final cartesian fix

Author: Jammy2211

autogalaxy/convert.py

@@ -336,4 +336,4 @@ def multipole_comps_from(
     multipole_comp_0 = k_m * xp.sin(phi_m * float(m) * units.deg.to(units.rad))
     multipole_comp_1 = k_m * xp.cos(phi_m * float(m) * units.deg.to(units.rad))
 
-    return (multipole_comp_0, multipole_comp_1)
+    return (multipole_comp_0, multipole_comp_1)

autogalaxy/profiles/light/linear/shapelets/polar.py

@@ -36,7 +36,7 @@ def __init__(
         q
             The axis-ratio of the elliptical coordinate system, where a perfect circle has q=1.0.
         phi
-            The position angle (in degrees) of the elliptical coordinate system, measured counter-clockwise from the 
+            The position angle (in degrees) of the elliptical coordinate system, measured counter-clockwise from the
             positive x-axis.
         intensity
             Overall intensity normalisation of the light profile (units are dimensionless and derived from the data
@@ -79,7 +79,7 @@ def __init__(
         centre
             The (y,x) arc-second coordinates of the profile (shapelet) centre.
         phi
-            The position angle (in degrees) of the elliptical coordinate system, measured counter-clockwise from the 
+            The position angle (in degrees) of the elliptical coordinate system, measured counter-clockwise from the
             positive x-axis.
         beta
             The characteristic length scale of the shapelet basis function, defined in arc-seconds.

autogalaxy/profiles/light/standard/shapelets/cartesian.py

@@ -8,6 +8,7 @@
 )
 from autogalaxy.profiles.light.standard.shapelets.abstract import AbstractShapelet
 
+
 def hermite_phys(n: int, x, xp=np):
     """
     Physicists' Hermite polynomial H_n(x), compatible with NumPy and JAX via `xp`.
@@ -35,6 +36,7 @@ def hermite_phys(n: int, x, xp=np):
         Hnm1, Hn = Hn, Hnp1
     return Hn
 
+
 class ShapeletCartesian(AbstractShapelet):
     def __init__(
         self,
@@ -89,11 +91,11 @@ def coefficient_tag(self) -> str:
     @check_operated_only
     @aa.grid_dec.transform
     def image_2d_from(
-            self,
-            grid: aa.type.Grid2DLike,
-            xp=np,
-            operated_only: Optional[bool] = None,
-            **kwargs,
+        self,
+        grid: aa.type.Grid2DLike,
+        xp=np,
+        operated_only: Optional[bool] = None,
+        **kwargs,
     ) -> np.ndarray:
         """
         Returns the Cartesian Shapelet light profile's 2D image from a 2D grid of Cartesian (y,x) coordinates.
@@ -117,19 +119,16 @@ def image_2d_from(
         shapelet_y = hermite_phys(self.n_y, y_ell / self.beta, xp=xp)
         shapelet_x = hermite_phys(self.n_x, x_ell / self.beta, xp=xp)
 
-        gaussian = xp.exp(-0.5 * (x_ell ** 2 + y_ell ** 2) / (self.beta ** 2))
+        gaussian = xp.exp(-0.5 * (x_ell**2 + y_ell**2) / (self.beta**2))
 
-        norm = (
-                self.beta
-                * xp.sqrt(
+        norm = self.beta * xp.sqrt(
             (2.0 ** (self.n_x + self.n_y))
             * xp.pi
             * factorial(self.n_y)
             * factorial(self.n_x)
         )
-        )
 
-        return (shapelet_y * shapelet_x * gaussian) / norm
+        return self._intensity * (shapelet_y * shapelet_x * gaussian) / norm
 
 
 class ShapeletCartesianSph(ShapeletCartesian):

autogalaxy/profiles/light/standard/shapelets/polar.py

@@ -26,7 +26,9 @@ def genlaguerre_jax(n, alpha, x):
     # 0. Input Validation (Requires static Python int n)
     if not isinstance(n, int) or n < 0:
         # Use Python's math.isnan/isinf check if n is float, otherwise type error
-        raise ValueError(f"Degree n must be a non-negative Python integer (static), got {n}.")
+        raise ValueError(
+            f"Degree n must be a non-negative Python integer (static), got {n}."
+        )
 
     # Base Case L0
     if n == 0:
@@ -45,9 +47,9 @@ def genlaguerre_jax(n, alpha, x):
     log_N_plus_alpha_fact = gammaln(n + alpha + 1)
 
     log_BF_k = (
-            log_N_plus_alpha_fact
-            - gammaln(n - k_values + 1)  # log( (n-k)! )
-            - gammaln(alpha + k_values + 1)  # log( (alpha+k)! )
+        log_N_plus_alpha_fact
+        - gammaln(n - k_values + 1)  # log( (n-k)! )
+        - gammaln(alpha + k_values + 1)  # log( (alpha+k)! )
     )
 
     BF_k = jnp.exp(log_BF_k)  # Shape: (n+1,)
@@ -56,7 +58,9 @@ def genlaguerre_jax(n, alpha, x):
     # TF = (-x)^k / k!
 
