Develop (#14)

* adding rrc filter, temporarily putting it in utilities.py
* added some docstring stuff
* added a simple loss class for fiber and cleaned up some duplicate convenience access properties
* Changed Carrier to Channel to reflect correct nomenclature for multi-carrier/superchannels
* in process fixes for main.py.  adding in amp spacings and spans to convert to start adding additional noded to Coronet network
* some simple additions to utilites
* adding stand alone edfa model

core/__main__.py

@@ -10,6 +10,17 @@ from networkx import (draw_networkx_nodes, draw_networkx_edges,
 
 from . import network_from_json
 from .elements import Transceiver, Fiber
+from .info import SpectralInformation, Channel, Power
+from .algorithms import closed_paths
+
+logger = getLogger(__package__ or __file__)
+
+def format_si(spectral_infos):
+    return '\n'.join([
+        f'#{idx} Carrier(frequency={c.frequency},\n  power=Power(signal={c.power.signal}, nli={c.power.nli}, ase={c.power.ase}))'
+        for idx, si in sorted(set(spectral_infos))
+        for c in set(si.carriers)
+    ])
 
 logger = getLogger('gnpy.core')
 
@@ -25,9 +36,38 @@ def main(args):
               for n in network.nodes()]
     labels = {n: n.location.city if isinstance(n, Transceiver) else ''
               for n in network.nodes()}
-    draw_networkx_nodes(network, pos=pos, node_size=size, node_color=color)
+
-    draw_networkx_edges(network, pos=pos)
+    si = SpectralInformation(
-    draw_networkx_labels(network, pos=labels_pos, labels=labels, font_size=14)
+        Channel(1, 193.95e12, '16-qam', 32e9, 0,  # 193.95 THz, 32 Gbaud
+            Power(1e-3, 1e-6, 1e-6)),             # 1 mW, 1uW, 1uW
+        Channel(1, 195.95e12, '16-qam', 32e9, 0,  # 195.95 THz, 32 Gbaud
+            Power(1.2e-3, 1e-6, 1e-6)),           # 1.2 mW, 1uW, 1uW
+    )
+
+    nodes = [n for n in network.nodes() if isinstance(n, Transceiver)]
+    source, sink = choice(nodes), choice(nodes)
+
+    results = list(islice(closed_paths(network, source, sink, si), 3))
+    paths = [[n for _, n, _ in r] for r in results]
+    infos = {}
+    for idx, r in enumerate(results):
+        for in_si, node, out_si in r:
+            infos.setdefault(node, []).append((idx, out_si))
+
+    node_color = ['#ff0000' if n is source or n is sink else
+                  '#900000' if any(n in p for p in paths) else
+                  '#ffdede' if isinstance(n, Transceiver) else '#dedeff'
+                  for n in network.nodes()]
+    edge_color = ['#ff9090' if any(u in p for p in paths) and
+                               any(v in p for p in paths) else '#dedede'
+                  for u, v in network.edges()]
+
+    fig = figure()
+    plot = draw_networkx_nodes(network, pos=pos, node_size=size, node_color=node_color, figure=fig)
+    draw_networkx_edges(network, pos=pos, figure=fig, edge_color=edge_color)
+    draw_networkx_labels(network, pos=labels_pos, labels=labels, font_size=14, figure=fig)
+
+    title(f'Propagating from {source.loc.city} to {sink.loc.city}')
     axis('off')
     show()
 

core/elements.py

@@ -1,9 +1,12 @@
 #!/usr/bin/env python3
-
 from core.node import Node
 from core.units import UNITS
+import numpy as np
+from scipy.constants import c
 
 
+# network elements
+
 class Transceiver(Node):
     def __init__(self, config):
         super().__init__(config)
@@ -11,7 +14,6 @@ class Transceiver(Node):
     def __call__(self, spectral_info):
         return spectral_info
 
-
 class Fiber(Node):
     def __init__(self, config):
         super().__init__(config)
@@ -21,6 +23,51 @@ class Fiber(Node):
     def __repr__(self):
         return f'{type(self).__name__}(uid={self.uid}, length={self.length})'
     
