Add DWBA and SDWBA classes (don't calculate TS yet)

docs/api_reference.md

@@ -14,11 +14,13 @@ Each type of model is contained in a separate Python class (with name ending in
 
 ## DWBA models
 
-There are several models that use the distorted wave Born approximation, documented below:
+There are several models that use the distorted-wave Born approximation, documented below:
 
 ### DWBA
 
-This is a placeholder for the distorted wave Born approximation model.
+::: echosms.DWBAModel
+    options:
+        heading_level: 4
 
 ### PT-DWBA
 
@@ -28,7 +30,9 @@ This is a placeholder for the distorted wave Born approximation model.
 
 ### SDWBA
 
-This is a placeholder for the stochastic distorted wave Born approximation model.
+::: echosms.SDWBAModel
+    options:
+        heading_level: 4
 
 ## ESModel
 

src/echosms/__init__.py

@@ -8,8 +8,11 @@
 from .psmsmodel import PSMSModel
 from .dcmmodel import DCMModel
 from .ptdwbamodel import PTDWBAModel
+from .dwbamodel import DWBAModel
+from .sdwbamodel import SDWBAModel
 
 __all__ = ['ScatterModelBase', 'BenchmarkData', 'ReferenceModels',
            'MSSModel', 'PSMSModel', 'DCMModel', 'ESModel', 'PTDWBAModel',
+           'DWBAModel', 'SDWBAModel',
            'wavenumber', 'eta', 'h1', 'spherical_jnpp',
            'as_dataframe', 'as_dataarray']

src/echosms/dwbamodel.py

@@ -0,0 +1,60 @@
+"""The distorted-wave Born approximation model."""
+
+from .scattermodelbase import ScatterModelBase
+
+
+class DWBAModel(ScatterModelBase):
+    """Distorted-wave Born approximation scattering model."""
+
+    def __init__(self):
+        super().__init__()
+        self.long_name = 'distorted-wave Born approximation'
+        self.short_name = 'dwba'
+        self.analytical_type = 'approximate'
+        self.boundary_types = 'weakly scattering'
+        self.shapes = ['any']
+        self.max_ka = 20
+
+    def calculate_ts_single(self, theta, phi, f, target_rho, target_c):
+        """Distorted-wave Born approximation scattering model.
+
+        Implements the distorted-wave Born approximation
+        model for calculating the acoustic backscatter from weakly scattering bodies.
+
+        Parameters
+        ----------
+        theta : float
+            Incident wave pitch angle [°].
+
+        phi : float
+            Incident wave roll angle [°].
+
+        f : float
+            Frequency to run the model at [Hz]
+
+        target_rho : iterable[float]
+            Densities of each material. Must have at least the same number of entries as unique
+            integers in `volume` [kg/m³].
+
+        target_c : iterable[float]
+            Sound speed of each material. Must have at least the same number of entries as unique
+            integers in `volume` [m/s].
+
+        Returns
+        -------
+        : float
+            The target strength (re 1 m²) [dB] of the target.
+
+        Notes
+        -----
+        This class implements the method presented in Chu et al. (1993).
+
+        References
+        ----------
+        Chu, D., Foote, K. G., & Stanton, T. K. (1993). Further analysis of target strength
+        measurements of Antarctic krill at 38 and 120 kHz: Comparison with deformed cylinder
+        model and inference or orientation distribution. The Journal of the Acoustical Society
+        of America, 93(5), 2985–2988. <https://doi.org/10.1121/1.405818>
+
+        """
+        return None

src/echosms/sdwbamodel.py

@@ -0,0 +1,69 @@
+"""The stochastic distorted-wave Born approximation model."""
+
+from .scattermodelbase import ScatterModelBase
+
+
+class SDWBAModel(ScatterModelBase):
+    """Stochastic distorted-wave Born approximation scattering model."""
+
+    def __init__(self):
+        super().__init__()
+        self.long_name = "stochastic distorted-wave Born approximation"
+        self.short_name = "sdwba"
+        self.analytical_type = "approximate"
+        self.boundary_types = "weakly scattering"
+        self.shapes = ["any"]
+        self.max_ka = 20
+
+    def calculate_ts_single(self, theta, phi, f, target_rho, target_c):
+        """Stochastic distorted-wave Born approximation scattering model.
+
+        Implements the stochastic distorted-wave Born approximation
+        model for calculating the acoustic backscatter from weakly scattering bodies.
+
+        Parameters
+        ----------
+        theta : float
+            Incident wave pitch angle [°].
+
+        phi : float
+            Incident wave roll angle [°].
+
+        f : float
+            Frequency to run the model at [Hz]
+
+        target_rho : iterable[float]
+            Densities of each material. Must have at least the same number of entries as unique
+            integers in `volume` [kg/m³].
+
+        target_c : iterable[float]
+            Sound speed of each material. Must have at least the same number of entries as unique
+            integers in `volume` [m/s].
+
+        Returns
+        -------
+        : float
+            The target strength (re 1 m²) [dB] of the target.
+
+        Notes
+        -----
+        This class implements the method presented in Demer & Conti (2003), Demer & Conti (2004),
+        and Conti & Demer (2006).
+
+        References
+        ----------
+        Demer, D. A., & Conti, S. G. (2003). Reconciling theoretical versus empirical target
+        strengths of krill: Effects of phase variability on the distorted-wave Born approximation.
+        ICES Journal of Marine Science, 60, 429-434.
+        <https://doi.org/10.1016/S1054-3139(03)00002-X>
+
+        Demer, D. A., & Conti, S. G. (2004). Reconciling theoretical versus empirical
+        target strengths of krill: Effects of phase variability on the distorted-wave Born
+        approximation. ICES Journal of Marine Science, 61(1), 157-158.
+        <https://doi.org/10.1016/j.icesjms.2003.12.003>
+
+        Conti, S. G., & Demer, D. A. (2006). Improved parameterization of the SDWBA for estimating
+        krill target strength. ICES Journal of Marine Science, 63(5), 928-935.
+        <https://doi.org/10.1016/j.icesjms.2006.02.007>
+        """
+        return None