I want to do a simple GUI that allows the user to add or remove traces from a plot for any number of traces. It looks like this:
The problems I'm having:
I don't know how to make the axes not to superpose with each other for a generic number of plots.
When I plot more than one trace, and then delete all but one, there are two axes showing for some reason. There should always be one axis per trace being shown.
Is there a way to fix these issues? You can find my code below. The only function that should be changed is update_canvas(), I believe. To try it out, just modify the list name_vars in the main with the number of variables you want. The rest of the example code is self-contained.
import numpy as np
from matplotlib.backends.qt_compat import QtWidgets
from matplotlib.backends.backend_qt5agg import FigureCanvas
from matplotlib.figure import Figure
class ApplicationWindow(QtWidgets.QMainWindow):
def __init__(self, parent=None):
super(ApplicationWindow, self).__init__(parent)
global name_vars
self.x = np.array([1,2,3,4,5])
self.y = np.random.random((5, len(name_vars)))
self.num_vars = np.size(self.y,1)
self.name_vars = name_vars
self.tags_on = [0] * self.num_vars
self.colors = ['#1F77B4','#FF7F0E','#2CA02C','#D62728','#9467BD',
'#8C564B','#E377C2','#F7F7F7','#BCBD22','#17BECF']
self._main = QtWidgets.QWidget()
self.setCentralWidget(self._main)
canvas = FigureCanvas(Figure(figsize=(10, 10)))
self.canvas_ax = canvas.figure.subplots()
self.canvas_ax.set_xlabel("Time")
self.canvas_ax_twin = []
self.list_tags = QtWidgets.QComboBox(self)
for name in self.name_vars:
self.list_tags.addItem(name)
button_add = QtWidgets.QPushButton('Add', self)
button_remove = QtWidgets.QPushButton('Remove', self)
button_add.clicked.connect(self.add_plot)
button_remove.clicked.connect(self.remove_plot)
layout = QtWidgets.QGridLayout(self._main)
layout.addWidget(canvas, 0, 0)
dropdown_layout = QtWidgets.QHBoxLayout()
dropdown_layout.addWidget(self.list_tags)
dropdown_layout.addWidget(button_add)
dropdown_layout.addWidget(button_remove)
layout.addLayout(dropdown_layout, 1, 0)
self.show()
def add_plot(self):
selected_tag = self.list_tags.currentIndex()
self.tags_on[selected_tag] = 1
self.update_canvas()
def remove_plot(self):
selected_tag = self.list_tags.currentIndex()
self.tags_on[selected_tag] = 0
self.update_canvas()
def update_canvas(self):
# Delete all traces
self.canvas_ax.clear()
[i.clear() for i in self.canvas_ax_twin]
self.canvas_ax_twin = []
num_plots = 0
for ii in range(self.num_vars):
if self.tags_on[ii] == 1:
# If it's not the first trace, create a twin axis
if num_plots != 0:
self.canvas_ax_twin.append(self.canvas_ax.twinx())
self.canvas_ax_twin[-1].plot(self.x, self.y[:,ii], self.colors[num_plots])
self.canvas_ax_twin[-1].set_ylabel(self.name_vars[ii])
self.canvas_ax_twin[-1].yaxis.label.set_color(self.colors[num_plots])
self.canvas_ax_twin[-1].tick_params(axis='y', colors=self.colors[num_plots])
num_plots += 1
# If it's the first trace, use the original axis
else:
self.canvas_ax.plot(self.x, self.y[:,ii], self.colors[num_plots])
self.canvas_ax.set_ylabel(self.name_vars[ii])
self.canvas_ax.yaxis.label.set_color(self.colors[num_plots])
self.canvas_ax.tick_params(axis='y', colors=self.colors[num_plots])
num_plots += 1
# Show the final plot
self.canvas_ax.figure.canvas.draw()
if __name__ == '__main__':
# Edit the number of elements in name_vars to try the code
name_vars = ['V1','V2','V3','V4']
app = QtWidgets.QApplication([])
ex = ApplicationWindow()
ex.show()
app.exec_()
I would suggest to separate the logic from the actual plotting. This makes it easier to follow through. This solves the second question about not removing all axes.
