actsnclass.Canonical¶
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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.
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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.
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snpcc_identify_samples
()¶ Identify training and test sample.
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find_neighbors
()¶ Identify 1 nearest neighbor for each object in training.
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__init__
()¶ Initialize self. See help(type(self)) for accurate signature.
Methods
__init__
()Initialize self. 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.