Python 在tkinter GUI中嵌入matplotlib
试图在tkinter GUI中嵌入matplotlib，但出现错误： TypeError:init（）为参数“master”获取了多个值 你能告诉我怎么处理吗 代码如下： 界面-tkinter GUI 可视化工具-使用pylive_mod文件中的函数live_绘图仪的selv更新图形 界面：Python 在tkinter GUI中嵌入matplotlib,python,python-3.x,matplotlib,user-interface,tkinter,Python,Python 3.x,Matplotlib,User Interface,Tkinter,试图在tkinter GUI中嵌入matplotlib，但出现错误： TypeError:init（）为参数“master”获取了多个值 你能告诉我怎么处理吗 代码如下： 界面-tkinter GUI 可视化工具-使用pylive_mod文件中的函数live_绘图仪的selv更新图形 界面： # -*- coding: utf-8 -*- """ Created on Tue Oct 6 10:24:35 2020 @author: Dar0 "
# -*- coding: utf-8 -*-
"""
Created on Tue Oct 6 10:24:35 2020
@author: Dar0
"""
from tkinter import * #import tkinter module
from visualizer import main #import module 'visualizer' that shows the graph in real time
class Application(Frame):
''' Interface for visualizing graphs, indicators and text-box. '''
def __init__(self, master):
super(Application, self).__init__(master)
self.grid()
self.create_widgets()
def create_widgets(self):
# Label of the 1st graph
Label(self,
text='Hook Load / Elevator Height / Depth vs Time'
).grid(row = 0, column = 0, sticky = W)
# Graph 1 - Hook Load / Elevator Height / Depth vs Time
# button that displays the plot
plot_button = Button(self,
master = root,
command = main,
height = 2,
width = 10,
text = "Plot").grid(row = 1, column = 0, sticky = W)
# place the button
# in main window
# Label of the 2nd graph
Label(self,
text = 'Hook Load / Elevator Height vs Time'
).grid(row = 2, column = 0, sticky = W)
# Graph 2 - Hook Load / Elevator Height vs Time
#Label of the 3rd graph
Label(self,
text = 'Hook Load vs Time'
).grid(row = 4, column = 0, sticky = W)
#Graph 3 - Hook Load vs Time
#Label of the 1st indicator
Label(self,
text = '1st performance indicator'
).grid(row = 0, column = 1, sticky = W)
#1st performance indicator
#Label of 2nd performance indicator
Label(self,
text = '2nd performance indicator'
).grid(row = 2, column = 1, sticky = W)
#2nd performance indicator
#Label of 3rd performance indicator
Label(self,
text = '3rd performance indicator'
).grid(row = 4, column = 1, sticky = W)
#Text-box showing comments based on received data
self.text_box = Text(self, width = 50, height = 10, wrap = WORD)
self.text_box.grid(row = 6, column = 0, columnspan = 1)
self.text_box.delete(0.0, END)
self.text_box.insert(0.0, 'My message will be here.')
#Main part
root = Tk()
root.title('WiTSML Visualizer by Dar0')
app = Application(root)
root.mainloop()
#WiTSML visualizer
#Created by Dariusz Krol
#import matplotlib
#matplotlib.use('TkAgg')
#from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg, NavigationToolbar2TkAgg
#from matplotlib.figure import Figure
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import random
class Visualizer(object):
""" Includes all the methods needed to show streamed data. """
def __init__(self):
self.file_path = 'C:/Anaconda/_my_files/witsml_reader/modified_witsml.csv' #Defines which file is streamed
self.datetime_mod = []
self.bpos_mod = []
self.woh_mod = []
self.torq_mod = []
self.spp_mod = []
self.depth_mod = []
self.flow_in_mod = []
self.rpm_mod = []
def open_file(self):
self.df = pd.read_csv(self.file_path, low_memory = False, nrows = 300000) #Opens the STREAMED file (already modified so that data convert is not required)
