所以我正在尝试弄清楚如何读取文本文件并从中绘制值...我有一个文本文件，每5秒更新一次，值写成如下：
"Day, Time, channel1, channel2, channel3, channel4"
每一行都是新的5秒数据标记。
我想绘制一条4行（channel1 - channel4）的动画图表 共享相同的x轴值...我该如何定义？以下是相关代码 到目前为止......
#MATPLOTLIB ANIMATED GRAPH
fig = plt.figure()
ax1 = fig.add_subplot(1,1,1)
ln1, = ax1.plot([], [], 'r-')
ln2, = ax1.plot([], [], 'g-')
ln3, = ax1.plot([], [], 'b-')
ln4, = ax1.plot([], [], 'p-')
def animate(i):
pullData = open("%s.txt" % FILE_NAME,"r").read()
dataArray = pullData.split('\n')
xar = []
yar = []
for eachLine in dataArray:
if len(eachLine)>1:
x,y = eachLine.split(',')
ln1.set_data(x1, y1)
ln2.set_data(x1, y2)
ln3.set_data(X1, y3)
ln4.set_data(x1, y4)
ax1.clear()
ax1.plot(ln1)
ax1.plot(ln2)
ax1.plot(ln3)
ax1.plot(ln4)
ani = animation.FuncAnimation(fig, animate, interval=5000)
plt.show()
我如何定义每条线的x和y？
------编辑＃3 -----
import Queue
import datetime as DT
import collections
import matplotlib.pyplot as plt
import numpy as np
import multiprocessing as mp
import time
import matplotlib.dates as mdates
import matplotlib.animation as animation
from ABE_DeltaSigmaPi import DeltaSigma
from ABE_helpers import ABEHelpers
i2c_helper = ABEHelpers()
bus = i2c_helper.get_smbus()
adc = DeltaSigma(bus, 0x68, 0x69, 18)
#Rename file to date
base_dir = '/home/pi/Desktop/DATA'
filename_time = datetime.datetime.fromtimestamp(ts).strftime('%Y-%m-%d')
filename_base = os.path.join(base_dir, filename_time)
filename = '%s.txt' % filename_base
# you will want to change read_delay to 5000
read_delay = int(5000) # in milliseconds
write_delay = read_delay/1000.0 # in seconds
window_size = 60
nlines = 8
datenums = collections.deque(maxlen=window_size)
ys = [collections.deque(maxlen=window_size) for i in range(nlines)]
def animate(i, queue):
try:
row = queue.get_nowait()
except Queue.Empty:
return
datenums.append(mdates.date2num(row[0]))
for i, y in enumerate(row[1:]):
ys[i].append(y)
for i, y in enumerate(ys):
lines[i].set_data(datenums, y)
ymin = min(min(y) for y in ys)
ymax = max(max(y) for y in ys)
xmin = min(datenums)
xmax = max(datenums)
if xmin < xmax:
ax.set_xlim(xmin, xmax)
ax.set_ylim(ymin, ymax)
fig.canvas.draw()
def write_data(filename, queue):
while True:
delay1 = DT.datetime.now()
row = []
for i in range(nlines):
# read from adc channels and print to screen
channel = adc.read_voltage(i)
row.append(channel)
queue.put([delay1]+row)
#print voltage variables to local file
with open(filename, 'a') as DAQrecording:
time1 = delay1.strftime('%Y-%m-%d')
time2 = delay1.strftime('%H:%M:%S')
row = [time1, time2] + row
row = map(str, row)
DAQrecording.write('{}\n'.format(', '.join(row)))
#Delay until next 5 second interval
delay2 = DT.datetime.now()
difference = (delay2 - delay1).total_seconds()
time.sleep(write_delay - difference)
def main():
global fig, ax, lines
queue = mp.Queue()
proc = mp.Process(target=write_data, args=(filename, queue))
# terminate proc when main process ends
proc.daemon = True
# spawn the writer in a separate process
proc.start()
fig, ax = plt.subplots()
xfmt = mdates.DateFormatter('%H:%M:%S')
ax.xaxis.set_major_formatter(xfmt)
# make matplotlib treat x-axis as times
ax.xaxis_date()
fig.autofmt_xdate(rotation=25)
lines = []
for i in range(nlines):
line, = ax.plot([], [])
