add dash outputs file, add tom client class, add fit_TOM function

resspect/database.py

@@ -276,7 +276,6 @@ def load_features_from_file(self, path_to_features_file: str, screen=False,
                                        'rp1', 'rp2', 'rp3', 'rmax_flux', 
                                        'ip1', 'ip2', 'ip3', 'imax_flux', 
                                        'zp1', 'zp2', 'zp3', 'zmax_flux']
-
             elif feature_extractor == 'malanchev':
                 self.features_names = ['ganderson_darling_normal','ginter_percentile_range_5',
                                        'gchi2','gstetson_K','gweighted_mean','gduration', 
@@ -310,12 +309,44 @@ def load_features_from_file(self, path_to_features_file: str, screen=False,
                     self.metadata_names = self.metadata_names + ['cost_' + name]
 
         elif survey == 'LSST':
-            self.features_names = ['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']
+            if feature_extractor == "bazin":
+                self.features_names = ['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']
+            elif feature_extractor == "malanchev":
+                self.features_names = ['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']
 
             if 'objid' in data.keys():
                 self.metadata_names = ['objid', 'redshift', 'type', 'code',

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 +32,15 @@
 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
+from resspect.tom_client import TomClient
 
-__all__ = ["fit_snpcc", "fit_plasticc"]
+__all__ = ["fit_snpcc", "fit_plasticc", "fit_TOM", "request_TOM_data"]
 
 
 FEATURE_EXTRACTOR_MAPPING = {
@@ -238,6 +241,86 @@ 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(
+        obj_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(obj_dic['photometry'])
+    light_curve_data.dataset_name = 'TOM'
+    light_curve_data.filters = ['u', 'g', 'r', 'i', 'z', 'Y']
+    light_curve_data.id = obj_dic['objectid']
+    light_curve_data.redshift = obj_dic['redshift']
+    light_curve_data.sntype = 'unknown'
+    light_curve_data.sncode = obj_dic['sncode']
+    light_curve_data.sample = 'N/A'
+
+    light_curve_data.fit_all()
+
+    return light_curve_data
+
+def fit_TOM(data_dic: dict, output_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.
+     output_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(output_features_file, 'w') as TOM_features_file:
+        TOM_features_file.write(','.join(header) + '\n')
+
+        for light_curve_data in multi_process.starmap(
+                _TOM_sample_fit, zip(
+                    data_dic, repeat(feature_extractor))):
+            if 'None' not in light_curve_data.features:
+                write_features_to_output_file(
+                    light_curve_data, TOM_features_file)
+    logging.info("Features have been saved to: %s", output_features_file)
+
+def request_TOM_data(url: str = "https://desc-tom-2.lbl.gov", username: str = None, 
+                     passwordfile: str = None, password: str = None, detected_since_mjd: float = None, 
+                     detected_in_last_days: float = None, mjdnow: float = None, cheat_gentypes: list = None):
+    tom = TomClient(url = url, username = username, passwordfile = passwordfile, 
+                    password = password)
+    dic = {}
+    if detected_since_mjd is not None:
+        dic['detected_since_mjd'] = detected_since_mjd
+    if detected_in_last_days is not None:
+        dic['detected_in_last_days'] = detected_in_last_days
+    if mjdnow is not None:
+        dic['mjd_now'] = mjdnow
+    if cheat_gentypes is not None:
+        dic['cheat_gentypes'] = cheat_gentypes
+    res = tom.post('elasticc2/gethottransients', json = dic)
+    data_dic = res.json()
+    return data_dic
+
 
 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

@@ -0,0 +1,43 @@
+# Copyright 2020 resspect software
+# Author: Amanda Wasserman
+#
+# created on 11 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 csv
+
+__all__ = ["get_id_type"]
+
+def get_id_type(file: str):
+    #read text file
+    data = open(file, "r")
+
+    #retrieve lines with id and type
+    sns =[]
+    for line in data:
+        if '.txt' in line:
+            sns.append(line)
+
+    ids = [id[0:7] for id in sns]
+
+    type = []
+    #parse file for type
+    for obj in sns:
+        indxb = obj.find("('")
+        indxe = obj.find(")  ")
+        temp = obj[indxb+2:indxe]
+        temp = temp.split(',')[0]
+        type.append(temp[0:-1])
+    return ids, type
+

resspect/time_domain_loop.py

@@ -14,7 +14,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-__all__ = ['time_domain_loop', 'load_dataset']
+__all__ = ['time_domain_loop', 'load_dataset', 'submit_queries_to_TOM']
 
 import os
 from typing import Union, Tuple
@@ -24,6 +24,7 @@
 import progressbar
 
 from resspect import DataBase
+from resspect.tom_client import TomClient
 
 
 def load_dataset(file_names_dict: dict, survey_name: str = 'DES',
@@ -799,6 +800,20 @@ def process_next_day_loop(
     return light_curve_data
 
 
+def submit_queries_to_TOM(username, passwordfile, objectids: list, priorities: list, requester: str='resspect'):
+    tom = TomClient(url = "https://desc-tom-2.lbl.gov", username = username, passwordfile = passwordfile)
+    req = { 'requester': requester,
+            'objectids': objectids,
+            'priorities': priorities}
+    res = tom.request( 'POST', 'elasticc2/askforspectrum', json=req )
+    dic = res.json()
+    if res.satus_code != 200:
+        raise ValueError('Request failed, ' + res.text + ". Status code: " + str(res.status_code))
+
+    if dic['status'] == 'error':
+        raise ValueError('Request failed, ' + dic.json()['error'])
+
+
 # TODO: Too many arguments. Refactor and update docs
 def run_time_domain_active_learning_loop(
         light_curve_data: DataBase, learning_days: list,