### Install Photontorch Development Version

Source: https://github.com/flaport/photontorch/blob/master/README.md

Clone the repository and install the development version of Photontorch using pip. Includes instructions for 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
```

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

### Setup Training Environment and Optimizer

Source: https://github.com/flaport/photontorch/blob/master/examples/00_introduction_to_photontorch.ipynb

Configure the simulation environment for frequency domain simulations with gradient tracking enabled. Initialize the Adam optimizer for circuit parameters.

```python
# define simualation environment
train_env = pt.Environment(
    wl=1525e-9, # we want minimal transmission at this wavelength
    freqdomain=True, # we will do frequency domain simulations
    grad=True, # gradient need to be tracked in order to use gradient descent.
)

# define target for the simulation, lets take 0 (ideally no transmission at resonance)
target = 0

# let's define an optimizer.
# The Adam optimizer is generally considered to be the best
# gradient descent optimizer out there:
optimizer = torch.optim.Adam(circuit.parameters(), lr=0.1)
```

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

### Install Photontorch Stable Version

Source: https://github.com/flaport/photontorch/blob/master/README.md

Install the latest stable version of Photontorch using pip.

```bash
pip install photontorch
```

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

### Initialize Environment and Source

Source: https://github.com/flaport/photontorch/blob/master/examples/07_unitary_matrix_network_in_the_time_domain.ipynb

Sets up the simulation environment with parameters like time start, end, step, and wavelength. Defines the input source tensor, ensuring it has named dimensions if less than 4D.

```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
```

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

### Import Libraries and Setup

Source: https://github.com/flaport/photontorch/blob/master/examples/07_unitary_matrix_network_in_the_time_domain.ipynb

Imports necessary libraries including Photontorch, PyTorch, NumPy, Matplotlib, and tqdm. Configures NumPy for better array printing and sets a random seed for reproducibility.

```python
# Photontorch
import photontorch as pt

# Python
import torch
import numpy as np
import matplotlib.pyplot as plt
from tqdm.notebook import trange

# numpy settings
np.random.seed(6) # seed for random numbers
np.set_printoptions(precision=2, suppress=True) # show less numbers while printing numpy arrays
```

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

### Initialize and Inspect Readout Network

Source: https://github.com/flaport/photontorch/blob/master/examples/05_optical_readout.ipynb

Initializes a Readout network with 4 weights and prints the initial weights. This demonstrates the setup before any training.

```python
nw = Readout(4)
with pt.Environment(wl=wl0, freqdomain=True):
    print('weights:')
    print(' '.join(['%.2f'%w for w in nw.allpasses(source=1)[-1,0,:,0]]))

nw.plot(nw(source=1))
plt.xticks([1549,wl0*1e9,1551], [1549,'$\\lambda_0$',1551]); plt.yticks([0.1,1])
plt.grid(True)
plt.show()
```

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

### Instantiate AllPass Network

Source: https://github.com/flaport/photontorch/blob/master/examples/04_crow_optimization.ipynb

Creates an instance of the custom AllPass Photontorch network. No specific setup is required beyond defining the class.

```python
allpass = AllPass()
```

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

### Instantiate AllPass Network

Source: https://github.com/flaport/photontorch/blob/master/examples/01_all_pass_filter.ipynb

Creates an instance of the `AllPass` network defined previously. This prepares the network for simulation setup.

```python
nw = AllPass()
```

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

### Import Libraries for Photontorch Simulation

Source: https://github.com/flaport/photontorch/blob/master/examples/04_crow_optimization.ipynb

Imports essential libraries including numpy, matplotlib, tqdm, torch, and photontorch. Ensure photontorch is installed via pip.

```python
%matplotlib inline

import numpy as np
import matplotlib.pyplot as plt

from tqdm.notebook import tqdm # [pip install tqdm]
import torch # [conda install pytorch -c pytorch, only python 3!]
import photontorch as pt # [pip install photontorch] my simulation/optimization library
```

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

### Import Libraries for PhotonTorch Simulation

Source: https://github.com/flaport/photontorch/blob/master/examples/03_circuit_optimization_by_backpropagation.ipynb

Imports necessary libraries including NumPy, Matplotlib, PyTorch, PhotonTorch, and tqdm for progress bars. Ensure these libraries are installed before running.

```python
# Python
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt

# Torch
import torch

# PhotonTorch
import photontorch as pt

# Progress Bars
from tqdm.notebook import tqdm
```

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

### Configure Simulation Environment for Optimization

Source: https://github.com/flaport/photontorch/blob/master/examples/04_crow_optimization.ipynb

Defines the simulation environment for frequency domain analysis with gradient computation enabled. This setup is crucial for optimizing the CROW parameters.

```python
train_env = pt.Environment(
    wavelength = 1e-6*np.linspace(1.53, 1.58, 1001), #[m]
    freqdomain=True, # we will be doing frequency domain simulations
    grad=True, # we need to enable gradients to be able to optimize
)
```

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

### Import Libraries and Set Seeds

Source: https://github.com/flaport/photontorch/blob/master/examples/08_general_ring_networks.ipynb

Imports necessary libraries and sets random seeds for reproducibility. Ensure all required libraries are installed.

```python
import torch
import numpy as np
import matplotlib.pyplot as plt
import photontorch as pt
from tqdm.notebook import trange

torch.manual_seed(42)
np.random.seed(33)
```

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

### Define Photontorch Networks

Source: https://context7.com/flaport/photontorch/llms.txt

Create custom photonic circuits by subclassing pt.Network, defining components, and linking their ports. This example shows an AllPass filter and a complete circuit with source and detector.

