Plots and Tables for the DOE Analyses¶

print(f"{'Scenario':>42} | {'N Events':>9} | {'N Operations':>12}")
data_frames = {}
for simulation_name in scenarios:
    events_path = os.path.join(library_path, "outputs", "csv_logs", f"{simulation_name}_events.csv")
    operations_path = os.path.join(library_path, "outputs", "csv_logs", f"{simulation_name}_operations.csv")
    
    events = pd.read_csv(events_path)
    events.datetime = pd.to_datetime(events.datetime)
    events.env_datetime = pd.to_datetime(events.env_datetime)
    events["year"] = events.env_datetime.dt.year
    events["month"] = events.env_datetime.dt.month
    events.index = events.datetime
    events = events.drop(labels="datetime", axis=1)
    
    operations = pd.read_csv(operations_path)
    operations.datetime = pd.to_datetime(operations.datetime)
    operations.env_datetime = pd.to_datetime(operations.env_datetime)
    operations["year"] = operations.env_datetime.dt.year
    operations["month"] = operations.env_datetime.dt.month
    operations.index = operations.datetime
    operations = operations.drop(labels="datetime", axis=1)
    
    data_frames[simulation_name] = {
        "events": events,
        "operations": operations,
    }
    
    print(f"{simulation_name:>42} | {events.shape[0]:>9,.0f} | {operations.shape[0]:>12,.0f}")

fig = plt.figure(figsize=(18, 12), dpi=300)
ax = fig.add_subplot(111)

env = WombatEnvironment(library_path, "weather_short.csv", simulation_name="plot_only", workday_start=8, workday_end=18)
manager = RepairManager(env)
windfarm = Windfarm(env, "offshore_dudgeon_array.csv", manager)

positions = {name: np.array([node["longitude"], node["latitude"]]) for name, node in windfarm.graph.nodes(data=True)}
nx.draw(windfarm.graph, with_labels=True, font_weight="bold", pos=positions, ax=ax, node_color="#E37225")

fig.tight_layout()
plt.savefig(os.path.join(library_path, "outputs/plots_and_tables/dudgeon_array_layout.png"), bbox_inches="tight")
plt.savefig(os.path.join(library_path, "outputs/plots_and_tables/dudgeon_array_layout.svg"), bbox_inches="tight")

fig, ax = plt.subplots(figsize=(15, 7), dpi=300)

line_legend = []
for formatting in format_categories:
    line = mlines.Line2D([], [], color=formatting["color"], linestyle=formatting["linestyle"], label=formatting["name"])
    line_legend.append(line)

ax.plot(np.arange(241), np.repeat(0.92, 241), alpha=0.8, color="black")
ax.text(242, 0.92, "Combined Industry Average: 92%*", va="center")

for scenario in data_frames:
    if scenario.startswith("onshore"):
        continue
    
    operations = data_frames[scenario]["operations"]
    monthly_availability, project_availability = get_availability_data(operations)
    data_frames[scenario]["monthly_availability"] = monthly_availability
    data_frames[scenario]["project_availability"] = project_availability
    
    fmt = format_dict[scenario]["format"]
    ax.plot(
        range(monthly_availability.shape[0]), monthly_availability.windfarm.values,
        color=fmt["color"], linestyle=fmt["linestyle"], alpha=fmt["alpha"]
    )

    name = format_dict[scenario]['name']
    text = f"{name}: {project_availability * 100 :.1f}%"
    y = monthly_availability.loc[2017].loc[12].values[0]
#     print(f"{name:>40}: {y:>4.2f}")
    if name == "Base (3 Month Summer Visit)":
        y *= 0.99
    elif name == "2 Month Visit w/o Weather":
        y *= 1.01
    elif name == "Fall Visit":
        y *= 0.93
    elif name == "1 Month Visit w/o Weather":
        y *= 0.98
    elif name == "2 Month Visit":
        y *= 1.04
    elif name == "Spring Visit":
        y *= 1.02
    elif name == "Winter Visit":
        y *= 0.99
    elif name == "1 Month Visit":
        y *= 0.95
    elif name == "12 Month Visit":
        y *= 0.99
    elif name == "Doubled MTBF":
        y *= 1.01
    ax.text(242, y, text, va="center")

