Drizzle3D API Reference

3D spectral image drizzle module for combining multiple SPHEREx observations into a single data cube (X, Y, λ).

Drizzle3D: SPHEREx 3D spectral image drizzle.

Combines multiple SPHEREx observations into a single data cube (X, Y, λ) using a decoupled spatial + spectral drizzle algorithm.

class spxquery.drizzle3d.Drizzle3DConfig(center_ra: float, center_dec: float, width: float, height: float, detector: int = 0, xy_shrink: float = 0.8, z_shrink: float | None = None, xy_oversample: float = 1.0, z_oversample: float = 1.0, z_lambda_edges: ~numpy.ndarray | None = None, mjd_range: ~typing.Tuple[float, float] | None = None, max_images: int = 500, download_workers: int = 4, skip_existing: bool = True, data_mirror: ~pathlib._local.Path | None = None, subtract_zodi: bool = True, static_zodi: bool = False, exclude_flags: ~typing.List[int] = <factory>, ivar_max: float = 10000000000.0, min_overlap: float = 0.0, output_dir: ~pathlib._local.Path = PosixPath('drizzle_output'), overwrite: bool = False)

Bases: object

Configuration for SPHEREx 3D spectral image drizzle.

Sky-region-driven: user specifies where on the sky and how big the output cube should be. The module queries IRSA, downloads matching observations, and drizzles them.

Two independent parameter groups: - Shrink (xy_shrink / z_shrink): controls input droplet size during

overlap calculation. Frequently tuned based on data volume.

• Oversample (xy_oversample / z_oversample): controls output grid resolution. Default = native SPHEREx resolution. Rarely changed.

center_ra: float

center_dec: float

width: float

height: float

detector: int = 0

xy_shrink: float = 0.8

z_shrink: float | None = None

xy_oversample: float = 1.0

z_oversample: float = 1.0

z_lambda_edges: ndarray | None = None

mjd_range: Tuple[float, float] | None = None

max_images: int = 500

download_workers: int = 4

skip_existing: bool = True

data_mirror: Path | None = None

subtract_zodi: bool = True

static_zodi: bool = False

exclude_flags: List[int]

ivar_max: float = 10000000000.0

min_overlap: float = 0.0

output_dir: Path = PosixPath('drizzle_output')

overwrite: bool = False

BASE_PIXSCALE: float = 6.15

__post_init__() → None

Validate configuration parameters.

effective_pixscale() → float

Output pixel scale [arcsec] after oversampling.

effective_z_shrink() → float

Spectral droplet shrink factor (z_shrink if set, else xy_shrink).

output_nx() → int

Output grid width in pixels.

output_ny() → int

Output grid height in pixels.

spatial_radius_deg() → float

Half-diagonal of the output region in degrees (for TAP INTERSECTS query).

to_json() → str

Serialize config to JSON (excludes array fields).

classmethod from_json(json_str: str) → Drizzle3DConfig

Deserialize config from JSON string.

to_yaml_file(filepath: Path) → None

Save config to a YAML file with descriptive header comments.

Parameters:

filepath (Path) – Output YAML file path. Parent directories are created if needed.

classmethod from_yaml_file(filepath: Path) → Drizzle3DConfig

Load config from a YAML file.

Parameters:

filepath (Path) – Path to the YAML config file.

Raises:

• FileNotFoundError – If the file does not exist.

• ValueError – If the file does not contain a valid YAML dict.

__init__(center_ra: float, center_dec: float, width: float, height: float, detector: int = 0, xy_shrink: float = 0.8, z_shrink: float | None = None, xy_oversample: float = 1.0, z_oversample: float = 1.0, z_lambda_edges: ~numpy.ndarray | None = None, mjd_range: ~typing.Tuple[float, float] | None = None, max_images: int = 500, download_workers: int = 4, skip_existing: bool = True, data_mirror: ~pathlib._local.Path | None = None, subtract_zodi: bool = True, static_zodi: bool = False, exclude_flags: ~typing.List[int] = <factory>, ivar_max: float = 10000000000.0, min_overlap: float = 0.0, output_dir: ~pathlib._local.Path = PosixPath('drizzle_output'), overwrite: bool = False) → None

spxquery.drizzle3d.drizzle(config: Drizzle3DConfig) → Dict[int, Path]

Top-level entry point: query → download → drizzle → save.