```python
import photontorch as pt

# Define an all-pass filter network
class AllPass(pt.Network):
    def __init__(self, length=1e-5, neff=2.34, ng=3.4, loss=1000):
        super(AllPass, self).__init__()
        self.wg = pt.Waveguide(length=length, neff=neff, ng=ng, loss=loss, trainable=True)
        self.dc = pt.DirectionalCoupler(coupling=0.3, trainable=True)
        # Link port 2 of dc -> port 0 of wg -> port 1 of wg -> port 3 of dc
        self.link("dc:2", "0:wg:1", "3:dc")

# Define a complete circuit with source and detector
class Circuit(pt.Network):
    def __init__(self, length=1e-5, neff=2.34, ng=3.4, loss=1000):
        super(Circuit, self).__init__()
        self.allpass = AllPass(length, neff, ng, loss)
        self.source = pt.Source()
        self.detector = pt.Detector()
        # Link source -> allpass filter -> detector
        self.link("source:0", "0:allpass:1", "0:detector")

# Create circuit instance
circuit = Circuit(length=1.2e-5, neff=2.84, ng=3.2, loss=3e4)
```

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

### Set Up and Run Photontorch Simulation Environment

Source: https://github.com/flaport/photontorch/blob/master/examples/01_all_pass_filter.ipynb

Configures the simulation environment with specific parameters like wavelength and time steps, then sets this environment globally. Finally, it runs the simulation for the instantiated network.

```python
# create environment
environment = pt.Environment(
    wl=np.mean(wavelengths),
    t=time,
)

# set environment
pt.set_environment(environment)

# run simulation
detected = nw(source=1)
```

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

### Initialize Photontorch Environment

Source: https://github.com/flaport/photontorch/blob/master/examples/06_unitary_matrix_network_in_the_frequency_domain.ipynb

Sets up the matplotlib backend, device, random seeds, and initializes the photontorch environment for frequency domain computations with gradient enabled.

```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()
```

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

### Get Current Environment in PhotonTorch

Source: https://github.com/flaport/photontorch/blob/master/examples/04_crow_optimization.ipynb

Retrieves the current simulation environment object from PhotonTorch. This object contains global simulation parameters.

```python
pt.current_environment()
```

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

### Configure Photontorch Environments

Source: https://context7.com/flaport/photontorch/llms.txt

Set up simulation parameters like wavelength, time steps, and gradient tracking. Environments are immutable after creation and can be used as context managers or set globally.

```python
import photontorch as pt
import numpy as np

# Create a frequency domain environment for wavelength sweep
freq_env = pt.Environment(
    wl=1e-6 * np.linspace(1.45, 1.65, 1000),  # Wavelength array in meters
    freqdomain=True  # Enable frequency domain simulation
)

# Create a time domain environment
time_env = pt.Environment(
    dt=1e-14,           # Time step in seconds
    t1=2.5e-12,         # End time in seconds
    wl=1.55e-6,         # Single wavelength
    bitrate=40e9,       # Bit rate for signal processing
    samplerate=160e9    # Sample rate
)

# Create environment with gradient tracking for training
train_env = pt.Environment(
    wl=1.55e-6,
    freqdomain=True,
    grad=True  # Enable gradient tracking for optimization
)

# Use environment as context manager
with freq_env:
    # Simulation code here
    pass

# Or set environment globally
pt.set_environment(time_env)
```

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

### Initialize Adam Optimizer using .parameters()

Source: https://github.com/flaport/photontorch/blob/master/examples/04_crow_optimization.ipynb

Demonstrates an alternative way to initialize the Adam optimizer by using the `.parameters()` method to include all optimizable parameters of the network.

```python
optimizer = torch.optim.Adam(allpass.parameters(), lr=0.03)
```

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

### Set Up Photontorch Simulation Environment

Source: https://github.com/flaport/photontorch/blob/master/examples/04_crow_optimization.ipynb

Configures the Photontorch simulation environment for frequency domain analysis. This includes setting the wavelength range and enabling frequency domain 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)
```

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

### Configure PhotonTorch Environment

Source: https://github.com/flaport/photontorch/blob/master/examples/08_general_ring_networks.ipynb

Sets up the simulation environment with specific time step, duration, and wavelength range. This configuration is crucial for accurate simulations.

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

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

### Configure Simulation Environment

Source: https://github.com/flaport/photontorch/blob/master/examples/05_optical_readout.ipynb

Sets up simulation parameters including CUDA availability, bitrate, time step, sampling rate, output angles, power, latencies, and number of bits. Initializes the photontorch environment with a specified wavelength range and sets it as the global environment.

```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()
```

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

### Frequency Domain Simulation with Context Manager

Source: https://github.com/flaport/photontorch/blob/master/examples/01_all_pass_filter.ipynb

Sets up and runs a frequency domain simulation using a context manager. This ensures the environment is properly closed after use. The `freqdomain=True` flag is used.

```python
# create simulation environment
with pt.Environment(wl=wavelengths, freqdomain=True) as env:
    detected = nw(source=1)
    print("This was detected inside the context manager:\n""We see an exact copy of the analytically predicted response, as is to be expected")
    nw.plot(detected, label="simulation")
    plt.plot(env.wavelength*1e9, detected_target, linestyle="dotted", linewidth=3, label="analytical")
    plt.legend()
    plt.show()
```

