def fig_compare_de21_region_borders():
ffe = geometries.load_shp(cfg.get("paths", "geo_plot"),
"de21_nach_ffe_grafik.geojson")
tso = geometries.load_shp(cfg.get("paths", "geo_plot"),
"de21_nach_tso_bild.geojson").set_index("region")
renpass = geometries.load_shp(cfg.get("paths", "geo_plot"),
"de21_renpass_frauke_wiese.geojson")
cmap = LinearSegmentedColormap.from_list("mycmap", [(0.0, "#000000"),
(0.99, "#ffffff"),
(1, "#a5bfdd")])
data = pd.Series({
"DE01": 0.9,
"DE02": 0.95,
"DE03": 0.5,
"DE04": 0.7,
"DE05": 0.5,
"DE06": 0.83,
"DE07": 0.65,
"DE08": 0.37,
"DE09": 0.45,
"DE10": 0.8,
"DE11": 0.75,
"DE12": 0.60,
"DE13": 0.4,
"DE14": 0.75,
"DE15": 0.2,
"DE16": 0.85,
"DE17": 0.5,
"DE18": 0.2,
"DE19": 1,
"DE20": 1,
"DE21": 1,
})
data = pd.DataFrame(data, columns=["value"])
ax = plot.plot_regions(
edgecolor="None",
data=data,
legend=False,
label_col="sp_id_1",
rmap=tso,
data_col="value",
cmap=cmap,
)
ax = ffe.boundary.plot(ax=ax,
color="#720000",
aspect="equal",
linewidth=0.8)
renpass.boundary.plot(ax=ax,
color="#064304",
aspect="equal",
linewidth=0.8)
plt.legend(["nach FfE", "renpass", "nach ÜNB"])
ax.axis("off")
plt.subplots_adjust(left=0, right=1, bottom=0, top=1)
return "netzregionen_vergleich", None
def fig_model_regions():
"""Plot one or more model regions in one plot."""
maps = ["de02", "de17", "de21", "de22"]
# maps = ["de22"]
add_title = True
top = 1
ax = None
ax_ar = []
width = len(maps * 3)
if len(maps) > 1:
f, ax_ar = plt.subplots(1, len(maps), figsize=(width, 2.5))
else:
ax = plt.figure(figsize=(width, 2.5)).add_subplot(1, 1, 1)
i = 0
for rmap in maps:
if len(maps) > 1:
ax = ax_ar[i]
de_map = geometries.load(
cfg.get("paths", "geo_deflex"),
cfg.get("geometry", "deflex_polygon").format(suffix=".geojson",
map=rmap,
type="polygons"),
)
# greyscale
colormap = LinearSegmentedColormap.from_list("mycmap",
[(0, "#dddddd"),
(1, "#333333")])
# colormap = None
plot.plot_regions(
rmap=de_map,
ax=ax,
legend=False,
simple=0.005,
offshore="auto",
cmap=colormap,
)
for spine in plt.gca().spines.values():
spine.set_visible(False)
ax.axis("off")
if add_title is True:
ax.set_title(rmap)
top = 0.88
i += 1
plt.subplots_adjust(right=1, left=0, wspace=0, bottom=0, top=top)
return "model_regions", None
def table_demand_federal_states(outpath):
ego_year = 2014
path = cfg.get('paths', 'data_my_reegis')
filename = 'lak_electricity_demand_federal_states.csv'
lak = pd.read_csv(os.path.join(path, filename), index_col=[0, 1], sep=';')
lak = lak.swaplevel()
federal_states = geometries.get_federal_states_polygon()
ego = oego.get_ego_demand_by_region(
federal_states, 'federal_states', grouped=True)
ego_sum = ego.sum()
lak = lak.rename(index=cfg.get_dict('STATES'))
lak['Strom'] = pd.to_numeric(lak['Strom'], errors='coerce')
new_table = pd.DataFrame(
columns=pd.MultiIndex(levels=[[], []], codes=[[], []]),
index=ego.index)
new_table[('openEgo', ego_year)] = ego
for y in [2010, 2011, 2012, 2013, 2014]:
key = ('lak', y)
new_table[key] = lak.loc[y]['Strom'] / 3.6
f = new_table[key].sum() / ego_sum
new_table[('frac', y)] = (((new_table[('openEgo', ego_year)] * f) -
new_table[key]) / new_table[key])
fk = new_table[('lak', ego_year)].sum() / ego_sum
new_table[('openEgo', ego_year)] = new_table[('openEgo', ego_year)] * fk
new_table.sort_index(axis=1, inplace=True)
new_table.to_excel(
os.path.join(outpath, 'federal_states_demand_ego_lak.xls'))
print(new_table)
def show_download_image(name, file_types):
create_subplot((12, 4.4))
fn = os.path.join(cfg.get("paths", "figure_source"),
"{0}.png".format(name))
img = mpimg.imread(fn)
plt.imshow(img)
plt.axis("off")
plt.subplots_adjust(left=0, top=0.93, bottom=0, right=1)
url = ("https://raw.githubusercontent.com/uvchik/reegis_phd/master/"
"reegis_phd/data/figures/{0}.{1}")
fn = os.path.join(cfg.get("paths", "figures"), "{0}.{1}")
for suffix in file_types:
download_file(fn.format(name, suffix), url.format(name, suffix), True)
plt.title("Image source downloaded to: {0}".format(
fn.format(name, file_types[0])))
return "{0}.png".format(name), None
def fig_tespy_heat_pumps_cop():
"""From TESPy examples."""