self.df = self.df.drop(0)
self.df = pd.DataFrame(self.df)
return self.df
def convert_dataframe(self):
self.df = self.df.values.T.tolist() #Do transposition of the dataframe and convert to list
#Columns are as following:
# - DATETIME
# - BPOS
# - WOH
# - TORQ
# - SPP
# - DEPTH
# - FLOW_IN
# - RPM
self.datetime_value = self.df[0]
self.bpos_value = self.df[1]
self.woh_value = self.df[2]
self.torq_value = self.df[3]
self.spp_value = self.df[4]
self.depth_value = self.df[5]
self.flow_in_value = self.df[5]
self.rpm_value = self.df[7]
return self.datetime_value, self.bpos_value, self.woh_value, self.torq_value, self.spp_value, self.depth_value, self.flow_in_value, self.rpm_value
#print(self.bpos_value)
def deliver_values(self, no_dp, columns):
''' Method gets no_dp amount of data points from the original file. '''
self.no_dp = no_dp #defines how many data points will be presented in the graph
val_dict = {
'datetime': [self.datetime_value, self.datetime_mod],
'bpos': [self.bpos_value, self.bpos_mod],
'woh': [self.woh_value, self.woh_mod],
'torq': [self.torq_value, self.torq_mod],
'spp': [self.spp_value, self.spp_mod],
'depth': [self.depth_value, self.depth_mod],
'flow_in': [self.flow_in_value, self.flow_in_mod],
'rpm': [self.rpm_value, self.rpm_mod]
}
for item in columns:
if self.no_dp > len(val_dict[item][0]):
dp_range = len(val_dict[item][0])
else:
dp_range = self.no_dp
for i in range(dp_range):
val_dict[item][1].append(val_dict[item][0][i])
return self.datetime_mod, self.bpos_mod, self.woh_mod, self.torq_mod, self.spp_mod, self.depth_mod, self.flow_in_mod, self.rpm_mod
def show_graph2(self):
from pylive_mod import live_plotter
self.open_file()
self.convert_dataframe()
self.deliver_values(no_dp = 100000, columns = ['datetime', 'depth', 'bpos', 'woh'])
fst_p = 0
size = 300 # density of points in the graph (100 by default)
x_vec = self.datetime_mod[fst_p:size]
y_vec = self.depth_mod[fst_p:size]
y2_vec = self.bpos_mod[fst_p:size]
y3_vec = self.woh_mod[fst_p:size]
line1 = []
line2 = []
line3 = []
for i in range(self.no_dp):
#print(self.datetime_mod[i:6+i])
#print('Ostatni element y_vec: ', y_vec[-1])
#print(x_vec)
x_vec[-1] = self.datetime_mod[size+i]
y_vec[-1] = self.depth_mod[size+i]
y2_vec[-1] = self.bpos_mod[size+i]
y3_vec[-1] = self.woh_mod[size+i]
line1, line2, line3 = live_plotter(x_vec, y_vec, y2_vec, y3_vec, line1, line2, line3)
x_vec = np.append(x_vec[1:], 0.0)
y_vec = np.append(y_vec[1:], 0.0)
y2_vec = np.append(y2_vec[1:], 0.0)
y3_vec = np.append(y3_vec[1:], 0.0)
def main():
Graph = Visualizer()
Graph.open_file() #Opens the streamed file
Graph.convert_dataframe() #Converts dataframe to readable format
Graph.show_graph2()
#Show us the graph
#main()
def live_plotter(x_data, y1_data, y2_data, y3_data, line1, line2, line3, identifier='',pause_time=1):
if line1 == [] and line2 == [] and line3 == []:
# this is the call to matplotlib that allows dynamic plotting
plt.ion()
#fig = Figure(figsize = (5, 4), dpi = 100)
#host = fig.add_subplot()
fig, host = plt.subplots()
fig.set_figheight(7) #adjust figure's height
fig.set_figwidth(14) #adjust figure's width
fig.subplots_adjust(0.15)
#Line1
#line1 = host
ln1 = host
ln2 = host.twinx()
ln3 = host.twinx()
ln2.spines['right'].set_position(('axes', 1.))