lines.append(line)
ani = animation.FuncAnimation(fig, animate, interval=read_delay, fargs=(queue,))
plt.show()
if __name__ == '__main__':
main()
答案 0 :(得分：1)
从同一文件中写入和读取将需要锁定以防止竞争条件 - 在文件完全写入之前从文件读取。这是可能的，但在下面我建议采用不同的方式。
由于两个程序都是用Python编写的，因此可以使用多处理模块生成编写器进程，并将其写入队列。
然后主进程可以animate从队列中获取值并绘制结果。队列为我们处理锁定和进程间通信，并允许我们将日期时间对象和浮点值作为Python对象传输，而不必从文件中读取它们并解析字符串。
import Queue
import datetime as DT
import collections
import matplotlib.pyplot as plt
import numpy as np
import multiprocessing as mp
import time
import matplotlib.dates as mdates
import matplotlib.animation as animation
try:
from ABE_DeltaSigmaPi import DeltaSigma
from ABE_helpers import ABEHelpers
i2c_helper = ABEHelpers()
bus = i2c_helper.get_smbus()
adc = DeltaSigma(bus, 0x68, 0x69, 18)
except ImportError:
class ADC(object):
"""
This is a dummy class to mock the adc.read_voltage calls.
"""
def __init__(self):
self.x = 0
def read_voltage(self, i):
if i == 0:
self.x += 0.1
return np.sin(self.x/10)*(i+1)
adc = ADC()
filename = 'data.txt'
# you will want to change read_delay to 5000
read_delay = int(0.05 * 1000) # in milliseconds
write_delay = read_delay/1000.0 # in seconds
window_size = 60
nlines = 8
datenums = collections.deque(maxlen=window_size)
ys = [collections.deque(maxlen=window_size) for i in range(nlines)]
def animate(i, queue):
try:
row = queue.get_nowait()
except Queue.Empty:
return
datenums.append(mdates.date2num(row[0]))
for i, y in enumerate(row[1:]):
ys[i].append(y)
for i, y in enumerate(ys):
lines[i].set_data(datenums, y)
ymin = min(min(y) for y in ys)
ymax = max(max(y) for y in ys)
xmin = min(datenums)
xmax = max(datenums)
if xmin < xmax:
ax.set_xlim(xmin, xmax)
ax.set_ylim(ymin, ymax)
fig.canvas.draw()
def write_data(filename, queue):
while True:
delay1 = DT.datetime.now()
row = []
for i in range(nlines):
# read from adc channels and print to screen
channel = adc.read_voltage(i)
temp = 3.45 * channel
row.append(temp)
queue.put([delay1]+row)
#print voltage variables to local file
with open(filename, 'a') as DAQrecording:
time1 = delay1.strftime('%Y-%m-%d')
time2 = delay1.strftime('%H:%M:%S')
row = [time1, time2] + row
row = map(str, row)
DAQrecording.write('{}\n'.format(', '.join(row)))
#Delay until next 5 second interval
delay2 = DT.datetime.now()
difference = (delay2 - delay1).total_seconds()
time.sleep(write_delay - difference)
def main():
global fig, ax, lines
queue = mp.Queue()
proc = mp.Process(target=write_data, args=(filename, queue))
# terminate proc when main process ends
proc.daemon = True
# spawn the writer in a separate process
proc.start()
fig, ax = plt.subplots()
xfmt = mdates.DateFormatter('%H:%M:%S')
ax.xaxis.set_major_formatter(xfmt)
# make matplotlib treat x-axis as times
ax.xaxis_date()
fig.autofmt_xdate(rotation=25)
lines = []
for i in range(nlines):
line, = ax.plot([], [])
lines.append(line)
ani = animation.FuncAnimation(fig, animate, interval=read_delay, fargs=(queue,))
plt.show()
if __name__ == '__main__':
main()
答案 1 :(得分：0)
我今天开始工作，做我需要的一切！感谢你的帮助。最终产品可以：
对于其他可能对此类事情感兴趣的人，代码如下：
import Queue
import os
import sys
import datetime as DT
import collections
import matplotlib.pyplot as plt
from matplotlib import gridspec
import numpy as np
import multiprocessing as mp
