Repository: MatthewFilipovich/torchoptics

Issue:
Fix grating profiles
- Fix `blazed_grating()` to return a real-valued tensor. 
- Combine `sinusoidal_amplitude_grating ()` and `sinusoidal_amplitude_grating()` into `sinusoidal_grating()` which returns a real-valued tensor.
- Update relevant Jupyter notebook in docs using `PolychromaticPhaseModulator` class.

Discussion:
@maujim: blazed_grating is such a cool name@tonyzhang-netizen: Torchoptics is a great library! I'm only a beginner in python, and I find it very friendly. The only thing is maybe you forgot to update torchoptics in PyPI, because blazed_grating() of pip installed version doesn't work, I also raised an issue about this. Anyway, thank you so much for your work! : ) I've tried other libraries for optical systems and didn't even get past installing the dependencies stage.

Solution patch:
diff --git a/docs/source/tutorials/polychromatic_grating.ipynb b/docs/source/tutorials/polychromatic_grating.ipynb
index 4abf350..60516ca 100644
--- a/docs/source/tutorials/polychromatic_grating.ipynb
+++ b/docs/source/tutorials/polychromatic_grating.ipynb
@@ -18,7 +18,7 @@
     "import matplotlib.pyplot as plt\n",
     "\n",
     "from torchoptics import Field, System\n",
-    "from torchoptics.elements import Modulator\n",
+    "from torchoptics.elements import PolychromaticPhaseModulator\n",
     "from torchoptics.profiles import gaussian, blazed_grating"
    ]
   },
@@ -31,7 +31,7 @@
     "# Set simulation properties\n",
     "shape = 500\n",
     "waist_radius = 150e-6\n",
-    "wavelengths = [700e-9, 550e-9, 450e-9]  # Red, green, blue\n",
+    "wavelengths = [450e-9, 550e-9, 700e-9]  # Blue, green, red\n",
     "grating_period = 100e-6\n",
     "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
     "\n",
@@ -88,7 +88,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x500 with 4 Axes>"
       ]
@@ -99,8 +99,9 @@
    ],
    "source": [
     "# Initialize a system with a blazed grating modulator\n",
-    "system = System(Modulator(blazed_grating(shape, grating_period))).to(device)\n",
-    "system[0].visualize()"
+    "phase = blazed_grating(shape, grating_period, height=wavelengths[0])\n",
+    "system = System(PolychromaticPhaseModulator(phase)).to(device)\n",
+    "system[0].visualize(wavelengths[0])"
    ]
   },
   {
@@ -120,7 +121,7 @@
     },
     {
      "data": {
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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -130,7 +131,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -140,7 +141,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -150,7 +151,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -160,7 +161,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
diff --git a/torchoptics/elements/lens.py b/torchoptics/elements/lens.py
index 6de5b61..b0009d1 100644
--- a/torchoptics/elements/lens.py
+++ b/torchoptics/elements/lens.py
@@ -51,9 +51,7 @@ class Lens(PolychromaticModulationElement):
         self.is_circular_lens = is_circular_lens
 
     def modulation_profile(self, wavelength: Optional[Scalar] = None) -> Tensor:
-        return lens(
-            self.shape, self.focal_length, self.z, wavelength, self.spacing, None, self.is_circular_lens
-        )
+        return lens(self.shape, self.focal_length, wavelength, self.spacing, None, self.is_circular_lens)
 
     @property
     def is_circular_lens(self) -> bool:
diff --git a/torchoptics/profiles/__init__.py b/torchoptics/profiles/__init__.py
index a8b711b..d71d773 100644
--- a/torchoptics/profiles/__init__.py
+++ b/torchoptics/profiles/__init__.py
@@ -1,10 +1,10 @@
 """This module contains functions to generate different types of profiles."""
 
