def plot_frontier(self,frontier_only=False,plot_samples=True) :
		""" Plot the frontier"""
		frontier = self.frontier
		frontier_energy = self.frontier_energy
		feat1,feat2 = self.feats	
	
		pl.figure()
		if not frontier_only :	
			ll_list1,ll_list2 = zip(*self.all_seq_energy)
			pl.plot(ll_list1,ll_list2,'b*')
		if plot_samples :
			ll_list1,ll_list2 = zip(*self.sample_seq_energy)
			pl.plot(ll_list1,ll_list2,'g*')
					
		pl.plot(*zip(*sorted(frontier_energy)),color='magenta',\
			marker='*',	linestyle='dashed')
		ctr = dict(zip(set(frontier_energy),[0]*
			len(set(frontier_energy))))
		for i,e in enumerate(frontier_energy) : 
			ctr[e] += 1
			pl.text(e[0],e[1]+0.1*ctr[e],str(i),fontsize=10)
			pl.text(e[0]+0.4,e[1]+0.1*ctr[e],frontier[i],fontsize=9)	
		pl.xlabel('Energy:'+feat1)
		pl.ylabel('Energy:'+feat2)
		pl.title('Energy Plot')
		xmin,xmax = pl.xlim()
		ymin,ymax = pl.ylim()
		pl.xlim(xmin,xmax)
		pl.ylim(ymin,ymax)
		pic_dir = '../docs/tex/pics/'
		pl.savefig(pic_dir+self.name+'.pdf')
		pl.savefig(pic_dir+self.name+'.png')

def plot_sphere_x( s, fname ):
  """ put plot of ionization fractions from sphere `s` into fname """

  plt.figure()
  s.Edges.units = 'kpc'
  s.r_c.units = 'kpc'
  xx = s.r_c
  L = s.Edges[-1]

  plt.plot( xx, np.log10( s.xHe1 ),
            color='green', ls='-', label = r'$x_{\rm HeI}$' )
  plt.plot( xx, np.log10( s.xHe2 ),
            color='green', ls='--', label = r'$x_{\rm HeII}$' )
  plt.plot( xx, np.log10( s.xHe3 ),
            color='green', ls=':', label = r'$x_{\rm HeIII}$' )

  plt.plot( xx, np.log10( s.xH1 ),
            color='red', ls='-', label = r'$x_{\rm HI}$' )
  plt.plot( xx, np.log10( s.xH2 ),
            color='red', ls='--', label = r'$x_{\rm HII}$' )

  plt.xlim( -L/20, L+L/20 )
  plt.xlabel( 'r_c [kpc]' )

  plt.ylim( -4.5, 0.2 )
  plt.ylabel( 'log 10 ( x )' )

  plt.grid()
  plt.legend(loc='best', ncol=2)
  plt.tight_layout()
  plt.savefig( 'doc/img/x_' + fname )

def geweke_plot(data, name, format='png', suffix='-diagnostic', path='./', fontmap = None, 
    verbose=1):
    # Generate Geweke (1992) diagnostic plots

    if fontmap is None: fontmap = {1:10, 2:8, 3:6, 4:5, 5:4}

    # Generate new scatter plot
    figure()
    x, y = transpose(data)
    scatter(x.tolist(), y.tolist())

    # Plot options
    xlabel('First iteration', fontsize='x-small')
    ylabel('Z-score for %s' % name, fontsize='x-small')

    # Plot lines at +/- 2 sd from zero
    pyplot((nmin(x), nmax(x)), (2, 2), '--')
    pyplot((nmin(x), nmax(x)), (-2, -2), '--')

    # Set plot bound
    ylim(min(-2.5, nmin(y)), max(2.5, nmax(y)))
    xlim(0, nmax(x))

    # Save to file
    if not os.path.exists(path):
        os.mkdir(path)
    if not path.endswith('/'):
        path += '/'
    savefig("%s%s%s.%s" % (path, name, suffix, format))

    def rmsdSpreadSubplot(multiplier=1.0, layout=(-1, -1)):
        rmsd_data   = dict( (e, rad_data[e]['innov'][quant])  for e in rad_data.iterkeys() )
        spread_data = dict( (e, rad_data[e]['spread'][quant]) for e in rad_data.iterkeys() )

        times = temp.getTimes()
        n_t = len(times)