ln3.spines['right'].set_position(('axes', 1.12))
make_patch_spines_invisible(ln2)
make_patch_spines_invisible(ln3)
ln2.spines['right'].set_visible(True)
ln3.spines['right'].set_visible(True)
ln1.set_xlabel('Date & Time') #main x axis
ln1.set_ylabel('Depth') #left y axis
ln2.set_ylabel('Elevator Height')
ln3.set_ylabel('Weight on Hook')
#
x_formatter = FixedFormatter([x_data])
x_locator = FixedLocator([x_data[5]])
ln1.xaxis.set_major_formatter(x_formatter)
ln1.xaxis.set_major_locator(x_locator)
#
ln1.locator_params(nbins = 5, axis = 'y')
ln1.tick_params(axis='x', rotation=90) #rotates x ticks 90 degrees down
ln2.axes.set_ylim(0, 30)
ln3.axes.set_ylim(200, 250)
line1, = ln1.plot(x_data, y1_data, color = 'black', linestyle = 'solid', alpha=0.8, label = 'Depth')
line2, = ln2.plot(x_data, y2_data, color = 'blue', linestyle = 'dashed', alpha=0.8, label = 'Elevator Height')
line3, = ln3.plot(x_data, y3_data, color = 'red', linestyle = 'solid', alpha=0.8, label = 'Weight on Hook')
# ----- embedding -----
canvas = FigureCanvasTkAgg(fig, master = root)
canvas.draw()
# placing the canvas on the Tkinter window
canvas.get_tk_widget().pack()
# creating the Matplotlib toolbar
toolbar = NavigationToolbar2Tk(canvas, root)
toolbar.update()
# placing the toolbar on the Tkinter window
canvas.get_tk_widget().pack()
#----- -----
plt.title('WiTSML Visualizer')
fig.tight_layout() #the graphs is not clipped on sides
#Shows legend
lines = [line1, line2, line3]
host.legend(lines, [l.get_label() for l in lines], loc = 'lower left')
#Shows grid
plt.grid(True)
#Shows the whole graph
plt.show()
# after the figure, axis, and line are created, we only need to update the y-data
mod_x_data = convert_x_data(x_data, 10)
line1.axes.set_xticklabels(mod_x_data)
line1.set_ydata(y1_data)
line2.set_ydata(y2_data)
line3.set_ydata(y3_data)
#Debugging
#rint('plt.lim: ', ln2.axes.get_ylim())
# adjust limits if new data goes beyond bounds
# limit for line 1
if np.min(y1_data)<=line1.axes.get_ylim()[0] or np.max(y1_data)>=line1.axes.get_ylim()[1]:
plt.ylim(0, 10)
line1.axes.set_ylim([np.min(y1_data)-np.std(y1_data),np.max(y1_data)+np.std(y1_data)])
# limit for line 2
if np.min(y2_data)<=line2.axes.get_ylim()[0] or np.max(y2_data)>=line2.axes.get_ylim()[1]:
plt.ylim([np.min(y2_data)-np.std(y2_data),np.max(y2_data)+np.std(y2_data)])
#plt.ylim(0, 25)
# limit for line 3
if np.min(y3_data)<=line3.axes.get_ylim()[0] or np.max(y3_data)>=line3.axes.get_ylim()[1]:
plt.ylim([np.min(y3_data)-np.std(y3_data),np.max(y3_data)+np.std(y3_data)])
#plt.ylim(0, 25)
# Adds lines to the legend
#host.legend(lines, [l.get_label() for l in lines])
# this pauses the data so the figure/axis can catch up - the amount of pause can be altered above
plt.pause(pause_time)
# return line so we can update it again in the next iteration
return line1, line2, line3
# -*- coding: utf-8 -*-
"""
Created on Tue Oct 6 10:24:35 2020
@author: Dar0
"""
from tkinter import * #import tkinter module
from visualizer import main #import module 'visualizer' that shows the graph in real time
class Application(Frame):
''' Interface for visualizing graphs, indicators and text-box. '''
def __init__(self, master):
super(Application, self).__init__(master)
self.grid()
self.create_widgets()
def create_widgets(self):
# Label of the 1st graph
Label(self,
text='Hook Load / Elevator Height / Depth vs Time'
).grid(row = 0, column = 0, sticky = W)
# Graph 1 - Hook Load / Elevator Height / Depth vs Time
# button that displays the plot
plot_button = Button(self,
master = root,
command = main,
height = 2,
width = 10,
text = "Plot").grid(row = 1, column = 0, sticky = W)
# place the button
# in main window
# Label of the 2nd graph
Label(self,
text = 'Hook Load / Elevator Height vs Time'
).grid(row = 2, column = 0, sticky = W)
# Graph 2 - Hook Load / Elevator Height vs Time
#Label of the 3rd graph
Label(self,
text = 'Hook Load vs Time'
).grid(row = 4, column = 0, sticky = W)
#Graph 3 - Hook Load vs Time
#Label of the 1st indicator
Label(self,
text = '1st performance indicator'
).grid(row = 0, column = 1, sticky = W)
#1st performance indicator
#Label of 2nd performance indicator
Label(self,
text = '2nd performance indicator'
).grid(row = 2, column = 1, sticky = W)
#2nd performance indicator
#Label of 3rd performance indicator
Label(self,
text = '3rd performance indicator'
).grid(row = 4, column = 1, sticky = W)
#Text-box showing comments based on received data
self.text_box = Text(self, width = 50, height = 10, wrap = WORD)
self.text_box.grid(row = 6, column = 0, columnspan = 1)
self.text_box.delete(0.0, END)
self.text_box.insert(0.0, 'My message will be here.')
