Python pylab.savefig() Examples

def plot_hits(filename_fil, filename_dat):
    """ Plot the hits in a .dat file. """
    table = find_event.read_dat(filename_dat)
    print(table)

    plt.figure(figsize=(10, 8))
    N_hit = len(table)
    if N_hit > 10:
        print("Warning: More than 10 hits found. Only plotting first 10")
        N_hit = 10

    for ii in range(N_hit):
        plt.subplot(N_hit, 1, ii+1)
        plot_event.plot_hit(filename_fil, filename_dat, ii)
    plt.tight_layout()
    plt.savefig(filename_dat.replace('.dat', '.png'))
    plt.show() 

def save(GUI):
    global txtResultPath
    if GUI:
        import pylab as pl
        import nest.raster_plot
        import nest.voltage_trace
        logger.debug("Saving IMAGES into {0}".format(SAVE_PATH))
        for key in spikedetectors:
            try:
                nest.raster_plot.from_device(spikedetectors[key], hist=True)
                pl.savefig(f_name_gen(SAVE_PATH, "spikes_" + key.lower()), dpi=dpi_n, format='png')
                pl.close()
            except Exception:
                print(" * * * from {0} is NOTHING".format(key))
    txtResultPath = SAVE_PATH + 'txt/'
    logger.debug("Saving TEXT into {0}".format(txtResultPath))
    if not os.path.exists(txtResultPath):
        os.mkdir(txtResultPath)
    for key in spikedetectors:
        save_spikes(spikedetectors[key], name=key)
    with open(txtResultPath + 'timeSimulation.txt', 'w') as f:
        for item in times:
            f.write(item) 

def train(self):
        # self.load_model('./save_model/cartpole_a3c.h5')
        agents = [Agent(i, self.actor, self.critic, self.optimizer, self.env_name, self.discount_factor,
                        self.action_size, self.state_size) for i in range(self.threads)]

        for agent in agents:
            agent.start()

        while True:
            time.sleep(20)

            plot = scores[:]
            pylab.plot(range(len(plot)), plot, 'b')
            pylab.savefig("./save_graph/cartpole_a3c.png")

            self.save_model('./save_model/cartpole_a3c.h5') 

def summarise_reads(path):
    """Count reads in all files in path"""

    resultfile = os.path.join(path, 'read_stats.csv')
    files = glob.glob(os.path.join(path,'*.fastq'))
    vals=[]
    rl=[]
    for f in files:
        label = os.path.splitext(os.path.basename(f))[0]
        s = utils.fastq_to_dataframe(f)
        l = len(s)
        vals.append([label,l])
        print (label, l)

    df = pd.DataFrame(vals,columns=['path','total reads'])
    df.to_csv(resultfile)
    df.plot(x='path',y='total reads',kind='barh')
    plt.tight_layout()
    plt.savefig(os.path.join(path,'total_reads.png'))
    #df = pd.concat()
    return df 

def main():

    l2_base_dir = '/media/admin228/00027E210001A5BD/train_pytorch/change_detection/CMU/prediction_cons/l2_5,6,7/roc'
    cos_base_dir = '/media/admin228/00027E210001A5BD/train_pytorch/change_detection/CMU/prediction_cons/dist_cos_new_5,6,7/roc'
    CSF_dir = os.path.join(l2_base_dir)
    CSF_fig_dir = os.path.join(l2_base_dir,'fig.png')
    end_number = 22
    csf_conv5_l2_ls,csf_fc6_l2_ls,csf_fc7_l2_ls,x_l2 = get_csf_ls(l2_base_dir,end_number)
    csf_conv5_cos_ls,csf_fc6_cos_ls,csf_fc7_cos_ls,x_cos = get_csf_ls(cos_base_dir,end_number)
    Fig = pylab.figure()
    setFigLinesBW(Fig)
    #pylab.plot(x,csf_conv4_ls, color='k',label= 'conv4')
    pylab.plot(x_l2,csf_conv5_l2_ls, color='m',label= 'l2:conv5')
    pylab.plot(x_l2,csf_fc6_l2_ls, color = 'b',label= 'l2:fc6')
    pylab.plot(x_l2,csf_fc7_l2_ls, color = 'g',label= 'l2:fc7')
    pylab.plot(x_cos,csf_conv5_cos_ls, color='c',label= 'cos:conv5')
    pylab.plot(x_cos,csf_fc6_cos_ls, color = 'r',label= 'cos:fc6')
    pylab.plot(x_cos,csf_fc7_cos_ls, color = 'y',label= 'cos:fc7')
    pylab.legend(loc='lower right', prop={'size': 10})
    pylab.ylabel('RMS Contrast', fontsize=14)
    pylab.xlabel('Epoch', fontsize=14)
    pylab.savefig(CSF_fig_dir) 

