punchbowl.level3.velocity#

Functions#

calc_ylims(→ tuple[int, int])

Convert y-coordinates of lower and upper row of bands to array indices for slicing.

preprocess_image(→ numpy.ndarray)

Normalize and preprocess FITS image by removing bad values and scaling.

calculate_cross_correlation(→ numpy.ndarray)

Perform cross-correlation for a range of offsets.

accumulate_cross_correlation_across_frames(→ numpy.ndarray)

Accumulate cross-correlation across frames in a list of FITS files.

compute_all_bands(→ tuple[numpy.ndarray, numpy.ndarray])

Compute speed and sigma for all radial bands.

process_corr(→ tuple[numpy.ndarray, numpy.ndarray])

Process the cross-correlation across frames in a list of FITS files with associated average speeds.

plot_flow_map(→ None)

Plot polar maps of the radial flows.

track_velocity(→ ndcube.NDCube)

Generate velocity map using flow tracking.

Module Contents#

punchbowl.level3.velocity.calc_ylims(ycen_band_rs: numpy.ndarray, r_band_width: float, arcsec_per_px: float) → tuple[int, int]#

Convert y-coordinates of lower and upper row of bands to array indices for slicing.

Parameters:

  • ycen_band_rs (np.ndarray) – y-coordinates of center of band in solar radii

  • r_band_width (float) – Half-width of each radial band in solar radii

  • arcsec_per_px (float) – Radial pixel scale in arcsec/px in the polar-remapped images

Returns:

Lower and upper Numpy array indices of the radial band

Return type:

list

punchbowl.level3.velocity.preprocess_image(data: ndcube.NDCube, max_radius_px: int, num_azimuth_bins: int, az_bin: int) → numpy.ndarray#

Normalize and preprocess FITS image by removing bad values and scaling.

Parameters:

  • data (NDCube) – Input data NDCube

  • max_radius_px (int) – Maximum radius to include for polar remapping

  • num_azimuth_bins (int) – Number of azimuthal samples to use in polar remapping

  • az_bin (int) – Binning factor for binning the polar remapped image over the azimuth. The binning rule is currently numpy.mean()

Returns:

  • Preprocecess polar-remapped image

  • associated metadata

Return type:

np.ndarray, dict

punchbowl.level3.velocity.calculate_cross_correlation(image1: numpy.ndarray, image2: numpy.ndarray, offsets: numpy.ndarray, delta_px: int, central_offset: int) → numpy.ndarray#

Perform cross-correlation for a range of offsets.

Parameters:

  • image1 (np.ndarray) – First image array for correlation

  • image2 (np.ndarray) – Second, time-offset image array for correlation

  • offsets (np.ndarray) – Array of pixel offsets to iterate over for cross-correlation

  • delta_px (int) – Pixel offset increment between samples

  • central_offset (int) – Central offset from which to start correlation

Returns:

Accumulated cross-correlation array over all offsets

Return type:

np.ndarray

punchbowl.level3.velocity.accumulate_cross_correlation_across_frames(files: list, delta_t: int, sparsity: int, n_ofs: int, max_radius_deg: int, num_azimuth_bins: int, az_bin: int, delta_px: int, central_offset: int) → numpy.ndarray#

Accumulate cross-correlation across frames in a list of FITS files.

Parameters:

  • files (list) – List of file paths to FITS files

  • delta_t (int) – Frame offset (in frames) between time-offset image pairs

  • sparsity (int) – Interval between frames to skip when accumulating cross-correlation

  • n_ofs (int) – Number of pixel offsets to use in cross-correlation

  • max_radius_deg (int) – Maximum radius in degrees to include for polar remapping

  • num_azimuth_bins (int) – Number of azimuthal samples to use in polar remapping

  • az_bin (int) – Binning factor for binning the polar remapped image over the azimuth. The binning rule is currently numpy.mean()

  • delta_px (int) – Pixel offset increment between samples

  • central_offset (int) – Central offset from which to start correlation

Returns:

Accumulated cross-correlation array over all frames and offsets

Return type:

np.ndarray

punchbowl.level3.velocity.compute_all_bands(acc: numpy.ndarray, ycen_band_rs: numpy.ndarray, r_band_half_width: float, arcsec_per_px: float, velocity_azimuth_bins: int, x_kps: numpy.ndarray) → tuple[numpy.ndarray, numpy.ndarray]#

Compute speed and sigma for all radial bands.

