added transient efficency for des

added transient_efficency for des. set deaful

Author: Little-Ryugu

src/sorcha/data/survey_setups/DES_config_file.ini

@@ -93,10 +93,14 @@ visits_query = SELECT llcra, llcdec, lrcra, lrcdec, urcra, urcdec, ulcra, ulcdec
 [FADINGFUNCTION]
 
 # Uses probability calculation from Bernardinelli et al (2022)
-# For DES, calculation is done per observation and so only need to turn fading function on with no parameters needed.
+# For DES, calculation is done per observation.
 # pointing sqlite database must include constants c and k. (can be seen in pointing_sql_query in the INPUT section.)
 fading_function_on = True
 
+# overall transient efficiency for moving object detection. 0.95529 in Bernardinelli et al (2022).
+# if not specifed will default to 1 and be neglible 
+self.des_transient_efficency = 0.95529
+
 
 [LINKINGFILTER]
 # For DES a single detection was considered a tracklet detection and 

src/sorcha/des.py

@@ -330,6 +330,7 @@ def runDESSimulation(args, sconfigs, return_only=False):
             verboselog("Number of rows BEFORE applying fading function: " + str(len(observations.index)))
             observations = DESFadingFunctionFilter(
                 observations,
+                sconfigs.fadingfunction.des_transient_efficency,
                 args._rngs,
                 verbose=args.loglevel,
             )

src/sorcha/modules/DESDetectionProbability.py

@@ -20,7 +20,7 @@
 import numpy as np
 
 
-def DEScalcDetectionProbability(mag, limmag, c, k, c_sharp=0.9555):
+def DEScalcDetectionProbability(mag, limmag, c, k, c_sharp):
     """
     Find the probability of a detection given a visual magnitude,
     limiting magnitude, a scaling factor c, transition sharpness k and a transient efficiency. Equation from
@@ -45,7 +45,7 @@ def DEScalcDetectionProbability(mag, limmag, c, k, c_sharp=0.9555):
     Returns
     ----------
     P : float or array of floats
-        Probability of detection.
+        Probability of detection
     """
 
     P = c_sharp * (c / (1 + np.exp(k * (mag - limmag))))
@@ -55,6 +55,7 @@ def DEScalcDetectionProbability(mag, limmag, c, k, c_sharp=0.9555):
 
 def DESDetectionProbability(
     eph_df,
+    transient_efficiency,
     magnitude_name="PSFMag",
     limiting_magnitude_name="fiveSigmaDepth_mag",
     scaling_factor_name="c",
@@ -90,6 +91,9 @@ def DESDetectionProbability(
         eph_df column name for transition_sharpness
         DEfault: k
 
+    transient_efficiency: float
+        overall transient efficiency for moving object detection
+
     Returns
     ----------
      : float or array of floats
@@ -102,4 +106,5 @@ def DESDetectionProbability(
         eph_df[limiting_magnitude_name],
         eph_df[scaling_factor_name],
         eph_df[transition_sharpness_name],
+        transient_efficiency,
     )

src/sorcha/modules/DESFadingFunctionFilter.py

@@ -5,7 +5,12 @@
 from .DESDetectionProbability import DESDetectionProbability
 
 
-def DESFadingFunctionFilter(observations, module_rngs, verbose=False):
+def DESFadingFunctionFilter(
+    observations,
+    transient_efficiency,
+    module_rngs,
+    verbose=False,
+):
     """
     Wrapper function for DESDetectionProbability and PPDropObservations.
 
@@ -17,6 +22,8 @@ def DESFadingFunctionFilter(observations, module_rngs, verbose=False):
     observations : Pandas dataframe
         Dataframe of observations with a column containing the probability of detection.
 
+    transient_efficiency: float
+        overall transient efficiency for moving object detection
 
     module_rngs : PerModuleRNG
         A collection of random number generators (per module).
@@ -34,7 +41,7 @@ def DESFadingFunctionFilter(observations, module_rngs, verbose=False):
     verboselog = pplogger.info if verbose else lambda *a, **k: None
 
     verboselog("Calculating probabilities of detections...")
-    observations["detection_probability"] = DESDetectionProbability(observations)
+    observations["detection_probability"] = DESDetectionProbability(observations, transient_efficiency)
 
     verboselog("Dropping observations below detection threshold...")
     observations = PPDropObservations(observations, module_rngs, "detection_probability")

src/sorcha/utilities/sorchaConfigs.py

@@ -454,6 +454,9 @@ class fadingfunctionConfigs:
     fading_function_peak_efficiency: float = None
     """Peak efficiency for the fading function, called the 'fill factor' in Chesley and Veres (2017)."""
 
+    des_transient_efficency: float = None
+    """Overall transient efficiency for moving object detection"""
+
     survey_name: str = None
 
     def __post_init__(self):
@@ -477,6 +480,11 @@ def _validate_fadingfunction_configs_DES(self):
         """
         check_key_exists(self.fading_function_on, "fadingfunction")
         self.fading_function_on = cast_as_bool(self.fading_function_on, "fading_function_on")
+        if self.fading_function_on:
+            if self.des_transient_efficency is not None:
+                cast_as_float(self.des_transient_efficency, "des_transient_efficency")
+            else:
+                self.des_transient_efficency = 1  # won't impact detection efficency when 1
         check_key_doesnt_exist(
             self.fading_function_peak_efficiency, "fading_function_peak_efficiency", "but survey is DES."
         )

tests/sorcha/test_DESDetectionProbaility.py

@@ -4,12 +4,12 @@
 import pandas as pd
 def test_DEScalcDetectionProbability():
     mag, limmag, c, k = 20,23.1,0.90,4.55
-    P = DEScalcDetectionProbability(mag,limmag,c,k)
+    P = DEScalcDetectionProbability(mag,limmag,c,k,0.9555)
 
     assert math.isclose(P, 0.8599493561841001,rel_tol=1e-10)
 
     mag, limmag, c, k = 24,23.1,0.90,3.99
-    P = DEScalcDetectionProbability(mag,limmag,c,k)
+    P = DEScalcDetectionProbability(mag,limmag,c,k,0.9555)
 
     assert math.isclose(P, 0.0230733144369,rel_tol=1e-10)
 
@@ -24,7 +24,7 @@ def test_DESDectionProbability():
         "k":[4.55,3.99],
         }
     obs = pd.DataFrame(obs)
-    P = DESDetectionProbability(obs)
+    P = DESDetectionProbability(obs,0.9555)
 
     assert math.isclose(P[0], 0.8599493561841001,rel_tol=1e-10)
 

tests/sorcha/test_sorchaConfigs.py

@@ -61,6 +61,7 @@
     "fading_function_width": 0.1,
     "fading_function_peak_efficiency": 1.0,
     "survey_name": "rubin_sim",
+    "des_transient_efficency": None,
 }
 
 correct_linkingfilter = {