def propagate_and_optimize_mode(path, req, equipment, show=False):
# if mode is unknown : loops on the modes starting from the highest baudrate fiting in the
# step 1: create an ordered list of modes based on baudrate
baudrate_to_explore = list(set([m['baud_rate'] for m in equipment['Transceiver'][req.tsp].mode
if float(m['min_spacing'])<= req.spacing]))
# TODO be carefull on limits cases if spacing very close to req spacing eg 50.001 50.000
baudrate_to_explore = sorted(baudrate_to_explore, reverse=True)
if baudrate_to_explore :
# at least 1 baudrate can be tested wrt spacing
for b in baudrate_to_explore :
modes_to_explore = [m for m in equipment['Transceiver'][req.tsp].mode
if m['baud_rate'] == b and float(m['min_spacing'])<= req.spacing]
modes_to_explore = sorted(modes_to_explore,
key = lambda x: x['bit_rate'], reverse=True)
# print(modes_to_explore)
# step2 : computes propagation for each baudrate: stop and select the first that passes
found_a_feasible_mode = False
# TODO : the case of roll of is not included: for now use SI one
# TODO : if the loop in mode optimization does not have a feasible path, then bugs
si = create_input_spectral_information(
req.f_min, req.f_max, equipment['SI']['default'].roll_off,
b, req.power, req.spacing)
for el in path:
si = el(si)
if show:
print(el)
for m in modes_to_explore :
if path[-1].snr is not None:
path[-1].update_snr(m['tx_osnr'], equipment['Roadm']['default'].add_drop_osnr)
if round(min(path[-1].snr+lin2db(b/(12.5e9))),2) > m['OSNR'] :
found_a_feasible_mode = True
return path, m
else:
return [], None
# only get to this point if no baudrate/mode satisfies OSNR requirement
# returns the last propagated path and mode
msg = f'\tWarning! Request {req.request_id}: no mode satisfies path SNR requirement.\n'
print(msg)
logger.info(msg)
return [],None
else :
# no baudrate satisfying spacing
msg = f'\tWarning! Request {req.request_id}: no baudrate satisfies spacing requirement.\n'
print(msg)
logger.info(msg)
return [], None
def propagate(self, pref, *carriers):
#pin_target and loss are read from eqpt_config.json['Roadm']
#all ingress channels in xpress are set to this power level
#but add channels are not, so we define an effective loss
#in the case of add channels
self.effective_pch_out_db = min(pref.p_spani, self.params.target_pch_out_db)
self.effective_loss = pref.p_spani - self.effective_pch_out_db
carriers_power = array([c.power.signal +c.power.nli+c.power.ase for c in carriers])
carriers_att = list(map(lambda x : lin2db(x*1e3)-self.params.target_pch_out_db, carriers_power))
exceeding_att = -min(list(filter(lambda x: x < 0, carriers_att)), default = 0)
carriers_att = list(map(lambda x: db2lin(x+exceeding_att), carriers_att))
for carrier_att, carrier in zip(carriers_att, carriers) :
pwr = carrier.power
pwr = pwr._replace( signal = pwr.signal/carrier_att,
nli = pwr.nli/carrier_att,
ase = pwr.ase/carrier_att)
yield carrier._replace(power=pwr)
def test_excel_service_json_generation(xls_input, expected_json_output):
""" test services creation
"""
equipment = load_equipment(eqpt_filename)
network = load_network(DATA_DIR / 'testTopology.xls', equipment)
# Build the network once using the default power defined in SI in eqpt config
p_db = equipment['SI']['default'].power_dbm
p_total_db = p_db + lin2db(
automatic_nch(equipment['SI']['default'].f_min,
equipment['SI']['default'].f_max,
equipment['SI']['default'].spacing))
build_network(network, equipment, p_db, p_total_db)
from_xls = read_service_sheet(xls_input,
equipment,
network,
network_filename=DATA_DIR /
'testTopology.xls')
assert from_xls == load_json(expected_json_output)
def test_setup():
''' common setup for tests: builds network, equipment and oms only once
'''
equipment = load_equipment(EQPT_LIBRARY_NAME)
network = load_network(NETWORK_FILE_NAME, equipment)
# Build the network once using the default power defined in SI in eqpt config
# power density : db2linp(ower_dbm': 0)/power_dbm': 0 * nb channels as defined by
# spacing, f_min and f_max
p_db = equipment['SI']['default'].power_dbm
p_total_db = p_db + lin2db(
automatic_nch(equipment['SI']['default'].f_min,
equipment['SI']['default'].f_max,
equipment['SI']['default'].spacing))
build_network(network, equipment, p_db, p_total_db)
build_oms_list(network, equipment)
return network, equipment
def test_compare_nf_models(gain, setup_edfa_variable_gain, si):
""" compare the 2 amplifier models (polynomial and estimated from nf_min and max)
=> nf_model vs nf_poly_fit for intermediate gain values:
between gain_min and gain_flatmax some discrepancy is expected but target < 0.5dB
=> unitary test for Edfa._calc_nf (and Edfa.interpol_params)"""
edfa = setup_edfa_variable_gain
frequencies = array([c.frequency for c in si.carriers])
pin = array(
[c.power.signal + c.power.nli + c.power.ase for c in si.carriers])
pin = pin / db2lin(gain)
baud_rates = array([c.baud_rate for c in si.carriers])
edfa.operational.gain_target = gain
pref = Pref(0, -gain, lin2db(len(frequencies)))
edfa.interpol_params(frequencies, pin, baud_rates, pref)
nf_model = edfa.nf[0]
edfa.interpol_params(frequencies, pin, baud_rates, pref)
nf_poly = edfa.nf[0]
assert pytest.approx(nf_model, abs=0.5) == nf_poly
def test_compare_nf_models(gain, setup_edfa_variable_gain, si):
""" compare the 2 amplifier models (polynomial and estimated from nf_min and max)
=> nf_model vs nf_poly_fit for intermediate gain values:
between gain_min and gain_flatmax some discrepancy is expected but target < 0.5dB
=> unitary test for Edfa._calc_nf (and Edfa.interpol_params)"""
edfa = setup_edfa_variable_gain
frequencies = array([c.frequency for c in si.carriers])
pin = array(
[c.power.signal + c.power.nli + c.power.ase for c in si.carriers])
pin = pin / db2lin(gain)
baud_rates = array([c.baud_rate for c in si.carriers])
edfa.operational.gain_target = gain
# edfa is variable gain type
pref = Pref(0, -gain, lin2db(len(frequencies)))
edfa.interpol_params(frequencies, pin, baud_rates, pref)
nf_model = edfa.nf[0]
# change edfa type variety to a polynomial
el_config = {