#Main part
root = Tk()
root.title('WiTSML Visualizer by Dar0')
app = Application(root)
root.mainloop()
#WiTSML visualizer
#Created by Dariusz Krol
#import matplotlib
#matplotlib.use('TkAgg')
#from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg, NavigationToolbar2TkAgg
#from matplotlib.figure import Figure
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import random
class Visualizer(object):
""" Includes all the methods needed to show streamed data. """
def __init__(self):
self.file_path = 'C:/Anaconda/_my_files/witsml_reader/modified_witsml.csv' #Defines which file is streamed
self.datetime_mod = []
self.bpos_mod = []
self.woh_mod = []
self.torq_mod = []
self.spp_mod = []
self.depth_mod = []
self.flow_in_mod = []
self.rpm_mod = []
def open_file(self):
self.df = pd.read_csv(self.file_path, low_memory = False, nrows = 300000) #Opens the STREAMED file (already modified so that data convert is not required)
self.df = self.df.drop(0)
self.df = pd.DataFrame(self.df)
return self.df
def convert_dataframe(self):
self.df = self.df.values.T.tolist() #Do transposition of the dataframe and convert to list
#Columns are as following:
# - DATETIME
# - BPOS
# - WOH
# - TORQ
# - SPP
# - DEPTH
# - FLOW_IN
# - RPM
self.datetime_value = self.df[0]
self.bpos_value = self.df[1]
self.woh_value = self.df[2]
self.torq_value = self.df[3]
self.spp_value = self.df[4]
self.depth_value = self.df[5]
self.flow_in_value = self.df[5]
self.rpm_value = self.df[7]
return self.datetime_value, self.bpos_value, self.woh_value, self.torq_value, self.spp_value, self.depth_value, self.flow_in_value, self.rpm_value
#print(self.bpos_value)
def deliver_values(self, no_dp, columns):
''' Method gets no_dp amount of data points from the original file. '''
self.no_dp = no_dp #defines how many data points will be presented in the graph
val_dict = {
'datetime': [self.datetime_value, self.datetime_mod],
'bpos': [self.bpos_value, self.bpos_mod],
'woh': [self.woh_value, self.woh_mod],
'torq': [self.torq_value, self.torq_mod],
'spp': [self.spp_value, self.spp_mod],
'depth': [self.depth_value, self.depth_mod],
'flow_in': [self.flow_in_value, self.flow_in_mod],
'rpm': [self.rpm_value, self.rpm_mod]
}
for item in columns:
if self.no_dp > len(val_dict[item][0]):
dp_range = len(val_dict[item][0])
else:
dp_range = self.no_dp
for i in range(dp_range):
val_dict[item][1].append(val_dict[item][0][i])
return self.datetime_mod, self.bpos_mod, self.woh_mod, self.torq_mod, self.spp_mod, self.depth_mod, self.flow_in_mod, self.rpm_mod
def show_graph2(self):
from pylive_mod import live_plotter
self.open_file()
self.convert_dataframe()
self.deliver_values(no_dp = 100000, columns = ['datetime', 'depth', 'bpos', 'woh'])
fst_p = 0
size = 300 # density of points in the graph (100 by default)
x_vec = self.datetime_mod[fst_p:size]
y_vec = self.depth_mod[fst_p:size]
y2_vec = self.bpos_mod[fst_p:size]
y3_vec = self.woh_mod[fst_p:size]
line1 = []
line2 = []
line3 = []
for i in range(self.no_dp):
#print(self.datetime_mod[i:6+i])
#print('Ostatni element y_vec: ', y_vec[-1])
#print(x_vec)
x_vec[-1] = self.datetime_mod[size+i]
y_vec[-1] = self.depth_mod[size+i]
y2_vec[-1] = self.bpos_mod[size+i]
y3_vec[-1] = self.woh_mod[size+i]
line1, line2, line3 = live_plotter(x_vec, y_vec, y2_vec, y3_vec, line1, line2, line3)
x_vec = np.append(x_vec[1:], 0.0)
y_vec = np.append(y_vec[1:], 0.0)
y2_vec = np.append(y2_vec[1:], 0.0)
y3_vec = np.append(y3_vec[1:], 0.0)
def main():
Graph = Visualizer()
Graph.open_file() #Opens the streamed file
Graph.convert_dataframe() #Converts dataframe to readable format
Graph.show_graph2()
#Show us the graph
#main()
def live_plotter(x_data, y1_data, y2_data, y3_data, line1, line2, line3, identifier='',pause_time=1):
if line1 == [] and line2 == [] and line3 == []:
# this is the call to matplotlib that allows dynamic plotting
plt.ion()
#fig = Figure(figsize = (5, 4), dpi = 100)
#host = fig.add_subplot()
fig, host = plt.subplots()
fig.set_figheight(7) #adjust figure's height
fig.set_figwidth(14) #adjust figure's width
fig.subplots_adjust(0.15)
#Line1
#line1 = host
ln1 = host
ln2 = host.twinx()
ln3 = host.twinx()
ln2.spines['right'].set_position(('axes', 1.))
