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Joseph Hopfmüller
167
.gitignore
vendored
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167
.gitignore
vendored
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.vscode
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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[cod]
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*$py.class
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# C extensions
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*.so
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!speedfiber.so
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# Distribution / packaging
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.Python
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build/
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develop-eggs/
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dist/
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downloads/
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eggs/
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.eggs/
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lib/
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lib64/
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parts/
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sdist/
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var/
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wheels/
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share/python-wheels/
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*.egg-info/
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.installed.cfg
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*.egg
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MANIFEST
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# PyInstaller
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# Usually these files are written by a python script from a template
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# before PyInstaller builds the exe, so as to inject date/other infos into it.
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*.manifest
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*.spec
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# Installer logs
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pip-log.txt
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pip-delete-this-directory.txt
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# Unit test / coverage reports
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htmlcov/
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.tox/
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.nox/
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.coverage
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.coverage.*
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.cache
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nosetests.xml
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coverage.xml
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*.cover
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*.py,cover
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.hypothesis/
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.pytest_cache/
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cover/
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# Translations
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*.mo
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*.pot
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# Django stuff:
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*.log
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local_settings.py
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db.sqlite3
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db.sqlite3-journal
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# Flask stuff:
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instance/
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.webassets-cache
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# Scrapy stuff:
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.scrapy
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# Sphinx documentation
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docs/_build/
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# PyBuilder
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.pybuilder/
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target/
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# Jupyter Notebook
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.ipynb_checkpoints
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# IPython
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profile_default/
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ipython_config.py
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# pyenv
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# For a library or package, you might want to ignore these files since the code is
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# intended to run in multiple environments; otherwise, check them in:
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# .python-version
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# pipenv
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# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
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# However, in case of collaboration, if having platform-specific dependencies or dependencies
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# having no cross-platform support, pipenv may install dependencies that don't work, or not
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# install all needed dependencies.
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#Pipfile.lock
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# poetry
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# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
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# This is especially recommended for binary packages to ensure reproducibility, and is more
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# commonly ignored for libraries.
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# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
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#poetry.lock
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# pdm
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# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
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#pdm.lock
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# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
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# in version control.
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# https://pdm.fming.dev/latest/usage/project/#working-with-version-control
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.pdm.toml
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.pdm-python
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.pdm-build/
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# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
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__pypackages__/
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# Celery stuff
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celerybeat-schedule
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celerybeat.pid
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# SageMath parsed files
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*.sage.py
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# Environments
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.env
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.venv
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env/
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venv/
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ENV/
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env.bak/
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venv.bak/
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# Spyder project settings
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.spyderproject
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.spyproject
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# Rope project settings
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.ropeproject
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# mkdocs documentation
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/site
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# mypy
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.mypy_cache/
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.dmypy.json
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dmypy.json
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# Pyre type checker
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.pyre/
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# pytype static type analyzer
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.pytype/
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# Cython debug symbols
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cython_debug/
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# PyCharm
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# JetBrains specific template is maintained in a separate JetBrains.gitignore that can
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# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
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# and can be added to the global gitignore or merged into this file. For a more nuclear
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# option (not recommended) you can uncomment the following to ignore the entire idea folder.
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#.idea/
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.python-version
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config/wisdom_pyfftw_4096.npy
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78
run2.py
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78
run2.py
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@@ -0,0 +1,78 @@
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import pypho
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from pypho import functions as pf
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# from pypho_functions import *
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# from pypho import functions as pf
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import numpy as np
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import copy
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import matplotlib.pyplot as plt
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from scipy.interpolate import UnivariateSpline
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# Define network elements
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gp = pypho.setup(nos =2**4, sps = 256, symbolrate = 10.0e9)
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symbolsrc = pypho.symbols(glova = gp, nos = gp.nos, pattern = 'debruijn')
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esigsrc = pypho.signalsrc(glova = gp, pulseshape = 'rect' , fwhm = 0.85)
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sig_1550 = pypho.lasmod(glova = gp, power = 0, Df = 0, theta = 0)
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SSMF = pypho.fiber(glova = gp, l = 60.0e3, D = 17.0, S = 0, alpha = 0.2e-12, gamma = 1.4e-12, phi_max = 10.0)
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# Simulation
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bits = symbolsrc()
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esig = esigsrc(bitsequence = bits)
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E_Tx = sig_1550(esig = esig)
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# Define your parameters here
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T_0 = 25.0e-12
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z = SSMF.l
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D = 17.0
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beta_2, beta_3 = pf.DS_to_beta(17.0, 0, gp.lambda0)
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# Create a single pulse with gaussian shape (not power!)
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E_Tx[0]['E'][0] = E_Tx[0]['E'][0]*0 + np.exp(-(gp.timeax()-gp.timeax()[-1]/2)**2 / (2.0*T_0**2) )
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E = copy.deepcopy(E_Tx)
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# Fiber transmission
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E = SSMF(E = E, D = D, l = z)
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#sys.exit()
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# Get FWHM of the input signal E_Tx
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spline_0 = UnivariateSpline(gp.timeax()*1.0e12, np.abs(E_Tx[0]['E'][0])-1*np.max(np.abs(E_Tx[0]['E'][0]))/2, s=0)
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r1_0, r2_0 = spline_0.roots() # find the roots
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# Get FWHM of the output signal E
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spline_1 = UnivariateSpline(gp.timeax()*1.0e12, np.abs(E[0]['E'][0])-1*np.max(np.abs(E[0]['E'][0]))/2, s=0)
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r1_1, r2_1 = spline_1.roots() # find the roots
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T_FWHM_0 = (r2_0-r1_0) * 1e-12
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T_0_plot = T_FWHM_0 / 2.35482
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T_FWHM_1 = (r2_1-r1_1) * 1e-12
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L_D = (T_0_plot)**2 / np.abs(beta_2)
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# Plot Input and Output signal
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plt.figure(1)
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plt.plot(gp.timeax()*1.0e12, np.abs(E_Tx[0]['E'][0]), 'r', label='$E(0, t)$')
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plt.plot(gp.timeax()*1.0e12, np.abs(E[0]['E'][0]), 'g', label=f'$E(z={SSMF.l/1000}km, t$)')
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plt.annotate(text='', xy=(r1_0,np.max(np.abs(E_Tx[0]['E'][0]))/2), xytext=(r2_0,np.max(np.abs(E_Tx[0]['E'][0]))/2), arrowprops=dict(arrowstyle='<->'))
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plt.text(np.max((r2_0,r2_1))+10, np.max(np.abs(E_Tx[0]['E'][0]))/2, f'$T_{{FWHM,0}}$ = {r2_0-r1_0:.2f} ps', fontsize=12, horizontalalignment='left', verticalalignment='center')
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plt.annotate(text='', xy=(r1_1,np.max(np.abs(E[0]['E'][0]))/2), xytext=(r2_1,np.max(np.abs(E[0]['E'][0]))/2), arrowprops=dict(arrowstyle='<->'))
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plt.text(np.max((r2_0,r2_1))+10, np.max(np.abs(E[0]['E'][0]))/2, f'$T_{{FWHM,1}}$ = {r2_1-r1_1:.2f} ps', fontsize=12, horizontalalignment='left', verticalalignment='center')
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plt.ylabel('$|E|$ a.u.')
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plt.xlabel('Time $t$ [ps]')
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plt.grid()
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legend = plt.legend(loc='upper right')
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# Print the results
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print(f'Input signal 1/e-pulse width by definition: T_0 = {T_0*1e12:.6f} ps')
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print(f'Input signal 1/e-pulse width from plot: T_0 = {T_0_plot*1e12:.6f} ps')
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print(f'Input signal FWHM-pulse width from plot: T_FWHM,0 = {T_FWHM_0*1e12:.6f} ps')
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print(f'Output signal FWHM-pulse width from plot: T_FWHM,1 = {T_FWHM_1*1e12:.6f} ps')
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print(f'Calculated output FWHM-pulse width: T_FWHM,1 = {T_FWHM_0 * np.sqrt(1 + (z/L_D)**2)*1e12:.6f} ps')
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plt.show()
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