xticks_major = [x * 12 for x in range(21)]
xticks_minor = list(range(0, 12 * 20, 3))
xlabels_major = [f"{year:>6}" for year in years]
xlabels_minor = ["Apr", "", "Oct"] + ["Apr", "", "Oct"] * 19
ax.set_xlabel("Simulation Time", fontsize=16)

ax.set_ylim(0, 1.1)
ax.set_yticks(np.linspace(0, 1, 11))
ax.set_ylabel("Monthly Availability", fontsize=16)
ax.set_yticklabels([f"{y * 100:.0f}%" for y in ax.get_yticks()], fontsize=12)

ax.set_xlim(0, 300)
ax.set_xticks(xticks_major)
for t in ax.get_xticklabels():
    t.set_y(-0.05)
ax.set_xticks(xticks_minor, minor=True)
ax.set_xticklabels(xlabels_major, ha="left", fontsize=12)
ax.set_xticklabels(xlabels_minor, minor=True, rotation=90)

ax.set_title("Offshore Windfarm Availability", fontsize=18)
ax.legend(handles=line_legend, loc="lower left", fontsize=14)

plt.grid(axis="x", which="major")
plt.grid(alpha=0.2, which="minor")
ax.hlines(np.linspace(0, 1, 11), 0, 240, alpha=0.2, color="gray")

fig.tight_layout()
plt.savefig(os.path.join(library_path, "outputs/plots_and_tables/offshore_availability.png"), bbox_to_inches="tight")
plt.savefig(os.path.join(library_path, "outputs/plots_and_tables/offshore_availability.svg"), bbox_to_inches="tight")

fig, ax = plt.subplots(figsize=(15, 7), dpi=300)

line_legend = []
for formatting in format_categories:
    line = mlines.Line2D([], [], color=formatting["color"], linestyle=formatting["linestyle"], label=formatting["name"])
    line_legend.append(line)

ax.plot(np.arange(241), np.repeat(0.92, 241), alpha=0.8, color="black")
ax.text(242, 0.92, "Combined Industry Average: 92%*", va="center")

for scenario in data_frames:
    if scenario.startswith("offshore") or scenario.endswith("v2"):
        continue
    
    operations = data_frames[scenario]["operations"]
    monthly_availability, project_availability = get_availability_data(operations)
    data_frames[scenario]["monthly_availability"] = monthly_availability
    data_frames[scenario]["project_availability"] = project_availability
    
    fmt = format_dict[scenario]["format"]
    ax.plot(
        range(monthly_availability.shape[0]), monthly_availability.windfarm.values,
        color=fmt["color"], linestyle=fmt["linestyle"], alpha=fmt["alpha"]
    )

    name = format_dict[scenario]['name']
    text = f"{name}: {project_availability * 100 :.1f}%"
    y = monthly_availability.loc[2017].loc[12].values[0]
    print(f"{name:>40}: {y:>4.2f}")
    if name == "Base (3 Month Summer Visit)":
        y *= 0.99
    elif name == "Fall Visit":
        y *= 1.01
# #     elif name == "No Weather":
# #         y *= 0.98
    elif name == "Doubled MTBF":
        y *= 1.01
    elif name == "12 Month Visit":
        y *= 0.99
#     elif name == "1 Month Visit w/o Weather":
#         y *= 1.01
    elif name == "Spring Visit":
        y *= 0.99
    elif name == "Halved MTBF":
        y *= 0.96
    elif name == "Winter Visit":
        y *= 0.99
#     elif name == "2 Month Visit w/o Weather":
#         y *= 0.96
#     elif name == "1 Month Visit":
#         y *= 0.99
    ax.text(242, y, f"{text}", va="center")