ln3.spines['right'].set_position(('axes', 1.12))
make_patch_spines_invisible(ln2)
make_patch_spines_invisible(ln3)
ln2.spines['right'].set_visible(True)
ln3.spines['right'].set_visible(True)
ln1.set_xlabel('Date & Time') #main x axis
ln1.set_ylabel('Depth') #left y axis
ln2.set_ylabel('Elevator Height')
ln3.set_ylabel('Weight on Hook')
#
x_formatter = FixedFormatter([x_data])
x_locator = FixedLocator([x_data[5]])
ln1.xaxis.set_major_formatter(x_formatter)
ln1.xaxis.set_major_locator(x_locator)
#
ln1.locator_params(nbins = 5, axis = 'y')
ln1.tick_params(axis='x', rotation=90) #rotates x ticks 90 degrees down
ln2.axes.set_ylim(0, 30)
ln3.axes.set_ylim(200, 250)
line1, = ln1.plot(x_data, y1_data, color = 'black', linestyle = 'solid', alpha=0.8, label = 'Depth')
line2, = ln2.plot(x_data, y2_data, color = 'blue', linestyle = 'dashed', alpha=0.8, label = 'Elevator Height')
line3, = ln3.plot(x_data, y3_data, color = 'red', linestyle = 'solid', alpha=0.8, label = 'Weight on Hook')
# ----- embedding -----
canvas = FigureCanvasTkAgg(fig, master = root)
canvas.draw()
# placing the canvas on the Tkinter window
canvas.get_tk_widget().pack()
# creating the Matplotlib toolbar
toolbar = NavigationToolbar2Tk(canvas, root)
toolbar.update()
# placing the toolbar on the Tkinter window
canvas.get_tk_widget().pack()
#----- -----
plt.title('WiTSML Visualizer')
fig.tight_layout() #the graphs is not clipped on sides
#Shows legend
lines = [line1, line2, line3]
host.legend(lines, [l.get_label() for l in lines], loc = 'lower left')
#Shows grid
plt.grid(True)
#Shows the whole graph
plt.show()
# after the figure, axis, and line are created, we only need to update the y-data
mod_x_data = convert_x_data(x_data, 10)
line1.axes.set_xticklabels(mod_x_data)
line1.set_ydata(y1_data)
line2.set_ydata(y2_data)
line3.set_ydata(y3_data)
#Debugging
#rint('plt.lim: ', ln2.axes.get_ylim())
# adjust limits if new data goes beyond bounds
# limit for line 1
if np.min(y1_data)<=line1.axes.get_ylim()[0] or np.max(y1_data)>=line1.axes.get_ylim()[1]:
plt.ylim(0, 10)
line1.axes.set_ylim([np.min(y1_data)-np.std(y1_data),np.max(y1_data)+np.std(y1_data)])
# limit for line 2
if np.min(y2_data)<=line2.axes.get_ylim()[0] or np.max(y2_data)>=line2.axes.get_ylim()[1]:
plt.ylim([np.min(y2_data)-np.std(y2_data),np.max(y2_data)+np.std(y2_data)])
#plt.ylim(0, 25)
# limit for line 3
if np.min(y3_data)<=line3.axes.get_ylim()[0] or np.max(y3_data)>=line3.axes.get_ylim()[1]:
plt.ylim([np.min(y3_data)-np.std(y3_data),np.max(y3_data)+np.std(y3_data)])
#plt.ylim(0, 25)
# Adds lines to the legend
#host.legend(lines, [l.get_label() for l in lines])
# this pauses the data so the figure/axis can catch up - the amount of pause can be altered above
plt.pause(pause_time)
# return line so we can update it again in the next iteration
return line1, line2, line3
# -*- coding: utf-8 -*-
"""
Created on Tue Oct 6 10:24:35 2020
@author: Dar0
"""
from tkinter import * #import tkinter module
from visualizer import main #import module 'visualizer' that shows the graph in real time
class Application(Frame):
''' Interface for visualizing graphs, indicators and text-box. '''
def __init__(self, master):
super(Application, self).__init__(master)
self.grid()
self.create_widgets()
def create_widgets(self):
# Label of the 1st graph
Label(self,
text='Hook Load / Elevator Height / Depth vs Time'
).grid(row = 0, column = 0, sticky = W)
# Graph 1 - Hook Load / Elevator Height / Depth vs Time
# button that displays the plot
plot_button = Button(self,
master = root,
command = main,
height = 2,
width = 10,
text = "Plot").grid(row = 1, column = 0, sticky = W)
# place the button
# in main window
# Label of the 2nd graph
Label(self,
text = 'Hook Load / Elevator Height vs Time'
).grid(row = 2, column = 0, sticky = W)
# Graph 2 - Hook Load / Elevator Height vs Time
#Label of the 3rd graph
Label(self,
text = 'Hook Load vs Time'
).grid(row = 4, column = 0, sticky = W)
#Graph 3 - Hook Load vs Time
#Label of the 1st indicator
Label(self,
text = '1st performance indicator'
).grid(row = 0, column = 1, sticky = W)
#1st performance indicator
#Label of 2nd performance indicator
Label(self,
text = '2nd performance indicator'
).grid(row = 2, column = 1, sticky = W)
#2nd performance indicator
#Label of 3rd performance indicator
Label(self,
text = '3rd performance indicator'
).grid(row = 4, column = 1, sticky = W)
#Text-box showing comments based on received data
self.text_box = Text(self, width = 50, height = 10, wrap = WORD)
self.text_box.grid(row = 6, column = 0, columnspan = 1)
self.text_box.delete(0.0, END)
self.text_box.insert(0.0, 'My message will be here.')
