# SPDX-FileCopyrightText: : 2017-2020 The PyPSA-Eur Authors # # SPDX-License-Identifier: GPL-3.0-or-later """ Plots map with pie charts and cost box bar charts. Relevant Settings ----------------- Inputs ------ Outputs ------- Description ----------- """ import logging from _helpers import (load_network_for_plots, aggregate_p, aggregate_costs, configure_logging) import pandas as pd import numpy as np from six.moves import zip import cartopy.crs as ccrs import matplotlib.pyplot as plt import matplotlib as mpl from matplotlib.patches import Circle, Ellipse from matplotlib.legend_handler import HandlerPatch to_rgba = mpl.colors.colorConverter.to_rgba logger = logging.getLogger(__name__) def make_handler_map_to_scale_circles_as_in(ax, dont_resize_actively=False): fig = ax.get_figure() def axes2pt(): return np.diff(ax.transData.transform([(0,0), (1,1)]), axis=0)[0] * (72./fig.dpi) ellipses = [] if not dont_resize_actively: def update_width_height(event): dist = axes2pt() for e, radius in ellipses: e.width, e.height = 2. * radius * dist fig.canvas.mpl_connect('resize_event', update_width_height) ax.callbacks.connect('xlim_changed', update_width_height) ax.callbacks.connect('ylim_changed', update_width_height) def legend_circle_handler(legend, orig_handle, xdescent, ydescent, width, height, fontsize): w, h = 2. * orig_handle.get_radius() * axes2pt() e = Ellipse(xy=(0.5*width-0.5*xdescent, 0.5*height-0.5*ydescent), width=w, height=w) ellipses.append((e, orig_handle.get_radius())) return e return {Circle: HandlerPatch(patch_func=legend_circle_handler)} def make_legend_circles_for(sizes, scale=1.0, **kw): return [Circle((0,0), radius=(s/scale)**0.5, **kw) for s in sizes] def set_plot_style(): plt.style.use(['classic', 'seaborn-white', {'axes.grid': False, 'grid.linestyle': '--', 'grid.color': u'0.6', 'hatch.color': 'white', 'patch.linewidth': 0.5, 'font.size': 12, 'legend.fontsize': 'medium', 'lines.linewidth': 1.5, 'pdf.fonttype': 42, }]) def plot_map(n, ax=None, attribute='p_nom', opts={}): if ax is None: ax = plt.gca() ## DATA line_colors = {'cur': "purple", 'exp': mpl.colors.rgb2hex(to_rgba("red", 0.7), True)} tech_colors = opts['tech_colors'] if attribute == 'p_nom': # bus_sizes = n.generators_t.p.sum().loc[n.generators.carrier == "load"].groupby(n.generators.bus).sum() bus_sizes = pd.concat((n.generators.query('carrier != "load"').groupby(['bus', 'carrier']).p_nom_opt.sum(), n.storage_units.groupby(['bus', 'carrier']).p_nom_opt.sum())) line_widths_exp = dict(Line=n.lines.s_nom_opt, Link=n.links.p_nom_opt) line_widths_cur = dict(Line=n.lines.s_nom_min, Link=n.links.p_nom_min) else: raise 'plotting of {} has not been implemented yet'.format(attribute) line_colors_with_alpha = \ dict(Line=(line_widths_cur['Line'] / n.lines.s_nom > 1e-3) .map({True: line_colors['cur'], False: to_rgba(line_colors['cur'], 0.)}), Link=(line_widths_cur['Link'] / n.links.p_nom > 1e-3) .map({True: line_colors['cur'], False: to_rgba(line_colors['cur'], 0.)})) ## FORMAT linewidth_factor = opts['map'][attribute]['linewidth_factor'] bus_size_factor = opts['map'][attribute]['bus_size_factor'] ## PLOT n.plot(line_widths=pd.concat(line_widths_exp)/linewidth_factor, line_colors=dict(Line=line_colors['exp'], Link=line_colors['exp']), bus_sizes=bus_sizes/bus_size_factor, bus_colors=tech_colors, boundaries=map_boundaries, geomap=True, ax=ax) n.plot(line_widths=pd.concat(line_widths_cur)/linewidth_factor, line_colors=pd.concat(line_colors_with_alpha), bus_sizes=0, bus_colors=tech_colors, boundaries=map_boundaries, geomap=False, ax=ax) ax.set_aspect('equal') ax.axis('off') # Rasterize basemap # TODO : Check if this also works with cartopy for c in ax.collections[:2]: c.set_rasterized(True) # LEGEND handles = [] labels = [] for s in (10, 1): handles.append(plt.Line2D([0],[0],color=line_colors['exp'], linewidth=s*1e3/linewidth_factor)) labels.append("{} GW".format(s)) l1_1 = ax.legend(handles, labels, loc="upper left", bbox_to_anchor=(0.24, 1.01), frameon=False, labelspacing=0.8, handletextpad=1.5, title='Transmission Exist./Exp. ') ax.add_artist(l1_1) handles = [] labels = [] for s in (10, 5): handles.append(plt.Line2D([0],[0],color=line_colors['cur'], linewidth=s*1e3/linewidth_factor)) labels.append("/") l1_2 = ax.legend(handles, labels, loc="upper left", bbox_to_anchor=(0.26, 