The question about not letting the axes superimpose may be solved by setting the position of additional twin axes to some distance from the axes, depending on how many axes you have.
ax.spines["right"].set_position(("axes", 1+(n-1)*0.1))
where n is the axes number starting from 0. The main axes (n=0) should be excluded, and the first axes will stay at position 1. Further axes are positionned in steps of 0.1.
Then it makes sense to also adjust the right margin of the main axes to give enough space for the extra spines.
import numpy as np
from matplotlib.backends.qt_compat import QtWidgets
from matplotlib.backends.backend_qt5agg import FigureCanvas
from matplotlib.figure import Figure
class ApplicationWindow(QtWidgets.QMainWindow):
def __init__(self, parent=None, name_vars=[]):
super(ApplicationWindow, self).__init__(parent)
self.x = np.array([1,2,3,4,5])
self.y = np.random.random((5, len(name_vars)))
self.num_vars = np.size(self.y,1)
self.name_vars = name_vars
self.tags_on = [0] * self.num_vars
self._main = QtWidgets.QWidget()
self.setCentralWidget(self._main)
self.figure = Figure(figsize=(10, 10))
canvas = FigureCanvas(self.figure)
self.left = self.figure.subplotpars.left
self.right = self.figure.subplotpars.right
self.canvas_ax = canvas.figure.subplots()
self.canvas_ax.set_xlabel("Time")
self.axes = [self.canvas_ax]
self.list_tags = QtWidgets.QComboBox(self)
for name in self.name_vars:
self.list_tags.addItem(name)
button_add = QtWidgets.QPushButton('Add', self)
button_remove = QtWidgets.QPushButton('Remove', self)
button_add.clicked.connect(self.add_plot)
button_remove.clicked.connect(self.remove_plot)
layout = QtWidgets.QGridLayout(self._main)
layout.addWidget(canvas, 0, 0)
dropdown_layout = QtWidgets.QHBoxLayout()
dropdown_layout.addWidget(self.list_tags)
dropdown_layout.addWidget(button_add)
dropdown_layout.addWidget(button_remove)
layout.addLayout(dropdown_layout, 1, 0)
self.show()
def add_plot(self):
selected_tag = self.list_tags.currentIndex()
self.tags_on[selected_tag] = 1
self.update_canvas()
def remove_plot(self):
selected_tag = self.list_tags.currentIndex()
self.tags_on[selected_tag] = 0
self.update_canvas()
def create_nth_axes(self, n, dataset):
if n == 0:
ax = self.canvas_ax
else:
ax = self.canvas_ax.twinx()
ax.spines["right"].set_position(("axes", 1+(n-1)*0.1))
for direction in ["left", "bottom", "top"]:
ax.spines[direction].set_visible(False)
# adjust subplotparams to make space for new axes spine
new_right = (self.right-self.left)/(1+(n-1)*0.1)+self.left
self.figure.subplots_adjust(right=new_right)
color = next(self.canvas_ax._get_lines.prop_cycler)['color']
ax.set_ylabel(self.name_vars[dataset], color=color)
ax.plot(self.x, self.y[:,dataset], color=color)
return ax
def clear_canvas(self):
# Clear main axes
self.canvas_ax.clear()
# clear and remove other axes
for ax in self.axes[1:]:
ax.clear()
ax.remove()
self.axes = [self.canvas_ax]
self.figure.subplots_adjust(right=0.9)
def update_canvas(self):
self.clear_canvas()
k = 0
for i, tag in enumerate(self.tags_on):
if tag:
ax = self.create_nth_axes(k, i)
if k > 0:
self.axes.append(ax)
k += 1
self.canvas_ax.figure.canvas.draw()
if __name__ == '__main__':
# Edit the number of elements in name_vars to try the code
name_vars = ['V1','V2','V3','V4']
app = QtWidgets.QApplication([])
ex = ApplicationWindow(name_vars=name_vars)
ex.show()
app.exec_()
Related
I am trying to remove an image from a figure and release the memory. when colorbar is not added for the image, memory can be released successfully, however, if colorbar is added, it fails. In the demo-code bellow:
click push button Add ColorBar will add a color bar for one image in the figure.
click push button remove will remove one image(and the related colorbar) from the figure.
each time i remove the image, the colorbar related is also removed, so i don't know why the memory recycle fails, I guess there must be some extra reference to the image when add a colorbar to it, which fails the memory recycle.
