Dynamic matplotlib pyside widget not displaying - matplotlib

I'm writing a python application using pyside and matplotlib. Following a combination of this tutorial and this SO post, I have created a matplotlib widget that I can successfully add to a parent. However when I go to actually add data to it, nothing seems to get displayed.
If I add static data like the SO post had, it shows up, but when I change it to update on the fly (currently every second on a timer, but it will eventually be using a signal from another class), I never get anything but the empty axes to appear. I suspect that I'm missing a call to force a draw or invalidate or that there is something wrong with the way I'm calling update_datalim (though the values that get passed to it seem correct).
from PySide import QtCore, QtGui
import matplotlib
import random
matplotlib.use('Qt4Agg')
matplotlib.rcParams['backend.qt4']='PySide'
from matplotlib import pyplot as plt
from matplotlib.backends.backend_qt4agg import FigureCanvasQTAgg as FigureCanvas
from matplotlib.figure import Figure
from matplotlib.patches import Rectangle
from collections import namedtuple
DataModel = namedtuple('DataModel', ['start_x', 'start_y', 'width', 'height'])
class BaseWidget(FigureCanvas):
def __init__(self, parent=None, width=5, height=4, dpi=100):
fig = Figure(figsize=(width, height), dpi=dpi)
self.axes = fig.add_subplot(111)
# We want the axes cleared every time plot() is called
self.axes.hold(False)
self.axes.set_xlabel('X Label')
self.axes.set_ylabel('Y Label')
self.axes.set_title('My Data')
FigureCanvas.__init__(self, fig)
self.setParent(parent)
FigureCanvas.setSizePolicy(self,
QtGui.QSizePolicy.Expanding,
QtGui.QSizePolicy.Expanding)
FigureCanvas.updateGeometry(self)
class DynamicWidget(BaseWidget):
def set_data(self, the_data):
self.axes.clear()
xys = list()
cmap = plt.cm.hot
for datum in the_data:
bottom_left = (datum.start_x, datum.start_y)
top_right = (bottom_left[0] + datum.width, bottom_left[1] + datum.height)
rect = Rectangle(
xy=bottom_left,
width=datum.width, height=datum.height, color=cmap(100)
)
xys.append(bottom_left)
xys.append(top_right)
self.axes.add_artist(rect)
self.axes.update_datalim(xys)
self.axes.figure.canvas.draw_idle()
class RandomDataWidget(DynamicWidget):
def __init__(self, *args, **kwargs):
DynamicWidget.__init__(self, *args, **kwargs)
timer = QtCore.QTimer(self)
timer.timeout.connect(self.generate_and_set_data)
timer.start(1000)
def generate_and_set_data(self):
fake_data = [DataModel(
start_x=random.randint(1, 100),
width=random.randint(20, 40),
start_y=random.randint(80, 160),
height=random.randint(20, 90)
) for i in range(100)]
self.set_data(fake_data)
Edit: I'm suspecting that there's an issue with updating the limits of the plot. When running the above code, the plot opens with limits of 0 and 1 on both the x and y axis. Since none of my generated data falls into that range, I created another subclass of DynamicWidget that plots only data between 0 and 1 (the same data from the linked SO post). When instantiating the class below, the data shows up successfully. Do I need to do something more than calling update_datalim to get the graph to re-bound itself?
class StaticWidget(DynamicWidget):
def __init__(self):
DynamicWidget.__init__(self)
static_data = [
DataModel(0.5, 0.05, 0.2, 0.05),
DataModel(0.1, 0.2, 0.7, 0.2),
DataModel(0.3, 0.1, 0.8, 0.1)
]
self.set_data(static_data)

Yes, update_datalim only updates the bounding box that is kept internally by the axes. You also need to enable auto scaling for it to be used. Add self.axes.autoscale(enable=True) after the self.axes.clear() statement and it will work. Or you can set the axes' range to a fixed value by using self.axes.set_xlim and self.axes.set_ylim.
