actsnclass.Canonical

class actsnclass.Canonical

Canonical sample object.

Variables:

  • canonical_ids (list) – List of ids for objects in the canonical sample.

  • canonical_sample (list) – Complete data matrix for the canonical sample.

  • meta_data (pd.DataFrame) – Metadata on sim peakmag and SNR for all objects in the original data set.

  • test_ia_data (pd.DataFrame) – Metadata on sim peakmag and SNR for SN Ias in the test sample.

  • test_ia_id (np.array) – Set of ids for all SN Ia in the test sample.

  • test_ibc_data (pd.DataFrame) – Metadata on sim peakmag and SNR for SN Ibcs in the test sample.

  • test_ibc_id (np.array) – Set of ids for all SN Ibc in the test sample.

  • test_ii_data (pd.DataFrame) – Metadata on sim peakmag and SNR for SN IIs in the test sample.

  • test_ii_id (np.array) – Set of ids for all SN II in the test sample.

  • train_ia_data (pd.DataFrame) – Metadata on sim peakmag and SNR for SN Ias in the train sample.

  • train_ia_id (np.array) – Set of ids for all SN Ia in the train sample.

  • train_ibc_data (pd.DataFrame) – Metadata on sim peakmag and SNR for SN Ibcs in the train sample.

  • train_ibc_id (np.array) – Set of ids for all SN Ibc in the train sample.

  • train_ii_data (pd.DataFrame) – Metadata on sim peakmag and SNR for SN IIs in the train sample.

  • train_ii_id (np.array) – Set of ids for all SN II in the train sample.

snpcc_get_canonical_info(path_to_rawdata_dir: str, canonical_output_file: st, compute: bool, save: bool, canonical_input_file: str)

Load SNPCC metada data required to characterize objects.

snpcc_identify_samples()

Identify training and test sample.

find_neighbors()

Identify 1 nearest neighbor for each object in training.

__init__()

Methods

__init__()

find_neighbors()

Identify 1 nearest neighbor for each object in training.

snpcc_get_canonical_info(...[, compute, ...])

Load SNPCC metada data required to characterize objects.

snpcc_identify_samples()

Identify training and test sample.