python类axes_style()的实例源码

def plot_1d(dataset, nbins, data):
    with sns.axes_style('white'):
        plt.rc('font', weight='bold')
        plt.rc('grid', lw=2)
        plt.rc('lines', lw=3)
        plt.figure(1)
        plt.hist(data, bins=np.arange(nbins+1), color='blue')
        plt.ylabel('Count', weight='bold', fontsize=24)
        xticks = list(plt.gca().get_xticks())
        while (nbins-1) / float(xticks[-1]) < 1.1:
            xticks = xticks[:-1]
        while xticks[0] < 0:
            xticks = xticks[1:]
        xticks.append(nbins-1)
        xticks = list(sorted(xticks))
        plt.gca().set_xticks(xticks)
        plt.xlim([int(np.ceil(-0.05*nbins)),int(np.ceil(nbins*1.05))])
        plt.legend(loc='upper right')
        plt.savefig('plots/marginals-{0}.pdf'.format(dataset.replace('_','-')), bbox_inches='tight')
        plt.clf()
        plt.close()

def plot_2d(dataset, nbins, data, extra=None):
    with sns.axes_style('white'):
        plt.rc('font', weight='bold')
        plt.rc('grid', lw=2)
        plt.rc('lines', lw=2)
        rows, cols = nbins
        im = np.zeros(nbins)
        for i in xrange(rows):
            for j in xrange(cols):
                im[i,j] = ((data[:,0] == i) & (data[:,1] == j)).sum()
        plt.imshow(im, cmap='gray_r', interpolation='none')
        if extra is not None:
            dataset += extra
        plt.savefig('plots/marginals-{0}.pdf'.format(dataset.replace('_','-')), bbox_inches='tight')
        plt.clf()
        plt.close()

def plot_1d(dataset, nbins):
    data = np.loadtxt('experiments/uci/data/splits/{0}_all.csv'.format(dataset), skiprows=1, delimiter=',')[:,-1]
    with sns.axes_style('white'):
        plt.rc('font', weight='bold')
        plt.rc('grid', lw=2)
        plt.rc('lines', lw=3)
        plt.figure(1)
        plt.hist(data, bins=np.arange(nbins+1), color='blue')
        plt.ylabel('Count', weight='bold', fontsize=24)
        xticks = list(plt.gca().get_xticks())
        while (nbins-1) / float(xticks[-1]) < 1.1:
            xticks = xticks[:-1]
        while xticks[0] < 0:
            xticks = xticks[1:]
        xticks.append(nbins-1)
        xticks = list(sorted(xticks))
        plt.gca().set_xticks(xticks)
        plt.xlim([int(np.ceil(-0.05*nbins)),int(np.ceil(nbins*1.05))])
        plt.legend(loc='upper right')
        plt.savefig('plots/marginals-{0}.pdf'.format(dataset.replace('_','-')), bbox_inches='tight')
        plt.clf()
        plt.close()

def plot_2d(dataset, nbins, data=None, extra=None):
    if data is None:
        data = np.loadtxt('experiments/uci/data/splits/{0}_all.csv'.format(dataset), skiprows=1, delimiter=',')[:,-2:]
    with sns.axes_style('white'):
        plt.rc('font', weight='bold')
        plt.rc('grid', lw=2)
        plt.rc('lines', lw=2)
        rows, cols = nbins
        im = np.zeros(nbins)
        for i in xrange(rows):
            for j in xrange(cols):
                im[i,j] = ((data[:,0] == i) & (data[:,1] == j)).sum()
        plt.imshow(im, cmap='gray_r', interpolation='none')
        if extra is not None:
            dataset += extra
        plt.savefig('plots/marginals-{0}.pdf'.format(dataset.replace('_','-')), bbox_inches='tight')
        plt.clf()
        plt.close()