"uid": "Edfa1",
"operational": {
"gain_target": gain,
"tilt_target": 0
},
"metadata": {
"location": {
"region": "",
"latitude": 2,
"longitude": 0
}
}
}
equipment = load_equipment(eqpt_library)
extra_params = equipment['Edfa']['CienaDB_medium_gain']
temp = el_config.setdefault('params', {})
temp = merge_amplifier_restrictions(temp, extra_params.__dict__)
el_config['params'] = temp
edfa = Edfa(**el_config)
# edfa is variable gain type
edfa.interpol_params(frequencies, pin, baud_rates, pref)
nf_poly = edfa.nf[0]
print(nf_poly, nf_model)
assert pytest.approx(nf_model, abs=0.5) == nf_poly
def test_no_amp_feature(node_uid):
''' Check that booster is not placed on a roadm if fused is specified
test_parser covers partly this behaviour. This test should guaranty that the
feature is preserved even if convert is changed
'''
equipment = load_equipment(EQPT_LIBRARY_NAME)
json_network = load_json(NETWORK_FILE_NAME)
for elem in json_network['elements']:
if elem['uid'] == node_uid:
#replace edfa node by a fused node in the topology
elem['type'] = 'Fused'
elem.pop('type_variety')
elem.pop('operational')
elem['params'] = {'loss': 0}
next_node_uid = next(conn['to_node'] for conn in json_network['connections'] \
if conn['from_node'] == node_uid)
previous_node_uid = next(conn['from_node'] for conn in json_network['connections'] \
if conn['to_node'] == node_uid)
network = network_from_json(json_network, equipment)
# Build the network once using the default power defined in SI in eqpt config
# power density : db2linp(ower_dbm": 0)/power_dbm": 0 * nb channels as defined by
# spacing, f_min and f_max
p_db = equipment['SI']['default'].power_dbm
p_total_db = p_db + lin2db(automatic_nch(equipment['SI']['default'].f_min,\
equipment['SI']['default'].f_max, equipment['SI']['default'].spacing))
build_network(network, equipment, p_db, p_total_db)
node = next(nd for nd in network.nodes() if nd.uid == node_uid)
next_node = next(network.successors(node))
previous_node = next(network.predecessors(node))
if not isinstance(node, Fused):
raise AssertionError()
if not node.params.loss == 0.0:
raise AssertionError()
if not next_node_uid == next_node.uid:
raise AssertionError()
if not previous_node_uid == previous_node.uid:
raise AssertionError()
def _calc_nf(self):
"""nf calculation based on 2 models: self.params.nf_model.enabled from json import:
True => 2 stages amp modelling based on precalculated nf1, nf2 and delta_p in build_OA_json
False => polynomial fit based on self.params.nf_fit_coeff"""
#TODO : tbd alarm rising or input VOA padding in case
#gain_min > gain_target TBD:
pad = max(self.params.gain_min - self.operational.gain_target, 0)
gain_target = self.operational.gain_target + pad
dg = gain_target - self.params.gain_flatmax # ! <0
if self.params.nf_model.enabled:
g1a = gain_target - self.params.nf_model.delta_p + dg
nf_avg = lin2db(
db2lin(self.params.nf_model.nf1) +
db2lin(self.params.nf_model.nf2) / db2lin(g1a))
else:
nf_avg = np.polyval(self.params.nf_fit_coeff, dg)
nf_array = self.interpol_nf_ripple + nf_avg + pad #input VOA = 1 for 1 NF degradation
return nf_array
def update_snr(self, *args):
"""
snr_added in 0.1nm
compute SNR penalties such as transponder Tx_osnr or Roadm add_drop_osnr
only applied in request.py / propagate on the last Trasceiver node of the path
all penalties are added in a single call because to avoid uncontrolled cumul
"""
# use raw_values so that the added SNR penalties are not cumulated
snr_added = 0
for s in args:
snr_added += db2lin(-s)
snr_added = -lin2db(snr_added)
self.osnr_ase = list(map(lambda x, y: snr_sum(x, y, snr_added),
self.raw_osnr_ase, self.baud_rate))
self.snr = list(map(lambda x, y: snr_sum(x, y, snr_added),
self.raw_snr, self.baud_rate))
self.osnr_ase_01nm = list(map(lambda x: snr_sum(x, 12.5e9, snr_added),
self.raw_osnr_ase_01nm))
self.snr_01nm = list(map(lambda x: snr_sum(x, 12.5e9, snr_added),
self.raw_snr_01nm))
def calc_average_gsnr_opt():
"""
Function to print the average GSNR for all channels for several .mat data files
"""
path_data = Path(
'/mnt/Bruno_Data/GoogleDrive/Material_Academico/PoliTo_WON/Research/Simulations_Data/Results/'
'JOCN_Power_Optimization/C_L_S/Future_scenarios_analyze/Profiles')
list_folder = [file for file in os.listdir(path_data)]
for folder in list_folder:
print(folder)
list_files = [file for file in os.listdir(path_data / folder)]
for file in list_files:
data = loadmat(path_data / (folder + '/' + file))
gsnr = np.transpose(data['GSNR'])
print('Name of profile: {}'.format(data['name'][0]))
print('Average GSNR: {} dB'.format(np.mean(lin2db(gsnr))))
print('\n')
def test_disjunction(net, eqpt, serv):
data = load_requests(serv, eqpt)
equipment = load_equipment(eqpt)
network = load_network(net, equipment)
# Build the network once using the default power defined in SI in eqpt config
# power density : db2linp(ower_dbm": 0)/power_dbm": 0 * nb channels as defined by
# spacing, f_min and f_max
p_db = equipment['SI']['default'].power_dbm
p_total_db = p_db + lin2db(automatic_nch(equipment['SI']['default'].f_min,\
equipment['SI']['default'].f_max, equipment['SI']['default'].spacing))
build_network(network, equipment, p_db, p_total_db)
rqs = requests_from_json(data, equipment)
rqs = correct_route_list(network, rqs)
dsjn = disjunctions_from_json(data)
pths = compute_path_dsjctn(network, equipment, rqs, dsjn)
print(dsjn)
dsjn_list = [d.disjunctions_req for d in dsjn]
# assumes only pairs in dsjn list
test = True
for e in dsjn_list:
rqs_id_list = [r.request_id for r in rqs]
p1 = pths[rqs_id_list.index(e[0])][1:-1]
p2 = pths[rqs_id_list.index(e[1])][1:-1]
if isdisjoint(p1, p2) + isdisjoint(p1, find_reversed_path(
p2, network)) > 0:
test = False
print(
f'Computed path (roadms):{[e.uid for e in p1 if isinstance(e, Roadm)]}\n'
)
print(
f'Computed path (roadms):{[e.uid for e in p2 if isinstance(e, Roadm)]}\n'
)
break
print(dsjn_list)
assert test
def test_auto_design_generation_fromjson(tmpdir, json_input, power_mode):
"""test that autodesign creates same file as an input file already autodesigned