import time
import datetime
import matplotlib.dates as mdates
import matplotlib.animation as animation
from ABE_DeltaSigmaPi import DeltaSigma
from ABE_helpers import ABEHelpers
i2c_helper = ABEHelpers()
bus = i2c_helper.get_smbus()
adc = DeltaSigma(bus, 0x68, 0x69, 16)
#Rename file to date
base_dir = '/home/pi/Desktop/DATA'
ts = time.time()
filename_time = datetime.datetime.fromtimestamp(ts).strftime('%Y-%m-%d')
filename_base = os.path.join(base_dir, filename_time)
filename = '%s.txt' % filename_base
# you will want to change read_delay to 5000
read_delay = int(5000) # in milliseconds
write_delay = read_delay/1000.0 # in seconds
window_size = 60
nlines = 8
ypadding = 0.5
datenums = collections.deque(maxlen=window_size)
ys = [collections.deque(maxlen=window_size) for i in range(nlines)]
def animate(i, queue):
try:
row = queue.get_nowait()
except Queue.Empty:
return
datenums.append(mdates.date2num(row[0]))
for i, y in enumerate(row[1:]):
ys[i].append(y)
for i, y in enumerate(ys):
lines[i].set_data(datenums, y)
ymin1 = min(min(y) for y in ys)
ymin = ymin1 - ypadding
ymax1 = max(max(y) for y in ys)
ymax = ymax1 + ypadding
xmin = min(datenums)
xmax = max(datenums)
if xmin < xmax:
ax1.set_xlim(xmin, xmax)
ax1.set_ylim(ymin, ymax)
ax2.plot(0, 0)
ax2.set_xlim(0, 1)
ax2.set_ylim(0, 1)
channel1 = row[-8]
channel2 = row[-7]
channel3 = row[-6]
channel4 = row[-5]
channel5 = row[-4]
channel6 = row[-3]
channel7 = row[-2]
channel8 = row[-1]
ax2.text(0.1,0.8,'CH1: %.02f \n CH2: %.02f \n CH3: %.02f \n CH4: %.02f \n CH5: %.02f \n CH6: %.02f \n CH7: %.02f \n CH8: %.02f \n' % (channel1,channel2,channel3,channel4,channel5,channel6,channel7,channel8) , ha='left', va='top', backgroundcolor='w')
fig.canvas.draw()
def write_data(filename, queue):
while True:
delay1 = DT.datetime.now()
row = []
for i in range(nlines):
# read from adc channels and print to screen
channel = adc.read_voltage(i)
row.append(channel)
queue.put([delay1]+row)
#print voltage variables to local file
with open(filename, 'a') as DAQrecording:
time1 = delay1.strftime('%Y-%m-%d')
time2 = delay1.strftime('%H:%M:%S')
row = [time1, time2] + row
row = map(str, row)
DAQrecording.write('{}\n'.format(', '.join(row)))
#Delay until next 5 second interval
delay2 = DT.datetime.now()
difference = (delay2 - delay1).total_seconds()
time.sleep(write_delay - difference)
def main():
global fig, ax1, ax2, lines
queue = mp.Queue()
proc = mp.Process(target=write_data, args=(filename, queue))
# terminate proc when main process ends
proc.daemon = True
# spawn the writer in a separate process
proc.start()
fig, (ax1, ax2) = plt.subplots(1, 2, sharey=False)
gs = gridspec.GridSpec(1,2, width_ratios=[3, 1] wspace=None)
ax1 = plt.subplot(gs[0])
ax2 = plt.subplot(gs[1])
ax2.axes.xaxis.set_ticklabels([])
ax2.axes.yaxis.set_ticklabels([])
xfmt = mdates.DateFormatter('%H:%M:%S')
ax1.xaxis.set_major_formatter(xfmt)
# make matplotlib treat x-axis as times
ax1.xaxis_date()
fig.autofmt_xdate()
fig.suptitle('Data Acquisition', fontsize=14, fontweight='bold')
lines = []
for i in range(nlines):
line, = ax1.plot([], [])
lines.append(line)
ani = animation.FuncAnimation(fig, animate, interval=read_delay, fargs=(queue,))
plt.show()
if __name__ == '__main__':
main()