edit: here is my code, which works for me
from PySide import QtCore, QtGui
import matplotlib
import random, sys
matplotlib.use('Qt4Agg')
matplotlib.rcParams['backend.qt4']='PySide'
from matplotlib import pyplot as plt
from matplotlib.backends.backend_qt4agg import FigureCanvasQTAgg as FigureCanvas
from matplotlib.figure import Figure
from matplotlib.patches import Rectangle
from collections import namedtuple
DataModel = namedtuple('DataModel', ['start_x', 'start_y', 'width', 'height'])
class BaseWidget(FigureCanvas):
def __init__(self, parent=None, width=5, height=4, dpi=100):
fig = Figure(figsize=(width, height), dpi=dpi)
self.axes = fig.add_subplot(111)
# We want the axes cleared every time plot() is called
self.axes.hold(False)
#self.axes.autoscale(enable=True)
self.axes.set_xlabel('X Label')
self.axes.set_ylabel('Y Label')
self.axes.set_title('My Data')
FigureCanvas.__init__(self, fig)
self.setParent(parent)
FigureCanvas.setSizePolicy(self,
QtGui.QSizePolicy.Expanding,
QtGui.QSizePolicy.Expanding)
FigureCanvas.updateGeometry(self)
class DynamicWidget(BaseWidget):
def set_data(self, the_data):
self.axes.clear()
self.axes.autoscale(enable=True)
#self.axes.set_xlim(0, 300)
#self.axes.set_ylim(0, 300)
xys = list()
cmap = plt.cm.hot
for datum in the_data:
print datum
bottom_left = (datum.start_x, datum.start_y)
top_right = (bottom_left[0] + datum.width, bottom_left[1] + datum.height)
rect = Rectangle(
xy=bottom_left,
width=datum.width, height=datum.height, color=cmap(100)
)
xys.append(bottom_left)
xys.append(top_right)
self.axes.add_artist(rect)
self.axes.update_datalim(xys)
self.axes.figure.canvas.draw_idle()
class RandomDataWidget(DynamicWidget):
def __init__(self, *args, **kwargs):
DynamicWidget.__init__(self, *args, **kwargs)
timer = QtCore.QTimer(self)
timer.timeout.connect(self.generate_and_set_data)
timer.start(1000)
def generate_and_set_data(self):
fake_data = [DataModel(
start_x=random.randint(1, 100),
width=random.randint(20, 40),
start_y=random.randint(80, 160),
height=random.randint(20, 90)) for i in range(100)]
self.set_data(fake_data)
print "done:...\n\n"
def main():
qApp = QtGui.QApplication(sys.argv)
aw = RandomDataWidget()
aw.show()
aw.raise_()
sys.exit(qApp.exec_())
if __name__ == "__main__":
main()

Related

Resize one subplot after removing another

Problem description:
I'm building an interface for my lab, I intergrarted matplotlib with pyqt5 widget, there is a real time video display widget working on multi-thread and queue. I managed to show single shot with cross-section plot by adding divider. However, when I remove the cross-section plots, and redraw the figure_idle, the video frame can never moved back to its initial position with its initial size. I adjust the figure with navigator_tool_bar (top, bottom...), However it seems that there are blank areas left after removing the cross-section plots. Do you have any idea?
The initial figure:
Display cross-sections:
Clear cross-section and redraw:
Video widget code:
class VideoViewer(FigureCanvas):
def __init__(self, parent=None, width=4, height=4, dpi=70):
self.figwidth = width
self.figheight = height
self.fig = Figure(figsize=(self.figwidth, self.figheight), dpi=dpi)
FigureCanvas.__init__(self, self.fig)
FigureCanvas.setSizePolicy(self, QSizePolicy.Expanding, QSizePolicy.Expanding)
FigureCanvas.updateGeometry(self)
self.axes = self.fig.add_subplot()
self.fig.subplots_adjust(top=0.975,bottom=0.048,left=0.029,right=0.983,hspace=0.2,wspace=0.2)
Cross section code:
#create cross-sections
self.divider = make_axes_locatable(self.axes)
self.top_ax = self.divider.append_axes("top", 1.05, pad=0.2,sharex=self.axes)
self.right_ax = self.divider.append_axes("right", 1.05,pad=0.2,sharey=self.axes)
#create lines
self.v_line = self.axes.axvline(xvlinepos, color='r')
self.h_line = self.axes.axhline(yhlinepos, color='g')
self.v_cross, = self.right_ax.plot(Norm_xvlinedata,np.arange(self.ImageData.shape[0]), 'r-')
self.h_cross, = self.top_ax.plot(np.arange(self.ImageData.shape[1]),Norm_yhlinedata, 'g-')
Clear cross-section code:
def ClearCrossSection(self):
self.fig.canvas.mpl_disconnect(self.pick_event_v)
self.fig.canvas.mpl_disconnect(self.pick_event_h)
self.h_line.remove()
self.v_line.remove()
self.v_cross.remove()
self.h_cross.remove()
self.right_ax.remove()
self.top_ax.remove()
self.fig.canvas.draw_idle()
What I did:
Light_layout + subplot_adjust -----> does not work.
Constrained_layout -----> does not work.
Constrained_layout + GridSpec by declaring at beginning self.axes takes all cols and rows.-----> Does not work.
An exemple of the problem:
import sys
# GUI
from PyQt5.QtWidgets import*
from PyQt5.QtCore import *
from PyQt5.QtGui import *
# Matplotlib
import matplotlib
matplotlib.use('Qt5Agg')
import matplotlib.pyplot as plt
from matplotlib.figure import Figure
import matplotlib.gridspec as gridspec
from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
from matplotlib.backends.backend_qt5 import NavigationToolbar2QT as NavigationToolbar
from mpl_toolkits.axes_grid1 import make_axes_locatable
from PIL import Image
import matplotlib.lines as lines
# Generate data
import numpy as np
'''
need to update video frame, I'm using blitting, so better not clear the whole figure.
the whole code could be too long to show here.