"""
equipment = load_equipment(eqpt_filename)
network = load_network(json_input, equipment)
# in order to test the Eqpt sheet and load gain target,
# change the power-mode to False (to be in gain mode)
equipment['Span']['default'].power_mode = power_mode
# Build the network once using the default power defined in SI in eqpt config
p_db = equipment['SI']['default'].power_dbm
p_total_db = p_db + lin2db(
automatic_nch(equipment['SI']['default'].f_min,
equipment['SI']['default'].f_max,
equipment['SI']['default'].spacing))
build_network(network, equipment, p_db, p_total_db)
actual_json_output = tmpdir / json_input.with_name(
json_input.stem + '_auto_design').with_suffix('.json').name
save_network(network, actual_json_output)
actual = load_json(actual_json_output)
unlink(actual_json_output)
assert actual == load_json(json_input)
def calc_best_gsnr(data_path, output_folder=None, thr=0.01, copy_files=False):
"""
Save a list of profiles with best GSNR average for a threshold value of difference
"""
config_path = data_path / 'config_file.json'
if not output_folder:
output_folder = data_path.parent / 'results/results_{}'.format(thr)
if not os.path.isdir(output_folder):
os.makedirs(output_folder)
# Create list of .mat files (combinations) and load configuration file
list_data_files = [file for file in os.listdir(data_path) if file.endswith('.mat')]
config = json.load(open(config_path, 'r'))
bands = config['spectral_config']['bands']
# Calculation only for the channels under test
ordered_list = []
files_bar = tqdm(iterable=list_data_files, desc='Calculating gsnr average for all combinations')
for file_name in files_bar:
data = DataQot.load_qot_mat(data_path / file_name)
calc_gsnr_lin = [gsnr for i, gsnr in enumerate(data.gsnr) if i in data.cut_index]
calc_gsnr_db = [lin2db(gsnr) for gsnr in calc_gsnr_lin]
gsnr_average = np.mean(calc_gsnr_db)
ordered_list.append([data.name, gsnr_average])
# Order list of combinations by GSNR average
ordered_list.sort(key=lambda value: value[1], reverse=True)
thr_gsnr = ordered_list[0][1] - (ordered_list[0][1] * thr)
# Remove items with smaller GSNR average than the threshold
ordered_list = [item for item in ordered_list if item[1] >= thr_gsnr]
print('{} profiles with acceptable GSNR average'.format(len(ordered_list)))
# Save best GSNR list and profiles
with open((output_folder / 'Best_GSNR_average_{}.txt'.format(thr)), 'w') as best_combs:
for comb in ordered_list:
best_combs.write('{}: {}\n'.format(comb[0], comb[1]))
if copy_files:
shutil.copy2((data_path / (comb[0] + '.mat')), output_folder)
def _calc_nf(self, avg=False):
"""nf calculation based on 2 models: self.params.nf_model.enabled from json import:
True => 2 stages amp modelling based on precalculated nf1, nf2 and delta_p in build_OA_json
False => polynomial fit based on self.params.nf_fit_coeff"""
# TODO|jla: TBD alarm rising or input VOA padding in case
# gain_min > gain_target TBD:
pad = max(self.params.gain_min - self.effective_gain, 0)
self.att_in = pad
gain_target = self.effective_gain + pad
dg = max(self.params.gain_flatmax - gain_target, 0)
if self.params.type_def == 'variable_gain':
g1a = gain_target - self.params.nf_model.delta_p - dg
nf_avg = lin2db(
db2lin(self.params.nf_model.nf1) +
db2lin(self.params.nf_model.nf2) / db2lin(g1a))
elif self.params.type_def == 'fixed_gain':
nf_avg = self.params.nf_model.nf0
else:
nf_avg = polyval(self.params.nf_fit_coeff, -dg)
if avg:
return nf_avg + pad
else:
return self.interpol_nf_ripple + nf_avg + pad # input VOA = 1 for 1 NF degradation
def _calc_nf(self, avg=False):
"""nf calculation based on 2 models: self.params.nf_model.enabled from json import:
True => 2 stages amp modelling based on precalculated nf1, nf2 and delta_p in build_OA_json
False => polynomial fit based on self.params.nf_fit_coeff"""
# gain_min > gain_target TBD:
if self.params.type_def == 'dual_stage':
g1 = self.params.preamp_gain_flatmax
g2 = self.effective_gain - g1
nf1_avg, pad = self._nf(self.params.preamp_type_def,
self.params.preamp_nf_model,
self.params.preamp_nf_fit_coeff,
self.params.preamp_gain_min,
self.params.preamp_gain_flatmax,
g1)
# no padding expected for the 1stage because g1 = gain_max
nf2_avg, pad = self._nf(self.params.booster_type_def,
self.params.booster_nf_model,
self.params.booster_nf_fit_coeff,
self.params.booster_gain_min,
self.params.booster_gain_flatmax,
g2)
nf_avg = lin2db(db2lin(nf1_avg) + db2lin(nf2_avg - g1))
# no padding expected for the 1stage because g1 = gain_max
pad = 0
else:
nf_avg, pad = self._nf(self.params.type_def,
self.params.nf_model,
self.params.nf_fit_coeff,
self.params.gain_min,
self.params.gain_flatmax,
self.effective_gain)
self.att_in = pad # not used to attenuate carriers, only used in _repr_ and _str_
if avg:
return nf_avg
else:
return self.interpol_nf_ripple + nf_avg # input VOA = 1 for 1 NF degradation
def calc_average_gsnr():
path_data = Path(
'/mnt/Bruno_Data/GoogleDrive/Material_Academico/PoliTo_WON/research/Simulations_Data/Results/'
'JOCN_Power_Optimization/Compare_GSNR')
config_path = path_data / 'config_file_cl.json'
config = json.load(open(config_path, 'r'))
bands = config['spectral_config']['bands']
data = loadmat(
path_data /
'jocn_2020_l_c_offset_l=0.5_tilt_l=0.1_offset_c=1.0_tilt_c=-0.3.mat')
num_ch_band = 0
for band in bands:
bands[band]['comp_channels_ind'] = [
(ch + num_ch_band - 1) for ch in bands[band]['comp_channels']
]
num_ch_band += bands[band]['nb_channels']
gsnr_per_band = 0.0
total_gsnr, freq_list = [], []
for band in bands:
gsnr_per_band = [
lin2db(gsnr) for i, gsnr in enumerate(data['GSNR'][0])
if i in bands[band]['comp_channels_ind']
]
total_gsnr.extend(gsnr_per_band)
freq_list.extend([
freq for i, freq in enumerate(data['frequencies'][0])
if i in bands[band]['comp_channels_ind']
])
print('Average GSNR for {} band is {}'.format(band,
np.mean(gsnr_per_band)))
print('Average GSNR is {}'.format(np.mean(total_gsnr)))
array = np.array([freq_list, total_gsnr])
np.save(path_data / 'C+L.npy', array)
def propagate(self, pref, *carriers, degree):