def plot(ifile, varkey, options, before='', after=''):
    import pylab as pl
    outpath = getattr(options, 'outpath', '.')
    var = ifile.variables[varkey]
    dims = [(k, l) for l, k in zip(var[:].shape, var.dimensions) if l > 1]
    if len(dims) > 1:
        raise ValueError(
            'Plots can have only 1 non-unity dimensions; got %d - %s' %
            (len(dims), str(dims)))
    exec(before)
    ax = pl.gca()
    print(varkey, end='')
    if options.logscale:
        ax.set_yscale('log')

    ax.plot(var[:].squeeze())
    ax.set_xlabel('unknown')
    ax.set_ylabel(getattr(var, 'standard_name',
                          varkey).strip() + ' ' + var.units.strip())
    fmt = 'png'
    figpath = os.path.join(outpath + '_1d_' + varkey + '.' + fmt)
    exec(after)
    pl.savefig(figpath)
    print('Saved fig', figpath)
    return figpath 

def on_epoch_end(self, epoch, logs={}):
        self.model.save_weights(os.path.join(self.output_dir, 'weights%02d.h5' % (epoch)))
        self.show_edit_distance(256)
        word_batch = next(self.text_img_gen)[0]
        res = decode_batch(self.test_func, word_batch['the_input'][0:self.num_display_words])
        if word_batch['the_input'][0].shape[0] < 256:
            cols = 2
        else:
            cols = 1
        for i in range(self.num_display_words):
            pylab.subplot(self.num_display_words // cols, cols, i + 1)
            if K.image_data_format() == 'channels_first':
                the_input = word_batch['the_input'][i, 0, :, :]
            else:
                the_input = word_batch['the_input'][i, :, :, 0]
            pylab.imshow(the_input.T, cmap='Greys_r')
            pylab.xlabel('Truth = \'%s\'\nDecoded = \'%s\'' % (word_batch['source_str'][i], res[i]))
        fig = pylab.gcf()
        fig.set_size_inches(10, 13)
        pylab.savefig(os.path.join(self.output_dir, 'e%02d.png' % (epoch)))
        pylab.close() 

def on_epoch_end(self, epoch, logs={}):
        self.model.save_weights(os.path.join(self.output_dir, 'weights%02d.h5' % (epoch)))
        self.show_edit_distance(256)
        word_batch = next(self.text_img_gen)[0]
        res = decode_batch(self.test_func, word_batch['the_input'][0:self.num_display_words])
        if word_batch['the_input'][0].shape[0] < 256:
            cols = 2
        else:
            cols = 1
        for i in range(self.num_display_words):
            pylab.subplot(self.num_display_words // cols, cols, i + 1)
            if K.image_data_format() == 'channels_first':
                the_input = word_batch['the_input'][i, 0, :, :]
            else:
                the_input = word_batch['the_input'][i, :, :, 0]
            pylab.imshow(the_input.T, cmap='Greys_r')
            pylab.xlabel('Truth = \'%s\'\nDecoded = \'%s\'' % (word_batch['source_str'][i], res[i]))
        fig = pylab.gcf()
        fig.set_size_inches(10, 13)
        pylab.savefig(os.path.join(self.output_dir, 'e%02d.png' % (epoch)))
        pylab.close() 

