scitex.plt

Figure and Axis Creation

scitex.plt.subplots(nrows=1, ncols=1, axes_width_mm=None, axes_height_mm=None, margin_left_mm=None, margin_right_mm=None, margin_bottom_mm=None, margin_top_mm=None, space_w_mm=None, space_h_mm=None, style=None, apply_style_mm=True, panel_labels=None, **kwargs)

Create a figure with recording-enabled axes.

This is a drop-in replacement for plt.subplots() that wraps the returned figure and axes with recording capabilities.

Supports mm-based layout control for publication-quality figures.

Parameters:

• nrows (int) – Number of rows and columns of subplots.

• ncols (int) – Number of rows and columns of subplots.

• axes_width_mm (float, optional) – Axes dimensions in mm.

• axes_height_mm (float, optional) – Axes dimensions in mm.

• margin_left_mm (float, optional) – Left/right margins in mm.

• margin_right_mm (float, optional) – Left/right margins in mm.

• margin_bottom_mm (float, optional) – Bottom/top margins in mm.

• margin_top_mm (float, optional) – Bottom/top margins in mm.

• space_w_mm (float, optional) – Horizontal/vertical spacing between axes in mm.

• space_h_mm (float, optional) – Horizontal/vertical spacing between axes in mm.

• style (dict, optional) – Style configuration dictionary.

• apply_style_mm (bool) – If True (default), apply loaded style to axes.

• panel_labels (bool or None) – If True, add panel labels (A, B, C, …).

• **kwargs – Additional arguments passed to plt.subplots().

Return type:

Tuple[RecordingFigure, Union[RecordingAxes, ndarray[tuple[Any, ...], dtype[TypeVar(_ScalarT, bound= generic)]]]]

Returns:

• fig (RecordingFigure) – Wrapped figure object.

• axes (RecordingAxes or ndarray) – Wrapped axes.

scitex.plt.figure(*args, **kwargs)

Create a new figure with optional recording support.

This is a pass-through to matplotlib.pyplot.figure(). For recording support, use subplots() instead.

Parameters:

• *args – Arguments passed to matplotlib.pyplot.figure().

• **kwargs – Arguments passed to matplotlib.pyplot.figure().

Returns:

The created figure.

Return type:

matplotlib.figure.Figure

Gallery

Demo plotters registry for all supported figrecipe plotting methods.

Usage

>>> from figrecipe._dev.demo_plotters import REGISTRY, create_all_plots_figure
>>>
>>> # List all available plot types
>>> print(list(REGISTRY.keys()))
>>>
>>> # List by category
>>> from figrecipe._dev.demo_plotters import CATEGORIES
>>> for cat, plots in CATEGORIES.items():
...     print(f"{cat}: {plots}")
>>>
>>> # Create a single figure with all plot types
>>> fig, axes = create_all_plots_figure(fr)
>>>
>>> # Run individual plotter
>>> fig, ax = REGISTRY['plot'](fr, rng, ax)

scitex.plt.gallery.generate(output_dir='./gallery', category=None, plot_type=None, dpi=150, save_csv=True, save_png=True, save_svg=True, save_plot=True, verbose=True)

Generate gallery plots (figrecipe bundles + extracted PNG/SVG/CSV).

Parameters:

• output_dir (str or Path) – Output directory for the gallery.

• category (str, optional) – Generate only plots in this category (see gallery.CATEGORIES).

• plot_type (str, optional) – Generate only this specific plot type.

• dpi (int) – Resolution for raster output.

• save_csv (bool) – Which artifacts to keep.

• save_png (bool) – Which artifacts to keep.

• save_svg (bool) – Which artifacts to keep.

• save_plot (bool) – Which artifacts to keep.

• verbose (bool) – Print progress messages.

Returns:

Generated file paths and any errors.

Return type:

dict

scitex.plt.gallery.get_plot_spec(category, plot_name)

Get a minimal spec dictionary for a gallery plot.

Parameters:

• category (str) – Plot category (see gallery.CATEGORIES).

• plot_name (str) – Plot name within the category.

Returns:

Spec dictionary for the plot type.