'''
class Window(QMainWindow):
def __init__(self):
super(Window, self).__init__()
self.resize(800,600)
self.setGeometry(350,250,950,600)
# Creat MainWidget
self.MainWidget = QWidget()
self.LayoutMainWidget = QGridLayout()
self.MainWidget.setLayout(self.LayoutMainWidget)
# Matplotlib widget
self.MatplotViewer = VideoViewer()
self.FigureTool = NavigationToolbar(self.MatplotViewer, self)
# Button plot image
self.ButtPltImg = QPushButton("Plot Image")
# BUtton plot cross
self.ButtPltCross = QPushButton("Cross View")
self.ButtPltCross.setCheckable(True)
self.ButtPltCross.setStyleSheet("background-color: Green")
# add widgets
self.LayoutMainWidget.addWidget(self.MatplotViewer,0,0,7,7)
self.LayoutMainWidget.addWidget(self.FigureTool, 7,0,1,7)
self.LayoutMainWidget.addWidget(self.ButtPltImg, 2,7,1,1)
self.LayoutMainWidget.addWidget(self.ButtPltCross, 3,7,1,1)
# Set central widget
self.setCentralWidget(self.MainWidget)
self.connection()
def GenerateImage(self, state):
if self.ButtPltCross.isChecked():
self.ButtPltCross.setChecked(False)
self.MatplotViewer.ClearCrossSection()
self.MatplotViewer.UpdateFrame()
else:
self.MatplotViewer.UpdateFrame()
def PlotCrossSection(self, state):
if self.ButtPltCross.isChecked():
self.MatplotViewer.PlotCrossSection()
def ClearCrossSection(self, state):
if not(self.ButtPltCross.isChecked()):
self.MatplotViewer.ClearCrossSection()
def connection(self):
self.ButtPltImg.clicked.connect(lambda state=True: self.GenerateImage(state))
self.ButtPltCross.clicked.connect(lambda state=True: self.PlotCrossSection(state))
self.ButtPltCross.clicked.connect(lambda state=True: self.ClearCrossSection(state))
class VideoViewer(FigureCanvas):
def __init__(self, parent=None, width=4, height=4, dpi=70):
# Figure
self.fig = Figure(figsize=(width, height), dpi=dpi)
# Init Parent
FigureCanvas.__init__(self, self.fig)
FigureCanvas.setSizePolicy(self, QSizePolicy.Expanding, QSizePolicy.Expanding)
FigureCanvas.updateGeometry(self)
# Ax
self.axes = self.fig.add_subplot(111)
self.fig.subplots_adjust(top=0.975,bottom=0.048,left=0.029,right=0.983,hspace=0.2,wspace=0.2)
# Plot init image
self.PlotInitFrame()
def PlotInitFrame(self):
self.ImageData = self.ImageGenerate()
self.image = self.axes.imshow(self.ImageData, cmap='Greys', interpolation='none')
self.fig.canvas.draw_idle()
def UpdateFrame(self):
self.ImageData = self.ImageGenerate()
self.image.set_data(self.ImageData)
self.fig.canvas.draw_idle()
def PlotCrossSection(self):
# create axes
self.divider = make_axes_locatable(self.axes)
self.top_ax = self.divider.append_axes("top", 1.05, pad=0.2,sharex=self.axes)
self.right_ax = self.divider.append_axes("right", 1.05, pad=0.2,sharey=self.axes)
self.top_ax.xaxis.set_tick_params(labelbottom=False)
self.right_ax.yaxis.set_tick_params(labelleft=False)
# set cross section limit
self.right_ax.set_xlim(right=1.05)
self.top_ax.set_ylim(top=1.05)
# some pars
xmin, xmax = self.axes.get_xlim()
ymin, ymax = self.axes.get_ylim()
v_mid = int((xmin + xmax)/2)
h_mid = int((ymin + ymax)/2)
# set line
self.v_line = lines.Line2D([v_mid, v_mid], [ymin, ymax], color='r', pickradius=5)
self.axes.add_line(self.v_line)
self.h_line = lines.Line2D([xmin, xmax], [h_mid, h_mid], color='g', pickradius=5)
self.axes.add_line(self.h_line)
# set cross section data
Norm_xvlinedata = self.NormalizeData(self.ImageData[:,v_mid])
self.v_cross, = self.right_ax.plot(Norm_xvlinedata, np.arange(self.ImageData.shape[0]), 'r-')
Norm_yhlinedata = self.NormalizeData(self.ImageData[h_mid,:])
self.h_cross, = self.top_ax.plot(np.arange(self.ImageData.shape[1]), Norm_yhlinedata, 'g-')
self.fig.canvas.draw_idle()
def NormalizeData(self, data_temp):
min_temp = np.min(data_temp)
max_temp = np.max(data_temp)
if min_temp != max_temp:
return (data_temp-min_temp)/(max_temp-min_temp)
else:
return data_temp/data_temp
def ClearCrossSection(self):
self.v_line.remove()
self.h_line.remove()
self.top_ax.remove()
self.right_ax.remove()
self.fig.canvas.draw_idle()
def ImageGenerate(self):
xx,yy = np.meshgrid(np.linspace(-502,502,1024),np.linspace(-502,502,1024))
r = np.sqrt(xx**2+yy**2)
AMP = np.random.randint(150,250)
SIG = np.random.randint(200,250)
T = np.random.randint(115,135)
return AMP*np.exp(-(r)**2/(2*SIG**2))*np.cos(2*np.pi/T*r)
if __name__ == '__main__':
app = QApplication(sys.argv)
MainWindow = Window()
MainWindow.showMaximized()
sys.exit(app.exec_())

Interactive matplotlib plot in PySimpleGUI

I'm trying to get the RectangleSelector form matplotlib.widgets to work with PySimpleGUI.