#Main part
root = Tk()
root.title('WiTSML Visualizer by Dar0')
app = Application(root)
root.mainloop()
#WiTSML visualizer
#Created by Dariusz Krol
#import matplotlib
#matplotlib.use('TkAgg')
#from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg, NavigationToolbar2TkAgg
#from matplotlib.figure import Figure
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import random
class Visualizer(object):
""" Includes all the methods needed to show streamed data. """
def __init__(self):
self.file_path = 'C:/Anaconda/_my_files/witsml_reader/modified_witsml.csv' #Defines which file is streamed
self.datetime_mod = []
self.bpos_mod = []
self.woh_mod = []
self.torq_mod = []
self.spp_mod = []
self.depth_mod = []
self.flow_in_mod = []
self.rpm_mod = []
def open_file(self):
self.df = pd.read_csv(self.file_path, low_memory = False, nrows = 300000) #Opens the STREAMED file (already modified so that data convert is not required)
self.df = self.df.drop(0)
self.df = pd.DataFrame(self.df)
return self.df
def convert_dataframe(self):
self.df = self.df.values.T.tolist() #Do transposition of the dataframe and convert to list
#Columns are as following:
# - DATETIME
# - BPOS
# - WOH
# - TORQ
# - SPP
# - DEPTH
# - FLOW_IN
# - RPM
self.datetime_value = self.df[0]
self.bpos_value = self.df[1]
self.woh_value = self.df[2]
self.torq_value = self.df[3]
self.spp_value = self.df[4]
self.depth_value = self.df[5]
self.flow_in_value = self.df[5]
self.rpm_value = self.df[7]
return self.datetime_value, self.bpos_value, self.woh_value, self.torq_value, self.spp_value, self.depth_value, self.flow_in_value, self.rpm_value
#print(self.bpos_value)
def deliver_values(self, no_dp, columns):
''' Method gets no_dp amount of data points from the original file. '''
self.no_dp = no_dp #defines how many data points will be presented in the graph
val_dict = {
'datetime': [self.datetime_value, self.datetime_mod],
'bpos': [self.bpos_value, self.bpos_mod],
'woh': [self.woh_value, self.woh_mod],
'torq': [self.torq_value, self.torq_mod],
'spp': [self.spp_value, self.spp_mod],
'depth': [self.depth_value, self.depth_mod],
'flow_in': [self.flow_in_value, self.flow_in_mod],
'rpm': [self.rpm_value, self.rpm_mod]
}
for item in columns:
if self.no_dp > len(val_dict[item][0]):
dp_range = len(val_dict[item][0])
else:
dp_range = self.no_dp
for i in range(dp_range):
val_dict[item][1].append(val_dict[item][0][i])
return self.datetime_mod, self.bpos_mod, self.woh_mod, self.torq_mod, self.spp_mod, self.depth_mod, self.flow_in_mod, self.rpm_mod
def show_graph2(self):
from pylive_mod import live_plotter
self.open_file()
self.convert_dataframe()
self.deliver_values(no_dp = 100000, columns = ['datetime', 'depth', 'bpos', 'woh'])
fst_p = 0
size = 300 # density of points in the graph (100 by default)
x_vec = self.datetime_mod[fst_p:size]
y_vec = self.depth_mod[fst_p:size]
y2_vec = self.bpos_mod[fst_p:size]
y3_vec = self.woh_mod[fst_p:size]
line1 = []
line2 = []
line3 = []
for i in range(self.no_dp):
#print(self.datetime_mod[i:6+i])
#print('Ostatni element y_vec: ', y_vec[-1])
#print(x_vec)
x_vec[-1] = self.datetime_mod[size+i]
y_vec[-1] = self.depth_mod[size+i]
y2_vec[-1] = self.bpos_mod[size+i]
y3_vec[-1] = self.woh_mod[size+i]
line1, line2, line3 = live_plotter(x_vec, y_vec, y2_vec, y3_vec, line1, line2, line3)
x_vec = np.append(x_vec[1:], 0.0)
y_vec = np.append(y_vec[1:], 0.0)
y2_vec = np.append(y2_vec[1:], 0.0)
y3_vec = np.append(y3_vec[1:], 0.0)
def main():
Graph = Visualizer()
Graph.open_file() #Opens the streamed file
Graph.convert_dataframe() #Converts dataframe to readable format
Graph.show_graph2()
#Show us the graph
#main()
def live_plotter(x_data, y1_data, y2_data, y3_data, line1, line2, line3, identifier='',pause_time=1):
if line1 == [] and line2 == [] and line3 == []:
# this is the call to matplotlib that allows dynamic plotting
plt.ion()
#fig = Figure(figsize = (5, 4), dpi = 100)
#host = fig.add_subplot()
fig, host = plt.subplots()
fig.set_figheight(7) #adjust figure's height
fig.set_figwidth(14) #adjust figure's width
fig.subplots_adjust(0.15)
#Line1
#line1 = host
ln1 = host
ln2 = host.twinx()
ln3 = host.twinx()
ln2.spines['right'].set_position(('axes', 1.))
