### Initialize Circuit with Filters

Source: https://docs.photontorch.com/_sources/examples/02_add_drop_filter.ipynb.txt

This example shows how to initialize a Photontorch circuit with pre-defined filters. This is useful for setting up complex optical systems from the start.

```python
from photontorch.circuit import Circuit
from photontorch.components import Filter

# Define filters to be included in the circuit
filter1 = Filter(wavelength=1550, bandwidth=10, transmission=0.9)
filter2 = Filter(wavelength=1610, bandwidth=5, transmission=0.8)

# Initialize the circuit with the filters
circuit = Circuit(components=[filter1, filter2])
```

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

### Basic Photontorch Setup and Usage

Source: https://docs.photontorch.com/_sources/examples/00_introduction_to_photontorch.ipynb.txt

Demonstrates the initial setup and basic usage of Photontorch, including importing the library and creating a simple simulation.

```python
import photontorch as pt
import torch

# Create a simple simulation
sim = pt.Simulation()

```

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

### Install PhotonTorch

Source: https://docs.photontorch.com/_sources/examples/00_introduction_to_photontorch.ipynb.txt

Install PhotonTorch using pip. This is the first step to using the library.

```bash
pip install -U photontorch
```

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

### Ring Network Example

Source: https://docs.photontorch.com/_sources/examples/08_general_ring_networks.ipynb.txt

This snippet demonstrates a basic ring network setup. Ensure all necessary libraries are imported before use.

```python
import torch
import torch.nn as nn

class RingNetwork(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(RingNetwork, self).__init__()
        self.layer1 = nn.Linear(input_size, hidden_size)
        self.relu = nn.ReLU()
        self.layer2 = nn.Linear(hidden_size, hidden_size)
        self.output_layer = nn.Linear(hidden_size, output_size)

    def forward(self, x):
        x = self.relu(self.layer1(x))
        x = self.relu(self.layer2(x))
        x = self.output_layer(x)
        return x

# Example Usage:
input_size = 10
hidden_size = 20
output_size = 5

model = RingNetwork(input_size, hidden_size, output_size)
print(model)
```

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

### Install Photontorch (Development)

Source: https://docs.photontorch.com/_sources/index.rst.txt

Install the development version of Photontorch by cloning the repository and linking with pip. Includes instructions for installing git hooks.

```bash
git clone https://git.photontorch.com/photontorch.git
./install-git-hooks.sh # Unix [Linux/Mac/BSD/...]
install-git-hooks.bat  # Windows
pip install -e photontorch
```

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

### Basic Crow Optimization Setup

Source: https://docs.photontorch.com/_sources/examples/04_crow_optimization.ipynb.txt

This snippet shows the fundamental setup for a crow optimization algorithm. It includes necessary imports and the initialization of the optimizer.

```python
from crow_optimization import CrowOptimizer

# Initialize the optimizer with a function to minimize and bounds
optimizer = CrowOptimizer(lambda x: x[0]**2 + x[1]**2, bounds=[(-5, 5), (-5, 5)])
```

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

### Install Photon-Torch

Source: https://docs.photontorch.com/_sources/examples/00_introduction_to_photontorch.ipynb.txt

Install Photon-Torch using pip. This command installs the library and its dependencies.

```bash
pip install "git+https://github.com/facebookresearch/photontorch.git"
```

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

### Basic Crow Optimization Setup

Source: https://docs.photontorch.com/_sources/examples/04_crow_optimization.ipynb.txt

This snippet shows the basic setup for the Crow Optimization Algorithm, including necessary imports and initialization.

```python
from crow_optimization import CrowOptimization
import numpy as np

def objective_function(x):
    return np.sum(x**2)

# Define problem parameters
num_variables = 10
lower_bound = -5
upper_bound = 5
population_size = 30
max_iterations = 100

# Initialize the optimizer
optimizer = CrowOptimization(
    objective_function,
    num_variables,
    lower_bound,
    upper_bound,
    population_size,
    max_iterations
)
```

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

### Basic Crow Optimization Setup

Source: https://docs.photontorch.com/_sources/examples/04_crow_optimization.ipynb.txt

This snippet shows the basic setup for Crow Optimization, including initialization and core parameters. It's useful for understanding the fundamental structure of the algorithm.

```python
from crow_optimization import CrowOptimization

# Define the objective function to minimize
def objective_function(x):
    return sum(xi**2 for xi in x)

# Initialize Crow Optimization
optimizer = CrowOptimization(
    objective_function=objective_function,
    dimensions=2,
    lower_bound=-5,
    upper_bound=5,
    population_size=30,
    max_iterations=100,
    crow_memory_size=2
)

# Run the optimization
best_solution, best_fitness = optimizer.optimize()

print(f"Best solution found: {best_solution}")
print(f"Best fitness: {best_fitness}")
```

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

### Basic Crow Optimization Setup

Source: https://docs.photontorch.com/_sources/examples/04_crow_optimization.ipynb.txt

This snippet shows the basic setup for a crow optimization algorithm, including necessary imports and initial parameter definitions.

```python
import numpy as np
from crow_optimization import CrowOptimizer

def objective_function(x):
    return np.sum(x**2)

# Define problem parameters
num_variables = 10
lower_bound = -5
upper_bound = 5

# Initialize the optimizer
crow_optimizer = CrowOptimizer(objective_function, num_variables, lower_bound, upper_bound)
```

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

### Basic Crow Optimization Setup

Source: https://docs.photontorch.com/_sources/examples/04_crow_optimization.ipynb.txt

Initializes and configures the Crow optimization algorithm. This snippet is useful for understanding the basic setup and parameters.

```python
from crow_optimization import CrowOptimizer

# Define the objective function to minimize
def objective_function(x):
    return sum(xi**2 for xi in x)

# Set the dimensions of the problem
dimensions = 10

# Set the bounds for each dimension
lower_bound = -5
upper_bound = 5

# Initialize the CrowOptimizer
crow_optimizer = CrowOptimizer(objective_function, dimensions, lower_bound, upper_bound)

```

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

### Basic Crow Optimization Setup

Source: https://docs.photontorch.com/_sources/examples/04_crow_optimization.ipynb.txt

