def plot_tracks(src, fakewcs, spa=None, **kwargs):
    # NOTE -- MAGIC 61 = monthly; this is ASSUMEd below.
    tt = np.linspace(2010., 2015., 61)
    t0 = TAITime(None, mjd=TAITime.mjd2k + 365.25*10)
    #rd0 = src.getPositionAtTime(t0)
    #print 'rd0:', rd0
    xx,yy = [],[]
    rr,dd = [],[]
    for t in tt:
        #print 'Time', t
        rd = src.getPositionAtTime(t0 + (t - 2010.)*365.25*24.*3600.)
        ra,dec = rd.ra, rd.dec
        rr.append(ra)
        dd.append(dec)
        ok,x,y = fakewcs.radec2pixelxy(ra,dec)
        xx.append(x - 1.)
        yy.append(y - 1.)

    if spa is None:
        spa = [None,None,None]
    for rows,cols,sub in spa:
        if sub is not None:
            plt.subplot(rows,cols,sub)
        ax = plt.axis()
        plt.plot(xx, yy, 'k-', **kwargs)
        plt.axis(ax)

    return rr,dd,tt

 def testTelescope(self):
     import matplotlib
     matplotlib.use('AGG')
     import matplotlib.mlab as ml
     import pylab as pl
     import time        
     w0 = 8.0
     k = 2*np.pi/3.0
     gb = GaussianBeam(w0, k)
     lens = ThinLens(150, 150)
     gb2 = lens*gb
     self.assertAlmostEqual(gb2._z0, gb._z0 + 2*150.0)
     lens2 = ThinLens(300, 600)
     gb3 = lens2*gb2
     self.assertAlmostEqual(gb3._z0, gb2._z0 + 2*300.0)
     self.assertAlmostEqual(gb._w0, gb3._w0/2.0)
     z = np.arange(0, 150)
     z2 = np.arange(150, 600)
     z3 = np.arange(600, 900)
     pl.plot(z, gb.w(z, k), z2, gb2.w(z2, k), z3, gb3.w(z3, k))
     pl.grid()
     pl.xlabel('z')
     pl.ylabel('w')
     pl.savefig('testTelescope1.png')
     time.sleep(0.1)
     pl.close('all')        

def plot_xc(t_years):
    """Plot the location of the calving front."""
    x = x_c(t_years * secpera) / 1000.0   # convert to km
    _, _, y_min, y_max = axis()

    hold(True)
    plot([x, x], [y_min, y_max], '--g')

def check_vpd_ks2_astrometry():
    """
    Check the VPD and quiver plots for our KS2-extracted, re-transformed astrometry.
    """
    catFile = workDir + '20.KS2_PMA/wd1_catalog.fits'
    tab = atpy.Table(catFile)

    good = (tab.xe_160 < 0.05) & (tab.ye_160 < 0.05) & \
        (tab.xe_814 < 0.05) & (tab.ye_814 < 0.05) & \
        (tab.me_814 < 0.05) & (tab.me_160 < 0.05)

    tab2 = tab.where(good)

    dx = (tab2.x_160 - tab2.x_814) * ast.scale['WFC'] * 1e3
    dy = (tab2.y_160 - tab2.y_814) * ast.scale['WFC'] * 1e3

    py.clf()
    q = py.quiver(tab2.x_814, tab2.y_814, dx, dy, scale=5e2)
    py.quiverkey(q, 0.95, 0.85, 5, '5 mas', color='red', labelcolor='red')
    py.savefig(workDir + '20.KS2_PMA/vec_diffs_ks2_all.png')

    py.clf()
    py.plot(dy, dx, 'k.', ms=2)
    lim = 30
    py.axis([-lim, lim, -lim, lim])
    py.xlabel('Y Proper Motion (mas)')
    py.ylabel('X Proper Motion (mas)')
    py.savefig(workDir + '20.KS2_PMA/vpd_ks2_all.png')

