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LSST Data Management Base Package
|
Functions | |
np.ndarray | integrated_gaussian_value (np.ndarray x, float sigma) |
np.ndarray | integrated_circular_gaussian (np.ndarray|None x=None, np.ndarray|None y=None, float sigma=0.8) |
get_circle_mask (int diameter, npt.DTypeLike dtype=np.float64) | |
is_attribute_safe_to_transfer (name, value) | |
continue_class (cls) | |
Variables | |
ScalarLike = bool | int | float | complex | |
tuple | ScalarTypes = (bool, int, float, complex) |
sqrt2 = np.sqrt(2) | |
INTRINSIC_SPECIAL_ATTRIBUTES | |
lsst.scarlet.lite.utils.continue_class | ( | cls | ) |
Re-open the decorated class, adding any new definitions into the original. For example: .. code-block:: python class Foo: pass @continueClass class Foo: def run(self): return None is equivalent to: .. code-block:: python class Foo: def run(self): return None .. warning:: Python's built-in `super` function does not behave properly in classes decorated with `continue_class`. Base class methods must be invoked directly using their explicit types instead. This is copied directly from lsst.utils. If any additional functions are used from that repo we should remove this function and make lsst.utils a dependency. But for now, it is easier to copy this single wrapper than to include lsst.utils and all of its dependencies.
Definition at line 158 of file utils.py.
lsst.scarlet.lite.utils.get_circle_mask | ( | int | diameter, |
npt.DTypeLike | dtype = np.float64 ) |
Get a boolean image of a circle Parameters ---------- diameter: The diameter of the circle and width of the image. dtype: The `dtype` of the image. Returns ------- circle: A boolean array with ones for the pixels with centers inside of the circle and zeros outside of the circle.
Definition at line 94 of file utils.py.
np.ndarray lsst.scarlet.lite.utils.integrated_circular_gaussian | ( | np.ndarray | None | x = None, |
np.ndarray | None | y = None, | ||
float | sigma = 0.8 ) |
Create a circular Gaussian that is integrated over pixels This is typically used for the model PSF, working well with the default parameters. Parameters ---------- x, y: The x,y-coordinates to evaluate the integrated Gaussian. If `X` and `Y` are `None` then they will both be given the default value `numpy.arange(-7, 8)`, resulting in a `15x15` centered image. sigma: The standard deviation of the Gaussian. Returns ------- image: A Gaussian function integrated over `X` and `Y`.
Definition at line 56 of file utils.py.
np.ndarray lsst.scarlet.lite.utils.integrated_gaussian_value | ( | np.ndarray | x, |
float | sigma ) |
A Gaussian function evaluated at `x` Parameters ---------- x: The coordinates to evaluate the integrated Gaussian (ie. the centers of pixels). sigma: The standard deviation of the Gaussian. Returns ------- gaussian: A Gaussian function integrated over `x`
Definition at line 35 of file utils.py.
lsst.scarlet.lite.utils.is_attribute_safe_to_transfer | ( | name, | |
value ) |
Return True if an attribute is safe to monkeypatch-transfer to another class. This rejects special methods that are defined automatically for all classes, leaving only those explicitly defined in a class decorated by `continueClass` or registered with an instance of `TemplateMeta`.
Definition at line 144 of file utils.py.
lsst.scarlet.lite.utils.INTRINSIC_SPECIAL_ATTRIBUTES |
lsst.scarlet.lite.utils.ScalarLike = bool | int | float | complex |
tuple lsst.scarlet.lite.utils.ScalarTypes = (bool, int, float, complex) |