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)
df_all = pd.concat((df_b, df_s1, df_s2, df_s3))
# mask missing data
dates_to_exclude = [
datetime.datetime(2017, 10, 2, 0, 0) + datetime.timedelta(days=i)
for i in range(14)
]
data_exclude_ix = df_all['start'].isin(dates_to_exclude)
df_all.loc[data_exclude_ix, 'co2'] = np.nan
journal = Journal.POWERPOINT_A3
fig, ax = init_figure(nrows=1,
ncols=1,
columnes=Columnes.ONE,
journal=journal,
sharex=True,
sharey=True)
sns.lineplot(data=df_all,
x="start",
y="co2",
hue="scenario",
linewidth=3,
ax=ax)
# ax.set_xticks(ax.get_xticks()[::2]) # skip every second tick
ax.xaxis.set_major_formatter(mdates.DateFormatter('%b'))
# ax.xaxis.set_minor_formatter(mdates.DateFormatter('%b'))
handles, labels = ax.get_legend_handles_labels()
with Pool(24) as pool:
daily_pv_per_user['generated_by_pv'] = pool.map(get_PV_generated_from_pandas_row_partial,
daily_pv_per_user.iterrows())
pool.close()
pool.join()
############### get kwp
df_kwp.set_index('vin', inplace=True)
df_kwp['area'] = 0
df_kwp['user'] = 0
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'))
all_users_df = pd.concat(user_df_resampled_list, axis=1).mean(axis=1)
all_users_df = pd.DataFrame(all_users_df, columns=['value'])
all_users_df['hour'] = all_users_df.index.hour
all_users_df['day'] = all_users_df.index.floor('D')
matrix = all_users_df.pivot(index=['day'], columns=['hour'], values='value')
matrix = matrix.transpose()
# plot
journal = Journal.POWERPOINT_A3
fig, ax = init_figure(
nrows=1,
ncols=1,
fig_height_mm=150,
# height_scale=5.0,
columnes=Columnes.ONE,
journal=journal,
disabled_spines=[],
# 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")
user_df_resampled_list.append(user_df_resampled)
all_users_df = pd.concat(user_df_resampled_list, axis=1).mean(axis=1)
all_users_df = pd.DataFrame(all_users_df, columns=['value'])
all_users_df['hour'] = all_users_df.index.hour
all_users_df['day'] = all_users_df.index.floor('D')
matrix = all_users_df.pivot(index=['day'], columns=['hour'], values='value')
matrix = matrix.transpose()
# plot
journal = Journal.POWERPOINT_A3
fig, ax = init_figure(
nrows=1,
ncols=1,
fig_height_mm=150,
columnes=Columnes.ONE,
journal=journal,
disabled_spines=[],
)
plt.sca(ax)
im = ax.imshow(matrix, aspect='auto', cmap='viridis', interpolation='None')
# plt.colorbar()
plt.grid(b=None)
plt.xlabel("Days in study")
plt.ylabel("Hours of the day")
plt.yticks([0, 6, 12, 18, 24])
im.set_clim([0, 1])
columns=['timestamp', 'soc'])
charging_state_timeseries = pd.concat([charging_state_timeseries1, charging_state_timeseries2],
axis=0,
sort=False,
ignore_index=True)
charging_state_timeseries['timestamp'] = charging_state_timeseries['timestamp'].astype(
'datetime64[ns]') # transform it to timestamps again!
# set index and plot
charging_state_timeseries.set_index('timestamp', inplace=True)
f, ax = init_figure(nrows=1,
ncols=1,
columnes=Columnes.ONE,
journal=Journal.POWERPOINT_A3,
disabled_spines=['top'])
charging_state_timeseries.plot(ax=ax,
color="k",
linewidth=3,
zorder=2,
legend=False)
lines, labels = ax.get_legend_handles_labels()
l1 = lines[0]
# %% plot ishome areas
with open(os.path.join(".", "data", "Car_is_at_home_table_UTC.csv"), 'r') as data_file:
all_data_df = pd.read_csv(data_file)
consumed_electric_energy_total
from bmw where zustand = 'fahrt'"""
df = pd.read_sql_query(query, con=engine)
df.to_csv(os.path.join('.', 'data', 'power_factor_data.csv'))
# data_PV_Solar preparation
df['dsoc'] = df['soc_customer_start'] - df['soc_customer_end']
df['pow'] = df['consumed_electric_energy_total']
# filter all that is zero
df = df.drop(df[(df['dsoc'] <= 0) | (df['pow'] <= 0)].index)
# create plot
f, ax = init_figure(nrows=1,
ncols=1,
columnes=Columnes.ONE,
journal=Journal.POWERPOINT_A3)
hb = ax.hexbin(df['dsoc'],
df['pow'],
gridsize=100,
bins='log',
cmap=plt.get_cmap('bone_r'),
mincnt=1,
linewidths=0.2,
edgecolors='slategray')
cb = f.colorbar(hb, ax=ax)
cb.set_label(r'log10(n), n = Number of points in cell', labelpad=20)
ax.set_ylabel(r"Consumed power [\si{\kilo\watthour}]", labelpad=20)