    idx = np.where((np.abs(dx) < 10) & (np.abs(dy) < 10))[0]
    print('Cluster Members (within dx < 10 mas and dy < 10 mas)')
    print(('   dx = {dx:6.2f} +/- {dxe:6.2f} mas'.format(dx=dx[idx].mean(),
                                                        dxe=dx[idx].std())))
    print(('   dy = {dy:6.2f} +/- {dye:6.2f} mas'.format(dy=dy[idx].mean(),
                                                        dye=dy[idx].std())))

def plot_data(x,y,Amp,freq):
    """
    Plot the actual data point x,y along with the fit Amp*sin(freq*x)
    """
    plb.plot(x,y,'b',linestyle=':')
    y_fit = Amp*np.sin(freq*x)
    plb.plot(x,y_fit,'r')

    def plot_matches(self, name, show_below = True, match_maximum = None):
        """ 対応点を線で結んで画像を表示する
          入力： im1,im2（配列形式の画像）、locs1,locs2（特徴点座標）
             machescores（match()の出力）、
             show_below（対応の下に画像を表示するならTrue）"""
        im1 = self._image_1.get_array_image()
        im2 = self._image_2.get_array_image()
        self.appendimages()
        im3 = self._append_image
        if self._match_score is None:
            self.match()
        locs1 = self._image_1.get_shift_location()
        locs2 = self._image_2.get_shift_location()
        if show_below:
            im3 = numpy.vstack((im3,im3))
        pylab.figure(dpi=160)
        pylab.gray()
        pylab.imshow(im3, aspect = 'auto')

        cols1 = im1.shape[1]
        match_num = 0
        for i,m in enumerate(self._match_score):
            if m > 0 : 
                pylab.plot([locs1[i][0],locs2[m][0]+cols1], [locs1[i][1],locs2[m][1]], 'c')
                match_num = match_num + 1
            if match_maximum is not None and match_num >= match_maximum:
                break
        pylab.axis('off')
        pylab.savefig(name, dpi=160)

def createPlot(dataY, dataX, ticksX, annotations, axisY, axisX, dostep, doannotate):
    if not ticksX:
        ticksX = dataX
    
    if dostep:
        py.step(dataX, dataY, where='post', linestyle='-', label=axisY) # where=post steps after point
    else:
        py.plot(dataX, dataY, marker='o', ms=5.0, linestyle='-', label=axisY)
    
    if annotations and doannotate:
        for note, x, y in zip(annotations, dataX, dataY):
            py.annotate(note, (x, y), xytext=(2,2), xycoords='data', textcoords='offset points')

    py.xticks(np.arange(1, len(dataX)+1), ticksX, horizontalalignment='left', rotation=30)
    leg = py.legend()
    leg.draggable()
    py.xlabel(axisX)
    py.ylabel('time (s)')

    # Set X axis tick labels as rungs
    #print zip(dataX, dataY)
  
    py.draw()
    py.show()
    
    return

def drawPr(tp,fp,tot,show=True):
    """
        draw the precision recall curve
    """
    det=numpy.array(sorted(tp+fp))
    atp=numpy.array(tp)
    afp=numpy.array(fp)
    #pylab.figure()
    #pylab.clf()
    rc=numpy.zeros(len(det))
    pr=numpy.zeros(len(det))
    #prc=0
    #ppr=1
    for i,p in enumerate(det):
        pr[i]=float(numpy.sum(atp>=p))/numpy.sum(det>=p)
        rc[i]=float(numpy.sum(atp>=p))/tot
        #print pr,rc,p
    ap=0
    for c in numpy.linspace(0,1,num=11):
        if len(pr[rc>=c])>0:
            p=numpy.max(pr[rc>=c])
        else:
            p=0
        ap=ap+p/11
    if show:
        pylab.plot(rc,pr,'-g')
        pylab.title("AP=%.3f"%(ap))
        pylab.xlabel("Recall")
        pylab.ylabel("Precision")
        pylab.grid()
        pylab.show()
        pylab.draw()
    return rc,pr,ap

def Doplots_monthly(mypathforResults,PlottingDF,variable_to_fill, Site_ID,units,item):   
    ANN_label=str(item+"_NN")     #Do Monthly Plots
    print "Doing MOnthly  plot"
    #t = arange(1, 54, 1)
    NN_label='Fc'
    Plottemp = PlottingDF[[NN_label,item]][PlottingDF['day_night']!=1]
    #Plottemp = PlottingDF[[NN_label,item]].dropna(how='any')
    figure(1)
    pl.title('Nightime ANN v Tower by year-month for '+item+' at '+Site_ID)

    try:
	xdata1a=Plottemp[item].groupby([lambda x: x.year,lambda x: x.month]).mean()
	plotxdata1a=True
    except:
	plotxdata1a=False
    try:
	xdata1b=Plottemp[NN_label].groupby([lambda x: x.year,lambda x: x.month]).mean()
	plotxdata1b=True
    except:
	plotxdata1b=False 
    if plotxdata1a==True:
	pl.plot(xdata1a,'r',label=item) 
    if plotxdata1b==True:
	pl.plot(xdata1b,'b',label=NN_label)
    pl.ylabel('Flux')    
    pl.xlabel('Year - Month')       
    pl.legend()
    pl.savefig(mypathforResults+'/ANN and Tower plots by year and month for variable '+item+' at '+Site_ID)
    #pl.show()
    pl.close()
    time.sleep(1)

 def plot(self):
     """Plot the scores"""
     from pylab import plot
     plot(self.xdata, self.ydata)
     xlabel("Number of computeScore calls")
     ylabel("Score")
     ylim([0, ylim()[1]])

    def plot_cost(self):
        if self.show_cost not in self.train_outputs[0][0]:
            raise ShowNetError("Cost function with name '%s' not defined by given convnet." % self.show_cost)
        train_errors = [o[0][self.show_cost][self.cost_idx] for o in self.train_outputs]
        test_errors = [o[0][self.show_cost][self.cost_idx] for o in self.test_outputs]

        numbatches = len(self.train_batch_range)
        test_errors = numpy.row_stack(test_errors)
        test_errors = numpy.tile(test_errors, (1, self.testing_freq))
        test_errors = list(test_errors.flatten())
        test_errors += [test_errors[-1]] * max(0,len(train_errors) - len(test_errors))
        test_errors = test_errors[:len(train_errors)]

        numepochs = len(train_errors) / float(numbatches)
        pl.figure(1)
        x = range(0, len(train_errors))
        pl.plot(x, train_errors, 'k-', label='Training set')
        pl.plot(x, test_errors, 'r-', label='Test set')
        pl.legend()
        ticklocs = range(numbatches, len(train_errors) - len(train_errors) % numbatches + 1, numbatches)
        epoch_label_gran = int(ceil(numepochs / 20.)) # aim for about 20 labels
        epoch_label_gran = int(ceil(float(epoch_label_gran) / 10) * 10) # but round to nearest 10
        ticklabels = map(lambda x: str((x[1] / numbatches)) if x[0] % epoch_label_gran == epoch_label_gran-1 else '', enumerate(ticklocs))

        pl.xticks(ticklocs, ticklabels)
        pl.xlabel('Epoch')
#        pl.ylabel(self.show_cost)
        pl.title(self.show_cost)

def plot_heatingrate(data_dict, filename, do_show=True):
    pl.figure(201)
    color_list = ['b','r','g','k','y','r','g','b','k','y','r',]
    fmtlist = ['s','d','o','s','d','o','s','d','o','s','d','o']
    result_dict = {}
    for key in data_dict.keys():
        x = data_dict[key][0]
        y = data_dict[key][1][:,0]
        y_err = data_dict[key][1][:,1]

