我在Python（x，y）包中使用QT附带的MPL小部件。我试图用新数据刷新或重绘我的情节。方法fig.canvas.draw（）仅刷新主图。子图是我的问题所在。之前的子图和与它们相关的所有内容（轴刻度，注释等）都保留在图表上。当我刷新时，新的子图数据被绘制在旧数据上，造成了相当大的混乱。与主图相关联的所有内容都会正确重绘。我尝试了所有我想要尝试的东西，包括clf和cla。如何使用Python（x，y）中包含的QT MPL Widget刷新子图？
这是我的代码：
def mpl_plot(self, plot_page, replot = 0): #Data stored in lists
if plot_page == 1: #Plot 1st Page
plt = self.mplwidget.axes
fig = self.mplwidget.figure #Add a figure
if plot_page == 2: #Plot 2nd Page
plt = self.mplwidget_2.axes
fig = self.mplwidget_2.figure #Add a figure
if plot_page == 3: #Plot 3rd Page
plt = self.mplwidget_3.axes
fig = self.mplwidget_3.figure #Add a figure
if replot == 1:
#self.mplwidget_2.figure.clear()
print replot
par1 = fig.add_subplot(111)
par2 = fig.add_subplot(111)
#Add Axes
ax1 = par1.twinx()
ax2 = par2.twinx()
impeller = str(self.comboBox_impellers.currentText()) #Get Impeller
fac_curves = self.mpl_factory_specs(impeller)
fac_lift = fac_curves[0]
fac_power = fac_curves[1]
fac_flow = fac_curves[2]
fac_eff = fac_curves[3]
fac_max_eff = fac_curves[4]
fac_max_eff_bpd = fac_curves[5]
fac_ranges = self.mpl_factory_ranges()
min_range = fac_ranges[0]
max_range = fac_ranges[1]
#bep = fac_ranges[2]
#Plot Chart
plt.hold(False) #Has to be included for multiple curves
#Plot Factory Pressure
plt.plot(fac_flow, fac_lift, 'b', linestyle = "dashed", linewidth = 1)
#Plot Factory Power
ax1.plot(fac_flow, fac_power, 'r', linestyle = "dashed", linewidth = 1)
ax2.plot(fac_flow, fac_eff, 'g', linestyle = "dashed", linewidth = 1)
#Move spines
ax2.spines["right"].set_position(("outward", 25))
self.make_patch_spines_invisible(ax2)
ax2.spines["right"].set_visible(True)
#Plot x axis minor tick marks
minorLocatorx = AutoMinorLocator()
ax1.xaxis.set_minor_locator(minorLocatorx)
ax1.tick_params(which='both', width= 0.5)
ax1.tick_params(which='major', length=7)
ax1.tick_params(which='minor', length=4, color='k')
#Plot y axis minor tick marks
minorLocatory = AutoMinorLocator()
plt.yaxis.set_minor_locator(minorLocatory)
plt.tick_params(which='both', width= 0.5)
plt.tick_params(which='major', length=7)
plt.tick_params(which='minor', length=4, color='k')
#Make Border of Chart White
#Plot Grid
plt.grid(b=True, which='both', color='k', linestyle='-')
#set shaded Area
plt.axvspan(min_range, max_range, facecolor='#9BE2FA', alpha=0.5) #Yellow rectangular shaded area
#Set Vertical Lines
plt.axvline(fac_max_eff_bpd, color = '#69767A')
#BEP MARKER *** Can change marker style if needed
bep = fac_max_eff * 0.90 #bep is 90% of maximum efficiency point
bep_corrected = bep * 0.90 # We knock off another 10% to place the arrow correctly on chart
ax2.annotate('BEP', xy=(fac_max_eff_bpd, bep_corrected), xycoords='data', #Subtract 2.5 shows up correctly on chart
xytext=(-50, 30), textcoords='offset points',
bbox=dict(boxstyle="round", fc="0.8"),
arrowprops=dict(arrowstyle="-|>",
shrinkA=0, shrinkB=10,
connectionstyle="angle,angleA=0,angleB=90,rad=10"),
)
#Set Scales
plt.set_ylim(0,max(fac_lift) + (max(fac_lift) * 0.40)) #Pressure
#plt.set_xlim(0,max(fac_flow))
ax1.set_ylim(0,max(fac_power) + (max(fac_power) * 0.40)) #Power
ax2.set_ylim(0,max(fac_eff) + (max(fac_eff) * 0.40)) #Effiency
# Set Axes Colors
plt.tick_params(axis='y', colors='b')
ax1.tick_params(axis='y', colors='r')
ax2.tick_params(axis='y', colors='g')
# Set Chart Labels
plt.set_xlabel("BPD")
plt.set_ylabel("Feet" , color = 'b')
#To redraw plot
fig.canvas.draw()
def mpl_plot(self, plot_page, replot = 0): #Data stored in lists
if plot_page == 1: #Plot 1st Page
plt = self.mplwidget.axes
fig = self.mplwidget.figure #Add a figure
if plot_page == 2: #Plot 2nd Page
plt = self.mplwidget_2.axes
fig = self.mplwidget_2.figure #Add a figure
if plot_page == 3: #Plot 3rd Page
plt = self.mplwidget_3.axes
fig = self.mplwidget_3.figure #Add a figure
if replot == 1:
#self.mplwidget_2.figure.clear()
print replot
par1 = fig.add_subplot(111)
par2 = fig.add_subplot(111)
#Add Axes
ax1 = par1.twinx()
ax2 = par2.twinx()