def on_epoch_end(self, epoch, logs={}):
        self.model.save_weights(os.path.join(self.output_dir, 'weights%02d.h5' % (epoch)))
        self.show_edit_distance(256)
        word_batch = next(self.text_img_gen)[0]
        res = decode_batch(self.test_func, word_batch['the_input'][0:self.num_display_words])
        if word_batch['the_input'][0].shape[0] < 256:
            cols = 2
        else:
            cols = 1
        for i in range(self.num_display_words):
            pylab.subplot(self.num_display_words // cols, cols, i + 1)
            if K.image_data_format() == 'channels_first':
                the_input = word_batch['the_input'][i, 0, :, :]
            else:
                the_input = word_batch['the_input'][i, :, :, 0]
            pylab.imshow(the_input.T, cmap='Greys_r')
            pylab.xlabel('Truth = \'%s\'\nDecoded = \'%s\'' % (word_batch['source_str'][i], res[i]))
        fig = pylab.gcf()
        fig.set_size_inches(10, 13)
        pylab.savefig(os.path.join(self.output_dir, 'e%02d.png' % (epoch)))
        pylab.close() 

def on_epoch_end(self, epoch, logs={}):
        self.model.save_weights(os.path.join(self.output_dir, 'weights%02d.h5' % (epoch)))
        self.show_edit_distance(256)
        word_batch = next(self.text_img_gen)[0]
        res = decode_batch(self.test_func, word_batch['the_input'][0:self.num_display_words])
        if word_batch['the_input'][0].shape[0] < 256:
            cols = 2
        else:
            cols = 1
        for i in range(self.num_display_words):
            pylab.subplot(self.num_display_words // cols, cols, i + 1)
            if K.image_data_format() == 'channels_first':
                the_input = word_batch['the_input'][i, 0, :, :]
            else:
                the_input = word_batch['the_input'][i, :, :, 0]
            pylab.imshow(the_input.T, cmap='Greys_r')
            pylab.xlabel('Truth = \'%s\'\nDecoded = \'%s\'' % (word_batch['source_str'][i], res[i]))
        fig = pylab.gcf()
        fig.set_size_inches(10, 13)
        pylab.savefig(os.path.join(self.output_dir, 'e%02d.png' % (epoch)))
        pylab.close() 

def plot_evaluation_episode_reward():
	pylab.clf()
	sns.set_context("poster")
	pylab.plot(0, 0)
	episodes = [0]
	average_scores = [0]
	median_scores = [0]
	for n in xrange(len(csv_evaluation)):
		params = csv_evaluation[n]
		episodes.append(params[0])
		average_scores.append(params[1])
		median_scores.append(params[2])
	pylab.plot(episodes, average_scores, sns.xkcd_rgb["windows blue"], lw=2)
	pylab.xlabel("episodes")
	pylab.ylabel("average score")
	pylab.savefig("%s/evaluation_episode_average_reward.png" % args.plot_dir)

	pylab.clf()
	pylab.plot(0, 0)
	pylab.plot(episodes, median_scores, sns.xkcd_rgb["windows blue"], lw=2)
	pylab.xlabel("episodes")
	pylab.ylabel("median score")
	pylab.savefig("%s/evaluation_episode_median_reward.png" % args.plot_dir) 

def plotdata(obsmode,spectrum,val,odict,sdict,
             instr,fieldname,outdir,outname):
    isetting=P.isinteractive()
    P.ioff()

    P.clf()
    P.plot(obsmode,val,'.')
    P.ylabel('(pysyn-syn)/syn')
    P.xlabel('obsmode')
    P.title("%s: %s"%(instr,fieldname))
    P.savefig(os.path.join(outdir,outname+'_obsmode.ps'))