xticks_major = [x * 12 for x in range(21)]
xticks_minor = list(range(0, 12 * 20, 3))
xlabels_major = [f"{year:>6}" for year in years]
xlabels_minor = ["Apr", "", "Oct"] + ["Apr", "", "Oct"] * 19
ax.set_xlabel("Simulation Time", fontsize=16)

ax.set_ylim(0, 1.1)
ax.set_yticks(np.linspace(0, 1, 11))
ax.set_ylabel("Monthly Availability", fontsize=16)
ax.set_yticklabels([f"{y * 100:.0f}%" for y in ax.get_yticks()], fontsize=12)

ax.set_xlim(0, 300)
ax.set_xticks(xticks_major)
for t in ax.get_xticklabels():
    t.set_y(-0.05)
ax.set_xticks(xticks_minor, minor=True)
ax.set_xticklabels(xlabels_major, ha="left", fontsize=12)
ax.set_xticklabels(xlabels_minor, minor=True, rotation=90)

ax.set_title("Onshore Windfarm Availability", fontsize=18)
ax.legend(handles=line_legend, loc="lower left", fontsize=14)

plt.grid(axis="x", which="major")
plt.grid(alpha=0.2, which="minor")
ax.hlines(np.linspace(0, 1, 11), 0, 240, alpha=0.2, color="gray")
fig.tight_layout()
plt.savefig(os.path.join(library_path, "outputs/plots_and_tables/onshore_availability.png"), bbox_to_inches="tight")
plt.savefig(os.path.join(library_path, "outputs/plots_and_tables/onshore_availability.svg"), bbox_to_inches="tight")

fig = plt.figure(figsize=(16, 12), dpi=300)
axes = fig.subplots(
    3, 4, sharey=True,
    subplot_kw=dict(ylim=(0, 200000), yticks=range(0, 200001, 20000)),
    gridspec_kw=dict(wspace=0.05, hspace=0.05)
)

x = range(len(cost_columns))
x_labels = [cost_format_dict[x]["name"] for x in cost_columns]
major_cat_colors = [cost_format_dict[x]["color"] for x in cost_columns]
offshore_equipment_colors = [cost_format_dict[e]["color"] for e in offshore_equipment]

patches = []
for name, color in [*zip(x_labels[:-2], major_cat_colors[:-2]), *reversed(list(zip(offshore_equipment, offshore_equipment_colors))), (x_labels[-1], major_cat_colors[-1])]:
    name = cost_format_dict[name]["name"] if name in offshore_equipment else name
    patch = mpatches.Patch(color=color, label=name)
    patches.append(patch)
    
scenario = "offshore_base_3_month_summer"
base_costs = costs = data_frames[scenario]["events"][cost_columns].sum(axis=0) / offshore_total_mw / len(years)
base_availability = data_frames[scenario]["project_availability"]

_data_frames = {name: values for name, values in data_frames.items() if name.startswith("offshore") and name != "offshore_0_month"}
for scenario, ax in zip(_data_frames, axes.flatten()):
    name = format_dict[scenario]["name"]
    events = data_frames[scenario]["events"]

    costs = events[cost_columns].sum(axis=0)
    annual_mw_costs = costs / offshore_total_mw / len(years)
    availability = data_frames[scenario]["project_availability"]
    
    vals = annual_mw_costs.copy()
    vals.loc["equipment_cost"] = 0
    
    equip_ix = cost_columns.index("equipment_cost")
    offshore_equipment_costs = events[events.equipment_cost > 0].groupby("agent").sum().equipment_cost / offshore_total_mw / len(years)
    
    bottom = np.zeros(annual_mw_costs.size)
    for i, e in enumerate(offshore_equipment):
        _colors = deepcopy(major_cat_colors)
        _colors[equip_ix] = cost_format_dict[e]["color"]
        y = vals.values
        if i > 0:
            y = np.zeros(vals.size)
        try:
            y[equip_ix] = offshore_equipment_costs.loc[e]
            ax.bar(x, y, color=_colors, bottom=bottom)
        except KeyError:
            pass
        bottom += y
        