+    def effective_length(self, loss_coef):
+        alpha_dict = self.dbkm_2_lin(loss_coef)
+        alpha = alpha_dict['alpha_acoef']
+        leff = 1 - np.exp(-2 * alpha * self.span_length)
+        return leff
+
+    def asymptotic_length(self, loss_coef):
+        alpha_dict = self.dbkm_2_lin(loss_coef)
+        alpha = alpha_dict['alpha_acoef']
+        aleff = 1/(2 * alpha)
+        return aleff
+
+    def dbkm_2_lin(self, 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 beta2(self, dispersion, ref_wavelength=None):
+        """ Returns beta2 from dispersion parameter.  Dispersion is entered in
+        ps/nm/km.  Disperion can be a numpy array or a single value.  If a
+        value ref_wavelength is not entered 1550e-9m will be assumed.
+        ref_wavelength can be a numpy array.
+        """
+        if ref_wavelength is None:
+            ref_wavelength = 1550e-9
+        wl = ref_wavelength
+        D = np.abs(dispersion)
+        b2 = (10**21) * (wl**2) * D / (2 * np.pi * c)
+#       10^21 scales to ps^2/km
+        return b2
+
+    # convenience access
+    loss = property(lambda self: self.loss_.value)
+    length = property(lambda self: self.length_.value)
+    loc  = property(lambda self: self.location)
+    lat  = property(lambda self: self.location.latitude)
+    long = property(lambda self: self.location.longitude)
+
     def propagate(self, *carriers):
         for carrier in carriers:
             power = carrier.power
@@ -32,3 +79,4 @@ class Fiber(Node):
     def __call__(self, spectral_info):
         carriers = tuple(self.propagate(*spectral_info.carriers))
         return spectral_info.update(carriers=carriers)
+

core/info.py

@@ -0,0 +1,42 @@
+#!/usr/bin/env python3
+
+from collections import namedtuple
+
+class ConvenienceAccess:
+    def __init_subclass__(cls):
+        for abbrev, field in getattr(cls, '_ABBREVS', {}).items():
+            setattr(cls, abbrev, property(lambda self, f=field: getattr(self, f)))
+
+    def update(self, **kwargs):
+        for abbrev, field in getattr(self, '_ABBREVS', {}).items():
+            if abbrev in kwargs:
+                kwargs[field] = kwargs.pop(abbrev)
+        return self._replace(**kwargs)
+
+class Power(namedtuple('Power', 'signal nonlinear_interference amplified_spontaneous_emission'), ConvenienceAccess):
+    _ABBREVS = {'nli': 'nonlinear_interference',
+                'ase': 'amplified_spontaneous_emission',}
+
+class Channel(namedtuple('Channel', 'channel_number frequency modulation baud_rate alpha power'), ConvenienceAccess):
+    _ABBREVS = {'channel': 'channel_number',
+                'num_chan':      'channel_number',
+                'num_carriers': 'num_carriers',
+                'ffs':     'frequency',
+                'freq':    'frequency',}
+
+class SpectralInformation(namedtuple('SpectralInformation', 'carriers'), ConvenienceAccess):
+    def __new__(cls, *carriers):
+        return super().__new__(cls, carriers)
+
+if __name__ == '__main__':
+    si = SpectralInformation(
+        Channel(1, 193.95e12, '16-qam', 32e9, 0,  # 193.95 THz, 32 Gbaud
+            Power(1e-3, 1e-6, 1e-6)),             # 1 mW, 1uW, 1uW
+        Channel(1, 195.95e12, '16-qam', 32e9, 0,  # 195.95 THz, 32 Gbaud
+            Power(1.2e-3, 1e-6, 1e-6)),           # 1.2 mW, 1uW, 1uW
+    )
+    print(f'si = {si}')
+    print(f'si = {si.carriers[0].power.nli}')
+    si2 = si.update(carriers=tuple(c.update(power = c.power.update(nli = c.power.nli * 1e5))
+                              for c in si.carriers))
+    print(f'si2 = {si2}')

examples/convert.py

@@ -10,12 +10,36 @@ from collections import namedtuple, Counter
 from itertools import chain
 from json import dumps
 from uuid import uuid4
+import math
+import numpy as np
 
 Node = namedtuple('Node', 'city state country region latitude longitude')
 class Link(namedtuple('Link', 'from_city to_city distance distance_units')):
     def __new__(cls, from_city, to_city, distance, distance_units='km'):
         return super().__new__(cls, from_city, to_city, distance, distance_units)
 
+def define_span_range(min_span, max_span, nspans):
+    srange = (max_span - min_span) + min_span*np.random.rand(nspans)
+    return srange
+
+def amp_spacings(min_span,max_span,length):
+    nspans =  math.ceil(length/100)
+    spans = define_span_range(min_span, max_span, nspans)
+    tot = spans.sum()
+    delta = length -tot
+    if delta > 0 and delta < 25:
+        ind  = np.where(np.min(spans))
+        spans[ind] = spans[ind] + delta
+    elif delta >= 25 and delta < 40:
+        spans = spans + delta/float(nspans)
+    elif delta > 40 and delta < 100:
+        spans = np.append(spans,delta)
+    elif delta > 100:
+        spans  = np.append(spans, [delta/2, delta/2])
+    elif delta < 0:
+        spans = spans + delta/float(nspans)
+    return list(spans)
+
 def parse_excel(args):
     with open_workbook(args.workbook) as wb:
         nodes_sheet = wb.sheet_by_name('Nodes')