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

### Create a Photontorch Waveguide

Source: https://context7.com/flaport/photontorch/llms.txt

Instantiate a Waveguide component with physical parameters like length, loss, effective index, and group index. The phase parameter can be made trainable.

```python
import photontorch as pt

# Create a waveguide with physical parameters
waveguide = pt.Waveguide(
    length=1e-5,      # Length in meters
    loss=0,           # Loss in dB/m
    neff=2.34,        # Effective index
    wl0=1.55e-6,      # Center wavelength for neff definition
    ng=3.40,          # Group index
    phase=0,          # Additional phase correction
    trainable=True    # Make phase parameter trainable
)

# Port configuration:
#   0 ---- 1
```

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

### Interactive CROW Plotting with ipywidgets

Source: https://github.com/flaport/photontorch/blob/master/examples/04_crow_optimization.ipynb

Provides an interactive way to visualize CROW filter responses by adjusting the number of rings using an IntSlider. This requires the ipywidgets library to be installed and used in an environment like a Jupyter notebook.

```python
from ipywidgets import interact, IntSlider
@interact(num_rings=IntSlider(min=1, max=10, step=1))
def plot_detected(num_rings=5):
    crow = Crow(num_rings=num_rings)
    detected = crow(source=1)
    crow.plot(detected)
    plt.show()
```

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

### Perform a time-domain simulation

Source: https://github.com/flaport/photontorch/blob/master/examples/00_introduction_to_photontorch.ipynb

Sets up a time-domain simulation environment using `pt.Environment` with specified time step (`dt`), total time (`t1`), and wavelength (`wl`). A time-dependent source is created, and the simulation is performed and plotted.

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

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

with env:
    # perform simulation
    detected = circuit(source=single_source)

    # plot detected power
    circuit.plot(detected);
    plt.show()
```

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

### Perform a frequency-domain simulation

Source: https://github.com/flaport/photontorch/blob/master/examples/00_introduction_to_photontorch.ipynb

Sets up a frequency-domain simulation environment using `pt.Environment` and performs a simulation on the `circuit` instance. The detected power is then plotted.

```python
# create simulation environment
freq_env = pt.Environment(
    wl=1e-6*np.linspace(1.45, 1.65, 1000),
    freqdomain=True
)

with freq_env:
    # perform simulation
    detected = circuit(source=1) # constant source with amplitude 1
    
    # plot detected power
    circuit.plot(detected);
    plt.show()
```

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

### Set Up Optimizer and Loss Function

Source: https://github.com/flaport/photontorch/blob/master/examples/03_circuit_optimization_by_backpropagation.ipynb

Initializes the Mean Squared Error loss function and the Adam optimizer for training the network parameters. The learning rate is set to 0.2.

```python
num_epochs = 10 # number of training cycles
learning_rate = 0.2 # multiplication factor for the gradients during optimization.
lossfunc = torch.nn.MSELoss()
optimizer = torch.optim.Adam(nw.parameters(), learning_rate)
```

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

### Create Source, Detector, and Term Components

Source: https://context7.com/flaport/photontorch/llms.txt

Initializes basic optical components for injecting power (Source), measuring power (Detector), and absorbing power (Term).

```python
import photontorch as pt

# Create a source (injects power)
source = pt.Source()  # Single port: --0

# Create a detector (measures power)
detector = pt.Detector()  # Single port: --0

# Create a termination (absorbs power without detection)
term = pt.Term()  # Single port: --0
```

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

### Create MZI Component

Source: https://context7.com/flaport/photontorch/llms.txt

Instantiates a Mach-Zehnder Interferometer (MZI) component with specified optical properties and trainability.

```python
import photontorch as pt
import numpy as np

mzi = pt.Mzi(
    phi=0,                    # Input phase
    theta=np.pi / 4,          # Phase difference between arms
    neff=2.34,                # Effective index
    ng=3.40,                  # Group index
    wl0=1.55e-6,              # Center wavelength
    length=1e-5,              # Waveguide length in meters
    loss=0,                   # Loss in dB/m
    trainable=True            # Make phi and theta trainable
)
```

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

### Model a Realistic Photodetector

Source: https://context7.com/flaport/photontorch/llms.txt

Initializes a `Photodetector` with realistic parameters for simulating optical-to-electrical conversion, including noise.

```python
import photontorch as pt
from photontorch.detectors import Photodetector

# Create photodetector with realistic parameters
detector = Photodetector(
    bitrate=40e9,           # Data rate [1/s]
    samplerate=160e9,       # Sample rate [1/s]
    cutoff_frequency=20e9,  # Cutoff frequency [1/s]
    responsivity=1.0,       # Responsivity [A/W]
    dark_current=1e-10,     # Dark current [A]
    load_resistance=166,    # Load resistance [Ohm]
    filter_order=4,         # Butterworth filter order
    seed=42                 # Random seed for noise
)

# Use in simulation
env = pt.Environment(dt=1e-14, t1=1e-11, wl=1.55e-6, bitrate=40e9, samplerate=160e9)
with env:
    optical_signal = circuit(source=1)
    electrical_signal = detector(optical_signal)
```

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

### Create Frequency Domain Environment with Gradient Tracking

Source: https://github.com/flaport/photontorch/blob/master/examples/04_crow_optimization.ipynb

Defines a Photontorch environment for frequency domain simulations at a single wavelength, enabling gradient tracking for optimization.

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

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

### Initialize CROW for Optimization on CUDA

Source: https://github.com/flaport/photontorch/blob/master/examples/04_crow_optimization.ipynb

Sets up a CROW instance with 10 rings and prepares it for computation on a CUDA-enabled device. This is a prerequisite for performing gradient-based optimization.