plt.rcParams.update({"font.size": 20})
f, ax_ar = plt.subplots(1, 2, sharey=True, sharex=True, figsize=(15, 5))
t_range = [6, 9, 12, 15, 18, 21, 24]
q_range = np.array([120e3, 140e3, 160e3, 180e3, 200e3, 220e3])
n = 0
for filename in ["COP_air.csv", "COP_water.csv"]:
fn = os.path.join(cfg.get("paths", "data_my_reegis"), filename)
df = pd.read_csv(fn, index_col=0)
# Colors
# colormap = "Greys"
# offset = 0.5
colormap = "tab10"
offset = 0
cmap = plt.get_cmap(colormap)
nc = len(t_range) # number of colors
p = ((nc-1)/(1-offset))
colors_bw = [cmap(i/p+offset) for i in range(nc)]
plt.sca(ax_ar[n])
i = 0
for t in t_range:
plt.plot(
q_range / 200e3,
df.loc[t],
"-x",
Color=colors_bw[i],
label="$T_{resvr}$ = " + str(t) + " °C",
MarkerSize=7,
LineWidth=2,
)
i += 1
ax_ar[n].set_xlabel("Relative Last")
if n == 0:
ax_ar[n].set_ylabel("COP")
n += 1
plt.ylim([0, 3.2])
plt.xlim([0.5, 1.2])
plt.legend(loc="upper right", bbox_to_anchor=(1.5, 1))
plt.subplots_adjust(
right=0.82, left=0.06, wspace=0.11, bottom=0.13, top=0.97
)
return "tespy_heat_pumps", None
def fig_deflex_de22_polygons(**kwargs):
ax = figures_base.create_subplot((9, 7), **kwargs)
# change for a better/worse resolution (
simple = 0.02
fn = os.path.join(cfg.get("paths", "geo_plot"),
"region_polygon_de22_reegis.csv")
reg_id = ["DE{num:02d}".format(num=x + 1) for x in range(22)]
idx = [x + 1 for x in range(22)]
data = pd.DataFrame({"reg_id": reg_id}, index=idx)
data["class"] = 0
data.loc[[19, 20, 21], "class"] = 1
data.loc[22, "class"] = 0.5
data.loc[22, "reg_id"] = ""
cmap = LinearSegmentedColormap.from_list("mycmap",
[(0.000000000, "#badd69"),
(0.5, "#dd5500"),
(1, "#a5bfdd")])
ax = plot.plot_regions(
edgecolor="#666666",
data=data,
legend=False,
label_col="reg_id",
fn=fn,
data_col="class",
cmap=cmap,
ax=ax,
simple=simple,
)
plt.subplots_adjust(right=1, left=0, bottom=0, top=1)
ax.set_axis_off()
return "deflex_de22_polygons", None
def fig_district_heating_areas(**kwargs):
from berlin_hp import heat
ax = figures_base.create_subplot((7.8, 4), **kwargs)
# get groups of district heating systems in Berlin
district_heating_groups = pd.DataFrame(pd.Series(
cfg.get_dict("district_heating_systems")),
columns=["name"])
# get district heating system areas in Berlin
distr_heat_areas = heat.get_district_heating_areas()
# Merge main groups on map
distr_heat_areas = distr_heat_areas.merge(district_heating_groups,
left_on="KLASSENNAM",
right_index=True)
# Create real geometries
distr_heat_areas = geometries.create_geo_df(distr_heat_areas)
# Plot berlin map
berlin_fn = os.path.join(cfg.get("paths", "geo_berlin"), "berlin.csv")
berlin = geometries.create_geo_df(pd.read_csv(berlin_fn))
ax = berlin.plot(color="#ffffff", edgecolor="black", ax=ax, aspect="equal")
# Plot areas of district heating system groups
ax = distr_heat_areas.loc[
distr_heat_areas["name"] != "decentralised_dh"].plot(column="name",
ax=ax,
cmap="tab10",
aspect="equal")
# Remove frame around plot
for spine in plt.gca().spines.values():
spine.set_visible(False)
ax.axis("off")
text = {
"Vattenfall 1": (13.3, 52.52),
"Vattenfall 2": (13.5, 52.535),
"Buch": (13.47, 52.63),
"Märkisches Viertel": (13.31, 52.61),
"Neukölln": (13.422, 52.47),
"BTB": (13.483, 52.443),
"Köpenick": (13.58, 52.425),
"Friedrichshagen": (13.653, 52.47),
}
for t, c in text.items():
plt.text(
c[0],
c[1],
t,
size=14,
ha="center",
va="center",
bbox=dict(boxstyle="round", alpha=0.5, ec=(1, 1, 1), fc=(1, 1, 1)),
)
plt.draw()
plt.subplots_adjust(left=0, right=1, bottom=0, top=1)
return "distric_heating_areas", None