import json
from gnpy.constants import c, h
import numpy as np
from itertools import tee, islice

nwise = lambda g, n=2: zip(*(islice(g, i, None)
                             for i, g in enumerate(tee(g, n))))


def read_config(filepath):
    with open(filepath, 'r') as f:
        return json.load(f)


def find_by_node_id(g, nid):
    # TODO: What if nid is not found in graph (g)?
    return next(n for n in g.nodes() if n.id == nid)


def dbkm_2_lin(loss_coef):
    """ calculates the linear loss coefficient
    """
    alpha_pcoef = loss_coef
    alpha_acoef = alpha_pcoef/(2*4.3429448190325184)
    s = 'alpha_pcoef is linear loss coefficient in [dB/km^-1] units'
    s = ''.join([s, "alpha_acoef is linear loss field amplitude \
                 coefficient in [km^-1] units"])
    d = {'alpha_pcoef': alpha_pcoef, 'alpha_acoef': alpha_acoef,
         'description:': s}
    return d


def db_to_lin(val):
    return 10 ** (val / 10)


def chan_osnr(chan_params, amp_params):
    in_osnr = db_to_lin(chan_params['osnr'])
    pin = db_to_lin(chan_params['power']) / 1e3
    nf = db_to_lin(amp_params.nf[0])
    ase_cont = nf * h * chan_params['frequency'] * 12.5 * 1e21
    ret = -10 * np.log10(1 / in_osnr + ase_cont / pin)
    return ret