This snippet shows the basic setup for a crow optimization algorithm, including initialization and parameter settings. Ensure all necessary libraries are imported.

```python
import numpy as np
import matplotlib.pyplot as plt

from crow import CrowOptimizer

def sphere(x):
    return np.sum(x**2)


# Parameters
num_crow = 20
num_dim = 2
max_iter = 100

# Initialize optimizer
crow_optimizer = CrowOptimizer(num_crow, num_dim, sphere, max_iter)
crow_optimizer.optimize()
```

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

### Initialize Quantum Circuit and Parameters

Source: https://docs.photontorch.com/_sources/examples/03_circuit_optimization_by_backpropagation.ipynb.txt

Sets up a basic quantum circuit with trainable parameters. This is the starting point for optimization.

```python
import pennylane as qml
from pennylane import numpy as np

num_qubits = 2
qlayer_depth = 2

dev = qml.device("default.qubit", wires=num_qubits)

def qlayer(weights):
    for i in range(num_qubits):
        qml.RY(weights[i, 0], wires=i)
        qml.RX(weights[i, 1], wires=i)

    for i in range(qlayer_depth):
        for j in range(num_qubits - 1):
            qml.CNOT(wires=[j, j + 1])
        qml.CNOT(wires=[num_qubits - 1, 0])

    for i in range(num_qubits):
        qml.RY(weights[i, 2], wires=i)
        qml.RX(weights[i, 3], wires=i)

@qml.qnode(dev)
def circuit(weights):
    qlayer(weights)
    return qml.expval(qml.PauliZ(0) @ qml.PauliZ(1))


# Initialize weights randomly
np.random.seed(0)
weights = np.random.uniform(0, 2 * np.pi, (num_qubits, 4))

```

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

### Basic Crow Optimization Setup

Source: https://docs.photontorch.com/_sources/examples/04_crow_optimization.ipynb.txt

This snippet shows the basic setup for a crow optimization algorithm. It involves defining the objective function and initializing the crow population.

```python
import numpy as np

def objective_function(x):
    return np.sum(x**2)

# Parameters
num_crows = 30
max_iter = 100

# Initialize crow positions randomly within bounds
lower_bound = -5
upper_bound = 5
crow_position = np.random.uniform(lower_bound, upper_bound, (num_crows, 2)) # Assuming 2D problem
crow_memory = np.copy(crow_position)

# Initialize best position found so far
best_position = crow_position[np.argmin(np.apply_along_axis(objective_function, 1, crow_position))]

```

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

### Install Photontorch (Stable)

Source: https://docs.photontorch.com/_sources/index.rst.txt

Install the stable version of Photontorch using pip. This is the recommended method for most users.

```bash
pip install photontorch
```

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

### Basic Crow Optimization Setup

Source: https://docs.photontorch.com/_sources/examples/04_crow_optimization.ipynb.txt

This snippet shows the basic setup for a crow optimization algorithm, including defining the objective function and initializing parameters.

```python
from crow_optimization import CrowOptimization

def objective_function(x):
    return sum(xi**2 for xi in x)

# Initialize the optimizer
optimizer = CrowOptimization(objective_function, bounds=[(-5, 5)], num_crows=20, max_iter=100)

```

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

### Basic Crow Optimization Setup

Source: https://docs.photontorch.com/_sources/examples/04_crow_optimization.ipynb.txt

This snippet shows the basic setup for a crow optimization algorithm. It involves defining the objective function, crow population, and parameters.

```python
import numpy as np

def crow_optimization(objective_func, bounds, num_crows, max_iter, crow_memory_size):
    # Initialize crow positions randomly within bounds
    crow_positions = np.random.uniform(bounds[:, 0], bounds[:, 1], size=(num_crows, len(bounds)))
    crow_memory = np.copy(crow_positions)
    crow_food_pos = np.zeros_like(crow_positions)
    crow_food_num = np.zeros(num_crows, dtype=int)

    for t in range(max_iter):
        for i in range(num_crows):
            # Update crow's food position based on its own experience or a random crow's experience
            if np.random.rand() < 0.1:  # Probability of exploring
                crow_food_pos[i] = np.random.uniform(bounds[:, 0], bounds[:, 1])
            else:
                # Choose a random crow to learn from
                j = np.random.randint(num_crows)
                crow_food_pos[i] = crow_memory[j]

            # Calculate the step size
            step = np.random.rand() * (crow_memory[i] - crow_positions[i])
            new_position = crow_positions[i] + step

            # Apply bounds
            new_position = np.clip(new_position, bounds[:, 0], bounds[:, 1])

            # Evaluate the new position
            if objective_func(new_position) < objective_func(crow_positions[i]):
                crow_positions[i] = new_position
                crow_memory[i] = new_position

        # Update the best known position for each crow
        for i in range(num_crows):
            if objective_func(crow_memory[i]) < objective_func(crow_food_pos[i]):
                crow_food_num[i] += 1
            else:
                crow_memory[i] = crow_food_pos[i]
                crow_food_num[i] = 0

    # Find the best crow position overall
    best_crow_index = np.argmin([objective_func(m) for m in crow_memory])
    best_position = crow_memory[best_crow_index]
    return best_position, objective_func(best_position)

# Example usage:
def sphere_function(x):
    return np.sum(x**2)

# Define problem parameters
bounds = np.array([[-5, 5], [-5, 5]])  # Bounds for each dimension
num_crows = 20
max_iter = 100
crow_memory_size = 10 # This parameter is not used in the provided snippet but is part of the algorithm's concept

# Run the optimization
best_solution, best_fitness = crow_optimization(sphere_function, bounds, num_crows, max_iter, crow_memory_size)

print(f"Best solution found: {best_solution}")
print(f"Best fitness: {best_fitness}")
```

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

### Run Full Optical Readout Example

Source: https://docs.photontorch.com/_sources/examples/05_optical_readout.ipynb.txt

Executes the complete optical readout example pipeline, from loading to visualization. This is a convenience function for running the entire example.

```python
from photon_torch.examples.optical_readout import run_optical_readout_example

# Run the example with a placeholder file path
# Replace 'path/to/your/optical_readout_data.npy' with an actual file path
run_optical_readout_example("path/to/your/optical_readout_data.npy")
```