Parameters:

config (Drizzle3DConfig) – Complete drizzle configuration.

Returns:

{detector_id: output_path} for each successfully processed detector.

Return type:

dict

Examples

>>> from spxquery.drizzle3d import Drizzle3DConfig, drizzle
>>> config = Drizzle3DConfig(
...     center_ra=186.4536,
...     center_dec=33.5468,
...     width=30.0,
...     height=30.0,
...     detector=3,
... )
>>> results = drizzle(config)

class spxquery.drizzle3d.DrizzleCube(wcs: WCS, pixscale: float, zgrid: ZGrid, flux_weighted: ndarray, weight_total: ndarray, var_accum: ndarray, count_map: ndarray, and_mask: ndarray, or_mask: ndarray, config: Drizzle3DConfig, detector: int, n_inputs: int = 0, n_rejected: int = 0)

Bases: object

In-memory accumulation buffers for one detector’s 3D drizzle.

All image arrays have shape (n_z, n_y, n_x) — FITS ordering with the spectral axis as the first dimension.

wcs: WCS

pixscale: float

zgrid: ZGrid

flux_weighted: ndarray

weight_total: ndarray

var_accum: ndarray

count_map: ndarray

and_mask: ndarray

or_mask: ndarray

config: Drizzle3DConfig

detector: int

n_inputs: int = 0

n_rejected: int = 0

property nz: int

property ny: int

property nx: int

property flux: ndarray

flux_weighted / weight_total.

Type:

Normalized flux

property variance: ndarray

var_accum / weight_total².

Type:

Per-voxel variance

finalize_masks() → None

Clean AND_MASK: set to 0 where no data was accumulated.

classmethod create(wcs: WCS, pixscale: float, zgrid: ZGrid, config: Drizzle3DConfig, detector: int) → DrizzleCube

Allocate an empty DrizzleCube with zero-initialized arrays.

__init__(wcs: WCS, pixscale: float, zgrid: ZGrid, flux_weighted: ndarray, weight_total: ndarray, var_accum: ndarray, count_map: ndarray, and_mask: ndarray, or_mask: ndarray, config: Drizzle3DConfig, detector: int, n_inputs: int = 0, n_rejected: int = 0) → None

Submodules

config — Pipeline Configuration

Drizzle3D configuration for SPHEREx 3D spectral image drizzle.

class spxquery.drizzle3d.config.Drizzle3DConfig(center_ra: float, center_dec: float, width: float, height: float, detector: int = 0, xy_shrink: float = 0.8, z_shrink: float | None = None, xy_oversample: float = 1.0, z_oversample: float = 1.0, z_lambda_edges: ~numpy.ndarray | None = None, mjd_range: ~typing.Tuple[float, float] | None = None, max_images: int = 500, download_workers: int = 4, skip_existing: bool = True, data_mirror: ~pathlib._local.Path | None = None, subtract_zodi: bool = True, static_zodi: bool = False, exclude_flags: ~typing.List[int] = <factory>, ivar_max: float = 10000000000.0, min_overlap: float = 0.0, output_dir: ~pathlib._local.Path = PosixPath('drizzle_output'), overwrite: bool = False)

Bases: object

Configuration for SPHEREx 3D spectral image drizzle.

Sky-region-driven: user specifies where on the sky and how big the output cube should be. The module queries IRSA, downloads matching observations, and drizzles them.

Two independent parameter groups: - Shrink (xy_shrink / z_shrink): controls input droplet size during

overlap calculation. Frequently tuned based on data volume.

• Oversample (xy_oversample / z_oversample): controls output grid resolution. Default = native SPHEREx resolution. Rarely changed.

center_ra: float

center_dec: float

width: float

height: float

detector: int = 0

xy_shrink: float = 0.8

z_shrink: float | None = None

xy_oversample: float = 1.0

z_oversample: float = 1.0

z_lambda_edges: ndarray | None = None

mjd_range: Tuple[float, float] | None = None

max_images: int = 500

download_workers: int = 4

skip_existing: bool = True

data_mirror: Path | None = None

subtract_zodi: bool = True

static_zodi: bool = False

exclude_flags: List[int]

ivar_max: float = 10000000000.0

min_overlap: float = 0.0

output_dir: Path = PosixPath('drizzle_output')

overwrite: bool = False

BASE_PIXSCALE: float = 6.15

__post_init__() → None

Validate configuration parameters.

effective_pixscale() → float

Output pixel scale [arcsec] after oversampling.

effective_z_shrink() → float

Spectral droplet shrink factor (z_shrink if set, else xy_shrink).

output_nx() → int

Output grid width in pixels.

output_ny() → int

Output grid height in pixels.

spatial_radius_deg() → float

Half-diagonal of the output region in degrees (for TAP INTERSECTS query).

to_json() → str

Serialize config to JSON (excludes array fields).

classmethod from_json(json_str: str) → Drizzle3DConfig

Deserialize config from JSON string.

to_yaml_file(filepath: Path) → None

Save config to a YAML file with descriptive header comments.

