How to draw dynamic plot – Records And Memorys

要求：随着数据的更新，图表也能更新，并且可以自动在图表上添加标注，

当前已实现的程度

import sys
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import pymysql
from scipy import stats
import numpy as np
import datetime
import time

x = datetime.datetime(2019,5,8,9,30)
firstTop =0
doubletop=0
FuPanIndexBegin = 0

sellingPrice = 0

lowPrice = 0
lastStopWinPrice = 0 

oporTime = 0
CurrentState = 0      #  0 mean observer ,  1 selling , 2 buying  

Todaymin=0
Todaymax =0

conn=pymysql.connect(host='localhost',user='root',password='MYSQLTB',db='shfuture')
a=conn.cursor()

t=[]
s=[]


annotateIndexX=[]
annotateIndexY=[]

while True: 
    sql = 'select happentime,lastprice from ' + sys.argv[1]  + ' where happentime<=%s and hour(happentime)>=9  order by happentime desc limit 2;'
    a.execute(sql,x)
    data=a.fetchall()
    for result in data:
        t.append(result[0])
        s.append(result[1])

    x= x + datetime.timedelta(seconds=1)
    
    print(s)

    plt.plot(t, s)
    #print(t[-1])
    #print(s[-1])
    if s[-1]>3050:       # 保存备注的坐标 
        #print(t.index(t[-1]))
        #print(s.index(s[-1]))
        #plt.annotate('Something', xy=(t[-1], s[-1]))
        annotateIndexX.append(t[-1])
        annotateIndexY.append(s[-1])        

    #print(annotateIndex)

    for i in range(len(annotateIndexX)):      #  试图加上备注  
        #print('i is ',i)
        plt.annotate('Something', xy=(annotateIndexX[i],annotateIndexY[i]))

    #plt.annotate('Something', xy=(t[-1],s[-1]))

    #plt.draw()
    #plt.pause(0.0001)
    plt.pause(3)    
    plt.clf()

import sys

import matplotlib.pyplot as plt

import matplotlib.animation as animation

import pymysql

from scipy import stats

import numpy as np

import datetime

import time

x = datetime.datetime(2019,5,8,9,30)

firstTop =0

doubletop=0

FuPanIndexBegin = 0

sellingPrice = 0

lowPrice = 0

lastStopWinPrice = 0

oporTime = 0

CurrentState = 0 # 0 mean observer , 1 selling , 2 buying

Todaymin=0

Todaymax =0

conn=pymysql.connect(host='localhost',user='root',password='MYSQLTB',db='shfuture')

a=conn.cursor()

t=[]

s=[]

annotateIndexX=[]

annotateIndexY=[]

while True:

sql = 'select happentime,lastprice from ' + sys.argv[1] + ' where happentime<=%s and hour(happentime)>=9 order by happentime desc limit 2;'

a.execute(sql,x)

data=a.fetchall()

for result in data:

t.append(result[0])

s.append(result[1])

x= x + datetime.timedelta(seconds=1)

print(s)

plt.plot(t, s)

#print(t[-1])

#print(s[-1])

if s[-1]>3050: # 保存备注的坐标

#print(t.index(t[-1]))

#print(s.index(s[-1]))

#plt.annotate('Something', xy=(t[-1], s[-1]))

annotateIndexX.append(t[-1])

annotateIndexY.append(s[-1])

#print(annotateIndex)

for i in range(len(annotateIndexX)): # 试图加上备注

#print('i is ',i)

plt.annotate('Something', xy=(annotateIndexX[i],annotateIndexY[i]))

#plt.annotate('Something', xy=(t[-1],s[-1]))

#plt.draw()

#plt.pause(0.0001)

plt.pause(3)

plt.clf()

功能是可以动态更新，也可以加标注，保存之前的标注坐标在数组里

用到的函数

matplotlib.pyplot.annotate(s, xy, *args, **kwargs) : Annotate the point xy with text s.