    if scenario != "offshore_base_3_month_summer":
        cost_diff = (annual_mw_costs.values - base_costs.values) / base_costs.values
        cost_diff_text = [
            f"{'+' if d > 0 else ''}{d * 100:.1f}%"
            for d in cost_diff
        ]
        for i, (y, text) in enumerate(zip(bottom, cost_diff_text)):
            ax.text(i, y + 2000, text, ha="center")
    
        availability_diff = (availability - base_availability) / base_availability
        availability_diff_text = f"Availability {'+' if availability_diff > 0 else ''}{availability_diff * 100:.1f}%"
        ax.text(-0.5, 130000, availability_diff_text, ha="left", va="center")
    else:
        cost_text = [f"${cost:,.0f}" for cost in base_costs]
        for i, (y, text) in enumerate(zip(base_costs, cost_text)):
            ax.text(i, y + 2000, text, ha="center")
        
        text = f"Availability: {base_availability * 100:.1f}%"
        ax.text(-0.5, 130000, text, ha="left", va="center")
    
    ax.text(1.5, 190000, name, fontsize=13, ha="center", va="center")
    
    ax.set_yticklabels([f"{y:,.0f}" for y in ax.get_yticks()])
    ax.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False)
    
    ax.grid(axis="y", alpha=0.5)
    ax.set_axisbelow(True)

# axes[0, 1].set_title(
#     "Operations and Maintenance Cost Breakdown by Category",
#     fontsize=20, x=1.1, y=1.05, ha="center"
# )
axes[0, 1].legend(handles=patches, loc="upper right", ncol=6, fontsize=12, bbox_to_anchor=(2.5, 1.15))
axes[1, 0].set_ylabel(
    "Annualized Cost per MW (USD/MW/year)",
    fontsize=14, x=-0.2, y=0.5, ha="center", va="bottom"
)
axes[2, 1].set_xlabel(
    "Cost Category",
    fontsize=14, x=1.05, ha="center", va="top",
)

for ax in axes[:, 1:].flatten():
    ax.tick_params(axis="y", which="both", left=False, right=False, labelleft=False, labelright=False)

# axes.flatten()[-1].legend(handles=patches, loc="center", fontsize=16)
# axes.flatten()[-1].tick_params(axis="both", which="both", bottom=False, top=False, left=False, right=False, labelbottom=False, grid_alpha=0)
# axes.flatten()[-1].axis('off')

fig.tight_layout()
plt.savefig(os.path.join(library_path, "outputs/plots_and_tables/offshore_costs.png"), bbox_inches="tight")
plt.savefig(os.path.join(library_path, "outputs/plots_and_tables/offshore_costs.svg"), bbox_inches="tight")

fig = plt.figure(figsize=(15, 6), dpi=300)
ax = fig.add_subplot(111)

colors = ["#035068", "#E37225", "#FBD876", "#83C554"]
indices = ['No Requests', 'Weather Delay', 'Outside Working Hours', 'Working']
_data_frames = {name: values for name, values in data_frames.items() if name.startswith("offshore")}
x_tick_labels = [format_dict[s]["name"] for s in _data_frames]
y_ticks = range(0, 160001, 20000)

scenario = "offshore_base_3_month_summer"
name = format_dict[scenario]["name"]
# events = data_frames[scenario]["events"]
# equip_labor = equipment_labor_cost_breakdown(events, equipment, offshore_total_mw, 20).values

patches = []
for name, color in zip(indices, colors):
    patch = mpatches.Patch(color=color, label=name)
    patches.append(patch)

for i, scenario in enumerate(_data_frames):
    if all(s is None for s in scenarios[scenario][2:]):
        continue
    name = format_dict[scenario]["name"]
    events = data_frames[scenario]["events"]
    equip_labor = equipment_labor_cost_breakdown(events, offshore_equipment, offshore_total_mw, 20)

    bottom = 0
    for c, ix in zip(colors, indices):
        if ix not in equip_labor.index:
            continue
        ax.bar(i, equip_labor.loc[ix], bottom=bottom, color=c)
        bottom += equip_labor.loc[ix]
        