examples/edfa_model/DFG_96.txt

@@ -0,0 +1 @@
+   2.5135969849999999e+01   2.5118228139999999e+01   2.5095421330000001e+01   2.5062457710000000e+01   2.5026027650000000e+01   2.4996379529999999e+01   2.4981672549999999e+01   2.4975306679999999e+01   2.4983207260000000e+01   2.4997185649999999e+01   2.5017572470000001e+01   2.5038327809999998e+01   2.5054955849999999e+01   2.5067071899999998e+01   2.5070914110000000e+01   2.5070943650000000e+01   2.5071143240000001e+01   2.5075336270000001e+01   2.5087310179999999e+01   2.5103139360000000e+01   2.5122762040000001e+01   2.5142394790000001e+01   2.5159456330000001e+01   2.5173927039999999e+01   2.5176737670000001e+01   2.5170371410000001e+01   2.5152162539999999e+01   2.5131143099999999e+01   2.5108023350000000e+01   2.5085487770000000e+01   2.5069166750000001e+01   2.5058481759999999e+01   2.5054473130000002e+01   2.5051544410000002e+01   2.5049460589999999e+01   2.5047178490000000e+01   2.5045516559999999e+01   2.5044676490000001e+01   2.5040729200000001e+01   2.5032854080000000e+01   2.5023488300000000e+01   2.5016592339999999e+01   2.5013321359999999e+01   2.5011234340000001e+01   2.5010300149999999e+01   2.5009365480000000e+01   2.5008739640000002e+01   2.5008425350000000e+01   2.5006964660000001e+01   2.5004043100000001e+01   2.5000709980000000e+01   2.4998423200000001e+01   2.4993063320000001e+01   2.4983524209999999e+01   2.4971251030000001e+01   2.4960381080000001e+01   2.4948887209999999e+01   2.4935314890000001e+01   2.4921319270000001e+01   2.4908986970000001e+01   2.4898965140000001e+01   2.4889584630000002e+01   2.4880838700000002e+01   2.4872100920000001e+01   2.4864620259999999e+01   2.4858397730000000e+01   2.4854458380000001e+01   2.4851554430000000e+01   2.4851766009999999e+01   2.4854080140000001e+01   2.4859096240000000e+01   2.4864744580000000e+01   2.4872034859999999e+01   2.4880365200000000e+01   2.4889106689999998e+01   2.4897213130000001e+01   2.4902826040000001e+01   2.4906566900000001e+01   2.4908650800000000e+01   2.4910939440000000e+01   2.4913430790000000e+01   2.4915923440000000e+01   2.4921553509999999e+01   2.4930318610000000e+01   2.4940528120000000e+01   2.4949046689999999e+01   2.4957571229999999e+01   2.4967818449999999e+01   2.4981800929999999e+01   2.4997826860000000e+01   2.5013931830000001e+01   2.5028098459999999e+01   2.5040325750000001e+01   2.5052569810000001e+01   2.5064797009999999e+01   2.5077046970000001e+01

examples/edfa_model/DGT_96.txt

@@ -0,0 +1 @@
+   2.7145266811316859e+00   2.7054438192385049e+00   2.6947834587664494e+00   2.6841217449620203e+00   2.6681935771243177e+00   2.6521732021128046e+00   2.6303964408153848e+00   2.6028603502864280e+00   2.5696460593920065e+00   2.5364027376452056e+00   2.4994462867966041e+00   2.4587748041127506e+00   2.4143984371852212e+00   2.3699990328716107e+00   2.3223736962293420e+00   2.2715207713712529e+00   2.2174389328192197e+00   2.1633756538423898e+00   2.1183028432496016e+00   2.0822250998736478e+00   2.0551007720052352e+00   2.0279625371819305e+00   2.0008103857988204e+00   1.9736443063300082e+00   1.9482128147680253e+00   1.9245345552113182e+00   1.9026104247588487e+00   1.8806927939516411e+00   1.8622356724442459e+00   1.8472755032011290e+00   1.8358140813807049e+00   1.8243814368429321e+00   1.8139629377087627e+00   1.8045606557581335e+00   1.7961751115773796e+00   1.7877868031023945e+00   1.7793941781790852e+00   1.7709972329654864e+00   1.7625959636196327e+00   1.7541903672600494e+00   1.7459181197626403e+00   1.7377807579136351e+00   1.7297783508684146e+00   1.7217732861435076e+00   1.7137640932265894e+00   1.7057507692361864e+00   1.6918150918099673e+00   1.6719047669939942e+00   1.6460167077689267e+00   1.6201194134191075e+00   1.5986915141218316e+00   1.5817353179379183e+00   1.5691997641843789e+00   1.5566577309558969e+00   1.5453741527614671e+00   1.5353620432989845e+00   1.5266220576235803e+00   1.5178910621476225e+00   1.5097346239790443e+00   1.5021530399096861e+00   1.4951454560626991e+00   1.4881342434792260e+00   1.4811193973568100e+00   1.4741004422522110e+00   1.4670307626366115e+00   1.4599103316162523e+00   1.4527395948591399e+00   1.4455651371583680e+00   1.4340878115214444e+00   1.4182738067303231e+00   1.3981208704326855e+00   1.3779439775587023e+00   1.3598972673004606e+00   1.3439818461440451e+00   1.3301807335621048e+00   1.3163839268630830e+00   1.3040618749785347e+00   1.2932153453410835e+00   1.2838336236692311e+00   1.2744470198196236e+00   1.2650555289898042e+00   1.2556591482982988e+00   1.2428104897182262e+00   1.2264996957264114e+00   1.2067249615595257e+00   1.1869318618366975e+00   1.1672278304018044e+00   1.1476135933863398e+00   1.1280891949729075e+00   1.1085552896156590e+00   1.0895983485572227e+00   1.0712204022764056e+00   1.0534217504465226e+00   1.0356155337864215e+00   1.0178077678537021e+00   1.0000000000000000e+00

