I have an issue. I'm using matplotlib on a project to do some real-time programming, I used this library because it was suggested by my mentor although I think now it was not a good idea since I learned that matplotlib is not real-time oriented. As I was working at the lab with the lab computer, real-time made the computer slow, but not that slow as now that I'm remote working on my laptop that is 5 years old, but I don't think that's the problem. In the project, I use Python 2.7, postgresql, pandas, wxpython, etc, so you get the idea. I have a notebook with multiple tabs and in each tab I implemented one figure of mpl for each instrument I start adding. For performance things I make it stop animation and star as you go between the tabs and only if the have been started with the desirable variable.
The thing is that as I started working on my laptop, as soon as I start the real-time plotting, the applications becomes very laggy, it's mostly unusable because it jumps too much when I'm trying to interact with it, or when is running too long. Then I think it's something more of the code and how it's plot. I'm seeking a way of making it run faster. My code for plotting is the following.
class live_panel_2(wx.Panel): # Real Time plotter for 2 Variable
def __init__(self, parent):
super(live_panel_2, self).__init__(parent)
#sns.set() # Set Plot Style
self.parent = parent
self.x_axis = [] # First x-axis
self.x2_axis = [] # Second x-axis
self.y_axis = [] # First y-axis
self.y2_axis = [] # Seconf y-axis
self.line_width = 1 # Line width for plots
self.flag = False
self.switch = True # Switch Flag
self.animation = False # Animation Flag
self.figure = Figure(figsize=(10, 3)) # Figure Size
self.canvas = FigureCanvas(self, -1, self.figure) # Canvas of Figure
self.axis = self.figure.add_subplot(1, 1, 1)
self.axis2 = self.axis.twinx() # Adding Second Axis
self.toolbar = NavigationToolbar(self.canvas) # Adding Navigation Tool
self.toolbar.Realize()
self.ani = None
self.figure.subplots_adjust(left=0.09, right=0.92, top=0.92, bottom=0.2, wspace=1) # Figure Space Adjust
self.axis.format_xdata = mdates.DateFormatter('%Y-%m-%d') # Format Dates
self.axis2.format_xdata = mdates.DateFormatter('%Y-%m-%d') # Format Dates
# self.axis.tick_params(axis='x', direction='inout', length=5, labelrotation=0)
# Sizer Adds
sizer = wx.BoxSizer(wx.VERTICAL)
sizer.Add(self.canvas, 0, wx.EXPAND, 5)
sizer.Add(self.toolbar, 0, wx.LEFT | wx.EXPAND, 5)
# sizer.Add(self.button, 0, wx.ALL, 5)
self.SetSizerAndFit(sizer)
self.Bind(wx.EVT_CLOSE, self.stop)
self.canvas.draw()
def build(self, model, model_2, y, y2, delta, localization): # Method that builds the plot based on info received:
""""
Model: Mapped Model to be Used.
Y: Mapped parameter to be plotted.
Delta: Plot Range
Localization: Legend localization
"""
self.axis.set_title(model.Datetime.name + ' vs ' + y.name + ' vs ' + y2.name)
self.axis.set_xlabel(model.Datetime.name)
self.axis.set_ylabel(y.name)
# self.axis.annotate(str(value), xy=(time, value), xytext=(10, -2), textcoords='offset pixels',
# bbox=dict(boxstyle=custom_box_style, alpha=0.2))
# Set Axis 2 Format
self.axis2.set_xlabel(model.Datetime.name)
self.axis2.set_ylabel(y2.name)
# self.axis2.annotate(str(value2), xy=(time, value2), xytext=(10, -2), textcoords='offset pixels',
# bbox=dict(boxstyle=custom_box_style, alpha=0.2))
# Turn of scientific notation
self.axis.yaxis.set_major_formatter(mticker.ScalarFormatter())
self.axis.yaxis.get_major_formatter().set_scientific(False)
self.axis.yaxis.get_major_formatter().set_useOffset(False)
self.axis2.yaxis.set_major_formatter(mticker.ScalarFormatter())
self.axis2.yaxis.get_major_formatter().set_scientific(False)
self.axis2.yaxis.get_major_formatter().set_useOffset(False)
# Set legend and turn axes 2 grid off
self.axis2.grid(False)
if self.animation: # If Animation is true
self.ani._stop() # Stop animation
self.x_axis = [] # Make Arrays empty
self.x2_axis = []
self.y_axis = []
self.y2_axis = []
self.delta = delta # Set Delta
self.ani = animation.FuncAnimation(self.figure, self.animate,
fargs=(
self.x_axis, self.x2_axis, self.y_axis, self.y2_axis, model, model_2, y,
y2,
localization), interval=500) # Call Animate Method.
self.animation = True # Set Animation to True
self.ani._start() # Start Animation
def close(self):
self.ani._stop()
def stop(self, event): # Stop method when changing between real-time notebook
if self.ani != None: # If animation have been created.
