Real-time graph display by matplotlib

monitoring

When tuning the parameters of a model, you can improve the work by displaying a graph and observing the learning progress in real time. I will introduce the script that I made when the tensorboard could not be displayed due to insufficient GPU resources when the calculation was flowing on the client machine. If you want to use it in another script, you can use it as it is if you read it with import realtime_plot and have realtime_plot declared as a class.

realtime_plot.py

import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt

#Change matplotlib default settings
font = {'family':'monospace', 'size':'9'}
mpl.rc('font', **font)

class realtime_plot(object):
    
    def __init__(self):
        self.fig = plt.figure(figsize=(12,8))
        self.initialize()

    def initialize(self):
        self.fig.suptitle('monitoring sample', size=12)
        self.fig.subplots_adjust(left=0.05, bottom=0.1, right=0.95, top=0.90, wspace=0.2, hspace=0.6)
        self.ax00 = plt.subplot2grid((2,2),(0,0))
        self.ax10 = plt.subplot2grid((2,2),(1,0))
        self.ax01 = plt.subplot2grid((2,2),(0,1))
        self.ax11 = plt.subplot2grid((2,2),(1,1))
        self.ax00.grid(True)
        self.ax10.grid(True)
        self.ax01.grid(True)
        self.ax11.grid(True)
        self.ax00.set_title('real time result')
        self.ax10.set_title('histogram')
        self.ax01.set_title('correlation')
        self.ax11.set_title('optimized result')
        self.ax00.set_xlabel('x')
        self.ax00.set_ylabel('y')
        self.ax01.set_xlabel('correct')
        self.ax01.set_ylabel('predict')
        self.ax11.set_xlabel('correct')
        self.ax11.set_ylabel('predict')

        #Plot initialization
        self.lines000, = self.ax00.plot([-1,-1],[1,1],label='y1')
        self.lines001, = self.ax00.plot([-1,-1],[1,1],label='y2')
        self.lines100 = self.ax10.hist([-1,-1],label='res1')
        self.lines101 = self.ax10.hist([-1,-1],label='res2')
        self.lines01, = self.ax01.plot([-1,-1],[1,1],'.')
        self.lines11, = self.ax11.plot([-1,-1],[1,1],'.r')
    
    #Subplot the required data from the dictionary type data to which the value name and value are assigned to the subplot value.
    def set_data(self,data):

        self.lines000.set_data(data['x'],data['y1'])
        self.lines001.set_data(data['x'],data['y2'])
        self.ax00.set_xlim((data['x'].min(),data['x'].max()))
        self.ax00.set_ylim((-1.2,1.2))
        #Needed to fix the legend
        self.ax00.legend(loc='upper right')

        self.lines01.set_data(data['corr'],data['pred'])
        self.ax01.set_xlim((-2,12))
        self.ax01.set_ylim((-2,12))

        #Histogram is set_Since there is no data, it will be recreated every time it is updated.
        self.ax10.cla()
        self.ax10.set_title('histogram')
        self.ax10.grid(True)
        self.lines100 = self.ax10.hist(data['corr'],label='corr')
        self.lines101 = self.ax10.hist(data['pred'],label='pred')
        self.ax10.set_xlim((-0.5,9.5))
        self.ax10.set_ylim((0,20))
        self.ax10.legend(loc='upper right')

        #When updating the title or text, the value before the update remains in the figure, so recreate it every time it is updated.
        bbox_props = dict(boxstyle='square,pad=0.3',fc='gray')
        self.ax11.cla()
        self.ax11.grid(True)
        self.ax11.set_xlabel('correct')
        self.ax11.set_ylabel('predict')
        self.ax11.set_title('optimized result')
        self.ax11.text(-1.5,10.5,data['text'], ha='left', va='center',size=11,bbox=bbox_props)
        self.lines = self.ax11.plot(data['opt_corr'],data['opt_pred'],'.')
        self.ax11.set_xlim((-2,12))
        self.ax11.set_ylim((-2,12))
    
    def pause(self,second):
        plt.pause(second)

#Example of use
RP = realtime_plot()
data = {}
x = np.arange(-np.pi,np.pi,0.1)
y1 = np.sin(x)
y2 = np.cos(x)
opt_coef = 0

while True:
    x += 0.1
    y1 = np.sin(x)
    y2 = np.cos(x)
    corr = np.random.randint(0,10,20)
    pred = np.random.randint(0,10,20)
    c = np.random.normal(0,1,1)

    data['x'] = np.pi * x
    data['y1'] = y1
    data['y2'] = y2
    data['corr'] = corr 
    data['pred'] = pred
    coef = np.corrcoef(c*corr,pred)[0,1]

    if abs(coef) > abs(opt_coef):
        data['opt_corr'] = corr
        data['opt_pred'] = pred
        data['text'] = 'c = ' + str(c[0])
        opt_coef = coef

    RP.set_data(data)
    RP.pause(0.1)

sample

reference

-Real-time drawing with matplotlib