ax.set_xticks(range(len(x_tick_labels)))
ax.set_xticklabels([make_label_multi_lines(x, longest_line_length=12) for x in x_tick_labels], fontsize=10)
ax.set_yticks(y_ticks)
ax.set_yticklabels([f"{y:,.0f}" for y in y_ticks], fontsize=12)

ax.set_title("Equipment and Labor Cost Breakdown by Work Status", fontsize=20)
ax.set_xlabel("Scenario", fontsize=16)
ax.set_ylabel("Annualized Cost per MW (USD/MW/year)", fontsize=14)

ax.set_xlim(-0.7, 12.7)
ax.legend(handles=list(reversed(patches)), ncol=1, fontsize=14, loc="upper left")

ax.grid(axis="y")
ax.set_axisbelow(True)
        
fig.tight_layout()
plt.savefig(os.path.join(library_path, "outputs/plots_and_tables/offshore_equipment_labor_breakdown.png"), bbox_inches="tight")
plt.savefig(os.path.join(library_path, "outputs/plots_and_tables/offshore_equipment_labor_breakdown.svg"), bbox_inches="tight")

availability, annual_mw_costs

fig = plt.figure(figsize=(9, 6), dpi=300)
ax = fig.add_subplot(111)

x_ticks = range(0, 180001, 20000)
ax.set_xticks(x_ticks)
ax.set_xticklabels([f"{x:,.0f}" for x in x_ticks])

y_ticks = range(0, 101, 10)
ax.set_yticks(y_ticks)
ax.set_yticklabels([f"{y:.0f}%" for y in y_ticks])

ax.grid(alpha=0.5)
ax.set_axisbelow(True)

ax.set_xlim(-1500, 180000)
ax.set_ylim(0, 100)

ax.set_xlabel("Annualized Cost (USD/MW/year)", fontsize=14)
ax.set_ylabel("Windfarm Availability", fontsize=14)

arrow_props = arrowprops=dict(arrowstyle="-", linewidth=0.5, zorder=0)

_data_frames = {name: values for name, values in data_frames.items() if name.startswith("offshore")}
for scenario in _data_frames:
    name = format_dict[scenario]["name"]
    events = data_frames[scenario]["events"]
    fmt = format_dict[scenario]["format"]
    annual_mw_costs = data_frames[scenario]["events"].total_cost.sum()
    annual_mw_costs = annual_mw_costs / offshore_total_mw / len(years)
    availability = data_frames[scenario]["project_availability"] * 100
    text_positions = (annual_mw_costs, availability)
    if name == "No Visit":
        text_positions = (annual_mw_costs + 7000, availability + 5)
    elif name == "12 Month Visit":
        text_positions = (annual_mw_costs - 12000, availability - 5)
    elif name == "Doubled MTBF":
        text_positions = (annual_mw_costs - 8000, availability - 4)
    elif name == "No Weather":
        text_positions = (annual_mw_costs - 6000, availability + 6)
    elif name == "Halved MTBF":
        text_positions = (annual_mw_costs - 4000, availability - 4)
    elif name == "2 Month Visit":
        text_positions = (annual_mw_costs - 4000, availability - 8)
    elif name == "1 Month Visit":
        text_positions = (annual_mw_costs - 4000, availability - 5)
    elif name == "2 Month Visit w/o Weather":
        text_positions = (annual_mw_costs - 6000, availability + 11)
    elif name == "1 Month Visit w/o Weather":
        text_positions = (annual_mw_costs - 8000, availability + 6)
    elif name == "Fall Visit":
        text_positions = (annual_mw_costs + 12000, availability + 2)
    elif name == "Spring Visit":
        text_positions = (annual_mw_costs + 10000, availability - 4)
    elif name == "Winter Visit":
        text_positions = (annual_mw_costs + 16000, availability)
    elif name == "Base (3 Month Summer Visit)":
        text_positions = (annual_mw_costs + 30000, availability + 7)

    ax.scatter(
        annual_mw_costs, availability,
        s=50, color=fmt["marker_color"], marker=fmt["marker"],
        edgecolor="black", linewidth=0.5
    )
    ax.annotate(
        name, (annual_mw_costs, availability),
        xytext=text_positions, xycoords="data",
        ha="center", va="center", fontsize=10,
        arrowprops=arrow_props,
    )

fig.tight_layout()
plt.savefig(os.path.join(library_path, "outputs/plots_and_tables/offshore_cost_availability.png"), bbox_to_inches="tight")
plt.savefig(os.path.join(library_path, "outputs/plots_and_tables/offshore_cost_availability.svg"), bbox_to_inches="tight")

