stdpipe.plots module

stdpipe.plots.colorbar(obj=None, ax=None, size='5%', pad=0.1)

stdpipe.plots.imshow(image, qq=None, mask=None, show_colorbar=True, show_axis=True, stretch='linear', r0=None, ax=None, max_plot_size=4096, fast=True, xlim=None, ylim=None, **kwargs)

Display a 2D image with percentile-based intensity scaling.

Parameters:

imagendarray

2D array to display.

qqlist of float, optional

Two-element [low, high] percentile range for intensity normalization. Default is [0.5, 99.5]. Overridden by explicit vmin / vmax.

maskndarray of bool, optional

Boolean mask; masked pixels are excluded from intensity normalization.

show_colorbarbool, optional

If True, display a colorbar alongside the image.

show_axisbool, optional

If True, display axis ticks and labels.

stretchstr, optional

Intensity stretch: 'linear', 'log', 'asinh', 'histeq', or any stretch supported by Astropy visualization.

r0float, optional

Gaussian smoothing sigma in pixels applied before display.

axmatplotlib.axes.Axes, optional

Axes to draw on; defaults to the current axes.

max_plot_sizeint or None, optional

Images larger than this (in pixels) are downscaled for rendering. Set to None to disable. Default is 4096.

fastbool, optional

If True, use faster approximate methods for large images (subsampled percentiles, float32, FFT convolution).

xlimtuple of float, optional

(xmin, xmax) region selection in original image coordinates.

ylimtuple of float, optional

(ymin, ymax) region selection in original image coordinates.

**kwargs

Additional keyword arguments passed to matplotlib.pyplot.imshow().

stdpipe.plots.binned_map(x, y, value, bins=16, statistic='mean', qq=[0.5, 97.5], color=None, show_colorbar=True, show_axis=True, show_dots=False, ax=None, range=None, **kwargs)

Plot statistical estimators binned onto a regular grid.

Parameters:

xarray_like

Abscissae of the data points.

yarray_like

Ordinates of the data points.

valuearray_like

Values to aggregate.

binsint, optional

Number of bins per axis.

statisticstr or callable, optional

Aggregation statistic: 'mean', 'median', or a callable.

qqlist of float, optional

[low, high] percentile range for color scaling. Default [0.5, 97.5]. Overridden by explicit vmin / vmax.

colorcolor, optional

Color used for the data-point overlay (requires show_dots=True).

show_colorbarbool, optional

If True, display a colorbar.

show_axisbool, optional

If True, display axis ticks and labels.

show_dotsbool, optional

If True, overlay the raw data point positions.

rangelist of list, optional

Data range [[xmin, xmax], [ymin, ymax]].

axmatplotlib.axes.Axes, optional

Axes to draw on; defaults to the current axes.

**kwargs

Additional keyword arguments passed to matplotlib.pyplot.imshow().

stdpipe.plots.adaptive_binned_map(x, y, value, target_count=50, target_sn=None, err=None, statistic='mean', method='auto', qq=[0.5, 97.5], color=None, show_colorbar=True, show_axis=True, show_dots=False, show_edges=True, ax=None, range=None, verbose=False, **kwargs)

Plot statistical estimators with adaptive (density-based) binning.

Bins have variable sizes: sparse regions get larger bins, dense regions get finer resolution. Bins target approximately target_count points each, or a specified target_sn S/N ratio.