# pin_target and loss are read from eqpt_config.json['Roadm']
# all ingress channels in xpress are set to this power level
# but add channels are not, so we define an effective loss
# in the case of add channels
# find the target power on this degree:
# if a target power has been defined for this degree use it else use the global one.
# if the input power is lower than the target one, use the input power instead because
# a ROADM doesn't amplify, it can only attenuate
# TODO maybe add a minimum loss for the ROADM
per_degree_pch = self.per_degree_pch_out_db[degree] if degree in self.per_degree_pch_out_db.keys() else self.params.target_pch_out_db
self.effective_pch_out_db = min(pref.p_spani, per_degree_pch)
self.effective_loss = pref.p_spani - self.effective_pch_out_db
carriers_power = array([c.power.signal + c.power.nli + c.power.ase for c in carriers])
carriers_att = list(map(lambda x: lin2db(x * 1e3) - per_degree_pch, carriers_power))
exceeding_att = -min(list(filter(lambda x: x < 0, carriers_att)), default=0)
carriers_att = list(map(lambda x: db2lin(x + exceeding_att), carriers_att))
for carrier_att, carrier in zip(carriers_att, carriers):
pwr = carrier.power
pwr = pwr._replace(signal=pwr.signal / carrier_att,
nli=pwr.nli / carrier_att,
ase=pwr.ase / carrier_att)
pmd = sqrt(carrier.pmd**2 + self.params.pmd**2)
yield carrier._replace(power=pwr, pmd=pmd)
def path_metric(pth, req):
""" creates the metrics dictionary
"""
return [{
'metric-type': 'SNR-bandwidth',
'accumulative-value': round(mean(pth[-1].snr), 2)
}, {
'metric-type':
'SNR-0.1nm',
'accumulative-value':
round(mean(pth[-1].snr + lin2db(req.baud_rate / 12.5e9)), 2)
}, {
'metric-type': 'OSNR-bandwidth',
'accumulative-value': round(mean(pth[-1].osnr_ase), 2)
}, {
'metric-type': 'OSNR-0.1nm',
'accumulative-value': round(mean(pth[-1].osnr_ase_01nm), 2)
}, {
'metric-type': 'reference_power',
'accumulative-value': req.power
}, {
'metric-type': 'path_bandwidth',
'accumulative-value': req.path_bandwidth
}]
def test_auto_design_generation_fromxlsgainmode(tmpdir, xls_input,
expected_json_output):
""" tests generation of topology json
test that the build network gives correct results in gain mode
"""
equipment = load_equipment(eqpt_filename)
network = load_network(xls_input, equipment)
# in order to test the Eqpt sheet and load gain target,
# change the power-mode to False (to be in gain mode)
equipment['Span']['default'].power_mode = False
# Build the network once using the default power defined in SI in eqpt config
p_db = equipment['SI']['default'].power_dbm
p_total_db = p_db + lin2db(
automatic_nch(equipment['SI']['default'].f_min,
equipment['SI']['default'].f_max,
equipment['SI']['default'].spacing))
build_network(network, equipment, p_db, p_total_db)
actual_json_output = tmpdir / xls_input.with_name(
xls_input.stem + '_auto_design').with_suffix('.json').name
save_network(network, actual_json_output)
actual = load_json(actual_json_output)
unlink(actual_json_output)
assert actual == load_json(expected_json_output)
except EquipmentConfigError as e:
print(f'{ansi_escapes.red}Configuration error in the equipment library:{ansi_escapes.reset} {e}')
exit(1)
except NetworkTopologyError as e:
print(f'{ansi_escapes.red}Invalid network definition:{ansi_escapes.reset} {e}')
exit(1)
except ConfigurationError as e:
print(f'{ansi_escapes.red}Configuration error:{ansi_escapes.reset} {e}')
exit(1)
# Build the network once using the default power defined in SI in eqpt config
# TODO power density : db2linp(ower_dbm": 0)/power_dbm": 0 * nb channels as defined by
# spacing, f_min and f_max
p_db = equipment['SI']['default'].power_dbm
p_total_db = p_db + lin2db(automatic_nch(equipment['SI']['default'].f_min,\
equipment['SI']['default'].f_max, equipment['SI']['default'].spacing))
build_network(network, equipment, p_db, p_total_db)
save_network(args.network_filename, network)
rqs = requests_from_json(data, equipment)
# check that request ids are unique. Non unique ids, may
# mess the computation : better to stop the computation
all_ids = [r.request_id for r in rqs]
if len(all_ids) != len(set(all_ids)):
for a in list(set(all_ids)):
all_ids.remove(a)
msg = f'Requests id {all_ids} are not unique'
logger.critical(msg)
exit()
rqs = correct_route_list(network, rqs)
def create_input_spectral_information(f_min, f_max, roll_off, baud_rate, power,
spacing):
# pref in dB : convert power lin into power in dB
pref = lin2db(power * 1e3)
nb_channel = automatic_nch(f_min, f_max, spacing)
si = SpectralInformation(pref=Pref(pref, pref, lin2db(nb_channel)),
carriers=[
Channel(f, (f_min + spacing * f), baud_rate,
roll_off, Power(power, 0, 0))
for f in range(1, nb_channel + 1)
])
return si
if __name__ == '__main__':
pref = lin2db(power * 1e3)
si = SpectralInformation(
Pref(pref, pref),
Channel(
1,
193.95e12,
32e9,
0.15, # 193.95 THz, 32 Gbaud
Power(1e-3, 1e-6, 1e-6)), # 1 mW, 1uW, 1uW
Channel(
1,
195.95e12,
32e9,
0.15, # 195.95 THz, 32 Gbaud
Power(1.2e-3, 1e-6, 1e-6)), # 1.2 mW, 1uW, 1uW
)
def test_excel_ila_constraints(source, destination, route_list, hoptype,
expected_correction):
""" add different kind of constraints to test all correct_route cases
"""
service_xls_input = DATA_DIR / 'testTopology.xls'
network_json_input = DATA_DIR / 'testTopology_auto_design_expected.json'
equipment = load_equipment(eqpt_filename)
network = load_network(network_json_input, equipment)
# increase length of one span to trigger automatic fiber splitting included by autodesign
# so that the test also covers this case
next(node for node in network.nodes()
if node.uid == 'fiber (Brest_KLA → Quimper)-').length = 200000
next(node for node in network.nodes()
if node.uid == 'fiber (Quimper → Brest_KLA)-').length = 200000
default_si = equipment['SI']['default']
p_db = default_si.power_dbm
p_total_db = p_db + lin2db(
automatic_nch(default_si.f_min, default_si.f_max, default_si.spacing))
build_network(network, equipment, p_db, p_total_db)
# create params for a request based on input
nodes_list = route_list.split(' | ') if route_list is not None else []
params = {
'request_id':
'0',
'source':
source,
'bidir':
False,
'destination':
destination,
'trx_type':
'',
'trx_mode':
'',
'format':
'',
'spacing':
'',
'nodes_list':
nodes_list,
'loose_list':
[hoptype for node in nodes_list] if route_list is not None else '',
'f_min':
0,
'f_max':
0,
'baud_rate':
0,
'OSNR':
None,
'bit_rate':
None,
'cost':
None,
'roll_off':
0,
'tx_osnr':
0,
'min_spacing':
None,
'nb_channel':
0,
'power':
0,
'path_bandwidth':
0,
}
request = PathRequest(**params)
if expected_correction != 'Fail':
[request] = correct_xls_route_list(service_xls_input, network,
[request])
assert request.nodes_list == expected_correction
else:
with pytest.raises(ServiceError):
[request] = correct_xls_route_list(service_xls_input, network,
[request])
def test_json_response_generation(xls_input, expected_response_file):
""" tests if json response is correctly generated for all combinations of requests
"""
equipment = load_equipment(eqpt_filename)
network = load_network(xls_input, equipment)
p_db = equipment['SI']['default'].power_dbm
p_total_db = p_db + lin2db(
automatic_nch(equipment['SI']['default'].f_min,
equipment['SI']['default'].f_max,
equipment['SI']['default'].spacing))
build_network(network, equipment, p_db, p_total_db)
data = read_service_sheet(xls_input, equipment, network)
# change one of the request with bidir option to cover bidir case as well
data['path-request'][2]['bidirectional'] = True
oms_list = build_oms_list(network, equipment)
rqs = requests_from_json(data, equipment)
dsjn = disjunctions_from_json(data)
dsjn = deduplicate_disjunctions(dsjn)
rqs, dsjn = requests_aggregation(rqs, dsjn)
pths = compute_path_dsjctn(network, equipment, rqs, dsjn)
propagatedpths, reversed_pths, reversed_propagatedpths = \
compute_path_with_disjunction(network, equipment, rqs, pths)
pth_assign_spectrum(pths, rqs, oms_list, reversed_pths)
result = []
for i, pth in enumerate(propagatedpths):
# test ServiceError handling : when M is zero at this point, the
# json result should not be created if there is no blocking reason
if i == 1:
my_rq = deepcopy(rqs[i])
my_rq.M = 0
with pytest.raises(ServiceError):
ResultElement(my_rq, pth, reversed_propagatedpths[i]).json
my_rq.blocking_reason = 'NO_SPECTRUM'
ResultElement(my_rq, pth, reversed_propagatedpths[i]).json
result.append(ResultElement(rqs[i], pth, reversed_propagatedpths[i]))
temp = {'response': [n.json for n in result]}
expected = load_json(expected_response_file)
for i, response in enumerate(temp['response']):
if i == 2:
# compare response must be False because z-a metric is missing
# (request with bidir option to cover bidir case)
assert not compare_response(expected['response'][i], response)
print(f'response {response["response-id"]} should not match')
expected['response'][2]['path-properties']['z-a-path-metric'] = [{
'metric-type':
'SNR-bandwidth',
'accumulative-value':
22.809999999999999
}, {
'metric-type':
'SNR-0.1nm',
'accumulative-value':
26.890000000000001
}, {
'metric-type':
'OSNR-bandwidth',
'accumulative-value':
26.239999999999998
}, {
'metric-type':
'OSNR-0.1nm',
'accumulative-value':
30.32
}, {
'metric-type':
'reference_power',
'accumulative-value':
0.0012589254117941673
}, {
'metric-type':
'path_bandwidth',
'accumulative-value':
60000000000.0
}]
# test should be OK now
else:
assert compare_response(expected['response'][i], response)
print(f'response {response["response-id"]} is not correct')
def _gain_profile(self, pin):
"""
Pin : input power / channel in W
:param gain_ripple: design flat gain
:param dgt: design gain tilt
:param Pin: total input power in W
:param gp: Average gain setpoint in dB units
:param gtp: gain tilt setting
:type gain_ripple: numpy.ndarray
:type dgt: numpy.ndarray
:type Pin: numpy.ndarray
:type gp: float
:type gtp: float
:return: gain profile in dBm
:rtype: numpy.ndarray
AMPLIFICATION USING INPUT PROFILE
INPUTS:
gain_ripple - vector of length number of channels or spectral slices
DGT - vector of length number of channels or spectral slices
Pin - input powers vector of length number of channels or
spectral slices
Gp - provisioned gain length 1
GTp - provisioned tilt length 1
OUTPUT:
amp gain per channel or spectral slice
NOTE: there is no checking done for violations of the total output
power capability of the amp.
