How to use the plotnine.aes function in plotnine

p1.save(os.path.join(outdir, 'protobowl_hist.pdf'))
    # p1.draw()
    print('p1 done')

    # histogram of accuracy
    p2 = ggplot(user_stat, aes(x='result', y='..density..')) \
        + geom_histogram(color='#31a354', fill='#e5f5e0') \
        + geom_density() \
        + labs(x='Accuracy', y='Density') \
        + theme(aspect_ratio=0.3)
    p2.save(os.path.join(outdir, 'protobowl_acc.pdf'))
    # p2.draw()
    print('p2 done')

    # histogram of buzzing position
    p3 = ggplot(user_stat, aes(x='relative_position', y='..density..')) \
        + geom_histogram(color='#3182bd', fill='#deebf7') \
        + geom_density() \
        + labs(x='Average buzzing position', y='Density') \
        + theme(aspect_ratio=0.3)
    p3.save(os.path.join(outdir, 'protobowl_pos.pdf'))
    # p3.draw()
    print('p3 done')

for i in range(len(color_order)):
                    for j in dm[facet_var].unique():
                        dm_sub = dm[(dm[group_by] == color_order[i]) & (dm[facet_var] == j)].sort_values('pos')
                        p += plotnine.geom_ribbon(data=dm_sub,
                                                  mapping=aes(ymin='ci_low_robust',
                                                              ymax='ci_high_robust'),
                                                  show_legend=False,
                                                  fill=list(dm_sub.color_palette.unique())[0],
                                                  color=None,
                                                  alpha=0.3)
        else:
            if stat == "mean":
                for i in range(len(color_order)):
                    dm_sub = dm[dm[group_by] == color_order[i]].sort_values('pos')
                    p += plotnine.geom_ribbon(data=dm_sub,
                                              mapping=aes(ymin='ci_low',
                                                          ymax='ci_high'),
                                              show_legend=False,
                                              fill=list(dm_sub.color_palette.unique())[0],
                                              color=None,
                                              alpha=0.3)
            elif stat == "median":
                for i in range(len(color_order)):
                    dm_sub = dm[dm[group_by] == color_order[i]].sort_values('pos')
                    p += plotnine.geom_ribbon(data=dm_sub,
                                              mapping=aes(ymin='ci_low_robust',
                                                          ymax='ci_high_robust'),
                                              show_legend=False,
                                              fill=list(dm_sub.color_palette.unique())[0],
                                              color=None,
                                              alpha=0.3)

mat_s = d[['feature_type',
                   'summed_bp_overlaps_log2_fold_change',
                   'summed_bp_overlaps_pvalue']]
        # Uncomputed pvalue are discarded
        mat_s = mat_s.drop(mat_s[mat_s.summed_bp_overlaps_pvalue == -1].index)
        # Pval set to 0 are changed to  1e-320
        mat_s.loc[mat_s['summed_bp_overlaps_pvalue'] == 0, 'summed_bp_overlaps_pvalue'] = 1e-320
        mat_s = mat_s.assign(minus_log10_pvalue=list(-np.log10(list(mat_s.summed_bp_overlaps_pvalue))))
        mat_s.columns = ['Feature', 'log2_FC', 'pvalue', 'minus_log10_pvalue']
        mat_s = mat_s.assign(Statistic=['S'] * mat_s.shape[0])

        df_volc = mat_n.append(mat_s)

        p = ggplot(data=df_volc, mapping=aes(x='log2_FC', y='minus_log10_pvalue'))
        p += geom_vline(xintercept=0, color='darkgray')
        p += geom_label(aes(label='Feature', fill='Statistic'),
                        size=5,
                        color='black',
                        alpha=.5,
                        label_size=0)
        p += ylab('-log10(pvalue)') + xlab('log2(FC)')
        p += ggtitle('Volcano plot (for both N and S statistics)')
        p += scale_fill_manual(values={'N': '#7570b3', 'S': '#e7298a'})
        p += theme_bw()

        return p

def bsuite_bar_plot(df_in: pd.DataFrame,
                    sweep_vars: Sequence[Text] = None) -> gg.ggplot:
  """Output bar plot of bsuite data."""
  df = _clean_bar_plot_data(df_in, sweep_vars)

  p = (gg.ggplot(df)
       + gg.aes(x='env', y='score', colour='type', fill='type')
       + gg.geom_bar(position='dodge', stat='identity')
       + gg.geom_hline(yintercept=1., linetype='dashed', alpha=0.5)
       + gg.scale_colour_manual(plotting.CATEGORICAL_COLOURS)
       + gg.scale_fill_manual(plotting.CATEGORICAL_COLOURS)
       + gg.xlab('experiment')
       + gg.theme(axis_text_x=gg.element_text(angle=25, hjust=1))
      )
  if not all(df.finished):  # add a layer of alpha for unfinished jobs
    p += gg.aes(alpha='finished')
    p += gg.scale_alpha_discrete([0.3, 1.0])