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

### Crow Optimization with Custom Parameters

Source: https://docs.photontorch.com/_sources/examples/04_crow_optimization.ipynb.txt

This example demonstrates how to configure the Crow Optimization algorithm with custom parameters such as population size and number of iterations. Ensure the `crow_optimization` library is installed.

```python
from crow_optimization import CrowOptimization

def another_function(x):
    return sum([xi**2 for xi in x])

custom_optimizer = CrowOptimization(another_function, bounds=[(-10, 10)], population_size=50, iterations=200, crow_memory_size=10, crow_step_size=0.1)
best_solution, best_fitness = custom_optimizer.optimize()

print(f"Custom best solution: {best_solution}")
print(f"Custom best fitness: {best_fitness}")
```

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

### Real-time Data Streaming Example

Source: https://docs.photontorch.com/_sources/examples/05_optical_readout.ipynb.txt

Demonstrates setting up the detector for real-time data streaming. This requires a compatible detector and sufficient bandwidth.

```python
from photon_readout import Detector

detector = Detector()
detector.initialize()

detector.start_streaming(callback=lambda frame: print(f"Received frame: {frame.shape}"))

# Keep the script running to receive frames
import time
try:
    while True:
        time.sleep(1)
except KeyboardInterrupt:
    detector.stop_streaming()
    print("Streaming stopped.")
```

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

### Basic Crow Optimization Setup

Source: https://docs.photontorch.com/_sources/examples/04_crow_optimization.ipynb.txt

This snippet shows the basic setup for the Crow optimization algorithm, including importing necessary libraries and defining the objective function.

```python
from crow_optimization import CrowOptimization
import numpy as np

def objective_function(x):
    return np.sum(x**2)

# Define problem parameters
num_variables = 10
lower_bound = -5
upper_bound = 5
population_size = 50
max_iterations = 100
```

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

### Basic Crow Optimization Setup

Source: https://docs.photontorch.com/_sources/examples/04_crow_optimization.ipynb.txt

This snippet shows the basic setup for the Crow optimization algorithm, including importing necessary libraries and defining the objective function.

```python
import numpy as np
from crow_optimization import CrowOptimization

def objective_function(x):
    return np.sum(x**2)

# Define problem parameters
num_variables = 10
lower_bound = -5
upper_bound = 5
population_size = 50
max_iterations = 100
```

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

### Ring Network Configuration Example

Source: https://docs.photontorch.com/_sources/examples/08_general_ring_networks.ipynb.txt

Demonstrates a basic configuration for a ring network, showing how ranks might be set up.

```python
import torch
import torch.distributed as dist

def setup_ring_network(rank, world_size, master_addr='localhost', master_port='29500'):
    # Initialize the distributed environment
    dist.init_process_group("gloo", rank=rank, world_size=world_size)
    print(f"Rank {rank}/{world_size} process group initialized.")

    # In a real ring, you'd establish neighbor connections here.
    # For simplicity, we'll just confirm initialization.
    print(f"Rank {rank} is ready for ring operations.")

# Example usage:
# if __name__ == "__main__":
#     world_size = 4
#     # This part would typically be run in separate processes
#     for r in range(world_size):
#         # In a real script, you'd launch these as separate processes
#         # For demonstration, imagine this is called for each rank
#         # setup_ring_network(r, world_size)
#         pass

```

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

### Basic Crow Optimization Setup

Source: https://docs.photontorch.com/_sources/examples/04_crow_optimization.ipynb.txt

Initializes the Crow optimization algorithm with basic parameters. Requires the 'crow_optimizer' library.

```python
from crow_optimizer import CrowOptimizer

# Define the objective function to minimize
def objective_function(x):
    return sum(xi**2 for xi in x)

# Initialize the optimizer
optimizer = CrowOptimizer(objective_function, bounds=[(-5, 5), (-5, 5)], n_crows=20, max_iter=100)

```

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

### Crow Optimization with All Custom Parameters Example 2

Source: https://docs.photontorch.com/_sources/examples/04_crow_optimization.ipynb.txt

A comprehensive example demonstrating the Crow Optimization Algorithm with a full set of custom parameters, including population size, dimensions, sizes, probabilities, and ranges.

```python
crow_population = crow_optimization.CrowPopulation(population_size=80, crow_dim=6, crow_memory_size=4, crow_flight_size=4, crow_speed_size=4, crow_flight_prob=0.18, crow_speed_prob=0.18, crow_memory_prob=0.18, crow_speed_range=[-1.4, 1.4], crow_flight_range=[-1.4, 1.4], crow_memory_range=[-1.4, 1.4])
crow_population.init_population()
crow_population.crow_optimization()
```

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

### Crow Optimization with All Custom Parameters Example

Source: https://docs.photontorch.com/_sources/examples/04_crow_optimization.ipynb.txt

A comprehensive example demonstrating the Crow Optimization Algorithm with a full set of custom parameters, including population size, dimensions, sizes, probabilities, and ranges.

```python
crow_population = crow_optimization.CrowPopulation(population_size=60, crow_dim=4, crow_memory_size=3, crow_flight_size=3, crow_speed_size=3, crow_flight_prob=0.15, crow_speed_prob=0.15, crow_memory_prob=0.15, crow_speed_range=[-1.3, 1.3], crow_flight_range=[-1.3, 1.3], crow_memory_range=[-1.3, 1.3])
crow_population.init_population()
crow_population.crow_optimization()
```

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

### Customizing Light Source

Source: https://docs.photontorch.com/_sources/examples/00_introduction_to_photontorch.ipynb.txt

Example of how to customize the light source's spectrum and intensity.

```python
import torch
import photontorch as pt

# Define a custom spectrum
custom_spectrum = pt.Spectrum.from_file("data/spectrum/led_blue.txt")

# Create a light source with custom spectrum and intensity
custom_light = pt.Light(spectrum=custom_spectrum, intensity=1000.0)

# Add to scene and simulate (assuming scene, detector are defined)
# scene.add(custom_light)
# result = scene.simulate()
```

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

### Get Detector Firmware Version

Source: https://docs.photontorch.com/_sources/examples/05_optical_readout.ipynb.txt

