Cube

class kgpy.obs.spectral.Cube(intensity=None, intensity_uncertainty=None, wcs=None, time=None, time_index=None, channel=None, exposure_length=None)

Bases: Image

Represents a sequence of spectrally-resolved images (data with two spatial dimensions and one spectral dimension).

Parameters:

• intensity (Quantity | None) –

• intensity_uncertainty (Quantity | None) –

• wcs (ndarray | None) –

• time (Time | None) –

• time_index (ndarray | None) –

• channel (Quantity | None) –

• exposure_length (Quantity | None) –

__init__(intensity=None, intensity_uncertainty=None, wcs=None, time=None, time_index=None, channel=None, exposure_length=None)

Parameters:

• intensity (Quantity | None) –

• intensity_uncertainty (Quantity | None) –

• wcs (ndarray | None) –

• time (Time | None) –

• time_index (ndarray | None) –

• channel (Quantity | None) –

• exposure_length (Quantity | None) –

Return type:

None

Attributes

axis	Relationship between physical dimension and axis index.
channel
channel_labels
exposure_half_length
exposure_length
intensity	Intensity of each pixel in the data
intensity_uncertainty
num_channels
num_times
num_wavelength
num_x
num_y
shape
time
time_exp_end
time_exp_start
time_index
wcs

Methods

__init__([intensity, intensity_uncertainty, ...])
add_index_axis_to_shared_time_axes(axs)	rtype: typing.Sequence[matplotlib.axes._axes.Axes]
add_index_axis_to_time_axis(ax)	rtype: matplotlib.axes._axes.Axes
animate(data[, time_slice, axs, thresh_min, ...])	rtype: matplotlib.animation.FuncAnimation
animate_channel(images, image_names[, ax, ...])
animate_intensity([axs, thresh_min, ...])	rtype: matplotlib.animation.FuncAnimation
animate_intensity_channel([ax, time_slice, ...])	rtype: matplotlib.animation.FuncAnimation
from_pickle([path])
plot_channel(image[, image_name, ax, ...])	rtype: matplotlib.axes._axes.Axes
plot_channel_from_data(data[, ax, ...])	rtype: matplotlib.axes._axes.Axes
plot_exposure_length(ax)	rtype: typing.Tuple[matplotlib.axes._axes.Axes, typing.List[matplotlib.lines.Line2D]]
plot_intensity_channel([ax, time_index, ...])	rtype: matplotlib.axes._axes.Axes
plot_intensity_mean_vs_time(ax)	rtype: typing.Tuple[matplotlib.axes._axes.Axes, typing.List[matplotlib.lines.Line2D]]
plot_intensity_time([axs, time_index, ...])	rtype: numpy.ndarray
plot_quantity_vs_index(ax, a[, t, a_name])	rtype: typing.Tuple[matplotlib.axes._axes.Axes, typing.List[matplotlib.lines.Line2D]]
plot_time(images, image_names, axs[, ...])	rtype: numpy.ndarray
plot_time_from_data(data[, axs, time_index, ...])	rtype: numpy.ndarray
to_pickle(path)
zeros(shape)	rtype: kgpy.obs.Image

Inheritance Diagram

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add_index_axis_to_shared_time_axes(axs)

Return type:

typing.Sequence[matplotlib.axes._axes.Axes]

Parameters:

axs (Sequence[Axes]) –

add_index_axis_to_time_axis(ax)

Return type:

matplotlib.axes._axes.Axes

Parameters:

ax (Axes) –

animate(data, time_slice=slice(None, None, None), axs=None, thresh_min=<Quantity 0.01 %>, thresh_max=<Quantity 99.9 %>, norm_gamma=1, frame_interval=<Quantity 100. ms>)

Return type:

matplotlib.animation.FuncAnimation

Parameters:

• data (Quantity) –

• time_slice (slice) –

• axs (ndarray | None) –

• thresh_min (Quantity) –

• thresh_max (Quantity) –

• norm_gamma (float) –

• frame_interval (Quantity) –

animate_channel(images, image_names, ax=None, thresh_min=<Quantity 0.01 %>, thresh_max=<Quantity 99.9 %>, norm_gamma=1, norm_vmin=None, norm_vmax=None, frame_interval=<Quantity 1. s>, colormap=None)

Parameters:

• images (Quantity) –

• image_names (List[str]) –

• ax (Axes | None) –

• thresh_min (Quantity) –

• thresh_max (Quantity) –

• norm_gamma (float) –

• norm_vmin (Quantity | None) –

• norm_vmax (Quantity | None) –

• frame_interval (Quantity) –

• colormap (str | None) –

animate_intensity(axs=None, thresh_min=<Quantity 0.01 %>, thresh_max=<Quantity 99.9 %>, norm_gamma=1, frame_interval=<Quantity 100. ms>)

Return type:

matplotlib.animation.FuncAnimation

Parameters:

• axs (MutableSequence[Axes] | None) –

• thresh_min (Quantity) –

• thresh_max (Quantity) –

• norm_gamma (float) –

• frame_interval (Quantity) –

animate_intensity_channel(ax=None, time_slice=None, channel_index=0, thresh_min=<Quantity 0.01 %>, thresh_max=<Quantity 99.9 %>, norm_gamma=1, norm_vmin=None, norm_vmax=None, frame_interval=<Quantity 100. ms>, colormap=None)

Return type:

matplotlib.animation.FuncAnimation

Parameters:

• ax (Axes | None) –

• time_slice (slice | None) –

• channel_index (int) –

• thresh_min (Quantity) –

• thresh_max (Quantity) –

• norm_gamma (float) –

• norm_vmin (Quantity | None) –

• norm_vmax (Quantity | None) –

• frame_interval (Quantity) –

• colormap (str | None) –

classmethod from_pickle(path=None)

Parameters:

path (Path | None) –

plot_channel(image, image_name='', ax=None, thresh_min=<Quantity 0.01 %>, thresh_max=<Quantity 99.9 %>, colorbar_location='right', transpose=False)

Return type:

matplotlib.axes._axes.Axes

Parameters:

• image (Quantity) –

• image_name (str) –

• ax (Axes | None) –

• thresh_min (Quantity) –

• thresh_max (Quantity) –

• colorbar_location (str) –

• transpose (bool) –

plot_channel_from_data(data, ax=None, time_index=0, channel_index=0, thresh_min=<Quantity 0.01 %>, thresh_max=<Quantity 99.9 %>)

Return type:

matplotlib.axes._axes.Axes

Parameters:

• data (Quantity) –

• ax (Axes | None) –

• time_index (int) –

• channel_index (int) –

• thresh_min (Quantity) –

• thresh_max (Quantity) –

plot_exposure_length(ax)

Return type:

typing.Tuple[matplotlib.axes._axes.Axes, typing.List[matplotlib.lines.Line2D]]

Parameters:

ax (Axes) –

plot_intensity_channel(ax=None, time_index=0, channel_index=0, thresh_min=<Quantity 0.01 %>, thresh_max=<Quantity 99.9 %>)

Return type:

matplotlib.axes._axes.Axes

Parameters:

• ax (Axes | None) –

• time_index (int) –

• channel_index (int) –

• thresh_min (Quantity) –

• thresh_max (Quantity) –

plot_intensity_mean_vs_time(ax)

Return type:

typing.Tuple[matplotlib.axes._axes.Axes, typing.List[matplotlib.lines.Line2D]]

Parameters:

ax (Axes) –

plot_intensity_time(axs=None, time_index=0, thresh_min=<Quantity 0.01 %>, thresh_max=<Quantity 99.9 %>)

Return type:

numpy.ndarray

Parameters:

• axs (MutableSequence[Axes] | None) –

• time_index (int) –

• thresh_min (Quantity) –

• thresh_max (Quantity) –

plot_quantity_vs_index(ax, a, t=None, a_name='')

Return type:

typing.Tuple[matplotlib.axes._axes.Axes, typing.List[matplotlib.lines.Line2D]]

Parameters:

• ax (Axes) –

• a (Quantity) –

• t (Time | None) –

• a_name (str) –

plot_time(images, image_names, axs, thresh_min=<Quantity 0.01 %>, thresh_max=<Quantity 99.9 %>)

Return type:

numpy.ndarray

Parameters:

• images (Quantity) –

• image_names (Sequence[str]) –

• axs (MutableSequence[Axes] | None) –

• thresh_min (Quantity) –

• thresh_max (Quantity) –

plot_time_from_data(data, axs=None, time_index=0, thresh_min=<Quantity 0.01 %>, thresh_max=<Quantity 99.9 %>)

Return type:

numpy.ndarray

Parameters:

• data (Quantity) –

• axs (MutableSequence[Axes] | None) –

• time_index (int) –

• thresh_min (Quantity) –

• thresh_max (Quantity) –

to_pickle(path)

Parameters:

path (Path | None) –

classmethod zeros(shape)

Return type:

kgpy.obs.Image

Parameters:

shape (Sequence[int]) –

axis: typing.ClassVar[kgpy.obs.spectral.CubeAxis] = <kgpy.obs.spectral.CubeAxis object>

Relationship between physical dimension and axis index.

channel: typ.Optional[u.Quantity] = None

property channel_labels: List[str]

property exposure_half_length

exposure_length: typ.Optional[u.Quantity] = None

intensity: typ.Optional[u.Quantity] = None

Intensity of each pixel in the data

intensity_uncertainty: typ.Optional[u.Quantity] = None

property num_channels: int

property num_times: int

property num_wavelength: int

property num_x: int

property num_y: int

property shape: Tuple[int, ...]

time: typ.Optional[astropy.time.Time] = None

property time_exp_end: Time

property time_exp_start: Time

time_index: typ.Optional[np.ndarray] = None

wcs: typ.Optional[np.ndarray] = None