mirror of
https://github.com/Telecominfraproject/oopt-gnpy.git
synced 2025-10-31 01:57:54 +00:00
7ab93e7cd9
I decided to keep it around because I know that some people would like to see those nanometers. Let's make sure it works. Change-Id: Ib279cc8380a77f478da7a2bbc1e045a718446404
247 lines
6.6 KiB
Python
247 lines
6.6 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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from csv import writer
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import numpy as np
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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 the dictionary format:
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::
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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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::
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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 arrange_frequencies(length, start, stop):
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"""Create an array of frequencies
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:param length: number of elements
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:param start: Start frequency in THz
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:param stop: Stop frequency in THz
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:type length: integer
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:type start: float
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:type stop: float
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:return: an array of frequencies determined by the spacing parameter
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:rtype: numpy.ndarray
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"""
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return np.linspace(start, stop, length)
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def lin2db(value):
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"""Convert linear unit to logarithmic (dB)
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>>> lin2db(0.001)
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-30.0
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>>> round(lin2db(1.0), 2)
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0.0
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>>> round(lin2db(1.26), 2)
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1.0
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>>> round(lin2db(10.0), 2)
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10.0
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>>> round(lin2db(100.0), 2)
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20.0
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"""
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return 10 * log10(value)
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def db2lin(value):
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"""Convert logarithimic units to linear
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>>> round(db2lin(10.0), 2)
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10.0
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>>> round(db2lin(20.0), 2)
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100.0
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>>> round(db2lin(1.0), 2)
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1.26
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>>> round(db2lin(0.0), 2)
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1.0
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>>> round(db2lin(-10.0), 2)
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0.1
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"""
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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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>>> round(freq2wavelength(191.35e12) * 1e9, 3)
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1566.723
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>>> round(freq2wavelength(196.1e12) * 1e9, 3)
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1528.773
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"""
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return constants.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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def merge_amplifier_restrictions(dict1, dict2):
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"""Updates contents of dicts recursively
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>>> d1 = {'params': {'restrictions': {'preamp_variety_list': [], 'booster_variety_list': []}}}
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>>> d2 = {'params': {'target_pch_out_db': -20}}
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>>> merge_amplifier_restrictions(d1, d2)
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{'params': {'restrictions': {'preamp_variety_list': [], 'booster_variety_list': []}, 'target_pch_out_db': -20}}
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>>> d3 = {'params': {'restrictions': {'preamp_variety_list': ['foo'], 'booster_variety_list': ['bar']}}}
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>>> merge_amplifier_restrictions(d1, d3)
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{'params': {'restrictions': {'preamp_variety_list': [], 'booster_variety_list': []}}}
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"""
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copy_dict1 = dict1.copy()
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for key in dict2:
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if key in dict1:
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if isinstance(dict1[key], dict):
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copy_dict1[key] = merge_amplifier_restrictions(copy_dict1[key], dict2[key])
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else:
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copy_dict1[key] = dict2[key]
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return copy_dict1
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def silent_remove(this_list, elem):
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"""Remove matching elements from a list without raising ValueError
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>>> li = [0, 1]
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>>> li = silent_remove(li, 1)
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>>> li
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[0]
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>>> li = silent_remove(li, 1)
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>>> li
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[0]
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"""
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try:
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this_list.remove(elem)
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except ValueError:
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pass
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return this_list
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