Retrieves the current firmware version installed on the detector.

```python
from photon_readout import Detector

detector = Detector()
firmware_version = detector.get_firmware_version()
print(f"Firmware version: {firmware_version}")
```

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

### Create a Simple Neural Network

Source: https://docs.photontorch.com/_sources/examples/00_introduction_to_photontorch.ipynb.txt

This example demonstrates how to define a simple feedforward neural network using PhotonTorch layers.

```python
class SimpleNet(pt.Module):
    def __init__(self):
        super().__init__()
        self.layer1 = pt.Linear(10, 5)
        self.layer2 = pt.Linear(5, 1)

    def forward(self, x):
        x = pt.relu(self.layer1(x))
        x = self.layer2(x)
        return x

net = SimpleNet()
print(net)
```

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

### Basic PhotonTorch Usage

Source: https://docs.photontorch.com/_sources/examples/00_introduction_to_photontorch.ipynb.txt

A simple example demonstrating the core usage of PhotonTorch for a basic simulation.

```python
import torch
import photontorch as pt

# Define a simple scene
scene = pt.Scene()

# Add a light source
light = pt.Light(spectrum=pt.Spectrum.from_file("data/spectrum/sun.txt"))
scene.add(light)

# Add a detector
detector = pt.Detector(position=[0, 0, 0], spectrum=pt.Spectrum.from_file("data/spectrum/vis.txt"))
scene.add(detector)

# Simulate
result = scene.simulate()

# Print the result
print(result)
```

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

### Network Creation with a With-Block

Source: https://docs.photontorch.com/networks.html

Shows an alternative method for creating a Network instance using a with-block for quick setup.

```APIDOC
## Network Creation with a With-Block

### Description
Quickly create a Network instance using a with-block, defining components and connections within the block.

### Example
```python
with pt.Network() as circuit:
    circuit.src = pt.Source()
    circuit.det = pt.Detector()
    circuit.wg = pt.Waveguide()
    circuit.link("src:0", "0:wg:1", "0:det")
```
```

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

### Multimode Simulation Setup

Source: https://docs.photontorch.com/_sources/examples/01_all_pass_filter.ipynb.txt

Demonstrates how to perform multimode simulations by specifying a range of wavelengths in the simulation environment. This is useful for analyzing device behavior across multiple frequencies.

```python
env = Environment(wl=np.linspace(1.55, 1.65, 100))
detected = nw(source=1, env=env)
nw.plot(detected)
```

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

### Display Data Example

Source: https://docs.photontorch.com/_sources/examples/04_crow_optimization.ipynb.txt

This snippet shows how to display data, likely for visualization purposes. It includes metadata for background.

```python
print(plt.figure(figsize=(6,4), dpi=72))
```

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

### Configure Simulation Parameters and Environment

Source: https://docs.photontorch.com/examples/05_optical_readout.html

Sets up simulation parameters such as bitrate, sampling time, and optical properties. Initializes the Photon-Torch environment for frequency domain calculations.

```python
cuda = torch.cuda.is_available()
bitrate = 50e9 # bps
dt = 1e-14 # new sampling timestep
samplerate = 1/dt # new sampling rate
angles = np.pi*np.array([0.5,0,-0.5,-0.5,-0.5,1,1]) # output angles of the output waveguides
power = 1e-3 #[W]
latencies = np.arange(0.01,2.5,0.1)
num_bits = 500

c = 299792458.0 #[m/s] speed of light
neff = 2.86 # effective index
ng = 3.0 # group index of waveguide
wl0 = 1.55e-6

# Set global environment
environment = pt.Environment(
    wl=np.linspace(1.549e-6,1.551e-6,10000),
    freqdomain=True,
)
pt.set_environment(environment);
pt.current_environment()

```

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

### Dynamic Quantization Example

Source: https://docs.photontorch.com/_sources/examples/01_all_pass_filter.ipynb.txt

Demonstrates applying dynamic quantization to a model in PhotonTorch, which quantizes weights post-training and activations dynamically during inference.

```python
from photon.quantization import quantize_dynamic

quantized_model = quantize_dynamic(model, dtype=photon.qint8)

```

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

### Create Simulation Environment and Source

Source: https://docs.photontorch.com/_sources/examples/00_introduction_to_photontorch.ipynb.txt

Sets up the simulation environment with specified time step, end time, and wavelength. Creates a time-dependent single-mode light source using a sine wave.

```python
env = pt.Environment(dt=1e-15, t1=2.5e-12, wl=1.55e-6)

single_source = torch.tensor(np.sin(env.time*5e13), dtype=torch.float32, names=["t"]) # lower dimensional source should have named dimensions.

```

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

### Initialize Quantum Circuit and Parameters

Source: https://docs.photontorch.com/_sources/examples/03_circuit_optimization_by_backpropagation.ipynb.txt

Sets up a basic quantum circuit with trainable parameters. This is the starting point for optimization, defining the structure of the circuit to be tuned.

```python
from pennylane import numpy as pnp

# Define the circuit structure
def circuit(params):
    qml.RX(params[0], wires=0)
    qml.RY(params[1], wires=1)
    qml.CNOT(wires=[0, 1])
    qml.RZ(params[2], wires=0)
    qml.CRX(params[3], wires=[0, 1])
    return qml.expval(qml.PauliZ(0) @ qml.PauliZ(1))
```

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

### Setting up an Optimizer (Adam)

Source: https://docs.photontorch.com/_sources/examples/00_introduction_to_photontorch.ipynb.txt

Demonstrates how to initialize an Adam optimizer. Adam is a popular optimization algorithm for training neural networks.

```python
model = pt.nn.Sequential(pt.nn.Linear(10, 2))
optimizer = pt.optim.Adam(model.parameters(), lr=0.001)
print(optimizer)
```

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

### Set Up Simulation Environment

Source: https://docs.photontorch.com/_sources/examples/04_crow_optimization.ipynb.txt

Define and set the simulation environment, including wavelength range and frequency domain settings. The environment contains global parameters for the simulation.

```python
# define the simulation environment:
env = pt.Environment(
    wavelength = 1e-6*np.linspace(1.50, 1.6, 1001), #[m]
    freqdomain=True, # we will be doing frequency domain simulations
)

# set the global simulation environment:
pt.set_environment(env)

# one can always get the current environment from photontorch:
pt.current_environment()
```

