https://github.com/peter-barrow/citrine
citrine, a small Python library for calculating spectral properties of down conversion based single-photon sources
https://github.com/peter-barrow/citrine
optics parametric-downconversion python quantum-optics quantum-photonics spdc spontaneous-parametric-downconversion
Last synced: 2 months ago
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citrine, a small Python library for calculating spectral properties of down conversion based single-photon sources
- Host: GitHub
- URL: https://github.com/peter-barrow/citrine
- Owner: Peter-Barrow
- License: agpl-3.0
- Created: 2024-09-19T18:09:20.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2024-11-28T12:40:55.000Z (5 months ago)
- Last Synced: 2024-12-29T11:36:39.515Z (4 months ago)
- Topics: optics, parametric-downconversion, python, quantum-optics, quantum-photonics, spdc, spontaneous-parametric-downconversion
- Language: Python
- Homepage: https://peter-barrow.github.io/citrine/
- Size: 1.54 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Welcome to citrine 💎
*citrine* is a small Python library to calculate the joint-spectra of nonlinear crystals used in parametric downconversion sources.
## About
## Features
- Calculate grating periods
- Produce Δk matrices for different crystals and phase matching conditions
- Pump envelope functions
- Phase matching functions
- Types for commonly used units
- e.g. ```citrine.Wavelength```
- Library of crystal parameters
- ``` citrine.crystals ``` sub-module
## Requirements
- numpy
## Installation
```sh title="pypi"
python3 -m pip install citrine
```
### Alternatively from Git
Install from git for the latest version
```sh title="git"
python3 -m pip install git+https://gitlab.com/Peter-Barrow/citrine.git
```
## Quick Examples
``` python
import citrine
import numpy as np
import matplotlib.pyplot as plt
# Define central wavelengths for pump, signal, and idler
lambda_p_central = citrine.Wavelength(775, Magnitude.nano)
lambda_s_central = citrine.Wavelength(1550, Magnitude.nano)
lambda_i_central = citrine.Wavelength(1550, Magnitude.nano)
# Define a range of wavelengths for signal and idler (in nm)
spectral_width = citrine.Wavelength(25, Magnitude.nano)
steps = 1024
lambda_s = citrine.spectral_window(lambda_s_central, spectral_width, steps)
lambda_i = citrine.spectral_window(lambda_i_central, spectral_width, steps)
# Define pump bandwidth (in nm and convert)
sigma_lambda_p = citrine.Wavelength(0.5, Magnitude.nano)
sigma_p = 2 * np.pi * citrine.bandwidth_conversion(sigma_lambda_p, lambda_p_central)
# Calculate grating period
grating_period = citrine.calculate_grating_period(
lambda_p_central,
lambda_s_central,
lambda_i_central,
citrine.crystals.ppKTP,
)
# Calculate Δk matrix
delta_k = citrine.delta_k_matrix(
lambda_p_central,
lambda_s,
lambda_i,
ppKTP,
)
# Calculate the phase matching function
phase_matching = citrine.phase_matching_function(delta_k, grating_period, 1e-2)
# Calculate the Gaussian pump envelope
pump_envelope = citrine.pump_envelope_gaussian(lambda_p_central, sigma_p, lambda_s, lambda_i)
# Calculate the Joint Spectral Amplitude (JSA)
JSA = pump_envelope * phase_matching
# Plotting
fig, axs = plt.subplots(
2, 2, figsize=(8, 8), sharex=True, sharey=True, squeeze=True
)
# Plot Δk
axs[0, 0].pcolormesh(
lambda_i.value, lambda_s.value, delta_k, cmap='viridis'
)
axs[0, 0].set_title(r'$\Delta k$')
axs[0, 0].set_xlabel(r'$\lambda_s$ (nm)')
axs[0, 0].set_ylabel(r'$\lambda_i$ (nm)')
# Plot the pump envelope
axs[0, 1].pcolormesh(
lambda_i.value,
lambda_s.value,
pump_envelope,
cmap='viridis',
)
axs[0, 1].set_title(r'$\alpha(\lambda_s, \lambda_i)$')
axs[0, 1].set_xlabel(r'$\lambda_s$ (nm)')
axs[0, 1].set_ylabel(r'$\lambda_i$ (nm)')
# Plot the phase matching function
axs[1, 0].pcolormesh(
lambda_i.value,
lambda_s.value,
phase_matching,
cmap='viridis',
)
axs[1, 0].set_title(r'$\phi(\lambda_s, \lambda_i)$')
axs[1, 0].set_xlabel(r'$\lambda_s$ (nm)')
axs[1, 0].set_ylabel(r'$\lambda_i$ (nm)')
# Plot the Joint Spectral Amplitude (JSA)
axs[1, 1].pcolormesh(lambda_i.value, lambda_s.value, JSA, cmap='viridis')
axs[1, 1].set_title(
r'$\alpha(\lambda_s, \lambda_i) \phi(\lambda_s, \lambda_i)$'
)
axs[1, 1].set_xlabel(r'$\lambda_s$ (nm)')
axs[1, 1].set_ylabel(r'$\lambda_i$ (nm)')
# Add overall title
fig.suptitle(
r'$\omega_p = {:.2f}\mathrm{{nm}}, \omega_s = {:.2f}\mathrm{{nm}}, \omega_i = {:.2f}\mathrm{{nm}}, \Lambda = {:.2f}\mathrm{{\mu m}}$'.format(
lambda_p_central.value,
lambda_s_central.value,
lambda_i_central.value,
grating_period * (1e6), # convert to microns for printing
)
)
# Show plot
plt.tight_layout()
plt.show()
```