import numpy as np
from PyQt5 import QtWidgets
from memory_profiler import profile
import matplotlib
from matplotlib.figure import Figure
import matplotlib.cm as cm
from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg
from matplotlib.axes._axes import Axes
matplotlib.use("Qt5Agg")
class MplCanvas(FigureCanvasQTAgg):
def __init__(self, parent=None, width=5, height=4, dpi=100):
self.fig = Figure(figsize=(width, height), dpi=dpi)
self.axe = self.fig.add_subplot(1, 1, 1, label='good')
super().__init__(self.fig)
class MainWindow(QtWidgets.QMainWindow):
def __init__(self):
super().__init__()
layout = QtWidgets.QVBoxLayout()
self.canvas = MplCanvas(self, width=5, height=4, dpi=100)
self.axe = self.canvas.axe
layout.addWidget(self.canvas)
self.pushButton_addColorBar = QtWidgets.QPushButton('Add ColorBar')
layout.addWidget(self.pushButton_addColorBar)
self.pushButton_remove = QtWidgets.QPushButton('remove')
layout.addWidget(self.pushButton_remove)
widget = QtWidgets.QWidget()
widget.setLayout(layout)
self.setCentralWidget(widget)
self.pushButton_remove.clicked.connect(self.removeImage)
self.pushButton_addColorBar.clicked.connect(self.createColorBar)
self.pcolormesh_test()
def pcolormesh_test(self):
"""add two images"""
delta = 0.01
x = y = np.arange(-3.0, 3.0, delta)
X, Y = np.meshgrid(x, y)
Z1 = np.exp(-X ** 2 - Y ** 2)
Z2 = np.exp(-(X - 1) ** 2 - (Y - 1) ** 2)
Z = (Z1 - Z2) * 2
im = self.axe.pcolormesh(X, Y, Z, cmap=cm.viridis, shading='auto')
im.set_clim(vmax=np.amax(Z), vmin=np.amin(Z))
Zx = (Z1 + Z2) * 2
imx = self.axe.pcolormesh(X, Y, Zx, cmap=cm.Blues, shading='auto')
imx.set_clim(vmax=np.amax(Zx), vmin=np.amin(Zx))
def createColorBar(self):
""" to create a color bar for an image. """
axe = self.axe
fig = axe.get_figure()
images = self.getImages(axe)
for image in images:
if not image.colorbar: # color bar doesn't exist
inset_axe = axe.inset_axes([1.0, 0, 0.05, 1], transform=axe.transAxes)
fig.colorbar(image, ax=axe, cax=inset_axe)
break # each trigger create one colorbar for one image
self.reDraw()
#profile
def removeImage(self, checked):
"""
Usage:
* each trigger remove one image
"""
images = self.getImages(self.axe)
# print(f'images={images}')
if images:
image = images[-1]
color_bar = image.colorbar
if color_bar:
color_bar.remove()
del color_bar
# remove image
image.remove()
del image
self.reDraw()
def getImages(self, axe: Axes):
"""to obtain the image list in the axe"""
images = []
images.extend(axe.images)
images.extend(axe.collections)
return images
def reDraw(self):
self.canvas.draw_idle()
self.canvas.flush_events()
if __name__ == '__main__':
import sys
app = QtWidgets.QApplication(sys.argv)
window = MainWindow()
window.show()
sys.exit(app.exec_())
I have found the solution, and post an answer to help.
we need to add gc.collect() at the end of removeImage() method. then the memory can be reclaimed when the image is removed.
Below is python code to demonstrate the problem.
If there are 2 rows and 2 columns of images, for example, typing/erasing in the textbox is reasonably fast. However, if there are 5 rows and 5 columns, typing/erasing in the textbox is quite slow. If the xticks and yticks are drawn, interaction is even slower. So, it seems as if the entire figure is redrawn after every keystroke.
Is there a solution for this (apart from putting the textbox on a separate figure)?
(My development platform is MacOS Mojave, Python 3.7.5.)