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

### Initialize Simulation Environment and Source

Source: https://docs.photontorch.com/examples/07_unitary_matrix_network_in_the_time_domain.html

Sets up the Photontorch simulation environment with specified time parameters, wavelength, and timestep. Defines the input source tensor.

```python
N = 4
length = 25e-6 #[m]
transmission = 0.5 #[]
neff = 2.86
env = pt.Environment(
    t_start = 0,
    t_end = 2000e-14,
    dt = 1e-13,
    wl = 1.55e-6,
)
pt.set_environment(env)

source = torch.ones(N, names=["s"])/np.sqrt(N) # Source tensors with less than 4D need to have named dimensions.

env

```

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

### Create and Add a Filter with Specific Parameters

Source: https://docs.photontorch.com/_sources/examples/02_add_drop_filter.ipynb.txt

This example illustrates creating a filter with custom transmission and wavelength parameters and adding it to a circuit.

```python
from photontorch.components import Filter

# Assuming 'circuit' is an existing Photontorch circuit object
custom_filter = Filter(wavelengths=[1310, 1550], transmission=[0.8, 0.95])
circuit.add_component(custom_filter)
```

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

### Initialize Simulation Environment and Source

Source: https://docs.photontorch.com/_sources/examples/07_unitary_matrix_network_in_the_time_domain.ipynb.txt

Sets up the simulation environment with parameters like time duration, timestep, and wavelength. Initializes a source tensor with specified dimensions and normalization.

```python
N = 4
length = 25e-6 #[m]
transmission = 0.5 #[]
neff = 2.86
env = pt.Environment(
    t_start = 0,
    t_end = 2000e-14,
    dt = 1e-13,
    wl = 1.55e-6,
)
pt.set_environment(env)

source = torch.ones(N, names=["s"])/np.sqrt(N) # Source tensors with less than 4D need to have named dimensions.

env
```

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

### PhotonTorch Optimizer Initialization

Source: https://docs.photontorch.com/_sources/examples/00_introduction_to_photontorch.ipynb.txt

Demonstrates initializing different optimizers available in PhotonTorch.

```python
import photontorch as pt

# Assume 'model_parameters' is a list of parameters from a PhotonTorch module
model_parameters = [pt.Parameter(pt.randn(2, 2))]

# SGD Optimizer
optimizer_sgd = pt.optim.SGD(model_parameters, lr=0.01)
print(f"SGD Optimizer: {optimizer_sgd}")

# Adam Optimizer
optimizer_adam = pt.optim.Adam(model_parameters, lr=0.001)
print(f"Adam Optimizer: {optimizer_adam}")

# RMSprop Optimizer
optimizer_rmsprop = pt.optim.RMSprop(model_parameters, lr=0.01)
print(f"RMSprop Optimizer: {optimizer_rmsprop}")
```

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

### Set Up Training Environment for Single Wavelength Simulation

Source: https://docs.photontorch.com/_sources/examples/04_crow_optimization.ipynb.txt

Creates a Photonic Integrated Circuit (PIC) environment for frequency-domain simulations at a specific wavelength (1.55e-6 m). Gradient tracking is enabled for optimization purposes.

```python
train_env = pt.Environment(
    wl=1.55e-6, #[m]
    freqdomain=True, # we will be doing frequency domain simulations

```

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

### Initialize Quantum Circuit and Parameters

Source: https://docs.photontorch.com/_sources/examples/03_circuit_optimization_by_backpropagation.ipynb.txt

Sets up a basic quantum circuit with trainable parameters. This is the starting point for optimization, defining the structure of the quantum computation.

```python
import pennylane as qml
from pennylane import numpy as np

num_qubits = 2
qlayer_depth = 2

# Define the quantum device
dev = qml.device("default.qubit", wires=num_qubits)

# Define the quantum circuit layer
def qlayer(weights):
    for i in range(num_qubits):
        qml.RY(weights[i, 0], wires=i)
        qml.RX(weights[i, 1], wires=i)

    for i in range(num_qubits):
        qml.CNOT(wires=[i, (i + 1) % num_qubits])

    for i in range(num_qubits):
        qml.RY(weights[i, 2], wires=i)
        qml.RX(weights[i, 3], wires=i)

# Initialize trainable weights
np.random.seed(42)
weights_init = np.random.uniform(0, 2 * np.pi, (num_qubits, 4))

```

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

### Initialize Quantum Circuit and Parameters

Source: https://docs.photontorch.com/_sources/examples/03_circuit_optimization_by_backpropagation.ipynb.txt

Sets up a basic quantum circuit with trainable parameters. This is the starting point for optimization, defining the structure of the quantum computation.

```python
from pennylane import numpy as np
import pennylane as qml

num_wires = 2
qlayer_depth = 2

# Define the quantum device
dev = qml.device("default.qubit", wires=num_wires)

# Define the quantum circuit (ansatz)
def qnode_layer(weights, wires):
    for i in range(len(wires)):
        qml.RY(weights[0, i], wires=wires[i])
    for l in range(1, qlayer_depth):
        for i in range(num_wires):
            qml.RY(weights[l, i], wires=wires[i])
        for i in range(num_wires - 1):
            qml.CNOT(wires=[wires[i], wires[i + 1]])

# Define the quantum circuit with measurement
@qml.qnode(dev)
def circuit(weights):
    qnode_layer(weights, wires=range(num_wires))
    return qml.expval(qml.PauliZ(0) @ qml.PauliZ(1))

# Initialize trainable parameters
param_shape = (qlayer_depth, num_wires)
weights = np.random.uniform(0, 2 * np.pi, size=param_shape)

```

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

### Unitary Matrix Network Example

Source: https://docs.photontorch.com/_sources/examples/07_unitary_matrix_network_in_the_time_domain.ipynb.txt

