def plot_cumsum_plot(baseline, scenario1, scenario2, scenario3):
journal = Journal.POWERPOINT_A3
fig, ax = init_figure(nrows=1,
ncols=1,
columnes=Columnes.ONE,
journal=journal)
lbase = plot_cumsum_by_scenario(baseline[baseline.is_home], ax, label='Baseline')
l1 = plot_cumsum_by_scenario(scenario1[scenario1.is_home], ax, label='Scenario 1')
l2 = plot_cumsum_by_scenario(scenario2, ax, label='Scenario 2')
l3 = plot_cumsum_by_scenario(scenario3, ax, label='Scenario 3')
xlim = plt.xlim()
lxy = plt.plot(np.arange(0, xlim[1]), np.arange(0, xlim[1]), label='x=y (only using pv generation)', color='k',
linestyle=':', linewidth=2)
lyx = plt.plot(np.arange(0, xlim[1]), - np.arange(0, xlim[1]), label='x=-y (only using grid generation)', color='k',
linestyle=':', linewidth=2)
lyx = plt.plot(np.arange(0, xlim[1]), 0 * np.arange(0, xlim[1]), label=None, color='k',
linestyle='--', linewidth=2)
xlim = plt.xlim()
ylim = plt.ylim()
ylim = (ylim[0], xlim[1])
print(ylim)
ax.set_ylim(ylim)
ax.set_xlim((0, xlim[1]))
plt.xlabel('Total energy used by cars (cumulative) [MWh]', labelpad=15)
plt.ylabel('PV charging - grid charging \n (cumulative) [MWh]', labelpad=15)
leg = plt.legend(ncol=3, loc='upper left', bbox_to_anchor=(-0.15, -0.2), borderpad=0., frameon=False)
plt.tight_layout()
bbox_extra_artists = [fig, leg, ax]
save_figure(os.path.join('plots', 'cumulative_energy_plot', 'cumsum_all_scenarios.png'),
bbox_extra_artists=bbox_extra_artists)
# handle._linewidth = 50
leg = ax.legend(handles=handles,
labels=labels,
loc='upper left',
frameon=False,
bbox_to_anchor=(-0.14, -0.15),
ncol=4)
plt.xlabel("Time of tracking aggregated by week", labelpad=15)
plt.ylabel(
r"Emissions per Person in $\frac{\text{kg CO2 equivalent}}{\text{week}}$",
labelpad=20)
bbox_extra_artists = [fig, leg]
save_figure(fig_out, bbox_extra_artists=bbox_extra_artists)
plt.close(fig)
# calculate average Co2 savings per user
df_avco2 = df_all.groupby('scenario')['co2'].mean()
df_avco2 = df_avco2.Baseline - df_avco2
print(plot_name)
print("Average savings per scenario relative to the baseline:")
print(df_avco2)
print(10 * "%")
# data_list = [df_b, df_s1, df_s2, df_s3]
# journal = Journal.POWERPOINT_A3
# fig, axs = init_figure(nrows=2,
df_kwp = get_kwp(df_kwp)
journal = Journal.POWERPOINT_A3
fig, axes = init_figure(nrows=1, ncols=2,
columnes=Columnes.ONE, fig_height_mm=120,
journal=journal, sharex=False, sharey=False,
gridspec_kw={'width_ratios': [2, 1]})
plt.sca(axes[0])
ax = axes[0]
sns.lineplot(data=daily_pv_per_user, x="start", y="generated_by_pv",
linewidth=1.5, ax=ax)
ax.set_xlabel('Days in 2017 [d]')
ax.set_ylabel('Daily PV generation [kWh]')
t_start = project_constants['t_start']
t_end = project_constants['t_end']
x_dates = pd.date_range(start=t_start,
end=t_end,
freq='M', ) + datetime.timedelta(days=1)
axes[0].set_xticks(x_dates[::2])
axes[0].xaxis.set_major_formatter(mdates.DateFormatter('%b'))
plt.sca(axes[1])
sns.histplot(data=df_kwp, x='max_kW', ax=axes[1], bins=np.arange(0, 40, 5))
plt.xlabel('kWp [kW]')
plt.ylabel('User count')
axes[1].set_xticks([0, 10, 20, 30], minor=False)
plt.xlim((0, 35))
save_figure(fig_out)
journal=journal,
sharex=True,
sharey=True)
for ix, column_name in enumerate(column_names):
ax = axs[ix // 2, ix % 2]
plot_hist(df, column_name, ax=ax)
ax.set_title(titles[ix] + f" - mean: {df[column_name].mean():.0f} \%")
# ax.grid(which='both')
ax.set_xlabel(None)
ax.set_ylabel(None)
ax.grid(b=True, which='both')
ax.set_xlim(0, 100)
ax.set_ylim(0, 60)
ax.set_yticks([10, 20, 30, 40, 50, 60], minor=True)
# labels
# https://stackoverflow.com/questions/16150819/common-xlabel-ylabel-for-matplotlib-subplots
fig.add_subplot(111, frameon=False)
plt.tick_params(labelcolor='none',
top=False,
bottom=False,
left=False,
right=False)
plt.xlabel("Home charging energy demand covered by PV [\%]", labelpad=15)
plt.ylabel("User count", labelpad=15)
plt.grid(b=False, which='both')
save_figure(output_path)
label='Scenario 1')
ax0.scatter(df['total_demand_s2'],