    P.clf()
    P.plot(spectrum,val,'.')
    P.ylabel('(pysyn-syn)/syn')
    P.xlabel('spectrum')
    P.title("%s: %s"%(instr,fieldname))
    P.savefig(os.path.join(outdir,outname+'_spectrum.ps'))

    matplotlib.interactive(isetting) 

def _plot_example(xv, yv, b):
    """Plot for debugging purposes."""

    matplotlib.rcParams['font.family'] = "serif"
    matplotlib.rcParams['font.sans-serif'] = "Times"
    matplotlib.rcParams["legend.edgecolor"] = "None"
    matplotlib.rcParams["axes.spines.top"] = False
    matplotlib.rcParams["axes.spines.bottom"] = True
    matplotlib.rcParams["axes.spines.left"] = True
    matplotlib.rcParams["axes.spines.right"] = False
    matplotlib.rcParams['axes.grid'] = True
    matplotlib.rcParams['axes.grid.axis'] = 'both'
    matplotlib.rcParams['axes.grid.which'] = 'major'
    matplotlib.rcParams['legend.edgecolor'] = '1.0'
    plt.plot(xv[:, 113], yv[:, 113], 'ko')
    plt.plot(xv[:, 113], xv[:, 113] * b[113, 1] + b[113, 0], 'nneighbors')
    # plt.plot(x[113], x[113]*b[113, 1] + b[113, 0], 'ro')
    plt.grid(True)
    plt.xlabel('Radiance, $\mu{W }nm^{-1} sr^{-1} cm^{-2}$')
    plt.ylabel('Reflectance')
    plt.show(block=True)
    plt.savefig('empirical_line.pdf') 

def on_epoch_end(self, epoch, logs={}):
        self.model.save_weights(os.path.join(self.output_dir, 'weights%02d.h5' % (epoch)))
        self.show_edit_distance(256)
        word_batch = next(self.text_img_gen)[0]
        res = decode_batch(self.test_func, word_batch['the_input'][0:self.num_display_words])
        if word_batch['the_input'][0].shape[0] < 256:
            cols = 2
        else:
            cols = 1
        for i in range(self.num_display_words):
            pylab.subplot(self.num_display_words // cols, cols, i + 1)
            if K.image_data_format() == 'channels_first':
                the_input = word_batch['the_input'][i, 0, :, :]
            else:
                the_input = word_batch['the_input'][i, :, :, 0]
            pylab.imshow(the_input.T, cmap='Greys_r')
            pylab.xlabel('Truth = \'%s\'\nDecoded = \'%s\'' % (word_batch['source_str'][i], res[i]))
        fig = pylab.gcf()
        fig.set_size_inches(10, 13)
        pylab.savefig(os.path.join(self.output_dir, 'e%02d.png' % (epoch)))
        pylab.close() 

def on_epoch_end(self, epoch, logs={}):
        self.model.save_weights(os.path.join(self.output_dir, 'weights%02d.h5' % (epoch)))
        self.show_edit_distance(256)
        word_batch = next(self.text_img_gen)[0]
        res = decode_batch(self.test_func, word_batch['the_input'][0:self.num_display_words])
        if word_batch['the_input'][0].shape[0] < 256:
            cols = 2
        else:
            cols = 1
        for i in range(self.num_display_words):
            pylab.subplot(self.num_display_words // cols, cols, i + 1)
            if K.image_data_format() == 'channels_first':
                the_input = word_batch['the_input'][i, 0, :, :]
            else:
                the_input = word_batch['the_input'][i, :, :, 0]
            pylab.imshow(the_input.T, cmap='Greys_r')
            pylab.xlabel('Truth = \'%s\'\nDecoded = \'%s\'' % (word_batch['source_str'][i], res[i]))
        fig = pylab.gcf()
        fig.set_size_inches(10, 13)
        pylab.savefig(os.path.join(self.output_dir, 'e%02d.png' % (epoch)))
        pylab.close() 