ln3.spines['right'].set_position(('axes', 1.12))
make_patch_spines_invisible(ln2)
make_patch_spines_invisible(ln3)
ln2.spines['right'].set_visible(True)
ln3.spines['right'].set_visible(True)
ln1.set_xlabel('Date & Time') #main x axis
ln1.set_ylabel('Depth') #left y axis
ln2.set_ylabel('Elevator Height')
ln3.set_ylabel('Weight on Hook')
#
x_formatter = FixedFormatter([x_data])
x_locator = FixedLocator([x_data[5]])
ln1.xaxis.set_major_formatter(x_formatter)
ln1.xaxis.set_major_locator(x_locator)
#
ln1.locator_params(nbins = 5, axis = 'y')
ln1.tick_params(axis='x', rotation=90) #rotates x ticks 90 degrees down
ln2.axes.set_ylim(0, 30)
ln3.axes.set_ylim(200, 250)
line1, = ln1.plot(x_data, y1_data, color = 'black', linestyle = 'solid', alpha=0.8, label = 'Depth')
line2, = ln2.plot(x_data, y2_data, color = 'blue', linestyle = 'dashed', alpha=0.8, label = 'Elevator Height')
line3, = ln3.plot(x_data, y3_data, color = 'red', linestyle = 'solid', alpha=0.8, label = 'Weight on Hook')
# ----- embedding -----
canvas = FigureCanvasTkAgg(fig, master = root)
canvas.draw()
# placing the canvas on the Tkinter window
canvas.get_tk_widget().pack()
# creating the Matplotlib toolbar
toolbar = NavigationToolbar2Tk(canvas, root)
toolbar.update()
# placing the toolbar on the Tkinter window
canvas.get_tk_widget().pack()
#----- -----
plt.title('WiTSML Visualizer')
fig.tight_layout() #the graphs is not clipped on sides
#Shows legend
lines = [line1, line2, line3]
host.legend(lines, [l.get_label() for l in lines], loc = 'lower left')
#Shows grid
plt.grid(True)
#Shows the whole graph
plt.show()
# after the figure, axis, and line are created, we only need to update the y-data
mod_x_data = convert_x_data(x_data, 10)
line1.axes.set_xticklabels(mod_x_data)
line1.set_ydata(y1_data)
line2.set_ydata(y2_data)
line3.set_ydata(y3_data)
#Debugging
#rint('plt.lim: ', ln2.axes.get_ylim())
# adjust limits if new data goes beyond bounds
# limit for line 1
if np.min(y1_data)<=line1.axes.get_ylim()[0] or np.max(y1_data)>=line1.axes.get_ylim()[1]:
plt.ylim(0, 10)
line1.axes.set_ylim([np.min(y1_data)-np.std(y1_data),np.max(y1_data)+np.std(y1_data)])
# limit for line 2
if np.min(y2_data)<=line2.axes.get_ylim()[0] or np.max(y2_data)>=line2.axes.get_ylim()[1]:
plt.ylim([np.min(y2_data)-np.std(y2_data),np.max(y2_data)+np.std(y2_data)])
#plt.ylim(0, 25)
# limit for line 3
if np.min(y3_data)<=line3.axes.get_ylim()[0] or np.max(y3_data)>=line3.axes.get_ylim()[1]:
plt.ylim([np.min(y3_data)-np.std(y3_data),np.max(y3_data)+np.std(y3_data)])
#plt.ylim(0, 25)
# Adds lines to the legend
#host.legend(lines, [l.get_label() for l in lines])
# this pauses the data so the figure/axis can catch up - the amount of pause can be altered above
plt.pause(pause_time)
# return line so we can update it again in the next iteration
return line1, line2, line3
def live_绘图仪（x_数据、y1_数据、y2_数据、y3_数据、第1行、第2行、第3行，标识符=“”，暂停时间=1）：
如果第1行==[]和第2行==[]和第3行==[]：
#这是对matplotlib的调用，允许动态打印
plt.ion（）
#图=图（figsize=（5,4），dpi=100）
#主机=图添加_子批次（）
图，主机=plt.subplot（）
图：设置figheight（7）#调整人物高度
图设置图宽（14）#调整图宽
图子批次调整（0.15）
#第1行
#line1=主机
ln1=主机
ln2=host.twinx（）
ln3=host.twinx（）
ln2.脊椎['right']设置位置（（'axes'，1.））
ln3.脊椎[‘右’]。设置位置（（‘轴’，1.12））
使_面片_刺_不可见（ln2）
使_补丁_刺_不可见（ln3）
ln2.脊椎['右'].设置为可见（真）
ln3.脊椎['right'].set_可见（真）
ln1.设置标签（“日期和时间”）#主x轴
ln1.设置_ylabel（'Depth'）#左y轴
ln2.设置标签（“电梯高度”）
ln3.设置标签（“吊钩上的重量”）
#
x_格式化程序=固定格式化程序（[x_数据]）
x_定位器=固定定位器（[x_数据[5]]）
ln1.xaxis.set_major_格式化程序（x_格式化程序）
ln1.xaxis.set_major_定位器（x_定位器）
#
ln1.定位器参数（nbins=5，轴='y'）
ln1.勾号参数（轴=x'，旋转=90）#将x勾号向下旋转90度
ln2.轴设置Y（0,30）
ln3.轴设置长度（200250）
line1，=ln1.绘图（x_数据，y1_数据，颜色='黑色'，线型='实心'，alpha=0.8，标签='深度'）
line2，=ln2.绘图（x_数据，y2_数据，颜色='蓝色'，线型='虚线'，alpha=0.8，标签='电梯高度'）
line3，=ln3.plot（x_数据，y3_数据，颜色='红色'，线型='实心'，alpha=0.8，标签='挂钩上的重量'）
#----嵌入-----
canvas=FigureCanvasTkAgg（图，主控=根）
canvas.draw（）
#将画布放置在Tkinter窗口上
canvas.get_tk_widget（）.pack（）
#创建Matplotlib工具栏
工具栏=导航工具栏2TK（画布，根）
toolbar.update（）
#将工具栏放置在Tkinter窗口上
canvas.get_tk_widget（）.pack（）
#----- -----
产品名称（“WiTSML可视化工具”）
图紧_布局（）#图形未剪裁在侧面
#显示图例
行=[line1，line2，line3]
图例（行，[l.get_label（）表示行中的l]，loc='lower left'）
#显示网格
plt.grid（真）
#显示整个图表
plt.show（）
#创建图形、轴和线之后，我们只需要更新y数据
mod_x_data=转换_x_数据（x_数据，10）
line1.轴.设置x刻度线（mod_x_数据）
第1行。设置数据（y1数据）
第2行。设置数据（y2数据）
第3行。设置数据（y3数据）
#调试
#rint（'plt.lim:'，ln2.axes.get_ylim（））
#如果新数据超出范围，则调整限制
#1号线的限制
如果np.min（y1_数据）=line1.axes.get_ylim（）[1]：
plt.ylim（0,10）
行1.轴设置Y轴（[np.min（y1_数据）-np.std（y1_数据），np.max（y1_数据）+np.std（y1_数据）]）
#2号线的限制
如果np.min（y2_数据）=line2.axes.get_ylim（）[1]：
plt.ylim（[np.min（y2_数据）-np.std（y2_数据），np.max（y2_数据）+np.std（y2_数据）]）
#plt.ylim（0,25）
#3号线的限制
如果np.min（y3_数据）=line3.axes.get_ylim（）[1]：
plt.ylim（[np.min（y3_数据）-np.std（y3_数据），np.max（y3_数据）+np.std（y3_数据）]）
#plt.ylim（0,25）
#向图例中添加线条
#图例（行，[l.get_label（）表示l行中的l]）
#这会暂停数据，以便图形/轴可以跟上-暂停的数量可以在上面更改
plt.暂停（暂停时间）
#返回行，以便我们可以在下一次迭代中再次更新它
返回行1、行2、行3
绘图按钮=按钮（self，master=root，…）selfmaster=rootmaster=root