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
from matplotlib.widgets import TextBox
class Textbox_Demo(object):
def __init__(self):
self.fig = plt.figure(figsize=(8,8))
self.string = 'label'
self.rows = 5 # reducing rows speeds up textbox interaction
self.cols = 5 # reducing cols speeds up textbox interaction
self.plot_count = self.rows * self.cols
self.gs = gridspec.GridSpec(self.rows, self.cols,
left=0.05, right=1-0.02, top=1-.02, bottom=0.10, wspace=0.3, hspace=0.4)
for k in range(self.plot_count):
ax = self.fig.add_subplot(self.gs[k])
#ax.set_xticks([]) # showing axes slows textbox interaction
#ax.set_yticks([]) # showing axes slows textbox interaction
data = np.atleast_2d(np.sin(np.linspace(1,255,255) * 50))
ax.imshow(data, aspect="auto", cmap='ocean')
# this is the user-input textbox
tb_axis = plt.axes([0.125, 0.02, 0.8, 0.05])
self.tb = TextBox(tb_axis, 'Enter label:', initial=self.string, label_pad=0.01)
self.tb.on_submit(self.on_submit)
plt.show()
def on_submit(self, text):
pass
if __name__ == "__main__":
Textbox_Demo()
Matplotlib's TextBox is inherently slow, because it uses the drawing tools provided by matplotlib itself and hence redraws the complete figure upon changes.
I would propose to use a text box of a GUI kit instead. For example for PyQt this might look like:
import numpy as np
import sys
from matplotlib.backends.backend_qt5agg import (
FigureCanvas, NavigationToolbar2QT as NavigationToolbar)
from matplotlib.backends.qt_compat import QtCore, QtWidgets
import matplotlib.gridspec as gridspec
from matplotlib.figure import Figure
class Textbox_Demo(QtWidgets.QMainWindow):
def __init__(self):
super().__init__()
self._main = QtWidgets.QWidget()
self.setCentralWidget(self._main)
layout = QtWidgets.QVBoxLayout(self._main)
layout.setContentsMargins(0,0,0,0)
layout.setSpacing(0)
self.fig = Figure(figsize=(8,8))
self.canvas = FigureCanvas(self.fig)
layout.addWidget(self.canvas)
self.addToolBar(NavigationToolbar(self.canvas, self))
self._textwidget = QtWidgets.QWidget()
textlayout = QtWidgets.QHBoxLayout(self._textwidget)
self.textbox = QtWidgets.QLineEdit(self)
self.textbox.editingFinished.connect(self.on_submit)
# or, if wanting to have changed apply directly:
# self.textbox.textEdited.connect(self.on_submit)
textlayout.addWidget(QtWidgets.QLabel("Enter Text: "))
textlayout.addWidget(self.textbox)
layout.addWidget(self._textwidget)
self.fill_figure()
def fill_figure(self):
self.string = 'label'
self.rows = 5 # reducing rows speeds up textbox interaction
self.cols = 5 # reducing cols speeds up textbox interaction
self.plot_count = self.rows * self.cols
self.gs = gridspec.GridSpec(self.rows, self.cols,
left=0.05, right=1-0.02, top=1-.02, bottom=0.10, wspace=0.3, hspace=0.4)
for k in range(self.plot_count):
ax = self.fig.add_subplot(self.gs[k])
#ax.set_xticks([]) # showing axes slows textbox interaction
#ax.set_yticks([]) # showing axes slows textbox interaction
data = np.atleast_2d(np.sin(np.linspace(1,255,255) * 50))
ax.imshow(data, aspect="auto", cmap='ocean')
def on_submit(self):
text = self.textbox.text()
print(text)
pass
if __name__ == "__main__":
qapp = QtWidgets.QApplication(sys.argv)
app = Textbox_Demo()
app.show()
qapp.exec_()
I'm using matplotlib with pyqt5 to draw data into 3 axes, and than user can make selection in one plot that will be shown in other two plots too. Since I'm working with big data (up to 10 millions of points), drawing selection could be slow, especially when I need to draw to scatterplot.
I am trying to use matplotlib blit function, but have some issues with result. Here is minimum simple example.
import matplotlib
matplotlib.use('Qt5Agg')
import numpy as np
import sys
from matplotlib.backends.qt_compat import QtCore, QtWidgets
from matplotlib.backends.backend_qt5agg import (FigureCanvas, NavigationToolbar2QT as NavigationToolbar)
from matplotlib.figure import Figure
class ApplicationWindow(QtWidgets.QMainWindow):
def __init__(self):
super().__init__()
self._main = QtWidgets.QWidget()
self.setCentralWidget(self._main)
layout = QtWidgets.QVBoxLayout(self._main)
self.static_canvas = FigureCanvas(Figure(figsize=(10, 10)))
layout.addWidget(self.static_canvas)
layout.addWidget(NavigationToolbar(self.static_canvas, self))
axes = self.static_canvas.figure.subplots(2, 1)
self.ax1 = axes[0]
self.ax2 = axes[1]
self.ax1.cla()
self.ax2.cla()
button = QtWidgets.QPushButton('Click me!')
