The MALT API reference¶
The Lightcurve class¶
-
class
malt.Lightcurve(filepath, interpolate=False, interp_func=<function get_gp>, ini_t='rand', obs_time=0.3333333333333333, sample_size=100, obj_type=None)¶ -
extract_features(feat_ex_method=<function get_wavelet_feature>)¶ Extracts features from the given lightcurve with assigned feature extraction method.
- self: Lightcurve object
- An instance of the Lightcurve class.
- feat_ex_method: python function
- Function to use for the feature extraction.
-
interpolate(interp_func=<function get_gp>, ini_t='rand', obs_time=0.3333333333333333, sample_size=100, aug_num=1)¶ Interpolates the given lightcurve with assigned interpolation function
- self: Lightcurve object
- An instance of the Lightcurve class.
- interp_func: python function
- A python function that takes in a lightcurve and interpolates it.
- ini_t: str or float
- Initial time to start sampling.
- obs_time: float
- The total length of the interpolated lightcurve.
- sample_size: int
- Number of data points in interpolated lightcurve.
- aug_num: int
- Number of lightcurves to augment to.
-
loadfile(filename)¶ Loads file to extract time, flux, flux_err ra_dec and class
filename: path to dataset
-
The Dataset class¶
-
class
malt.Dataset(configFile='', feat_ex_method=<function get_wavelet_feature>, interpolate=True, interp_func=<function get_gp>, ini_t='rand', obs_time=0.3333333333333333, sample_size=100, aug_num=1, ml_method=<class 'malt.machine_learning.RFclassifier'>, hyperparams={'criterion': ['gini', 'entropy'], 'n_estimators': array([70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89])}, n_jobs=-1, pca=True, n_components=20)¶ -
add(new_lightcurve)¶ - Adds new lightcurve to the Dataset then retrains Dataset.
- self: Dataset object
- An instance of the Dataset class containing instances of the Lightcurve class.
- lightcurve: Lightcurve object
- Lightcurve object to add to dataset.
-
extract_features()¶ Extracts features from all the lightcurves in the given dataset with assigned feature extraction method.
- self: Dataset object
- An instance of the Dataset class containing instances of the Lightcurve class.
-
get_pca()¶ Performs PCA decomposition of a feature array X.
- self: Dataset object
- An instance of the Dataset class containing instances of the Lightcurve class.
-
interpolate()¶ Interpolates all the lightcurves in the given dataset with assigned interpolation function.
- self: Dataset object
- An instance of the Dataset class containing instances of the Lightcurve class.
-
classmethod
load_from_save(filename)¶ - Returns a saved Dataset instance using pickle
- self: Dataset object
- An instance of the Dataset class containing instances of the Lightcurve class.
- filename: str
- filename under which the Dataset instance was saved.
-
populate(filepaths)¶ Initialises an instance of the Dataset class.
- self: Database object
- An instance of the Database class.
- filepaths: list
- List containing the paths to the data files.
-
predict(lightcurve, show_prob=False)¶ - Predicts the type of given lightcurve object using classifier trained on Dataset.
- self: Dataset object
- An instance of the Dataset class containing instances of the Lightcurve class.
- lightcurve: Lightcurve object
- Lightcurve object for which to predict
- show_prob: boolean.
- If True will print full output from predict_proba()
-
project_pca(lightcurve=None)¶ Projects self.features onto calculated PCA axis from self.pca
- self: Dataset object
- An instance of the Dataset class containing instances of the Lightcurve class.
-
run_diagnostic()¶ Runs the Diagnostic test which trains n classifiers on different subsets of the Dataset to test how well it can classify objects.
- self: Dataset object
- An instance of the Dataset class containing instances of the Lightcurve class.
-
save(filename='saved_dataset')¶ - Saves a Dataset instance using a pickle dump
- self: Dataset object
- An instance of the Dataset class containing instances of the Lightcurve class.
- filename: str
- filename under which to store the Dataset instance
-
train(verbose=1)¶ - Trains a ML algorithm on the Dataset with the parameters specified on initialisation.
- self: Dataset object
- An instance of the Dataset class containing instances of the Lightcurve class.
verbose: How much information to print out.
-
types(show_aug_num=False)¶ Prints out the counts of each object type stored in the dataset.
- self: Dataset object
- An instance of the Dataset class containing instances of the Lightcurve class.
- show_aug_num: boolean
- Use augmented lightcurve when counting type numbers.
-
MALT interpolator¶
-
malt.interpolator.get_gp(lightcurve, t0, obs_time, sample_size, aug_num)¶ Returns a Gaussian Process (george) object marginalised on the data in file.
- lightcurve: Lightcurve object
- An instance of the Lightcurve class.
- t0: float
- Initial time to start sampling.
- obs_time: float
- The total length of the interpolated lightcurve.
- sample_size: int
- Number of data points in interpolated lightcurve.