Return type:

dict

scitex.plt.gallery.get_plot_data(category, plot_name)

Get sample data for a gallery plot as a DataFrame.

Parameters:

• category (str) – Plot category (see gallery.CATEGORIES).

• plot_name (str) – Plot name within the category.

Returns:

Sample data for the plot, or None if it has no extractable data.

Return type:

pandas.DataFrame or None

scitex.plt.gallery.list_plots()

List all available plot types.

Return type:

List[str]

scitex.plt.gallery.list_plots_by_category()

List all available plot types organized by category.

Return type:

Dict[str, List[str]]

scitex.plt.gallery.get_representative_plots()

Get one representative plot from each category for demos.

Return type:

List[str]

scitex.plt.gallery.get_plotter(name)

Get plotter function by name.

Return type:

Optional[Callable]

scitex.plt.gallery.create_all_plots_figure(plt, seed=42)

Create a single figure with all supported plot types.

Parameters:

• plt (module) – figrecipe module (import figrecipe as fr).

• seed (int) – Random seed for reproducibility.

Returns:

(fig, axes, results) where results maps name to success/error.

Return type:

tuple

scitex.plt.gallery.run_individual_demos(plt, output_dir=None, show=False)

Run all demo plotters individually and optionally save outputs.

Parameters:

• plt (module) – figrecipe or matplotlib.pyplot module.

• output_dir (Path, optional) – Directory to save output images.

• show (bool) – Whether to show figures.

Returns:

Results for each demo: {name: {‘success’: bool, ‘error’: str or None}}

Return type:

dict

scitex.plt.gallery.run_all_demos(plt, output_dir=None, show=False)

Run all demo plotters individually and optionally save outputs.

Parameters:

• plt (module) – figrecipe or matplotlib.pyplot module.

• output_dir (Path, optional) – Directory to save output images.

• show (bool) – Whether to show figures.

Returns:

Results for each demo: {name: {‘success’: bool, ‘error’: str or None}}

Return type:

dict

scitex.plt.gallery.plot_errorbar(plt, rng, ax=None)

Error bar plot demo with multiple series and legend.

Demonstrates: ax.errorbar() with SCITEX color palette and legend

scitex.plt.gallery.plot_fill(plt, rng, ax=None)

Filled polygon demo with multiple shapes and legend.

Demonstrates: ax.fill() with SCITEX color palette and legend

scitex.plt.gallery.plot_fill_between(plt, rng, ax=None)

Filled area between curves demo with multiple regions and legend.

Demonstrates: ax.fill_between() with SCITEX color palette and legend

scitex.plt.gallery.plot_fill_betweenx(plt, rng, ax=None)

Horizontal filled area demo with multiple regions and legend.

Demonstrates: ax.fill_betweenx() with SCITEX color palette and legend

scitex.plt.gallery.plot_plot(plt, rng, ax=None)

Line plot demo.

Demonstrates: ax.plot()

scitex.plt.gallery.plot_stackplot(plt, rng, ax=None)

Stacked area plot demo with legend.

Demonstrates: ax.stackplot() with SCITEX color palette and legend

scitex.plt.gallery.plot_stairs(plt, rng, ax=None)

Staircase plot demo with legend.

Demonstrates: ax.stairs() with SCITEX color palette and legend

scitex.plt.gallery.plot_step(plt, rng, ax=None)

Step plot demo with multiple series and legend.

Demonstrates: ax.step() with SCITEX color palette and legend

scitex.plt.gallery.plot_scatter(plt, rng, ax=None)

Scatter plot demo with multiple groups in different colors.

Demonstrates: ax.scatter() with SCITEX color palette and legend

scitex.plt.gallery.plot_bar(plt, rng, ax=None)

Bar chart demo with grouped bars and legend.

Demonstrates: ax.bar() with SCITEX color palette and legend

scitex.plt.gallery.plot_barh(plt, rng, ax=None)

Horizontal bar chart demo with grouped bars and legend.

Demonstrates: ax.barh() with SCITEX color palette and legend

scitex.plt.gallery.plot_boxplot(plt, rng, ax=None)

Box plot demo with different colors for each group and legend.