 from .bessel import bessel
-from .gratings import blazed_grating, sinusoidal_amplitude_grating, sinusoidal_phase_grating
+from .gratings import binary_grating, blazed_grating, sinusoidal_grating
 from .hermite_gaussian import gaussian, hermite_gaussian
 from .laguerre_gaussian import laguerre_gaussian
 from .lens import lens
-from .shapes import checkerboard, circle, rectangle, square
+from .shapes import checkerboard, circle, rectangle, square, triangle
 from .spatial_coherence import gaussian_schell_model, schell_model
 from .special import airy, sinc
diff --git a/torchoptics/profiles/bessel.py b/torchoptics/profiles/bessel.py
index fbc91bd..e9ee658 100644
--- a/torchoptics/profiles/bessel.py
+++ b/torchoptics/profiles/bessel.py
@@ -1,6 +1,5 @@
 """This module defines a function to generate the bessel beam profile."""
 
-import math
 from typing import Optional
 
 import torch
@@ -9,6 +8,7 @@ from torch import Tensor
 from ..config import wavelength_or_default
 from ..planar_geometry import PlanarGeometry
 from ..type_defs import Scalar, Vector2
+from ..utils import initialize_tensor
 
 __all__ = ["bessel"]
 
@@ -48,12 +48,11 @@ def bessel(
 
     """
     wavelength = wavelength_or_default(wavelength)
+    cone_angle = initialize_tensor("cone_angle", cone_angle, is_scalar=True)
 
-    # Calculate the wave number k and its radial component
     k = 2 * torch.pi / wavelength
-    k_r = k * math.sin(cone_angle)
+    k_r = k * torch.sin(cone_angle)
 
-    # Generate the planar grid
     x, y = PlanarGeometry(shape, spacing=spacing, offset=offset).meshgrid()
     r = torch.sqrt(x**2 + y**2)
 
diff --git a/torchoptics/profiles/gratings.py b/torchoptics/profiles/gratings.py
index 3049631..bc39a2c 100644
--- a/torchoptics/profiles/gratings.py
+++ b/torchoptics/profiles/gratings.py
@@ -1,129 +1,148 @@
 """This module defines functions to generate grating profiles."""
 
-import math
 from typing import Optional
 
 import torch
 
 from ..planar_geometry import PlanarGeometry
 from ..type_defs import Scalar, Vector2
+from ..utils import initialize_tensor
 
-__all__ = ["blazed_grating", "sinusoidal_amplitude_grating", "sinusoidal_phase_grating"]
+__all__ = ["binary_grating", "blazed_grating", "sinusoidal_grating"]
 
 
-def blazed_grating(
+def binary_grating(
     shape: Vector2,
     period: Scalar,
     spacing: Optional[Vector2] = None,
     offset: Optional[Vector2] = None,
+    height: Scalar = 1,
     theta: Scalar = 0,
+    duty_cycle: Scalar = 0.5,
 ) -> torch.Tensor:
     r"""
-    Generates a blazed grating profile.
+    Generates a binary grating profile.
 
-    The blazed grating profile is defined by the following equation:
+    The binary grating profile is defined by the following equation:
 
     .. math::
-        \mathcal{M}(x, y) = \exp\left( i \frac{2\pi}{\Lambda} (x \cos(\theta) + y \sin(\theta)) \right)
+        \psi(x, y) = 
+        \begin{cases} 
+        0 & \text{if } \mod \left(\frac{x \cos \theta + y \sin \theta}{\Lambda}, 1\right) < d \\
+        h & \text{otherwise} 
+        \end{cases}
 
     where:
 
-    - :math:`\Lambda` is the period of the grating, and
-    - :math:`\theta` is the angle of the grating.
+    - :math:`h` is the height of the grating,
+    - :math:`\Lambda` is the period of the grating,
+    - :math:`\theta` is the angle of the grating, and
+    - :math:`d` is the duty cycle of the grating.
 
