punchbowl.util#

Attributes#

T

Classes#

DataLoader

Interface for passing callable objects instead of file paths to be loaded.

Functions#

`validate_image_is_square`(→ None)	Check that the input array is square.
`output_image_task`(→ None)	Prefect task to write an image to disk.
`load_image_task`(→ ndcube.NDCube)	Prefect task to load data for processing.
`average_datetime`(→ datetime.datetime)	Compute average datetime from a list of datetimes.
`_zvalue_from_index`(arr, ind)	Do math.
`nan_percentile`(→ numpy.ndarray)	Calculate the nan percentile faster of a 3D cube.
`interpolate_data`(→ numpy.ndarray)	Interpolates between two data objects.
`find_first_existing_file`(→ ndcube.NDCube \| None)	Find the first cube that's not None in a list of NDCubes.

Module Contents#

punchbowl.util.validate_image_is_square(image: numpy.ndarray) → None[source]#: Check that the input array is square.

punchbowl.util.output_image_task(data: ndcube.NDCube, output_filename: str) → None[source]#

Prefect task to write an image to disk.

Parameters:

data (NDCube) – data that is to be written
output_filename (str) – where to write the file out

Return type:

None

punchbowl.util.load_image_task(input_filename: str, include_provenance: bool = True, include_uncertainty: bool = True) → ndcube.NDCube[source]#

Prefect task to load data for processing.

Parameters:

input_filename (str) – path to file to load
include_provenance (bool) – whether to load the provenance layer
include_uncertainty (bool) – whether to load the uncertainty layer

Returns:

loaded version of the image

Return type:

NDCube

punchbowl.util.average_datetime(datetimes: list[datetime.datetime]) → datetime.datetime[source]#: Compute average datetime from a list of datetimes.

punchbowl.util._zvalue_from_index(arr, ind)[source]#

Do math.

Private helper function to work around the limitation of np.choose() by employing np.take(). arr has to be a 3D array ind has to be a 2D array containing values for z-indicies to take from arr See: http://stackoverflow.com/a/32091712/4169585 This is faster and more memory efficient than using the ogrid based solution with fancy indexing.

punchbowl.util.nan_percentile(arr: numpy.ndarray, q: list[float] | float, modify_arr_in_place: bool = False) → numpy.ndarray[source]#: Calculate the nan percentile faster of a 3D cube.

punchbowl.util.interpolate_data(data_before: ndcube.NDCube, data_after: ndcube.NDCube, reference_time: datetime.datetime, time_key: str = 'DATE-OBS', allow_extrapolation: bool = False) → numpy.ndarray[source]#: Interpolates between two data objects.

punchbowl.util.find_first_existing_file(inputs: list[ndcube.NDCube]) → ndcube.NDCube | None[source]#: Find the first cube that’s not None in a list of NDCubes.

punchbowl.util.T#

class punchbowl.util.DataLoader[source]#

Bases: abc.ABC, Generic[T]

Interface for passing callable objects instead of file paths to be loaded.

abstractmethod load() → T[source]#: Load the data.

abstractmethod src_repr() → str[source]#: Return a string representation of the data source.