impeller = str(self.comboBox_impellers.currentText()) #Get Impeller
fac_curves = self.mpl_factory_specs(impeller)
fac_lift = fac_curves[0]
fac_power = fac_curves[1]
fac_flow = fac_curves[2]
fac_eff = fac_curves[3]
fac_max_eff = fac_curves[4]
fac_max_eff_bpd = fac_curves[5]
fac_ranges = self.mpl_factory_ranges()
min_range = fac_ranges[0]
max_range = fac_ranges[1]
#bep = fac_ranges[2]
#Plot Chart
plt.hold(False) #Has to be included for multiple curves
#Plot Factory Pressure
plt.plot(fac_flow, fac_lift, 'b', linestyle = "dashed", linewidth = 1)
#Plot Factory Power
ax1.plot(fac_flow, fac_power, 'r', linestyle = "dashed", linewidth = 1)
ax2.plot(fac_flow, fac_eff, 'g', linestyle = "dashed", linewidth = 1)
#Move spines
ax2.spines["right"].set_position(("outward", 25))
self.make_patch_spines_invisible(ax2)
ax2.spines["right"].set_visible(True)
#Plot x axis minor tick marks
minorLocatorx = AutoMinorLocator()
ax1.xaxis.set_minor_locator(minorLocatorx)
ax1.tick_params(which='both', width= 0.5)
ax1.tick_params(which='major', length=7)
ax1.tick_params(which='minor', length=4, color='k')
#Plot y axis minor tick marks
minorLocatory = AutoMinorLocator()
plt.yaxis.set_minor_locator(minorLocatory)
plt.tick_params(which='both', width= 0.5)
plt.tick_params(which='major', length=7)
plt.tick_params(which='minor', length=4, color='k')
#Make Border of Chart White
#Plot Grid
plt.grid(b=True, which='both', color='k', linestyle='-')
#set shaded Area
plt.axvspan(min_range, max_range, facecolor='#9BE2FA', alpha=0.5) #Yellow rectangular shaded area
#Set Vertical Lines
plt.axvline(fac_max_eff_bpd, color = '#69767A')
#BEP MARKER *** Can change marker style if needed
bep = fac_max_eff * 0.90 #bep is 90% of maximum efficiency point
bep_corrected = bep * 0.90 # We knock off another 10% to place the arrow correctly on chart
ax2.annotate('BEP', xy=(fac_max_eff_bpd, bep_corrected), xycoords='data', #Subtract 2.5 shows up correctly on chart
xytext=(-50, 30), textcoords='offset points',
bbox=dict(boxstyle="round", fc="0.8"),
arrowprops=dict(arrowstyle="-|>",
shrinkA=0, shrinkB=10,
connectionstyle="angle,angleA=0,angleB=90,rad=10"),
)
#Set Scales
plt.set_ylim(0,max(fac_lift) + (max(fac_lift) * 0.40)) #Pressure
#plt.set_xlim(0,max(fac_flow))
ax1.set_ylim(0,max(fac_power) + (max(fac_power) * 0.40)) #Power
ax2.set_ylim(0,max(fac_eff) + (max(fac_eff) * 0.40)) #Effiency
# Set Axes Colors
plt.tick_params(axis='y', colors='b')
ax1.tick_params(axis='y', colors='r')
ax2.tick_params(axis='y', colors='g')
# Set Chart Labels
plt.set_xlabel("BPD")
plt.set_ylabel("Feet" , color = 'b')
#To redraw plot
fig.canvas.draw()
答案 0 :(得分：1)
从示例中不清楚如何创建self.mpl_widget.axes。它没有被使用，如下所述，绘制到轴实例的旧引用可能会导致问题。
因为根本没有使用mpl_widget.axes，我建议不要在轴上保留参考。然后在示例中使用twinx是不正确的。以下可能有效：
if plot_page == 1: #Plot 1st Page
widget = self.mplwidget
if plot_page == 2: #Plot 2nd Page
widget = self.mplwidget_3
if plot_page == 3: #Plot 3rd Page
widget = self.mplwidget_3
if replot == 1:
widget.figure.clear()
ax1 = widget.figure.add_subplot(111)
ax2 = ax1.twinx()
...
widget.figure.canvas.draw()
另一个问题是self.mpl_widgetXX.axes已分配给plt，下面示例plt中的self.mpl_widgetXX.axes用于绘制新数据。由于ax1未更新为包含新创建的轴实例之一，因此示例代码将绘制到旧轴，从而可能导致问题中描述的效果。
您只应使用ax2和widget.figure进行绘图和勾选设置，并使用public static void main(String[] args) {
int[] array = {1, 2, 2, 3, 3, 4, 5, 5, 3, 4, 3, 4, 6, 6, 1, 3, 3, 3};
Arrays.sort(array);
List<Integer> list = new ArrayList<>();
for (int x : array) {
list.add(x);
}
for (int i = 0; i < list.size(); i++) {
System.out.print(list.get(i) + " ");
}
System.out.println();
System.out.println("\nDuplicate element(s)");
Integer last = null;
Iterator<Integer> it = list.iterator();
while (it.hasNext()){
Integer n = it.next();
if (n.equals(last)){
System.out.print(n + " ");
it.remove();
}
last = n;
}
System.out.println();
System.out.println("\nDuplicate element(s) removed");
for (int i = 0; i < list.size(); i++) {
System.out.print(list.get(i) + " ");
}
System.out.println();
}
来访问数字实例。