Level_3¶
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class
esis.data.level_3.
Level_3
(observation, transformation_objects, lev1_sequences, lev1_cameras)¶ Bases:
kgpy.mixin.Pickleable
The ESIS Level_3 data will be stored in an NDCube. The NDCube will contain a 4 axis (time, camera_id, solarx, solary) WCS object.
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__init__
(observation, transformation_objects, lev1_sequences, lev1_cameras)¶ Initialize self. See help(type(self)) for accurate signature.
- Parameters
observation (ndcube.ndcube.NDCube) –
transformation_objects (pathlib.Path) –
lev1_sequences (numpy.ndarray) –
lev1_cameras (numpy.ndarray) –
- Return type
Methods
__init__
(observation, …)Initialize self.
add_mask
([line])Transform masks created for Level1 data into Level_3 coordinates and add to Level_3 NDCube
correct_vignetting
(scale_factor[, fudge_angle])- rtype
- rtype
from_aia_level1
([aia_path, level1_path, hei])Create a Level_3 Obj through a linear co-alignment of ESIS Level1 to AIA 304.
from_pickle
([path])Given a masked level3 object this routine will return the mean taken from the portion of the sun seen by all 4 cameras that does not contain the bright MgX line as a cube by which level3.observation.data can be divided.
to_fits
(path)In need of a rework since moving to NDCube.
to_pickle
([path])to_test_object
([aia_path, level1_path])Replace all images in a Level 3 object with co-temporal AIA 304 images for testing.
update_internal_alignment
([ref_channel, heI])Inheritance Diagram
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add_mask
(line=None)¶ Transform masks created for Level1 data into Level_3 coordinates and add to Level_3 NDCube
- Return type
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correct_vignetting
(scale_factor, fudge_angle=array([0, 0, 0, 0]))¶ - Return type
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static
default_pickle_path
()¶ - Return type
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classmethod
from_aia_level1
(aia_path=None, level1_path=WindowsPath('C:/Users/byrdie/Kankelborg-Group/ESIS/esis/flight/esis_Level1.pickle'), hei=False)¶ Create a Level_3 Obj through a linear co-alignment of ESIS Level1 to AIA 304.
NOTE!!! This contains hard coded variables that only pertain to the 2019 ESIS Flight, will need to be made more general for future launches. Including a rough FOV and pointing when choosing an AIA cutout should do the trick.
- Return type
- Parameters
aia_path (Optional[pathlib.Path]) –
level1_path (pathlib.Path) –
hei (bool) –
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classmethod
from_pickle
(path=None)¶ - Return type
- Parameters
path (Optional[pathlib.Path]) –
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masked_mean_normalization
()¶ Given a masked level3 object this routine will return the mean taken from the portion of the sun seen by all 4 cameras that does not contain the bright MgX line as a cube by which level3.observation.data can be divided.
For best results correct vignetting first.
- Return type
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to_fits
(path)¶ In need of a rework since moving to NDCube. Note that WCS.to_header does not output naxis keywords
- Parameters
path (pathlib.Path) –
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to_pickle
(path=None)¶ - Parameters
path (Optional[pathlib.Path]) –
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to_test_object
(aia_path=WindowsPath('c:/users/byrdie/kankelborg-group/kgpy/kgpy/observatories/aia/data'), level1_path=WindowsPath('C:/Users/byrdie/Kankelborg-Group/ESIS/esis/flight/esis_Level1.pickle'))¶ Replace all images in a Level 3 object with co-temporal AIA 304 images for testing.
- Return type
- Parameters
aia_path (pathlib.Path) –
level1_path (pathlib.Path) –
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update_internal_alignment
(ref_channel=1, heI=False)¶ - Return type
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lev1_cameras
: numpy.ndarray¶
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lev1_sequences
: numpy.ndarray¶
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observation
: ndcube.ndcube.NDCube¶
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transformation_objects
: pathlib.Path¶
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