EDIT OF PREVIOUS NOTE: power violation now added in interpol_params
Ported from Matlab version written by David Boerges at Ciena.
Based on:
R. di Muro, "The Er3+ fiber gain coefficient derived from a dynamic
gain
tilt technique", Journal of Lightwave Technology, Vol. 18, Iss. 3,
Pp. 343-347, 2000.
"""
err_tolerance = 1.0e-11
simple_opt = True
# TODO check what param should be used (currently length(dgt))
nchan = np.arange(len(self.interpol_dgt))
# TODO find a way to use these or lose them. Primarily we should have
# a way to determine if exceeding the gain or output power of the amp
tot_in_power_db = lin2db(np.sum(pin * 1e3)) # ! Pin expressed in W
# Linear fit to get the
p = np.polyfit(nchan, self.interpol_dgt, 1)
dgt_slope = p[0]
# Calculate the target slope- Currently assumes equal spaced channels
# TODO make it so that supports arbitrary channel spacing.
targ_slope = self.operational.tilt_target / (len(nchan) - 1)
# 1st estimate of DGT scaling
if abs(dgt_slope) > 0.001: # add check for div 0 due to flat dgt
dgts1 = targ_slope / dgt_slope
else:
dgts1 = 0
# when simple_opt is true code makes 2 attempts to compute gain and
# the internal voa value. This is currently here to provide direct
# comparison with original Matlab code. Will be removed.
# TODO replace with loop
if simple_opt:
# 1st estimate of Er gain & voa loss
g1st = np.array(self.interpol_gain_ripple) + self.params.gain_flatmax + \
np.array(self.interpol_dgt) * dgts1
voa = lin2db(np.mean(db2lin(g1st))) - self.operational.gain_target
# 2nd estimate of Amp ch gain using the channel input profile
g2nd = g1st - voa
pout_db = lin2db(np.sum(pin * 1e3 * db2lin(g2nd)))
dgts2 = self.operational.gain_target - (pout_db - tot_in_power_db)
# Center estimate of amp ch gain
xcent = dgts2
gcent = g1st - voa + np.array(self.interpol_dgt) * xcent
pout_db = lin2db(np.sum(pin * 1e3 * db2lin(gcent)))
gavg_cent = pout_db - tot_in_power_db
# Lower estimate of Amp ch gain
deltax = np.max(g1st) - np.min(g1st)
# ! if no ripple deltax = 0 => xlow = xcent: div 0
# add check for flat gain response :
if abs(
deltax
) > 0.05: #enough ripple to consider calculation and avoid div 0
xlow = dgts2 - deltax
glow = g1st - voa + np.array(self.interpol_dgt) * xlow
pout_db = lin2db(np.sum(pin * 1e3 * db2lin(glow)))
gavg_low = pout_db - tot_in_power_db
# Upper gain estimate
xhigh = dgts2 + deltax
ghigh = g1st - voa + np.array(self.interpol_dgt) * xhigh
pout_db = lin2db(np.sum(pin * 1e3 * db2lin(ghigh)))
gavg_high = pout_db - tot_in_power_db
# compute slope
slope1 = (gavg_low - gavg_cent) / (xlow - xcent)
slope2 = (gavg_cent - gavg_high) / (xcent - xhigh)
if np.abs(self.operational.gain_target -
gavg_cent) <= err_tolerance:
dgts3 = xcent
elif self.operational.gain_target < gavg_cent:
dgts3 = xcent - (gavg_cent -
self.operational.gain_target) / slope1
else:
dgts3 = xcent + (-gavg_cent +
self.operational.gain_target) / slope2
gprofile = g1st - voa + np.array(self.interpol_dgt) * dgts3
else: #not enough ripple
gprofile = g1st - voa
else: #simple_opt
gprofile = None
return gprofile
def test_automaticmodefeature(net, eqpt, serv, expected_mode):
equipment = load_equipment(eqpt)
network = load_network(net, equipment)
data = load_requests(serv,
eqpt,
bidir=False,
network=network,
network_filename=net)
# Build the network once using the default power defined in SI in eqpt config
# power density : db2linp(ower_dbm": 0)/power_dbm": 0 * nb channels as defined by
# spacing, f_min and f_max
p_db = equipment['SI']['default'].power_dbm
p_total_db = p_db + lin2db(
automatic_nch(equipment['SI']['default'].f_min,
equipment['SI']['default'].f_max,
equipment['SI']['default'].spacing))
build_network(network, equipment, p_db, p_total_db)
rqs = requests_from_json(data, equipment)
rqs = correct_json_route_list(network, rqs)
dsjn = []
pths = compute_path_dsjctn(network, equipment, rqs, dsjn)
path_res_list = []
for i, pathreq in enumerate(rqs):
# use the power specified in requests but might be different from the one specified for design
# the power is an optional parameter for requests definition
# if optional, use the one defines in eqt_config.json
p_db = lin2db(pathreq.power * 1e3)
p_total_db = p_db + lin2db(pathreq.nb_channel)
print(f'request {pathreq.request_id}')
print(f'Computing path from {pathreq.source} to {pathreq.destination}')
# adding first node to be clearer on the output
print(f'with path constraint: {[pathreq.source]+pathreq.nodes_list}')
total_path = pths[i]
print(
f'Computed path (roadms):{[e.uid for e in total_path if isinstance(e, Roadm)]}\n'
)
# for debug
# print(f'{pathreq.baud_rate} {pathreq.power} {pathreq.spacing} {pathreq.nb_channel}')
if pathreq.baud_rate is not None:
print(pathreq.format)
path_res_list.append(pathreq.format)
total_path = propagate(total_path, pathreq, equipment)
else:
total_path, mode = propagate_and_optimize_mode(
total_path, pathreq, equipment)
# if no baudrate satisfies spacing, no mode is returned and an empty path is returned
# a warning is shown in the propagate_and_optimize_mode
if mode is not None:
print(mode['format'])
path_res_list.append(mode['format'])
else:
print('nok')
path_res_list.append('nok')
print(path_res_list)
assert path_res_list == expected_mode
def jsontocsv(json_data, equipment, fileout):
