Plotting

psPlotKit provides several high-level plotters and a low-level FigureGenerator for creating publication-quality matplotlib figures.

Plot Types

Plotter	Class	Use Case
Line plots	linePlotter	X-Y curves, parameter sweeps
Box plots	boxPlotter	Distribution comparisons
Breakdown plots	BreakDownPlotter	Stacked cost breakdowns (CAPEX/OPEX)
Map/contour plots	MapPlotter	2D heatmaps, contour plots
Low-level	FigureGenerator	Direct matplotlib access

Using High-Level Plotters

All plotters follow the same pattern:

1. Prepare data with PsDataManager.select_data() + get_selected_data()
2. Create a plotter instance
3. Configure and plot
4. Generate the figure

Line Plots

from psPlotKit.data_plotter.ps_line_plotter import linePlotter

dm.select_data(["LCOW", "recovery"])
selected = dm.get_selected_data()

lp = linePlotter(selected, save_name="my_line_plot")
lp.plot_line(
    xdata="recovery",
    ydata="LCOW",
    axis_options={"xticks": [0, 25, 50, 75, 100]},
)
lp.generate_figure()

Box Plots

from psPlotKit.data_plotter.ps_box_plotter import boxPlotter

bp = boxPlotter(selected, save_name="my_box_plot")
bp.plot_tornado_plot(xdata="recovery", ydata="LCOW")
bp.generate_figure()

Breakdown Plots

from psPlotKit.data_plotter.ps_break_down_plotter import BreakDownPlotter

plotter = BreakDownPlotter(selected, save_name="cost_breakdown")
plotter.define_area_groups({"RO": {}, "Pumps": {}})
plotter.define_hatch_groups({"CAPEX": {}, "OPEX": {"hatch": "//"}})
plotter.plotbreakdown(
    xdata="Water recovery",
    ydata=["cost_breakdown", "levelized"],
)
plotter.generate_figure()

Map / Contour Plots

from psPlotKit.data_plotter.ps_map_plotter import MapPlotter

mp = MapPlotter(selected, save_name="my_map")
mp.plot_map(
    data_dir=dir_key,
    xdata="param_a",
    ydata="param_b",
    zdata="LCOW",
)
mp.generate_figure()

Using FigureGenerator Directly

For full control, use FigureGenerator:

from psPlotKit.data_plotter.fig_generator import FigureGenerator

fig = FigureGenerator(font_size=10, save_data=True)
fig.init_figure(width=3.25, height=3.25)

# Line
fig.plot_line(xdata=[1, 2, 3], ydata=[4, 5, 6], label="Series A")

# Bar
fig.plot_bar(x_pos=0, x_value=10, label="Bar 1")

# Scatter with color-mapped z
fig.plot_scatter(xdata=[1, 2], ydata=[3, 4], zdata=[0.1, 0.9])

# Error bars
fig.plot_errorbar(xdata=[1, 2], ydata=[3, 4], yerr=[0.5, 0.3])

# Histogram / CDF
fig.plot_hist(data=[1, 2, 2, 3, 3, 3])
fig.plot_cdf(data=[1, 2, 2, 3, 3, 3])

# Box plot
fig.plot_box(position=1, data=[1, 2, 3, 4, 5])

# Map / heatmap
fig.plot_map(xdata=x, ydata=y, zdata=z)

# Configure axes
fig.set_axis(
    xlabel="X Label",
    ylabel="Y Label",
    xlims=[0, 10],
    ylims=[0, 10],
)

# Legend and colorbar
fig.add_legend(loc="best")
fig.add_colorbar(zlabel="Z Values")

# Save
fig.save(save_location="figs/", file_name="my_figure")
fig.show()

Figure Configuration

init_figure Options

Parameter	Default	Description
width	3.25	Figure width in inches
height	3.25	Figure height in inches
dpi	150	Resolution
nrows / ncols	1	Subplot grid
sharex / sharey	False	Share axes across subplots
twinx / twiny	False	Create twin axes
projection	None	e.g., "3d" for 3D plots

Color Maps

fig.gen_colormap(
    num_samples=10,
    map_name="viridis",  # any matplotlib colormap
)

Saving Figures

Figures are saved as both JPG and SVG by default. When save_data=True, plotted data is also exported to CSV:

fig = FigureGenerator(save_data=True)
# ... plot ...
fig.save(save_location="figs/", file_name="my_plot")