Parameters:

filepath (Path) – Output YAML file path. Parent directories are created if needed.

classmethod from_yaml_file(filepath: Path) → Drizzle3DConfig

Load config from a YAML file.

Parameters:

filepath (Path) – Path to the YAML config file.

Raises:

• FileNotFoundError – If the file does not exist.

• ValueError – If the file does not contain a valid YAML dict.

__init__(center_ra: float, center_dec: float, width: float, height: float, detector: int = 0, xy_shrink: float = 0.8, z_shrink: float | None = None, xy_oversample: float = 1.0, z_oversample: float = 1.0, z_lambda_edges: ~numpy.ndarray | None = None, mjd_range: ~typing.Tuple[float, float] | None = None, max_images: int = 500, download_workers: int = 4, skip_existing: bool = True, data_mirror: ~pathlib._local.Path | None = None, subtract_zodi: bool = True, static_zodi: bool = False, exclude_flags: ~typing.List[int] = <factory>, ivar_max: float = 10000000000.0, min_overlap: float = 0.0, output_dir: ~pathlib._local.Path = PosixPath('drizzle_output'), overwrite: bool = False) → None

grid — Spatial WCS Construction

Output spatial WCS construction for Drizzle3D.

Builds a tangent-plane (TAN) projection centered on the user-specified (ra, dec) with pixel scale determined by xy_oversample.

spxquery.drizzle3d.grid.build_output_wcs(config: Drizzle3DConfig) → WCS

Build output spatial WCS from Drizzle3DConfig.

Creates a 2-D gnomonic (TAN) projection centered on (center_ra, center_dec) with pixel scale = 6.15” / xy_oversample. The image extent matches (output_ny, output_nx) from the config.

Parameters:

config (Drizzle3DConfig) – Drizzle configuration with center, size, and oversampling parameters.

Returns:

2-D celestial WCS for the output spatial grid.

Return type:

WCS

Notes

CRPIX is placed at the geometric center of the output image so that pixel (nx//2, ny//2) maps to (center_ra, center_dec).

spectral — Spectral Z Grid

Spectral axis (Z) grid construction for Drizzle3D.

Reads SPECTRAL_CHANNELS from the bundled aux FITS file and builds the wavelength bin grid (native, oversampled, or user-defined custom).

class spxquery.drizzle3d.spectral.ZGrid(edges: ndarray, centers: ndarray, widths: ndarray)

Bases: object

Spectral axis grid descriptor for one detector.

edges

(n_z+1,) bin edges [μm].

Type:

np.ndarray

centers

(n_z,) bin centers [μm].

Type:

np.ndarray

widths

(n_z,) bin widths Δλ [μm].

Type:

np.ndarray

edges: ndarray

centers: ndarray

widths: ndarray

property n_z: int

frequencies() → ndarray

Central frequencies [Hz] for each bin.

delta_nu() → ndarray

Frequency bin widths [Hz] for each bin.

resolving_power() → ndarray

Effective resolving power λ/Δλ per bin.

__init__(edges: ndarray, centers: ndarray, widths: ndarray) → None

spxquery.drizzle3d.spectral.load_spectral_table() → DataFrame

Load SPECTRAL_CHANNELS (HDU 2) from the bundled aux FITS file.

Returns:

102 rows × (DETECTOR, SUBCHAN, WAVELENGTH, WL_MIN, WL_MAX, R, R_STD, BANDWIDTH).

Return type:

pd.DataFrame

spxquery.drizzle3d.spectral.detector_spectral_table(detector: int) → DataFrame

Get the 17-row spectral table for a single detector.

Parameters:

detector (int) – Detector number 1–6.

Returns:

17 rows sorted by WAVELENGTH.