# read json path result file in accordance with:
# Yang model for requesting Path Computation
# draft-ietf-teas-yang-path-computation-01.txt.
# and write results in an CSV file
mywriter = writer(fileout)
mywriter.writerow(('response-id','source','destination','path_bandwidth','Pass?',\
'nb of tsp pairs','total cost','transponder-type','transponder-mode',\
'OSNR-0.1nm','SNR-0.1nm','SNR-bandwidth','baud rate (Gbaud)',\
'input power (dBm)','path'))
tspjsondata = equipment['Transceiver']
#print(tspjsondata)
for pth_el in json_data['response']:
path_id = pth_el['response-id']
try:
if pth_el['no-path']:
source = ''
destination = ''
tsp = ''
mode = ''
isok = False
nb_tsp = 0
pthbdbw = ''
rosnr = ''
rsnr = ''
rsnrb = ''
br = ''
pw = ''
total_cost = ''
pth = ''
except KeyError:
source = pth_el['path-properties']['path-route-objects'][0]\
['path-route-object']['num-unnum-hop']['node-id']
destination = pth_el['path-properties']['path-route-objects'][-2]\
['path-route-object']['num-unnum-hop']['node-id']
# selects only roadm nodes
temp = []
for e in pth_el['path-properties']['path-route-objects']:
try:
temp.append(
e['path-route-object']['num-unnum-hop']['node-id'])
except KeyError:
pass
pth = ' | '.join(temp)
temp_tsp = pth_el['path-properties']['path-route-objects'][1]\
['path-route-object']['transponder']
tsp = temp_tsp['transponder-type']
mode = temp_tsp['transponder-mode']
# find the min acceptable OSNR, baud rate from the eqpt library based on tsp (tupe) and mode (format)
# loading equipment already tests the existence of tsp type and mode:
if mode != 'not feasible with this transponder':
[minosnr, baud_rate, bit_rate, cost] = next(
[m['OSNR'], m['baud_rate'], m['bit_rate'], m['cost']]
for m in equipment['Transceiver'][tsp].mode
if m['format'] == mode)
# else:
# [minosnr, baud_rate, bit_rate] = ['','','','']
output_snr = next(e['accumulative-value']
for e in pth_el['path-properties']['path-metric']
if e['metric-type'] == 'SNR-0.1nm')
output_snrbandwidth = next(
e['accumulative-value']
for e in pth_el['path-properties']['path-metric']
if e['metric-type'] == 'SNR-bandwidth')
output_osnr = next(
e['accumulative-value']
for e in pth_el['path-properties']['path-metric']
if e['metric-type'] == 'OSNR-0.1nm')
output_osnrbandwidth = next(
e['accumulative-value']
for e in pth_el['path-properties']['path-metric']
if e['metric-type'] == 'OSNR-bandwidth')
power = next(e['accumulative-value']
for e in pth_el['path-properties']['path-metric']
if e['metric-type'] == 'reference_power')
path_bandwidth = next(
e['accumulative-value']
for e in pth_el['path-properties']['path-metric']
if e['metric-type'] == 'path_bandwidth')
if isinstance(output_snr, str):
isok = False
nb_tsp = 0
pthbdbw = round(path_bandwidth * 1e-9, 2)
rosnr = ''
rsnr = ''
rsnrb = ''
br = ''
pw = ''
total_cost = ''
else:
isok = output_snr >= minosnr
nb_tsp = ceil(path_bandwidth / bit_rate)
pthbdbw = round(path_bandwidth * 1e-9, 2)
rosnr = round(output_osnr, 2)
rsnr = round(output_snr, 2)
rsnrb = round(output_snrbandwidth, 2)
br = round(baud_rate * 1e-9, 2)
pw = round(lin2db(power) + 30, 2)
total_cost = nb_tsp * cost
mywriter.writerow(
(path_id, source, destination, pthbdbw, isok, nb_tsp, total_cost,
tsp, mode, rosnr, rsnr, rsnrb, br, pw, pth))
def pathresult(self):
if not self.computed_path:
return {
'response-id':
self.path_id,
'no-path':
"Response without path information, due to failure performing the path computation"
}
else:
index = 0
pro_list = []
for element in self.computed_path:
temp = {
'path-route-object': {
'index': index,
'num-unnum-hop': {
'node-id': element.uid,
'link-tp-id': element.uid,
# TODO change index in order to insert transponder attribute
}
}
}
pro_list.append(temp)
index += 1
if isinstance(element, Transceiver):
temp = {
'path-route-object': {
'index': index,
'transponder': {
'transponder-type': self.path_request.tsp,
'transponder-mode': self.path_request.tsp_mode,
}
}
}
pro_list.append(temp)
index += 1
response = {
'response-id': self.path_id,
'path-properties':{
'path-metric': [
{
'metric-type': 'SNR-bandwidth',
'accumulative-value': round(mean(self.computed_path[-1].snr), 2)
},
{
'metric-type': 'SNR-0.1nm',
'accumulative-value': round(mean(self.computed_path[-1]. snr + \
lin2db(self.path_request.baud_rate/12.5e9)), 2)
},
{
'metric-type': 'OSNR-bandwidth',
'accumulative-value': round(mean(self.computed_path[-1].osnr_ase), 2)
},
{
'metric-type': 'OSNR-0.1nm',
'accumulative-value': round(mean(self.computed_path[-1].osnr_ase_01nm), 2)
},
{
'metric-type': 'reference_power',
'accumulative-value': self.path_request.power
},
{
'metric-type': 'path_bandwidth',
'accumulative-value': self.path_request.path_bandwidth
}
],
'path-route-objects': pro_list
}
}
return response
def update_pref(self, pref, *carriers):
pch_out_db = lin2db(
mean([carrier.power.signal for carrier in carriers])) + 30
self.pch_out_db = round(pch_out_db, 2)
return pref._replace(p_span0=pref.p_span0, p_spani=self.pch_out_db)
def path_requests_run(args=None):
parser = argparse.ArgumentParser(
description=
'Compute performance for a list of services provided in a json file or an excel sheet',
epilog=_help_footer,
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
)
_add_common_options(parser,
network_default=_examples_dir /
'meshTopologyExampleV2.xls')
parser.add_argument('service_filename',
nargs='?',
type=Path,
metavar='SERVICES-REQUESTS.(json|xls|xlsx)',
default=_examples_dir / 'meshTopologyExampleV2.xls',
help='Input service file')
parser.add_argument('-bi',
'--bidir',
action='store_true',
help='considers that all demands are bidir')
parser.add_argument(
'-o',
'--output',
type=Path,
metavar=_help_fname_json_csv,
help='Store satisifed requests into a JSON or CSV file')
args = parser.parse_args(args if args is not None else sys.argv[1:])
_setup_logging(args)
_logger.info(
f'Computing path requests {args.service_filename} into JSON format')
print(
f'{ansi_escapes.blue}Computing path requests {os.path.relpath(args.service_filename)} into JSON format{ansi_escapes.reset}'
)
(equipment,
network) = load_common_data(args.equipment, args.topology,
args.sim_params,
args.save_network_before_autodesign)
# Build the network once using the default power defined in SI in eqpt config
# TODO power density: db2linp(ower_dbm": 0)/power_dbm": 0 * nb channels as defined by
# spacing, f_min and f_max
p_db = equipment['SI']['default'].power_dbm
p_total_db = p_db + lin2db(
automatic_nch(equipment['SI']['default'].f_min,
equipment['SI']['default'].f_max,
equipment['SI']['default'].spacing))
try:
build_network(network, equipment, p_db, p_total_db)
except exceptions.NetworkTopologyError as e:
print(
f'{ansi_escapes.red}Invalid network definition:{ansi_escapes.reset} {e}'
)
sys.exit(1)
except exceptions.ConfigurationError as e:
print(
f'{ansi_escapes.red}Configuration error:{ansi_escapes.reset} {e}')
sys.exit(1)
if args.save_network is not None:
save_network(network, args.save_network)
print(
f'{ansi_escapes.blue}Network (after autodesign) saved to {args.save_network}{ansi_escapes.reset}'
)
oms_list = build_oms_list(network, equipment)
try:
data = load_requests(args.service_filename,
equipment,
bidir=args.bidir,
network=network,
network_filename=args.topology)
rqs = requests_from_json(data, equipment)
except exceptions.ServiceError as e:
print(f'{ansi_escapes.red}Service error:{ansi_escapes.reset} {e}')
sys.exit(1)
# check that request ids are unique. Non unique ids, may
# mess the computation: better to stop the computation
all_ids = [r.request_id for r in rqs]
if len(all_ids) != len(set(all_ids)):
for item in list(set(all_ids)):
all_ids.remove(item)
msg = f'Requests id {all_ids} are not unique'
_logger.critical(msg)
sys.exit()
rqs = correct_json_route_list(network, rqs)
# pths = compute_path(network, equipment, rqs)
dsjn = disjunctions_from_json(data)
print(f'{ansi_escapes.blue}List of disjunctions{ansi_escapes.reset}')
print(dsjn)
# need to warn or correct in case of wrong disjunction form
# disjunction must not be repeated with same or different ids
dsjn = deduplicate_disjunctions(dsjn)
# Aggregate demands with same exact constraints
print(
f'{ansi_escapes.blue}Aggregating similar requests{ansi_escapes.reset}')
rqs, dsjn = requests_aggregation(rqs, dsjn)
# TODO export novel set of aggregated demands in a json file
print(
f'{ansi_escapes.blue}The following services have been requested:{ansi_escapes.reset}'
)
print(rqs)
print(
f'{ansi_escapes.blue}Computing all paths with constraints{ansi_escapes.reset}'
)
try:
pths = compute_path_dsjctn(network, equipment, rqs, dsjn)
except exceptions.DisjunctionError as this_e:
print(
f'{ansi_escapes.red}Disjunction error:{ansi_escapes.reset} {this_e}'
)
sys.exit(1)
print(
f'{ansi_escapes.blue}Propagating on selected path{ansi_escapes.reset}')
propagatedpths, reversed_pths, reversed_propagatedpths = compute_path_with_disjunction(
network, equipment, rqs, pths)
# Note that deepcopy used in compute_path_with_disjunction returns
# a list of nodes which are not belonging to network (they are copies of the node objects).
# so there can not be propagation on these nodes.
pth_assign_spectrum(pths, rqs, oms_list, reversed_pths)
print(f'{ansi_escapes.blue}Result summary{ansi_escapes.reset}')
header = [
'req id', ' demand', ' snr@bandwidth A-Z (Z-A)',
' [email protected] A-Z (Z-A)', ' Receiver minOSNR', ' mode', ' Gbit/s',
' nb of tsp pairs', 'N,M or blocking reason'
]
data = []
data.append(header)
for i, this_p in enumerate(propagatedpths):
rev_pth = reversed_propagatedpths[i]
if rev_pth and this_p:
psnrb = f'{round(mean(this_p[-1].snr),2)} ({round(mean(rev_pth[-1].snr),2)})'
psnr = f'{round(mean(this_p[-1].snr_01nm), 2)}' +\
f' ({round(mean(rev_pth[-1].snr_01nm),2)})'
elif this_p:
psnrb = f'{round(mean(this_p[-1].snr),2)}'
psnr = f'{round(mean(this_p[-1].snr_01nm),2)}'
try:
if rqs[i].blocking_reason in BLOCKING_NOPATH:
line = [
f'{rqs[i].request_id}',
f' {rqs[i].source} to {rqs[i].destination} :', f'-', f'-',
f'-', f'{rqs[i].tsp_mode}',
f'{round(rqs[i].path_bandwidth * 1e-9,2)}', f'-',
f'{rqs[i].blocking_reason}'
]
else:
line = [
f'{rqs[i].request_id}',
f' {rqs[i].source} to {rqs[i].destination} : ', psnrb,
psnr, f'-', f'{rqs[i].tsp_mode}',
f'{round(rqs[i].path_bandwidth * 1e-9, 2)}', f'-',
f'{rqs[i].blocking_reason}'
]
except AttributeError:
line = [
f'{rqs[i].request_id}',
f' {rqs[i].source} to {rqs[i].destination} : ', psnrb, psnr,
f'{rqs[i].OSNR + equipment["SI"]["default"].sys_margins}',
f'{rqs[i].tsp_mode}',
f'{round(rqs[i].path_bandwidth * 1e-9,2)}',
f'{ceil(rqs[i].path_bandwidth / rqs[i].bit_rate) }',
f'({rqs[i].N},{rqs[i].M})'
]
data.append(line)
col_width = max(len(word) for row in data for word in row[2:]) # padding
firstcol_width = max(len(row[0]) for row in data) # padding
secondcol_width = max(len(row[1]) for row in data) # padding
for row in data:
firstcol = ''.join(row[0].ljust(firstcol_width))
secondcol = ''.join(row[1].ljust(secondcol_width))
remainingcols = ''.join(
word.center(col_width, ' ') for word in row[2:])
print(f'{firstcol} {secondcol} {remainingcols}')
print(
f'{ansi_escapes.yellow}Result summary shows mean SNR and OSNR (average over all channels){ansi_escapes.reset}'
)
if args.output:
result = []
# assumes that list of rqs and list of propgatedpths have same order
for i, pth in enumerate(propagatedpths):
result.append(
ResultElement(rqs[i], pth, reversed_propagatedpths[i]))
temp = _path_result_json(result)
if args.output.suffix.lower() == '.json':
save_json(temp, args.output)
print(
f'{ansi_escapes.blue}Saved JSON to {args.output}{ansi_escapes.reset}'
)
elif args.output.suffix.lower() == '.csv':
with open(args.output, "w", encoding='utf-8') as fcsv:
jsontocsv(temp, equipment, fcsv)
print(
f'{ansi_escapes.blue}Saved CSV to {args.output}{ansi_escapes.reset}'
)
else:
print(
f'{ansi_escapes.red}Cannot save output: neither JSON nor CSV file{ansi_escapes.reset}'
)
sys.exit(1)
def transmission_main_example(args=None):
parser = argparse.ArgumentParser(
description=
'Send a full spectrum load through the network from point A to point B',
epilog=_help_footer,
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
)
_add_common_options(parser,
network_default=_examples_dir /
'edfa_example_network.json')
parser.add_argument('--show-channels',
action='store_true',
help='Show final per-channel OSNR summary')
parser.add_argument('-pl', '--plot', action='store_true')
parser.add_argument('-l',
'--list-nodes',
action='store_true',
help='list all transceiver nodes')
parser.add_argument('-po',
'--power',
default=0,
help='channel ref power in dBm')
parser.add_argument('source', nargs='?', help='source node')
parser.add_argument('destination', nargs='?', help='destination node')
args = parser.parse_args(args if args is not None else sys.argv[1:])
_setup_logging(args)
(equipment,
network) = load_common_data(args.equipment, args.topology,
args.sim_params,
args.save_network_before_autodesign)
if args.plot:
plot_baseline(network)
transceivers = {
n.uid: n
for n in network.nodes() if isinstance(n, Transceiver)
}
if not transceivers:
sys.exit('Network has no transceivers!')