examples/edfa_model/NFR_96.txt

@@ -0,0 +1 @@
+  -3.1537433199999998e-01  -3.1537433199999998e-01  -3.1540091571002721e-01  -3.1849146117510951e-01  -3.2158358425400546e-01  -3.2467728615499991e-01  -3.2762368641496226e-01  -3.2054138461232762e-01  -3.1345546385118733e-01  -3.0636592135697482e-01  -2.9920267890990127e-01  -2.7061972852631744e-01  -2.4202215770774693e-01  -2.1340995523361256e-01  -1.8478227130158695e-01  -1.4809761118389625e-01  -1.1139416731807622e-01  -7.4671925273579881e-02  -3.8026748965679924e-02  -1.9958469399422092e-02  -1.8809287980157928e-03   1.6205879960573561e-02   3.4301964005709673e-02   5.2407330474054062e-02   7.0521986509597359e-02   7.9578036683472006e-02   8.8546647361909522e-02   9.7519863231965306e-02   1.0649768784154924e-01   9.7741380449907406e-02   8.8803437172660038e-02   7.9860899732845866e-02   7.0913764587403796e-02   6.3335892740565308e-02   5.5756212252058776e-02   4.8172631747863209e-02   4.0585148217162359e-02   3.3381591675710129e-02   2.6178308595650738e-02   1.8971315351761126e-02   1.1760609076833628e-02   1.6950294922759991e-02   2.2274991357701439e-02   2.7602433189104329e-02   3.2932622540790261e-02   3.8265561538776145e-02   4.3601252311271169e-02   3.4856990743481552e-02   2.5991055149117932e-02   1.7120541224980364e-02   8.2757587359203223e-03   1.9423214065246042e-03  -4.3943890171043590e-03  -1.0734375072893196e-02  -1.7077639301414434e-02  -2.4679702899572852e-02  -3.2297970403821680e-02  -3.9920180090477250e-02  -4.7534566327530239e-02  -4.9234003141433724e-02  -5.0934320036547187e-02  -5.2635517696692252e-02  -5.4337596806402461e-02  -5.6040558050919301e-02  -5.7718452237076875e-02  -5.6840590379175944e-02  -5.5962273198734966e-02  -5.5083500341416583e-02  -5.4204271452516814e-02  -5.8396088726955113e-02  -6.2627330169715334e-02  -6.6860769089203700e-02  -7.0901736256069450e-02  -5.2096097309052243e-02  -3.3280684121412940e-02  -1.4455489070928059e-02   4.3150387579057158e-03   1.4839202394482527e-02   2.5368841662503576e-02   3.5903960836465652e-02   4.6444564195321399e-02   5.6990656022467459e-02   6.7542240605774059e-02   1.0002709623672751e-01   1.3258013095133617e-01   1.6515013362773309e-01   1.9773711753599391e-01   2.3194802687829724e-01   2.6618779883837107e-01   3.0044543658085349e-01   3.3472095409250663e-01   3.5929034770587287e-01   3.8384389188855605e-01   4.0841026111391787e-01   4.3298946543290784e-01   4.3298946543290784e-01

examples/edfa_model/Pchan2D.txt

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+  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01  -1.4460000000000001e+01
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examples/edfa_model/Pchan2DLegend.txt