Parameters:

xarray_like

Abscissae of the data points.

yarray_like

Ordinates of the data points.

valuearray_like

Values to aggregate.

target_countint, optional

Target number of points per bin.

target_snfloat, optional

Target S/N per bin (alternative to target_count).

errarray_like, optional

Per-point value errors; required when target_sn is set.

statisticstr or callable, optional

Aggregation: 'mean', 'median', 'std', 'count', or callable.

methodstr, optional

Binning algorithm: 'auto', 'powerbin', or 'kdtree'.

qqlist of float, optional

[low, high] percentile range for color scaling. Default [0.5, 97.5].

colorcolor, optional

Color for the data-point overlay (requires show_dots=True).

show_colorbarbool, optional

If True, display a colorbar.

show_axisbool, optional

If True, display axis ticks and labels.

show_dotsbool, optional

If True, overlay the raw data point positions.

show_edgesbool, optional

If True, draw bin boundaries.

axmatplotlib.axes.Axes, optional

Axes to draw on; defaults to the current axes.

rangelist of list, optional

Data range [[xmin, xmax], [ymin, ymax]].

verbosebool, optional

If True, enable verbose output from the binning backend.

**kwargs

Additional keyword arguments passed to matplotlib.

stdpipe.plots.plot_cutout(cutout, planes=['image', 'template', 'diff', 'mask'], fig=None, axs=None, mark_x=None, mark_y=None, mark_r=5.0, mark_r2=None, mark_r3=None, mark_color='red', mark_lw=2, mark_ra=None, mark_dec=None, r0=None, show_title=True, title=None, additional_title=None, **kwargs)

Display image planes from a cutout structure in a single row.

Parameters:

cutoutdict

Cutout structure as returned by stdpipe.cutouts.get_cutout().

planeslist of str, optional

Names of cutout planes to show (in order).

figmatplotlib.figure.Figure, optional

Figure to draw into; a new figure is created if not provided.

axslist of matplotlib.axes.Axes, optional

Axes to draw into; must be the same length as planes.

mark_xfloat, optional

X coordinate (in cutout pixels) of the circular overlay mark.

mark_yfloat, optional

Y coordinate (in cutout pixels) of the circular overlay mark.

mark_rfloat, optional

Radius of the overlay mark in pixels.

mark_r2float, optional

Radius of a secondary dashed overlay circle.

mark_r3float, optional

Radius of a tertiary dashed overlay circle.

mark_colorcolor, optional

Color of the overlay mark.

mark_lwfloat, optional

Line width of the overlay mark.

mark_rafloat, optional

RA of the overlay mark; overrides mark_x / mark_y.

mark_decfloat, optional

Dec of the overlay mark; overrides mark_x / mark_y.

r0float, optional

Gaussian smoothing sigma applied to image, template, and diff planes.

show_titlebool, optional

If True (default), display a title above the cutout row.

titlestr, optional

Title text. Auto-generated from cutout metadata if not provided.

additional_titlestr, optional

Text appended to the auto-generated title.

**kwargs

Additional keyword arguments passed to imshow() for each plane.

stdpipe.plots.plot_photometric_match(m, ax=None, mode='mag', show_masked=True, show_final=True, **kwargs)

Plot photometric match diagnostics.

Displays various representations of the results returned by stdpipe.photometry.match() or stdpipe.pipeline.calibrate_photometry().

Available modes:

• 'mag' — photometric residuals vs catalogue magnitude

• 'normed' — residuals divided by errors vs catalogue magnitude

• 'color' — residuals vs catalogue color

• 'zero' — spatial map of the empirical zero point

• 'model' — spatial map of the zero-point model

• 'residuals' — spatial map of zero-point fitting residuals

• 'dist' — spatial map of angular separation (arcsec)

Parameters:

mdict

Photometric match results dictionary.

axmatplotlib.axes.Axes, optional

Axes to draw on; defaults to the current axes.

modestr, optional

Plotting mode (see above).

show_maskedbool, optional

If True, include masked objects in the plot.

show_finalbool, optional

If True, highlight objects used in the final fit.

**kwargs

Additional keyword arguments passed to binned_map() where applicable.

stdpipe.plots.plot_detection_limit(obj, sn=5, mag_name=None, obj_col_mag='mag_calib', obj_col_mag_err='magerr', show_local=True, ax=None)

Plot S/N vs magnitude with detection limit model.

