print type(data_train_output[0])
print type(data_test_input[0])
print type(data_test_output[0])
print
print "Type:"
print type(data_train_input)
print type(data_train_output)
print type(data_test_input)
print type(data_test_output)
print
print "shape of [0]:"
print data_train_input[0].shape
print data_train_output[0].shape
print data_test_input[0].shape
print data_test_output[0].shape
list_of_input = [Input(shape = (78,3)) for i in range(72)]
list_of_conv_output = []
list_of_max_out = []
for i in range(72):
list_of_conv_output.append(Conv1D(filters = 32 , kernel_size = 6 , padding = "same", activation = 'relu')(list_of_input[i]))
list_of_max_out.append(MaxPooling1D(pool_size=3)(list_of_conv_output[i]))
merge = keras.layers.concatenate(list_of_max_out)
reshape = Flatten()(merge)
dense1 = Dense(units = 500, activation = 'relu', name = "dense_1")(reshape)
dense2 = Dense(units = 250, activation = 'relu', name = "dense_2")(dense1)
dense3 = Dense(units = 24 , activation = 'softmax', name = "dense_3")(dense2)
model = Model(inputs = list_of_input , outputs = dense3)
model.compile(loss="categorical_crossentropy", optimizer="adam" , metrics = [metrics.sparse_categorical_accuracy])
reduce_lr=ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=3, verbose=1, mode='auto', epsilon=0.01, cooldown=0, min_lr=0.000000000000000000001)
stop = EarlyStopping(monitor='val_loss', min_delta=0, patience=5, verbose=1, mode='auto')
print "Train!"
print model.summary()
hist_current = model.fit(x = ,
y = ,
shuffle=False,
validation_data=(,),
validation_split=0.1,
epochs=150000,
verbose=1,
callbacks=[reduce_lr,stop])
model(nano_train_input,nano_train_output,nano_test_input, nano_test_output)
The input and output is stored as a list of numpy.ndarrays.
This is a minimal working example.. how am I supposed to pass the input an output?
I would try:
merge = keras.layers.concatenate(list_of_max_out)
merge = Flatten()(merge) # or GlobalMaxPooling1D or GlobalAveragePooling1D
dense1 = Dense(500, activation = 'relu')(merge)
You probably want to apply something to transform your output from Convolutional layers. In order to do that - you need to squash the time / sequential dimension. In order to do that try techniques I provided.
If you take a look at your code and outputs you indeed have what you say: 24 outputs (data_train_outputs[0].shape). However, if you look at your layer output of Keras, you have this as output:
dense_3 (Dense) (None, 26, 145) 36395
I would say that this should be an array with shape (None, 24)....
I suggest you add a reshape layer to get the output you want to have!