@@ -0,0 +1,8 @@
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+   1.0400000000000000e+02	   1.0100000000000000e+02	   9.7000000000000000e+01	   1.1800000000000000e+02	   1.2100000000000000e+02	   3.2000000000000000e+01	   1.1400000000000000e+02	   1.0100000000000000e+02	   1.0000000000000000e+02	   3.2000000000000000e+01	   3.2000000000000000e+01	   3.2000000000000000e+01	   3.2000000000000000e+01	   3.2000000000000000e+01	   3.2000000000000000e+01	
+   1.1900000000000000e+02	   1.1100000000000000e+02	   1.1400000000000000e+02	   1.1500000000000000e+02	   1.1600000000000000e+02	   3.2000000000000000e+01	   9.9000000000000000e+01	   9.7000000000000000e+01	   1.1500000000000000e+02	   1.0100000000000000e+02	   3.2000000000000000e+01	   3.2000000000000000e+01	   3.2000000000000000e+01	   3.2000000000000000e+01	   3.2000000000000000e+01	

examples/edfa_model/amplifier.py

@@ -0,0 +1,300 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Nov 27 12:32:04 2017
+
+@author: briantaylor
+"""
+import numpy as np
+from numpy import polyfit, polyval, mean
+from utilities import lin2db, db2lin, itufs, freq2wavelength
+import matplotlib.pyplot as plt
+from scipy.constants import h
+
+
+def noise_profile(nf, gain, ffs, df):    
+    """ noise_profile(nf, gain, ffs, df) computes amplifier ase
+    
+    :param nf: Noise figure in dB
+    :param gain: Actual gain calculated for the EDFA in dB units
+    :param ffs: A numpy array of frequencies
+    :param df: the reference bw in THz
+    :type nf: numpy.ndarray 
+    :type gain: numpy.ndarray 
+    :type ffs: numpy.ndarray 
+    :type df: float
+    :return: the asepower in dBm
+    :rtype: numpy.ndarray
+    
+    ASE POWER USING PER CHANNEL GAIN PROFILE
+    INPUTS:
+    NF_dB - Noise figure in dB, vector of length number of channels or 
+            spectral slices
+    G_dB  - Actual gain calculated for the EDFA, vector of length number of 
+            channels or spectral slices
+    ffs     - Center frequency grid of the channels or spectral slices in THz,
+            vector of length number of channels or spectral slices
+    dF    - width of each channel or spectral slice in THz, 
+            vector of length number of channels or spectral slices
+    OUTPUT:
+        ase_dBm - ase in dBm per channel or spectral slice
+    NOTE: the output is the total ASE in the channel or spectral slice. For 
+    50GHz channels the ASE BW is effectively 0.4nm. To get to noise power in
+    0.1nm, subtract 6dB.
+    
+    ONSR is usually quoted as channel power divided by
+    the ASE power in 0.1nm RBW, regardless of the width of the actual
+    channel.  This is a historical convention from the days when optical
+    signals were much smaller (155Mbps, 2.5Gbps, ... 10Gbps) than the
+    resolution of the OSAs that were used to measure spectral power which
+    were set to 0.1nm resolution for convenience.  Moving forward into
+    flexible grid and high baud rate signals, it may be convenient to begin
+    quoting power spectral density in the same BW for both signal and ASE,
+    e.g. 12.5GHz."""
+    
+    h_mWThz = 1e-3*h*(1e14)**2 
+    nf_lin = db2lin(nf)
+    g_lin = db2lin(gain)
+    ase = h_mWThz*df*ffs*(nf_lin*g_lin - 1)
+    asedb = lin2db(ase)
+    
+    return asedb
+
+
+def gain_profile(dfg, dgt, Pin , gp , gtp):
+    """ 
+    :param dfg: design flat gain
+    :param dgt: design gain tilt
+    :param Pin: channing input power profile
+    :param gp: Average gain setpoint in dB units
+    :param gtp: gain tilt setting
+    :type dfg: 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:
+        DFG - 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.
+        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 make all values linear unit and convert to dB units as needed within
+    # this function.
+    nchan = list(range(len(Pin)))
+    
+    # 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(db2lin(Pin)))
+    avg_gain_db = lin2db(mean(db2lin(dfg)))
+    
+    #Linear fit to get the
+    p = polyfit(nchan, 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 = gtp / (len(nchan)-1)
+    
+    # 1st estimate of DGT scaling
+    dgts1 = targ_slope / dgt_slope
+    
+    # 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 = dfg + dgt*dgts1
+        voa = lin2db(mean(db2lin(g1st))) - gp
+        
+        # 2nd estimate of Amp ch gain using the channel input profile
+        g2nd = g1st - voa
+        pout_db = lin2db(np.sum(db2lin(Pin + g2nd)))
+        dgts2 = gp - (pout_db - tot_in_power_db)
+        
+        #Center estimate of amp ch gain
+        xcent = dgts2
+        gcent = g1st - voa + dgt*xcent
+        pout_db = lin2db(np.sum(db2lin(Pin + gcent)))
+        gavg_cent = pout_db - tot_in_power_db
+        
+        # Lower estimate of Amp ch gain
+        deltax = np.max(g1st) - np.min(g1st)
+        xlow = dgts2 - deltax
+        glow = g1st - voa + xlow*dgt
+        pout_db = lin2db(np.sum(db2lin(Pin + glow)))
+        gavg_low = pout_db - tot_in_power_db
+ 
+        # Upper gain estimate
+        xhigh =dgts2 + deltax
+        ghigh = g1st - voa + xhigh*dgt
+        pout_db = lin2db(np.sum(db2lin(Pin + 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(gp - gavg_cent) <= err_tolerance:
+            dgts3 = xcent
+        elif gp < gavg_cent:
+            dgts3 = xcent - (gavg_cent - gp)/slope1
+        else:
+            dgts3 = xcent + (-gavg_cent + gp)/slope2
