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