def plot_number_alteration_by_tissue(self, fontsize=10, width=0.9):
        """Plot number of alterations

        .. plot::
            :width: 100%
            :include-source:

            from gdsctools import *
            data = gdsctools_data("test_omnibem_genomic_alterations.csv.gz")
            bem = OmniBEMBuilder(data)
            bem.filter_by_gene_list(gdsctools_data("test_omnibem_genes.txt"))
            bem.plot_number_alteration_by_tissue()

        """
        count = self.unified.groupby(['TISSUE_TYPE'])['GENE'].count()
        try:
            count.sort_values(inplace=True, ascending=False)
        except:
            count.sort(inplace=True, ascending=False)
        count.plot(kind="bar", width=width)
        pylab.grid()
        pylab.xlabel("Tissue Type", fontsize=fontsize)
        pylab.ylabel("Total number of alterations in cell lines",
                     fontsize=fontsize)
        try:pylab.tight_layout()
        except:pass

def plotLists(xList, xLabel=None, eListTitle=None, eList=None, eLabel=None, fListTitle=None, fList=None, fLabel=None):
    if h2o.python_username!='kevin':
        return

    import pylab as plt
    print "xList", xList
    print "eList", eList
    print "fList", fList

    font = {'family' : 'normal',
            'weight' : 'normal',
            'size'   : 26}
    ### plt.rc('font', **font)
    plt.rcdefaults()

    if eList:
        if eListTitle:
            plt.title(eListTitle)
        plt.figure()
        plt.plot (xList, eList)
        plt.xlabel(xLabel)
        plt.ylabel(eLabel)
        plt.draw()

    if fList:
        if fListTitle:
            plt.title(fListTitle)
        plt.figure()
        plt.plot (xList, fList)
        plt.xlabel(xLabel)
        plt.ylabel(fLabel)
        plt.draw()

    if eList or fList:
        plt.show()

def plotear(xi,yi,zi):
    # mask inner circle
    interior1 = sqrt(((xi+1.5)**2) + (yi**2)) < 1.0 
    interior2 = sqrt(((xi-1.5)**2) + (yi**2)) < 1.0
    zi[interior1] = ma.masked
    zi[interior2] = ma.masked
    p.figure(figsize=(16,10))
    pyplot.jet()
    max=2.8
    min=0.4
    steps = 50
    levels=list()
    labels=list()
    for i in range(0,steps):
	levels.append(int((max-min)/steps*100*i)*0.01+min)
    for i in range(0,steps/2):
	labels.append(levels[2*i])
    CSF = p.contourf(xi,yi,zi,levels,norm=colors.LogNorm())
    CS = p.contour(xi,yi,zi,levels, format='%.3f', labelsize='18')
    p.clabel(CS,labels,inline=1,fontsize=9)
    p.title('electrostatic potential of two spherical colloids, R=lambda/3',fontsize=24)
    p.xlabel('z-coordinate (3*lambda)',fontsize=18)
    p.ylabel('radial coordinate r (3*lambda)',fontsize=18)
    # add a vertical bar with the color values
    cbar = p.colorbar(CSF,ticks=labels,format='%.3f')
    cbar.ax.set_ylabel('potential (reduced units)',fontsize=18)
    cbar.add_lines(CS)
    p.show()

def param_set_averages_plot(results):
    averages_ocr = [
        a[1] for a in sorted(
            param_set_averages(results, metric='ocr').items(),
            key=lambda x: int(x[0].split('-')[1]))
    ]
    averages_q = [
        a[1] for a in sorted(
            param_set_averages(results, metric='q').items(),
            key=lambda x: int(x[0].split('-')[1]))
    ]
    averages_mse = [
        a[1] for a in sorted(
            param_set_averages(results, metric='mse').items(),
            key=lambda x: int(x[0].split('-')[1]))
    ]
    fig = plt.figure(figsize=(6, 4))
    # plt.tight_layout()
    plt.plot(averages_ocr, label='OCR', linewidth=2.0)
    plt.plot(averages_q, label='Q', linewidth=2.0)
    plt.plot(averages_mse, label='MSE', linewidth=2.0)
    plt.ylim([0, 1])
    plt.xlabel(u'Paslėptų neuronų skaičius')
    plt.ylabel(u'Vidurinė Q įverčio pokyčio reikšmė')
    plt.grid(True)
    plt.tight_layout()
    plt.legend(loc='lower right')
    plt.show()

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 plotEventFlop(library, num, eventNames, sizes, times, events, filename = None):
  from pylab import legend, plot, savefig, semilogy, show, title, xlabel, ylabel
  import numpy as np

  arches = sizes.keys()
  bs     = events[arches[0]].keys()[0]
  data   = []
  names  = []
  for event, color in zip(eventNames, ['b', 'g', 'r', 'y']):
    for arch, style in zip(arches, ['-', ':']):
      if event in events[arch][bs]:
        names.append(arch+'-'+str(bs)+' '+event)
        data.append(sizes[arch][bs])
        data.append(1e-3*np.array(events[arch][bs][event])[:,1])
        data.append(color+style)
      else:
        print 'Could not find %s in %s-%d events' % (event, arch, bs)
  semilogy(*data)
  title('Performance on '+library+' Example '+str(num))
  xlabel('Number of Dof')
  ylabel('Computation Rate (GF/s)')
  legend(names, 'upper left', shadow = True)
  if filename is None:
    show()
  else:
    savefig(filename)
  return