        for exp, exp_name in exp_names.iteritems():
            pylab.plot(sawtooth(times, times)[:(n_t + 1)], rmsd_data[exp][:(n_t + 1)], color=colors[exp], linestyle='-')
            pylab.plot(times[(n_t / 2):], rmsd_data[exp][n_t::2], color=colors[exp], linestyle='-')
 
        for exp, exp_name in exp_names.iteritems():
            pylab.plot(sawtooth(times, times)[:(n_t + 1)], spread_data[exp][:(n_t + 1)], color=colors[exp], linestyle='--')
            pylab.plot(times[(n_t / 2):], spread_data[exp][n_t::2], color=colors[exp], linestyle='--')

        ylim = pylab.ylim()
        pylab.plot(times, -1 * np.ones((len(times),)), color='#999999', linestyle='-', label="RMS Innovation")
        pylab.plot(times, -1 * np.ones((len(times),)), color='#999999', linestyle='--', label="Spread")

        pylab.axhline(y=7, color='k', linestyle=':')
        pylab.axvline(x=14400, color='k', linestyle=':')

        pylab.ylabel("RMS Innovation/Spread (dBZ)", size='large')

        pylab.xlim(times[0], times[-1])
        pylab.ylim(ylim)

        pylab.legend(loc=4)

        pylab.xticks(times[::2], [ "" for t in times[::2] ])
        pylab.yticks(size='x-large')
        return

def plotB3reg():
    w=loadStanFit('revE2B3BHreg.fit')
    printCI(w,'mmu')
    printCI(w,'mr')
    for b in range(2):
        subplot(1,2,b+1)
        plt.title('')
        px=np.array(np.linspace(-0.5,0.5,101),ndmin=2)
        a0=np.array(w['mmu'][:,b],ndmin=2).T
        a1=np.array(w['mr'][:,b],ndmin=2).T
        y=np.concatenate([sap(a0+a1*px,97.5,axis=0),sap(a0+a1*px[:,::-1],2.5,axis=0)])
        x=np.squeeze(np.concatenate([px,px[:,::-1]],axis=1))
        plt.plot(px[0,:],np.median(a0)+np.median(a1)*px[0,:],'red')
        #plt.plot([-1,1],[0.5,0.5],'grey')
        ax=plt.gca()
        ax.set_aspect(1)
        ax.add_patch(plt.Polygon(np.array([x,y]).T,alpha=0.2,fill=True,fc='red',ec='w'))
        y=np.concatenate([sap(a0+a1*px,75,axis=0),sap(a0+a1*px[:,::-1],25,axis=0)])
        ax.add_patch(plt.Polygon(np.array([x,y]).T,alpha=0.2,fill=True,fc='red',ec='w'))
        man=np.array([-0.4,-0.2,0,0.2,0.4])
        mus=[]
        for m in range(len(man)):
            mus.append(loadStanFit('revE2B3BH%d.fit'%m)['mmu'][:,b])
        mus=np.array(mus).T
        errorbar(mus,x=man)
        ax.set_xticks(man)
        plt.xlim([-0.5,0.5])
        plt.ylim([-0.4,0.8])
        #plt.xlabel('Manipulated Displacement')
        if b==0:
            plt.ylabel('Perceived Displacemet')
            plt.gca().set_yticklabels([])
        subplot_annotate()
    plt.text(-1.1,-0.6,'Pivot Displacement',fontsize=8);

def plotFeatureImportance(featureImportance, title, originalImage=None, lim=0.06, colorate=None):
    """
    originalImage : the index of the original image. If None, ignore
    """
    indices = featureImportanceIndices(len(featureImportance), originalImage)
    pl.figure()
    pl.title(title)
    if colorate is not None:
        nbType = len(colorate)
        X = [[] for i in range(nbType)]
        Y = [[] for i in range(nbType)]
        for j, f in enumerate(featureImportance):
            X[j % nbType].append(j)
            Y[j % nbType].append(f)
        for i in range(nbType):
            pl.bar(X[i], Y[i], align="center", label=colorate[i][0], color=colorate[i][1])
        pl.legend()
    else:
        pl.bar(range(len(featureImportance)), featureImportance, align="center")
    #pl.xticks(pl.arange(len(indices)), indices, rotation=-90)
    pl.xlim([-1, len(indices)])
    pl.ylabel("Feature importance")
    pl.xlabel("Filter indices")
    pl.ylim(0, lim)
    pl.show()

    def InitializePlot(self, goal_config):
        self.fig = pl.figure()
        lower_limits, upper_limits = self.boundary_limits
        pl.xlim([lower_limits[0], upper_limits[0]])
        pl.ylim([lower_limits[1], upper_limits[1]])
        pl.plot(goal_config[0], goal_config[1], 'gx')