def on_epoch_end(self, epoch, logs={}):
        self.model.save_weights(os.path.join(self.output_dir, 'weights%02d.h5' % (epoch)))
        self.show_edit_distance(256)
        word_batch = next(self.text_img_gen)[0]
        res = decode_batch(self.test_func, word_batch['the_input'][0:self.num_display_words])
        if word_batch['the_input'][0].shape[0] < 256:
            cols = 2
        else:
            cols = 1
        for i in range(self.num_display_words):
            pylab.subplot(self.num_display_words // cols, cols, i + 1)
            if K.image_data_format() == 'channels_first':
                the_input = word_batch['the_input'][i, 0, :, :]
            else:
                the_input = word_batch['the_input'][i, :, :, 0]
            pylab.imshow(the_input.T, cmap='Greys_r')
            pylab.xlabel('Truth = \'%s\'\nDecoded = \'%s\'' % (word_batch['source_str'][i], res[i]))
        fig = pylab.gcf()
        fig.set_size_inches(10, 13)
        pylab.savefig(os.path.join(self.output_dir, 'e%02d.png' % (epoch)))
        pylab.close() 

def on_epoch_end(self, epoch, logs={}):
        self.model.save_weights(os.path.join(self.output_dir, 'weights%02d.h5' % (epoch)))
        self.show_edit_distance(256)
        word_batch = next(self.text_img_gen)[0]
        res = decode_batch(self.test_func, word_batch['the_input'][0:self.num_display_words])
        if word_batch['the_input'][0].shape[0] < 256:
            cols = 2
        else:
            cols = 1
        for i in range(self.num_display_words):
            pylab.subplot(self.num_display_words // cols, cols, i + 1)
            if K.image_data_format() == 'channels_first':
                the_input = word_batch['the_input'][i, 0, :, :]
            else:
                the_input = word_batch['the_input'][i, :, :, 0]
            pylab.imshow(the_input.T, cmap='Greys_r')
            pylab.xlabel('Truth = \'%s\'\nDecoded = \'%s\'' % (word_batch['source_str'][i], res[i]))
        fig = pylab.gcf()
        fig.set_size_inches(10, 13)
        pylab.savefig(os.path.join(self.output_dir, 'e%02d.png' % (epoch)))
        pylab.close() 

def solid_plot():
	# reference values, see
	sref=0.0924102
	wref=0.000170152
	# List of the element types to process (text files)
	eltyps=["C3D8",
		"C3D8R",
		"C3D8I",
		"C3D20",
		"C3D20R",
		"C3D4",
		"C3D10"]
	pylab.figure(figsize=(10, 5.0), dpi=100)
	pylab.subplot(1,2,1)
	pylab.title("Stress")
	# pylab.hold(True) # deprecated
	for elty in eltyps:
		data = numpy.genfromtxt(elty+".txt")
		pylab.plot(data[:,1],data[:,2]/sref,"o-")
	pylab.xscale("log")
	pylab.xlabel('Number of nodes')
	pylab.ylabel('Max $\sigma / \sigma_{\mathrm{ref}}$')
	pylab.grid(True)
	pylab.subplot(1,2,2)
	pylab.title("Displacement")
	# pylab.hold(True) # deprecated
	for elty in eltyps:
		data = numpy.genfromtxt(elty+".txt")
		pylab.plot(data[:,1],data[:,3]/wref,"o-")
	pylab.xscale("log")
	pylab.xlabel('Number of nodes')
	pylab.ylabel('Max $u / u_{\mathrm{ref}}$')
	pylab.ylim([0,1.2])
	pylab.grid(True)
	pylab.legend(eltyps,loc="lower right")
	pylab.tight_layout()
	pylab.savefig("solid.svg",format="svg")
	# pylab.show()