I'm basing my test code on the RectangleSelector demo shown in the accepted answer on this question.
I'm getting the plot to show in PySimpleGUI but it's not interactive. Is it even possible in PySimpleGUI to have interactive matplotlib widgets?
import PySimpleGUI as sg
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
from matplotlib.widgets import RectangleSelector
import matplotlib
matplotlib.use('TkAgg')
xdata = np.linspace(0,9*np.pi, num=301)
ydata = np.sin(xdata)
fig, ax = plt.subplots()
line, = ax.plot(xdata, ydata)
def draw_figure(canvas, figure):
figure_canvas_agg = FigureCanvasTkAgg(figure, canvas)
figure_canvas_agg.draw()
figure_canvas_agg.get_tk_widget().pack(side="top", fill="both", expand=1)
return figure_canvas_agg
def line_select_callback(eclick, erelease):
x1, y1 = eclick.xdata, eclick.ydata
x2, y2 = erelease.xdata, erelease.ydata
rect = plt.Rectangle( (min(x1,x2),min(y1,y2)), np.abs(x1-x2), np.abs(y1-y2) )
ax.add_patch(rect)
rs = RectangleSelector(ax, line_select_callback,
drawtype='box', useblit=False, button=[1],
minspanx=5, minspany=5, spancoords='pixels',
interactive=True)
layout = [[sg.Canvas(key="-CANVAS-")]]
window = sg.Window('test', layout, finalize=True, element_justification='center', font='Helvetica 16')
draw_figure(window["-CANVAS-"].TKCanvas, fig)
event, values = window.read()
Edit: Thanks to MikeyB for the pointer, I now have the following code, which shows an interactive plot, but it's still not possible to draw rectangles. The callback function doesn't seem to be firing. New code below:
import PySimpleGUI as sg
import numpy as np
from matplotlib.widgets import RectangleSelector
import matplotlib.figure as figure
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg, NavigationToolbar2Tk
# instantiate matplotlib figure
fig = figure.Figure()
ax = fig.add_subplot(111)
DPI = fig.get_dpi()
fig.set_size_inches(505 * 2 / float(DPI), 707 / float(DPI))
# ------------------------------- This is to include a matplotlib figure in a Tkinter canvas
def draw_figure_w_toolbar(canvas, fig, canvas_toolbar):
if canvas.children:
for child in canvas.winfo_children():
child.destroy()
if canvas_toolbar.children:
for child in canvas_toolbar.winfo_children():
child.destroy()
figure_canvas_agg = FigureCanvasTkAgg(fig, master=canvas)
figure_canvas_agg.draw()
toolbar = Toolbar(figure_canvas_agg, canvas_toolbar)
toolbar.update()
figure_canvas_agg.get_tk_widget().pack(side='right', fill='both', expand=1)
def line_select_callback(eclick, erelease):
x1, y1 = eclick.xdata, eclick.ydata
x2, y2 = erelease.xdata, erelease.ydata
rect = plt.Rectangle( (min(x1,x2),min(y1,y2)), np.abs(x1-x2), np.abs(y1-y2) )
print(rect)
ax.add_patch(rect)
class Toolbar(NavigationToolbar2Tk):
def __init__(self, *args, **kwargs):
super(Toolbar, self).__init__(*args, **kwargs)
# ------------------------------- PySimpleGUI CODE
layout = [
[sg.B('start', key='start')],
[sg.Canvas(key='controls_cv')],
[sg.Column(
layout=[
[sg.Canvas(key='fig_cv',
# it's important that you set this size
size=(500 * 2, 700)
)]
],
background_color='#DAE0E6',
pad=(0, 0)
)],
]
window = sg.Window('Test', layout)
while True:
event, values = window.read()
print(event, values)
if event == sg.WIN_CLOSED:
break
elif event == 'start':
x = np.linspace(0, 2 * np.pi)
y = np.sin(x)
line, = ax.plot(x, y)
rs = RectangleSelector(ax, line_select_callback,
drawtype='box', useblit=False, button=[1],
minspanx=5, minspany=5, spancoords='pixels',
interactive=True)
draw_figure_w_toolbar(window['fig_cv'].TKCanvas, fig, window['controls_cv'].TKCanvas)
window.close()
Is it even possible in PySimpleGUI to have interactive matplotlib widgets?