     Args:
         shape (Vector2): Number of grid points along the planar dimensions.
         period (Scalar): The grating period (distance between adjacent grooves).
+        duty_cycle (Scalar): The duty cycle of the grating. Default: `0.5`.
         spacing (Optional[Vector2]): Distance between grid points along planar dimensions. Default: if
             `None`, uses a global default (see :meth:`torchoptics.set_default_spacing()`).
         offset (Optional[Vector2]): Center coordinates of the beam. Default: `(0, 0)`.
+        height (Scalar): The height of the grating. Default: `1`.
         theta (Scalar): The angle of the grating in radians. Default: `0`.
 
-
     Returns:
         Tensor: The generated transmission function.
     """
 
-    x, y = PlanarGeometry(shape, 0, spacing, offset).meshgrid()
-    phase = 2 * torch.pi * (x * math.cos(theta) + y * math.sin(theta)) / period
-    return torch.exp(1j * phase)
+    duty_cycle = initialize_tensor("duty_cycle", duty_cycle, is_scalar=True)
+    grating = blazed_grating(shape, period, spacing, offset, 1, theta)
+    return torch.where(grating < duty_cycle, 0, height)
 
 
-def sinusoidal_amplitude_grating(
+def blazed_grating(
     shape: Vector2,
-    m: Scalar,
     period: Scalar,
     spacing: Optional[Vector2] = None,
     offset: Optional[Vector2] = None,
+    height: Scalar = 1,
     theta: Scalar = 0,
 ) -> torch.Tensor:
     r"""
-    Generates a sinusoidal amplitude grating profile.
+    Generates a blazed grating profile.
 
-    The sinusoidal amplitude grating profile is defined by the following equation:
+    The blazed grating profile is defined by the following equation:
 
     .. math::
-        \mathcal{M}(x, y) = \frac{1}{2} + \frac{m}{2} \cos\left(2\pi \frac{x \cos(\theta)
-        + y \sin(\theta)}{\Lambda}\right)
+        \psi(x, y) = h \cdot \mod \left(\frac{x \cos \theta + y \sin \theta}{\Lambda}, 1\right)
 
     where:
 
-    - :math:`m` is the amplitude contrast (:math:`0-1`), and
-    - :math:`\Lambda` is the period of the grating.
+    - :math:`h` is the height of the grating,
+    - :math:`\Lambda` is the period of the grating, and
+    - :math:`\theta` is the angle of the grating.
 
     Args:
         shape (Vector2): Number of grid points along the planar dimensions.
-        m (Scalar): The amplitude contrast.
         period (Scalar): The grating period (distance between adjacent grooves).
         spacing (Optional[Vector2]): Distance between grid points along planar dimensions. Default: if
             `None`, uses a global default (see :meth:`torchoptics.set_default_spacing()`).
         offset (Optional[Vector2]): Center coordinates of the beam. Default: `(0, 0)`.
+        height (Scalar): The height of the grating. Default: `1`.
         theta (Scalar): The angle of the grating in radians. Default: `0`.
 
     Returns:
         Tensor: The generated transmission function.
     """
 
-    x, y = PlanarGeometry(shape, 0, spacing, offset).meshgrid()
-    amplitude = 0.5 + m / 2 * torch.cos(2 * torch.pi * (x * math.cos(theta) + y * math.sin(theta)) / period)
-    return amplitude
+    period = initialize_tensor("period", period, is_scalar=True)
+    height = initialize_tensor("height", height, is_scalar=True)
+    theta = initialize_tensor("theta", theta, is_scalar=True)
+    x, y = PlanarGeometry(shape, spacing=spacing, offset=offset).meshgrid()
+
+    grating = ((x * torch.cos(theta) + y * torch.sin(theta)) / period) % 1
+    grating = grating.where(grating < 1 - 1e-10, 0)  # Avoid numerical issues from modulus
+    return height * grating
 
 
-def sinusoidal_phase_grating(
+def sinusoidal_grating(
     shape: Vector2,
-    m: Scalar,
     period: Scalar,
     spacing: Optional[Vector2] = None,
     offset: Optional[Vector2] = None,
+    height: Scalar = 1,
     theta: Scalar = 0,
 ) -> torch.Tensor:
     r"""
-    Generates a sinusoidal phase grating profile.
+    Generates a sinusoidal grating profile.
 