# Move new files and folders to 'Refs' 

def timeseries(ifile, varkey, options, before='', after=''):
    import pylab as pl
    outpath = getattr(options, 'outpath', '.')
    time = gettime(ifile)
    var = ifile.variables[varkey]
    dims = [(k, l) for l, k in zip(var[:].shape, var.dimensions) if l > 1]
    if len(dims) > 1:
        raise ValueError(
            'Time series can have 1 non-unity dimensions; got %d - %s' %
            (len(dims), str(dims)))
    exec(before)
    ax = pl.gca()
    print(varkey, end='')
    if options.logscale:
        ax.set_yscale('log')

    ax.plot_date(time[:].squeeze(), var[:].squeeze())
    ax.set_xlabel(time.units.strip())
    ax.set_ylabel(getattr(var, 'standard_name',
                          varkey).strip() + ' ' + var.units.strip())
    fmt = 'png'
    figpath = os.path.join(outpath + '_ts_' + varkey + '.' + fmt)
    exec(after)
    pl.savefig(figpath)
    print('Saved fig', figpath)
    return figpath 

def saveBEVImageWithAxes(data, outputname, cmap = None, xlabel = 'x [m]', ylabel = 'z [m]', rangeX = [-10, 10], rangeXpx = None, numDeltaX = 5, rangeZ = [7, 62], rangeZpx = None, numDeltaZ = 5, fontSize = 16):
    '''
    
    :param data:
    :param outputname:
    :param cmap:
    '''
    aspect_ratio = float(data.shape[1])/data.shape[0]
    fig = pylab.figure()
    Scale = 8
    # add +1 to get axis text
    fig.set_size_inches(Scale*aspect_ratio+1,Scale*1)
    ax = pylab.gca()
    #ax.set_axis_off()
    #fig.add_axes(ax)
    if cmap != None:
        pylab.set_cmap(cmap)
    
    #ax.imshow(data, interpolation='nearest', aspect = 'normal')
    ax.imshow(data, interpolation='nearest')
    
    if rangeXpx == None:
        rangeXpx = (0, data.shape[1])
    
    if rangeZpx == None:
        rangeZpx = (0, data.shape[0])
        
    modBev_plot(ax, rangeX, rangeXpx, numDeltaX, rangeZ, rangeZpx, numDeltaZ, fontSize, xlabel = xlabel, ylabel = ylabel)
    #plt.savefig(outputname, bbox_inches='tight', dpi = dpi)
    pylab.savefig(outputname, dpi = data.shape[0]/Scale)
    pylab.close()
    fig.clear() 

def create_figure():
    newpsd = test_modcovar()

    pylab.plot(pylab.linspace(-0.5, 0.5, 4096),
               10 * pylab.log10(newpsd/max(newpsd)))
    pylab.axis([-0.5,0.5,-60,0])
    pylab.savefig('psd_modcovar.png') 

def create_figure():
    psd = test_burg()
    pylab.plot(pylab.linspace(-0.5, 0.5, len(psd)),
               10 * pylab.log10(psd/max(psd)))
    pylab.axis([-0.5,0.5,-60,0])
    pylab.savefig('psd_burg.png') 

def create_figure():
    psd = test_pcovar()
    pylab.axis([-0.5,0.5,-60,0])
    pylab.savefig('psd_covar.png') 

def create_figure():
    res = test_minvar()
    psd = res[0]
    f = pylab.linspace(-0.5, 0.5, len(psd))
    pylab.plot(f, 10 * pylab.log10(psd/max(psd)), label='minvar 15')
    pylab.savefig('psd_minvar.png') 

