punchbowl.data.visualize#

Attributes#

cmap_punch

cmap_punch_r

Functions#

_cmap_punch(→ matplotlib.colors.LinearSegmentedColormap)

Generate PUNCH colormap.

radial_distance(→ numpy.ndarray)

Create radial distance array.

radial_filter(→ numpy.ndarray)

Filter data with radial distance function.

generate_mzp_to_rgb_map(→ numpy.ndarray)

Create an RGB composite from a MZP cube.

_render_frame(→ pathlib.Path)

Frame rendering helper function.

animate_punch(→ None)

Create an animation from a sequence of PUNCH data.

plot_punch(, axes_labels, str] =, axes_off, annotate, ...)

Plot a PUNCH NDCube data object.

Module Contents#

punchbowl.data.visualize._cmap_punch() → matplotlib.colors.LinearSegmentedColormap[source]#

Generate PUNCH colormap.

punchbowl.data.visualize.cmap_punch#
punchbowl.data.visualize.cmap_punch_r#
punchbowl.data.visualize.radial_distance(h: int, w: int, center: tuple[int, int] | None = None, radius: float | None = None) → numpy.ndarray[source]#

Create radial distance array.

punchbowl.data.visualize.radial_filter(data: numpy.ndarray) → numpy.ndarray[source]#

Filter data with radial distance function.

punchbowl.data.visualize.generate_mzp_to_rgb_map(data_cube: numpy.ndarray, gamma: float = 0.7, frac: float = 0.125, s_boost: float = 2.25) → numpy.ndarray[source]#

Create an RGB composite from a MZP cube.

Parameters:

  • data_cube (NDData-like or numpy array) – Expected shape: (3, ny, nx) Channels correspond to M, Z, P images.

  • gamma (float) – Power-law exponent to apply to each channel.

  • frac (float) – Fractional scaling applied after median normalization.

  • s_boost (float) – HSV saturation boost factor (>1 increases color saturation).

Returns:

  • rgb_sat (ndarray (ny, nx, 3)) – Float RGB array in [0,1] with enhanced saturation.

  • color_image (ndarray (3, ny, nx)) – 8-bit RGB image before HSV saturation.

punchbowl.data.visualize._render_frame(args: tuple[int, pathlib.Path | ndcube.NDCube, str, dict]) → pathlib.Path[source]#

Frame rendering helper function.

punchbowl.data.visualize.animate_punch(data_list: list[pathlib.Path | ndcube.NDCube], output_path: str | pathlib.Path, fps: int = 10, n_jobs: int | None = None, persistence: bool = False, **plot_kwargs: dict) → None[source]#

Create an animation from a sequence of PUNCH data.

Parameters:

  • data_list (list of Path or NDCube) – PUNCH data to animate

  • output_path (str or Path) – Output path to write generated animation (.mp4)

  • fps (int, optional) – Frames per second

  • n_jobs (int or None, optional) – Number of parallel processes (None uses all available cores)

  • persistence (bool, optional) – Toggle for persistence of vision animation, which updates each frame in valid data areas, keeping a running value elsewhere. False by default.

  • **plot_kwargs – Additional formatting arguments passed to plot_punch

punchbowl.data.visualize.plot_punch(data: pathlib.Path | ndcube.NDCube, layer: int = 0, cmap: str | matplotlib.colors.Colormap | None = cmap_punch, norm: matplotlib.colors.Normalize | None = PowerNorm, vmin: float = 1e-14, vmax: float = 1e-12, gamma: float = 1 / 2.2, figsize: tuple[float, float] = (10, 8), axes_labels: tuple[str, str] = ('Helioprojective longitude', 'Helioprojective latitude'), axes_off: bool = False, annotate: bool = True, grid_spacing: int = 15, grid_alpha: float = 0.25, title_prefix: str | None = None, colorbar: bool = True, colorbar_label: str = 'Mean Solar Brightness (MSB)', persistence_array: numpy.ndarray | ndcube.NDCube | None = None, trim_edge: float | tuple[float, float] | list[float, float] | None = None, save_path: str | pathlib.Path | None = None, dpi: int = 300) → tuple[matplotlib.figure.Figure, matplotlib.axes.Axes][source]#

Plot a PUNCH NDCube data object.

Parameters:

  • data (Path | NDCube) – PUNCH data to plot, either a filepath or an NDCube

  • layer (int) – Data layer to plot when using three-dimensional data cubes

  • cmap (str or Colormap, optional) – Colormap to use for plot

  • norm (Normalize, optional) – Normalization function for image

  • vmin (float, optional) – Normalization vmin value

  • vmax (float, optional) – Normalization vmax value

  • gamma (float, optional) – Normalization gamma scaling value

  • figsize (tuple, optional) – Figure size

  • axes_labels (tuple[str, str], optional) – Axes labels (x, y)

  • axes_off (bool, optional) – Remove axes and labels

  • annotate (bool, optional) – Toggles display of corner annotation when axes_off is True

  • grid_spacing (int, optional) – Coordinate grid spacing in degrees, removes grid for None

  • grid_alpha (float, optional) – Coordinate grid transparency (1: opaque, 0: transparent)

  • title_prefix (str, optional) – Prefix to prepend to plot title

  • colorbar (bool, optional) – Toggle for plotting colorbar

  • colorbar_label (str, optional) – Label to use for the colorbar

  • persistence_array (np.ndarray or NDCube or None) – When not None, data is plotted where valid atop this existing array.

  • trim_edge (float, tuple[float, float], list[float, float], None) – Option to trim the edges of low-noise mosaic products to the specified fractional radial distance. One input value trims the outer boundary only, while two trim both the inner and outer boundaries. A reasonable set of values are (0.13, 0.68) for the inner and outer boundaries.

  • save_path (str or Path, optional) – When provided, saves the figure to file directly without plotting on screen

  • dpi (int, optional) – DPI for output plots saved to file

Return type:

tuple of (figure, axes)