Yes.
The demo program on the project's GitHub shows how to make an interactive Matplotlib drawing.
https://github.com/PySimpleGUI/PySimpleGUI/blob/master/DemoPrograms/Demo_Matplotlib_Embedded_Toolbar.py
You need to embed the controls into the window.
you need to add
fig.canvas.draw()
to your callback-function if you want the plot to be updated after the callback has triggered!
here's an updated version of your code that works just fine:
import PySimpleGUI as sg
import numpy as np
from matplotlib.widgets import RectangleSelector
import matplotlib.figure as figure
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg, NavigationToolbar2Tk
import matplotlib.pyplot as plt
# instantiate matplotlib figure
fig = figure.Figure()
ax = fig.add_subplot(111)
DPI = fig.get_dpi()
fig.set_size_inches(505 * 2 / float(DPI), 707 / float(DPI))
# ------------------------------- This is to include a matplotlib figure in a Tkinter canvas
def draw_figure_w_toolbar(canvas, fig, canvas_toolbar):
if canvas.children:
for child in canvas.winfo_children():
child.destroy()
if canvas_toolbar.children:
for child in canvas_toolbar.winfo_children():
child.destroy()
figure_canvas_agg = FigureCanvasTkAgg(fig, master=canvas)
figure_canvas_agg.draw()
toolbar = Toolbar(figure_canvas_agg, canvas_toolbar)
toolbar.update()
figure_canvas_agg.get_tk_widget().pack(side='right', fill='both', expand=1)
def line_select_callback(eclick, erelease):
x1, y1 = eclick.xdata, eclick.ydata
x2, y2 = erelease.xdata, erelease.ydata
rect = plt.Rectangle( (min(x1,x2),min(y1,y2)), np.abs(x1-x2), np.abs(y1-y2) )
print(rect)
ax.add_patch(rect)
fig.canvas.draw()
class Toolbar(NavigationToolbar2Tk):
def __init__(self, *args, **kwargs):
super(Toolbar, self).__init__(*args, **kwargs)
# ------------------------------- PySimpleGUI CODE
layout = [
[sg.B('start', key='start')],
[sg.Canvas(key='controls_cv')],
[sg.Column(
layout=[
[sg.Canvas(key='fig_cv',
# it's important that you set this size
size=(500 * 2, 700)
)]
],
background_color='#DAE0E6',
pad=(0, 0)
)],
]
window = sg.Window('Test', layout)
while True:
event, values = window.read()
print(event, values)
if event == sg.WIN_CLOSED:
break
elif event == 'start':
x = np.linspace(0, 2 * np.pi)
y = np.sin(x)
line, = ax.plot(x, y)
rs = RectangleSelector(ax, line_select_callback,
drawtype='box', useblit=False, button=[1],
minspanx=5, minspany=5, spancoords='pixels',
interactive=True)
draw_figure_w_toolbar(window['fig_cv'].TKCanvas, fig, window['controls_cv'].TKCanvas)
window.close()

pyqt5: how to zoom in a figure on both x axes and y axes together?

I want to plot a figure by pyqt5 as below codes, and I want to see all the data in one figure and zoom in to see some detals; I hope when Izoom in a part of this figure, x axes and y axes fit the detail auto together;
import sys
from PyQt5 import QtWidgets
from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
from matplotlib.backends.backend_qt5 import NavigationToolbar2QT as NavigationToolbar
import matplotlib.pyplot as plt
import matplotlib.finance as mpf
class Window(QtWidgets.QDialog):
def __init__(self,Data,parent=None):
super().__init__(parent)
self.candleData=Data[0]
self.plots=len(Data)
if self.plots>1:
self.lineData=Data[1]
self.figure = plt.figure(figsize=(30,18))
self.axes = self.figure.add_subplot(111)
self.axes.hold(True)
self.canvas = FigureCanvas(self.figure)
self.toolbar = NavigationToolbar(self.canvas, self)
self.toolbar.hide()
self.button2 = QtWidgets.QPushButton('Zoom')
self.button2.clicked.connect(self.zoom)
self.button3 = QtWidgets.QPushButton('Pan')
self.button3.clicked.connect(self.pan)
self.button4 = QtWidgets.QPushButton('Home')
self.button4.clicked.connect(self.home)
layout = QtWidgets.QVBoxLayout()
layout.addWidget(self.toolbar)
layout.addWidget(self.canvas)
btnlayout = QtWidgets.QHBoxLayout()
btnlayout.addWidget(self.button2)
btnlayout.addWidget(self.button3)
btnlayout.addWidget(self.button4)
qw = QtWidgets.QWidget(self)
qw.setLayout(btnlayout)
layout.addWidget(qw)
self.setLayout(layout)
def home(self):
self.toolbar.home()
def zoom(self):
self.toolbar.zoom()
def pan(self):
self.toolbar.pan()
def plot(self):
[obj.insert(0,i) for i,obj in enumerate(self.candleData)]
mpf.candlestick_ohlc(self.axes,self.candleData,width=0.8,colorup='r',colordown='g')
self.axes.grid()
print(self.plots)
if self.plots>1:
for i in range(len(self.lineData)):
self.axes.plot(self.lineData[i][0],self.lineData[i][1],color=self.lineData[i][2])
self.canvas.draw()
Now I plot a figure and zoom in for some small part as blow:
Screenshot:
but I should use "pan" to see all of it; shall I see all of it auto when I zoom in?