```python
device = 'cuda' # 'cpu' or 'cuda'
crow = crow = Crow(num_rings=10, random_parameters=False).to(device)
```

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

### Create and Plot Ring Network (Time Domain)

Source: https://github.com/flaport/photontorch/blob/master/examples/08_general_ring_networks.ipynb

Initializes a RingNetwork with specified parameters, terminates it with sources and detectors, and plots the detected signals in the time domain. Ensure the environment is copied with a specific wavelength for this simulation.

```python
nw = pt.RingNetwork(
    N=2,
    capacity = 3,
    wg_factory = lambda: pt.Waveguide(length=50e-6/2, neff=2.86),
    mzi_factory = lambda: pt.DirectionalCoupler(coupling=0.3),
).terminate([pt.Source("term_in"), pt.Detector("term_pass"), pt.Detector("term_drop"), pt.Detector("term_add")])
with env.copy(wl=env.wavelength.mean()):
    detected = nw(source=1)
    nw.plot(detected)
    plt.show()
```

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

### Simulate with Time-Dependent Source

Source: https://github.com/flaport/photontorch/blob/master/examples/00_introduction_to_photontorch.ipynb

Simulate a circuit using a time-dependent source and plot the detected signal. Ensure the environment is set up correctly.

```python
with env:
    detected = circuit(source=double_source)
    circuit.plot(detected)
    plt.show()
```

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

### Enable CUDA Acceleration for Circuits

Source: https://context7.com/flaport/photontorch/llms.txt

Shows how to move a Photontorch circuit to a CUDA-enabled GPU for accelerated computation and check its device status.

```python
import photontorch as pt

circuit = Circuit(length=1.2e-5, neff=2.84, ng=3.2, loss=3e4)

# Move to GPU
circuit = circuit.cuda()  # or circuit.cuda(0) for specific device

# Check if on GPU
print(circuit.is_cuda)  # True

# Move back to CPU
circuit = circuit.cpu()
```

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

### Import Libraries for Photon-Torch Simulation

Source: https://github.com/flaport/photontorch/blob/master/examples/02_add_drop_filter.ipynb

Imports necessary libraries including numpy for numerical operations, matplotlib for plotting, and photontorch for photonic circuit simulation.

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

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

### Training Loop with Context Manager

Source: https://github.com/flaport/photontorch/blob/master/examples/04_crow_optimization.ipynb

Performs a training loop using a context manager to temporarily apply a specific environment. Includes zeroing gradients, calculating loss, and updating parameters.

```python
with train_env: # temporarily override the global environment
    # we train for 400 training steps
    for i in tqdm(range(400)):
        optimizer.zero_grad() # set all the gradients to zero
        result = torch.squeeze(allpass(source=1)) # squeeze: 4D -> 0D
        loss = lossfunc(result, target) # MSE loss
        loss.backward() # calculate the gradients
        optimizer.step() # use the calculated gradients to perform an optimization step
```

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

### Import Libraries for Photonic Simulation

Source: https://github.com/flaport/photontorch/blob/master/examples/09_XOR_task_with_MZI.ipynb

Imports necessary libraries including torch, numpy, photontorch, matplotlib, ipywidgets, and tqdm for simulation and visualization.

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

from ipywidgets import interact
from tqdm.notebook import trange
```

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

### Define Simulation Parameters

Source: https://github.com/flaport/photontorch/blob/master/examples/03_circuit_optimization_by_backpropagation.ipynb

Sets up physical and temporal parameters for the simulation, including effective refractive index, wavelength, time step, and total simulation time. These values are crucial for accurate optical simulations.

```python
neff = np.sqrt(12.1)
wl = 1.55e-6
dt = 0.5e-9
total_time = 2e-6
time = np.arange(0,total_time,dt)
```

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

### Define Simulation and Design Parameters

Source: https://github.com/flaport/photontorch/blob/master/examples/02_add_drop_filter.ipynb

Sets up parameters for time-domain simulation and optical design, including time step, total simulation time, speed of light, ring length, and transmission.

```python
dt = 1e-14 #[s]
total_time = 2000*dt #[s]
time = np.arange(0, total_time, dt)
c = 299792458 #[m/s]
ring_length = 50e-6 #[m]
transmission = 0.7 #[]
wavelengths = 1e-6*np.linspace(1.50, 1.6, 1000) #[m]
```

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

### Instantiate ReckNxN Network

Source: https://github.com/flaport/photontorch/blob/master/examples/07_unitary_matrix_network_in_the_time_domain.ipynb

Creates a ReckNxN network with specified dimensions and factories for waveguides and MZIs. The network is terminated to handle boundary conditions.

```python
nw = pt.ReckNxN(
    N=N, 
    wg_factory=lambda: pt.Waveguide(length=1e-4, phase=2*np.pi*np.random.rand(), trainable=True),
    mzi_factory=lambda: pt.Mzi(length=1e-4, phi=2*np.pi*np.random.rand(), theta=2*np.pi*np.random.rand(), trainable=True),
).terminate()
```

