sorcha.modules.DESDetectionProbability
Functions
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Find the probability of a detection given a visual magnitude, |
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Find probability of observations being observable for objectInField output. |
Module Contents
- DEScalcDetectionProbability(mag, limmag, c, k, c_sharp)[source]
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 Bernardinelli et al., 2022
- Parameters:
mag (float or array of floats) -- Magnitude of object in filter used for that field.
limmag (float or array of floats) -- Limiting magnitude of the field.
c (float or array of floats) -- scaling factor
k (float or array of floats) -- transition sharpness
c_sharp (float) -- transient efficiency.
- Returns:
P -- Probability of detection
- Return type:
float or array of floats
- DESDetectionProbability(eph_df, transient_efficiency, magnitude_name='PSFMag', limiting_magnitude_name='fiveSigmaDepth_mag', scaling_factor_name='c', transition_sharpness_name='k')[source]
Find probability of observations being observable for objectInField output. Wrapper for calcDetectionProbability which takes into account column names. Used by DESFadingFunctionFilter.
- Parameters:
eph_df (Pandas dataframe) -- Dataframe of observations.
- magnitude_namestring, optional
eph_df column name for observation limiting magnitude Default = PSFMag
- limiting_magnitude_namestring, optional
eph_df column used for observation limiting magnitude. Default = fiveSigmaDepth_mag
- field IDstring, optional
eph_df column name for observation field_id Default = FieldID
- scaling_factor_name: str, optional
eph_df column name for scaling factor Default: c
- transition_sharpness_name: str, optional
eph_df column name for transition_sharpness DEfault: k
- transient_efficiency: float
overall transient efficiency for moving object detection
- Returns:
Probability of detection.
- Return type:
float or array of floats