Embedding "Figure Type" Seaborn Plot in PyQt (pyqtgraph)

I am using a wrapper of PyQt (pyqtgraph) to build a GUI application.
I wish to embed a Seaborn plot within it using the MatplotlibWidget. However, my problem is that the Seaborn wrapper method such as FacetGrid do not accept an external figure handle. Moreover, when I try to update the MatplotlibWidget object underlying figure (.fig) with the figure produced by the FacetGrid it doesn't work (no plot after draw). Any suggestion for a workaround?
Seaborn's Facetgrid provides a convenience function to quickly connect pandas dataframes to the matplotlib pyplot interface.
However in GUI applications you rarely want to use pyplot, but rather the matplotlib API.
The problem you are facing here is that Facetgrid already creates its own matplotlib.figure.Figure object (Facetgrid.fig). Also, the MatplotlibWidget
creates its own figure, so you end up with two figures.
Now, let's step back a bit:
In principle it is possible to use a seaborn Facetgrid plot in PyQt, by first creating the plot and then providing the resulting figure to the figure canvas (matplotlib.backends.backend_qt4agg.FigureCanvasQTAgg). The following is an example of how to do that.
from PyQt4 import QtGui, QtCore
from matplotlib.backends.backend_qt4agg import FigureCanvasQTAgg as FigureCanvas
import sys
import seaborn as sns
import matplotlib.pyplot as plt
tips = sns.load_dataset("tips")
def seabornplot():
g = sns.FacetGrid(tips, col="sex", hue="time", palette="Set1",
hue_order=["Dinner", "Lunch"])
g.map(plt.scatter, "total_bill", "tip", edgecolor="w")
return g.fig
class MainWindow(QtGui.QMainWindow):
send_fig = QtCore.pyqtSignal(str)
def __init__(self):
super(MainWindow, self).__init__()
self.main_widget = QtGui.QWidget(self)
self.fig = seabornplot()
self.canvas = FigureCanvas(self.fig)
self.canvas.setSizePolicy(QtGui.QSizePolicy.Expanding,
QtGui.QSizePolicy.Expanding)
self.canvas.updateGeometry()
self.button = QtGui.QPushButton("Button")
self.label = QtGui.QLabel("A plot:")
self.layout = QtGui.QGridLayout(self.main_widget)
self.layout.addWidget(self.button)
self.layout.addWidget(self.label)
self.layout.addWidget(self.canvas)
self.setCentralWidget(self.main_widget)
self.show()
if __name__ == '__main__':
app = QtGui.QApplication(sys.argv)
win = MainWindow()
sys.exit(app.exec_())
While this works fine, it is a bit questionable, if it's useful at all. Creating a plot inside a GUI in most cases has the purpose of beeing updated depending on user interactions. In the example case from above, this is pretty inefficient, as it would require to create a new figure instance, create a new canvas with this figure and replace the old canvas instance with the new one, adding it to the layout.
Note that this problematics is specific to those plotting functions in seaborn, which work on a figure level, like lmplot, factorplot, jointplot, FacetGrid and possibly others.
Other functions like regplot, boxplot, kdeplot work on an axes level and accept a matplotlib axes object as argument (sns.regplot(x, y, ax=ax1)).
A possibile solution is to first create the subplot axes and later plot to those axes, for example using the pandas plotting functionality.
df.plot(kind="scatter", x=..., y=..., ax=...)
where ax should be set to the previously created axes.