+        
+        gprofile = g1st - voa +dgt*dgts3
+    else:
+        gprofile = None
+        
+    return gprofile
+
+if __name__ == '__main__':
+    
+    plt.close('all')
+    fc = itufs(0.05)
+    lc = freq2wavelength(fc)/1000
+    nchan = list(range(len(lc)))
+    df = np.array([0.05]*(nchan[-1] + 1))
+    # TODO remove path dependence
+    path = ''
+    
+    """
+    DFG_96:  Design flat gain at each wavelength in the 96 channel 50GHz ITU
+    grid in dB.  This can be experimentally determined by measuring the gain 
+    at each wavelength using a full, flat channel (or ASE) load at the input.
+    The amplifier should be set to its maximum flat gain (tilt = 0dB).  This 
+    measurement captures the ripple of the amplifier.  If the amplifier was 
+    designed to be mimimum ripple at some other tilt value, then the ripple
+    reflected in this measurement will not be that minimum.  However, when
+    the DGT gets applied through the provisioning of tilt, the model should
+    accurately reproduce the expected ripple at that tilt value.  One could
+    also do the measurement at some expected tilt value and back-calculate
+    this vector using the DGT method.  Alternatively, one could re-write the
+    algorithm to accept a nominal tilt and a tiled version of this vector.
+    """
+    
+    dfg_96 =  np.loadtxt(path + 'DFG_96.txt')
+    
+    """maximum gain for flat operation - the amp in the data file was designed
+    for 25dB gain and has an internal VOA for setting the external gain
+    """
+    
+    avg_dfg = dfg_96.mean()
+    
+    """
+    DGT_96:  This is the so-called Dynamic Gain Tilt of the EDFA in dB/dB. It
+    is the change in gain at each wavelength corresponding to a 1dB change at
+    the longest wavelength supported.  The value can be obtained
+    experimentally or through analysis of the cross sections or Giles
+    parameters of the Er fibre.  This is experimentally measured by changing 
+    the gain of the amplifier above the maximum flat gain while not changing 
+    the internal VOA (i.e. the mid-stage VOA is set to minimum and does not 
+    change during the measurement). Note that the measurement can change the 
+    gain by an arbitrary amount and divide by the gain change (in dB) which
+    is measured at the reference wavelength (the red end of the band).
+    """
+    
+    dgt_96 = np.loadtxt(path + 'DGT_96.txt')
+  
+    """
+    pNFfit3:  Cubic polynomial fit coefficients to noise figure in dB 
+    averaged across wavelength as a function of gain change from design flat:  
+    
+        NFavg = pNFfit3(1)*dG^3 + pNFfit3(2)*dG^2 pNFfit3(3)*dG + pNFfit3(4)
+    where 
+        dG = GainTarget - average(DFG_96)
+    note that dG will normally be a negative value.
+    """
+    
+    nf_fitco = np.loadtxt(path + 'pNFfit3.txt')
+    
+    """NFR_96:  Noise figure ripple in dB away from the average noise figure
+    across the band.  This captures the wavelength dependence of the NF.  To
+    calculate the NF across channels, one uses the cubic fit coefficients
+    with the external gain target to get the average nosie figure, NFavg and 
+    then adds this to NFR_96:
+    NF_96 = NFR_96 + NFavg
+    """ 
+    
+    nf_ripple = np.loadtxt(path + 'NFR_96.txt')
+    
+    # This is an example to set the provisionable gain and gain-tilt values
+    # Tilt is in units of dB/THz
+    gain_target = 20.0
+    tilt_target = -0.7
+    
+    # calculate the NF for the EDFA at this gain setting
+    dg = gain_target - avg_dfg
+    nf_avg = polyval(nf_fitco, dg)
+    nf_96 = nf_ripple + nf_avg
+    
+    # get the input power profiles to show
+    pch2d =  np.loadtxt(path + 'Pchan2D.txt')
+    
+    # Load legend and assemble legend text
+    pch2d_legend_data = np.loadtxt(path + 'Pchan2DLegend.txt')
+    pch2d_legend = []
+    for ea in pch2d_legend_data:
+        s = ''.join([chr(xx) for xx in ea.astype(dtype=int)]).strip()
+        pch2d_legend.append(s)
+        
+    #assemble plot
+    axis_font = {'fontname': 'Arial', 'size':'16', 'fontweight':'bold'}
+    title_font = {'fontname': 'Arial', 'size':'17', 'fontweight':'bold'}
+    tic_font = {'fontname': 'Arial', 'size':'12'} 
+
+    plt.rcParams["font.family"] = "Arial"
+    plt.figure()
+    plt.plot(nchan, pch2d.T, '.-', lw=2)
+    plt.xlabel('Channel Number', **axis_font)
+    plt.ylabel('Channel Power [dBm]', **axis_font)
+    plt.title('Input Power Profiles for Different Channel Loading', 
+              **title_font)
+    plt.legend(pch2d_legend, loc=5)
+    plt.grid()
+    plt.ylim((-100, -10))
+    plt.xlim((0,110))
+    plt.xticks(np.arange(0,100,10), **tic_font)
+    plt.yticks(np.arange(-110,-10,10), **tic_font)
+
+    plt.figure()
+    ea = pch2d[1,:]
+    for ea in pch2d:
+        chgain = gain_profile(dfg_96, dgt_96, ea, gain_target, tilt_target)
+        pase = noise_profile(nf_96, chgain, fc, df)
+        pout = lin2db(db2lin(ea + chgain) + db2lin(pase))
+        plt.plot(nchan, pout, '.-', lw=2)
+    plt.title('Output Power with ASE for Different Channel Loading', 
+              **title_font)
+    plt.xlabel('Channel Number', **axis_font)
+    plt.ylabel('Channel Power [dBm]', **axis_font)
+    plt.grid()
+    plt.ylim((-50, 10))
+    plt.xlim((0,100))
+    plt.xticks(np.arange(0,100,10), **tic_font)
+    plt.yticks(np.arange(-50,10,10), **tic_font)
+    plt.legend(pch2d_legend, loc=5)
+ 