button.clicked.connect(self.update_canvas_blit)
layout.addWidget(button)
# Fixing random state for reproducibility
np.random.seed(19680801)
# Create random data
N = 50000
x = np.random.rand(N)
y = np.random.rand(N)
self.ax1.scatter(x, y)
self.points = self.ax1.scatter([],[], s=5, color='red')
x = np.linspace(0, 1000, 100000)
self.ax2.plot(x, np.sin(x))
self.lines, = self.ax2.plot([],[], color='red')
self.static_canvas.draw()
self.background1 = self.static_canvas.copy_from_bbox(self.ax1.bbox)
self.background2 = self.static_canvas.copy_from_bbox(self.ax2.bbox)
def update_canvas_blit(self):
N = 50
x = np.random.rand(N)
y = np.random.rand(N)
self.static_canvas.restore_region(self.background1)
self.points.set_offsets(np.c_[x,y])
self.ax1.draw_artist(self.points)
self.ax1.figure.canvas.blit(self.ax1.bbox)
self.static_canvas.restore_region(self.background2)
x = np.linspace(0, np.random.randint(500,1000), 1000)
self.lines.set_data(x, np.sin(x))
self.ax2.draw_artist(self.lines)
self.ax2.figure.canvas.blit(self.ax2.bbox)
if __name__ == "__main__":
qapp = QtWidgets.QApplication(sys.argv)
app = ApplicationWindow()
app.show()
qapp.exec_()
When clicking button, expected output should be still same background with random points/lines redrawing. In a way it is happening but there are some strange artifacts that looks like somehow axes are drawn to each other. But when I try to save it to .png, it will restore to good state.
The problem is that the snapshot of the background is taken at a moment in time where the figure has not yet been shown on screen. At that point the figure is 10 by 10 inches large. Later, it is shown inside the QMainWindow and resized to fit into the widget.
Only once that has happened, it makes sense to take the background snapshot.
One option is to use a timer of 1 second and only then copy the background. This would look as follows.
import numpy as np
import sys
from matplotlib.backends.qt_compat import QtCore, QtWidgets
from matplotlib.backends.backend_qt5agg import (FigureCanvas, NavigationToolbar2QT as NavigationToolbar)
from matplotlib.figure import Figure
class ApplicationWindow(QtWidgets.QMainWindow):
def __init__(self):
super().__init__()
self._main = QtWidgets.QWidget()
self.setCentralWidget(self._main)
layout = QtWidgets.QVBoxLayout(self._main)
self.static_canvas = FigureCanvas(Figure(figsize=(10, 10)))
layout.addWidget(self.static_canvas)
layout.addWidget(NavigationToolbar(self.static_canvas, self))
axes = self.static_canvas.figure.subplots(2, 1)
self.ax1 = axes[0]
self.ax2 = axes[1]
self.ax1.cla()
self.ax2.cla()
button = QtWidgets.QPushButton('Click me!')
button.clicked.connect(self.update_canvas_blit)
layout.addWidget(button)
# Fixing random state for reproducibility
np.random.seed(19680801)
# Create random data
N = 50000
x = np.random.rand(N)
y = np.random.rand(N)
self.ax1.scatter(x, y)
self.points = self.ax1.scatter([],[], s=5, color='red')
x = np.linspace(0, 1000, 100000)
self.ax2.plot(x, np.sin(x))
self.lines, = self.ax2.plot([],[], color='red')
self.static_canvas.draw()
self._later()
def _later(self, evt=None):
self.timer = self.static_canvas.new_timer(interval=1000)
self.timer.single_shot = True
self.timer.add_callback(self.update_background)
self.timer.start()
def update_background(self, evt=None):
self.background1 = self.static_canvas.copy_from_bbox(self.ax1.bbox)
self.background2 = self.static_canvas.copy_from_bbox(self.ax2.bbox)
def update_canvas_blit(self):
N = 50
x = np.random.rand(N)
y = np.random.rand(N)
self.static_canvas.restore_region(self.background1)
self.points.set_offsets(np.c_[x,y])
self.ax1.draw_artist(self.points)
self.ax1.figure.canvas.blit(self.ax1.bbox)
self.static_canvas.restore_region(self.background2)
x = np.linspace(0, np.random.randint(500,1000), 1000)
self.lines.set_data(x, np.sin(x))
self.ax2.draw_artist(self.lines)
self.ax2.figure.canvas.blit(self.ax2.bbox)
if __name__ == "__main__":
qapp = QtWidgets.QApplication(sys.argv)
app = ApplicationWindow()
app.show()
qapp.exec_()
I want to display sensor data on a PyQT GUI with a matplotlib animation.