if len(transceivers) < 2:
sys.exit('Network has only one transceiver!')
if args.list_nodes:
for uid in transceivers:
print(uid)
sys.exit()
# First try to find exact match if source/destination provided
if args.source:
source = transceivers.pop(args.source, None)
valid_source = True if source else False
else:
source = None
_logger.info('No source node specified: picking random transceiver')
if args.destination:
destination = transceivers.pop(args.destination, None)
valid_destination = True if destination else False
else:
destination = None
_logger.info(
'No destination node specified: picking random transceiver')
# If no exact match try to find partial match
if args.source and not source:
# TODO code a more advanced regex to find nodes match
source = next(
(transceivers.pop(uid)
for uid in transceivers if args.source.lower() in uid.lower()),
None)
if args.destination and not destination:
# TODO code a more advanced regex to find nodes match
destination = next((transceivers.pop(uid) for uid in transceivers
if args.destination.lower() in uid.lower()), None)
# If no partial match or no source/destination provided pick random
if not source:
source = list(transceivers.values())[0]
del transceivers[source.uid]
if not destination:
destination = list(transceivers.values())[0]
_logger.info(f'source = {args.source!r}')
_logger.info(f'destination = {args.destination!r}')
params = {}
params['request_id'] = 0
params['trx_type'] = ''
params['trx_mode'] = ''
params['source'] = source.uid
params['destination'] = destination.uid
params['bidir'] = False
params['nodes_list'] = [destination.uid]
params['loose_list'] = ['strict']
params['format'] = ''
params['path_bandwidth'] = 0
trx_params = trx_mode_params(equipment)
# Randomly generate input power
input_power = round(random.random(), 2)
input_power = -2 + (input_power * (8))
args.power = input_power
if args.power:
trx_params['power'] = db2lin(float(args.power)) * 1e-3
params.update(trx_params)
req = PathRequest(**params)
power_mode = equipment['Span']['default'].power_mode
print('\n'.join([
f'Power mode is set to {power_mode}',
f'=> it can be modified in eqpt_config.json - Span'
]))
pref_ch_db = lin2db(req.power *
1e3) # reference channel power / span (SL=20dB)
pref_total_db = pref_ch_db + lin2db(
req.nb_channel) # reference total power / span (SL=20dB)
try:
build_network(network, equipment, pref_ch_db, pref_total_db)
except exceptions.NetworkTopologyError as e:
print(
f'{ansi_escapes.red}Invalid network definition:{ansi_escapes.reset} {e}'
)
sys.exit(1)
except exceptions.ConfigurationError as e:
print(
f'{ansi_escapes.red}Configuration error:{ansi_escapes.reset} {e}')
sys.exit(1)
path = compute_constrained_path(network, req)
spans = [
s.params.length for s in path
if isinstance(s, RamanFiber) or isinstance(s, Fiber)
]
print(
f'\nThere are {len(spans)} fiber spans over {sum(spans)/1000:.0f} km between {source.uid} '
f'and {destination.uid}')
print(f'\nNow propagating between {source.uid} and {destination.uid}:')
try:
p_start, p_stop, p_step = equipment['SI']['default'].power_range_db
p_num = abs(int(round(
(p_stop - p_start) / p_step))) + 1 if p_step != 0 else 1
power_range = list(linspace(p_start, p_stop, p_num))
except TypeError:
print(
'invalid power range definition in eqpt_config, should be power_range_db: [lower, upper, step]'
)
power_range = [0]
if not power_mode:
# power cannot be changed in gain mode
power_range = [0]
for dp_db in power_range:
req.power = db2lin(pref_ch_db + dp_db) * 1e-3
if power_mode:
print(
f'\nPropagating with input power = {ansi_escapes.cyan}{lin2db(req.power*1e3):.2f} dBm{ansi_escapes.reset}:'
)
else:
print(
f'\nPropagating in {ansi_escapes.cyan}gain mode{ansi_escapes.reset}: power cannot be set manually'
)
infos = propagate(path, req, equipment)
if len(power_range) == 1:
for elem in path:
print(elem)
if power_mode:
print(
f'\nTransmission result for input power = {lin2db(req.power*1e3):.2f} dBm:'
)
else:
print(f'\nTransmission results:')
# print('-------------')
# print(destination.snr_01nm)
# print('-------------')
print(
f' Final SNR total (0.1 nm): {ansi_escapes.cyan}{mean(destination.snr_01nm):.02f} dB{ansi_escapes.reset}'
)
else:
print(path[-1])
if args.save_network is not None:
save_network(network, args.save_network)
print(
f'{ansi_escapes.blue}Network (after autodesign) saved to {args.save_network}{ansi_escapes.reset}'
)
if args.show_channels:
print('\nThe total SNR per channel at the end of the line is:')
print('{:>5}{:>26}{:>26}{:>28}{:>28}{:>28}'.format(
'Ch. #', 'Channel frequency (THz)', 'Channel power (dBm)',
'OSNR ASE (signal bw, dB)', 'SNR NLI (signal bw, dB)',
'SNR total (signal bw, dB)'))
# print(dir(info))
for final_carrier, ch_osnr, ch_snr_nl, ch_snr in zip(
infos.carriers, path[-1].osnr_ase, path[-1].osnr_nli,
path[-1].snr):
ch_freq = final_carrier.frequency * 1e-12
ch_power = lin2db(final_carrier.power.signal * 1e3)
print('{:5}{:26.2f}{:26.2f}{:28.2f}{:28.2f}{:28.2f}'.format(
final_carrier.channel_number, round(ch_freq, 2),
round(ch_power, 2), round(ch_osnr, 2), round(ch_snr_nl, 2),
round(ch_snr, 2)))
if not args.source:
print(f'\n(No source node specified: picked {source.uid})')
elif not valid_source:
print(
f'\n(Invalid source node {args.source!r} replaced with {source.uid})'
)
if not args.destination:
print(f'\n(No destination node specified: picked {destination.uid})')
elif not valid_destination:
print(
f'\n(Invalid destination node {args.destination!r} replaced with {destination.uid})'
)
if args.plot:
plot_results(network, path, source, destination)
# MY ADDITION
# just to see what the different contributions of ASE and NLI are
# return input_power, path[-1].osnr_ase, path[-1].osnr_nli, path[-1].snr
# to test Raman
return input_power, destination.snr_01nm, mean(destination.snr_01nm)