implement fitting TOM inputs

resspect/fit_lightcurves.py

@@ -1,5 +1,5 @@
 # Copyright 2020 resspect software
-# Author: Rupesh Durgesh and Emille Ishida
+# Author: Rupesh Durgesh, Emille Ishida, and Amanda Wasserman
 #
 # created on 14 April 2022
 #
@@ -32,12 +32,14 @@
 from resspect.lightcurves_utils import get_resspect_header_data
 from resspect.lightcurves_utils import read_plasticc_full_photometry_data
 from resspect.lightcurves_utils import SNPCC_FEATURES_HEADER
+from resspect.lightcurves_utils import TOM_FEATURES_HEADER
+from resspect.lightcurves_utils import TOM_MALANCHEV_FEATURES_HEADER
 from resspect.lightcurves_utils import SNPCC_MALANCHEV_FEATURES_HEADER
 from resspect.lightcurves_utils import find_available_key_name_in_header
 from resspect.lightcurves_utils import PLASTICC_TARGET_TYPES
 from resspect.lightcurves_utils import PLASTICC_RESSPECT_FEATURES_HEADER
 
-__all__ = ["fit_snpcc", "fit_plasticc"]
+__all__ = ["fit_snpcc", "fit_plasticc", "fit_TOM"]
 
 
 FEATURE_EXTRACTOR_MAPPING = {
@@ -238,6 +240,64 @@ def fit_plasticc(path_photo_file: str, path_header_file: str,
                     light_curve_data, plasticc_features_file)
     logging.info("Features have been saved to: %s", output_file)
 
+def _TOM_sample_fit(
+        id: str, dic: dict, feature_extractor: str):
+    """
+    Reads SNPCC file and performs fit.
+    
+    Parameters
+    ----------
+    id
+        SNID
+    feature_extractor
+        Function used for feature extraction.
+        Options are 'bazin', 'bump', or 'malanchev'.
+    """
+    light_curve_data = FEATURE_EXTRACTOR_MAPPING[feature_extractor]()
+    light_curve_data.photometry = pd.DataFrame(dic[id]['photometry'])
+    light_curve_data.dataset_name = 'TOM'
+    light_curve_data.filters = ['u', 'g', 'r', 'i', 'z', 'Y']
+
+    light_curve_data.fit_all()
+
+    return light_curve_data
+
+def fit_TOM(data_dic: dict, features_file: str,
+            number_of_processors: int = 1,
+            feature_extractor: str = 'bazin'):
+    """
+    Perform fit to all objects from the TOM data.
+
+     Parameters
+     ----------
+     data_dic: str
+         Dictionary containing the photometry for all light curves.
+     features_file: str
+         Path to output file where results should be stored.
+     number_of_processors: int, default 1
+        Number of cpu processes to use.
+     feature_extractor: str, default bazin
+        Function used for feature extraction.
+    """
+    if feature_extractor == 'bazin':
+        header = TOM_FEATURES_HEADER
+    elif feature_extractor == 'malanchev':
+        header = TOM_MALANCHEV_FEATURES_HEADER
+
+    multi_process = multiprocessing.Pool(number_of_processors)
+    logging.info("Starting TOM " + feature_extractor + " fit...")
+    with open(features_file, 'w') as snpcc_features_file:
+        snpcc_features_file.write(','.join(header) + '\n')
+
+        for light_curve_data in multi_process.starmap(
+                _TOM_sample_fit, zip(
+                    data_dic, repeat(data_dic), repeat(feature_extractor))):
+            if 'None' not in light_curve_data.features:
+                write_features_to_output_file(
+                    light_curve_data, snpcc_features_file)
+    logging.info("Features have been saved to: %s", features_file)
+
+
 
 def main():
     return None

resspect/lightcurves_utils.py

@@ -79,6 +79,37 @@
     'zotsu_lower_to_all_ratio', 'zlinear_fit_slope', 'zlinear_fit_slope_sigma','zlinear_fit_reduced_chi2'
 ]
 
