Plotting¶

The openpytea.plotting module wraps matplotlib to produce publication-quality figures using the SciencePlots style. All functions return a matplotlib.axes.Axes object so you can further customize the figure before saving.

To see the outputs of all code examples below, refer to the walkthrough notebook.

Cost breakdown charts¶

Stacked bar charts visualize cost structure data returned by the *_data helper functions in openpytea.analysis.

from openpytea.analysis import (
    direct_costs_data,
    fixed_capital_data,
    fixed_opex_data,
    variable_opex_data,
)
from openpytea.plotting import plot_stacked_bar

# Equipment-level direct costs
equip_data = direct_costs_data(plants=plant)
ax = plot_stacked_bar(equip_data)

# Capital cost breakdown (ISBL, OSBL, D&E, Contingency)
capex_data = fixed_capital_data(plants=plant)
ax = plot_stacked_bar(capex_data)

# Fixed OPEX breakdown
fopex_data = fixed_opex_data(plants=plant)
ax = plot_stacked_bar(fopex_data)

# Variable OPEX breakdown
vopex_data = variable_opex_data(plants=plant)
ax = plot_stacked_bar(vopex_data)

Sensitivity plots¶

from openpytea.analysis import sensitivity_data
from openpytea.plotting import plot_sensitivity

# Vary electricity price ±50 % and plot LCOP
sens = sensitivity_data(plants=plant, parameter="electricity", plus_minus_value=0.5)
ax = plot_sensitivity(sens)

ax.figure.savefig("sensitivity.pdf")

Axis labels and the legend are set automatically from the data returned by sensitivity_data(). Pass a custom figsize to resize the chart:

ax = plot_sensitivity(sens, figsize=(5, 3))

Comparing multiple plants¶

Pass a list of plants to sensitivity_data() to plot all curves on the same axes:

sens_multi = sensitivity_data(
    plants=[plant, plant_b],
    parameter="electricity",
    metric="NPV",
    plus_minus_value=0.5,
)
ax = plot_sensitivity(sens_multi)

Tornado diagrams¶

from openpytea.analysis import tornado_data
from openpytea.plotting import plot_tornado

# Default metric is LCOP
td = tornado_data(plant=plant, plus_minus_value=0.5)
ax = plot_tornado(td)

# Profit-oriented metric
td_roi = tornado_data(plant=plant, plus_minus_value=0.5, metric="ROI")
ax = plot_tornado(td_roi)

ax.figure.savefig("tornado.pdf")

Monte Carlo histograms¶

from openpytea.analysis import monte_carlo
from openpytea.plotting import plot_monte_carlo

mc_results = monte_carlo(plant, num_samples=1_000_000, batch_size=10_000)

# Distribution of the LCOP
ax = plot_monte_carlo(plant, metric="LCOP", bins=30)

ax.figure.savefig("monte_carlo_lcop.pdf")

Visualizing input distributions¶

Use plot_monte_carlo_inputs() to verify that the std/min/max settings produce the intended input distributions:

from openpytea.plotting import plot_monte_carlo_inputs

axes = plot_monte_carlo_inputs(mc_results, bins=40)

Comparing scenarios¶

from openpytea.plotting import plot_multiple_monte_carlo

mc_b = monte_carlo(plant_b, num_samples=1_000_000, batch_size=10_000)

ax = plot_multiple_monte_carlo(
    data_list=[plant, plant_b],
    metric="LCOP",
    bins=30,
)

Saving figures¶

All functions return an Axes object. Access the parent figure via ax.figure to save:

ax = plot_stacked_bar(capex_data)
ax.figure.savefig("capex.png", dpi=300, bbox_inches="tight")
ax.figure.savefig("capex.pdf")   # vector format for publications

Customizing axes¶

You can modify the returned axes object with standard matplotlib calls:

ax = plot_sensitivity(sens)
ax.set_title("Custom title", fontsize=14)
ax.set_xlim(-0.6, 0.6)
ax.legend(loc="upper left")