# If animation is running and is not current page.
if self.flag == False and self.animation == True and self.parent != self.parent.parent.GetCurrentPage():
self.ani.event_source.stop() # Stop animation
self.flag = True
self.animation = False # Set Animation flag to False
# If animation is not running and is current page.
elif self.flag == True and self.animation == False and self.parent == self.parent.parent.GetCurrentPage():
self.ani.event_source.start()
self.flag = False
self.animation = True
event.Skip()
def mode_stop(self, event): # Stop method when changing between views.
if self.ani != None:
if self.switch != True and self.parent == self.parent.parent.GetCurrentPage():
self.ani.event_source.start()
self.switch = True
else:
self.ani.event_source.stop()
self.switch = False
event.Skip()
# Animation
def animate(self, i, x_axis, x2_axis, y_axis, y2_axis, model, model2, y, y2, localization):
# Data query
self.now = datetime.now() # Get Present Time
self.now_delta = self.now - timedelta(minutes=self.delta) # Get past time with delta.
if not x_axis: # If x_axis array have not been created.
with session_scope() as s: # Open SQL Session
value = s.query(y).order_by(model.Datetime.desc()).filter(model.Datetime > self.now_delta).filter(
model.Datetime < self.now).all() # Query Values for y array in axis 1.
value2 = s.query(y2).order_by(model2.Datetime.desc()).filter(model2.Datetime > self.now_delta).filter(
model2.Datetime < self.now).all() # Query Values for y array in axis 2.
time = s.query(model.Datetime).order_by(model.Datetime.desc()).filter(
model.Datetime > self.now_delta).filter(
model.Datetime < self.now).all() # Query dates for x array in axis 1.
time2 = s.query(model2.Datetime).order_by(model2.Datetime.desc()).filter(
model2.Datetime > self.now_delta).filter(
model2.Datetime < self.now).all() # Query date for x array in axis 2.
# Reverse List.
for i in reversed(value):
y_axis.append(i[0])
for i in reversed(value2):
y2_axis.append(i[0])
for i in reversed(time):
x_axis.append(i[0])
for i in reversed(time2):
x2_axis.append(i[0])
if x_axis: # If X Axis Array Exist
with session_scope() as s: # Open SQL Session
value = s.query(y).filter(model.Datetime > x_axis[
-1]).all() # Query Valyes for Y array in axis 1, after the date of the last point in the array.
value2 = s.query(y2).filter(model2.Datetime > x2_axis[
-1]).all() # Query Valyes for Y array in axis 2, after the date of the last point in the array.
time = s.query(model.Datetime).filter(model.Datetime > x_axis[
-1]).all() # Query Valyes for X array in axis 1, after the date of the last point in the array.
time2 = s.query(model2.Datetime).filter(model2.Datetime > x2_axis[
-1]).all() # Query Valyes for X array in axis 2, after the date of the last point in the array.
# Appent in Array
for i in value:
y_axis.append(i[0])
for i in value2:
y2_axis.append(i[0])
for i in time:
x_axis.append(i[0])
for i in time2:
x2_axis.append(i[0])
# Alinate Array
x_axis, y_axis = array_alinate(x_axis, y_axis)
x2_axis, y2_axis = array_alinate(x2_axis, y2_axis)
# Create Dataframe from the arrays
data_1 = pd.DataFrame(y_axis, x_axis, [y.name], dtype=float)
data_2 = pd.DataFrame(y2_axis, x2_axis, [y2.name], dtype=float)
# Set Index Name to Models Datetime Name
data_1.index.name = model.Datetime.name
data_2.index.name = model2.Datetime.name
# Resapmle to second if the models are from wibs-neo
if model == (wibs_neo_monitoring or wibs_neo_particle):
data_1 = data_1.resample('S').mean()
data_2 = data_2.resample('S').mean()
# Secure data points dont past delta, asuming each datapoint lapse one second.
data_1 = data_1.iloc[-self.delta * 60:]
data_2 = data_2.iloc[-self.delta * 60:]
# Clear Axis
#self.axis.clear()
#self.axis2.clear()
self.axis.set_xlim(min(data_1.index), max(data_1.index))
self.axis2.set_xlim(min(data_2.index), max(data_2.index))
line_2 = self.axis2.plot(data_2, linewidth=self.line_width, color='G', label=y2.name)
line_1 = self.axis.plot(data_1, linewidth=self.line_width, color='B', label=y.name)
lines = line_1 + line_2
labels = [l.get_label() for l in lines]
self.axis2.legend(lines, labels, loc=localization)
# Format plot
self.figure.canvas.mpl_connect('close_event', self.close)
# tm.sleep(1)