+TOM_FEATURES_HEADER = [
+    'id', 'redshift', 'type', 'code', 'orig_sample',
+    'uA', 'uB', 'ut0', 'utfall', 'utrise',
+    'gA', 'gB', 'gt0', 'gtfall', 'gtrise', 'rA', 'rB',
+    'rt0', 'rtfall', 'rtrise', 'iA', 'iB', 'it0', 'itfall',
+    'itrise', 'zA', 'zB', 'zt0', 'ztfall', 'ztrise',
+    'YA', 'YB', 'Yt0', 'Ytfall', 'Ytrise'
+]
+
+TOM_MALANCHEV_FEATURES_HEADER = [
+    'id', 'redshift', 'type', 'code', 'orig_sample',
+    'uanderson_darling_normal','uinter_percentile_range_5',
+    'uchi2','ustetson_K','uweighted_mean','uduration', 'uotsu_mean_diff','uotsu_std_lower', 'uotsu_std_upper',
+    'uotsu_lower_to_all_ratio', 'ulinear_fit_slope', 'ulinear_fit_slope_sigma','ulinear_fit_reduced_chi2',
+    'ganderson_darling_normal','ginter_percentile_range_5',
+    'gchi2','gstetson_K','gweighted_mean','gduration', 'gotsu_mean_diff','gotsu_std_lower', 'gotsu_std_upper',
+    'gotsu_lower_to_all_ratio', 'glinear_fit_slope', 'glinear_fit_slope_sigma','glinear_fit_reduced_chi2',
+    'randerson_darling_normal', 'rinter_percentile_range_5',
+    'rchi2', 'rstetson_K', 'rweighted_mean','rduration', 'rotsu_mean_diff','rotsu_std_lower', 'rotsu_std_upper',
+    'rotsu_lower_to_all_ratio', 'rlinear_fit_slope', 'rlinear_fit_slope_sigma','rlinear_fit_reduced_chi2',
+    'ianderson_darling_normal','iinter_percentile_range_5',
+    'ichi2', 'istetson_K', 'iweighted_mean','iduration', 'iotsu_mean_diff','iotsu_std_lower', 'iotsu_std_upper',
+    'iotsu_lower_to_all_ratio', 'ilinear_fit_slope', 'ilinear_fit_slope_sigma','ilinear_fit_reduced_chi2',
+    'zanderson_darling_normal','zinter_percentile_range_5',
+    'zchi2', 'zstetson_K', 'zweighted_mean','zduration', 'zotsu_mean_diff','zotsu_std_lower', 'zotsu_std_upper',
+    'zotsu_lower_to_all_ratio', 'zlinear_fit_slope', 'zlinear_fit_slope_sigma','zlinear_fit_reduced_chi2',
+    'Yanderson_darling_normal','Yinter_percentile_range_5',
+    'Ychi2','Ystetson_K','Yweighted_mean','Yduration', 'Yotsu_mean_diff','Yotsu_std_lower', 'Yotsu_std_upper',
+    'Yotsu_lower_to_all_ratio', 'Ylinear_fit_slope', 'Ylinear_fit_slope_sigma','Ylinear_fit_reduced_chi2'
+]
+
 PLASTICC_RESSPECT_FEATURES_HEADER = [
     'id', 'redshift', 'type', 'code', 'orig_sample', 'uA', 'uB', 'ut0',
     'utfall', 'utrise', 'gA', 'gB', 'gt0', 'gtfall','gtrise', 'rA', 'rB',

resspect/read_dash.py

@@ -15,17 +15,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-#import logging
-#import numpy as np
-#import pandas as pd
 import csv
 
 __all__ = ["get_id_type"]
 
-
-
-
-def get_id_type(file='/Users/arw/Desktop/spec_sims_binned_spaced/DASH_matches.txt'):
+def get_id_type(file: str):
     #read text file
     data = open(file, "r")
 

resspect/request_webservice_elasticc.py

@@ -0,0 +1,59 @@
+# Copyright 2020 resspect software
+# Author: Amanda Wasserman
+#
+# created on 12 March 2024
+#
+# Licensed GNU General Public License v3.0;
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.gnu.org/licenses/gpl-3.0.en.html
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import requests
+
+class TomClient:
+        def __init__(self, url='https://desc-tom.lbl.gov', username=None, 
+                     password=None, passwordfile = None, connect=True):
+            self._url = url
+            self._username = username
+            self._password = password
+            self._rqs=None
+            if self._password is None:
+                 if passwordfile is None:
+                      raise RuntimeError('No password or passwordfile provided')
+                 with open(passwordfile) as ifp:
+                      self._password = ifp.readline().strip()
+
+            if connect:
+                self.connect()
+
+        def connect(self):
+            self._rqs = requests.session()  #should this be capitalized S?
+            res = self._rqs.get(f'{self._url}/acounts/login/')
+            if res.status_code != 200:
+                raise RuntimeError(f'Failed to connect to {self._url}')
+            res = self._rqs.post(f'{self._url}/accounts/login/', 
+                                 data={'username':self._username, 
+                                       'password':self._password,
+                                       'csrfmiddlewaretoken': self._rqs.cookies['csrftoken']})
+            if res.status_code != 200:
+                 raise RuntimeError(f'Failed to login.')
+            if 'Please enter a correct' in res.text:
+                 raise RuntimeError("failed to log in.")
+            self._rqs.headers.update({'X-CSRFToken': self._rqs.cookies['csrftoken']})
+
+        def request(self, method="GET", page=None, **kwargs):
+
+             return self._rqs.request(method=method, url=f"{self._url}/{page}", **kwargs)
+
+#def request(website: str):
+#    r = requests.get(website)
+#    status = r.status_code
+#    text = r.text
+#    return text