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

### Initialize Adam Optimizer for Phase and Coupling

Source: https://github.com/flaport/photontorch/blob/master/examples/04_crow_optimization.ipynb

Initializes an Adam optimizer to adjust both the phase and coupling parameters of the device.

```python
# let's just optimize both the phase and the coupling
optimizer = torch.optim.Adam([allpass.wg.phase, allpass.dc.parameter], lr=0.03)
```

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

### Instantiate a circuit with specific parameters

Source: https://github.com/flaport/photontorch/blob/master/examples/00_introduction_to_photontorch.ipynb

Creates an instance of the `Circuit` network with custom parameters for length, effective refractive index (neff), group index (ng), and loss.

```python
circuit = Circuit(length=1.2e-5, neff=2.84, ng=3.2, loss=3e4)
```

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

### Instantiate and Plot a CROW

Source: https://github.com/flaport/photontorch/blob/master/examples/04_crow_optimization.ipynb

Demonstrates how to create a CROW instance with a specified number of rings, simulate its response, and plot the detected output. Ensure matplotlib.pyplot is imported as plt.

```python
crow = Crow(num_rings=1)
detected = crow(source=1)
crow.plot(detected)
plt.show()
```

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

### Create a Simple Add-Drop Filter Network

Source: https://github.com/flaport/photontorch/blob/master/examples/02_add_drop_filter.ipynb

Constructs a simple add-drop filter network using Photon-Torch's Network context manager. This approach avoids boilerplate code for network creation.

```python
with pt.Network() as nw:
    nw.term_in = pt.Source()
    nw.term_pass = nw.term_add = nw.term_drop = pt.Detector()
    nw.dc1 = nw.dc2 = pt.DirectionalCoupler(1-transmission)
    nw.wg1 = nw.wg2 = pt.Waveguide(0.5*ring_length, loss=0, neff=2.86)
    nw.link('term_in:0', '0:dc1:2', '0:wg1:1', '1:dc2:3', '0:term_drop')
    nw.link('term_pass:0', '1:dc1:3', '0:wg2:1', '0:dc2:2', '0:term_add')
```

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

### Define Simulation and Design Parameters

Source: https://github.com/flaport/photontorch/blob/master/examples/01_all_pass_filter.ipynb

Sets up key parameters for the all-pass filter simulation, including timestep, total simulation time, loss, effective index, waveguide length, coupler transmission, and wavelengths for sweeping.

```python
dt = 1e-14 # Timestep of the simulation
total_time = 2.5e-12 # Total time to simulate
time = np.arange(0, total_time, dt) # Total time array
loss = 1 # [dB] (alpha) roundtrip loss in ring
neff = 2.34 # Effective index of the waveguides
ng = 3.4
ring_length = 1e-5 #[m] Length of the ring
transmission = 0.5 #[] transmission of directional coupler
wavelengths = 1e-6*np.linspace(1.5,1.6,1000) #[m] Wavelengths to sweep over
```

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

### NxN Unitary Matrix (Clements) Network Creation

Source: https://github.com/flaport/photontorch/blob/master/examples/06_unitary_matrix_network_in_the_frequency_domain.ipynb

Creates NxN unitary matrix networks using the Clements decomposition with specified dimensions. Requires Photontorch library and a DEVICE object. Includes initialization and termination steps.

```python
clem5x5 = pt.ClementsNxN(N=5).to(DEVICE).terminate().initialize()
clem6x6 = pt.ClementsNxN(N=6).to(DEVICE).terminate().initialize()
# quick monkeypatch to have the same behavior as the classes defined above
clem5x5.__class__ = clem6x6.__class__ = Network
```

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

### Simulate and Plot Initial Response of Multiple All-Pass Filters

Source: https://github.com/flaport/photontorch/blob/master/examples/05_optical_readout.ipynb

Instantiates a network with four AllPass filters and simulates their combined response. The resulting transmission spectrum is then plotted.

```python
nw = MultipleAllPasses(4)
nw.plot(nw(source=1))
plt.xticks([1549,wl0*1e9,1551], [1549,'$\\lambda_0$',1551]); plt.yticks([0.1,1])
plt.grid(True)
plt.show()
```

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

### Create Time Domain Simulation Environment

Source: https://github.com/flaport/photontorch/blob/master/examples/04_crow_optimization.ipynb

Defines a Photontorch environment for time domain simulations across multiple wavelengths. Specifies the time vector and wavelength list.

```python
# define the simulation environment:
time_domain_env = pt.Environment(
    time = np.linspace(0, 1e-11, 1000),
    wavelength = [1.547e-6, 1.550e-6, 1.560e-6], #[m]
    freqdomain=False, # we will be doing a time domain simulation
)
```

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

### Utilize Specialized Loss Functions

Source: https://context7.com/flaport/photontorch/llms.txt

Demonstrates the use of `MSELoss` and `BERLoss` for training, which include latency and warmup handling.

```python
import photontorch as pt
from photontorch.nn import MSELoss, BERLoss

# Mean Squared Error Loss
mse_loss = MSELoss(
    latency=0.5,        # Latency in bits
    warmup=2,           # Warmup bits to ignore
    bitrate=40e9,
    samplerate=160e9
)

# Bit Error Rate Loss (non-differentiable)
ber_loss = BERLoss(
    threshold=0.5,      # Decision threshold
    latency=0.5,
    warmup=2,
    bitrate=40e9,
    samplerate=160e9
)

# Usage in training
loss = mse_loss(prediction, target)
ber = ber_loss(prediction, target)
```