        # Show all obstacles in environment
        for b in self.robot.GetEnv().GetBodies():
            if b.GetName() == self.robot.GetName():
                continue
            bb = b.ComputeAABB()
            pl.plot([bb.pos()[0] - bb.extents()[0],
                     bb.pos()[0] + bb.extents()[0],
                     bb.pos()[0] + bb.extents()[0],
                     bb.pos()[0] - bb.extents()[0],
                     bb.pos()[0] - bb.extents()[0]],
                    [bb.pos()[1] - bb.extents()[1],
                     bb.pos()[1] - bb.extents()[1],
                     bb.pos()[1] + bb.extents()[1],
                     bb.pos()[1] + bb.extents()[1],
                     bb.pos()[1] - bb.extents()[1]], 'r')
                    
                     
        pl.ion()
        pl.show()

    def updatePlot(self):
        """ Updates the antenna config plot"""
                
        self.sp_ax.clear()
        self.sp_fig.clear()
        
        row = self.slider.value()
        self.spinner.setValue(row)
        
        data_x    = self.fits_data[row,0,0,0,:]
        data_y    = self.fits_data[row,0,0,1,:]
        flagged_x = self.fits_flagged[row,0,0,0,:]
        flagged_y = self.fits_flagged[row,0,0,1,:]        
        freqs     = self.fits_freqs
        tsys      = self.fits_tsys[row,:]
        
        #plt.plot(freqs, data_x)
        plt.plot(freqs[flagged_x == 0], data_x[flagged_x == 0], color='#333333', label='Pol A [%2.1f Jy]'%tsys[0])  
        plt.plot(freqs[flagged_y == 0], data_y[flagged_y == 0], color='#CC0000', label='Pol B [%2.1f Jy]'%tsys[1])         
        
        
        plt.ylabel('%s [%s]'%(self.fits_data_name, self.fits_data_unit))
        plt.xlabel('%s [%s]'%(self.fits_freq_type, self.fits_freq_unit))
        plt.title('Beam %s: %s %s'%(self.fits_beam[row], self.fits_date[row], self.fits_time[row]))
        plt.xlim(np.min(freqs[flagged_x == 0]), np.max(freqs[flagged_x == 0]))
        plt.legend()
        plt.show()

        
        self.sp_fig.canvas.draw()
              
        self.lab_info.setText(self.fits_filename)       

    def draw(self):

        print self.edgeno

        pos = 0
        dy = 8
        edgeno = self.edgeno
        edge = self.edges[edgeno]
        edgeprev = self.edges[edgeno-1]
        p = np.round(edge["top"](1024))
        top = min(p+2*dy, 2048)
        bot = min(p-2*dy, 2048)
        self.cutout = self.flat[1][bot:top,:].copy()

        pl.figure(1)
        pl.clf()
        start = 0
        dy = 512
        for i in xrange(2048/dy):
            pl.subplot(2048/dy,1,i+1)
            pl.xlim(start, start+dy)

            if i == 0: pl.title("edge %i] %s|%s" % (edgeno,
                edgeprev["Target_Name"], edge["Target_Name"]))


            pl.subplots_adjust(left=.07,right=.99,bottom=.05,top=.95)

            pl.imshow(self.flat[1][bot:top,start:start+dy], extent=(start,
                start+dy, bot, top), cmap='Greys', vmin=2000, vmax=6000)

            pix = np.arange(start, start+dy)
            pl.plot(pix, edge["top"](pix), 'r', linewidth=1)
            pl.plot(pix, edgeprev["bottom"](pix), 'r', linewidth=1)
            pl.plot(edge["xposs_top"], edge["yposs_top"], 'o')
            pl.plot(edgeprev["xposs_bot"], edgeprev["yposs_bot"], 'o')


