Python(x,y) Matplotlib Widget. Plots fine but how do I get it to refresh?

Question

I'm using the Python(xy) package and QT designer. Python(xy) has a built-in MPL widget for QT which is what I'm using. Works fine for me until I replot: Is there any way to make the current setup (pyplot) redraw?

Here's my code:

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            
        fig = self.mplwidget.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    


    par1 = fig.add_subplot(1,1,1)
    par2 = fig.add_subplot(1,1,1)      

    #Add Axes
    ax1 = par1.twinx()        
    ax2 = par2.twinx()  



    ax2.spines["right"].set_position(("outward", 25))
    self.make_patch_spines_invisible(ax2)
    ax2.spines["right"].set_visible(True)  
    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]

    #Plot Chart
    plt.hold(True)    #Has to be included for  multiple curves

    plt.plot(fac_flow, fac_lift, 'b', linestyle = "dashed", linewidth = 1)

    #plt.plot(flow,f_lift,'b.')  #Plot datapoints only

    #Plot Factory Power
    ax1.plot(fac_flow, fac_power, 'r', linestyle = "dashed", linewidth = 1)
    #ax1.plot(flow,f_power,'r.')    #Plot datapoints only

    ax2.plot(fac_flow, fac_eff, 'g', linestyle = "dashed", linewidth = 1)

    #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 = fac_max_eff * 0.90    

    bep_corrected = bep * 0.90  

    ax2.annotate('BEP', xy=(fac_max_eff_bpd, bep_corrected), xycoords='data',  
            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')
    #ax1.set_ylabel("BHP", color = 'r')
    #ax1.set_ylabel("Effiency", color = 'g')

score 0 · Answer 1 · answered Aug 18 '16 at 17:42

0

I recommended

figure.canvas.draw()
figure.canvas.update()

This is from the documentation of matplotlib, and could you please delete any unnecessary code? That will help others to quickly find your problem.

answered Aug 18 '16 at 17:42

disccip

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Thank you. I added fig.canvas.draw(). It worked perfectly on the main plot but did not affect the subplots. Is there a fix for the subplots also? – Mike C. Aug 18 '16 at 19:59
Actually you overwrite your figure, you double assign fig = self.mplwidget.figure, use different name or try this: axes.figure.canvas.draw(), replace 'axes' to your subplot ax1 or something. – disccip Aug 19 '16 at 20:35
I tried calling: ax1.figure.canvas.draw() and ax1.figure.canvas.draw(). That had no effect on refreshing the plots. As for the double assignment of 'fig', are you referring to the 'if' statements at the beginning? If so I am only calling one of them while I am trying to work out the refresh problem. If you have any other ideas please let me know. I am at a loss here. – Mike C. Aug 19 '16 at 21:48
par1 = fig.add_subplot(1,1,1) par2 = fig.add_subplot(1,1,1), could you please explain this? This will only overwrite the subplot. And don't forget the canvas.update() – disccip Aug 19 '16 at 22:37
I am new to matplotlib. I got the code from an example somewhere. I needed two extra y axes. When I plotted the first time, it worked fine. Do you think that could be the problem? Do I put canvas.update() at the end, by draw()? – Mike C. Aug 19 '16 at 23:06
I looked at the code again. I should have done something like: – Mike C. Aug 19 '16 at 23:19

Python(x,y) Matplotlib Widget. Plots fine but how do I get it to refresh?

1 Answers1