I already have a working Plot which gets updates every time I receive new sensor value from an external source with this code:
def __init__(self):
self.fig = Figure(figsize=(width, height), dpi=dpi)
self.axes = self.fig.add_subplot(111)
self.axes.grid()
self.xdata = []
self.ydata = []
self.entry_limit = 50
self.line, = self.axes.plot([0], [0], 'r')
def update_figure_with_new_value(self, xval: float, yval: float):
self.xdata.append(xval)
self.ydata.append(yval)
if len(self.xdata) > self.entry_limit:
self.xdata.pop(0)
self.ydata.pop(0)
self.line.set_data(self.xdata, self.ydata)
self.axes.relim()
self.axes.autoscale_view()
self.fig.canvas.draw()
self.fig.canvas.flush_events()
I want now to extend the plot to show another data series with the same x-axis. I tried to achieve this with the following additions to the init-code above:
self.axes2 = self.axes.twinx()
self.y2data = []
self.line2, = self.axes2.plot([0], [0], 'b')
and in the update_figure_with_new_value() function (for test purpose I just tried to add 1 to yval, I will extend the params of the function later):
self.y2data.append(yval+1)
if len(self.y2data) > self.entry_limit:
self.y2data.pop(0)
self.line2.set_data(self.xdata, self.ydata)
self.axes2.relim()
self.axes2.autoscale_view()
But instead of getting two lines in the plot which should have the exact same movement but just shifted by one I get vertical lines for the second plot axis (blue). The first axis (red) remains unchanged and is ok.
How can I use matplotlib to update multiple axis so that they display the right values?
I'm using python 3.4.0 with matplotlib 2.0.0.
Since there is no minimal example available, it's hard to tell the reason for this undesired behaviour. In principle ax.relim() and ax.autoscale_view() should do what you need.
So here is a complete example which works fine and updates both scales when being run with python 2.7, matplotlib 2.0 and PyQt4:
import numpy as np
import matplotlib.pyplot as plt
from PyQt4 import QtGui, QtCore
from matplotlib.figure import Figure
from matplotlib.backends.backend_qt4agg import FigureCanvasQTAgg as FigureCanvas
from matplotlib.backends.backend_qt4agg import NavigationToolbar2QT as NavigationToolbar
class Window(QtGui.QMainWindow):
def __init__(self):
QtGui.QMainWindow.__init__(self)
self.widget = QtGui.QWidget()
self.setCentralWidget(self.widget)
self.widget.setLayout(QtGui.QVBoxLayout())
self.widget.layout().setContentsMargins(0,0,0,0)
self.widget.layout().setSpacing(0)
self.fig = Figure(figsize=(5,4), dpi=100)
self.axes = self.fig.add_subplot(111)
self.axes.grid()
self.xdata = [0]
self.ydata = [0]
self.entry_limit = 50
self.line, = self.axes.plot([], [], 'r', lw=3)
self.axes2 = self.axes.twinx()
self.y2data = [0]
self.line2, = self.axes2.plot([], [], 'b')
self.canvas = FigureCanvas(self.fig)
self.canvas.draw()
self.nav = NavigationToolbar(self.canvas, self.widget)
self.widget.layout().addWidget(self.nav)
self.widget.layout().addWidget(self.canvas)
self.show()
self.ctimer = QtCore.QTimer()
self.ctimer.timeout.connect(self.update)
self.ctimer.start(150)
def update(self):
y = np.random.rand(1)
self.update_figure_with_new_value(self.xdata[-1]+1,y)
def update_figure_with_new_value(self, xval,yval):
self.xdata.append(xval)
self.ydata.append(yval)
if len(self.xdata) > self.entry_limit:
self.xdata.pop(0)
self.ydata.pop(0)
self.y2data.pop(0)
self.line.set_data(self.xdata, self.ydata)
self.axes.relim()
self.axes.autoscale_view()
self.y2data.append(yval+np.random.rand(1)*0.17)
self.line2.set_data(self.xdata, self.y2data)
self.axes2.relim()
self.axes2.autoscale_view()
self.fig.canvas.draw()
self.fig.canvas.flush_events()
if __name__ == "__main__":
qapp = QtGui.QApplication([])
a = Window()
exit(qapp.exec_())
You may want to test this and report back if it is working or not.