            hpp = edge["hpps"]
            pl.axvline(hpp[0],ymax=.5, color='blue', linewidth=5)
            pl.axvline(hpp[1],ymax=.5, color='red', linewidth=5)

            hpp = edgeprev["hpps"]
            pl.axvline(hpp[0],ymin=.5,color='blue', linewidth=5)
            pl.axvline(hpp[1],ymin=.5,color='red', linewidth=5)


            if False:
                L = top-bot
                Lx = len(edge["xposs"])
                for i in xrange(Lx):
                    xp = edge["xposs"][i]
                    frac1 = (edge["top"](xp)-bot-1)/L
                    pl.axvline(xp,ymin=frac1)

                for xp in edgeprev["xposs"]: 
                    frac2 = (edgeprev["bottom"](xp)-bot)/L
                    pl.axvline(xp,ymax=frac2)

            start += dy

def plot_file_color(base, thin=True, start=0, size=14, save=False):
    conf, track, pegs = load(base)

    fig = pl.figure(figsize=(size,size*conf['top']/conf['wall']))

    track = track[start:]
    x = track[:,0];   y = track[:,1]
    t = np.linspace(0,1,x.shape[0])
    points = np.array([x,y]).transpose().reshape(-1,1,2)
    segs = np.concatenate([points[:-1],points[1:]],axis=1)
    lc = LineCollection(segs, linewidths=0.25, cmap=pl.cm.coolwarm)
    lc.set_array(t)
    pl.gca().add_collection(lc)

    #pl.scatter(x, y, c=np.arange(len(x)),linestyle='-',cmap=pl.cm.coolwarm)
    #pl.plot(track[-1000000:,0], track[-1000000:,1], '-', linewidth=0.0125, alpha=0.8)
    for peg in pegs:
        pl.gca().add_artist(pl.Circle(peg, conf['radius'], color='k', alpha=0.3))
    pl.xlim(0, conf['wall'])
    pl.ylim(0, conf['top'])
    pl.xticks([])
    pl.yticks([])
    pl.tight_layout()
    pl.show()
    if save:
        pl.savefig(base+".png", dpi=200)

def tracks_movie(base, skip=1, frames=500, size=10):
    """
    A movie of each particle as a point
    """
    conf, track, pegs = load(base)

    fig = pl.figure(figsize=(size,size*conf['top']/conf['wall']))
    plot = None

    for t in xrange(1,max(frames, track.shape[1]/skip)):
        tmp = track[:,t*skip,:]
        if not ((tmp[:,0] > 0) & (tmp[:,1] > 0) & (tmp[:,0] < conf['wall']) & (tmp[:,1] < conf['top'])).any():
            continue

        if plot is None:
            plot = pl.plot(tmp[:,0], tmp[:,1], 'k,', alpha=1.0, ms=0.1)[0]
            pl.xticks([])
            pl.yticks([])
            pl.xlim(0,conf['wall'])
            pl.ylim(0,conf['top'])
            pl.tight_layout()
        else:
            plot.set_xdata(tmp[:,0])
            plot.set_ydata(tmp[:,1])
        pl.draw()
        pl.savefig(base+'-movie-%05d.png' % (t-1))

def plotFreqVsGoodTuring(counts, confidence=1.96, loglog=False):
    """
    Draws a scatterplot of the empirical frequencies of the counted species
    versus their Simple Good Turing smoothed values, in rank order. Depends on
    pylab and matplotlib.
    """
    import pylab
    from matplotlib import rc

    tot = float(sum(counts.values()))
    freqs = dict([(species, cnt/tot) for species, cnt in counts.iteritems()])
    sgt, p0 = simpleGoodTuringProbs(counts, confidence)

    if loglog:
        plotFunc = pylab.loglog
    else:
        plotFunc = pylab.plot
    plotFunc(sorted(freqs.values(), reverse=True), 'kD', mfc='white',
            label="Observed")
    plotFunc(sorted(sgt.values(), reverse=True), 'k+', 
            label="Simple Good-Turing Estimate")
    pylab.xlim(-0.5, len(freqs)+0.5)
    pylab.xlabel("Rank")
    pylab.ylabel("Frequency")
    pylab.legend(numpoints=1)

    def plotVOI(self,n,points,L,data,kern,temp1,temp2,a,m,path):
        z=np.zeros(m)
        
        for i in xrange(m):
            z[i]=self.VOIfunc(n,points[i,:],L,data,kern,temp1,temp2,False,a,False)
            