Demonstrates: ax.boxplot() with SCITEX color palette and legend

scitex.plt.gallery.plot_ecdf(plt, rng, ax=None)

Empirical CDF demo with multiple distributions and legend.

Demonstrates: ax.ecdf() with SCITEX color palette and legend

scitex.plt.gallery.plot_hist(plt, rng, ax=None)

Histogram demo with multiple distributions and legend.

Demonstrates: ax.hist() with SCITEX color palette and legend

scitex.plt.gallery.plot_hist2d(plt, rng, ax=None)

2D histogram demo.

Demonstrates: ax.hist2d()

scitex.plt.gallery.plot_violinplot(plt, rng, ax=None)

Violin plot demo with colors for each group and legend.

Demonstrates: ax.violinplot() with SCITEX color palette and legend

scitex.plt.gallery.plot_hexbin(plt, rng, ax=None)

Hexagonal binning demo.

Demonstrates: ax.hexbin()

scitex.plt.gallery.plot_imshow(plt, rng, ax=None)

Image display demo.

Demonstrates: ax.imshow()

Uses the scitex icon if available, otherwise falls back to synthetic data.

scitex.plt.gallery.plot_matshow(plt, rng, ax=None)

Matrix display demo.

Demonstrates: ax.matshow()

scitex.plt.gallery.plot_pcolor(plt, rng, ax=None)

Pseudocolor plot demo.

Demonstrates: ax.pcolor()

scitex.plt.gallery.plot_pcolormesh(plt, rng, ax=None)

Pseudocolor mesh plot demo.

Demonstrates: ax.pcolormesh()

scitex.plt.gallery.plot_spy(plt, rng, ax=None)

Sparse matrix visualization demo.

Demonstrates: ax.spy()

scitex.plt.gallery.plot_contour(plt, rng, ax=None)

Contour plot demo.

Demonstrates: ax.contour()

scitex.plt.gallery.plot_contourf(plt, rng, ax=None)

Filled contour plot demo.

Demonstrates: ax.contourf()

scitex.plt.gallery.plot_tricontour(plt, rng, ax=None)

Triangular contour demo.

Demonstrates: ax.tricontour()

scitex.plt.gallery.plot_tricontourf(plt, rng, ax=None)

Filled triangular contour demo.

Demonstrates: ax.tricontourf()

scitex.plt.gallery.plot_tripcolor(plt, rng, ax=None)

Unstructured triangular grid demo.

Demonstrates: ax.tripcolor()

scitex.plt.gallery.plot_triplot(plt, rng, ax=None)

Triangular mesh plot demo.

Demonstrates: ax.triplot()

scitex.plt.gallery.plot_acorr(plt, rng, ax=None)

Autocorrelation demo with legend.

Demonstrates: ax.acorr() with SCITEX color palette and legend

scitex.plt.gallery.plot_angle_spectrum(plt, rng, ax=None)

Phase angle spectrum demo.

Demonstrates: ax.angle_spectrum()

scitex.plt.gallery.plot_cohere(plt, rng, ax=None)

Coherence demo.

Demonstrates: ax.cohere()

scitex.plt.gallery.plot_csd(plt, rng, ax=None)

Cross spectral density demo.

Demonstrates: ax.csd()

scitex.plt.gallery.plot_magnitude_spectrum(plt, rng, ax=None)

Magnitude spectrum demo.

Demonstrates: ax.magnitude_spectrum()

scitex.plt.gallery.plot_phase_spectrum(plt, rng, ax=None)

Phase spectrum demo.

Demonstrates: ax.phase_spectrum()

scitex.plt.gallery.plot_psd(plt, rng, ax=None)

Power spectral density demo.

Demonstrates: ax.psd()

scitex.plt.gallery.plot_specgram(plt, rng, ax=None)

Spectrogram demo.

Demonstrates: ax.specgram()

scitex.plt.gallery.plot_xcorr(plt, rng, ax=None)

Cross-correlation demo.

Demonstrates: ax.xcorr()

scitex.plt.gallery.plot_barbs(plt, rng, ax=None)

Wind barbs demo.

Demonstrates: ax.barbs()

scitex.plt.gallery.plot_quiver(plt, rng, ax=None)

Vector field demo.