-    The sinusoidal phase grating profile is defined by the following equation:
+    The sinusoidal grating profile is defined by the following equation:
 
     .. math::
-        \mathcal{M}(x, y) = \exp\left( i \frac{m}{2} \sin\left(2\pi \frac{x \cos(\theta)
-        + y \sin(\theta)}{\Lambda}\right) \right)
+        \psi(x, y) = h \left(\frac{1}{2} + \frac{1}{2} \cos\left(\frac{2 \pi}{\Lambda} (x \cos \theta
+        + y \sin \theta)\right)\right)
 
     where:
 
-    - :math:`m` is the phase contrast (:math:`0-2\pi`), and
-    - :math:`\Lambda` is the period of the grating.
+    - :math:`h` is the height of the grating,
+    - :math:`\Lambda` is the period of the grating, and
+    - :math:`\theta` is the angle of the grating.
 
     Args:
         shape (Vector2): Number of grid points along the planar dimensions.
-        m (Scalar): The phase contrast.
         period (Scalar): The grating period (distance between adjacent grooves).
         spacing (Optional[Vector2]): Distance between grid points along planar dimensions. Default: if
             `None`, uses a global default (see :meth:`torchoptics.set_default_spacing()`).
         offset (Optional[Vector2]): Center coordinates of the beam. Default: `(0, 0)`.
+        height (Scalar): The height of the grating. Default: `1`.
         theta (Scalar): The angle of the grating in radians. Default: `0`.
 
     Returns:
         Tensor: The generated transmission function.
     """
 
-    x, y = PlanarGeometry(shape, 0, spacing, offset).meshgrid()
-    phase = m / 2 * torch.sin(2 * torch.pi * (x * math.cos(theta) + y * math.sin(theta)) / period)
-    return torch.exp(1j * phase)
+    period = initialize_tensor("period", period, is_scalar=True)
+    height = initialize_tensor("height", height, is_scalar=True)
+    theta = initialize_tensor("theta", theta, is_scalar=True)
+    x, y = PlanarGeometry(shape, spacing=spacing, offset=offset).meshgrid()
+
+    grating = 0.5 + 0.5 * torch.cos(2 * torch.pi * (x * torch.cos(theta) + y * torch.sin(theta)) / period)
+    return height * grating
diff --git a/torchoptics/profiles/hermite_gaussian.py b/torchoptics/profiles/hermite_gaussian.py
index a95eaad..4780086 100644
--- a/torchoptics/profiles/hermite_gaussian.py
+++ b/torchoptics/profiles/hermite_gaussian.py
@@ -8,15 +8,16 @@ from torch import Tensor
 
 from ..config import wavelength_or_default
 from ..planar_geometry import PlanarGeometry
-from ..type_defs import Scalar, Vector2
+from ..type_defs import Int, Scalar, Vector2
+from ..utils import initialize_tensor
 
 __all__ = ["hermite_gaussian", "gaussian"]
 
 
 def hermite_gaussian(
     shape: Vector2,
-    m: int,
-    n: int,
+    m: Int,
+    n: Int,
     waist_radius: Scalar,
     wavelength: Optional[Scalar] = None,
     waist_z: Scalar = 0,
@@ -83,13 +84,17 @@ def hermite_gaussian(
     """
     # pylint: disable=too-many-locals
 