Parameters:

objastropy.table.Table

Table with calibrated object detections.

snfloat, optional

S/N threshold defining the detection limit.

mag_namestr, optional

Axis label for the magnitude.

obj_col_magstr, optional

Column name for calibrated magnitude.

obj_col_mag_errstr, optional

Column name for magnitude error.

show_localbool, optional

If True, overlay local per-object detection limits from bg_fluxerr.

axmatplotlib.axes.Axes, optional

Axes to draw on; defaults to the current axes.

stdpipe.plots.plot_mag_histogram(obj, cat=None, cat_col_mag=None, sn=None, obj_col_mag='mag_calib', obj_col_mag_err='magerr', accept_flags=0, ax=None)

Plot a histogram of calibrated magnitudes.

Parameters:

objastropy.table.Table

Table with calibrated object detections.

catastropy.table.Table, optional

Reference catalogue; its magnitudes are overlaid as a separate histogram.

cat_col_magstr, optional

Column name for catalogue magnitudes.

snfloat, optional

If set, overplot the S/N detection limit as a vertical line.

obj_col_magstr, optional

Column name for object calibrated magnitude.

obj_col_mag_errstr, optional

Column name for object magnitude error.

accept_flagsint, optional

Bitmask of acceptable object flags (objects matching this mask are shown as unflagged).

axmatplotlib.axes.Axes, optional

Axes to draw on; defaults to the current axes.

stdpipe.plots.figure_saver(filename=None, show=False, tight_layout=True, **kwargs)

Context manager that creates a Figure, saves it, and optionally displays it.

Example:

with figure_saver('/tmp/figure.png', show=True, figsize=(10, 6)) as fig:
    ax = fig.add_subplot(111)
    ax.plot(x, y, '.-')

Parameters:

filenamestr, optional

Output file path. Any format supported by Matplotlib is accepted.

showbool, optional

If True, display the figure in a Jupyter notebook after saving.

tight_layoutbool, optional

If True, call fig.tight_layout() before saving.

**kwargs

Additional keyword arguments passed to matplotlib.pyplot.Figure.

stdpipe.plots.plot_outline(x, y, *args, ax=None, **kwargs)

Plot the convex hull outline of (x, y) points.

Parameters:

x, yarray-like

Coordinates of points to outline.

*args, **kwargs

Passed through to matplotlib.axes.Axes.plot for line styling.

axmatplotlib.axes.Axes or None

Target axes; defaults to current axes when None.

Notes

• Uses scipy.spatial.ConvexHull to compute the outline.

• Requires at least 3 non-collinear points; otherwise ConvexHull may fail.

• If you pass masked arrays or NaNs, pre-filter to finite values to avoid distorting the hull.

stdpipe.plots.cornerplot(features, scales=None, lines=None, subsets=None, show_all=True, extra=None, fig=None, **kwargs)

Plot pairwise feature scatter panels in an upper-left triangular corner layout.

Parameters:

featureslist[tuple[array-like, str]]

List of (values, label) pairs. Each values array is 1D and aligned.

scaleslist[str] or None

Per-feature axis scale (e.g., ‘linear’, ‘log’); length must match features.

lineslist[float] or None

Per-feature reference line positions; length must match features.

subsetslist[dict] or None

Optional subsets to overlay. Each dict must include an ‘idx’ boolean mask, plus any matplotlib style kwargs (e.g., label, color, marker).

extracallable or None

If set, this callable will be called for every panel, with axes instance and two features (for x and y axes) as arguments

show_allbool

If True, plot the full dataset in each panel excluding subset masks.

figmatplotlib.figure.Figure or None

Figure to plot into; a new figure is created when None.

**kwargs

Additional matplotlib plot kwargs for the main dataset and subsets.

Notes

• Only off-diagonal panels are drawn, resulting in a (N-1)x(N-1) grid.

• Subsets are excluded from the “all data” layer when show_all=True.