This allows to update the plot within the GUI. See the example below. Of course normal matplotlib plotting (ax.plot(x,y)) or the use of the seaborn axes level function discussed above work equally well.
from PyQt4 import QtGui, QtCore
from matplotlib.backends.backend_qt4agg import FigureCanvasQTAgg as FigureCanvas
from matplotlib.figure import Figure
import sys
import seaborn as sns
tips = sns.load_dataset("tips")
class MainWindow(QtGui.QMainWindow):
send_fig = QtCore.pyqtSignal(str)
def __init__(self):
super(MainWindow, self).__init__()
self.main_widget = QtGui.QWidget(self)
self.fig = Figure()
self.ax1 = self.fig.add_subplot(121)
self.ax2 = self.fig.add_subplot(122, sharex=self.ax1, sharey=self.ax1)
self.axes=[self.ax1, self.ax2]
self.canvas = FigureCanvas(self.fig)
self.canvas.setSizePolicy(QtGui.QSizePolicy.Expanding,
QtGui.QSizePolicy.Expanding)
self.canvas.updateGeometry()
self.dropdown1 = QtGui.QComboBox()
self.dropdown1.addItems(["sex", "time", "smoker"])
self.dropdown2 = QtGui.QComboBox()
self.dropdown2.addItems(["sex", "time", "smoker", "day"])
self.dropdown2.setCurrentIndex(2)
self.dropdown1.currentIndexChanged.connect(self.update)
self.dropdown2.currentIndexChanged.connect(self.update)
self.label = QtGui.QLabel("A plot:")
self.layout = QtGui.QGridLayout(self.main_widget)
self.layout.addWidget(QtGui.QLabel("Select category for subplots"))
self.layout.addWidget(self.dropdown1)
self.layout.addWidget(QtGui.QLabel("Select category for markers"))
self.layout.addWidget(self.dropdown2)
self.layout.addWidget(self.canvas)
self.setCentralWidget(self.main_widget)
self.show()
self.update()
def update(self):
colors=["b", "r", "g", "y", "k", "c"]
self.ax1.clear()
self.ax2.clear()
cat1 = self.dropdown1.currentText()
cat2 = self.dropdown2.currentText()
print cat1, cat2
for i, value in enumerate(tips[cat1].unique().get_values()):
print "value ", value
df = tips.loc[tips[cat1] == value]
self.axes[i].set_title(cat1 + ": " + value)
for j, value2 in enumerate(df[cat2].unique().get_values()):
print "value2 ", value2
df.loc[ tips[cat2] == value2 ].plot(kind="scatter", x="total_bill", y="tip",
ax=self.axes[i], c=colors[j], label=value2)
self.axes[i].legend()
self.fig.canvas.draw_idle()
if __name__ == '__main__':
app = QtGui.QApplication(sys.argv)
win = MainWindow()
sys.exit(app.exec_())
A final word about pyqtgraph: I wouldn't call pyqtgraph a wrapper for PyQt but more an extention. Although pyqtgraph ships with its own Qt (which makes it portable and work out of the box), it is also a package one can use from within PyQt. You can therefore add a GraphicsLayoutWidget to a PyQt layout simply by
self.pgcanvas = pg.GraphicsLayoutWidget()
self.layout().addWidget(self.pgcanvas)
The same holds for a MatplotlibWidget (mw = pg.MatplotlibWidget()). While you can use this kind of widget, it's merely a convenience wrapper, since all it's doing is finding the correct matplotlib imports and creating a Figure and a FigureCanvas instance. Unless you are using other pyqtgraph functionality, importing the complete pyqtgraph package just to save 5 lines of code seems a bit overkill to me.
Here is exact copy of the accepted answer but using PYQT5:
from PyQt5 import QtCore, QtGui, QtWidgets
from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
from matplotlib.figure import Figure
import sys
import seaborn as sns
tips = sns.load_dataset("tips")
class MainWindow(QtWidgets.QMainWindow):
send_fig = QtCore.pyqtSignal(str)
def __init__(self):
super(MainWindow, self).__init__()
self.main_widget = QtWidgets.QWidget(self)
self.fig = Figure()
self.ax1 = self.fig.add_subplot(121)
self.ax2 = self.fig.add_subplot(122, sharex=self.ax1, sharey=self.ax1)
self.axes=[self.ax1, self.ax2]
self.canvas = FigureCanvas(self.fig)
self.canvas.setSizePolicy(QtWidgets.QSizePolicy.Expanding,
QtWidgets.QSizePolicy.Expanding)
self.canvas.updateGeometry()
self.dropdown1 = QtWidgets.QComboBox()
self.dropdown1.addItems(["sex", "time", "smoker"])
self.dropdown2 = QtWidgets.QComboBox()
self.dropdown2.addItems(["sex", "time", "smoker", "day"])
self.dropdown2.setCurrentIndex(2)
self.dropdown1.currentIndexChanged.connect(self.update)
self.dropdown2.currentIndexChanged.connect(self.update)
self.label = QtWidgets.QLabel("A plot:")
self.layout = QtWidgets.QGridLayout(self.main_widget)
self.layout.addWidget(QtWidgets.QLabel("Select category for subplots"))
self.layout.addWidget(self.dropdown1)
self.layout.addWidget(QtWidgets.QLabel("Select category for markers"))
self.layout.addWidget(self.dropdown2)
self.layout.addWidget(self.canvas)
self.setCentralWidget(self.main_widget)
self.show()
self.update()
def update(self):
colors=["b", "r", "g", "y", "k", "c"]
self.ax1.clear()
self.ax2.clear()
cat1 = self.dropdown1.currentText()
cat2 = self.dropdown2.currentText()
print (cat1, cat2)
for i, value in enumerate(tips[cat1].unique().get_values()):
print ("value ", value)
df = tips.loc[tips[cat1] == value]
self.axes[i].set_title(cat1 + ": " + value)
for j, value2 in enumerate(df[cat2].unique().get_values()):
print ("value2 ", value2)
df.loc[ tips[cat2] == value2 ].plot(kind="scatter", x="total_bill", y="tip",
ax=self.axes[i], c=colors[j], label=value2)
self.axes[i].legend()
self.fig.canvas.draw_idle()
if __name__ == '__main__':
import sys
app = QtWidgets.QApplication(sys.argv)
ex = MainWindow()
sys.exit(app.exec_())
While any matplotlib plots can be embedded in pyqt5 the same way, it's important to note that the UI could get slow as the sizeof the dataset grows. But I found such approaches handy to parse and plot log files by employing regex functionalities.

