WIP - Making changes to RESSPECT as I test working with the external …

…laiss_resspect_classifier library.

src/resspect/database.py

@@ -10,6 +10,7 @@
 # limitations under the License.
 
 import io
+import logging
 import os
 import pandas as pd
 import tarfile
@@ -28,6 +29,7 @@
 
 __all__ = ['DataBase']
 
+logger = logging.getLogger(__name__)
 
 class DataBase:
     """DataBase object, upon which the active learning loop is performed.
@@ -239,9 +241,9 @@ def load_features_from_file(self, path_to_features_file: str, screen=False,
             "Bump", or "Malanchev". Default is "Bazin".
         """
 
-        if survey not in ['DES', 'LSST']:
-            raise ValueError('Only "DES" and "LSST" filters are ' + \
-                             'implemented at this point!')
+        # if survey not in ['DES', 'LSST']:
+        #     raise ValueError('Only "DES" and "LSST" filters are ' + \
+        #                      'implemented at this point!')
 
         # read matrix with features
         if '.tar.gz' in path_to_features_file:
@@ -264,7 +266,9 @@ def load_features_from_file(self, path_to_features_file: str, screen=False,
         # Create the filter-feature names based on the survey.
         survey_filters = FILTER_SETS[survey]
         self.features_names = feature_extractor_class.get_features(survey_filters)
+        self.metadata_names = feature_extractor_class.get_metadata_header()
 
+        #! This section needs to be made dynamic between this line and the following comment
         self.metadata_names = ['id', 'redshift', 'type', 'code', 'orig_sample', 'queryable']
         if 'objid' in data.keys():
             self.metadata_names = ['objid', 'redshift', 'type', 'code', 'orig_sample', 'queryable']
@@ -275,6 +279,7 @@ def load_features_from_file(self, path_to_features_file: str, screen=False,
         for name in self.telescope_names:
             if 'cost_' + name in data.keys():
                 self.metadata_names = self.metadata_names + ['cost_' + name]
+        #! End of section that needs to be made dynamic
 
         if sample == None:
             self.features = data[self.features_names].values
@@ -414,14 +419,14 @@ def load_features(self, path_to_file: str, feature_extractor: str ='Bazin',
         if feature_extractor == "photometry":
             self.load_photometry_features(path_to_file, screen=screen,
                                           survey=survey, sample=sample)
-        elif feature_extractor in FEATURE_EXTRACTOR_REGISTRY:
+        else: # feature_extractor in FEATURE_EXTRACTOR_REGISTRY:
             self.load_features_from_file(
                 path_to_file, screen=screen, survey=survey,
                 sample=sample, feature_extractor=feature_extractor)
-        else:
-            feature_extractors = ', '.join(FEATURE_EXTRACTOR_REGISTRY.keys())
-            raise ValueError(f'Only {feature_extractors} or photometry features are implemented!'
-                             '\n Feel free to add other options.')
+        # else:
+        #     feature_extractors = ', '.join(FEATURE_EXTRACTOR_REGISTRY.keys())
+        #     raise ValueError(f'Only {feature_extractors} or photometry features are implemented!'
+        #                      '\n Feel free to add other options.')
 
     def load_plasticc_mjd(self, path_to_data_dir):
         """Return all MJDs from 1 file from PLAsTiCC simulations.
@@ -478,6 +483,9 @@ def identify_keywords(self):
             id_name = 'id'
         elif 'objid' in self.metadata_names:
             id_name = 'objid'
+        else:
+            logger.warning('No object identification found in metadata - using first column as object identification!')
+            id_name = self.metadata_names[0]
 
         return id_name
 

src/resspect/feature_extractors/feature_extractor.py

@@ -0,0 +1,18 @@
+from abc import ABC, abstractmethod
+
+class ResspectFeatureExtractor(ABC):
+    def __init__(self):
+        pass
+
+    @abstractmethod
+    def fit(self, band: str) -> list:
+        pass
+
+    @abstractmethod
+    def fit_all(self):
+        raise NotImplementedError()
+
+    @classmethod
+    @abstractmethod
+    def get_features(cls, filters: list) -> list[str]:
+        raise NotImplementedError()

src/resspect/feature_extractors/feature_extractor_utils.py

@@ -1,23 +1,20 @@
 import itertools
 from typing import List
 
-def make_features_header(
+def make_column_headers(
         filters: List[str],
         features: List[str],
         **kwargs
-    ) -> list:
+    ) -> List[str]:
     """
-    This function returns header list for given filters and features. The default
-    header names are: ['id', 'redshift', 'type', 'code', 'orig_sample'].
+    This function returns the full header list for given filters and features.
 