def presslat(ifile, varkey, options, before='', after=''):
    import matplotlib.pyplot as plt
    from matplotlib.colors import Normalize, LogNorm
    outpath = getattr(options, 'outpath', '.')
    vert = cu.getpresbnds(ifile)
    lat, latunit = cu.getlatbnds(ifile)
    lat = np.append(lat.squeeze()[..., :2].mean(
        1), lat.squeeze()[-1, 2:].mean(0))
    var = ifile.variables[varkey]
    dims = [(k, l) for l, k in zip(var[:].shape, var.dimensions) if l > 1]
    if len(dims) > 2:
        raise ValueError(
            'Press-lat can have 2 non-unity dimensions; got %d - %s' %
            (len(dims), str(dims)))
    if options.logscale:
        norm = LogNorm()
    else:
        norm = Normalize()
    exec(before)
    ax = plt.gca()
    print(varkey, end='')
    patches = ax.pcolor(lat, vert, var[:].squeeze(), norm=norm)
    # ax.set_xlabel(X.units.strip())
    # ax.set_ylabel(Y.units.strip())
    cbar = plt.colorbar(patches)
    vunit = getattr(var, 'units', 'unknown').strip()
    cbar.set_label(varkey + ' (' + vunit + ')')
    cbar.ax.text(.5, 1, '%.2g' % var[:].max(
    ), horizontalalignment='center', verticalalignment='bottom')
    cbar.ax.text(.5, 0, '%.2g' % var[:].min(
    ), horizontalalignment='center', verticalalignment='top')
    ax.set_ylim(vert.max(), vert.min())
    ax.set_xlim(lat.min(), lat.max())
    fmt = 'png'
    figpath = os.path.join(outpath + '_PRESSLAT_' + varkey + '.' + fmt)
    exec(after)
    plt.savefig(figpath)
    print('Saved fig', figpath)
    return figpath 

def presslon(ifile, varkey, options, before='', after=''):
    import matplotlib.pyplot as plt
    from matplotlib.colors import Normalize, LogNorm
    outpath = getattr(options, 'outpath', '.')
    vert = cu.getpresbnds(ifile)
    lon, lonunit = cu.getlonbnds(ifile)
    lon = np.append(lon.squeeze()[..., [0, 3]].mean(
        1), lon.squeeze()[-1, [1, 2]].mean(0))
    var = ifile.variables[varkey]
    dims = [(k, l) for l, k in zip(var[:].shape, var.dimensions) if l > 1]
    if len(dims) > 2:
        raise ValueError(
            'Press-lon plots can have 2 non-unity dimensions; got %d - %s' %
            (len(dims), str(dims)))
    if options.logscale:
        norm = LogNorm()
    else:
        norm = Normalize()
    exec(before)
    ax = plt.gca()
    print(varkey, end='')
    patches = ax.pcolor(lon, vert, var[:].squeeze(), norm=norm)
    # ax.set_xlabel(X.units.strip())
    # ax.set_ylabel(Y.units.strip())
    cbar = plt.colorbar(patches)
    vunit = getattr(var, 'units', 'unknown').strip()
    cbar.set_label(varkey + ' (' + vunit + ')')
    cbar.ax.text(.5, 1, '%.2g' % var[:].max(
    ), horizontalalignment='center', verticalalignment='bottom')
    cbar.ax.text(.5, 0, '%.2g' % var[:].min(
    ), horizontalalignment='center', verticalalignment='top')

    ax.set_ylim(vert.max(), vert.min())
    ax.set_xlim(lon.min(), lon.max())
    fmt = 'png'
    figpath = os.path.join(outpath + '_PRESLON_' + varkey + '.' + fmt)
    exec(after)
    plt.savefig(figpath)
    print('Saved fig', figpath)
    return figpath 

def profile(ifile, varkey, options, before='', after=''):
    import matplotlib.pyplot as plt
    print(varkey, end='')
    outpath = getattr(options, 'outpath', '.')
    try:
        vert = cu.getpresmid(ifile)
        vunit = 'Pa'
    except Exception:
        vert = cu.getsigmamid(ifile)
        vunit = r'\sigma'
    var = ifile.variables[varkey]

