punchbowl.util

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.

Module Contents

punchbowl.util.validate_image_is_square(image: numpy.ndarray) → None

Check that the input array is square.

punchbowl.util.output_image_task(data: ndcube.NDCube, output_filename: str) → None

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) → ndcube.NDCube

Prefect task to load data for processing.

Parameters:

input_filename (str) – path to file to load

Returns:

loaded version of the image

Return type:

NDCube

punchbowl.util.average_datetime(datetimes: list[datetime.datetime]) → datetime.datetime

Compute average datetime from a list of datetimes.

punchbowl.util._zvalue_from_index(arr, ind)

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) → numpy.ndarray

Calculate the nan percentile faster of a 3D cube.