def joint_plot(x, y, xlabel=None,
               ylabel=None, xlim=None, ylim=None,
               loc="best", color='#0485d1',
               size=8, markersize=50, kind="kde",
               scatter_color="r"):
    with sns.axes_style("darkgrid"):
        if xlabel and ylabel:
            g = SubsampleJointGrid(xlabel, ylabel,
                    data=DataFrame(data={xlabel: x, ylabel: y}),
                    space=0.1, ratio=2, size=size, xlim=xlim, ylim=ylim)
        else:
            g = SubsampleJointGrid(x, y, size=size,
                    space=0.1, ratio=2, xlim=xlim, ylim=ylim)
        g.plot_joint(sns.kdeplot, shade=True, cmap="Blues")
        g.plot_sub_joint(plt.scatter, 1000, s=20, c=scatter_color, alpha=0.3)
        g.plot_marginals(sns.distplot, kde=False, rug=False)
        g.annotate(ss.pearsonr, fontsize=25, template="{stat} = {val:.2g}\np = {p:.2g}")
        g.ax_joint.set_yticklabels(g.ax_joint.get_yticks())
        g.ax_joint.set_xticklabels(g.ax_joint.get_xticks())
    return g

def plot_settings():
    style = sns.axes_style('darkgrid')
    style['font.size'] = 12.0
    style['font.family'] = 'monospace'
    style['font.weight'] = 'normal'
    style['font.sans-serif'] = ('Helveitca', 'Bitstream Vera Sans',
                                'Lucida Grande', 'Verdana', 'Geneva', 'Lucid',
                                'Arial', 'Avant Garde', 'sans-serif')
    style['font.monospace'] = ('Decima Mono', 'Bitstream Vera Sans Mono',
                               'Andale Mono', 'Nimbus Mono L', 'Courier New',
                               'Courier', 'Fixed', 'Terminal', 'monospace')
    style['text.color'] = '#222222'
    style['pdf.fonttype'] = 42
    return style

def setup_figure(self):
        with sns.axes_style("whitegrid"):
            super(Visualizer, self).setup_figure()

def get_grid(self, **kwargs):
        with sns.axes_style("whitegrid"):
            grid = super(TimeSeries, self).get_grid(**kwargs)
        return grid

def setup_figure(self):
        with sns.axes_style("whitegrid"):
            super(Visualizer, self).setup_figure()

def setup_figure(self):
        with sns.axes_style("whitegrid"):
            super(Visualizer, self).setup_figure()

def setup_figure(self):
        with sns.axes_style("ticks"):
            super(Visualizer, self).setup_figure()

def get_grid(self, **kwargs):
        kwargs["data"] = self.df
        with sns.axes_style(self.axes_style):
            with sns.plotting_context(self.plotting_context):
                grid = sns.FacetGrid(**kwargs)
        return grid

def setup_figure(self):
        with sns.axes_style("white"):
            super(Visualizer, self).setup_figure()

def setup_figure(self):
        with sns.axes_style("whitegrid"):
            super(Visualizer, self).setup_figure()

def setup_figure(self):
        with sns.axes_style("white"):
            super(Visualizer, self).setup_figure()

def plot_learning_function(xdata, ydata, yerr, order, aplot, poly):
    with sns.axes_style('whitegrid'):
        sns.set_context('paper')
        xp = np.linspace(np.min(xdata), np.max(xdata), 1000)[:, None]
        plt.figure()
        plt.errorbar(xdata, ydata, yerr,
                     label='Nearest similarity', marker='s')
        plt.plot(xp, poly(xp), '-',
                 label='Learning function (poly of order {})'.format(order))
        # plt.plot(xp, least_squares_mdl(res.x, xp), '-', label='least squares')
        plt.xlabel(r'Mutation level')
        plt.ylabel(r'Average similarity (not normalised)')
        plt.legend(loc='lower left')
        plt.savefig(aplot, transparent=True, bbox_inches='tight')
        plt.close()