using ginput in embedded matplotlib figure in PyQt4

I'm trying to use the 'ginput' to measure distance in a matplotlib figure by allowing the user to mouse click the locations. I am able to do this independently in the matplotlib figure, but I'm having problems when I tried to set the figure onto a matplotlib canvas and then embed it into PyQt4 widget. Below is my code, most of which were taken from the matplotlib examples. My solution will be to click a set of locations, and pass the (x,y) coordinates to the 'dist_calc' function to get the distance.
import sys
from PyQt4 import QtGui
from matplotlib.backends.backend_qt4agg import FigureCanvasQTAgg as FigureCanvas
from matplotlib.backends.backend_qt4agg import NavigationToolbar2QT as NavigationToolbar
import matplotlib.pyplot as plt
from matplotlib.figure import Figure
import random
import numpy as np
class Window(QtGui.QWidget):
def __init__(self, parent=None):
super(Window, self).__init__(parent)
self.fig = Figure((6.5, 5.0), tight_layout=True)
self.ax = self.fig.add_subplot(111)
self.canvas = FigureCanvas(self.fig)
self.toolbar = NavigationToolbar(self.canvas, self)
self.button = QtGui.QPushButton('Plot')
self.button.clicked.connect(self.plot)
self.ndist = QtGui.QPushButton('Measure')
self.ndist.clicked.connect(self.draw_line)
self.toolbar.addWidget(self.button)
self.toolbar.addWidget(self.ndist)
self.fig.tight_layout()
layout = QtGui.QVBoxLayout()
layout.addWidget(self.toolbar)
layout.addWidget(self.canvas)
self.setLayout(layout)
def plot(self):
data = [random.random() for i in range(20)]
self.ax.hold(False)
self.ax.plot(data, '*-')
self.canvas.draw()
def draw_line(self):
self.xy = plt.ginput(0)
x = [p[0] for p in self.xy]
y = [p[1] for p in self.xy]
self.ax.plot(x,y)
self.ax.figure.canvas.draw()
self.get_dist(x, y)
def get_dist(self, xpts, ypts):
npts = len(xpts)
distArr = []
for i in range(npts-1):
apt = [xpts[i], ypts[i]]
bpt = [xpts[i+1], ypts[i+1]]
dist =self.calc_dist(apt,bpt)
distArr.append(dist)
tdist = np.sum(distArr)
print(tdist)
def calc_dist(self,apt, bpt):
apt = np.asarray(apt)
dist = np.sum((apt - bpt)**2)
dist = np.sqrt(dist)
return dist
if __name__ == '__main__':
app = QtGui.QApplication(sys.argv)
main = Window()
main.show()
sys.exit(app.exec_())
According to this comment by one of the lead Matplotlib developers, you must not import pyplot when you're embedding Matplotlib in Qt. Pyplot sets up its own gui, mainloop and canvas, which interfere with the Qt event loop.
Changing the line self.xy = plt.ginput(0) into self.xy = self.fig.ginput(0) did not help but gave an insightful error:
AttributeError: 'FigureCanvasQTAgg' object has no attribute 'manager'
Figure.show works only for figures managed by pyplot, normally created by pyplot.figure().
In short, I don't think this is possible. ginput is a blocking function and seems only to be implemented for a Matplotlib event loop. I'm afraid that you will have to build the functionality you want using Matplotlib mouse events, which do work when embedding in PyQt. Just be sure not to use pyplot!
Edit: I just remembered, perhaps the LassoSelector is what you need.