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 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 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 plotHistogram(data, preTime):
    pylab.figure(1)
    pylab.hist(data, bins=10)
    pylab.xlabel("Virus Population At End of Simulation")
    pylab.ylabel("Number of Trials")
    pylab.title("{0} Time Steps Before Treatment Simulation".format(preTime))
    pylab.show()

def getOptCandGamma(cv_train, cv_label):
    print "Finding optimal C and gamma for SVM with RBF Kernel"
    C_range = 10.0 ** np.arange(-2, 9)
    gamma_range = 10.0 ** np.arange(-5, 4)
    param_grid = dict(gamma=gamma_range, C=C_range)
    cv = StratifiedKFold(y=cv_label, n_folds=40)

    # Use the svm.SVC() as the cost function to evaluate parameter choices
    # NOTE: Perhaps we should run computations in parallel if needed. Does it
    # do that already within the class?
    grid = GridSearchCV(svm.SVC(), param_grid=param_grid, cv=cv)
    grid.fit(cv_train, cv_label)

    score_dict = grid.grid_scores_
    scores = [x[1] for x in score_dict]
    scores = np.array(scores).reshape(len(C_range), len(gamma_range))
    pl.figure(figsize=(8,6))
    pl.subplots_adjust(left=0.05, right=0.95, bottom=0.15, top=0.95)
    pl.imshow(scores, interpolation='nearest', cmap=pl.cm.spectral)
    pl.xlabel('gamma')
    pl.ylabel('C')
    pl.colorbar()
    pl.xticks(np.arange(len(gamma_range)), gamma_range, rotation=45)
    pl.yticks(np.arange(len(C_range)), C_range)
    pl.show()

    print "The best classifier is: ", grid.best_estimator_

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 simulation1(numTrials, numSteps, loc):
    results = {'UsualDrunk': [], 'ColdDrunk': [], 'EDrunk': [], 'PhotoDrunk': [], 'DDrunk': []}
    drunken_types = {'UsualDrunk': UsualDrunk, 'ColdDrunk': ColdDrunk, 'EDrunk': EDrunk, 'PhotoDrunk': PhotoDrunk,
                     'DDrunk': DDrunk}
    for drunken in drunken_types.keys():
        #Create field
        initial_loc = Location(loc[0], loc[1])
        field = Field()
        print "Simu", drunken
        drunk = Drunk(drunken)
        drunk_man = drunken_types[drunken](drunk)
        field.addDrunk(drunk_man, initial_loc)
        #print drunk_man
        for trial in range(numTrials):
            distance = walkVector(field, drunk_man, numSteps)
            results[drunken].append((round(distance[0], 1), round(distance[1], 1)))
        print drunken, "=", results[drunken]
    for result in results.keys():
        # x, y = zip(*results[result])
        # print "x", x
        # print "y", y
        pylab.plot(*zip(*results[result]), marker='o', color='r', ls='')
        pylab.title(result)
        pylab.xlabel('X coordinateds')
        pylab.ylabel('Y coordinateds')
        pylab.xlim(-100, 100)
        pylab.ylim(-100, 100)
        pylab.figure()
    pylab.show

	def plot_stress(self, block_ids=None, fignum=0):
		block_ids = self.check_block_ids_list(block_ids)
		#
		plt.figure(fignum)
		ax1=plt.gca()
		plt.clf()
		plt.figure(fignum)
		plt.clf()
		ax0=plt.gca()

		#
		for block_id in block_ids:
			rws = numpy.core.records.fromarrays(zip(*filter(lambda x: x['block_id']==block_id, self.shear_stress_sequences)), dtype=self.shear_stress_sequences.dtype)
			stress_seq = []
			for rw in rws:
				stress_seq += [[rw['sweep_number'], rw['shear_init']]]
				stress_seq += [[rw['sweep_number'], rw['shear_final']]]
			X,Y = zip(*stress_seq)
			#
			ax0.plot(X,Y, '.-', label='block_id: %d' % block_id)
			#
			plt.figure(fignum+1)
			plt.plot(rws['sweep_number'], rws['shear_init'], '.-', label='block_id: %d' % block_id)
			plt.plot(rws['sweep_number'], rws['shear_final'], '.-', label='block_id: %d' % block_id)
			plt.figure(fignum)
		ax0.plot([min(self.shear_stress_sequences['sweep_number']), max(self.shear_stress_sequences['sweep_number'])], [0., 0.], 'k-')
		ax0.legend(loc=0, numpoints=1)
		plt.figure(fignum)
		plt.title('Block shear_stress sequences')
		plt.xlabel('sweep number')
		plt.ylabel('shear stress')

def plot_Barycenter(dataset_name, feat, unfeat, repo):

    if dataset_name==MNIST:
        _, _, test=get_data(dataset_name, repo, labels=True)
        xtest1,_,_, labels,_=test
    else:
        _, _, test=get_data(dataset_name, repo, labels=False)
        xtest1,_,_ =test
        labels=np.zeros((len(xtest1),))
    # get labels
    def bary_wdl2(index): return _bary_wdl2(index, xtest1, feat, unfeat)
    
    n=xtest1.shape[-1]
    
    num_class = (int)(max(labels)+1)
    barys=[bary_wdl2(np.where(labels==i)) for i in range(num_class)]
    pl.figure(1, (num_class, 1))
    for i in range(num_class):
        pl.subplot(1,10,1+i)
        pl.imshow(barys[i][0,0,:,:],cmap='Blues',interpolation='nearest')
        pl.xticks(())
        pl.yticks(())
        if i==0:
            pl.ylabel('DWE Bary.')
        if num_class >1:
            pl.title('{}'.format(i))
    pl.tight_layout(pad=0,h_pad=-2,w_pad=-2) 
    pl.savefig("imgs/{}_dwe_bary.pdf".format(dataset_name))

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 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 showHistory(self, figNum):
     pylab.figure(figNum)
     plot = pylab.plot(self.history, label = 'Test Stock')
     plot
     pylab.title('Closing Price, Test ' + str(figNum))
     pylab.xlabel('Day')
     pylab.ylabel('Price')