def powerplant_map():
    with sns.axes_style('darkgrid'):
        df = set_uncommon_fueltypes_to_other(Carma_ENTSOE_ESE_GEO_OPSD_WRI_matched_reduced())
        shown_fueltypes = ['Hydro', 'Natural Gas', 'Nuclear', 'Hard Coal', 'Lignite', 'Oil']
        df = df[df.Fueltype.isin(shown_fueltypes) & df.lat.notnull()]
        fig, ax = plt.subplots(figsize=(7,5))

        scale = 5e1

        #df.plot.scatter('lon', 'lat', s=df.Capacity/scale, c=df.Fueltype.map(utils.tech_colors),
        #                ax=ax)
        ax.scatter(df.lon, df.lat, s=df.Capacity/scale, c=df.Fueltype.map(tech_colors2))

        ax.set_xlabel('')
        ax.set_ylabel('')
        draw_basemap()
        ax.set_xlim(-13, 34)
        ax.set_ylim(35, 71.65648314)
        ax.set_axis_bgcolor('white')
        fig.tight_layout(pad=0.5)

        legendcols = pd.Series(tech_colors2).reindex(shown_fueltypes)
        handles = sum(legendcols.apply(lambda x :
            make_legend_circles_for([10.], scale=scale, facecolor=x)).tolist(), [])
        fig.legend(handles, legendcols.index,
                   handler_map=make_handler_map_to_scale_circles_as_in(ax),
                   ncol=3, loc="upper left", frameon=False, fontsize=11)
        return fig, ax

def bar_fueltype_totals(dfs, keys, figsize=(7,4), unit='GW', show_totals=False, 
                        last_as_marker=False):
    with sns.axes_style('darkgrid'):
        fig, ax = plt.subplots(1,1, figsize=figsize)
        if last_as_marker:
            as_marker = dfs[-1]
            dfs = dfs[:-1]
            as_marker_key = keys[-1]
            keys = keys[:-1]
        fueltotals = lookup(dfs,
                   keys=keys, by='Fueltype'
                   ,show_totals=show_totals, unit=unit)
        fueltotals.plot(kind="bar",
                           ax=ax, legend='reverse', edgecolor='none', rot=75)
        if last_as_marker:
            fueltotals = lookup(as_marker,
                        keys=as_marker_key, by='Fueltype'
                       ,show_totals=show_totals, unit=unit)
            fueltotals.plot(ax=ax, label=as_marker_key, markeredgecolor='none', rot=75, 
                            marker='D', markerfacecolor='darkslategray', linestyle='None')

        ax.legend(loc=0)
        ax.set_ylabel(r'Capacity [$%s$]'%unit)
        ax.xaxis.grid(False)
        fig.tight_layout(pad=0.5)
        return fig, ax

def bar_matching_fueltype_totals(figsize=(7,4)):
    from . import data
    from .collection import Carma_ENTSOE_GEO_OPSD_WRI_matched_reduced
    matched = set_uncommon_fueltypes_to_other(
            Carma_ENTSOE_GEO_OPSD_WRI_matched_reduced())
    matched.loc[matched.Fueltype=='Waste', 'Fueltype']='Other'
    geo=set_uncommon_fueltypes_to_other(data.GEO())
    carma=set_uncommon_fueltypes_to_other(data.CARMA())
    wri=set_uncommon_fueltypes_to_other(data.WRI())
    ese=set_uncommon_fueltypes_to_other(data.ESE())
    entsoe = set_uncommon_fueltypes_to_other(data.Capacity_stats())
    opsd = set_uncommon_fueltypes_to_other(data.OPSD())
    entsoedata = set_uncommon_fueltypes_to_other(data.ENTSOE())

    matched.Capacity = matched.Capacity/1000.
    geo.Capacity = geo.Capacity/1000.
    carma.Capacity = carma.Capacity/1000.
    wri.Capacity = wri.Capacity/1000.
    ese.Capacity = ese.Capacity/1000.
    entsoe.Capacity = entsoe.Capacity/1000.
    opsd.Capacity = opsd.Capacity/1000.
    entsoedata.Capacity =  entsoedata.Capacity/1000.