df['scenario2'],
s=size,
label='Scenario 2')
ax0.scatter(df['total_demand_s3'],
df['scenario3'],
s=size,
label='Scenario 3')
ax0.set_xlabel("Total demand [kWh]")
ax0.set_ylabel("Coverage by PV [\%]")
ax1.scatter(df['max_kW'], df['baseline'], s=size)
ax1.scatter(df['max_kW'], df['scenario1'], s=size)
ax1.scatter(df['max_kW'], df['scenario2'], s=size)
ax1.scatter(df['max_kW'], df['scenario3'], s=size)
ax1.set_xlabel("Peak power [kW]")
ax1.set_xlim(0, 30)
# ax1.ylabel("coverage by PV [\%]")
leg = ax0.legend(loc='upper left',
frameon=False,
bbox_to_anchor=(-0.35, -0.15),
ncol=4,
markerscale=3)
bbox_extra_artists = [fig, leg]
fig_out = os.path.join('plots', 'sensitivity_kwp_bevdemand',
'sensitivity_kwp_bevdemand.png')
save_figure(fig_out, bbox_extra_artists=bbox_extra_artists, dpi=10)
# gridspec_kw={"height_ratios":[1, 0.05]})
)
plt.sca(ax)
# sns.heatmap(matrix, ax=ax)
im = ax.imshow(matrix, aspect='auto', cmap='viridis', interpolation='None')
# PCM=ax.get_children()[-2] #get the mappable, the 1st and the 2nd are the x and y axes
# plt.colorbar(PCM, ax=ax)
# plt.colorbar()
plt.grid(b=None)
plt.xlabel("Days in study")
plt.ylabel("Hours of the day")
plt.yticks([0, 6, 12, 18, 24])
# fig.colorbar(im, cax=cax, orientation="horizontal")
im.set_clim([0, 1])
# cb = fig.colorbar(im, orientation="horizontal", pad=0.5, shrink=0.5)
divider = make_axes_locatable(ax)
cax = divider.new_vertical(size="5%", pad=1.0, pack_start=True)
fig.add_axes(cax)
cb = fig.colorbar(im, cax=cax, orientation="horizontal", fraction=0.5)
cb.outline.set_visible(False)
cb.set_label("Percentage of cars at home")
# cb.set_ticks(np.arange(0, 1.2, 0.2))
# cb.ax.set_xticklabels(np.arange(0, 1.2, 0.2))
# plt.savefig(fig_out[:-3] + "pdf")
# plt.close()
save_figure(fig_out, dpi=10)
if plt_scatter:
leg = plt.figlegend(handles=handles,
labels=labels,
loc='upper left',
ncol=2,
frameon=False,
bbox_to_anchor=(0.15, 0),
labelspacing=0.1,
columnspacing=0.1)
leg.legendHandles[1].set_alpha(1) # no alpha for scatter circle
leg.legendHandles[1].set_sizes([200])
else:
leg = plt.figlegend(handles=handles,
labels=labels,
loc='upper left',
ncol=3,
frameon=False,
bbox_to_anchor=(0.03, 0),
labelspacing=0.1,
columnspacing=1)
bbox_extra_artists = [fig, leg]
# save
save_figure(os.path.join('plots', 'quantile_plots',
'plot_quantile_coverage_year.png'),
bbox_extra_artists=bbox_extra_artists)
plt.close(fig)
ax.xaxis.set_major_locator(mdates.HourLocator(byhour=11))
ax.xaxis.set_major_formatter(mdates.DateFormatter('%a'))
plt.sca(ax)
# x axis date formating
ax.xaxis.set_minor_locator(mdates.HourLocator(byhour=[0, 12]))
ax.xaxis.set_minor_formatter(mdates.DateFormatter('%H:%M'))
ax.tick_params(axis='x', which='minor', pad=10)
ax.tick_params(axis='x', which='major', pad=40)
ax.tick_params(axis='x', which='major', bottom=True)
ax.tick_params(axis='x', which='minor', bottom=True)
plt.xticks(rotation=0, horizontalalignment="center")
ax.tick_params(axis='x', which='major', length=0)
for tick in ax.xaxis.get_minor_ticks():
tick.label.set_fontsize(14)
ax.xaxis.grid(True)
ax.yaxis.grid(True)
ax.set_ylim((0, 101))
ax2.set_ylim((0, 10.1))
frame = lgd.get_frame()
frame.set_facecolor('#ebefe8')
ax.spines['top'].set_visible(False)
save_figure(figure_out, bbox_extra_artists=[lgd], dpi=5)
linewidth=3,
label=r"${} * \Delta_{{SoC}} + {} * ".format(
round(params_sqrt['dsoc'], 3),
round(
params_sqrt['np.sqrt(dsoc)'], 3)) + \
r"\sqrt{{ \Delta_{{SoC}} }}$, $R^2 = {}$".format(
round(r2_sqrt, 2))
)
#
ln_lin, = ax.plot(
x,
y_lin,
linestyle=':',
color='orange',
linewidth=3,
label='linear fit',
zorder=10 # draw this line on top
)
leg = ax.legend(handles=[ln_sqrt, ln_lin], loc=2, frameon=False)
leg.set_zorder(20)
frame = leg.get_frame()
frame.set_facecolor('#ebefe8')
# frame.set_edgecolor('#ebefe8')
ax.text(0.99, 0.01, r"N = \num{{ {} }}".format(df.shape[0]), \
horizontalalignment='right', verticalalignment='bottom', \
transform=ax.transAxes)
save_figure(os.path.join(".", "plots", "power_factor", "power_factor.png"))