This example constructs and uses a unitary matrix network. It initializes layers and passes data through the network, demonstrating the sequential application of unitary transformations.

```python
import torch
import torch.nn as nn

# Assuming UnitaryNetwork and UnitaryLayer classes are defined as above

# Example: Create a network with two 2x2 unitary layers
layer1 = UnitaryLayer(size=2, angle=torch.pi/4)
layer2 = UnitaryLayer(size=2, angle=torch.pi/2)

network = UnitaryNetwork([layer1, layer2])

# Create a sample input vector
input_vector = torch.tensor([[1.0, 0.0]]) # Example input for a 2D system

# Pass the input through the network
output_vector = network(input_vector)

print("Input:", input_vector)
print("Output:", output_vector)

# Verify unitarity (optional, requires specific checks based on layer implementation)
# For example, check if U @ U.T.conj() is close to identity matrix
```

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

### Basic Crow Optimization Setup

Source: https://docs.photontorch.com/_sources/examples/04_crow_optimization.ipynb.txt

Initializes the crow population and sets up the optimization problem. Requires defining the objective function and search space.

```python
from crow_optimization import CrowOptimization

def objective_function(x):
    return sum(xi**2 for xi in x)

# Define search space bounds (e.g., for a 2D problem)
lower_bound = [-5, -5]
upper_bound = [5, 5]

# Initialize the optimizer
crow_optimizer = CrowOptimization(objective_function, lower_bound, upper_bound)

```

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

### Crow Optimization Algorithm - Rastrigin Function Example

Source: https://docs.photontorch.com/_sources/examples/04_crow_optimization.ipynb.txt

Applies the Crow Optimization Algorithm to the Rastrigin function, a non-convex multimodal function. This example demonstrates the algorithm's capability to escape local optima and find the global minimum.

```python
import math
import random

# Assuming CrowOptimization class is defined as above

def rastrigin_function(position):
    A = 10
    return A * len(position) + sum([(x**2 - A * math.cos(2 * math.pi * x)) for x in position])

# Example usage:
crow_opt_rastrigin = CrowOptimization(population_size=100, max_iter=200, crow_memory_size=5, crow_flight_length=5, crow_awareness_probability=0.2)
bounds_rastrigin = [(-5.12, 5.12)] * 10
best_position_rastrigin, best_fitness_rastrigin = crow_opt_rastrigin.optimize(rastrigin_function, bounds=bounds_rastrigin)

print(f"Rastrigin Function - Best Position: {best_position_rastrigin}")
print(f"Rastrigin Function - Best Fitness: {best_fitness_rastrigin}")
```

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

### Static Quantization Example

Source: https://docs.photontorch.com/_sources/examples/01_all_pass_filter.ipynb.txt

Shows how to apply static quantization to a model, which requires calibration data to determine quantization parameters for weights and activations.

```python
from photon.quantization import quantize_static

# Requires a calibration data loader
# calibrated_model = quantize_static(model, calibration_loader, dtype=photon.qint8)

```

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

### Initialize Quantum Circuit and Parameters

Source: https://docs.photontorch.com/_sources/examples/03_circuit_optimization_by_backpropagation.ipynb.txt

Sets up a basic quantum circuit with trainable parameters. This is a common starting point for variational quantum algorithms.

```python
import pennylane as qml
from pennylane import numpy as np

num_qubits = 2
qlayer_depth = 2

# Define the quantum device
dev = qml.device("default.qubit", wires=num_qubits)

# Define the quantum circuit layer
def qlayer(weights):
    for k in range(num_qubits):
        qml.RY(weights[k, 0], wires=k)
        qml.RZ(weights[k, 1], wires=k, آموزش=False)

    for k in range(num_qubits):
        qml.CNOT(wires=[k, (k + 1) % num_qubits])

    for k in range(num_qubits):
        qml.RY(weights[k, 2], wires=k)
        qml.RZ(weights[k, 3], wires=k, آموزش=False)

    for k in range(num_qubits):
        qml.CNOT(wires=[k, (k + 1) % num_qubits])

# Define the quantum circuit
@qml.qnode(dev)
def circuit(weights):
    qlayer(weights)
    return qml.expval(qml.PauliZ(0) @ qml.PauliZ(1))

# Initialize trainable parameters
param_shape = (num_qubits, 4)
weights = np.random.uniform(0, 2 * np.pi, size=param_shape)

print(f"Initial circuit output: {circuit(weights)}")
```

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

### Crow Optimization Algorithm - Sphere Function Example

Source: https://docs.photontorch.com/_sources/examples/04_crow_optimization.ipynb.txt

Applies the Crow Optimization Algorithm to the Sphere function, a common benchmark function in optimization. This example shows the algorithm's ability to find the global minimum of a simple convex function.

```python
import math

class CrowOptimization:
    def __init__(self, population_size=50, max_iter=100, crow_memory_size=2, crow_flight_length=2, crow_awareness_probability=0.1):
        self.population_size = population_size
        self.max_iter = max_iter
        self.crow_memory_size = crow_memory_size
        self.crow_flight_length = crow_flight_length
        self.crow_awareness_probability = crow_awareness_probability

    def optimize(self, objective_function, bounds):
        num_dimensions = len(bounds)
        # Initialize crows' positions and memories
        crow_positions = [[random.uniform(bounds[d][0], bounds[d][1]) for d in range(num_dimensions)] for _ in range(self.population_size)]
        crow_memories = [list(pos) for pos in crow_positions]
        crow_fitness = [objective_function(pos) for pos in crow_positions]
        crow_memory_fitness = list(crow_fitness)

        for _ in range(self.max_iter):
            for i in range(self.population_size):
                # Choose a crow to steal from
                j = random.randint(0, self.population_size - 1)
                while i == j:
                    j = random.randint(0, self.population_size - 1)

                # Update crow's position
                if random.random() > self.crow_awareness_probability:
                    # Fly towards the crow's memory
                    new_position = [crow_positions[i][d] + random.uniform(-self.crow_flight_length, self.crow_flight_length) * (crow_memories[j][d] - crow_positions[i][d]) for d in range(num_dimensions)]
                else:
                    # Fly randomly
                    new_position = [random.uniform(bounds[d][0], bounds[d][1]) for d in range(num_dimensions)]

                # Ensure new position is within bounds
                new_position = [max(bounds[d][0], min(bounds[d][1], new_position[d])) for d in range(num_dimensions)]

                # Evaluate new position
                new_fitness = objective_function(new_position)

                # Update crow's position and memory if better
                if new_fitness < crow_fitness[i]:
                    crow_positions[i] = new_position
                    crow_fitness[i] = new_fitness
                    if new_fitness < crow_memory_fitness[i]:
                        crow_memories[i] = new_position
                        crow_memory_fitness[i] = new_fitness

        # Find the best overall position and fitness
        best_fitness_idx = crow_memory_fitness.index(min(crow_memory_fitness))
        best_position = crow_memories[best_fitness_idx]
        best_fitness = crow_memory_fitness[best_fitness_idx]

        return best_position, best_fitness

import random

def sphere_function(position):
    return sum(x**2 for x in position)

# Example usage:
crow_opt_sphere = CrowOptimization(population_size=50, max_iter=100, crow_memory_size=2, crow_flight_length=2, crow_awareness_probability=0.1)
bounds_sphere = [(-5, 5)] * 10
best_position_sphere, best_fitness_sphere = crow_opt_sphere.optimize(sphere_function, bounds=bounds_sphere)

print(f"Sphere Function - Best Position: {best_position_sphere}")
print(f"Sphere Function - Best Fitness: {best_fitness_sphere}")
```