    with sns.axes_style('whitegrid'):
        fig, (ax1,ax2) = plt.subplots(1,2, figsize=figsize, sharey=True)
        databases = lookup([carma, entsoedata, ese, geo, opsd, wri],
                   keys=[ 'CARMA', 'ENTSOE','ESE', 'GEO','OPSD', 'WRI'], by='Fueltype')

        databases.plot(kind='bar', ax=ax1, edgecolor='none')
        datamatched = lookup(matched, by='Fueltype')
        datamatched.index.name=''
        datamatched.name='Matched Database'
        datamatched.plot(kind='bar', ax=ax2, #color=cmap[3:4],
                         edgecolor='none')
        ax2.legend()
        ax1.set_ylabel('Capacity [GW]')
        ax1.xaxis.grid(False)
        ax2.xaxis.grid(False)
        fig.tight_layout(pad=0.5)
        return fig, [ax1,ax2]

def hbar_country_totals(dfs, keys, exclude_fueltypes=['Solar', 'Wind'],
                        figsize=(7,5), unit='GW'):
    with sns.axes_style('whitegrid'):
        fig, ax = plt.subplots(1,1, figsize=figsize)
        countrytotals = lookup(dfs,
                   keys=keys, by='Country',
                    exclude=exclude_fueltypes,show_totals=True,
                    unit=unit)
        countrytotals[::-1][1:].plot(kind="barh",
                           ax=ax, legend='reverse', edgecolor='none')
        ax.set_xlabel('Capacity [%s]'%unit)
        ax.yaxis.grid(False)
        ax.set_ylabel('')
        fig.tight_layout(pad=0.5)
        return fig, ax

def start_plotting(fig_size, fig_pos, style="white", rc=None, despine=False):
    with sns.axes_style(style, rc):
        fig = plt.figure(figsize=fig_size)
        if not fig_pos:
            ax = fig.add_subplot(111)
        else:
            ax = fig.add_axes(fig_pos)
    if despine:
        sns.despine(left=True)
    return fig, ax

def improvement_plot(consensus_data, ordered_genomes, improvement_tgt):
    def do_kdeplot(x, y, ax, n_levels=None, bw='scott'):
        try:
            sns.kdeplot(x, y, ax=ax, cut=0, cmap='Purples_d', shade=True, shade_lowest=False, n_levels=n_levels, bw=bw,
                        rasterized=True)
        except:
            logger.warning('Unable to do a KDE fit to AUGUSTUS improvement.')
            pass