        p0 = np.polyfit(x,y,1)
        fit = LinFit(np.array([x,y,y_err]).transpose(), show_graph=False)
        p1 = [0,0]
        p1[0] = fit.param_dict[0]['Slope'][0]
        p1[1] = fit.param_dict[0]['Offset'][0]
        print fit
        x0 = np.linspace(0,max(x))
        cstr = color_list.pop(0)
        fstr = fmtlist.pop(0)
        lstr = key + " heating: {0:.2f} ph/ms".format((p1[0]*1e3)) 
        pl.errorbar(x/1e3,y,y_err,fmt=fstr + cstr,label=lstr)
        pl.plot(x0/1e3,np.polyval(p0,x0),cstr)
        pl.plot(x0/1e3,np.polyval(p1,x0),cstr)
        result_dict[key] = 1e3*np.array(fit.param_dict[0]['Slope'])
    pl.xlabel('Heating time (ms)')
    pl.ylabel('nbar')
    if do_show:
        pl.legend()
        pl.show()
    if filename != None:
        pl.savefig(filename)
    return result_dict

def plotForce():
    figure(size=3,aspect=0.5)
    subplot(1,2,1)
    from EvalTraj import plotFF
    plotFF(vp=351,t=28,f=900,cm=0.6,foffset=8)
    subplot_annotate()
    
    subplot(1,2,2)
    for i in [1,2,3,4]:
        R=np.squeeze(np.load('Rdpse%d.npy'%i))
        R=stats.nanmedian(R,axis=2)[:,1:,:]
        dps=np.linspace(-1,1,201)[1:]
        plt.plot(dps,R[:,:,2].mean(0));
    plt.legend([0,0.1,0.2,0.3],loc=3) 
    i=2
    R=np.squeeze(np.load('Rdpse%d.npy'%i))
    R=stats.nanmedian(R,axis=2)[:,1:,:]
    mn=np.argmin(R,axis=1)
    y=np.random.randn(mn.shape[0])*0.00002+0.0438
    plt.plot(np.sort(dps[mn[:,2]]),y,'+',mew=1,ms=6,mec=[ 0.39  ,  0.76,  0.64])
    plt.xlabel('Displacement of Force Origin')
    plt.ylabel('Average Net Force Magnitude')
    hh=dps[mn[:,2]]
    err=np.std(hh)/np.sqrt(hh.shape[0])*stats.t.ppf(0.975,hh.shape[0])
    err2=np.std(hh)/np.sqrt(hh.shape[0])*stats.t.ppf(0.75,hh.shape[0])
    m=np.mean(hh)
    print m, m-err,m+err
    np.save('force',[m, m-err,m+err,m-err2,m+err2])
    plt.xlim([-0.5,0.5])
    plt.ylim([0.0435,0.046])
    plt.grid(b=True,axis='x')
    subplot_annotate()

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 plotB2reg(prefix=''):
    w=loadStanFit(prefix+'revE2B2LHregCa.fit')
    px=np.array(np.linspace(-0.5,0.5,101),ndmin=2)
    a1=np.array(w['ma'][:,4],ndmin=2).T+1
    a0=np.array(w['ma'][:,3],ndmin=2).T
    printCI(w,'ma')
    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))
    man=np.array([-0.4,-0.2,0,0.2,0.4])
    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'))
    mus=[]
    for m in range(len(man)):
        mus.append(loadStanFit(prefix+'revE2B2LHC%d.fit'%m)['ma4']+man[m])
    mus=np.array(mus).T
    errorbar(mus,x=man)
    ax.set_xticks(man)
    plt.xlim([-0.5,0.5])
    plt.ylim([-0.6,0.8])
    plt.xlabel('Pivot Displacement')
    plt.ylabel('Perceived Displacemet')