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

### Get Gain

Source: https://docs.photontorch.com/_sources/examples/05_optical_readout.ipynb.txt

Retrieves the current gain setting of the detector.

```python
from photon_readout import Detector

detector = Detector()
current_gain = detector.get_gain()
print(f"Current gain: {current_gain}")
```

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

### Initialize Photontorch Environment

Source: https://docs.photontorch.com/examples/08_general_ring_networks.html

Sets up the simulation environment with a specified time step and duration, and defines the wavelength range for the simulation.

```python
env = pt.Environment(
    dt = 1e-14,
    t_end=2000e-14,
    wl=1e-6*np.linspace(1.5, 1.6, 1000),
)
env
```

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

### Get Detector Configuration

Source: https://docs.photontorch.com/_sources/examples/05_optical_readout.ipynb.txt

Retrieves the current configuration settings of the detector.

```python
from photon_readout import Detector

detector = Detector()
config = detector.get_configuration()
print(config)
```

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

### Network Initialization

Source: https://docs.photontorch.com/networks.html

Demonstrates how to initialize a Network by subclassing it, defining components, and establishing connections.

```APIDOC
## Network Initialization

### Description
Initialize a Network by subclassing it and defining its components and connections.

### Method
`__init__`

### Parameters
* **components** (dict) - A dictionary containing the components of the network. Keys are strings representing new names for the components, and values are Component objects.
* **connections** (list) - A list of strings representing the connections between component ports. Connections can be specified as 'comp1:port1:comp2:port2' or 'comp:port:output_port'.
* **name** (str, optional) - The name of the network.
```

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

### Get Detector Model

Source: https://docs.photontorch.com/_sources/examples/05_optical_readout.ipynb.txt

Retrieves the model name or identifier of the detector.

```python
from photon_readout import Detector

detector = Detector()
model = detector.get_model()
print(f"Detector model: {model}")
```

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

### Get Readout Mode

Source: https://docs.photontorch.com/_sources/examples/05_optical_readout.ipynb.txt

Retrieves the current readout mode of the detector.

```python
from photon_readout import Detector

detector = Detector()
current_mode = detector.get_readout_mode()
print(f"Current readout mode: {current_mode}")
```

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

### Get ROI Status

Source: https://docs.photontorch.com/_sources/examples/05_optical_readout.ipynb.txt

Retrieves the current region of interest configuration.

```python
from photon_readout import Detector

detector = Detector()
roi_config = detector.get_roi_status()
print(roi_config)
```

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

### Get Trigger Status

Source: https://docs.photontorch.com/_sources/examples/05_optical_readout.ipynb.txt

Retrieves the current status of the trigger system.

```python
from photon_readout import Detector

detector = Detector()
trigger_status = detector.get_trigger_status()
print(trigger_status)
```

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

### Rendering a Simple Scene with PhotonTorch

Source: https://docs.photontorch.com/_sources/examples/00_introduction_to_photontorch.ipynb.txt

Illustrates how to set up a basic scene, camera, and render it using PhotonTorch. Requires defining geometry, materials, and lights.

```python
import photontorch as pt

# Define scene parameters
width, height = 256, 256

# Create a camera
camera = pt.Camera(width, height)

# Create a scene
scene = pt.Scene()

# Add geometry (e.g., a sphere)
sphere = pt.Sphere(center=[0, 0, -5], radius=1)
scene.add_geometry(sphere)

# Add a light source
light = pt.Light(position=[5, 5, 5], intensity=1.0)
scene.add_light(light)

# Render the scene
image = pt.render(scene, camera)

# Display or save the image (implementation depends on backend)
# For example, using matplotlib:
# import matplotlib.pyplot as plt
# plt.imshow(image.numpy())
# plt.show()
```

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

### Get Detector Temperature

Source: https://docs.photontorch.com/_sources/examples/05_optical_readout.ipynb.txt

Retrieves the current operating temperature of the detector.

```python
from photon_readout import Detector

detector = Detector()
current_temp = detector.get_temperature()
print(f"Current temperature: {current_temp}°C")
```

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

### Create a Simple Scene

Source: https://docs.photontorch.com/_sources/examples/00_introduction_to_photontorch.ipynb.txt

Demonstrates how to create a basic scene in PhotonTorch, which is the container for all physical objects and simulation parameters.

```python
scene = pt.Scene()
scene.add_object(pt.Sphere(radius=1.0, center=[0, 0, 0]))
```

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

### photontorch.components.waveguides.Waveguide

Source: https://docs.photontorch.com/genindex.html

Represents a waveguide component. Used for guiding optical signals.

```APIDOC
## Waveguide (class in photontorch.components.waveguides)

### Description
Represents a waveguide component. Used for guiding optical signals.
```

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

### Get Detector Connection Status

Source: https://docs.photontorch.com/_sources/examples/05_optical_readout.ipynb.txt

Checks if the detector is currently connected and responsive.

```python
from photon_readout import Detector

detector = Detector()
is_connected = detector.is_connected()
print(f"Detector connected: {is_connected}")
```

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

### Frequency Domain Simulation Setup in Photontorch

Source: https://docs.photontorch.com/_sources/examples/01_all_pass_filter.ipynb.txt

Sets up a frequency domain simulation using a context manager. This method is faster for frequency response calculation as it bypasses internal delays.

```python
with nw.SimEnv(frequency_domain=True) as env:
    detected = env.detect()
    print(f"This was detected inside the context manager:")
    print(detected)
```

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

### Get Detector Serial Number

Source: https://docs.photontorch.com/_sources/examples/05_optical_readout.ipynb.txt

Retrieves the unique serial number of the detector.

```python
from photon_readout import Detector

detector = Detector()
serial_number = detector.get_serial_number()
print(f"Detector serial number: {serial_number}")
```

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

### Initialize Photon-Torch and MZI

Source: https://docs.photontorch.com/_sources/examples/09_XOR_task_with_MZI.ipynb.txt

Imports necessary libraries and initializes the MZI component for the XOR task.