Return type:

pd.DataFrame

spxquery.drizzle3d.spectral.build_z_grid_native(detector: int) → ZGrid

Build Z grid from native 17 subchannels of a detector.

Uses WL_MIN / WL_MAX from SPECTRAL_CHANNELS as bin edges. Adjacent subchannels may overlap; edges are taken directly from the table.

spxquery.drizzle3d.spectral.build_z_grid_oversampled(detector: int, z_oversample: float) → ZGrid

Build oversampled Z grid with constant effective resolving power.

Algorithm

1. Compute mean R-bar across all 17 subchannels of the detector.

2. R_eff = R-bar / z_oversample (z_oversample=2 → double resolution).

3. Generate edges using constant-R logarithmic spacing: λ_{k+1} = λ_k * (1 + 1/R_eff)

4. Start from λ_min (first subchannel WL_MIN), stop at λ_max (last WL_MAX).

spxquery.drizzle3d.spectral.build_z_grid_custom(lambda_edges: ndarray) → ZGrid

Build Z grid from user-provided bin edges [μm].

Parameters:

lambda_edges (np.ndarray) – (n_z+1,) monotonically increasing bin edges in μm.

spxquery.drizzle3d.spectral.build_z_grid(detector: int, z_oversample: float = 1.0, z_lambda_edges: ndarray | None = None) → ZGrid

Dispatch to the appropriate Z-grid builder.

Parameters:

• detector (int) – Detector number (1–6).

• z_oversample (float) – Spectral oversampling factor (default 1.0 = native).

• z_lambda_edges (np.ndarray, optional) – Custom bin edges [μm]; overrides z_oversample if provided.

Return type:

ZGrid

spatial — XY Mapping

Spatial (XY) drizzle kernel for Drizzle3D.

Computes the mapping from input image pixels to output grid pixels, including pixfrac shrinkage and fractional overlap areas.

Uses vectorised numpy operations for efficiency on full-frame images.

spxquery.drizzle3d.spatial.compute_spatial_mapping(input_wcs: WCS, input_shape: Tuple[int, int], output_wcs: WCS, output_shape: Tuple[int, int], xy_shrink: float) → Tuple[ndarray, ndarray, ndarray, ndarray, ndarray]

Compute spatial overlap mapping from input pixels to output grid.

For each input pixel, computes the output pixel(s) it contributes to and the fractional overlap area using bilinear weight distribution.

Parameters:

• input_wcs (WCS) – Input image spatial WCS.

• input_shape (tuple of int) – (ny_in, nx_in) shape of the input image.

• output_wcs (WCS) – Output grid spatial WCS (2-D tangent plane).

• output_shape (tuple of int) – (ny_out, nx_out) shape of the output grid.

• xy_shrink (float) – Droplet shrink factor (0, 1]. 1.0 = full pixel footprint.

Returns:

• valid_mask (np.ndarray (bool)) – (ny_in, nx_in) — True for pixels that land inside the output grid.

• pixel_idx (np.ndarray (int64)) – Flat index into the input image for each output contribution. Use rows, cols = np.unravel_index(pixel_idx, input_shape) to recover (y, x) coordinates.

• out_y (np.ndarray (int32)) – Output Y pixel indices.

• out_x (np.ndarray (int32)) – Output X pixel indices.

• f_xy (np.ndarray (float64)) – Spatial overlap fractions.

spxquery.drizzle3d.spatial.compute_spectral_overlaps(lambda_c: float, delta_lambda: float, z_shrink: float, z_edges: ndarray, z_centers: ndarray, z_widths: ndarray) → Tuple[ndarray, ndarray]

Compute spectral overlap fractions for a single input pixel.

Uses a top-hat (boxcar) kernel centered at lambda_c with width delta_lambda * z_shrink.

Parameters:

• lambda_c (float) – Input pixel central wavelength [μm].

• delta_lambda (float) – Input pixel bandwidth [μm].

• z_shrink (float) – Spectral droplet shrink factor.

• z_edges (np.ndarray) – (n_z+1,) output Z bin edges [μm].

• z_centers (np.ndarray) – (n_z,) output Z bin centers [μm].

• z_widths (np.ndarray) – (n_z,) output Z bin widths [μm].

Returns:

• z_idx (np.ndarray (int32)) – Indices of overlapping Z bins.

• f_z (np.ndarray (float64)) – Fractional overlap for each Z bin, summing to ~1.

accumulate — Drizzle Engine

Core accumulation for Drizzle3D.