     Parameters
     ----------
     filters : list
         Filter values. e.g. ['g', 'r', 'i', 'z']
     features : list
         Feature values. e.g. ['A', 'B']
-    with_cost : bool
-        Flag for adding cost values. Default is False
     kwargs
         Can include the following flags:
         - override_primary_columns: List[str] of primary columns to override the default ones
@@ -27,33 +24,61 @@ def make_features_header(
 
     Returns
     -------
-    header
-        header list
+    List[str]
+        The complete metadata and feature header list
     """
 
     header = []
-    header.extend(['id', 'redshift', 'type', 'code', 'orig_sample'])
+
+    # Create metadata columns
+    metadata_columns = make_metadata_column_names(**kwargs)
+    header += metadata_columns
+
+    # Create all pairs of filter + feature strings and append to the header
+    filter_features = make_filter_feature_names(filters, features)
+    header += filter_features
+
+    return header
+
+def make_metadata_column_names(**kwargs) -> List[str]:
+    """The default header names are: ['id', 'redshift', 'type', 'code', 'orig_sample'].
+    Using the keys in kwargs, we can add additional columns to the header.
+
+    Parameters
+    ----------
+    kwargs
+        Can include the following flags:
+        - override_primary_columns: List[str] of primary columns to override the default ones
+        - with_queryable: flag for adding "queryable" column
+        - with_last_rmag: flag for adding "last_rmag" column
+        - with_cost: flag for adding "cost_4m" and "cost_8m" columns
+
+    Returns
+    -------
+    List[str]
+        metadata header list
+    """
+
+    metadata_columns = []
+    metadata_columns.extend(['id', 'redshift', 'type', 'code', 'orig_sample'])
 
     # There are rare instances where we need to override the primary columns
     if kwargs.get('override_primary_columns', False):
-        header = kwargs.get('override_primary_columns')
+        metadata_columns = kwargs.get('override_primary_columns')
 
     if kwargs.get('with_queryable', False):
-        header.append('queryable')
+        metadata_columns.append('queryable')
+
     if kwargs.get('with_last_rmag', False):
-        header.append('last_rmag')
+        metadata_columns.append('last_rmag')
 
     #TODO: find where the 'with_cost' flag is used to make sure we apply there
     if kwargs.get('with_cost', False):
-        header.extend(['cost_4m', 'cost_8m'])
+        metadata_columns.extend(['cost_4m', 'cost_8m'])
 
-    # Create all pairs of filter + feature strings and append to the header
-    filter_features = create_filter_feature_names(filters, features)
-    header += filter_features
-
-    return header
+    return metadata_columns
 
-def create_filter_feature_names(filters: List[str], features: List[str]) -> List[str]:
+def make_filter_feature_names(filters: List[str], features: List[str]) -> List[str]:
     """This function returns the list of concatenated filters and features. e.g.
     filter = ['g', 'r'], features = ['A', 'B'] => ['gA', 'gB', 'rA', 'rB']
 

src/resspect/feature_extractors/light_curve.py

@@ -26,8 +26,9 @@
     read_plasticc_full_photometry_data,
 )
 from resspect.feature_extractors.feature_extractor_utils import (
-    create_filter_feature_names,
-    make_features_header,
+    make_filter_feature_names,
+    make_column_headers,
+    make_metadata_column_names,
 )
 