  # Compute the necessary size of the plot
  if sweep_vars:
    p += gg.facet_wrap(sweep_vars, labeller='label_both', ncol=1)
    n_hypers = df[sweep_vars].drop_duplicates().shape[0]
  else:
    n_hypers = 1
  return p + gg.theme(figure_size=(14, 3 * n_hypers + 1))

if fc[i] > 1: signif_color[i] = '#3c9040'

        text = text.apply(format_pvalue)
        text_pos = (maximum + 0.05 * max(maximum)).append(na_series)
        text_pos.index = range(len(text_pos))

        if display_fit_quality:
            fit_qual_text = dm[statname + '_negbinom_fit_quality'].append(na_series)
            fit_qual_text.index = range(len(fit_qual_text))

            text_with_fit = list()
            for t, f in zip(text.tolist(), fit_qual_text.tolist()):
                text_with_fit += [t + "\n" + 'fit={0:.2g}'.format(f)]
            text = pd.Series(text_with_fit)

        aes_plot = aes(x='Feature', y=text_pos, label=text)
        p += geom_label(mapping=aes_plot, stat='identity',
                        size=5, boxstyle='round', label_size=0.2,
                        color='white', fill=signif_color)

        # Theme
        p += theme(legend_title=element_blank(),
                   legend_position="top",
                   legend_box_spacing=0.65,
                   legend_key_size=8,
                   legend_text=element_text(size=8),
                   legend_key=element_blank(),
                   axis_title_x=element_blank(),
                   axis_title_y=element_text(colour='#333333',
                                             size=8,
                                             hjust=4,
                                             angle=90,

def create_readme_image():
    kwargs = dict(width=6, height=4)
    df = pd.DataFrame({'x': np.linspace(0, 2*np.pi, 500)})

    p = (df
         >> define(y='np.sin(x)')
         >> define_where('y>=0', sign=('"positive"', '"negative"'))
         >> (ggplot(aes('x', 'y'))
             + geom_line(aes(color='sign'), size=1.5))
         )
    p.save('readme-image.png', **kwargs)

+ aes(x='depth', y='y', color='dataset')
        + facet_wrap('metric', scales='free_y', nrow=2, labeller=label_facet)
        + geom_line() + geom_point()
        + xlab('Parse Truncation Depth') + ylab('')
        + scale_color_discrete(name='Dataset')
        + scale_y_continuous(labels=label_y)
        + scale_x_continuous(
            breaks=list(range(1, 11)),
            minor_breaks=list(range(1, 11)),
            limits=[1, 10])
        + theme_fs()
    )
    p.save(path.join(output_path, 'syn_div_plot.pdf'))
    p = (
    ggplot(parse_df)
        + aes(x='depth', y='unique_parses', color='dataset')
        + geom_line() + geom_point()
        + xlab('Parse Truncation Depth')
        + ylab('Count of Unique Parses')
        + scale_color_discrete(name='Dataset')
        + scale_x_continuous(
            breaks=list(range(1, 11)),
            minor_breaks=list(range(1, 11)),
            limits=[1, 10])
        + theme_fs()
    )
    p.save(path.join(output_path, 'n_unique_parses.pdf'))
    p = (
        ggplot(parse_df)
        + aes(x='depth', y='parse_ratio', color='dataset')
        + geom_line() + geom_point()
        + xlab('Parse Truncation Depth')

def _bar_plot_compare(df: pd.DataFrame) -> gg.ggplot:
  """Bar plot of buite score data, comparing agents on each experiment."""
  p = (gg.ggplot(df)
       + gg.aes(x='agent', y='score', colour='agent', fill='agent')
       + gg.geom_bar(position='dodge', stat='identity')
       + gg.geom_hline(yintercept=1., linetype='dashed', alpha=0.5)
       + gg.theme(axis_text_x=gg.element_text(angle=25, hjust=1))
       + gg.scale_colour_manual(plotting.CATEGORICAL_COLOURS)
       + gg.scale_fill_manual(plotting.CATEGORICAL_COLOURS)
      )
  if not all(df.finished):  # add a layer of alpha for unfinished jobs
    p += gg.aes(alpha='finished')
    p += gg.scale_alpha_discrete([0.3, 1.0])
  return p

def plot_learning(df: pd.DataFrame,
                  sweep_vars: Sequence[Text] = None,
                  group_col: Text = 'noise_scale') -> gg.ggplot:
  """Plots the average regret through time."""
  p = plotting.plot_regret_learning(
      df_in=df, group_col=group_col, sweep_vars=sweep_vars,
      max_episode=sweep.NUM_EPISODES)
  p += gg.geom_hline(gg.aes(yintercept=mnist_analysis.BASE_REGRET),
                     linetype='dashed', alpha=0.4, size=1.75)
  return p