Parameters:

  • acc (np.ndarray) – Cross-correlation array accumulated across frames

  • ycen_band_rs (np.ndarray) – y-coordinates of band centers in solar radii

  • r_band_half_width (float) – Half-width of each radial band in solar radii

  • arcsec_per_px (float) – Radial pixel scale in arcsec/px in the polar-remapped images

  • velocity_azimuth_bins (int) – Number of azimuthal bins in the output flow maps

  • x_kps (np.ndarray) – Array mapping pixel offsets to speed in km/s

Returns:

Tuple containing: - np.ndarray : Average speed per angular bin for each radial band - np.ndarray : Sigma (standard deviation) of speed per angular bin for each radial band

Return type:

tuple

punchbowl.level3.velocity.process_corr(files: list, arcsec_per_px: float, expected_kps_windspeed: float, delta_t: int, sparsity: int, delta_px: int, ycens: numpy.ndarray, r_band_half_width: float, n_ofs: int, max_radius_deg: int, num_azimuth_bins: int, az_bin: int, velocity_azimuth_bins: int) → tuple[numpy.ndarray, numpy.ndarray]#

Process the cross-correlation across frames in a list of FITS files with associated average speeds.

Parameters:

  • files (list) – List of file paths to FITS files

  • arcsec_per_px (float) – pixel scale in arcsec over the radial axis in the polar-remapped image

  • expected_kps_windspeed (float) – Expected Wind Speed in km/s for narrowing the cross-correlation

  • delta_t (float) – Time offset (in nb of frames) between for an image pair

  • sparsity (int) – Interval between frames to skip when accumulating cross-correlation

  • delta_px (int) – Pixel offset increment between samples

  • ycens (np.ndarray) – y-coordinates of center of bands in solar radii

  • r_band_half_width (float) – Half-width of each radial band in solar radii

  • n_ofs (int) – Number of pixel offsets to use in cross-correlation

  • max_radius_deg (int) – Maximum radius in degrees to include for polar remapping

  • num_azimuth_bins (int) – Number of azimuthal samples to use in polar remapping

  • az_bin (int) – Binning factor for binning the polar remapped image over the azimuth. The binning rule is currently numpy.mean()

  • velocity_azimuth_bins (int) – Number of azimuthal bins in the output flow maps

Returns:

Average speed and 1-sigma uncertainty over radius and angular bins

Return type:

[np.ndarray, np.ndarray]

punchbowl.level3.velocity.plot_flow_map(filename: str, data: ndcube.NDCube, cmap: str = 'magma') → None#

Plot polar maps of the radial flows.

Parameters:

  • filename (str) – Output plot filename

  • data (NDCube) – Flow tracking data NDCube

  • cmap (str, optional) – Colormap for the plot (default is ‘magma’)

punchbowl.level3.velocity.track_velocity(files: list[str], delta_t: int = 12, sparsity: int = 2, n_ofs: int = 151, delta_px: int = 2, expected_kps_windspeed: int = 300, r_band_half_width: float = 0.5, max_radius_deg: int = 45, num_azimuth_bins: int = 1440 * 8, az_bin: int = 4, velocity_azimuth_bins: int = 36, ycens: numpy.ndarray | None = None, rbands: list[int] | None = None) → ndcube.NDCube#

Generate velocity map using flow tracking.

Parameters:

  • files (list[str]) – List of file paths for input data

  • delta_t (int, optional) – Time offset in frames between images

  • sparsity (int, optional) – Frame skip interval for averaging

  • n_ofs (int, optional) – Number of spatial offsets for cross-correlation

  • delta_px (int, optional) – Pixel offset increment per sample

  • expected_kps_windspeed (int, optional) – Expected wind speed in km/s

  • r_band_half_width (float, optional) – Half-width of each radial band in solar radii

  • max_radius_deg (int, optional) – The maximum radius in degrees

  • num_azimuth_bins (int, optional) – Number of azimuthal bins in the polar remapped images

  • az_bin (int, optional) – Binning factor for binning the polar remapped image over the azimuth

  • velocity_azimuth_bins (int, optional) – Number of azimuthal bins in the output flow maps

  • ycens (numpy.ndarray, optional) – Radial band centers in solar radii

  • rbands (list[int], optional) – Indices of radial bands to visualize

Returns:

The generated velocity map

Return type:

ndcube.NDCube