     # Note: jnp.math.gamma(k_values + 1) is equivalent to k! in log-gamma space
-    TF_k = jnp.power(-x_expanded, k_values_expanded) / jnp.exp(gammaln(k_values_expanded + 1))
+    TF_k = jnp.power(-x_expanded, k_values_expanded) / jnp.exp(
+        gammaln(k_values_expanded + 1)
+    )
     # TF_k Shape: (M, n+1)
 
     # --- C. Final Summation ---
@@ -67,13 +71,13 @@ def genlaguerre_jax(n, alpha, x):
 
 class ShapeletPolar(AbstractShapelet):
     def __init__(
-            self,
-            n: int,
-            m: int,
-            centre: Tuple[float, float] = (0.0, 0.0),
-            ell_comps: Tuple[float, float] = (0.0, 0.0),
-            intensity: float = 1.0,
-            beta: float = 1.0,
+        self,
+        n: int,
+        m: int,
+        centre: Tuple[float, float] = (0.0, 0.0),
+        ell_comps: Tuple[float, float] = (0.0, 0.0),
+        intensity: float = 1.0,
+        beta: float = 1.0,
     ):
         """
         Shapelets where the basis function is defined according to a Polar (r,theta) grid of coordinates.
@@ -153,18 +157,18 @@ def image_2d_from(
             from jax.scipy.special import factorial
 
         const = (
-                ((-1) ** ((self.n - xp.abs(self.m)) // 2))
-                * xp.sqrt(
+            ((-1) ** ((self.n - xp.abs(self.m)) // 2))
+            * xp.sqrt(
                 factorial((self.n - xp.abs(self.m)) // 2)
                 / factorial((self.n + xp.abs(self.m)) // 2)
-        )
-                / self.beta
-                / xp.sqrt(xp.pi)
+            )
+            / self.beta
+            / xp.sqrt(xp.pi)
         )
         y = grid.array[:, 0]
         x = grid.array[:, 1]
 
-        rsq = (x ** 2 + (y / self.axis_ratio(xp)) ** 2) / self.beta ** 2
+        rsq = (x**2 + (y / self.axis_ratio(xp)) ** 2) / self.beta**2
         theta = xp.arctan2(y, x)
 
         m_abs = abs(self.m)
@@ -174,7 +178,9 @@ def image_2d_from(
 
             from scipy.special import genlaguerre
 
-            laguerre = genlaguerre(n=(self.n - xp.abs(self.m)) / 2.0, alpha=xp.abs(self.m))
+            laguerre = genlaguerre(
+                n=(self.n - xp.abs(self.m)) / 2.0, alpha=xp.abs(self.m)
+            )
             laguerre_vals = laguerre(rsq)
 
         else:
@@ -200,12 +206,12 @@ def image_2d_from(
 
 class ShapeletPolarSph(ShapeletPolar):
     def __init__(
-            self,
-            n: int,
-            m: int,
-            centre: Tuple[float, float] = (0.0, 0.0),
-            intensity: float = 1.0,
-            beta: float = 1.0,
+        self,
+        n: int,
+        m: int,
+        centre: Tuple[float, float] = (0.0, 0.0),
+        intensity: float = 1.0,
+        beta: float = 1.0,
     ):
         """
         Shapelets where the basis function is defined according to a Polar (r,theta) grid of coordinates.

autogalaxy/profiles/mass/total/dual_pseudo_isothermal_mass.py

@@ -275,7 +275,9 @@ def deflections_yx_2d_from(self, grid: aa.type.Grid2DLike, xp=np, **kwargs):
         """
         ellip = self._ellip(xp)
         factor = self.b0
-        zis = _ci05(x=grid.array[:, 1], y=grid.array[:, 0], eps=ellip, rcore=self.ra, xp=xp)
+        zis = _ci05(
+            x=grid.array[:, 1], y=grid.array[:, 0], eps=ellip, rcore=self.ra, xp=xp
+        )
 
         # This is in axes aligned to the major/minor axis
         deflection_x = zis.real
@@ -287,17 +289,13 @@ def deflections_yx_2d_from(self, grid: aa.type.Grid2DLike, xp=np, **kwargs):
             xp=xp,
             **kwargs,
         )
-    
+
     def _convergence(self, radii, xp=np):
 
         radsq = radii * radii
         a = self.ra
 
-        return (
-            self.b0
-            / 2
-            * (1 / xp.sqrt(a**2 + radsq))
-        )
+        return self.b0 / 2 * (1 / xp.sqrt(a**2 + radsq))
 