Demonstrates: ax.quiver()

scitex.plt.gallery.plot_streamplot(plt, rng, ax=None)

Streamline plot demo.

Demonstrates: ax.streamplot()

scitex.plt.gallery.plot_eventplot(plt, rng, ax=None)

Event/spike raster demo with multiple channels and legend.

Demonstrates: ax.eventplot() with SCITEX color palette and legend

scitex.plt.gallery.plot_graph(plt, rng, ax=None)

Graph/network visualization demo.

Demonstrates: ax.graph() with NetworkX

scitex.plt.gallery.plot_loglog(plt, rng, ax=None)

Log-log plot demo with multiple power laws and legend.

Demonstrates: ax.loglog() with SCITEX color palette and legend

scitex.plt.gallery.plot_pie(plt, rng, ax=None)

Pie chart demo.

Demonstrates: ax.pie()

scitex.plt.gallery.plot_semilogx(plt, rng, ax=None)

Semi-log X plot demo with multiple series and legend.

Demonstrates: ax.semilogx() with SCITEX color palette and legend

scitex.plt.gallery.plot_semilogy(plt, rng, ax=None)

Semi-log Y plot demo with multiple exponentials and legend.

Demonstrates: ax.semilogy() with SCITEX color palette and legend

scitex.plt.gallery.plot_stem(plt, rng, ax=None)

Stem plot demo with multiple series and legend.

Demonstrates: ax.stem() with SCITEX color palette and legend

Axis Methods

The following methods are available on ax objects returned by stx.plt.subplots().

Line Plots

fig, ax = stx.plt.subplots()

ax.stx_line(values_1d, xx=None, track=True, id=None, **kwargs)

Plot a simple line from 1D array.

Parameters:

• values_1d – Y values

• xx – Optional X values

• track – Track data for CSV export

• id – Identifier for tracking

ax.stx_shaded_line(xs, ys_lower, ys_middle, ys_upper, color=None, label=None, track=True, id=None, **kwargs)

Plot a line with shaded area between bounds.

Parameters:

• xs – X values

• ys_lower – Lower bound Y values

• ys_middle – Middle line Y values

• ys_upper – Upper bound Y values

Statistical Plots

ax.stx_mean_std(values_2d, xx=None, sd=1, track=True, id=None, **kwargs)

Plot mean line with standard deviation shading.

Parameters:

• values_2d – 2D array (samples x timepoints)

• xx – Optional X values

• sd – Number of standard deviations for shading

ax.stx_mean_ci(values_2d, xx=None, perc=95, track=True, id=None, **kwargs)

Plot mean line with confidence interval shading.

Parameters:

• values_2d – 2D array (samples x timepoints)

• xx – Optional X values

• perc – Confidence interval percentage (default: 95)

ax.stx_median_iqr(values_2d, xx=None, track=True, id=None, **kwargs)

Plot median line with interquartile range shading.

Parameters:

• values_2d – 2D array (samples x timepoints)

• xx – Optional X values

ax.stx_errorbar(x, y, yerr=None, xerr=None, track=True, id=None, **kwargs)

Error bar plot with scitex styling.

Parameters:

• x – X coordinates

• y – Y coordinates

• yerr – Y error values

• xerr – X error values

Distribution Plots

ax.stx_kde(values_1d, cumulative=False, fill=False, track=True, id=None, **kwargs)

Plot kernel density estimate.

Parameters:

• values_1d – 1D array of values

• cumulative – Plot cumulative distribution

• fill – Fill under curve

ax.stx_ecdf(values_1d, track=True, id=None, **kwargs)

Plot empirical cumulative distribution function.

Parameters:

values_1d – 1D array of values

ax.hist(x, bins=10, range=None, align_bins=True, track=True, id=None, **kwargs)

Plot histogram with bin alignment support.

Parameters:

• x – Input data

• bins – Number of bins or bin edges

• align_bins – Align bins with other histograms on same axis

Categorical Plots

ax.stx_box(values_list, colors=None, track=True, id=None, **kwargs)

Boxplot with scitex styling.

