HMI¶

class kgpy.observatories.sdo.hmi.HMI(intensity=None, intensity_uncertainty=None, wcs=None, time=None, time_index=None, channel=None, exposure_length=None)¶

Bases: Image

A class for downloading and storing a sequence of HMI images

Parameters

intensity (Optional[Quantity]) –
intensity_uncertainty (Optional[Quantity]) –
wcs (Optional[ndarray]) –
time (Optional[Time]) –
time_index (Optional[ndarray]) –
channel (Optional[Quantity]) –
exposure_length (Optional[Quantity]) –

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

Parameters

intensity (Optional[Quantity]) –
intensity_uncertainty (Optional[Quantity]) –
wcs (Optional[ndarray]) –
time (Optional[Time]) –
time_index (Optional[ndarray]) –
channel (Optional[Quantity]) –
exposure_length (Optional[Quantity]) –

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`]
`add_index_axis_to_time_axis`(ax)	rtype `matplotlib.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_path_array`(path_array)	rtype `kgpy.observatories.sdo.hmi.HMI`
`from_pickle`([path])
`from_time_range`(time_start, time_end[, ...])
`plot_channel`(image[, image_name, ax, ...])	rtype `matplotlib.axes.Axes`
`plot_channel_from_data`(data[, ax, ...])	rtype `matplotlib.axes.Axes`
`plot_exposure_length`(ax)	rtype `typing.Tuple`[`matplotlib.axes.Axes`, `typing.List`[`matplotlib.lines.Line2D`]]
`plot_intensity_channel`([ax, time_index, ...])	rtype `matplotlib.axes.Axes`
`plot_intensity_mean_vs_time`(ax)	rtype `typing.Tuple`[`matplotlib.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`, `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

add_index_axis_to_shared_time_axes(axs)¶

Return type: typing.Sequence[matplotlib.axes.Axes]
Parameters: axs (Sequence[Axes]) –

add_index_axis_to_time_axis(ax)¶

Return type: matplotlib.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 (Optional[ndarray]) –
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 (Optional[Axes]) –
thresh_min (Quantity) –
thresh_max (Quantity) –
norm_gamma (float) –
norm_vmin (Optional[Quantity]) –
norm_vmax (Optional[Quantity]) –
frame_interval (Quantity) –
colormap (Optional[str]) –

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 (Optional[MutableSequence[Axes]]) –
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 (Optional[Axes]) –
time_slice (Optional[slice]) –
channel_index (int) –
thresh_min (Quantity) –
thresh_max (Quantity) –
norm_gamma (float) –
norm_vmin (Optional[Quantity]) –
norm_vmax (Optional[Quantity]) –
frame_interval (Quantity) –
colormap (Optional[str]) –

classmethod from_path_array(path_array)¶

Return type: kgpy.observatories.sdo.hmi.HMI
Parameters: path_array (ndarray) –

classmethod from_pickle(path=None)¶

Parameters: path (Optional[Path]) –

classmethod from_time_range(time_start, time_end, download_path=None, channels=None, user_email='jacobdparker@gmail.com')¶

Parameters

time_start (Time) –
time_end (Time) –
download_path (Optional[Path]) –
channels (Optional[Quantity]) –
user_email (str) –

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