示例代码

a) 在指定坐标加标注 https://jakevdp.github.io/PythonDataScienceHandbook/04.09-text-and-annotation.html

import numpy as np
import matplotlib.pyplot as plt

fig, ax = plt.subplots()

x = np.linspace(0, 20, 1000)
ax.plot(x, np.cos(x))
ax.axis('equal')

ax.annotate('local maximum', xy=(6.28, 1), xytext=(10, 4),
            arrowprops=dict(facecolor='black', shrink=0.05))

ax.annotate('local minimum', xy=(5 * np.pi, -1), xytext=(2, -6),
            arrowprops=dict(arrowstyle="->",
                            connectionstyle="angle3,angleA=0,angleB=-90"))
plt.show()

import numpy as np

import matplotlib.pyplot as plt

fig, ax = plt.subplots()

x = np.linspace(0, 20, 1000)

ax.plot(x, np.cos(x))

ax.axis('equal')

ax.annotate('local maximum', xy=(6.28, 1), xytext=(10, 4),

arrowprops=dict(facecolor='black', shrink=0.05))

ax.annotate('local minimum', xy=(5 * np.pi, -1), xytext=(2, -6),

arrowprops=dict(arrowstyle="->",

connectionstyle="angle3,angleA=0,angleB=-90"))

plt.show()

2021-04-11 最新

import sys
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import pymysql
from scipy import stats
import numpy as np
import datetime as dt
import time
from datetime import datetime, timedelta
import matplotlib.dates as mdates     # 设置x轴坐标用


plt.ion()
class DynamicUpdate():

    def on_launch(self):
        #Set up plot
        self.figure, self.ax = plt.subplots()
        # self.lines, = self.ax.plot([],[], 'o')     # o 意思是用圆点画
        self.lines, = self.ax.plot([],[])

        # plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%m/%d/%Y'))
        plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%M:%S'))

        
        #Autoscale on unknown axis and known lims on the other
        self.ax.set_autoscaley_on(True)

    def on_running(self, xdata, ydata):
        #Update data (with the new _and_ the old points)
        self.lines.set_xdata(xdata)
        self.lines.set_ydata(ydata)
        #Need both of these in order to rescale
        self.ax.relim()
        self.ax.autoscale_view()
        #We need to draw *and* flush
        self.figure.canvas.draw()
        self.figure.canvas.flush_events()

    #Example
    def __call__(self):
        import numpy as np
        import time
        self.on_launch()
        xdata = []
        ydata = []

        x = dt.datetime(2021,4,9,21,0)
        conn=pymysql.connect(host='localhost',user='root',password='MYSQLTB',db='shfuture')
        while True: 
            a=conn.cursor()
            strdatetime = x.strftime('%Y-%m-%d %H:%M:%S')
            sql = 'select happentime,lastprice from oi2109_20210412 where happentime<="' + strdatetime + '" and hour(happentime)>=9  order by happentime desc limit 2;'
            a.execute(sql)
            data=a.fetchall()
            for result in data:
                xdata.append(result[0])
                ydata.append(result[1])
            x= x + dt.timedelta(seconds=1) 
            self.on_running(xdata, ydata)
            # time.sleep(1)
        return xdata, ydata
    
d = DynamicUpdate()
d()

import sys

import matplotlib.pyplot as plt

import matplotlib.animation as animation

import pymysql

from scipy import stats

import numpy as np

import datetime as dt

import time

from datetime import datetime, timedelta

import matplotlib.dates as mdates # 设置x轴坐标用

plt.ion()

class DynamicUpdate():

def on_launch(self):

#Set up plot

self.figure, self.ax = plt.subplots()

# self.lines, = self.ax.plot([],[], 'o') # o 意思是用圆点画

self.lines, = self.ax.plot([],[])

# plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%m/%d/%Y'))

plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%M:%S'))

#Autoscale on unknown axis and known lims on the other

self.ax.set_autoscaley_on(True)

def on_running(self, xdata, ydata):

#Update data (with the new _and_ the old points)

self.lines.set_xdata(xdata)

self.lines.set_ydata(ydata)

#Need both of these in order to rescale

self.ax.relim()

self.ax.autoscale_view()

#We need to draw *and* flush

self.figure.canvas.draw()

self.figure.canvas.flush_events()

#Example

def __call__(self):

import numpy as np

import time

self.on_launch()

xdata = []

ydata = []

x = dt.datetime(2021,4,9,21,0)

conn=pymysql.connect(host='localhost',user='root',password='MYSQLTB',db='shfuture')

while True:

a=conn.cursor()

strdatetime = x.strftime('%Y-%m-%d %H:%M:%S')

sql = 'select happentime,lastprice from oi2109_20210412 where happentime<="' + strdatetime + '" and hour(happentime)>=9 order by happentime desc limit 2;'

a.execute(sql)

data=a.fetchall()

for result in data:

xdata.append(result[0])

ydata.append(result[1])

x= x + dt.timedelta(seconds=1)

self.on_running(xdata, ydata)

# time.sleep(1)

return xdata, ydata

d = DynamicUpdate()

d()