+    m = initialize_tensor("m", m, is_scalar=True, is_integer=True, is_non_negative=True)
+    n = initialize_tensor("n", n, is_scalar=True, is_integer=True, is_non_negative=True)
+    waist_radius = initialize_tensor("waist_radius", waist_radius, is_scalar=True, is_positive=True)
+
     x, y, phase_shift, wz, waist_ratio = calculate_beam_properties(
         (m, n), waist_radius, shape, wavelength, waist_z, spacing, offset, True
     )
 
     u_x = 2.0**0.5 * x / wz
     u_y = 2.0**0.5 * y / wz
-    normalization_constant = math.sqrt(
+    normalization_constant = torch.sqrt(
         2.0 ** (1 - m - n) / (math.factorial(m) * math.factorial(n) * torch.pi * waist_radius**2)
     )
 
@@ -164,7 +169,7 @@ def gaussian(
     return hermite_gaussian(shape, 0, 0, waist_radius, wavelength, waist_z, spacing, offset)
 
 
-def hermite_poly(n: int) -> Callable[[Tensor], Tensor]:
+def hermite_poly(n: Int) -> Callable[[Tensor], Tensor]:
     """Compute the Hermite polynomial of order n using an iterative approach."""
 
     def poly(x: Tensor) -> Tensor:
diff --git a/torchoptics/profiles/laguerre_gaussian.py b/torchoptics/profiles/laguerre_gaussian.py
index 09751c8..cd1e7c3 100644
--- a/torchoptics/profiles/laguerre_gaussian.py
+++ b/torchoptics/profiles/laguerre_gaussian.py
@@ -6,7 +6,8 @@ from typing import Callable, Optional
 import torch
 from torch import Tensor
 
-from ..type_defs import Scalar, Vector2
+from ..type_defs import Int, Scalar, Vector2
+from ..utils import initialize_tensor
 from .hermite_gaussian import calculate_beam_properties
 
 __all__ = ["laguerre_gaussian"]
@@ -14,8 +15,8 @@ __all__ = ["laguerre_gaussian"]
 
 def laguerre_gaussian(
     shape: Vector2,
-    p: int,
-    l: int,
+    p: Int,
+    l: Int,
     waist_radius: Scalar,
     wavelength: Optional[Scalar] = None,
     waist_z: Scalar = 0,
@@ -81,6 +82,9 @@ def laguerre_gaussian(
         Tensor: The generated Laguerre-Gaussian profile.
     """
     # pylint: disable=too-many-locals
+    p = initialize_tensor("p", p, is_scalar=True, is_integer=True, is_non_negative=True)
+    l = initialize_tensor("l", l, is_scalar=True, is_integer=True)
+    waist_radius = initialize_tensor("waist_radius", waist_radius, is_scalar=True, is_positive=True)
 
     x, y, phase_shift, wz, waist_ratio = calculate_beam_properties(
         (p, l), waist_radius, shape, wavelength, waist_z, spacing, offset, False
@@ -88,7 +92,7 @@ def laguerre_gaussian(
 
     r = torch.sqrt(x**2 + y**2) * 2.0**0.5 / wz
     phi = torch.atan2(y, x)
-    normalization_constant = math.sqrt(
+    normalization_constant = torch.sqrt(
         2 * math.factorial(p) / (torch.pi * math.factorial(p + abs(l))) / waist_radius**2
     )
 
@@ -103,7 +107,7 @@ def laguerre_gaussian(
     )
 
 
-def laguerre_poly(p: int, l: int) -> Callable[[torch.Tensor], torch.Tensor]:
+def laguerre_poly(p: Int, l: Int) -> Callable[[torch.Tensor], torch.Tensor]:
     """Compute the generalized Laguerre polynomial L_p^l(x)."""
 
     def poly(x):
diff --git a/torchoptics/profiles/lens.py b/torchoptics/profiles/lens.py
index 094eac4..a8e39f9 100644
--- a/torchoptics/profiles/lens.py
+++ b/torchoptics/profiles/lens.py
@@ -14,7 +14,6 @@ __all__ = ["lens"]
 def lens(
     shape: Vector2,
     focal_length: Scalar,
-    z: Scalar = 0,
     wavelength: Optional[Scalar] = None,
     spacing: Optional[Vector2] = None,
     offset: Optional[Vector2] = None,
@@ -34,7 +33,6 @@ def lens(
 
     Args:
         shape (Vector2): Number of grid points along the planar dimensions.
-        z (Scalar): Position along the z-axis. Default: `0`.
         focal_length (Scalar): Focal length of the lens.
         wavelength (Optional[Scalar]): Wavelength used for lens operation. Default: if `None`, uses a
             global default (see :meth:`torchoptics.config.set_default_wavelength()`).
@@ -46,7 +44,7 @@ def lens(
     """
     wavelength = wavelength_or_default(wavelength)
 
-    planar_geometry = PlanarGeometry(shape, z, spacing, offset)
+    planar_geometry = PlanarGeometry(shape, spacing=spacing, offset=offset)
     x, y = planar_geometry.meshgrid()
     radial_dist = torch.sqrt(x**2 + y**2)
     phase_profile = torch.exp(-1j * torch.pi / (wavelength * focal_length) * radial_dist**2)
diff --git a/torchoptics/profiles/shapes.py b/torchoptics/profiles/shapes.py
index 1b85f08..3b34fdf 100644
--- a/torchoptics/profiles/shapes.py
+++ b/torchoptics/profiles/shapes.py
@@ -9,7 +9,7 @@ from ..planar_geometry import PlanarGeometry
 from ..type_defs import Scalar, Vector2
 from ..utils import initialize_tensor
 
-__all__ = ["checkerboard", "circle", "rectangle", "square"]
+__all__ = ["checkerboard", "circle", "rectangle", "square", "triangle"]
 
 
 def checkerboard(
@@ -33,9 +33,9 @@ def checkerboard(
     Returns:
         Tensor: The generated checkerboard pattern with internal padding.
     """
-    x, y = PlanarGeometry(shape, 0, spacing, offset).meshgrid()
     tile_length = initialize_tensor("tile_length", tile_length, is_vector2=True, is_positive=True)
     num_tiles = initialize_tensor("num_tiles", num_tiles, is_vector2=True, is_integer=True, is_positive=True)
+    x, y = PlanarGeometry(shape, spacing=spacing, offset=offset).meshgrid()
 
     x_tile = (x + (tile_length[0] / 2 if num_tiles[0] % 2 == 1 else 0)) // tile_length[0]
     y_tile = (y + (tile_length[1] / 2 if num_tiles[1] % 2 == 1 else 0)) // tile_length[1]
@@ -63,7 +63,8 @@ def circle(
     Returns:
         Tensor: The generated circular profile.
     """
-    x, y = PlanarGeometry(shape, 0, spacing, offset).meshgrid()
+    radius = initialize_tensor("radius", radius, is_scalar=True, is_positive=True)
+    x, y = PlanarGeometry(shape, spacing=spacing, offset=offset).meshgrid()
     r = torch.sqrt(x**2 + y**2)
     return r <= radius
 
@@ -84,8 +85,8 @@ def rectangle(
     Returns:
         Tensor: The generated rectangle profile.
     """
-    x, y = PlanarGeometry(shape, 0, spacing, offset).meshgrid()
     side = initialize_tensor("side", side, is_vector2=True, is_positive=True)
+    x, y = PlanarGeometry(shape, spacing=spacing, offset=offset).meshgrid()
     return (x.abs() <= side[0] / 2) & (y.abs() <= side[1] / 2)
 
 
@@ -106,3 +107,45 @@ def square(
         Tensor: The generated square profile.
     """
     return rectangle(shape, (side, side), spacing, offset)
+
+
+def triangle(
+    shape: Vector2,
+    base: Scalar,
+    height: Scalar,
+    spacing: Optional[Vector2] = None,
+    offset: Optional[Vector2] = None,
+    theta: Scalar = 0,
+) -> Tensor:
+    """
+    Generates a triangular profile.
+
+    Args:
+        shape (Vector2): Number of grid points along the planar dimensions.
+        base (Scalar): The base length of the triangle.
+        height (Scalar): The height of the triangle.
+        spacing (Optional[Vector2]): Distance between grid points along planar dimensions. Default: if
+            `None`, uses a global default (see :meth:`torchoptics.set_default_spacing()`).
+        offset (Optional[Vector2]): Center coordinates of the profile. Default: `(0, 0)`.
+        theta (Scalar): The angle of rotation of the triangle in radians. Default: `0`.
+
+    Returns:
+        Tensor: The generated triangular profile.
+    """
+    base = initialize_tensor("base", base, is_scalar=True, is_positive=True)
+    height = initialize_tensor("height", height, is_scalar=True, is_positive=True)
+    theta = initialize_tensor("theta", theta, is_scalar=True)
+    x, y = PlanarGeometry(shape, spacing=spacing, offset=offset).meshgrid()
+
+    theta -= torch.pi / 2
+    x_rot = x * torch.cos(theta) - y * torch.sin(theta)
+    y_rot = x * torch.sin(theta) + y * torch.cos(theta)
+
+    triangle_profile = (
+        (y_rot >= -height / 2)
+        & (y_rot <= height / 2)
+        & (x_rot.abs() <= base / 2)
+        & (y_rot <= height / 2 - (2 * height / base) * x_rot.abs())
+    )
+
+    return triangle_profile
diff --git a/torchoptics/profiles/spatial_coherence.py b/torchoptics/profiles/spatial_coherence.py
index 4f4fd07..e4d78e7 100644
--- a/torchoptics/profiles/spatial_coherence.py
+++ b/torchoptics/profiles/spatial_coherence.py
@@ -8,6 +8,7 @@ from torch import Tensor
 from ..functional import outer2d
 from ..planar_geometry import PlanarGeometry
 from ..type_defs import Scalar, Vector2
+from ..utils import initialize_tensor
 
 
 def schell_model(
@@ -47,7 +48,7 @@ def schell_model(
         torch.Tensor: A 4D tensor representing the mutual coherence function
         :math:`\Gamma(x_1, y_1, x_2, y_2)`.
     """
-    x, y = PlanarGeometry(shape, 0, spacing, offset).meshgrid()
+    x, y = PlanarGeometry(shape, spacing=spacing, offset=offset).meshgrid()
     intensity = intensity_func(x, y)
 
     # Compute pairwise differences for coherence function
@@ -104,4 +105,5 @@ def gaussian_schell_model(
         return 2 / (torch.pi * waist_radius**2) * torch.exp(-(2 * (x**2 + y**2)) / waist_radius**2)
 
     # Use the general Schell model to compute the coherence profile
+    waist_radius = initialize_tensor("waist_radius", waist_radius, is_positive=True)
     return schell_model(shape, intensity_func, coherence_func, spacing, offset)
diff --git a/torchoptics/profiles/special.py b/torchoptics/profiles/special.py
index ab134b9..ca05bdb 100644
--- a/torchoptics/profiles/special.py
+++ b/torchoptics/profiles/special.py
@@ -40,7 +40,8 @@ def airy(
     Returns:
         Tensor: The generated Airy profile.
     """
-    x, y = PlanarGeometry(shape, 0, spacing, offset).meshgrid()
+    scale = initialize_tensor("scale", scale, is_scalar=True, is_positive=True)
+    x, y = PlanarGeometry(shape, spacing=spacing, offset=offset).meshgrid()
     r = torch.sqrt(x**2 + y**2)
     scaled_r = r / scale
     airy_pattern = (2 * bessel_j1(scaled_r) / (scaled_r)) ** 2  # pylint: disable=not-callable
@@ -75,7 +76,7 @@ def sinc(
     Returns:
         Tensor: The generated sinc profile.
     """
-    x, y = PlanarGeometry(shape, 0, spacing, offset).meshgrid()
     scale = initialize_tensor("scale", scale, is_vector2=True, is_positive=True)
+    x, y = PlanarGeometry(shape, spacing=spacing, offset=offset).meshgrid()
     sinc_pattern = torch.sinc(x / scale[0]) * torch.sinc(y / scale[1]) / (scale[0] * scale[1]) ** 0.5
     return sinc_pattern
diff --git a/torchoptics/type_defs.py b/torchoptics/type_defs.py
index 0ad59b7..dcd361d 100644
--- a/torchoptics/type_defs.py
+++ b/torchoptics/type_defs.py
@@ -6,5 +6,6 @@ from torch import Tensor
 
 __all__ = ["Scalar", "Vector2"]
 
+Int = Union[int, Tensor]
 Scalar = Union[int, float, Tensor]
 Vector2 = Union[int, float, Tensor, Sequence]


Test patch:
diff --git a/tests/test_profiles.py b/tests/test_profiles.py
index 2b84b5b..16a0f94 100644
--- a/tests/test_profiles.py
+++ b/tests/test_profiles.py
@@ -223,6 +223,19 @@ class TestShapes(unittest.TestCase):
         self.assertEqual(profile.shape, self.shape)
         self.assertTrue(torch.all((profile == 0) | (profile == 1)))
 
+    def test_triangle(self):
+        base = 10.0
+        height = 20.0
+        profile = shapes.triangle(
+            shape=self.shape,
+            base=base,
+            height=height,
+            spacing=self.spacing,
+            offset=self.offset,
+        )
+        self.assertEqual(profile.shape, self.shape)
+        self.assertTrue(torch.all((profile == 0) | (profile == 1)))
+
 
 class TestGratings(unittest.TestCase):
     def setUp(self):
@@ -233,43 +246,46 @@ class TestGratings(unittest.TestCase):
 
     def test_blazed_grating(self):
         period = 10.0
+        height = 2.0
         profile = gratings.blazed_grating(
             shape=self.shape,
             period=period,
             spacing=self.spacing,
             offset=self.offset,
             theta=self.theta,
+            height=height,
         )
         self.assertEqual(profile.shape, self.shape)
-        self.assertTrue(torch.is_complex(profile))
+        self.assertFalse(torch.is_complex(profile))
 
-    def test_sinusoidal_amplitude_grating(self):
+    def test_sinusoidal_grating(self):
         period = 10.0
-        m = 0.5
-        profile = gratings.sinusoidal_amplitude_grating(
+        height = 1.0
+        profile = gratings.sinusoidal_grating(
             shape=self.shape,
-            m=m,
             period=period,
+            height=height,
             spacing=self.spacing,
             offset=self.offset,
             theta=self.theta,
         )
         self.assertEqual(profile.shape, self.shape)
-        self.assertTrue(torch.all((profile >= 0) & (profile <= 1)))
+        self.assertFalse(torch.is_complex(profile))
+        self.assertTrue(torch.all(profile >= -height))
+        self.assertTrue(torch.all(profile <= height))
 
-    def test_sinusoidal_phase_grating(self):
+    def test_binary_grating(self):
         period = 10.0
-        m = 0.5
-        profile = gratings.sinusoidal_phase_grating(
+        profile = gratings.binary_grating(
             shape=self.shape,
-            m=m,
             period=period,
             spacing=self.spacing,
             offset=self.offset,
             theta=self.theta,
         )
         self.assertEqual(profile.shape, self.shape)
-        self.assertTrue(torch.is_complex(profile))
+        self.assertFalse(torch.is_complex(profile))
+        self.assertTrue(torch.all((profile == 0) | (profile == 1)))
 
 
 class TestSpecialProfiles(unittest.TestCase):