Contains the DrizzleCube data container and the per-image accumulation loop that combines spatial + spectral overlaps into the output 3D cube.

class spxquery.drizzle3d.accumulate.DrizzleCube(wcs: WCS, pixscale: float, zgrid: ZGrid, flux_weighted: ndarray, weight_total: ndarray, var_accum: ndarray, count_map: ndarray, and_mask: ndarray, or_mask: ndarray, config: Drizzle3DConfig, detector: int, n_inputs: int = 0, n_rejected: int = 0)

Bases: object

In-memory accumulation buffers for one detector’s 3D drizzle.

All image arrays have shape (n_z, n_y, n_x) — FITS ordering with the spectral axis as the first dimension.

wcs: WCS

pixscale: float

zgrid: ZGrid

flux_weighted: ndarray

weight_total: ndarray

var_accum: ndarray

count_map: ndarray

and_mask: ndarray

or_mask: ndarray

config: Drizzle3DConfig

detector: int

n_inputs: int = 0

n_rejected: int = 0

property nz: int

property ny: int

property nx: int

property flux: ndarray

flux_weighted / weight_total.

Type:

Normalized flux

property variance: ndarray

var_accum / weight_total².

Type:

Per-voxel variance

finalize_masks() → None

Clean AND_MASK: set to 0 where no data was accumulated.

classmethod create(wcs: WCS, pixscale: float, zgrid: ZGrid, config: Drizzle3DConfig, detector: int) → DrizzleCube

Allocate an empty DrizzleCube with zero-initialized arrays.

__init__(wcs: WCS, pixscale: float, zgrid: ZGrid, flux_weighted: ndarray, weight_total: ndarray, var_accum: ndarray, count_map: ndarray, and_mask: ndarray, or_mask: ndarray, config: Drizzle3DConfig, detector: int, n_inputs: int = 0, n_rejected: int = 0) → None

spxquery.drizzle3d.accumulate.drizzle_image(cube: DrizzleCube, image: ndarray, variance: ndarray, flags: ndarray, lambda_c_map: ndarray, delta_lambda_map: ndarray, pixel_idx: ndarray, out_y: ndarray, out_x: ndarray, f_xy: ndarray, exclude_mask: ndarray | None = None) → None

Drizzle one input image into the cube (in-place accumulation).

Parameters:

• cube (DrizzleCube) – Target accumulation cube (modified in place).

• image (np.ndarray) – (ny_in, nx_in) input flux in MJy/sr.

• variance (np.ndarray) – (ny_in, nx_in) input per-pixel variance.

• flags (np.ndarray) – (ny_in, nx_in) input pixel quality flags.

• lambda_c_map (np.ndarray) – (ny_in, nx_in) per-pixel central wavelength [μm].

• delta_lambda_map (np.ndarray) – (ny_in, nx_in) per-pixel bandwidth [μm].

• pixel_idx (np.ndarray (int64)) – Flat input-pixel indices from compute_spatial_mapping. Each entry identifies which input pixel this spatial contribution belongs to.

• out_y (np.ndarray (int32)) – Output pixel coordinates from compute_spatial_mapping.

• out_x (np.ndarray (int32)) – Output pixel coordinates from compute_spatial_mapping.

• f_xy (np.ndarray (float64)) – Spatial overlap fractions from compute_spatial_mapping.

• exclude_mask (np.ndarray, optional) – (ny_in, nx_in) bool — True for pixels to exclude (flagged).

Notes

The spatial mapping arrays (pixel_idx, out_y, out_x, f_xy) may have multiple entries per input pixel (bilinear produces up to 4). pixel_idx maps each contribution back to its source input pixel.

pipeline — Orchestrator

High-level Drizzle3D pipeline.

Orchestrates: query → download → per-detector drizzle → save.

spxquery.drizzle3d.pipeline.drizzle_detector(fits_paths: List[Path], config: Drizzle3DConfig, detector: int, output_wcs: WCS) → Path | None

Run the drizzle pipeline for one detector.

Parameters:

• fits_paths (list of Path) – Downloaded FITS files for this detector.

• config (Drizzle3DConfig) – Drizzle configuration.

• detector (int) – Detector number (1–6).

• output_wcs (WCS) – Output spatial WCS.

Returns:

Path to the output FITS file, or None if no valid inputs.