    dims = list(var.dimensions)
    for knownvert in ['layer', 'LAY'] + ['layer%d' % i for i in range(72)]:
        if knownvert in dims:
            vidx = dims.index(knownvert)
            break
    else:
        raise KeyError("No known vertical coordinate; got %s" % str(dims))
    vert = vert[:var[:].shape[vidx]]
    units = var.units.strip()
    vals = np.rollaxis(var[:], vidx, start=0).view(
        np.ma.MaskedArray).reshape(vert.size, -1)
    ax = plt.gca()
    minmaxmean(ax, vals, vert)
    ax.set_xlabel(varkey + ' (' + units + ')')
    ax.set_ylabel(vunit)
    ax.set_ylim(vert.max(), vert.min())
    if options.logscale:
        ax.set_xscale('log')
    fmt = 'png'
    figpath = os.path.join(outpath + '_profile_' + varkey + '.' + fmt)
    exec(after)
    plt.savefig(figpath)
    print('Saved fig', figpath)
    return figpath 

def generateImages(picklefile, pickledir, filehash, imagedir, pietype):

	leaf_file = open(os.path.join(pickledir, picklefile), 'rb')
	(piedata, pielabels) = cPickle.load(leaf_file)
	leaf_file.close()

	pylab.figure(1, figsize=(6.5,6.5))
	ax = pylab.axes([0.2, 0.15, 0.6, 0.6])

	pylab.pie(piedata, labels=pielabels)

	pylab.savefig(os.path.join(imagedir, '%s-%s.png' % (filehash, pietype)))
	pylab.gcf().clear()
	os.unlink(os.path.join(pickledir, picklefile)) 

def saveBEVImageWithAxes(data, outputname, cmap = None, xlabel = 'x [m]', ylabel = 'z [m]', rangeX = [-10, 10], rangeXpx = None, numDeltaX = 5, rangeZ = [7, 62], rangeZpx = None, numDeltaZ = 5, fontSize = 16):
    '''
    
    :param data:
    :param outputname:
    :param cmap:
    '''
    aspect_ratio = float(data.shape[1])/data.shape[0]
    fig = pylab.figure()
    Scale = 8
    # add +1 to get axis text
    fig.set_size_inches(Scale*aspect_ratio+1,Scale*1)
    ax = pylab.gca()
    #ax.set_axis_off()
    #fig.add_axes(ax)
    if cmap != None:
        pylab.set_cmap(cmap)
    
    #ax.imshow(data, interpolation='nearest', aspect = 'normal')
    ax.imshow(data, interpolation='nearest')
    
    if rangeXpx == None:
        rangeXpx = (0, data.shape[1])
    
    if rangeZpx == None:
        rangeZpx = (0, data.shape[0])
        
    modBev_plot(ax, rangeX, rangeXpx, numDeltaX, rangeZ, rangeZpx, numDeltaZ, fontSize, xlabel = xlabel, ylabel = ylabel)
    #plt.savefig(outputname, bbox_inches='tight', dpi = dpi)
    pylab.savefig(outputname, dpi = data.shape[0]/Scale)
    pylab.close()
    fig.clear() 

def pressx(ifile, varkey, options, before='', after=''):
    import matplotlib.pyplot as plt
    from matplotlib.colors import Normalize, LogNorm
    outpath = getattr(options, 'outpath', '.')
    vert = cu.getpresbnds(ifile)
    var = ifile.variables[varkey]
    dims = [(k, l) for l, k in zip(var[:].shape, var.dimensions) if l > 1]
    if len(dims) > 2:
        raise ValueError(
            'Press-x can have 2 non-unity dimensions; got %d - %s' %
            (len(dims), str(dims)))
    if options.logscale:
        norm = LogNorm()
    else:
        norm = Normalize()
    exec(before)
    ax = plt.gca()
    print(varkey, end='')
    vals = var[:].squeeze()
    x = np.arange(vals.shape[1])
    patches = ax.pcolor(x, vert, vals, norm=norm)
    # ax.set_xlabel(X.units.strip())
    # ax.set_ylabel(Y.units.strip())
    plt.colorbar(patches)
    ax.set_ylim(vert.max(), vert.min())
    ax.set_xlim(x.min(), x.max())
    fmt = 'png'
    figpath = os.path.join(outpath + '_PRESX_' + varkey + '.' + fmt)
    exec(after)
    plt.savefig(figpath)
    print('Saved fig', figpath)
    return figpath