plotops

Structured plotting utilities built on top of matplotlib.

plotops provides:

• Reproducible subplot layouts with cm-based sizing
• Clean multi-source plotting helpers
• Publication-ready export utilities
• Interactive workflow tools such as log toggle, pop-out, and tiling

Designed for engineering and scientific workflows where layout consistency matters.

Install

pip install plotops

Current runtime dependencies:

• numpy
• matplotlib
• mplcursors
• PyQt5

Core Modules

plotops.figure

Layout and figure control:

• layout() computes figure size and normalized layout parameters
• subplot() creates tightly controlled subplot grids as a 2D axes array and can apply labels, log scaling, limits, and grids; ylabel may be a single string, one label per row, or one label per axis in row-major order
• finish() applies standard post-plot finishing for custom subplot workflows
• axistight() applies MATLAB-style axis padding
• size() resizes and repositions the figure window
• tile() tiles all open figures on screen
• log_toggle() toggles linear and log scale interactively
• enable_popout() pops the active axes into a new figure

plotops.plot

High-level plotting utilities:

• plotxy() creates multi-source row-wise plots and accepts suptitle=...
• plot_timefreq() creates 2-column time/FFT subplot grids and supports the same common plotting arguments as plotxy() such as suptitle=..., plus time/FFT-specific options
• plot3d() flattens 3D matrix data into structured subplot layouts
• corr() converts covariance matrices to correlation plots
• surfiso() plots triangulated 3D surfaces with isolines

plotxy(..., return_all=True) returns all generated figures, axes groups, and line groups when one call spans multiple figures.

plotxy() accepts either one shared x-array per source or one x-array per subplot row, which allows wrappers such as plot_timefreq() to reuse the same legend and layout behavior.

The top-level alias plotops.multiplot(...) points to plotxy(), so it also supports suptitle=....

plotops.legacy

Legacy APIs are kept in plotops.legacy so older workflows remain available without crowding the main modules.

Quickstart

import numpy as np
import plotops

x1 = np.linspace(0, 10, 100)
x2 = np.linspace(0, 10, 400)
y1 = 20 * np.cos(x1) + 40
y2 = 20 * np.sin(x2) + 50

fig_layout = plotops.figure.layout(1, 1)

fig_out = plotops.plot.plotxy(
    [x1, x2],
    [y1, y2],
    labels=["Case A", "Case B"],
    suptitle="Main title",
    layout_kwargs=fig_layout,
)

plotops.print.savefig(
    fig_out["fig"],
    "plot_example_1",
    ".",
    figsize=fig_layout["figsize"],
    formats=["pdf", "png"],
)

Manual Plotting

For cases where you want the layout helper but will add lines or scatter plots manually:

layout = plotops.layout(2, 1)
axes, fig, _ = plotops.subplot(
    2, 1,
    layout=layout,
    xlabel="t [s]",
    ylabel=["Signal 1", "Signal 2"],
    ylog=False,
)

axes[0, 0].plot(x, y1)
axes[1, 0].scatter(x, y2)

plotops.finish(
    fig,
    axes,
    suptitle="Main title",
)

plotops.finish() is the recommended way to give custom subplot-based figures the same kind of post-plot behavior that high-level helpers already provide automatically, including axis tightening, figure-level legend placement, hiding unused padded axes, cursor hookup, and interactive figure helpers.

For plotops.subplot(...), ylabel supports three explicit forms:

• a single string to apply to every axis
• a list of length nrow to apply one y-label per subplot row across all columns
• a list of length nrow * ncol to apply one y-label per axis in row-major order

For example, ylabel=["MSD", ""] on a 2 x 1 or 2 x 2 subplot grid labels the first row as MSD and leaves the second row unlabeled.

Time And Frequency

For repeated "time on the left, FFT on the right" plots, use plot_timefreq():

fig_layout = plotops.layout(2, 2)

fig_out = plotops.plot_timefreq(
    [t_a, t_b],
    [y_a, y_b],
    labels=["Case A", "Case B"],
    ylabel=["Response 1", "Response 2"],
    suptitle="Main title",
    layout_kwargs=fig_layout,
)

This keeps the same legend/layout behavior as plotxy() while internally building one time row and one positive-frequency FFT row per signal. In practice, plot_timefreq() supports the common plotxy() options such as labels, color, linestyle, linewidth, legend, layout_kwargs, and suptitle, and adds time/frequency-specific arguments such as time_xlabel, freq_xlabel, fft_ylabel, time_xlim, freq_xlim, and ylog_freq.

Examples

The example scripts live in examples:

• example1.py: 2D plotting workflows
• example2.py: surface and isoline plotting
• example3.py: matrix-style subplot layouts
• example4.py: 3D matrix plotting

Example outputs: