TorchOptics

https://torchoptics.readthedocs.io/en/stable/
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TorchOptics is an open-source Python library for simulating optical systems using Fourier optics...
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### Setup TorchOptics Defaults

Source: https://torchoptics.readthedocs.io/en/stable/quickstart/index.html

Import necessary libraries and set global defaults for grid spacing and wavelength. These defaults apply to all subsequent fields and elements.

```python
import torch
import torchoptics
from torchoptics import Field, System
from torchoptics.elements import AmplitudeModulator, Lens
from torchoptics.profiles import checkerboard, circle, gaussian

torchoptics.set_default_spacing(10e-6)       # 10 µm grid spacing
torchoptics.set_default_wavelength(700e-9)   # 700 nm (red light)
```

--------------------------------

### Install TorchOptics

Source: https://torchoptics.readthedocs.io/en/stable/index.html

Install the TorchOptics library using pip. This command installs the latest stable version.

```bash
pip install torchoptics

```

--------------------------------

### Import Libraries and Setup

Source: https://torchoptics.readthedocs.io/en/stable/examples/optical_systems/lens_focusing.html

Imports necessary libraries for optical system simulation and visualization. Sets default simulation parameters like grid spacing and wavelength.

```python
import matplotlib.pyplot as plt
import torch

import torchoptics
from torchoptics import Field, System, visualize_tensor
from torchoptics.elements import Lens
from torchoptics.profiles import gaussian
```

```python
shape = 512  # Grid size
spacing = 10e-6  # Grid spacing (m)
wavelength = 632.8e-9  # HeNe laser wavelength (m)

focal_length = 100e-3  # Lens focal length (m)
beam_waist = 1e-3  # Input beam waist (m)

w_focus = wavelength * focal_length / (torch.pi * beam_waist)

torchoptics.set_default_spacing(spacing)
torchoptics.set_default_wavelength(wavelength)

device = "cuda" if torch.cuda.is_available() else "cpu"

print(f"Focal length:         {focal_length * 1e3:.0f} mm")
print(f"Input beam waist:     {beam_waist * 1e3:.1f} mm")
print(f"Predicted spot size:  {w_focus * 1e6:.1f} µm")
```

--------------------------------

### Initialize Phase Modulator

Source: https://torchoptics.readthedocs.io/en/stable/_downloads/c0654258368bca714c02af51f1bd0b47/training_multi_plane_focusing.ipynb

Initializes a PhaseModulator with a zero-initialized phase pattern. This is the starting point for optimization.

```python
phase_modulator = PhaseModulator(Parameter(torch.zeros(shape, shape)), z=0).to(device)
```

--------------------------------

### Create a Lens Phase Profile

Source: https://torchoptics.readthedocs.io/en/stable/user-guide/profiles.html

Use `lens_phase()` to generate the quadratic phase profile of a thin lens. Wrap in `torch.exp(1j * phase)` to get the complex field.

```python
field = torch.exp(1j * lens_phase(300, focal_length=300e-3, wavelength=700e-9))
visualize_tensor(field, title="Lens Phase")
```

--------------------------------

### Import Libraries for Quantum State Discrimination

Source: https://torchoptics.readthedocs.io/en/stable/_downloads/a5e21e778007342c988dc75ccded572b/quantum_state_discrimination.ipynb

Imports necessary libraries including matplotlib, torch, and torchoptics modules for field operations and Hermite-Gaussian profiles. Ensure these libraries are installed before running.

```python
import math

import matplotlib.pyplot as plt
import torch

import torchoptics
from torchoptics import Field
from torchoptics.profiles import hermite_gaussian
```

--------------------------------

### Simulate and Visualize Zernike Aberrations

Source: https://torchoptics.readthedocs.io/en/stable/_downloads/c96987df88734d623c47f161047e78d8/zernike_aberrations.ipynb

Iterates through a list of Zernike aberrations, applies them to a lens phase, and visualizes the resulting Point Spread Function (PSF). Requires prior setup of optical components and parameters.

```python
aberrations_for_psf = [
    (2, 0, "Defocus"),
    (2, 2, "Astigmatism"),
    (3, 1, "Coma"),
    (4, 0, "Spherical"),
]

for n, m, name in aberrations_for_psf:
    # Add wavefront error to lens phase
    z_poly = zernike(shape, n, m, aperture_radius)
    aberrated_phase = phase + aberration_strength * z_poly

    lens = Modulator(pupil * torch.exp(1j * aberrated_phase), z=d_o).to(device)
    psf = System(lens).measure_at_z(point_source, z=d_o + d_i)
    visualize_tensor(psf.intensity(), title=rf"{name} ($Z_{{{n}}}^{{{m}}}$)")
```

--------------------------------

### Example Output of Performance Metrics

Source: https://torchoptics.readthedocs.io/en/stable/examples/optimization/training_multi_plane_focusing.html

This is an example output showing the performance metrics for a bifocal lens, including overlap at specified focal planes and total efficiency.

```text
Final Performance:
  Overlap at z = 50 mm: 0.347
  Overlap at z = 100 mm: 0.671
  Total efficiency: 0.509

```

--------------------------------

### Configure simulation parameters and defaults

Source: https://torchoptics.readthedocs.io/en/stable/_downloads/4f85eaf72985ad118485913f6a3c9328/mach_zehnder_interferometer.ipynb

Sets up simulation grid size, spacing, wavelength, and beam waist. It also configures default spacing and wavelength for torchoptics.

```python
shape = 400  # Grid size (number of points per dimension)
spacing = 10e-6  # Grid spacing (m)
wavelength = 700e-9  # Wavelength (m)
waist_radius = 800e-6  # Gaussian beam waist (m)

# Configure torchoptics defaults
torchoptics.set_default_spacing(spacing)
torchoptics.set_default_wavelength(wavelength)
```

--------------------------------

### Configure Simulation Parameters

Source: https://torchoptics.readthedocs.io/en/stable/examples/optical_phenomena/animate_propagation.html

Sets up the grid size, spacing, wavelength, and aperture radius for the simulation. Default spacing and wavelength are configured using torchoptics functions.

```python
shape = 500  # Grid size
spacing = 10e-6  # Grid spacing (m)
wavelength = 700e-9  # Red light (m)
aperture_radius = 1.5e-3  # Octagon circumradius (m)

# Configure torchoptics defaults
torchoptics.set_default_spacing(spacing)
torchoptics.set_default_wavelength(wavelength)
```

--------------------------------

### Retrieve Current Global Defaults

Source: https://torchoptics.readthedocs.io/en/stable/user-guide/configuration.html

Get the currently set global default spacing and wavelength. These values are returned as PyTorch Tensors.

```python
torchoptics.get_default_spacing()       # Returns Tensor
torchoptics.get_default_wavelength()    # Returns Tensor
```

--------------------------------

### Apply Amplitude Mask Modulation

Source: https://torchoptics.readthedocs.io/en/stable/user-guide/fields.html

Modulate a Field using a real-valued profile, acting as an amplitude mask. This example uses a circular aperture profile.

```python
# Real amplitude mask
from torchoptics.profiles import circle

apertured = field.modulate(circle(300, radius=1e-3))
```

--------------------------------

### Set up simulation parameters and print predictions

Source: https://torchoptics.readthedocs.io/en/stable/_downloads/a262ed80a7d535a8333c44b2b97bce2b/lens_focusing.ipynb

Defines grid size, wavelength, focal length, and beam waist. Calculates and prints the predicted focal spot size and sets default simulation parameters.

```python
shape = 512  # Grid size
spacing = 10e-6  # Grid spacing (m)
wavelength = 632.8e-9  # HeNe laser wavelength (m)

focal_length = 100e-3  # Lens focal length (m)
beam_waist = 1e-3  # Input beam waist (m)

w_focus = wavelength * focal_length / (torch.pi * beam_waist)

torchoptics.set_default_spacing(spacing)
torchoptics.set_default_wavelength(wavelength)

device = "cuda" if torch.cuda.is_available() else "cpu"

print(f"Focal length:         {focal_length * 1e3:.0f} mm")
print(f"Input beam waist:     {beam_waist * 1e3:.1f} mm")
print(f"Predicted spot size:  {w_focus * 1e6:.1f} µm")
```

--------------------------------

### Define simulation parameters and defaults

Source: https://torchoptics.readthedocs.io/en/stable/_downloads/c2898a92809c5997752dec643f23f776/polarized_field.ipynb

Sets up the grid size, spacing, wavelength, and beam waist for the simulation. Configures default spacing and wavelength for torchoptics.

```python
shape = 100  # Grid size (number of points per dimension)
spacing = 10e-6  # Grid spacing (m)
wavelength = 700e-9  # Wavelength (m)
beam_waist = 250e-6  # Beam waist radius (m)
extent_mm = (shape - 1) * spacing / 2 * 1e3
x_coords = torch.linspace(-extent_mm, extent_mm, shape)

# Configure torchoptics defaults
torchoptics.set_default_spacing(spacing)
torchoptics.set_default_wavelength(wavelength)
```

--------------------------------

### Get Intensity and Power

Source: https://torchoptics.readthedocs.io/en/stable/user-guide/spatial_coherence.html

Extracts the time-averaged intensity profile and calculates the total power of a spatial coherence object. Intensity is returned as a tensor, and power as a scalar.

```python
I = low_coh.intensity()  # shape (H, W)
P = low_coh.power()      # scalar
```

--------------------------------

### Define simulation parameters

Source: https://torchoptics.readthedocs.io/en/stable/_downloads/390d20ff2921546a8e1e4f121c4b9e5a/4f_system.ipynb

Sets up simulation parameters including grid size, spacing, wavelength, focal length, checkerboard tile size, and device selection (CPU/GPU).

```python
shape = 500  # Grid size (number of points per dimension)
spacing = 10e-6  # Grid spacing (m)
wavelength = 700e-9  # Wavelength (m)
focal_length = 50e-3  # Lens focal length (m)

tile_length = 200e-6  # Checkerboard tile size (m)
num_tiles = 15  # Number of tiles in each dimension

device = "cuda" if torch.cuda.is_available() else "cpu"

torchoptics.set_default_spacing(spacing)
torchoptics.set_default_wavelength(wavelength)
```

--------------------------------

### Define Simulation Parameters

Source: https://torchoptics.readthedocs.io/en/stable/_downloads/a8691a27817e3d2b42391a0a9e44f889/spatial_coherence.ipynb

Sets up simulation parameters such as grid size, spacing, wavelength, beam waist, and coherence widths. It also configures torchoptics defaults and prints a summary of the parameters.

```python
shape = 30  # Grid size (number of points per dimension)
spacing = 10e-6  # Grid spacing (m)
wavelength = 700e-9  # Wavelength (m)
waist_radius = 40e-6  # Waist radius of the Gaussian beam (m)

# Define coherence widths
low_coherence_width = 10e-6  # Low spatial coherence (m)
high_coherence_width = 1e-3  # High spatial coherence (m)

# Determine computation device
device = "cuda" if torch.cuda.is_available() else "cpu"

# Configure torchoptics defaults
torchoptics.set_default_spacing(spacing)
torchoptics.set_default_wavelength(wavelength)

print(f"Beam waist: {waist_radius * 1e6:.0f} m")
low_ratio = low_coherence_width / waist_radius
print(f"Low coherence width: {low_coherence_width * 1e6:.0f} m (́c/w₀ = {low_ratio:.2f})")
high_ratio = high_coherence_width / waist_radius
print(f"High coherence width: {high_coherence_width * 1e3:.1f} mm (́c/w₀ = {high_ratio:.0f})")
```

--------------------------------

### Create a System with Lenses

Source: https://torchoptics.readthedocs.io/en/stable/user-guide/systems.html

Instantiate a System by passing optical elements to its constructor. Element positions are determined by their 'z' attribute. Default spacing and wavelength can be set globally.

```python
import torch
import torchoptics
from torchoptics import Field, System
from torchoptics.elements import AmplitudeModulator, Lens
from torchoptics.profiles import circle, gaussian

torchoptics.set_default_spacing(10e-6)
torchoptics.set_default_wavelength(700e-9)

shape = 1000
f = 200e-3

system = System(
    Lens(shape, f, z=1 * f),
    Lens(shape, f, z=3 * f),
)
```

--------------------------------

### Create a Spherical Wave Phase Profile

Source: https://torchoptics.readthedocs.io/en/stable/user-guide/profiles.html

Use `spherical_wave_phase()` to generate the phase profile for a diverging spherical wave. Wrap in `torch.exp(1j * phase)` to get the complex field.

```python
field = torch.exp(1j * spherical_wave_phase(300, z=0.5, wavelength=700e-9))
visualize_tensor(field, title="Spherical Wave Phase")
```

--------------------------------

### Create a Plane Wave Phase Profile

Source: https://torchoptics.readthedocs.io/en/stable/user-guide/profiles.html

Use `plane_wave_phase()` to generate the phase profile for a tilted plane wave. Wrap in `torch.exp(1j * phase)` to get the complex field.

```python
field = torch.exp(1j * plane_wave_phase(300, theta=0.001, wavelength=700e-9))
visualize_tensor(field, title="Plane Wave Phase")
```

--------------------------------

### Import Libraries and Configure Defaults

Source: https://torchoptics.readthedocs.io/en/stable/examples/optimization/training_petal_beam.html

Imports necessary libraries for simulation, visualization, and optimization. Configures default spacing and wavelength for TorchOptics.

```python
import matplotlib.animation as animation
import matplotlib.pyplot as plt
import torch
from torch.nn import Parameter

import torchoptics
from torchoptics import Field, System, visualize_tensor
from torchoptics.elements import PhaseModulator
from torchoptics.profiles import gaussian, laguerre_gaussian
```

```python
shape = 250  # Grid size (number of points per dimension)
waist_radius = 300e-6  # Waist radius of the Gaussian beam (m)

# Select computation device
device = "cuda" if torch.cuda.is_available() else "cpu"

# Configure torchoptics defaults
torchoptics.set_default_spacing(10e-6)
torchoptics.set_default_wavelength(700e-9)
```

--------------------------------

### Initialize Trainable Diffractive System

Source: https://torchoptics.readthedocs.io/en/stable/quickstart/index.html

Initializes a diffractive system with three phase modulators. These elements are trainable parameters, allowing for gradient-based optimization.

```python
# Trainable diffractive system: three phase planes initialized to zero
system = System(
    PhaseModulator(Parameter(torch.zeros(shape, shape)), z=0.2),
    PhaseModulator(Parameter(torch.zeros(shape, shape)), z=0.4),
    PhaseModulator(Parameter(torch.zeros(shape, shape)), z=0.6),
)

optimizer = torch.optim.Adam(system.parameters(), lr=0.05)
for iteration in range(200):
    optimizer.zero_grad()
    output = system.measure_at_z(input_field, z=0.8)
    loss = 1 - output.inner(target_field).abs().square()  # 1 - |η|²
    loss.backward()
    optimizer.step()
```

--------------------------------

### Create and Visualize Checkerboard Input Field

Source: https://torchoptics.readthedocs.io/en/stable/quickstart/index.html

Create a Field object representing a checkerboard pattern. This is used as input for a 4f system example. The `tile_length` and `num_tiles` parameters define the pattern's characteristics.

```python
input_field = Field(checkerboard(shape, tile_length=200e-6, num_tiles=15))
input_field.visualize(title="Input Field", vmax=1)
```

--------------------------------

### Create a 4f system with two lenses

Source: https://torchoptics.readthedocs.io/en/stable/autoapi/torchoptics/index.html

Demonstrates the creation of a 4f optical system using the System class and Lens elements. The system is configured with lenses placed at specific z-positions and moved to a specified device.

```python
system = System(
    Lens(shape, focal_length, z=1 * focal_length),
    Lens(shape, focal_length, z=3 * focal_length),
).to(device)
```

--------------------------------

### Import Libraries and Configure Defaults

Source: https://torchoptics.readthedocs.io/en/stable/examples/aberrations/zernike_aberrations.html

Imports necessary libraries like matplotlib and torchoptics. Configures default simulation parameters such as grid spacing and wavelength. Selects the computation device (CUDA or CPU).

```python
import matplotlib.pyplot as plt
import torch

import torchoptics
from torchoptics import Field, System, visualize_tensor
from torchoptics.elements import Modulator
from torchoptics.profiles import circle, lens_phase, zernike
```

```python
shape = 500  # Grid size (number of points per dimension)
spacing = 10e-6  # Grid spacing (m)
wavelength = 500e-9  # Wavelength (m)

focal_length = 0.5  # Lens focal length (m)
aperture_radius = 1.5e-3  # Lens aperture radius (m)
d_o = 1.0  # Object distance (m)
d_i = 1.0  # Image distance (m), satisfies 1/f = 1/d_o + 1/d_i

aberration_strength = 4 * torch.pi  # Peak-to-valley wavefront error (~2 waves)

# Configure torchoptics defaults
torchoptics.set_default_spacing(spacing)
torchoptics.set_default_wavelength(wavelength)

# Select computation device
device = "cuda" if torch.cuda.is_available() else "cpu"
```

--------------------------------

### Create a Cylindrical Lens Phase Profile

Source: https://torchoptics.readthedocs.io/en/stable/user-guide/profiles.html

Use `cylindrical_lens_phase()` to generate a quadratic phase profile in one direction, simulating a cylindrical lens. Wrap in `torch.exp(1j * phase)` to get the complex field.

```python
field = torch.exp(1j * cylindrical_lens_phase(300, focal_length=300e-3, wavelength=700e-9))
visualize_tensor(field, title="Cylindrical Lens Phase")
```

--------------------------------

### Import Libraries and Define Constants

Source: https://torchoptics.readthedocs.io/en/stable/examples/aberrations/chromatic_aberration.html

Imports necessary libraries and defines simulation parameters including material properties, spectral lines, and lens geometry. Sets up the default spacing and device for PyTorchOptics.

```python
import matplotlib.pyplot as plt
import torch

import torchoptics
from torchoptics import Field, System
from torchoptics.elements import PolychromaticPhaseModulator
from torchoptics.profiles import gaussian
from torchoptics.profiles._profile_meshgrid import profile_meshgrid
```

```python
shape = 512
spacing = 10e-6  # 10 µm grid spacing — large enough that the 1 mm beam
# decays to < 0.2 % amplitude at the boundary
waist_radius = 1e-3  # 1 mm Gaussian beam waist

# BK7 glass Cauchy coefficients
A_cauchy = 1.5044
B_cauchy = 4.24e-15  # m²


def n_bk7(wavelength):
    """Two-term Cauchy dispersion model for BK7 glass."""
    return A_cauchy + B_cauchy / wavelength**2


# Fraunhofer spectral lines
wavelengths = [486.1e-9, 589.3e-9, 656.3e-9]  # F, d, C lines (m)
colors = ["royalblue", "goldenrod", "firebrick"]
labels = ["F-line 486 nm", "d-line 589 nm", "C-line 656 nm"]

# Lens geometry: plano-convex, designed for f_d = 150 mm at the d-line
n_d = n_bk7(589.3e-9)
focal_d = 150e-3  # design focal length at d-line (m)
R = focal_d * (n_d - 1)  # radius of curvature (m)

# Abbe number and theoretical focal lengths
n_F, n_C = n_bk7(486.1e-9), n_bk7(656.3e-9)
V_abbe = (n_d - 1) / (n_F - n_C)
focal_theory = [R / (n_bk7(wl) - 1) for wl in wavelengths]
lca_theory = focal_theory[2] - focal_theory[0]

torchoptics.set_default_spacing(spacing)
device = "cuda" if torch.cuda.is_available() else "cpu"

print(f"BK7 refractive indices:  nF = {n_F:.4f},  nd = {n_d:.4f},  nC = {n_C:.4f}")
print(f"Abbe number:             Vd = {V_abbe:.1f}")
print(f"Radius of curvature:     R  = {R * 1e3:.2f} mm")
print(f"Theoretical LCA:         Δf = f_d / Vd = {lca_theory * 1e3:.2f} mm")
```

--------------------------------

### Configure simulation parameters and device

Source: https://torchoptics.readthedocs.io/en/stable/_downloads/13efaad8b0089b497a876083d6446158/fresnel_zone_plate.ipynb

Sets up simulation parameters such as grid size, spacing, wavelength, and focal length. It also configures TorchOptics defaults and selects the computation device (CUDA or CPU).

```python
shape = 600  # Grid size (number of points per dimension)
spacing = 5e-6  # Grid spacing (m)
wavelength = 500e-9  # Wavelength (m)
focal_length = 0.3  # Desired focal length (m)

# Configure torchoptics defaults
torchoptics.set_default_spacing(spacing)
torchoptics.set_default_wavelength(wavelength)

# Select computation device
device = "cuda" if torch.cuda.is_available() else "cpu"
```

--------------------------------

### Import Libraries and Configure Defaults

Source: https://torchoptics.readthedocs.io/en/stable/examples/coherence_and_polarization/laser_speckle_patterns.html

Imports necessary libraries for plotting, tensor operations, and torchoptics. Sets default spacing and wavelength for torchoptics simulations.

```python
import matplotlib.pyplot as plt
import torch

import torchoptics
from torchoptics import Field, visualize_tensor
from torchoptics.profiles import circle, gaussian
```

```python
# Configure torchoptics defaults
torchoptics.set_default_spacing(spacing)
torchoptics.set_default_wavelength(wavelength)

device = "cuda" if torch.cuda.is_available() else "cpu"
```

--------------------------------

### Define Simulation Parameters and Calculate Theoretical Angles

Source: https://torchoptics.readthedocs.io/en/stable/_downloads/c1f92605fa06ef38297a1129ed425027/polychromatic_light_with_grating.ipynb

Sets up simulation parameters such as grid size, spacing, beam waist, wavelengths for RGB light, and grating properties. It also calculates and prints the theoretical first-order diffraction angles for each wavelength.

```python
shape = 500  # Grid size (number of points per dimension)
spacing = 10e-6  # Grid spacing (m)
waist_radius = 300e-6  # Input Gaussian beam waist (m)

wavelengths = [450e-9, 550e-9, 700e-9]  # Blue, green, red (m)
colors = ["blue", "green", "red"]
labels = ["Blue (450 nm)", "Green (550 nm)", "Red (700 nm)"]

grating_period = 100e-6  # Grating period d (m)
blaze_wavelength = wavelengths[0]  # Blaze condition optimized for blue

# Configure torchoptics defaults (spacing only; wavelength set per-field)
torchoptics.set_default_spacing(spacing)

device = "cuda" if torch.cuda.is_available() else "cpu"

# Theoretical first-order deflection angles (radians)
theta_theory = [wl / grating_period for wl in wavelengths]
print("Theoretical first-order diffraction angles:")
for lbl, theta in zip(labels, theta_theory):
    print(f"  {lbl}: {theta * 1e3:.2f} mrad")
```

--------------------------------

### Configure Simulation Parameters

Source: https://torchoptics.readthedocs.io/en/stable/_downloads/b7e205eaab4bdb61763a0c7f7b81755a/laser_speckle_patterns.ipynb

Sets up the simulation grid size, spacing, wavelength, and illumination beam radius. It also configures torchoptics defaults for spacing and wavelength and determines the computation device (CUDA or CPU).

```python
shape = 512  # Grid size
spacing = 5e-6  # Grid spacing (m)
wavelength = 632.8e-9  # HeNe laser (m)

beam_radius = 1e-3  # Illumination beam radius (m)

# Configure torchoptics defaults
torchoptics.set_default_spacing(spacing)
torchoptics.set_default_wavelength(wavelength)

device = "cuda" if torch.cuda.is_available() else "cpu"
```

--------------------------------

### Configure Simulation Parameters and Device

Source: https://torchoptics.readthedocs.io/en/stable/_downloads/424f19c8cf6e3c2ef21198f68d83155d/talbot_effect.ipynb

Sets up simulation grid size, physical parameters like spacing and wavelength, grating period, and calculates the Talbot distance. It also configures torchoptics defaults and selects the computation device (CUDA or CPU).

```python
shape = 300  # Grid size (number of points per dimension)
spacing = 5e-6  # Grid spacing (m)
wavelength = 500e-9  # Wavelength (m), green light
grating_period = 50e-6  # Grating period (m)
# Talbot distance
z_talbot = 2 * grating_period**2 / wavelength
print(f"Talbot distance: z_T = {z_talbot * 1e3:.1f} mm")

# Configure torchoptics defaults
torchoptics.set_default_spacing(spacing)
torchoptics.set_default_wavelength(wavelength)

# Select computation device
device = "cuda" if torch.cuda.is_available() else "cpu"
```

--------------------------------

### Create Gaussian Schell Model Sources

Source: https://torchoptics.readthedocs.io/en/stable/user-guide/spatial_coherence.html

Constructs low and high coherence light sources using the gaussian_schell_model function. Ensure default spacing and wavelength are set before use.

```python
import torchoptics
from torchoptics import SpatialCoherence, visualize_tensor
from torchoptics.profiles import gaussian_schell_model

torchoptics.set_default_spacing(10e-6)
torchoptics.set_default_wavelength(700e-9)

low_coh = SpatialCoherence(
    gaussian_schell_model(30, waist_radius=40e-6, coherence_width=10e-6)
)
high_coh = SpatialCoherence(
    gaussian_schell_model(30, waist_radius=40e-6, coherence_width=1e-3)
)

low_coh.visualize(title="Low Coherence  (σ_c = 10 µm)")
```

```python
high_coh.visualize(title="High Coherence  (σ_c = 1 mm)")
```

--------------------------------

### System Indexing, Slicing, and Iteration

Source: https://torchoptics.readthedocs.io/en/stable/user-guide/systems.html

Systems support standard Python sequence operations like indexing to retrieve a single element, slicing to create a new System from a subset of elements, and iteration to loop through elements.

```python
first = system[0]             # Single element
sub = system[0:2]             # New System from slice
n = len(system)               # Element count

for element in system:
    print(element)
```

--------------------------------

### System Measurement with Phase Modulator

Source: https://torchoptics.readthedocs.io/en/stable/_downloads/5d503a871a061d2c5bab6d90fe2b43ae/phase_only_modulators.ipynb

Sets up a system with a phase modulator and a lens, then measures the output field at 2f for a uniform plane wave input.

```python
system = System(phase_modulator, Lens(shape, focal_length, z=focal_length)).to(device)

# Input is a uniform plane wave
input_field = Field(torch.ones(shape, shape)).to(device)

# Measure the output field at z = 2f
output_field = system.measure_at_z(input_field, z=2 * focal_length)
visualize_tensor(output_field.intensity(), title="Output Field at 2f", vmax=1)
```

--------------------------------

### Define simulation parameters

Source: https://torchoptics.readthedocs.io/en/stable/_downloads/c96987df88734d623c47f161047e78d8/zernike_aberrations.ipynb

Sets up simulation parameters such as grid size, spacing, wavelength, lens properties, and aberration strength. Configures torchoptics defaults and selects the computation device.

```python
shape = 500  # Grid size (number of points per dimension)
spacing = 10e-6  # Grid spacing (m)
wavelength = 500e-9  # Wavelength (m)

focal_length = 0.5  # Lens focal length (m)
aperture_radius = 1.5e-3  # Lens aperture radius (m)
d_o = 1.0  # Object distance (m)
d_i = 1.0  # Image distance (m), satisfies 1/f = 1/d_o + 1/d_i

aberration_strength = 4 * torch.pi  # Peak-to-valley wavefront error (~2 waves)

# Configure torchoptics defaults
torchoptics.set_default_spacing(spacing)
torchoptics.set_default_wavelength(wavelength)

# Select computation device
device = "cuda" if torch.cuda.is_available() else "cpu"
```

--------------------------------

### Configure simulation parameters

Source: https://torchoptics.readthedocs.io/en/stable/_downloads/18d5a120189995289163cf9a80b3a5bc/gaussian_beam_propagation.ipynb

Sets up simulation parameters such as grid size, spacing, wavelength, and beam waist radius. It calculates the Rayleigh range and configures torchoptics defaults for spacing and wavelength. The device (CPU or CUDA) is also determined.

```python
shape = 256  # Grid size
spacing = 10e-6  # Grid spacing (m)
wavelength = 632.8e-9  # HeNe laser wavelength (m)

waist_radius = 200e-6  # Beam waist radius w_0 (m)
rayleigh_range = torch.pi * waist_radius**2 / wavelength  # z_R (m)

# Configure torchoptics defaults
torchoptics.set_default_spacing(spacing)
torchoptics.set_default_wavelength(wavelength)

device = "cuda" if torch.cuda.is_available() else "cpu"

print(f"Waist radius: {waist_radius * 1e6:.0f} µm")
print(f"Rayleigh range: {rayleigh_range * 1e3:.1f} mm")
```

--------------------------------

### Simulate 4f System with High-Pass Filter

Source: https://torchoptics.readthedocs.io/en/stable/quickstart/index.html

Sets up and simulates a 4f optical system that includes a high-pass filter at the Fourier plane. This demonstrates how to chain optical elements and measure the output field.

```python
system = System(
    Lens(shape, f, z=1 * f),
    aperture,
    Lens(shape, f, z=3 * f),
)

output = system.measure_at_z(input_field, z=4 * f)
output.visualize(title="Output: Edges", vmax=1)
```

--------------------------------

### Create and use Waveplate elements

Source: https://torchoptics.readthedocs.io/en/stable/user-guide/elements.html

Instantiate Waveplate, QuarterWaveplate, or HalfWaveplate to introduce phase delays between polarization axes without attenuation. These elements modify the polarization state.

```python
from torchoptics.elements import HalfWaveplate, QuarterWaveplate, Waveplate

# General waveplate
wp = Waveplate(shape, phi=math.pi/3, theta=math.pi/4, z=0)

# Quarter waveplate at 45°: converts x-linear to circular
qwp = QuarterWaveplate(shape, theta=math.pi/4, z=0)

# Half waveplate at 22.5°: rotates polarization by 45°
hwp = HalfWaveplate(shape, theta=math.pi/8, z=0)
```

--------------------------------

### Initialize Input Gaussian Field

Source: https://torchoptics.readthedocs.io/en/stable/quickstart/index.html

Sets up the default spacing and wavelength, then defines and visualizes an input Gaussian beam field for inverse design.

```python
import torch
from torch.nn import Parameter
import torchoptics
from torchoptics import Field, System
from torchoptics.elements import PhaseModulator
from torchoptics.profiles import gaussian, laguerre_gaussian

torchoptics.set_default_spacing(10e-6)
torchoptics.set_default_wavelength(700e-9)

shape = 250
waist_radius = 300e-6

# Input: Gaussian beam
input_field = Field(gaussian(shape, waist_radius=waist_radius), z=0)
input_field.visualize(title="Input: Gaussian")
```

--------------------------------

### Create a Field with Default Configuration

Source: https://torchoptics.readthedocs.io/en/stable/user-guide/fields.html

Construct a Field from a profile, using default spacing and wavelength if not specified. The print statement shows the resulting Field object's properties.

```python
import torch
import torchoptics
from torchoptics import Field
from torchoptics.profiles import gaussian, circle

torchoptics.set_default_spacing(10e-6)
torchoptics.set_default_wavelength(700e-9)

field = Field(circle(300, radius=1e-3))
field.visualize(title="Circular Aperture")
print(field)
```

```python
Field(shape=(300, 300), z=0.00e+00, spacing=(1.00e-05, 1.00e-05), offset=(0.00e+00, 0.00e+00), wavelength=7.00e-07)
```

--------------------------------

### Initialize interferometer components and input field

Source: https://torchoptics.readthedocs.io/en/stable/_downloads/4f85eaf72985ad118485913f6a3c9328/mach_zehnder_interferometer.ipynb

Creates two BeamSplitter elements for the interferometer and defines the input field as a Gaussian beam. Visualizes the input beam's intensity.

```python
bs1 = BeamSplitter(shape, theta=torch.pi / 4, phi_0=0, phi_r=0, phi_t=0)
bs2 = BeamSplitter(shape, theta=torch.pi / 4, phi_0=0, phi_r=0, phi_t=0)

# Input: Gaussian beam
input_field = Field(gaussian(shape, waist_radius))
visualize_tensor(input_field.intensity(), title="Input Gaussian Beam")
```

--------------------------------

### Define Target Patterns and Visualize

Source: https://torchoptics.readthedocs.io/en/stable/_downloads/c0654258368bca714c02af51f1bd0b47/training_multi_plane_focusing.ipynb

Generates target Gaussian spot patterns at two different focal planes and visualizes them.

```python
# Target: Gaussian spots at each focal plane
target_waist = 30e-6  # Target spot size (m)

target_1 = Field(gaussian(shape, waist_radius=target_waist), z=focal_plane_1).to(device)
target_2 = Field(gaussian(shape, waist_radius=target_waist), z=focal_plane_2).to(device)

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4), constrained_layout=True)

ax1.imshow(target_1.intensity().cpu(), cmap="inferno")
ax1.set_title(f"Target at z = {focal_plane_1 * 1e3:.0f} mm")
ax1.axis("off")

ax2.imshow(target_2.intensity().cpu(), cmap="inferno")
ax2.set_title(f"Target at z = {focal_plane_2 * 1e3:.0f} mm")
ax2.axis("off")

plt.suptitle("Target Intensity Patterns", fontsize=12)
plt.show()
```

--------------------------------

### Import Necessary Libraries

Source: https://torchoptics.readthedocs.io/en/stable/_downloads/5d503a871a061d2c5bab6d90fe2b43ae/phase_only_modulators.ipynb

Imports the required modules from torchoptics and torch for optical field manipulation and simulation.

```python
import torch

import torchoptics
from torchoptics import Field, System, visualize_tensor
from torchoptics.elements import AmplitudeModulator, Lens, PhaseModulator
from torchoptics.profiles import circle, hermite_gaussian
```

--------------------------------

### Import necessary libraries for TorchOptics

Source: https://torchoptics.readthedocs.io/en/stable/_downloads/3dcbeb94f5127f913ee859586069e3c5/aperture_limited_psf.ipynb

Imports the required libraries for setting up and running optical simulations with TorchOptics, including plotting utilities.

```python
import matplotlib.pyplot as plt
import torch

import torchoptics
from torchoptics import Field, System
from torchoptics.elements import Modulator
from torchoptics.profiles import circle, lens_phase
```

--------------------------------

### Create and use a Detector

Source: https://torchoptics.readthedocs.io/en/stable/user-guide/elements.html

Instantiate a Detector to convert a field into an intensity measurement. Gradients can flow back through the detector.

```python
from torchoptics.elements import Detector

detector = Detector(shape, z=0.5)
power_map = detector(field)  # Tensor of shape (H, W)
```

--------------------------------

### Import necessary libraries

Source: https://torchoptics.readthedocs.io/en/stable/_downloads/a8691a27817e3d2b42391a0a9e44f889/spatial_coherence.ipynb

Imports the required libraries for spatial coherence simulation, including torch, torchoptics, and the Gaussian-Schell model profile.

```python
import torch

import torchoptics
from torchoptics import SpatialCoherence
from torchoptics.profiles import gaussian_schell_model as gsm
```

--------------------------------

### Set Simulation Parameters

Source: https://torchoptics.readthedocs.io/en/stable/_downloads/a5e21e778007342c988dc75ccded572b/quantum_state_discrimination.ipynb

Configures simulation parameters such as grid size, beam waist radius, grid spacing, and wavelength. These values are crucial for accurate optical field calculations.

```python
shape = 300  # Grid size (number of points per dimension)
waist_radius = 300e-6  # Beam waist radius (m)
spacing = 10e-6  # Grid spacing (m)
wavelength = 700e-9  # Wavelength (m)

torchoptics.set_default_spacing(spacing)
torchoptics.set_default_wavelength(wavelength)
```

--------------------------------

### Initialize Polychromatic Phase Modulator

Source: https://torchoptics.readthedocs.io/en/stable/_downloads/19d7e956cba886cfe6ecf959fca0e850/chromatic_aberration.ipynb

Initializes a PolychromaticPhaseModulator with specified thickness and refractive index. The system is then converted to the appropriate device (e.g., GPU).

```python
lens = PolychromaticPhaseModulator(thickness, n=n_bk7)
system = System(lens).to(device)
```

--------------------------------

### Create a Checkerboard Pattern Profile

Source: https://torchoptics.readthedocs.io/en/stable/user-guide/profiles.html

Use `checkerboard()` to create a tiled checkerboard pattern. Specify the number of pixels, tile length, and number of tiles.

```python
profile = checkerboard(300, tile_length=200e-6, num_tiles=10)
visualize_tensor(profile, title="Checkerboard")
```

--------------------------------

### Set Simulation Parameters

Source: https://torchoptics.readthedocs.io/en/stable/_downloads/c0654258368bca714c02af51f1bd0b47/training_multi_plane_focusing.ipynb

Configures simulation parameters including grid size, spacing, wavelength, beam waist, and target focal planes. It also sets up the computation device and default TorchOptics settings.

```python
shape = 256  # Grid size (smaller for faster training)
spacing = 10e-6  # Grid spacing (m)
wavelength = 632.8e-9  # HeNe laser (m)

waist_radius = 500e-6  # Input beam waist (m)

# Target focal distances
focal_plane_1 = 50e-3  # First focus (m)
focal_plane_2 = 100e-3  # Second focus (m)

# Select computation device
device = "cuda" if torch.cuda.is_available() else "cpu"

# Configure torchoptics defaults
torchoptics.set_default_spacing(spacing)
torchoptics.set_default_wavelength(wavelength)

print(f"Target focal planes: {focal_plane_1 * 1e3:.0f} mm and {focal_plane_2 * 1e3:.0f} mm")
```

--------------------------------

### Demonstrate sequential polarizer effects

Source: https://torchoptics.readthedocs.io/en/stable/examples/coherence_and_polarization/polarized_field.html

Illustrates how sequential polarizers affect light polarization. It shows that an intermediate polarizer can enable transmission through a subsequent polarizer that would otherwise block the light.

```python
polarizer_0 = LinearPolarizer(shape, theta=0)
polarizer_45 = LinearPolarizer(shape, theta=torch.pi / 4)
polarizer_90 = LinearPolarizer(shape, theta=torch.pi / 2)

after_0 = polarizer_0(field)
after_45 = polarizer_45(field)
after_90 = polarizer_90(field)
after_45_90 = polarizer_90(after_45)

stage_labels = ["Input", "0°", "45°", "90°", "45° → 90°"]
stage_powers = [
    field.power().sum().item(),
    after_0.power().sum().item(),
    after_45.power().sum().item(),
    after_90.power().sum().item(),
    after_45_90.power().sum().item(),
]

print("Sequential Polarizer Experiment")
for label, power in zip(stage_labels, stage_powers):
    print(f"  {label:<10}: {power:.3f}")
```

```text
Sequential Polarizer Experiment
  Input     : 1.000
  0°        : 1.000
  45°       : 0.500
  90°       : 0.000
  45° → 90° : 0.250
```

--------------------------------

### Define simulation parameters and BK7 dispersion model

Source: https://torchoptics.readthedocs.io/en/stable/_downloads/19d7e956cba886cfe6ecf959fca0e850/chromatic_aberration.ipynb

Sets up simulation parameters like grid shape, spacing, beam waist, and BK7 glass Cauchy coefficients. Defines a function for the refractive index of BK7 based on wavelength and specifies the Fraunhofer spectral lines to be simulated.

```python
shape = 512
spacing = 10e-6  # 10 µm grid spacing — large enough that the 1 mm beam
# decays to < 0.2 % amplitude at the boundary
waist_radius = 1e-3  # 1 mm Gaussian beam waist

# BK7 glass Cauchy coefficients
A_cauchy = 1.5044
B_cauchy = 4.24e-15  # m²


def n_bk7(wavelength):
    """Two-term Cauchy dispersion model for BK7 glass."""
    return A_cauchy + B_cauchy / wavelength**2


# Fraunhofer spectral lines
wavelengths = [486.1e-9, 589.3e-9, 656.3e-9]  # F, d, C lines (m)
colors = ["royalblue", "goldenrod", "firebrick"]
labels = ["F-line 486 nm", "d-line 589 nm", "C-line 656 nm"]

# Lens geometry: plano-convex, designed for f_d = 150 mm at the d-line
n_d = n_bk7(589.3e-9)
focal_d = 150e-3  # design focal length at d-line (m)
R = focal_d * (n_d - 1)  # radius of curvature (m)

# Abbe number and theoretical focal lengths
n_F, n_C = n_bk7(486.1e-9), n_bk7(656.3e-9)
V_abbe = (n_d - 1) / (n_F - n_C)
focal_theory = [R / (n_bk7(wl) - 1) for wl in wavelengths]
lca_theory = focal_theory[2] - focal_theory[0]

torchoptics.set_default_spacing(spacing)
device = "cuda" if torch.cuda.is_available() else "cpu"

print(f"BK7 refractive indices:  nF = {n_F:.4f},  nd = {n_d:.4f},  nC = {n_C:.4f}")
print(f"Abbe number:             Vd = {V_abbe:.1f}")
print(f"Radius of curvature:     R  = {R * 1e3:.2f} mm")
print(f"Theoretical LCA:         Δf = f_d / Vd = {lca_theory * 1e3:.2f} mm")
```