(events.groupby("part_name").sum()[["total_cost", "materials_cost"]] / offshore_total_mw / 20).sort_values(["total_cost"], ascending=False)

data = events.groupby("part_name").sum()[["total_cost", "materials_cost"]]
data = data / offshore_total_mw / 20
data = data.sort_values(["total_cost"], ascending=False)

fig = plt.figure(figsize=(12, 4))
ax = fig.add_subplot(111)

bottom = 0
for part in data.index.values:
    ax.barh(0, data.loc[part, "total_cost"], left=bottom)
    bottom += data.loc[part, "total_cost"]
    
fig.tight_layout()

fig = plt.figure(figsize=(16, 12), dpi=300)
axes = fig.subplots(
    3, 4, sharey=True,
    subplot_kw=dict(ylim=(0, 70000), yticks=range(0, 70001, 10000)),
    gridspec_kw=dict(wspace=0.05, hspace=0.05)
)

x = range(len(cost_columns))
x_labels = [cost_format_dict[x]["name"] for x in cost_columns]
major_cat_colors = [cost_format_dict[x]["color"] for x in cost_columns]
onshore_equipment_colors = [cost_format_dict[e]["color"] for e in onshore_equipment]

patches = []
for name, color in [*zip(x_labels[:-2], major_cat_colors[:-2]), *reversed(list(zip(onshore_equipment, onshore_equipment_colors))), (x_labels[-1], major_cat_colors[-1])]:
    name = cost_format_dict[name]["name"] if name in onshore_equipment else name
    patch = mpatches.Patch(color=color, label=name)
    patches.append(patch)
    
scenario = "onshore_base_3_month_summer"
base_costs = costs = data_frames[scenario]["events"][cost_columns].sum(axis=0) / onshore_total_mw / len(years)
base_availability = data_frames[scenario]["project_availability"]

_data_frames = {name: values for name, values in data_frames.items() if name.startswith("onshore") and name != "onshore_0_month"}
for scenario, ax in zip(_data_frames, axes.flatten()):
    name = format_dict[scenario]["name"]
    events = data_frames[scenario]["events"]

    costs = events[cost_columns].sum(axis=0)
    annual_mw_costs = costs / onshore_total_mw / len(years)
    availability = data_frames[scenario]["project_availability"]
    
    vals = annual_mw_costs.copy()
    vals.loc["equipment_cost"] = 0
    
    equip_ix = cost_columns.index("equipment_cost")
    onshore_equipment_costs = events[events.equipment_cost > 0].groupby("agent").sum().equipment_cost / onshore_total_mw / len(years)
    
    bottom = np.zeros(annual_mw_costs.size)
    for i, e in enumerate(onshore_equipment):
        _colors = deepcopy(major_cat_colors)
        _colors[equip_ix] = cost_format_dict[e]["color"]
        y = vals.values
        if i > 0:
            y = np.zeros(vals.size)
        try:
            y[equip_ix] = onshore_equipment_costs.loc[e]
            ax.bar(x, y, color=_colors, bottom=bottom)
        except KeyError:
            pass
        bottom += y
        
    if scenario != "onshore_base_3_month_summer":
        cost_diff = (annual_mw_costs.values - base_costs.values) / base_costs.values
        cost_diff_text = [
            f"{'+' if d > 0 else ''}{d * 100:.1f}%"
            for d in cost_diff
        ]
        for i, (y, text) in enumerate(zip(bottom, cost_diff_text)):
            ax.text(i, y + 2000, text, ha="center")
    