Parameters:

• values_list – List of arrays for each box

• colors – Optional colors for boxes

ax.stx_violin(values_list, x=None, y=None, hue=None, labels=None, colors=None, half=False, track=True, id=None, **kwargs)

Violin plot with scitex styling.

Parameters:

• values_list – List of arrays or DataFrame

• half – Show half violins

Scatter Plots

ax.stx_scatter(x, y, track=True, id=None, **kwargs)

Scatter plot with scitex styling.

Parameters:

• x – X coordinates

• y – Y coordinates

ax.stx_scatter_hist(x, y, hist_bins=20, scatter_alpha=0.6, track=True, id=None, **kwargs)

Scatter plot with marginal histograms.

Parameters:

• x – X coordinates

• y – Y coordinates

• hist_bins – Number of histogram bins

Bar Plots

ax.stx_bar(x, height, track=True, id=None, **kwargs)

Vertical bar plot with scitex styling.

Parameters:

• x – X coordinates

• height – Bar heights

ax.stx_barh(y, width, track=True, id=None, **kwargs)

Horizontal bar plot with scitex styling.

Parameters:

• y – Y coordinates

• width – Bar widths

Heatmaps

ax.stx_heatmap(values_2d, x_labels=None, y_labels=None, cmap='viridis', cbar_label='', show_annot=True, track=True, id=None, **kwargs)

Plot annotated heatmap.

Parameters:

• values_2d – 2D array

• x_labels – Column labels

• y_labels – Row labels

• show_annot – Show value annotations

ax.stx_conf_mat(conf_mat_2d, x_labels=None, y_labels=None, title='Confusion Matrix', track=True, id=None, **kwargs)

Plot confusion matrix.

Parameters:

• conf_mat_2d – 2D confusion matrix array

• x_labels – Predicted class labels

• y_labels – True class labels

ax.stx_image(arr_2d, track=True, id=None, **kwargs)

Display 2D array as image.

Parameters:

arr_2d – 2D array

Special Plots

ax.stx_raster(spike_times_list, time=None, labels=None, colors=None, track=True, id=None, **kwargs)

Plot spike raster.

Parameters:

• spike_times_list – List of spike time arrays per neuron

• time – Optional time array

ax.stx_fillv(starts_1d, ends_1d, color='red', alpha=0.2, track=True, id=None, **kwargs)

Fill vertical regions.

Parameters:

• starts_1d – Start positions

• ends_1d – End positions

ax.stx_rectangle(xx, yy, width, height, track=True, id=None, **kwargs)

Draw rectangle.

Parameters:

• xx – X position

• yy – Y position

• width – Rectangle width

• height – Rectangle height

ax.stx_joyplot(arrays, track=True, id=None, **kwargs)

Plot joyplot (ridgeline plot).

Parameters:

arrays – List of arrays

Seaborn Wrappers

ax.sns_barplot(data=None, x=None, y=None, track=True, id=None, **kwargs)

ax.sns_boxplot(data=None, x=None, y=None, strip=False, track=True, id=None, **kwargs)

ax.sns_violinplot(data=None, x=None, y=None, track=True, id=None, **kwargs)

ax.sns_stripplot(data=None, x=None, y=None, track=True, id=None, **kwargs)

ax.sns_swarmplot(data=None, x=None, y=None, track=True, id=None, **kwargs)

ax.sns_heatmap(*args, xyz=False, track=True, id=None, **kwargs)

ax.sns_histplot(data=None, x=None, y=None, bins=10, track=True, id=None, **kwargs)

ax.sns_kdeplot(data=None, x=None, y=None, track=True, id=None, **kwargs)

ax.sns_scatterplot(data=None, x=None, y=None, track=True, id=None, **kwargs)

ax.sns_lineplot(data=None, x=None, y=None, track=True, id=None, **kwargs)

ax.sns_jointplot(*args, track=True, id=None, **kwargs)

ax.sns_pairplot(*args, track=True, id=None, **kwargs)

Axis Utilities

ax.set_xyt(x=None, y=None, t=None)

Set xlabel, ylabel, and title in one call.

ax.hide_spines(*spines)

Hide specified spines (top, right, bottom, left).