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	-add f_min & f_max frequency definition in amplifier json -improve interpolation algorithm to support length differences between the spectrum information and the amplifier ripple and dgt frequency definition Signed-off-by: Jean-Luc Auge <jeanluc.auge@orange.com>
		
			
				
	
	
		
			202 lines
		
	
	
		
			5.4 KiB
		
	
	
	
		
			Python
		
	
	
	
	
	
			
		
		
	
	
			202 lines
		
	
	
		
			5.4 KiB
		
	
	
	
		
			Python
		
	
	
	
	
	
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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'''
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gnpy.core.utils
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===============
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This module contains utility functions that are used with gnpy.
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'''
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import json
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import numpy as np
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from csv import writer
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from numpy import pi, cos, sqrt, log10
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from scipy import constants
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def load_json(filename):
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    with open(filename, 'r', encoding='utf-8') as f:
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        data = json.load(f)
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    return data
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def save_json(obj, filename):
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    with open(filename, 'w', encoding='utf-8') as f:
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        json.dump(obj, f, indent=2, ensure_ascii=False)
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def write_csv(obj, filename):
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    """
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    convert dictionary items to a csv file
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    the dictionary format :
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    {'result category 1':
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                        [
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                        # 1st line of results
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                        {'header 1' : value_xxx,
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                         'header 2' : value_yyy},
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                         # 2nd line of results: same headers, different results
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                        {'header 1' : value_www,
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                         'header 2' : value_zzz}
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                        ],
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    'result_category 2':
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                        [
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                        {},{}
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                        ]
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    }
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    the generated csv file will be:
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    result_category 1
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    header 1    header 2
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    value_xxx   value_yyy
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    value_www   value_zzz
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    result_category 2
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    ...
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    """
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    with open(filename, 'w', encoding='utf-8') as f:
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        w = writer(f)
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        for data_key, data_list in obj.items():
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            #main header
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            w.writerow([data_key])
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            #sub headers:
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            headers = [_ for _ in data_list[0].keys()]
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            w.writerow(headers)
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            for data_dict in data_list:
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                w.writerow([_ for _ in data_dict.values()])
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def c():
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    """
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    Returns the speed of light in meters per second
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    """
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    return constants.c
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def itufs(spacing, startf=191.35, stopf=196.10):
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    """Creates an array of frequencies whose default range is
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    191.35-196.10 THz
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    :param spacing: Frequency spacing in THz
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    :param starf: Start frequency in THz
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    :param stopf: Stop frequency in THz
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    :type spacing: float
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    :type startf: float
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    :type stopf: float
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    :return an array of frequnecies determined by the spacing parameter
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    :rtype: numpy.ndarray
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    """
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    return np.arange(startf, stopf + spacing / 2, spacing)
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def itufl(length, startf=191.35, stopf=196.10):
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    """Creates an array of frequencies whose default range is
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    191.35-196.10 THz
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    :param length: number of elements
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    :param starf: Start frequency in THz
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    :param stopf: Stop frequency in THz
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    :type length: integer
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    :type startf: float
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    :type stopf: float
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    :return an array of frequnecies determined by the spacing parameter
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    :rtype: numpy.ndarray
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    """
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    return np.linspace(startf, stopf, length)
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def h():
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    """
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    Returns plank's constant in J*s
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    """
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    return constants.h
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def lin2db(value):
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    return 10 * log10(value)
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def db2lin(value):
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    return 10**(value / 10)
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def round2float(number, step):
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    step = round(step, 1)
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    if step >= 0.01:
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        number = round(number / step, 0)
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        number = round(number * step, 1)
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    else:
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        number = round(number, 2)
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    return number
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wavelength2freq = constants.lambda2nu
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freq2wavelength = constants.nu2lambda
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def freq2wavelength(value):
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    """ Converts frequency units to wavelength units.
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    """
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    return c() / value
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def snr_sum(snr, bw, snr_added, bw_added=12.5e9):
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    snr_added = snr_added - lin2db(bw/bw_added)
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    snr = -lin2db(db2lin(-snr)+db2lin(-snr_added))
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    return snr
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def deltawl2deltaf(delta_wl, wavelength):
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    """ deltawl2deltaf(delta_wl, wavelength):
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    delta_wl is BW in wavelength units
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    wavelength is the center wl
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    units for delta_wl and wavelength must be same
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    :param delta_wl: delta wavelength BW in same units as wavelength
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    :param wavelength: wavelength BW is relevant for
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    :type delta_wl: float or numpy.ndarray
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    :type wavelength: float
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    :return: The BW in frequency units
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    :rtype: float or ndarray
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    """
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    f = wavelength2freq(wavelength)
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    return delta_wl * f / wavelength
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def deltaf2deltawl(delta_f, frequency):
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    """ deltawl2deltaf(delta_f, frequency):
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        converts delta frequency to delta wavelength
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        units for delta_wl and wavelength must be same
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    :param delta_f: delta frequency in same units as frequency
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    :param frequency: frequency BW is relevant for
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    :type delta_f: float or numpy.ndarray
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    :type frequency: float
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    :return: The BW in wavelength units
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    :rtype: float or ndarray
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    """
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    wl = freq2wavelength(frequency)
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    return delta_f * wl / frequency
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def rrc(ffs, baud_rate, alpha):
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    """ rrc(ffs, baud_rate, alpha): computes the root-raised cosine filter
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    function.
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    :param ffs: A numpy array of frequencies
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    :param baud_rate: The Baud Rate of the System
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    :param alpha: The roll-off factor of the filter
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    :type ffs: numpy.ndarray
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    :type baud_rate: float
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    :type alpha: float
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    :return: hf a numpy array of the filter shape
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    :rtype: numpy.ndarray
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    """
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    Ts = 1 / baud_rate
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    l_lim = (1 - alpha) / (2 * Ts)
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    r_lim = (1 + alpha) / (2 * Ts)
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    hf = np.zeros(np.shape(ffs))
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    slope_inds = np.where(
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        np.logical_and(np.abs(ffs) > l_lim, np.abs(ffs) < r_lim))
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    hf[slope_inds] = 0.5 * (1 + cos((pi * Ts / alpha) *
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                                    (np.abs(ffs[slope_inds]) - l_lim)))
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    p_inds = np.where(np.logical_and(np.abs(ffs) > 0, np.abs(ffs) < l_lim))
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    hf[p_inds] = 1
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    return sqrt(hf)
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