 warnings.filterwarnings("ignore", category=RuntimeWarning)
@@ -39,66 +40,64 @@
 
 class LightCurve:
     """ Light Curve object, holding meta and photometric data.
-
-    Attributes
-    ----------
-    feature_names: list
-        Class attribute, a list of names of the feature extraction parameters.
-    features: list
-        List with the 5 best-fit feature extraction parameters in all filters.
-        Concatenated from blue to red.
-    dataset_name: str
-        Name of the survey or data set being analyzed.
-    exp_time: dict
-        Exposure time required to take a spectra.
-        Keywords indicate telescope e.g.['4m', '8m'].
-    filters: list
-        List of broad band filters.
-    full_photometry: pd.DataFrame
-        Photometry for a set of light curves read from file.
-    id: int
-        SN identification number.
-    id_name:
-        Column name of object identifier.
-    last_mag: float
-        r-band magnitude of last observed epoch.
-    photometry: pd.DataFrame
-        Photometry information.
-        Minimum keys --> [mjd, band, flux, fluxerr].
-    redshift: float
-        Redshift
-    sample: str
-        Original sample to which this light curve is assigned.
-    sim_peakmag: np.array
-        Simulated peak magnitude in each filter.
-    sim_pkmjd: float
-        Simulated day of maximum, observer frame.
-    sncode: int
-        Number identifying the SN model used in the simulation.
-    sntype: str
-        General classification, possibilities are: Ia, II or Ibc.
-    unique_ids: str or array
-        List of unique ids available in the photometry file.
-        Only used for PLAsTiCC data.
-
-    Methods
-    -------
-    calc_exp_time(telescope_diam: float, SNR: float, telescope_name: str)
-        Calculates time required to take a spectra in the last obs epoch.
-    check_queryable(mjd: float, r_lim: float)
-        Check if this light can be queried in a given day.
-    conv_flux_mag(flux: np.array)
-        Convert positive flux into magnitude.
-    load_snpcc_lc(path_to_data: str)
-        Reads header and photometric information for 1 light curve.
-    load_plasticc_lc(photo_file: str, snid: int)
-    	Load photometric information for 1 PLAsTiCC light curve.
-    load_resspect_lc(photo_file: str, snid: int)
-    	Load photometric information for 1 RESSPECT light curve.
-    plot_bump_fit(save: bool, show: bool, output_file: srt)
-        Plot photometric points and Bump fitted curve.
-
     """
+    # Attributes
+    # ----------
+    # feature_names: list
+    #     Class attribute, a list of names of the feature extraction parameters.
+    # features: list
+    #     List with the 5 best-fit feature extraction parameters in all filters.
+    #     Concatenated from blue to red.
+    # dataset_name: str
+    #     Name of the survey or data set being analyzed.
+    # exp_time: dict
+    #     Exposure time required to take a spectra.
+    #     Keywords indicate telescope e.g.['4m', '8m'].
+    # filters: list
+    #     List of broad band filters.
+    # full_photometry: pd.DataFrame
+    #     Photometry for a set of light curves read from file.
+    # id: int
+    #     SN identification number.
+    # id_name:
+    #     Column name of object identifier.
+    # last_mag: float
+    #     r-band magnitude of last observed epoch.
+    # photometry: pd.DataFrame
+    #     Photometry information.
+    #     Minimum keys --> [mjd, band, flux, fluxerr].
+    # redshift: float
+    #     Redshift
+    # sample: str
+    #     Original sample to which this light curve is assigned.
+    # sim_peakmag: np.array
+    #     Simulated peak magnitude in each filter.
+    # sim_pkmjd: float
+    #     Simulated day of maximum, observer frame.
+    # sncode: int
+    #     Number identifying the SN model used in the simulation.
+    # sntype: str
+    #     General classification, possibilities are: Ia, II or Ibc.
+    # unique_ids: str or array
+    #     List of unique ids available in the photometry file.
+    #     Only used for PLAsTiCC data.
+
+    # Methods
+    # -------
+    # calc_exp_time(telescope_diam: float, SNR: float, telescope_name: str)
+    #     Calculates time required to take a spectra in the last obs epoch.
+    # check_queryable(mjd: float, r_lim: float)
+    #     Check if this light can be queried in a given day.
+    # conv_flux_mag(flux: np.array)
+    #     Convert positive flux into magnitude.
+    # load_snpcc_lc(path_to_data: str)
+    #     Reads header and photometric information for 1 light curve.
+    # load_plasticc_lc(photo_file: str, snid: int)
+    # 	Load photometric information for 1 PLAsTiCC light curve.
+    # load_resspect_lc(photo_file: str, snid: int)
+    # 	Load photometric information for 1 RESSPECT light curve.
+    # plot_bump_fit(save: bool, show: bool, output_file: srt)
+    #     Plot photometric points and Bump fitted curve.
 
     feature_names = []
 
@@ -165,7 +164,24 @@ def get_features(cls, filters: list) -> list[str]:
         -------
         list
         """
-        return create_filter_feature_names(filters, cls.feature_names)
+
+        if cls.feature_names is None:
+            raise ValueError("Feature names not defined for this class.")
+
+        return make_filter_feature_names(filters, cls.feature_names)
+
+    @classmethod
+    def get_metadata_header(cls, **kwargs) -> list[str]:
+        """
+        Returns the metadata columns for the feature extractor.
+        i.e. id, redshift, sntype, sncode, sample
+
+        Returns
+        -------
+        list
+        """
+
+        return make_metadata_column_names(**kwargs)
 
     @classmethod
     def get_feature_header(cls, filters: list, **kwargs) -> list[str]:
@@ -183,7 +199,10 @@ def get_feature_header(cls, filters: list, **kwargs) -> list[str]:
         list
         """
 
-        return make_features_header(filters, cls.feature_names, **kwargs)
+        if cls.feature_names is None:
+            raise ValueError("Feature names not defined for this class.")
+
+        return make_column_headers(filters, cls.feature_names, **kwargs)
 
     def _get_snpcc_photometry_raw_and_header(
             self, lc_data: np.ndarray,

src/resspect/filter_sets.py

@@ -4,4 +4,5 @@
     "SNPCC": ["g", "r", "i", "z"],
     "DES": ["g", "r", "i", "z"],
     "LSST": ["u", "g", "r", "i", "z", "Y"],
+    "ZTF": ["g", "r"],
 }