examples/edfa_model/pNFfit3.txt

@@ -0,0 +1 @@
+   1.6824099999999999e-04   4.6996099999999999e-02   3.5954899999999998e-02   5.8285099999999996e+00

examples/edfa_model/utilities.py

@@ -0,0 +1,120 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Fri Nov 10 17:50:46 2017
+
+@author: briantaylor
+"""
+import numpy as np
+from numpy import pi, cos, sqrt, log10
+
+def c():
+    """
+    Returns the speed of light in meters per second
+    """
+    return 299792458.0
+
+def itufs(spacing, startf=191.35, stopf=196.10):
+    """Creates an array of frequencies whose default range is
+    191.35-196.10 THz
+    
+    :param spacing: Frequency spacing in THz
+    :param starf: Start frequency in THz
+    :param stopf: Stop frequency in THz
+    :type spacing: float
+    :type startf: float
+    :type stopf: float
+    :return an array of frequnecies determined by the spacing parameter
+    :rtype: numpy.ndarray
+    """
+    return np.arange(startf, stopf + spacing/2, spacing)
+
+def h():
+    """
+    Returns plank's constant in J*s
+    """
+    return 6.62607004e-34
+
+def lin2db(value):
+    return 10*log10(value)
+
+
+def db2lin(value):
+    return 10**(value/10)
+    
+
+def wavelength2freq(value):
+    """ Converts wavelength units to frequeuncy units.
+    """
+    return c()/value
+
+
+def freq2wavelength(value):
+    """ Converts frequency units to wavelength units.
+    """
+    return c()/value
+
+
+def deltawl2deltaf(delta_wl, wavelength):
+    """ deltawl2deltaf(delta_wl, wavelength):
+    delta_wl is BW in wavelength units
+    wavelength is the center wl
+    units for delta_wl and wavelength must be same
+    
+        
+
+    :param delta_wl: delta wavelength BW in same units as wavelength
+    :param wavelength: wavelength BW is relevant for
+    :type delta_wl: float or numpy.ndarray
+    :type wavelength: float
+    :return: The BW in frequency units
+    :rtype: float or ndarray
+
+    """ 
+    f = wavelength2freq(wavelength)
+    return delta_wl*f/wavelength
+
+
+def deltaf2deltawl(delta_f, frequency):
+    """ deltawl2deltaf(delta_f, frequency):
+        converts delta frequency to delta wavelength
+        units for delta_wl and wavelength must be same
+    
+    
+    :param delta_f: delta frequency in same units as frequency
+    :param frequency: frequency BW is relevant for
+    :type delta_f: float or numpy.ndarray
+    :type frequency: float
+    :return: The BW in wavelength units
+    :rtype: float or ndarray
+    
+    """
+    wl = freq2wavelength(frequency)
+    return delta_f*wl/frequency
+
+
+def rrc(ffs, baud_rate, alpha):
+    """ rrc(ffs, baud_rate, alpha): computes the root-raised cosine filter 
+    function.
+    
+    :param ffs: A numpy array of frequencies
+    :param baud_rate: The Baud Rate of the System
+    :param alpha: The roll-off factor of the filter
+    :type ffs: numpy.ndarray
+    :type baud_rate: float
+    :type alpha: float
+    :return: hf a numpy array of the filter shape
+    :rtype: numpy.ndarray
+    
+    """
+    Ts = 1/baud_rate
+    l_lim = (1 - alpha)/(2 * Ts)
+    r_lim = (1 + alpha)/(2 * Ts)
+    hf = np.zeros(np.shape(ffs))
+    slope_inds = np.where(
+            np.logical_and(np.abs(ffs) > l_lim, np.abs(ffs) < r_lim))
+    hf[slope_inds] = 0.5 * (1 + cos((pi * Ts / alpha) * 
+            (np.abs(ffs[slope_inds]) - l_lim)))
+    p_inds = np.where(np.logical_and(np.abs(ffs) > 0, np.abs(ffs) < l_lim))
+    hf[p_inds] =  1
+    return sqrt(hf)