1.01), frameon=False, labelspacing=0.8, handletextpad=0.5, title=' ') ax.add_artist(l1_2) handles = make_legend_circles_for([10e3, 5e3, 1e3], scale=bus_size_factor, facecolor="w") labels = ["{} GW".format(s) for s in (10, 5, 3)] l2 = ax.legend(handles, labels, loc="upper left", bbox_to_anchor=(0.01, 1.01), frameon=False, labelspacing=1.0, title='Generation', handler_map=make_handler_map_to_scale_circles_as_in(ax)) ax.add_artist(l2) techs = (bus_sizes.index.levels[1]) & pd.Index(opts['vre_techs'] + opts['conv_techs'] + opts['storage_techs']) handles = [] labels = [] for t in techs: handles.append(plt.Line2D([0], [0], color=tech_colors[t], marker='o', markersize=8, linewidth=0)) labels.append(opts['nice_names'].get(t, t)) l3 = ax.legend(handles, labels, loc="upper center", bbox_to_anchor=(0.5, -0.), # bbox_to_anchor=(0.72, -0.05), handletextpad=0., columnspacing=0.5, ncol=4, title='Technology') return fig def plot_total_energy_pie(n, ax=None): if ax is None: ax = plt.gca() ax.set_title('Energy per technology', fontdict=dict(fontsize="medium")) e_primary = aggregate_p(n).drop('load', errors='ignore').loc[lambda s: s>0] patches, texts, autotexts = ax.pie(e_primary, startangle=90, labels = e_primary.rename(opts['nice_names']).index, autopct='%.0f%%', shadow=False, colors = [opts['tech_colors'][tech] for tech in e_primary.index]) for t1, t2, i in zip(texts, autotexts, e_primary.index): if e_primary.at[i] < 0.04 * e_primary.sum(): t1.remove() t2.remove() def plot_total_cost_bar(n, ax=None): if ax is None: ax = plt.gca() total_load = (n.snapshot_weightings * n.loads_t.p.sum(axis=1)).sum() tech_colors = opts['tech_colors'] def split_costs(n): costs = aggregate_costs(n).reset_index(level=0, drop=True) costs_ex = aggregate_costs(n, existing_only=True).reset_index(level=0, drop=True) return (costs['capital'].add(costs['marginal'], fill_value=0.), costs_ex['capital'], costs['capital'] - costs_ex['capital'], costs['marginal']) costs, costs_cap_ex, costs_cap_new, costs_marg = split_costs(n) costs_graph = pd.DataFrame(dict(a=costs.drop('load', errors='ignore')), index=['AC-AC', 'AC line', 'onwind', 'offwind-ac', 'offwind-dc', 'solar', 'OCGT','CCGT', 'battery', 'H2']).dropna() bottom = np.array([0., 0.]) texts = [] for i,ind in enumerate(costs_graph.index): data = np.asarray(costs_graph.loc[ind])/total_load ax.bar([0.5], data, bottom=bottom, color=tech_colors[ind], width=0.7, zorder=-1) bottom_sub = bottom bottom = bottom+data if ind in opts['conv_techs'] + ['AC line']: for c in [costs_cap_ex, costs_marg]: if ind in c: data_sub = np.asarray([c.loc[ind]])/total_load ax.bar([0.5], data_sub, linewidth=0, bottom=bottom_sub, color=tech_colors[ind], width=0.7, zorder=-1, alpha=0.8) bottom_sub += data_sub if abs(data[-1]) < 5: continue text = ax.text(1.1,(bottom-0.5*data)[-1]-3,opts['nice_names'].get(ind,ind)) texts.append(text) ax.set_ylabel("Average system cost [Eur/MWh]") ax.set_ylim([0, opts.get('costs_max', 80)]) ax.set_xlim([0, 1]) ax.set_xticklabels([]) ax.grid(True, axis="y", color='k', linestyle='dotted') if __name__ == "__main__": if 'snakemake' not in globals(): from _helpers import mock_snakemake snakemake = mock_snakemake('plot_network', network='elec', simpl='', clusters='5', ll='copt', opts='Co2L-24H', attr='p_nom', ext="pdf") configure_logging(snakemake) set_plot_style() opts = snakemake.config['plotting'] map_figsize = opts['map']['figsize'] map_boundaries = opts['map']['boundaries'] n = load_network_for_plots(snakemake.input.network, snakemake.input.tech_costs, snakemake.config) scenario_opts = snakemake.wildcards.opts.split('-') fig, ax = plt.subplots(figsize=map_figsize, subplot_kw={"projection": ccrs.PlateCarree()}) plot_map(n, ax, snakemake.wildcards.attr, opts) fig.savefig(snakemake.output.only_map, dpi=150, bbox_inches='tight') ax1 = fig.add_axes([-0.115, 0.625, 0.2, 0.2]) plot_total_energy_pie(n, ax1) ax2 = fig.add_axes([-0.075, 0.1, 0.1, 0.45]) plot_total_cost_bar(n, ax2) ll = snakemake.wildcards.ll ll_type = ll[0] ll_factor = ll[1:] lbl = dict(c='line cost', v='line volume')[ll_type] amnt = '{ll} x today\'s'.format(ll=ll_factor) if ll_factor != 'opt' else 'optimal' fig.suptitle('Expansion to {amount} {label} at {clusters} clusters' .format(amount=amnt, label=lbl, clusters=snakemake.wildcards.clusters)) fig.savefig(snakemake.output.ext, transparent=True, bbox_inches='tight')