Slice

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

Bases: Image

Represents of sequence of images with one spectral axis and one spatial axis. This is the type of data that is natively gathered by slit imaging spectrographs such as IRIS.

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_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.SliceAxis] = <kgpy.obs.spectral.SliceAxis 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_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