        fig=plt.figure()
        fig.set_size_inches(21, 21)
        plt.plot(points,z,'-')
        plt.xlabel('x',fontsize=60)
        Xp=data.Xhist[0:self._numberTraining,0]
        pylab.plot(Xp,np.zeros(len(Xp))+0.00009,'o',color='red',markersize=40,label="Training point")
        if n>0:
            Xp=data.Xhist[self._numberTraining:self._numberTraining+n,0]
            pylab.plot(Xp,np.zeros(len(Xp))+0.00009,'o',color='firebrick',markersize=40,label="Chosen point")
        ax = plt.subplot(111)
        box = ax.get_position()
        ax.set_position([box.x0, box.y0+box.height*0.1, box.width, box.height*0.9])

        # Put a legend to the right of the current axis
        ax.legend(loc='upper center', bbox_to_anchor=(0.5, -0.09),ncol=2,fontsize=50)
        
        pylab.xlim([-0.5,0.5])
      #  plt.legend()
        plt.savefig(os.path.join(path,'%d'%n+"VOI_n.pdf"))
        plt.close(fig)

def plot_prob_effector(sens, fpr, xmax=1, baserate=0.1):
    """Plots a line graph of P(effector|positive test) against
    the baserate of effectors in the input set to the classifier.
        
    The baserate argument draws an annotation arrow
    indicating P(pos|+ve) at that baserate
    """
    assert 0.1 <= xmax <= 1, "Max x axis value must be in range [0,1]"
    assert 0.01 <= baserate <= 1, "Baserate annotation must be in range [0,1]"
    baserates = pylab.arange(0, 1.05, xmax * 0.005)  
    probs = [p_correct_given_pos(sens, fpr, b) for b in baserates]
    pylab.plot(baserates, probs, 'r')
    pylab.title("P(eff|pos) vs baserate; sens: %.2f, fpr: %.2f" % (sens, fpr))
    pylab.ylabel("P(effector|positive)")
    pylab.xlabel("effector baserate")
    pylab.xlim(0, xmax)
    pylab.ylim(0, 1)
    # Add annotation arrow
    xpos, ypos = (baserate, p_correct_given_pos(sens, fpr, baserate))
    if baserate < xmax:
        if xpos > 0.7 * xmax:
            xtextpos = 0.05 * xmax
        else:
            xtextpos = xpos + (xmax-xpos)/5.
        if ypos > 0.5:
            ytextpos = ypos - 0.05
        else:
            ytextpos = ypos + 0.05
        pylab.annotate('baserate: %.2f, P(pos|+ve): %.3f' % (xpos, ypos), 
                       xy=(xpos, ypos), 
                       xytext=(xtextpos, ytextpos),
                       arrowprops=dict(facecolor='black', shrink=0.05))
    else:
        pylab.text(0.05 * xmax, 0.95, 'baserate: %.2f, P(pos|+ve): %.3f' % \
                   (xpos, ypos))

def drunkTest(numTrials = 1000):
    #stepsTaken = [10, 100, 1000, 10000]
    stepsTaken = 1000
    
    for dClass in (UsualDrunk, ColdDrunk, EDrunk, PhotoDrunk, DDrunk):
        #initialize field
        field = Field()
        origin = Location(0, 0)
        
        # initialize drunk 
        drunk = dClass('Drunk')
        field.addDrunk(drunk, origin)

        x_pos, y_pos = [], [] # initialize to empty
        x, y = 0.0, 0.0
        
        for trial in range(numTrials): # trials 
            x, y = walkVector(field, drunk, stepsTaken)
            x_pos.append(x)
            y_pos.append(y)
            
        #pylab.plot(x_pos, y_pos, 'ro', s=5,
        #           label = dClass.__name__)
        pylab.scatter(x_pos, y_pos,s=5, color='red')
        pylab.title(str(dClass))
        pylab.xlabel('x')
        pylab.grid()
        pylab.xlim(-100, 100)
        pylab.ylim(-100,100)
        pylab.ylabel('y')
        pylab.show()

    def InitializePlot(self, goal_config):  # default
        self.fig = pl.figure()
        pl.xlim([self.lower_limits[0], self.upper_limits[0]])
        pl.ylim([self.lower_limits[1], self.upper_limits[1]])
        pl.plot(goal_config[0], goal_config[1], "gx")