        availability_diff = (availability - base_availability) / base_availability
        availability_diff_text = f"Availability {'+' if availability_diff > 0 else ''}{availability_diff * 100:.1f}%"
        ax.text(-0.5, 45000, availability_diff_text, ha="left", va="center")
    else:
        cost_text = [f"${cost:,.0f}" for cost in base_costs]
        for i, (y, text) in enumerate(zip(base_costs, cost_text)):
            ax.text(i, y + 1000, text, ha="center")
        
        text = f"Availability: {base_availability * 100:.1f}%"
        ax.text(-0.5, 45000, text, ha="left", va="center")
    
    ax.text(1.5, 65000, name, fontsize=13, ha="center", va="center")
    
    ax.set_yticklabels([f"{y:,.0f}" for y in ax.get_yticks()])
    ax.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False)
    
    ax.grid(axis="y", alpha=0.5)
    ax.set_axisbelow(True)

# axes[0, 1].set_title(
#     "Operations and Maintenance Cost Breakdown by Category",
#     fontsize=20, x=1.1, y=1.05, ha="center"
# )
axes[0, 1].legend(handles=patches, loc="upper right", ncol=6, fontsize=12, bbox_to_anchor=(2.5, 1.15))
axes[1, 0].set_ylabel(
    "Annualized Cost per MW (USD/MW/year)",
    fontsize=14, x=-0.2, y=0.5, ha="center", va="bottom"
)
axes[2, 1].set_xlabel(
    "Cost Category",
    fontsize=14, x=1.05, ha="center", va="top",
)

for ax in axes[:, 1:].flatten():
    ax.tick_params(axis="y", which="both", left=False, right=False, labelleft=False, labelright=False)

# axes.flatten()[-1].legend(handles=patches, loc="center", fontsize=16)
# axes.flatten()[-1].tick_params(axis="both", which="both", bottom=False, top=False, left=False, right=False, labelbottom=False, grid_alpha=0)
# axes.flatten()[-1].axis('off')

fig.tight_layout()
plt.savefig(os.path.join(library_path, "outputs/plots_and_tables/onshore_costs.png"), bbox_inches="tight")
plt.savefig(os.path.join(library_path, "outputs/plots_and_tables/onshore_costs.svg"), bbox_inches="tight")

fig = plt.figure(figsize=(15, 6), dpi=300)
ax = fig.add_subplot(111)

colors = ["#035068", "#E37225", "#FBD876", "#83C554"]
indices = ['No Requests', 'Weather Delay', 'Outside Working Hours', 'Working']
_data_frames = {name: values for name, values in data_frames.items() if name.startswith("onshore")}
x_tick_labels = [format_dict[s]["name"] for s in _data_frames]
y_ticks = range(0, 50001, 5000)

patches = []
for name, color in zip(indices, colors):
    patch = mpatches.Patch(color=color, label=name)
    patches.append(patch)

for i, scenario in enumerate(_data_frames):
    if all(s is None for s in scenarios[scenario][2:]):
        continue
    name = format_dict[scenario]["name"]
    events = data_frames[scenario]["events"]
    equip_labor = equipment_labor_cost_breakdown(events, onshore_equipment, onshore_total_mw, 20)

    bottom = 0
    for c, ix in zip(colors, indices):
        if ix not in equip_labor.index:
            continue
        ax.bar(i, equip_labor.loc[ix], bottom=bottom, color=c)
        bottom += equip_labor.loc[ix]
        
ax.set_xticks(range(len(x_tick_labels)))
ax.set_xticklabels([make_label_multi_lines(x, longest_line_length=12) for x in x_tick_labels], fontsize=10)
ax.set_yticks(y_ticks)
ax.set_yticklabels([f"{y:,.0f}" for y in y_ticks], fontsize=12)

ax.set_title("Equipment and Labor Cost Breakdown by Work Status", fontsize=20)
ax.set_xlabel("Scenario", fontsize=16)
ax.set_ylabel("Annualized Cost per MW (USD/MW/year)", fontsize=14)