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

### Create and Simulate a Ring Network

Source: https://context7.com/flaport/photontorch/llms.txt

Defines a ring network with a specified number of waveguides and MZI layers, then terminates and simulates it within an environment.

```python
ring = pt.RingNetwork(
    N=4,           # Number of input/output waveguides (must be even)
    capacity=2     # Number of consecutive MZI layers
)

# Terminate and simulate
ring_circuit = ring.terminate()

env = pt.Environment(wl=1.55e-6, freqdomain=True)
with env:
    detected = ring_circuit(source=1)
```

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

### NxN Unitary Matrix (Reck) Network Creation

Source: https://github.com/flaport/photontorch/blob/master/examples/06_unitary_matrix_network_in_the_frequency_domain.ipynb

Creates NxN unitary matrix networks using the Reck decomposition with specified dimensions. Requires Photontorch library and a DEVICE object. Includes initialization and termination steps.

```python
reck2x2 = pt.ReckNxN(N=2).to(DEVICE).terminate().initialize()
reck5x5 = pt.ReckNxN(N=5).to(DEVICE).terminate().initialize()
# quick monkeypatch to have the same behavior as the classes defined above
reck5x5.__class__ = Network
```

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

### Photontorch Utility Functions

Source: https://github.com/flaport/photontorch/blob/master/examples/06_unitary_matrix_network_in_the_frequency_domain.ipynb

Provides utility functions for converting between PyTorch tensors and NumPy arrays, generating random phases, and creating unitary matrices. These are essential for setting up and manipulating quantum optical circuits.

```python
def array(tensor):
    arr = tensor.data.cpu().numpy()
    if arr.shape[0] == 2:
        arr = arr[0] + 1j * arr[1]
    return arr

def tensor(array):
    if array.dtype == np.complex64 or array.dtype == np.complex128:
        array = np.stack([np.real(array), np.imag(array)])
    return torch.tensor(array, dtype=torch.get_default_dtype(), device=DEVICE)

def rand_phase():
    return float(2*np.pi*np.random.rand())
```

```python
def unitary_matrix(m,n):
    real_part = np.random.rand(m,n)
    imag_part = np.random.rand(m,n)
    complex_matrix = real_part + 1j*imag_part
    if m >= n:
        unitary_matrix, _, _ = np.linalg.svd(complex_matrix, full_matrices = False)
    else:
        _, _, unitary_matrix = np.linalg.svd(complex_matrix, full_matrices = False)
    return unitary_matrix
```

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

### Import Libraries and Set Seeds

Source: https://github.com/flaport/photontorch/blob/master/examples/05_optical_readout.ipynb

Imports necessary libraries for numerical computation, plotting, and photontorch. Sets random seeds for reproducibility.

```python
import torch
import numpy as np
import matplotlib.pyplot as plt
from tqdm.notebook import tqdm, trange
from numpy.fft import fft, ifft, fftfreq
from scipy.signal import butter, lfilter

import photontorch as pt

torch.manual_seed(33)
np.random.seed(34)
```

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

### Define Physical and Simulation Parameters

Source: https://github.com/flaport/photontorch/blob/master/examples/09_XOR_task_with_MZI.ipynb

Sets up physical constants like the speed of light, bitrate, and cutoff frequency, along with simulation parameters such as effective index, number of bits, and seeds for reproducibility. It also defines the default device and data type.

```python
c = 299792458.0 #[m/s] speed of light
Rb = 20e9 #[1/s] bitrate
Rs = 320e9 #[1/s] samplerate
fc = 20e9 #[1/s] cutoff frequency for bit generation
wl0 = 1550e-9 #[m] center wavelength
neff = 2.34 #[1] effective index
ng = 3.4 #[1] group index
N = 100 #[1] number of bits per bit stream
B = 10 #[1] number of bit streams in training batch
Str, Sva, Ste = (1, 2, 3) #[1] train seed, validation seed, test seed
Lr = (1/Rb) * (c/ng) #[m] reference length (distance the signal travels during one bit period)
device = torch.device("cpu") # default device used
torch.set_default_dtype(torch.float32) # default dtype used
torch.manual_seed(123) # pytorch seed
np.random.seed(42) # numpy seed
```

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

### Ring Network Creation

Source: https://context7.com/flaport/photontorch/llms.txt

Demonstrates the creation of a ring network by modifying MZI orientations within a Clements-like structure.

```python
import photontorch as pt
```

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

### Simulate Multiple Wavelengths

Source: https://github.com/flaport/photontorch/blob/master/examples/00_introduction_to_photontorch.ipynb

Simulate a circuit with multiple wavelengths defined in the environment. A constant source with amplitude 1 is used.

```python
with env.copy(wl=[1.545e-6, 1.55e-6, 1.56e-6]):
    detected = circuit(source=1) # constant source with amplitude 1
    circuit.plot(detected)
    plt.show()
```

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

### Initialize Adam Optimizer for Phase

Source: https://github.com/flaport/photontorch/blob/master/examples/04_crow_optimization.ipynb

Initializes an Adam optimizer to adjust the phase parameter of a component. Ensure the learning rate is appropriate to avoid long optimization times.

```python
optimizer = torch.optim.Adam([allpass.wg.phase], lr=0.03) # let's just optimize the phase of the ring
```

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

### View Optimization Results

Source: https://github.com/flaport/photontorch/blob/master/examples/00_introduction_to_photontorch.ipynb