        # Show all obstacles in environment
        for b in self.robot.GetEnv().GetBodies():
            if b.GetName() == self.robot.GetName():
                continue
            bb = b.ComputeAABB()
            pl.plot(
                [
                    bb.pos()[0] - bb.extents()[0],
                    bb.pos()[0] + bb.extents()[0],
                    bb.pos()[0] + bb.extents()[0],
                    bb.pos()[0] - bb.extents()[0],
                    bb.pos()[0] - bb.extents()[0],
                ],
                [
                    bb.pos()[1] - bb.extents()[1],
                    bb.pos()[1] - bb.extents()[1],
                    bb.pos()[1] + bb.extents()[1],
                    bb.pos()[1] + bb.extents()[1],
                    bb.pos()[1] - bb.extents()[1],
                ],
                "r",
            )

        pl.ion()
        pl.show()

def run(steps=10):
	for i in range(steps):
	    Q.time_step()
	    #Q.plot_links()
	    py.xlim((0,config['XSIZE']))
	    py.ylim((0,config['YSIZE']))
	    py.draw()

def plot_spectrum():
    #get the data...    
    a_0=struct.unpack('>1024l',fpga.read('even',1024*4,0))
    a_1=struct.unpack('>1024l',fpga.read('odd',1024*4,0))

    interleave_a=[]

    for i in range(1024):
        interleave_a.append(a_0[i])
        interleave_a.append(a_1[i])

    pylab.figure(num=1,figsize=(10,10))
    pylab.ioff()
    pylab.plot(interleave_a)
    #pylab.semilogy(interleave_a)
    pylab.title('Integration number %i.'%prev_integration)
    pylab.ylabel('Power (arbitrary units)')
    pylab.grid()
    pylab.xlabel('Channel')
    pylab.xlim(0,2048)
    pylab.ioff()

    pylab.hold(False)
    pylab.show()
    pylab.draw()

def correlation_matrix(data, size=8.0):
    """ Calculates and shows the correlation matrix of the pandas data frame
        'data' as a heat map.
        Only the correlations between numerical variables are calculated!
    """
    # calculate the correlation matrix
    corr = data.corr()
    #print corr
    lc = len(corr.columns)
    # set some settings for plottin'
    pl.pcolor(corr, vmin = -1, vmax = 1, edgecolor = "black")
    pl.colorbar()
    pl.xlim([-5,lc])
    pl.ylim([0,lc+5])
    pl.axis('off')
    # anotate the rows and columns with their corresponding variables
    ax = pl.gca()            
    for i in range(0,lc):
        ax.annotate(corr.columns[i], (-0.5, i+0.5), \
            size='large', horizontalalignment='right', verticalalignment='center')
        ax.annotate(corr.columns[i], (i+0.5, lc+0.5),\
            size='large', rotation='vertical',\
            horizontalalignment='center', verticalalignment='right')
    # change the size of the image
    fig = pl.figure(num=1)    
    fig.set_size_inches(size+(size/4), size)     
    
    pl.show()

def plot_importances(imp, clfName, obj):
    imp=np.vstack(imp)
    print imp
    mean_importance = np.mean(imp,axis=0)
    std_importance = np.std(imp,axis=0)
    indices = np.argsort(mean_importance)[::-1]
    print indices
    print featureNames
    featureList = []
    # num_features = len(featureNames)
    print("Feature ranking:")
    for f in range(num_features):
        featureList.append(featureNames[indices[f]])
        print("%d. feature %s (%.2f)" % (f, featureNames[indices[f]], mean_importance[indices[f]]))
    fig = pl.figure(figsize=(8,6),dpi=150)
    pl.title("Feature importances",fontsize=30)
    pl.bar(range(num_features), mean_importance[indices],
            yerr = std_importance[indices], color=paired[0], align="center",
            edgecolor=paired[0],ecolor=paired[1])
    pl.xticks(range(num_features), featureList, size=15,rotation=90)
    pl.ylabel("Importance",size=30)
    pl.yticks(size=20)
    pl.xlim([-1, num_features])
    # fix_axes()
    pl.tight_layout()
    save_path = 'plots/'+obj+'/'+clfName+'_feature_importances.pdf'
    fig.savefig(save_path)