utilities.py

@@ -0,0 +1,120 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Fri Nov 10 17:50:46 2017
+
+@author: briantaylor
+"""
+import numpy as np
+from numpy import pi, cos, sqrt, log10
+
+def c():
+    """
+    Returns the speed of light in meters per second
+    """
+    return 299792458.0
+
+def itufs(spacing, startf=191.35, stopf=196.10):
+    """Creates an array of frequencies whose default range is
+    191.35-196.10 THz
+    
+    :param spacing: Frequency spacing in THz
+    :param starf: Start frequency in THz
+    :param stopf: Stop frequency in THz
+    :type spacing: float
+    :type startf: float
+    :type stopf: float
+    :return an array of frequnecies determined by the spacing parameter
+    :rtype: numpy.ndarray
+    """
+    return np.arange(startf, stopf + spacing/2, spacing)
+
+def h():
+    """
+    Returns plank's constant in J*s
+    """
+    return 6.62607004e-34
+
+def lin2db(value):
+    return 10*log10(value)
+
+
+def db2lin(value):
+    return 10**(value/10)
+    
+
+def wavelength2freq(value):
+    """ Converts wavelength units to frequeuncy units.
+    """
+    return c()/value
+
+
+def freq2wavelength(value):
+    """ Converts frequency units to wavelength units.
+    """
+    return c()/value
+
+
+def deltawl2deltaf(delta_wl, wavelength):
+    """ deltawl2deltaf(delta_wl, wavelength):
+    delta_wl is BW in wavelength units
+    wavelength is the center wl
+    units for delta_wl and wavelength must be same
+    
+        
+
+    :param delta_wl: delta wavelength BW in same units as wavelength
+    :param wavelength: wavelength BW is relevant for
+    :type delta_wl: float or numpy.ndarray
+    :type wavelength: float
+    :return: The BW in frequency units
+    :rtype: float or ndarray
+
+    """ 
+    f = wavelength2freq(wavelength)
+    return delta_wl*f/wavelength
+
+
+def deltaf2deltawl(delta_f, frequency):
+    """ deltawl2deltaf(delta_f, frequency):
+        converts delta frequency to delta wavelength
+        units for delta_wl and wavelength must be same
+    
+    
+    :param delta_f: delta frequency in same units as frequency
+    :param frequency: frequency BW is relevant for
+    :type delta_f: float or numpy.ndarray
+    :type frequency: float
+    :return: The BW in wavelength units
+    :rtype: float or ndarray
+    
+    """
+    wl = freq2wavelength(frequency)
+    return delta_f*wl/frequency
+
+
+def rrc(ffs, baud_rate, alpha):
+    """ rrc(ffs, baud_rate, alpha): computes the root-raised cosine filter 
+    function.
+    
+    :param ffs: A numpy array of frequencies
+    :param baud_rate: The Baud Rate of the System
+    :param alpha: The roll-off factor of the filter
+    :type ffs: numpy.ndarray
+    :type baud_rate: float
+    :type alpha: float
+    :return: hf a numpy array of the filter shape
+    :rtype: numpy.ndarray
+    
+    """
+    Ts = 1/baud_rate
+    l_lim = (1 - alpha)/(2 * Ts)
+    r_lim = (1 + alpha)/(2 * Ts)
+    hf = np.zeros(np.shape(ffs))
+    slope_inds = np.where(
+            np.logical_and(np.abs(ffs) > l_lim, np.abs(ffs) < r_lim))
+    hf[slope_inds] = 0.5 * (1 + cos((pi * Ts / alpha) * 
+            (np.abs(ffs[slope_inds]) - l_lim)))
+    p_inds = np.where(np.logical_and(np.abs(ffs) > 0, np.abs(ffs) < l_lim))
+    hf[p_inds] =  1
+    return sqrt(hf)