After training, simulate the circuit again with the optimized parameters and plot the detected signal. This helps visualize the effect of the optimization.

```python
# view result
with freq_env:
    detected = circuit(source=1) # constant source with amplitude 1
    circuit.plot(detected)
    plt.show()
```

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

### Simulate Simple Add-Drop Filter in Time Domain

Source: https://github.com/flaport/photontorch/blob/master/examples/02_add_drop_filter.ipynb

Runs a time-domain simulation of the simple add-drop filter. The simulation uses a mean wavelength and the defined time array. Results are plotted.

```python
with pt.Environment(wl=np.mean(wavelengths), t=time):
    detected = nw(source=1)
    nw.plot(detected);
```

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

### Training Loop

Source: https://github.com/flaport/photontorch/blob/master/examples/04_crow_optimization.ipynb

Executes a training loop for 150 steps, optimizing the 'crow' model using the Adam optimizer. It calculates loss using MSE and updates model parameters. Requires 'tqdm' for progress visualization.

```python
range_ = tqdm(range(150)) # we train for 150 training steps
with train_env: # temporarily override the global environment
    for i in range_:
        crow.initialize()
        optimizer.zero_grad() # set all the gradients to zero
        result = crow(source=1)[0,:,0,0] # single timestep, all wls, (drop detector=0; through detector=2), single batch
        loss = lossfunc(result, target) # MSE loss
        loss.backward() # calculate the gradients
        optimizer.step() # use the calculated gradients to perform an optimization step
        range_.set_postfix(loss=loss.item())

print("loss: %.5f"%loss.item())
```

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

### Initialize Loss Functions

Source: https://github.com/flaport/photontorch/blob/master/examples/09_XOR_task_with_MZI.ipynb

Initializes Mean Squared Error (MSE) and Bit Error Rate (BER) loss functions with specified latency, threshold, and simulation parameters. These will be used during the training process.

```python
mse = pt.MSELoss(latency=1.0, warmup=0.0, bitrate=Rb, samplerate=Rs)
ber = pt.BERLoss(latency=1.0, threshold=0.5, warmup=0.0, bitrate=Rb, samplerate=Rs)
```

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

### Define Optimizer Parameters

Source: https://github.com/flaport/photontorch/blob/master/examples/04_crow_optimization.ipynb

Provides a comment explaining the requirements for defining a PyTorch optimizer, specifically mentioning the need for parameters to optimize and a learning rate. It suggests Adam as a good default choice.

```python
# to define an optimizer, one needs to provide the parameters to optimize
# and the learning rate of the optimizer. 
# the learning rate is an important parameter that needs to be tuned manually to 
# the right value. A too large learning rate will result in an optimizer that cannot find the loss
```

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

### Create and Plot Ring Network (Frequency Domain)

Source: https://github.com/flaport/photontorch/blob/master/examples/08_general_ring_networks.ipynb

Simulates and plots the RingNetwork's response in the frequency domain. This requires copying the environment with the 'freqdomain' flag set to True.

```python
with env.copy(freqdomain=True):
    detected = nw(source=1)
    nw.plot(detected)
    plt.show()
```

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

### Time Domain Simulation

Source: https://context7.com/flaport/photontorch/llms.txt

Simulates the dynamic propagation of optical signals through a circuit using a time-dependent source. Requires torch and matplotlib.

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

circuit = Circuit(length=1.2e-5, neff=2.84, ng=3.2, loss=3e4)

# Create time domain environment
env = pt.Environment(dt=1e-15, t1=2.5e-12, wl=1.55e-6)

# Create time-dependent source signal
source_signal = torch.tensor(
    np.sin(env.time * 5e13),
    dtype=torch.float32,
    names=["t"]  # Named dimension for time
)

with env:
    # Perform time domain simulation
    detected = circuit(source=source_signal)

    # Plot detected power vs time
    circuit.plot(detected)
    plt.xlabel('Time')
    plt.ylabel('Power')
    plt.show()
```

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

### Import Libraries for Photontorch

Source: https://github.com/flaport/photontorch/blob/master/examples/06_unitary_matrix_network_in_the_frequency_domain.ipynb

Imports necessary standard libraries, photontorch, and other utilities like numpy and matplotlib for frequency domain operations.

```python
# standard library
from collections import OrderedDict

# photontorch
import torch
import photontorch as pt

# other
import numpy as np
import matplotlib.pyplot as plt
from tqdm.notebook import trange
```

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

### Run Photontorch Tests

Source: https://github.com/flaport/photontorch/blob/master/README.md

Execute tests for Photontorch from the root of the git repository using pytest.

```bash
pytest tests
```

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

### Default Environment Simulation

Source: https://github.com/flaport/photontorch/blob/master/examples/01_all_pass_filter.ipynb

Runs a simulation using the default environment after exiting a context manager. This demonstrates the state of the environment outside the `with` block.

```python
print("This was detected outside the context manager, "
      "with the default environment:")
detected = nw(source=1)
nw.plot(detected)
plt.show()
```

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

### Print Circuit Parameters

Source: https://github.com/flaport/photontorch/blob/master/examples/00_introduction_to_photontorch.ipynb

Iterate through and print the optimizable parameters of a circuit, similar to how it's done in PyTorch.