--------------------------------

### Registering Optics Properties with OpticsModule

Source: https://torchoptics.readthedocs.io/en/stable/autoapi/torchoptics/index.html

Demonstrates how to subclass OpticsModule and register trainable and non-trainable properties using register_optics_property. This is useful for creating custom optical components that manage their own parameters.

```python
from torchoptics import OpticsModule
from torch.nn import Parameter

class MyOpticsModule(OpticsModule):
    def __init__(self, trainable_property, non_trainable_property):
        super().__init__()
        self.register_optics_property("trainable_property", Parameter(trainable_property), shape=())
        self.register_optics_property("non_trainable_property", non_trainable_property, shape=())

```

--------------------------------

### Set default simulation parameters

Source: https://torchoptics.readthedocs.io/en/stable/_downloads/3dcbeb94f5127f913ee859586069e3c5/aperture_limited_psf.ipynb

Configures the default spacing and wavelength for the TorchOptics simulation. This sets up the environment for subsequent optical element and field definitions.

```python
torchoptics.set_default_spacing(10e-6)
torchoptics.set_default_wavelength(700e-9)

shape = 500
wavelength = 700e-9  # m
spacing = 10e-6  # m

focal_length = 0.5  # m
d_o = 1.0  # Object distance (m)
d_i = 1.0  # Image distance (m), satisfies 1/f = 1/d_o + 1/d_i
lens_z = d_o
image_z = d_o + d_i

device = "cuda" if torch.cuda.is_available() else "cpu"

print(f"Focal length: {focal_length} m")
print(f"Object distance: {d_o} m, Image distance: {d_i} m")
print(f"Wavelength: {wavelength * 1e9:.0f} nm")
```

--------------------------------

### Create Random Phase Screen and Input Field

Source: https://torchoptics.readthedocs.io/en/stable/_downloads/b7e205eaab4bdb61763a0c7f7b81755a/laser_speckle_patterns.ipynb

Generates a random phase screen with uniformly distributed phases to simulate surface roughness. It then creates a Gaussian beam amplitude and combines it with the phase to form the input Field object, visualizing the initial field.

```python
# Create random phase screen (fully developed speckle)
random_phase = 2 * torch.pi * torch.rand(shape, shape)

# Illumination: Gaussian beam
amplitude = gaussian(shape, waist_radius=beam_radius)

# Create Field object
input_field = Field(amplitude * torch.exp(1j * random_phase)).to(device)

input_field.visualize(title="Input Field")
```

--------------------------------

### Import necessary libraries

Source: https://torchoptics.readthedocs.io/en/stable/examples/optical_phenomena/animate_propagation.html

Imports the required libraries for the simulation, including torch and torchoptics modules.

```python
import torch

import torchoptics
from torchoptics import Field, animate_tensor
from torchoptics.profiles import octagon
```

--------------------------------

### Define Simulation Parameters

Source: https://torchoptics.readthedocs.io/en/stable/_downloads/9e7c9ae99ba5cb1b1663c1e074cbf4ad/youngs_double_slit.ipynb

Sets up the physical and computational parameters for the simulation, including grid dimensions, spacing, wavelength, slit dimensions, and separation. It also configures torchoptics defaults and selects the computation device (CUDA or CPU).

```python
shape = 500  # Grid size (number of points per dimension)
spacing = 5e-6  # Grid spacing (m)
wavelength = 500e-9  # Wavelength (m), green light

slit_width = 40e-6  # Width of each slit (m)
slit_height = 1.5e-3  # Height of each slit (m)
slit_separation = 250e-6  # Center-to-center separation (m)

# Configure torchoptics defaults
torchoptics.set_default_spacing(spacing)
torchoptics.set_default_wavelength(wavelength)

# Select computation device
device = "cuda" if torch.cuda.is_available() else "cpu"
```

--------------------------------

### Import necessary libraries

Source: https://torchoptics.readthedocs.io/en/stable/_downloads/a262ed80a7d535a8333c44b2b97bce2b/lens_focusing.ipynb

Imports required modules from matplotlib, torch, and torchoptics for optical simulations and visualizations.

```python
import matplotlib.pyplot as plt
import torch

import torchoptics
from torchoptics import Field, System, visualize_tensor
from torchoptics.elements import Lens
from torchoptics.profiles import gaussian
```

--------------------------------

### Import necessary libraries for simulation

Source: https://torchoptics.readthedocs.io/en/stable/_downloads/4f85eaf72985ad118485913f6a3c9328/mach_zehnder_interferometer.ipynb

Imports required modules from matplotlib, torch, and torchoptics for setting up and visualizing the simulation.

```python
import matplotlib.pyplot as plt
import torch

import torchoptics
from torchoptics import Field, visualize_tensor
from torchoptics.elements import BeamSplitter
from torchoptics.profiles import gaussian
```

--------------------------------

### Set Simulation Parameters

Source: https://torchoptics.readthedocs.io/en/stable/_downloads/5d503a871a061d2c5bab6d90fe2b43ae/phase_only_modulators.ipynb

Configures the simulation parameters including grid size, spacing, wavelength, focal length, and computation device. It also sets default values for spacing and wavelength in torchoptics.

```python
shape = 1000  # Grid size (number of points per dimension)
spacing = 10e-6  # Grid spacing (m)
wavelength = 700e-9  # Wavelength (m)
focal_length = 200e-3  # Lens focal length (m)

waist_radius = 0.3e-3

# Select computation device
device = "cuda" if torch.cuda.is_available() else "cpu"

# Configure torchoptics defaults
torchoptics.set_default_spacing(spacing)
torchoptics.set_default_wavelength(wavelength)
```
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