     @aa.grid_dec.to_array
     @aa.grid_dec.transform
@@ -316,14 +314,14 @@ def convergence_2d_from(self, grid: aa.type.Grid2DLike, xp=np, **kwargs):
         """
         ellip = self._ellip(xp)
         grid_radii = xp.sqrt(
-            grid.array[:, 1] ** 2 / (1 + ellip) ** 2 + grid.array[:, 0] ** 2 / (1 - ellip) ** 2
+            grid.array[:, 1] ** 2 / (1 + ellip) ** 2
+            + grid.array[:, 0] ** 2 / (1 - ellip) ** 2
         )
         # Compute the convergence and deflection of a *circular* profile
-        kappa = self._convergence(grid_radii,xp)
+        kappa = self._convergence(grid_radii, xp)
 
         return kappa
 
-
     @aa.grid_dec.transform
     def analytical_hessian_2d_from(self, grid: "aa.type.Grid2DLike", xp=np, **kwargs):
         """
@@ -340,7 +338,12 @@ def analytical_hessian_2d_from(self, grid: "aa.type.Grid2DLike", xp=np, **kwargs
         ellip = self._ellip()
 
         hessian_xx, hessian_xy, hessian_yx, hessian_yy = _mdci05(
-            x=grid.array[:, 1], y=grid.array[:, 0], eps=ellip, rcore=self.ra, b0=self.b0, xp=xp
+            x=grid.array[:, 1],
+            y=grid.array[:, 0],
+            eps=ellip,
+            rcore=self.ra,
+            b0=self.b0,
+            xp=xp,
         )
 
         return hessian_yy, hessian_xy, hessian_yx, hessian_xx
@@ -433,7 +436,7 @@ def deflections_yx_2d_from(self, grid: aa.type.Grid2DLike, xp=np, **kwargs):
             eps=ellip,
             rcore=self.ra,
             rcut=self.rs,
-            xp=xp
+            xp=xp,
         )
 
         # This is in axes aligned to the major/minor axis
@@ -476,11 +479,12 @@ def convergence_2d_from(self, grid: aa.type.Grid2DLike, xp=np, **kwargs):
         """
         ellip = self._ellip(xp)
         grid_radii = xp.sqrt(
-            grid.array[:, 1] ** 2 / (1 + ellip) ** 2 + grid.array[:, 0] ** 2 / (1 - ellip) ** 2
+            grid.array[:, 1] ** 2 / (1 + ellip) ** 2
+            + grid.array[:, 0] ** 2 / (1 - ellip) ** 2
         )
-        kappa = self._convergence(grid_radii,xp)
+        kappa = self._convergence(grid_radii, xp)
         return kappa
-    
+
     @aa.grid_dec.transform
     def analytical_hessian_2d_from(self, grid: "aa.type.Grid2DLike", xp=np, **kwargs):
         """
@@ -499,10 +503,20 @@ def analytical_hessian_2d_from(self, grid: "aa.type.Grid2DLike", xp=np, **kwargs
 
         t05 = self.rs / (self.rs - self.ra)
         g05c_a, g05c_b, g05c_c, g05c_d = _mdci05(
-            x=grid.array[:, 1], y=grid.array[:, 0], eps=ellip, rcore=self.ra, b0=self.b0, xp=xp
+            x=grid.array[:, 1],
+            y=grid.array[:, 0],
+            eps=ellip,
+            rcore=self.ra,
+            b0=self.b0,
+            xp=xp,
         )
         g05cut_a, g05cut_b, g05cut_c, g05cut_d = _mdci05(
-            x=grid.array[:, 1], y=grid.array[:, 0], eps=ellip, rcore=self.rs, b0=self.b0, xp=xp
+            x=grid.array[:, 1],
+            y=grid.array[:, 0],
+            eps=ellip,
+            rcore=self.rs,
+            b0=self.b0,
+            xp=xp,
         )
 
         # Compute Hessian matrix components
@@ -619,7 +633,7 @@ def deflections_yx_2d_from(self, grid: aa.type.Grid2DLike, xp=np, **kwargs):
             xp=xp,
             **kwargs,
         )
-    
+
     @aa.grid_dec.to_array
     @aa.grid_dec.transform
     def convergence_2d_from(self, grid: aa.type.Grid2DLike, xp=np, **kwargs):

test_autogalaxy/profiles/light/shapelets/test_polar.py

@@ -26,7 +26,9 @@ def test__elliptical__image_2d_from():
 
     image = shapelet.image_2d_from(grid=ag.Grid2DIrregular([[0.0, 1.0], [0.5, 0.25]]))
 
-    assert image == pytest.approx(np.array([0.02577349206014249, -0.17434262753]), abs=1e-4)
+    assert image == pytest.approx(
+        np.array([0.02577349206014249, -0.17434262753]), abs=1e-4
+    )
 
     shapelet = ag.lp_linear.ShapeletPolar(
         n=2, m=0, centre=(0.0, 0.0), ell_comps=(0.5, 0.7), beta=1.0