    with improvement_tgt.open('w') as outf, PdfPages(outf) as pdf, sns.axes_style("whitegrid"):
        for genome in ordered_genomes:
            data = pd.DataFrame(consensus_data[genome]['Evaluation Improvement']['changes'])
            unchanged = consensus_data[genome]['Evaluation Improvement']['unchanged']
            if len(data) == 0:
                continue
            data.columns = ['transMap original introns',
                            'transMap intron annotation support',
                            'transMap intron RNA support',
                            'Original introns',
                            'Intron annotation support',
                            'Intron RNA support',
                            'transMap alignment goodness',
                            'Alignment goodness']
            fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(ncols=2, nrows=2)
            for ax in [ax1, ax2, ax3]:  # goodness plots are allowed to auto-set scale
                ax.set_xlim(0, 100)
                ax.set_ylim(0, 100)
            goodness_min = min(data['Alignment goodness'])
            ax4.set_xlim(goodness_min, 100)
            ax4.set_ylim(goodness_min, 100)
            do_kdeplot(data['transMap original introns'], data['Original introns'], ax1, n_levels=25, bw=2)
            sns.regplot(x=data['transMap original introns'], y=data['Original introns'], ax=ax1,
                        color='#A9B36F', scatter_kws={"s": 3, 'alpha': 0.7, 'rasterized': True}, fit_reg=False)
            do_kdeplot(data['transMap intron annotation support'], data['Intron annotation support'], ax2,
                       n_levels=25, bw=2)
            sns.regplot(x=data['transMap intron annotation support'], y=data['Intron annotation support'], ax=ax2,
                        color='#A9B36F', scatter_kws={"s": 3, 'alpha': 0.7, 'rasterized': True}, fit_reg=False)
            do_kdeplot(data['transMap intron RNA support'], data['Intron RNA support'], ax3, n_levels=25, bw=2)
            sns.regplot(x=data['transMap intron RNA support'], y=data['Intron RNA support'], ax=ax3,
                        color='#A9B36F', scatter_kws={"s": 3, 'alpha': 0.7, 'rasterized': True}, fit_reg=False)
            do_kdeplot(data['transMap alignment goodness'], data['Alignment goodness'], ax4, n_levels=20, bw=1)
            sns.regplot(x=data['transMap alignment goodness'], y=data['Alignment goodness'], ax=ax4,
                        color='#A9B36F', scatter_kws={"s": 3, 'alpha': 0.7, 'rasterized': True}, fit_reg=False)
            fig.suptitle('AUGUSTUS metric improvements for {:,} transcripts in {}.\n'
                         '{:,} transMap transcripts were chosen.'.format(len(data), genome, unchanged))
            for ax in [ax1, ax2, ax3, ax4]:
                ax.set(adjustable='box-forced', aspect='equal')
            fig.subplots_adjust(hspace=0.3)
            multipage_close(pdf, tight_layout=False)

def comparison_1dim(by='Country', include_WEPP=True, include_VRE=False,
                    year=2015, how='hbar', figsize=(7,5)):
    """
    Plots a horizontal bar chart with capacity on x-axis, ``by`` on y-axis.

    Parameters
    ----------
    by : string, defines how to group data
        Allowed values: 'Country' or 'Fueltype'

    """
    red_w_wepp, red_wo_wepp, wepp, statistics = gather_comparison_data(include_WEPP=include_WEPP,
                                                                       include_VRE=include_VRE,
                                                                       year=year)
    if include_WEPP:
        stats = lookup([red_w_wepp, red_wo_wepp, wepp, statistics],
                       keys=['Matched dataset w/ WEPP', 'Matched dataset w/o WEPP',
                             'WEPP only', 'Statistics OPSD'], by=by)/1000
    else:
        stats = lookup([red_wo_wepp, statistics],
                       keys=['Matched dataset w/o WEPP', 'Statistics OPSD'],
                       by=by)/1000

    if how == 'hbar':
        with sns.axes_style('darkgrid'):
            font={'size'   : 24}
            plt.rc('font', **font)
            fig, ax = plt.subplots(figsize=figsize)
            stats.plot.barh(ax=ax, stacked=False, colormap='jet')
            ax.set_xlabel('Installed Capacity [GW]')
            ax.yaxis.label.set_visible(False)
            #ax.set_facecolor('#d9d9d9')                  # gray background
            ax.set_axisbelow(True)                       # puts the grid behind the bars
            ax.grid(color='white', linestyle='dotted')   # adds white dotted grid
            ax.legend(loc='best')
            ax.invert_yaxis()
        return fig, ax
    if how == 'scatter':
        stats.loc[:, by] = stats.index.astype(str) #Needed for seaborne
        if len(stats.columns)-1 >= 3:
            g = sns.pairplot(stats, diag_kind='kde', hue=by, palette='Set2',
                             size=figsize[1], aspect=figsize[0]/figsize[1])
        else:
            g = sns.pairplot(stats, diag_kind='kde', hue=by, palette='Set2',
                             size=figsize[1], aspect=figsize[0]/figsize[1],
                             x_vars=stats.columns[0], y_vars=stats.columns[1])
        for i in range(0, len(g.axes)):
            for j in range(0, len(g.axes[0])):
                g.axes[i,j].set(xscale='log', yscale='log', xlim=(1,200), ylim=(1,200))
        return g.fig, g.axes