Return type:

Path or None

spxquery.drizzle3d.pipeline.drizzle(config: Drizzle3DConfig) → Dict[int, Path]

Top-level entry point: query → download → drizzle → save.

Parameters:

config (Drizzle3DConfig) – Complete drizzle configuration.

Returns:

{detector_id: output_path} for each successfully processed detector.

Return type:

dict

Examples

>>> from spxquery.drizzle3d import Drizzle3DConfig, drizzle
>>> config = Drizzle3DConfig(
...     center_ra=186.4536,
...     center_dec=33.5468,
...     width=30.0,
...     height=30.0,
...     detector=3,
... )
>>> results = drizzle(config)

query — IRSA TAP Query & Download

IRSA TAP query for SPHEREx observations intersecting a sky region.

Delegates to spxquery’s shared TAP infrastructure in core.query, then groups results by detector for drizzle processing.

class spxquery.drizzle3d.query.DrizzleObservation(obs_id: str, band: str, detector: int, mjd: float, download_url: str)

Bases: object

Minimal observation descriptor for drizzle downloads.

obs_id: str

band: str

detector: int

mjd: float

download_url: str

__init__(obs_id: str, band: str, detector: int, mjd: float, download_url: str) → None

spxquery.drizzle3d.query.query_observations(config: Drizzle3DConfig) → Dict[int, List[DrizzleObservation]]

Query IRSA TAP for observations INTERSECTING the target sky region.

Delegates to spxquery.core.query.query_spherex_observations() and converts the results into detector-grouped DrizzleObservation objects.

Parameters:

config (Drizzle3DConfig) – Drizzle configuration with center, size, detector, and mjd_range.

Returns:

{detector_id: [DrizzleObservation, …]} grouped by detector. Only detectors with results are included.

Return type:

dict

spxquery.drizzle3d.query.download_observations(observations: List[DrizzleObservation], output_dir, max_workers: int = 4, skip_existing: bool = True, data_mirror: Path | None = None) → List

Resolve FITS file paths for a list of observations.

Two modes:

• Mirror mode (data_mirror is set): constructs local paths from the IRSA download URL by stripping /ibe/data/spherex and prepending data_mirror. Verifies each file exists. No network access.

• Download mode (default): downloads FITS files from IRSA via HTTP, delegating to spxquery.core.download.download_file().

Parameters:

• observations (list of DrizzleObservation) – Observations to resolve.

• output_dir (Path) – Base directory; files go into output_dir/images/D{N}/ (download mode only).

• max_workers (int) – Parallel download threads (download mode only).

• skip_existing (bool) – Skip already-downloaded files (download mode only).

• data_mirror (Path, optional) – Local mirror root directory. When set, resolves paths from the mirror instead of downloading.

Returns:

Paths to resolved FITS files.

Return type:

list of Path

io — FITS I/O

FITS I/O for Drizzle3D output cubes.

Writes and reads the 7-HDU format:

HDU 0 — PRIMARY (empty, with metadata headers) HDU 1 — SCI (float32, flux-weighted mean surface brightness) HDU 2 — VARIANCE (float32) HDU 3 — AND_MASK (uint32, conservative flag) HDU 4 — OR_MASK (uint32, inclusive flag) HDU 5 — COUNT (uint16, contribution count) HDU 6 — WAVELENGTH (BinTableHDU, spectral axis lookup)

spxquery.drizzle3d.io.save_cube(cube: DrizzleCube, path: Path, overwrite: bool = False) → None

Write DrizzleCube to 7-HDU FITS.

Parameters:

• cube (DrizzleCube) – Completed accumulation cube.

• path (Path) – Output FITS file path.

• overwrite (bool) – Whether to overwrite an existing file.

Notes

After saving, the FITS file can be opened in DS9, CARTA, or any standard FITS viewer. The SCI HDU contains an approximate linear 3-D WCS for display; the exact per-plane wavelengths are in the WAVELENGTH table (HDU 6).

spxquery.drizzle3d.io.load_cube(path) → dict

Load a drizzled cube from FITS for inspection.

Returns a dict with ‘sci’, ‘variance’, ‘and_mask’, ‘or_mask’, ‘count’, ‘wavelength’ (BinTable), and ‘header’ keys.

Parameters:

path (Path) – Path to the drizzled FITS file.

Returns:

Dictionary of HDU data and header.

Return type:

dict