```python
for p in circuit.parameters():
    print(p)
```

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

### Initial Circuit Plotting with Latency Adjustment

Source: https://github.com/flaport/photontorch/blob/master/examples/09_XOR_task_with_MZI.ipynb

Initializes the circuit and plots the detected signal against the target signal for validation data. It uses an interactive widget to adjust the latency and visualize its effect on the detected signal, helping to find an optimal latency value.

```python
nw = Circuit()
with pt.Environment(t=t):
    detected = nw(source=vastream.rename("t", "b"))[:,0,0,:] # lower dimensional sources should have named dimensions
    
mse = pt.MSELoss(latency=0.0, warmup=0.0, bitrate=Rb, samplerate=Rs)
@interact(latency=(0,3,0.1))
def _(latency=1.0):
    mse.plot(vastream, label="input")
    mse.plot(vatarget, label="target")
    mse.plot(detected, latency=latency, label="detected")
    plt.legend()
    plt.xlim(0,1)
    plt.show()
```

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

### Simulate and Plot Optimized Filter

Source: https://github.com/flaport/photontorch/blob/master/examples/04_crow_optimization.ipynb

Simulates the device with the current parameters and plots the detected output along with a marker for the target wavelength.

```python
detected = allpass(source=1)
allpass.plot(detected)
plt.plot([1550,1550],[0,1])
plt.ylim(0,1)
plt.show()
```

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

### Create Add-Drop Filter Network with Grating Couplers

Source: https://github.com/flaport/photontorch/blob/master/examples/02_add_drop_filter.ipynb

Builds an add-drop filter network incorporating grating couplers. This network includes sources, detectors, directional couplers, waveguides, and grating couplers, with defined links between them.

```python
with pt.Network() as nw:
    # components
    nw.src = pt.Source()
    nw.through = nw.add = nw.drop = pt.Detector()
    nw.dc1 = nw.dc2 = pt.DirectionalCoupler(1 - transmission)
    nw.wg1 = nw.wg2 = pt.Waveguide(0.5 * ring_length, loss=0, neff=2.86)
    nw.wg_in = nw.wg_through = nw.wg_add = nw.wg_drop = pt.Waveguide(
        length=wg_length, loss=0.0, neff=2.86, trainable=True
    )
    nw.gc_in = nw.gc_through = nw.gc_add = nw.gc_drop = pt.GratingCoupler(
            R=reflection,
            R_in=0,
            Tmax=peak_transmission,
            bandwidth=bandwidth,
            wl0=center_wavelength,
        )
    
    # links
    nw.link('src:0', '0:gc_in:1', '0:wg_in:1', '0:dc1:2', '0:wg2:1',
            '1:dc2:3', '1:wg_drop:0', '1:gc_drop:0', '0:drop')
    
    nw.link('through:0', '0:gc_through:1', '0:wg_through:1', '1:dc1:3', '0:wg1:1',
            '0:dc2:2', '1:wg_add:0', '1:gc_add:0', '0:add')
```

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

### Import necessary libraries

Source: https://github.com/flaport/photontorch/blob/master/examples/00_introduction_to_photontorch.ipynb

Imports the required libraries for Photontorch, including torch, numpy, and matplotlib.

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

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

### Simulate Simple Add-Drop Filter in Frequency Domain

Source: https://github.com/flaport/photontorch/blob/master/examples/02_add_drop_filter.ipynb

Performs a frequency-domain simulation of the simple add-drop filter. The simulation sweeps across a range of wavelengths. Results are plotted.

```python
with pt.Environment(wl=wavelengths, freqdomain=True):
    detected = nw(source=1)
    nw.plot(detected)
```

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

### Gradient-Based Optimization

Source: https://context7.com/flaport/photontorch/llms.txt

Optimizes photonic circuit parameters using PyTorch optimizers for inverse design. Requires gradient computation enabled in the environment.

```python
import photontorch as pt
import torch

circuit = Circuit(length=1.2e-5, neff=2.84, ng=3.2, loss=3e4)

# Define training environment with gradient tracking
train_env = pt.Environment(
    wl=1525e-9,      # Target wavelength for optimization
    freqdomain=True,
    grad=True        # Enable gradient computation
)

# Define target (zero transmission at resonance)
target = 0

# Use PyTorch Adam optimizer
optimizer = torch.optim.Adam(circuit.parameters(), lr=0.1)

# Training loop
with train_env:
    for epoch in range(100):
        optimizer.zero_grad()
        detected = circuit(source=1)
        loss = ((detected - target) ** 2).mean()
        loss.backward()
        optimizer.step()

        if epoch % 20 == 0:
            print(f"Epoch {epoch}, Loss: {loss.item():.6f}")

# Print optimized parameters
for p in circuit.parameters():
    print(p)
```

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

### Frequency Domain Simulation

Source: https://context7.com/flaport/photontorch/llms.txt

Performs a wavelength sweep to analyze the spectral response of a photonic circuit. Requires matplotlib for plotting.

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

# Define circuit (from earlier)
circuit = Circuit(length=1.2e-5, neff=2.84, ng=3.2, loss=3e4)

# Create frequency domain environment
freq_env = pt.Environment(
    wl=1e-6 * np.linspace(1.45, 1.65, 1000),
    freqdomain=True
)

with freq_env:
    # Simulate with constant source amplitude
    detected = circuit(source=1)

    # Plot transmission spectrum
    circuit.plot(detected)
    plt.xlabel('Wavelength (m)')
    plt.ylabel('Power')
    plt.title('Frequency Response')
    plt.show()
```