def plot_res(ext, show):
end_year = '2105'
data = {}
for name, df in TablesRecorder.generate_dataframes('thames_output.h5'):
df.columns = ['Very low', 'Low', 'Central', 'High', 'Very high']
data[name] = df
fig1, ax1 = plt.subplots(figsize=(16, 5), dpi=300)
data['reservoir1'].loc[:end_year, 'Central'].plot(ax=ax1)
ax1.set_ylabel('Volume [$Mm^3$]')
plt.tight_layout()
fig2, ax2 = plt.subplots(figsize=(16, 5), dpi=300)
data['demand_saving_level'].loc[:end_year, 'Central'].plot(ax=ax2)
ax2.set_ylabel('Demand saving level')
plt.tight_layout()
fig3, ax3 = plt.subplots(figsize=(16, 5), dpi=300)
data['demand_max_flow'].loc[:end_year, 'Central'].plot(ax=ax3)
ax3.set_ylabel('Demand [$Mm^3/day$]')
plt.tight_layout()
for ax in (ax1, ax2, ax3):
ax.grid(True)
if ext is not None:
fig1.savefig(f'Reservoir.{ext}', dpi=300)
fig2.savefig(f'Demand saving level.{ext}', dpi=300)
fig3.savefig(f'Demand.{ext}', dpi=300)
if show:
plt.show()
def figures(ext, show):
for name, df in TablesRecorder.generate_dataframes('thames_output.h5'):
df.columns = ['Very low', 'Low', 'Central', 'High', 'Very high']
fig, (ax1, ax2) = plt.subplots(figsize=(12, 4),
ncols=2,
sharey='row',
gridspec_kw={'width_ratios': [3, 1]})
df['2100':'2125'].plot(ax=ax1)
df.quantile(np.linspace(0, 1)).plot(ax=ax2)
if name.startswith('reservoir'):
ax1.set_ylabel('Volume [$Mm^3$]')
else:
ax1.set_ylabel('Flow [$Mm^3/day$]')
for ax in (ax1, ax2):
ax.set_title(name)
ax.grid(True)
plt.tight_layout()
if ext is not None:
fig.savefig(f'{name}.{ext}', dpi=300)
if show:
plt.show()
def plot_res(ext, show):
end_year = "2105"
data = {}
for name, df in TablesRecorder.generate_dataframes("thames_output.h5"):
df.columns = ["Very low", "Low", "Central", "High", "Very high"]
data[name] = df
fig1, ax1 = plt.subplots(figsize=(16, 5), dpi=300)
data["reservoir1"].loc[:end_year, "Central"].plot(ax=ax1)
ax1.set_ylabel("Volume [$Mm^3$]")
plt.tight_layout()
fig2, ax2 = plt.subplots(figsize=(16, 5), dpi=300)
data["demand_saving_level"].loc[:end_year, "Central"].plot(ax=ax2)
ax2.set_ylabel("Demand saving level")
plt.tight_layout()
fig3, ax3 = plt.subplots(figsize=(16, 5), dpi=300)
data["demand_max_flow"].loc[:end_year, "Central"].plot(ax=ax3)
ax3.set_ylabel("Demand [$Mm^3/day$]")
plt.tight_layout()
for ax in (ax1, ax2, ax3):
ax.grid(True)
if ext is not None:
fig1.savefig(f"Reservoir.{ext}", dpi=300)
fig2.savefig(f"Demand saving level.{ext}", dpi=300)
fig3.savefig(f"Demand.{ext}", dpi=300)
if show:
plt.show()
def figures(ext, show):
for name, df in TablesRecorder.generate_dataframes("thames_output.h5"):
df.columns = ["Very low", "Low", "Central", "High", "Very high"]
fig, (ax1, ax2) = plt.subplots(
figsize=(12, 4), ncols=2, sharey="row", gridspec_kw={"width_ratios": [3, 1]}
)
df["2100":"2125"].plot(ax=ax1)
df.quantile(np.linspace(0, 1)).plot(ax=ax2)
if name.startswith("reservoir"):
ax1.set_ylabel("Volume [$Mm^3$]")
else:
ax1.set_ylabel("Flow [$Mm^3/day$]")
for ax in (ax1, ax2):
ax.set_title(name)
ax.grid(True)
plt.tight_layout()
if ext is not None:
fig.savefig(f"{name}.{ext}", dpi=300)
if show:
plt.show()
def plot_res2(ext, show):
end_year = '2105'
data = {}
for name, df in TablesRecorder.generate_dataframes('thames_output.h5'):
df.columns = ['Very low', 'Low', 'Central', 'High', 'Very high']
data[name] = df
fig1, ax1 = plt.subplots(figsize=(16, 5), dpi=300)
data['reservoir1'].loc[:end_year].plot(ax=ax1)
ax1.set_ylabel('Volume [$Mm^3$]')
plt.legend()
plt.tight_layout()
fig2, ax2 = plt.subplots(figsize=(16, 5), dpi=300)
data['reservoir1'].quantile(np.linspace(0, 1)).plot(ax=ax2)
ax2.set_ylabel('Volume [$Mm^3$]')
ax2.set_xlabel('Quantile')
plt.tight_layout()
fig3, ax3 = plt.subplots(figsize=(16, 5), dpi=300)
df = data['demand_saving_level'].apply(pandas.Series.value_counts)
df /= df.sum(axis=0)
df.plot.bar(ax=ax3)
ax3.set_ylabel('Proportion of time.')
ax3.set_xlabel('Demand saving level')
plt.tight_layout()
for ax in (ax1, ax2, ax3):
ax.grid(True)
if ext is not None:
fig1.savefig(f'Reservoir (scenarios).{ext}', dpi=300)
fig2.savefig(f'Reservoir SDC (scenarios).{ext}', dpi=300)
fig3.savefig(f'Demand saving level count (scenarios).{ext}', dpi=300)
if show:
plt.show()
def plot_res2(ext, show):
end_year = "2105"
data = {}
for name, df in TablesRecorder.generate_dataframes("thames_output.h5"):
df.columns = ["Very low", "Low", "Central", "High", "Very high"]
data[name] = df
fig1, ax1 = plt.subplots(figsize=(16, 5), dpi=300)
data["reservoir1"].loc[:end_year].plot(ax=ax1)
ax1.set_ylabel("Volume [$Mm^3$]")
plt.legend()
plt.tight_layout()
fig2, ax2 = plt.subplots(figsize=(16, 5), dpi=300)
data["reservoir1"].quantile(np.linspace(0, 1)).plot(ax=ax2)
ax2.set_ylabel("Volume [$Mm^3$]")
ax2.set_xlabel("Quantile")
plt.tight_layout()
fig3, ax3 = plt.subplots(figsize=(16, 5), dpi=300)
df = data["demand_saving_level"].apply(pandas.Series.value_counts)
df /= df.sum(axis=0)
df.plot.bar(ax=ax3)
ax3.set_ylabel("Proportion of time.")
ax3.set_xlabel("Demand saving level")
plt.tight_layout()
for ax in (ax1, ax2, ax3):
ax.grid(True)
if ext is not None:
fig1.savefig(f"Reservoir (scenarios).{ext}", dpi=300)
fig2.savefig(f"Reservoir SDC (scenarios).{ext}", dpi=300)
fig3.savefig(f"Demand saving level count (scenarios).{ext}", dpi=300)
if show:
plt.show()