Matplotlib 从文本文件定义数组
Matplotlib define array from text file
所以我想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，值是这样写的：
"Day, Time, channel1, channel2, channel3, channel4"
每一行都是一个新的 5 秒数据戳。
我想绘制一个由 4 条线 (channel1 - channel4) 组成的动画图
共享相同的 x 轴值...我该如何定义它？下面是相关代码
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()
如何为每条线定义 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()
写入和读取同一文件需要锁定以防止竞争条件——在文件完全写入之前读取文件。这是可能的，但下面我建议一个不同的方式。
由于这两个程序都是用 Python 编写的，您可以使用多处理模块来生成编写器进程，并将值写入队列。
然后主进程可以让 animate 从队列中获取值并绘制结果。 Queue 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，而无需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()
我今天让它工作了，做我需要的一切！感谢你的帮助。最终产品可以：
- 左侧一张图中显示8个模拟传感器的读数，每5秒刷新一次，只显示最后5分钟的数据。
- 右图为白色 canvas，显示已读取和绘制的最后一个值。我无法弄清楚如何每 5 秒清除一次，所以文本框有一个白色背景，覆盖了旧值。
- 所有数据都写入并保存到一个文本文件中，日期和时间标记为日期 (D/M/Y) 和时间 (H:M:S)。
对于任何可能有兴趣做这样的事情的人，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()
所以我想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，值是这样写的：
"Day, Time, channel1, channel2, channel3, channel4"
每一行都是一个新的 5 秒数据戳。
我想绘制一个由 4 条线 (channel1 - channel4) 组成的动画图 共享相同的 x 轴值...我该如何定义它？下面是相关代码 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()
如何为每条线定义 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()
写入和读取同一文件需要锁定以防止竞争条件——在文件完全写入之前读取文件。这是可能的，但下面我建议一个不同的方式。
由于这两个程序都是用 Python 编写的，您可以使用多处理模块来生成编写器进程，并将值写入队列。
然后主进程可以让 animate 从队列中获取值并绘制结果。 Queue 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，而无需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()
我今天让它工作了，做我需要的一切！感谢你的帮助。最终产品可以：
- 左侧一张图中显示8个模拟传感器的读数，每5秒刷新一次，只显示最后5分钟的数据。
- 右图为白色 canvas，显示已读取和绘制的最后一个值。我无法弄清楚如何每 5 秒清除一次，所以文本框有一个白色背景，覆盖了旧值。
- 所有数据都写入并保存到一个文本文件中，日期和时间标记为日期 (D/M/Y) 和时间 (H:M:S)。
对于任何可能有兴趣做这样的事情的人，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()