Plotting

The openpytea.plotting module wraps matplotlib to produce publication-quality figures using the SciencePlots style. All functions return a (fig, ax) tuple — a matplotlib.figure.Figure and a matplotlib.axes.Axes — so you can further customize or save the figure directly.

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)
fig, ax = plot_stacked_bar(equip_data)

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

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

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

Levelized cost breakdown

levelized_cost_data() feeds the same plot_stacked_bar() function, but its “Side revenue” component (stored as a negative value) is rendered as a waterfall-style base below zero instead of stacking like a normal cost — so CAPEX and OPEX still stack up to the true net LCOP at the top of the bar. The side-revenue segment reuses the color of the largest stacked component, distinguished only by a hatch pattern, so the CAPEX/OPEX ratio stays easy to read.

from openpytea.analysis import levelized_cost_data
from openpytea.plotting import plot_stacked_bar

lcop = levelized_cost_data(plants=plant)
fig, ax = plot_stacked_bar(lcop)

Cash flow diagram

plot_cash_flow() draws the classic cumulative cash flow curve: a dip into debt during construction/start-up, a minimum (“maximum investment”), a break-even point where the curve crosses back above zero, and a climb into profit for the remainder of the project life. The region where the cumulative cash flow is negative is shaded (hatched) as debt, and the break-even point (if any) is marked with a dashed vertical line in the same color as the curve.

from openpytea.analysis import cash_flow_data
from openpytea.plotting import plot_cash_flow

cash_flow = cash_flow_data(plant)
fig, ax = plot_cash_flow(cash_flow)

fig.savefig("cash_flow.pdf")

Comparing multiple plants

Pass a list of plants to cash_flow_data() to overlay their cumulative cash flow curves — each with its own shaded debt region and break-even line — for direct comparison:

cash_flow_multi = cash_flow_data([plant, plant_b])
fig, ax = plot_cash_flow(cash_flow_multi, figsize=(4.5, 3))

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)
fig, ax = plot_sensitivity(sens)

fig.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:

fig, 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,
)
fig, 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)
fig, ax = plot_tornado(td)

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

fig.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
fig, ax = plot_monte_carlo(plant, metric="LCOP", bins=30)

fig.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

fig, 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)

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

Saving figures

All functions return a (fig, ax) tuple. Use fig directly to save:

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

Customizing axes

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

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

See also

• openpytea.plotting — full API reference

• openpytea.analysis — data preparation functions

• Walkthrough notebook — end-to-end worked example