ramjet.photometric_database.lightcurve_database

Code for a base generalized database for photometric data to be subclassed.

Module Contents

class LightcurveDatabase(data_directory='data')

Bases: abc.ABC

A base generalized database for photometric data to be subclassed.

__init__(self, data_directory='data')

log_dataset_file_names(self, dataset: tf.data.Dataset, dataset_name: str)

Saves the names of the files used in a dataset to a CSV file in the trail directory.

static normalize(lightcurve: np.ndarray)

Normalizes from 0 to 1 on the logarithm of the lightcurve.

static shuffle_in_unison(a, b, seed=None)

Shuffle two arrays in unison.

static remove_random_values(lightcurve: np.ndarray)

Removes random values from the lightcurve.

get_ratio_enforced_dataset(self, positive_training_dataset: tf.data.Dataset, negative_training_dataset: tf.data.Dataset, positive_to_negative_data_ratio: float)

Generates a dataset with an enforced data ratio.

static repeat_dataset_to_size(dataset: tf.data.Dataset, size: int)

Repeats a dataset to make it a desired length.

is_positive(self, example_path)

Checks if an example contains a microlensing event or not.

Parameters:example_path – The path to the example to check. Returns:Whether or not the example contains a microlensing event.

static make_uniform_length(example: np.ndarray, length: int)

Makes the example a specific length, by clipping those too large and repeating those too small.

get_training_and_validation_datasets_for_file_paths(self, example_paths: List[Union[str, Path]])

Creates a TensorFlow Dataset from a list of file names and desired label for those files.