I would like to know how to perform the following pseudocode in python when embedding a matplotlib figure inside of a wxPython FigureCanvasWxAgg instance:
the following items need to be used:
---- IMPORTS THAT CAN BE USED ----
import wx
from matplotlib.figure import Figure
from matplotlib.backends.backend_wxagg import FigureCanvasWxAgg as FigureCanvas
-------------------------------------------------------
main_canvas;
shadow_canvas;
big_plot [a matplotlib figure instance with one big plot in it -- like the one you would make with figure.add_subplots(1,1,1)];
small_subplots [a matplotlib figure instance with, say, 2 subplots in it -- you would make with figure.add_subplots(2,1,i), where 1<=i<=2]
a function called SwapView(main_canvas,shadow_canvas,big_plot,small_subplots) that essentially swaps the figure that is currently in shadow_canvas with the one in main_canvas (so keep switching between the one with a big plot and the one with many small plots)
a function UpdateDisplay() that dynamically updates the display every time you call SwapView()
******* PSEUDOCODE *******
main_canvas.show()
shadow_canvas.hide()
main_canvas has big_plot initially
shadow_canvas has small_subplots initially
if big_plot in main_canvas:
SwapView(...) ---> should put big_plot in shadow_canvas and small_subplots in the main_canvas
else:
SwapView(...) ---> should put the small_subplots in shadow_canvas and the big_plot in main_canvas
UpdateDisplay()
******* END OF CODE *******
Here is my initial attempt at this code and unfortunately I can't find a way to find which figure is the one currently displayed.
#!/usr/bin/python
# -*- coding: utf-8 -*-
import numpy as np
import wx
import time
from matplotlib.figure import Figure
from matplotlib.backends.backend_wxagg import FigureCanvasWxAgg as FigureCanvas
class myframe(wx.Frame):
def __init__(self):
wx.Frame.__init__(self,parent = None, id = -1, title = 'LoadFigure()', size = (800,800))
self.figurePanel = FigurePanel(parent = self)
canvas1 = self.figurePanel.canvas
canvas2 = self.figurePanel.enlarged_canvas
fig1 = self.figurePanel.enlarged_figure
fig2 = self.figurePanel.figure
fig1.set_canvas(canvas1) #enlarged_fig resides in canvas1
fig2.set_canvas(canvas2) #fig resides in canvas2
#Show both canvases ---> canvas2 will override canvas1, but when canvas2 hides canvas1 should show
canvas2.Show()
canvas1.Show()
self.Show()
print "Starting to swap displays!"
time.sleep(1)
for i in range(10):
print "run: %d"%i
self.SwapView(big_plot = fig1,small_plots = fig2,main_canvas = canvas1,shadow_canvas = canvas2)
time.sleep(1)
def SwapView(self,big_plot,small_plots,main_canvas,shadow_canvas):
'''
Keep swapping the main_canvas with the shadow_canvas to show either fig1 or fig2.