ax.set_xlim(-0.7, 12.7)
ax.set_ylim(0, 50000)
ax.legend(handles=list(reversed(patches)), ncol=1, fontsize=14, loc="upper left")

ax.grid(axis="y")
ax.set_axisbelow(True)
        
fig.tight_layout()
plt.savefig(os.path.join(library_path, "outputs/plots_and_tables/onshore_equipment_labor_breakdown.png"), bbox_inches="tight")
plt.savefig(os.path.join(library_path, "outputs/plots_and_tables/onshore_equipment_labor_breakdown.svg"), bbox_inches="tight")

fig = plt.figure(figsize=(9, 6), dpi=300)
ax = fig.add_subplot(111)

x_ticks = range(0, 60001, 10000)
ax.set_xticks(x_ticks)
ax.set_xticklabels([f"{x:,.0f}" for x in x_ticks])

y_ticks = range(0, 101, 10)
ax.set_yticks(y_ticks)
ax.set_yticklabels([f"{y:.0f}%" for y in y_ticks])

ax.grid(alpha=0.5)
ax.set_axisbelow(True)

ax.set_xlim(-1000, 60000)
ax.set_ylim(0, 100)

ax.set_xlabel("Annualized Cost (USD/MW/year)", fontsize=14)
ax.set_ylabel("Windfarm Availability", fontsize=14)

arrow_props = arrowprops=dict(arrowstyle="-", linewidth=0.5, zorder=0)

_data_frames = {name: values for name, values in data_frames.items() if name.startswith("onshore")}
for scenario in _data_frames:
    name = format_dict[scenario]["name"]
    events = data_frames[scenario]["events"]
    fmt = format_dict[scenario]["format"]
    annual_mw_costs = data_frames[scenario]["events"].total_cost.sum()
    annual_mw_costs = annual_mw_costs / onshore_total_mw / len(years)
    availability = data_frames[scenario]["project_availability"] * 100
    text_positions = (annual_mw_costs, availability)
    if name == "No Visit":
        text_positions = (annual_mw_costs + 5000, availability + 4)
    elif name == "12 Month Visit":
        text_positions = (annual_mw_costs - 7000, availability - 7)
    elif name == "Doubled MTBF":
        text_positions = (annual_mw_costs - 5000, availability - 7)
    elif name == "No Weather":
        text_positions = (annual_mw_costs + 3000, availability + 5)
    elif name == "Halved MTBF":
        text_positions = (annual_mw_costs, availability - 7)
    elif name == "2 Month Visit":
        text_positions = (annual_mw_costs - 4000, availability + 5)
    elif name == "1 Month Visit":
        text_positions = (annual_mw_costs + 4000, availability - 6)
    elif name == "2 Month Visit w/o Weather":
        text_positions = (annual_mw_costs + 6000, availability - 10)
    elif name == "1 Month Visit w/o Weather":
        text_positions = (annual_mw_costs - 4000, availability + 4)
    elif name == "Fall Visit":
        text_positions = (annual_mw_costs + 6000, availability + 4)
    elif name == "Spring Visit":
        text_positions = (annual_mw_costs + 6000, availability - 2)
    elif name == "Winter Visit":
        text_positions = (annual_mw_costs + 7000, availability - 2)
    elif name == "Base (3 Month Summer Visit)":
        text_positions = (annual_mw_costs + 15500, availability)

    ax.scatter(
        annual_mw_costs, availability,
        s=50, color=fmt["marker_color"], marker=fmt["marker"],
        edgecolor="black", linewidth=0.5
    )
    ax.annotate(
        name, (annual_mw_costs, availability),
        xytext=text_positions, xycoords="data",
        ha="center", va="center", fontsize=10,
        arrowprops=arrow_props,
    )

fig.tight_layout()
plt.savefig(os.path.join(library_path, "outputs/plots_and_tables/onshore_cost_availability.png"), bbox_to_inches="tight")
plt.savefig(os.path.join(library_path, "outputs/plots_and_tables/onshore_cost_availability.svg"), bbox_to_inches="tight")