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

from photon_torch.components.mzi import MZI
from photon_torch.simulation import Simulation
from photon_torch.sources import GaussianSource
from photon_torch.detector import Detector

# Initialize MZI
mzi = MZI(phi=torch.tensor([0.0]), phase_step=torch.tensor([torch.pi / 2.0]))
```

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

### Get Pixel Value

Source: https://docs.photontorch.com/_sources/examples/05_optical_readout.ipynb.txt

Retrieves the value of a specific pixel in the acquired image.

```python
from photon_readout import Detector

detector = Detector()
image = detector.readout()

# Get value of pixel at (50, 75)
pixel_value = detector.get_pixel_value(image, x=50, y=75)
```

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

### Get Image Dimensions

Source: https://docs.photontorch.com/_sources/examples/05_optical_readout.ipynb.txt

Retrieves the dimensions (width and height) of the acquired image.

```python
from photon_readout import Detector

detector = Detector()
image = detector.readout()

width, height = detector.get_image_dimensions(image)
```

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

### PhotonTorch Environment Setup

Source: https://docs.photontorch.com/examples/06_unitary_matrix_network_in_the_frequency_domain.html

Configures the simulation environment for frequency-domain calculations with gradient tracking. Ensures reproducibility by setting random seeds.

```python
DEVICE = 'cpu'
np.random.seed(0)
torch.manual_seed(0)
np.set_printoptions(precision=2, suppress=True)
env = pt.Environment(freqdomain=True, num_t=1, grad=True)
pt.set_environment(env);
pt.current_environment()
```

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

### Basic Simulation Environment

Source: https://docs.photontorch.com/_sources/examples/01_all_pass_filter.ipynb.txt

Sets up a default simulation environment and detects a network. Use this for basic simulations when no specific environment parameters are needed.

```python
detected = nw(source=1)
nw.plot(detected)
plt.show()
```

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

### Get Exposure Time

Source: https://docs.photontorch.com/_sources/examples/05_optical_readout.ipynb.txt

Retrieves the current exposure time setting of the detector.

```python
from photon_readout import Detector

detector = Detector()
current_exposure = detector.get_exposure_time()
print(f"Current exposure time: {current_exposure}s")
```

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

### Histogram Example

Source: https://docs.photontorch.com/_sources/examples/05_optical_readout.ipynb.txt

Shows how to generate a histogram to visualize the distribution of a dataset. Requires Matplotlib.

```python
import matplotlib.pyplot as plt
import numpy as np

data = np.random.randn(1000)

plt.hist(data, bins=30, color='skyblue', edgecolor='black')
plt.xlabel("Value")
plt.ylabel("Frequency")
plt.title("Histogram of Random Data")
plt.show()

```

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

### Set up the Quantum Device and Cost Function

Source: https://docs.photontorch.com/_sources/examples/03_circuit_optimization_by_backpropagation.ipynb.txt

Initialize the quantum device and define the cost function. The cost function measures the performance of the circuit and is what we aim to minimize.

```python
dev = qml.device("default.qubit", wires=2)

@qml.qnode(dev)
def cost_fn(params):
    return circuit(params, wires=[0, 1])
```

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

### Get Current Network Context

Source: https://docs.photontorch.com/networks.html

Retrieves the network object that is currently being defined or is in context.

```python
photontorch.networks.network.current_network()
```

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

### Visualize Optical Data

Source: https://docs.photontorch.com/_sources/examples/05_optical_readout.ipynb.txt

Visualizes optical data for better understanding. Requires matplotlib to be installed.

```python
from photonorch.visualization import visualize_optical_data

visualize_optical_data(
    optical_data,
    "/mnt/data/visualization_config.json"
)
```

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

### Basic Plotting with Matplotlib

Source: https://docs.photontorch.com/_sources/examples/05_optical_readout.ipynb.txt

Demonstrates a basic plot using Matplotlib. Ensure Matplotlib is installed.

```python
import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 10, 100)
y = np.sin(x)

plt.plot(x, y)
plt.xlabel("X-axis")
plt.ylabel("Y-axis")
plt.title("Simple Sine Wave Plot")
plt.show()

```

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

### Import Libraries

Source: https://docs.photontorch.com/examples/02_add_drop_filter.html

Imports necessary libraries for simulation and plotting. Ensure these are installed before running the code.

```python
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import photontorch as pt
```

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

### Parameter Sweeps and Optimization

Source: https://docs.photontorch.com/_sources/examples/00_introduction_to_photontorch.ipynb.txt

Demonstrates how to perform parameter sweeps and optimization routines within Photontorch to find optimal system configurations.

```python
# Perform a parameter sweep
results = pt.optimize(sim, parameters={'focal_length': [0.05, 0.1, 0.2]})

```

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

### Configure Photon-Torch Environment

Source: https://docs.photontorch.com/_sources/examples/06_unitary_matrix_network_in_the_frequency_domain.ipynb.txt

Sets up the simulation environment for frequency-domain calculations with gradient tracking enabled. This configuration is crucial for subsequent photonic simulations and model training.

```python
%matplotlib inline
DEVICE = 'cpu'
np.random.seed(0)
torch.manual_seed(0)
np.set_printoptions(precision=2, suppress=True)
env = pt.Environment(freqdomain=True, num_t=1, grad=True)
pt.set_environment(env);
pt.current_environment()
```