Initially, big_plot has main_canvas and small_plots have shadow_canvas
'''
wx.Yield()
print list(main_canvas)
print list(big_plot.get_children())
time.sleep(2)
for child in big_plot.get_children():
if child == main_canvas:
print 'big_plot has main_canvas'
big_plot.set_canvas(shadow_canvas)
small_plots.set_canvas(main_canvas)
main_canvas.draw()
wx.Yield()
main_canvas.Show()
else:
print 'big_plot has shadow_canvas'
for child in small_plots.get_children():
if child == main_canvas:
print 'small_plots has main_canvas'
small_plots.set_canvas(shadow_canvas)
big_plot.set_canvas(main_canvas)
main_canvas.draw()
wx.Yield()
main_canvas.Show()
else:
print 'small_plots has shadow_canvas'
class FigurePanel(wx.Panel):
def __init__(self, parent):
wx.Panel.__init__(self, parent)
self.figPanel = self
self.sizer = wx.BoxSizer(wx.VERTICAL)
self.figure = Figure(figsize = (8,6.1), dpi =60)
self.ax = self.figure.add_subplot(1,1,1)
self.ax.plot([1,2,3],[1,2,3])
self.enlarged_figure = Figure(figsize = (8,6.1), dpi = 60)
self.ax1 = self.enlarged_figure.add_subplot(2,1,1)
self.ax2 = self.enlarged_figure.add_subplot(2,1,2)
self.ax1.plot([1,2,3],[1,4,9])
self.ax2.plot([1,2,3],[1,4,9])
self.canvas = FigureCanvas(self, -1, self.figure)
self.enlarged_canvas = FigureCanvas(self,-1,self.enlarged_figure)
self.Layout()
self.Fit()
if __name__ == "__main__":
app = wx.App(False)
fr = myframe()
app.MainLoop()
For anyone that might need it, here's the solution that I came up with:
#!/usr/bin/python
# -*- coding: utf-8 -*-
import numpy as np
import wx
import time
from matplotlib.figure import Figure
from matplotlib.backends.backend_wxagg import FigureCanvasWxAgg as FigureCanvas
class myframe(wx.Frame):
def __init__(self):
wx.Frame.__init__(self,parent = None, id = -1, title = 'SWAP!', size = (480,390))
self.figurePanel = FigurePanel(parent = self)
self.canvas1 = self.figurePanel.canvas
self.canvas2 = self.figurePanel.enlarged_canvas
self.fig1 = self.figurePanel.enlarged_figure
self.fig2 = self.figurePanel.figure
self.fig1.set_canvas(self.canvas1) #enlarged_fig resides in canvas1
self.canvas1.Show()
self.Show()
self.canvas2.mpl_connect("button_release_event",self.OnLoadFigure) #Enable the detection of mouseclicks for the plots in the plotting window
print "Click anywhere on the figure to swap the plots!"
self.display = 1
def OnLoadFigure(self,event = None):
print "Tried to load figure"
if event != None:
self.display = self.SwapView(big_plot = self.fig1 ,small_plots = self.fig2 , display = self.display, main_canvas = self.canvas1 , shadow_canvas = 0)
def SwapView(self,big_plot = None,display = -1, small_plots = None,main_canvas = None,shadow_canvas = None):
'''
Keep swapping the main_canvas with the shadow_canvas to show either fig1 or fig2.
Initially, big_plot has main_canvas and small_plots have shadow_canvas
'''
wx.Yield()
print display
if display == 1: #Show the big plot
print 'big_plot showing'
big_plot.set_canvas(main_canvas)
main_canvas.Show()
time.sleep(0.01) #Fastest time you can pick
wx.Yield()
else:
print 'small_plots showing'
main_canvas.Hide()
wx.Yield()
self.Refresh(canvas = main_canvas)
display = not(display)
return display
def Refresh(self,canvas = None,figure = None):
wx.Yield()
if canvas != None:
print "draw"
canvas.draw()
self.Update()
self.figurePanel.Update()
wx.Yield()
class FigurePanel(wx.Panel):
def __init__(self, parent):
wx.Panel.__init__(self, parent)
self.figPanel = self
self.sizer = wx.BoxSizer(wx.VERTICAL)
self.figure = Figure(figsize = (8,6.1), dpi =60)
self.ax = self.figure.add_subplot(1,1,1)
self.ax.plot([1,2,3],[1,2,3])
self.enlarged_figure = Figure(figsize = (8,6.1), dpi = 60)
self.ax1 = self.enlarged_figure.add_subplot(2,1,1)
self.ax2 = self.enlarged_figure.add_subplot(2,1,2)
self.ax1.plot([1,2,3],[1,4,9])
self.ax2.plot([1,2,3],[1,4,9])
self.canvas = FigureCanvas(self, -1, self.figure)
self.enlarged_canvas = FigureCanvas(self,-1,self.enlarged_figure)
self.Layout()
self.Fit()
if __name__ == "__main__":
app = wx.App(False)
fr = myframe()
app.MainLoop()
To make the display change, click on the figure.