# -*- coding: utf-8 -*-
# SPDX-FileCopyrightText: : 2017-2023 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: MIT
"""
Plots map with pie charts and cost box bar charts.
Relevant Settings
-----------------
Inputs
------
Outputs
-------
Description
-----------
"""
import logging
import cartopy.crs as ccrs
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from _helpers import (
aggregate_costs,
aggregate_p,
configure_logging,
load_network_for_plots,
)
from matplotlib.legend_handler import HandlerPatch
from matplotlib.patches import Circle, Ellipse
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] * (
72.0 / fig.dpi
)
ellipses = []
if not dont_resize_actively:
def update_width_height(event):
dist = axes2pt()
for e, radius in ellipses:
e.width, e.height = 2.0 * 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 = 2.0 * 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": "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, opts, ax=None, attribute="p_nom"):
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 = n.lines.s_nom_opt
line_widths_cur = n.lines.s_nom_min
link_widths_exp = n.links.p_nom_opt
link_widths_cur = n.links.p_nom_min
else:
raise "plotting of {} has not been implemented yet".format(attribute)
line_colors_with_alpha = (line_widths_cur / n.lines.s_nom > 1e-3).map(
{True: line_colors["cur"], False: to_rgba(line_colors["cur"], 0.0)}
)
link_colors_with_alpha = (link_widths_cur / n.links.p_nom > 1e-3).map(
{True: line_colors["cur"], False: to_rgba(line_colors["cur"], 0.0)}
)
## FORMAT
linewidth_factor = opts["map"][attribute]["linewidth_factor"]
bus_size_factor = opts["map"][attribute]["bus_size_factor"]
## PLOT
n.plot(
line_widths=line_widths_exp / linewidth_factor,
link_widths=link_widths_exp / linewidth_factor,
line_colors=line_colors["exp"],
link_colors=line_colors["exp"],
bus_sizes=bus_sizes / bus_size_factor,
bus_colors=tech_colors,
boundaries=map_boundaries,
color_geomap=True,
geomap=True,
ax=ax,
)
n.plot(
line_widths=line_widths_cur / linewidth_factor,
link_widths=link_widths_cur / linewidth_factor,
line_colors=line_colors_with_alpha,
link_colors=link_colors_with_alpha,
bus_sizes=0,
boundaries=map_boundaries,
color_geomap=True,
geomap=True,
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 Exp./Exist. ",
)
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]).intersection(
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, -0.0), # bbox_to_anchor=(0.72, -0.05),
handletextpad=0.0,
columnspacing=0.5,
ncol=4,
title="Technology",
)
return fig
def plot_total_energy_pie(n, opts, 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, opts, ax=None):
if ax is None:
ax = plt.gca()
total_load = (n.snapshot_weightings.generators * 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=0.0),
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, 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",
simpl="",
clusters="5",
ll="copt",
opts="Co2L-24H",
attr="p_nom",
ext="pdf",
)
configure_logging(snakemake)
set_plot_style()
config, wildcards = snakemake.config, snakemake.wildcards
map_figsize = config["plotting"]["map"]["figsize"]
map_boundaries = config["plotting"]["map"]["boundaries"]
n = load_network_for_plots(
snakemake.input.network, snakemake.input.tech_costs, config
)
scenario_opts = wildcards.opts.split("-")
fig, ax = plt.subplots(
figsize=map_figsize, subplot_kw={"projection": ccrs.PlateCarree()}
)
plot_map(n, config["plotting"], ax=ax, attribute=wildcards.attr)
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, config["plotting"], ax=ax1)
ax2 = fig.add_axes([-0.075, 0.1, 0.1, 0.45])
plot_total_cost_bar(n, config["plotting"], ax=ax2)
ll = 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=wildcards.clusters
)
)
fig.savefig(snakemake.output.ext, transparent=True, bbox_inches="tight")