 def yfromx(self, newtimeaxis, doplot=False, debug=False):
     if debug:
         print('fastresampler: yfromx called with following parameters')
         print('    padvalue:, ', self.padvalue)
         print('    initstep, hiresstep:', self.initstep, self.hiresstep)
         print('    initial axis limits:', self.initstart, self.initend)
         print('    hires axis limits:', self.hiresstart, self.hiresend)
         print('    requested axis limits:', newtimeaxis[0], newtimeaxis[-1])
     outindices = ((newtimeaxis - self.hiresstart) // self.hiresstep).astype(int)
     if debug:
         print('len(self.hires_y):', len(self.hires_y))
     try:
         out_y = self.hires_y[outindices]
     except IndexError:
         print('')
         print('indexing out of bounds in fastresampler')
         print('    padvalue:, ', self.padvalue)
         print('    initstep, hiresstep:', self.initstep, self.hiresstep)
         print('    initial axis limits:', self.initstart, self.initend)
         print('    hires axis limits:', self.hiresstart, self.hiresend)
         print('    requested axis limits:', newtimeaxis[0], newtimeaxis[-1])
         sys.exit()
     if doplot:
         fig = pl.figure()
         ax = fig.add_subplot(111)
         ax.set_title('fastresampler timecourses')
         pl.plot(self.hires_x, self.hires_y, newtimeaxis, out_y)
         pl.legend(('hires', 'output'))
         pl.show()
     return out_y

def drawPrfastscore(tp,fp,scr,tot,show=True):
    tp=numpy.cumsum(tp)
    fp=numpy.cumsum(fp)
    rec=tp/tot
    prec=tp/(fp+tp)
    #dif=numpy.abs(prec[1:]-rec[1:])
    dif=numpy.abs(prec[::-1]-rec[::-1])
    pos=dif.argmin()
    pos=len(dif)-pos-1
    ap=0
    for t in numpy.linspace(0,1,11):
        pr=prec[rec>=t]
        if pr.size==0:
            pr=0
        p=numpy.max(pr);
        ap=ap+p/11;
    if show:    
        pylab.plot(rec,prec,'-g')
        pylab.title("AP=%.3f EPRthr=%.3f"%(ap,scr[pos]))
        pylab.xlabel("Recall")
        pylab.ylabel("Precision")
        pylab.grid()
        pylab.show()
        pylab.draw()
    return rec,prec,scr,ap,scr[pos]

def cmap_plot(cmdLine):

    pylab.figure(figsize=[5,10])
    a=outer(ones(10),arange(0,1,0.01))
    subplots_adjust(top=0.99,bottom=0.00,left=0.01,right=0.8)
    maps=[m for m in cm.datad if not m.endswith("_r")]
    maps.sort()
    l=len(maps)+1
    for i, m in enumerate(maps):
        print m
        subplot(l,1,i+1)
        pylab.setp(pylab.gca(),xticklabels=[],xticks=[],yticklabels=[],yticks=[])
        imshow(a,aspect='auto',cmap=get_cmap(m),origin="lower")
        pylab.text(100.85,0.5,m,fontsize=10)

# render plot

    if cmdLine: 
        pylab.show(block=True)
    else: 
        pylab.ion()
        pylab.plot([])
        pylab.ioff()
	
    status = 1
    return status

def plotKerasExperimentcifar10():

    index = 5
    for experiment_number in range(1,index+1):
        outputPath_part_final = os.path.realpath( "/home/jie/docker_folder/random_keras/output_cifar10_mlp/errorFile/hyperopt_experiment_withoutparam_accuracy" + str(experiment_number) + ".txt")
        output_plot = os.path.realpath(
                "/home/jie/docker_folder/random_keras/output_cifar10_mlp/errorFile/plotErrorCurve" + str(experiment_number) + ".pdf")

        df = pd.read_csv(outputPath_part_final,delimiter='\t',header=None)
        df.drop(df.columns[[600]], axis=1, inplace=True)

        i=1
        epochnum = []
        while i<=250:
            epochnum.append(i)
            i = i+1
        i=0
        while i<10:
            df_1=df[df.columns[0:250]].ix[i]
            np.reshape(df_1, (1,250))
            plt.plot(epochnum,df_1)

            i = i+1
        # plt.show()
        # plt.show()
        plt.savefig(output_plot)
        plt.close()

def create_figure():
    psd = test_correlog()
    f = linspace(-0.5, 0.5, len(psd))

    psd = cshift(psd, len(psd)/2)
    plot(f, 10*log10(psd/max(psd)))
    savefig('psd_corr.png')