diff --git a/docs/json.rst b/docs/json.rst
index c5087a1a..535ef3c4 100644
--- a/docs/json.rst
+++ b/docs/json.rst
@@ -93,6 +93,33 @@ The fiber library currently describes SSMF and NZDF but additional fiber types c
 
 .. _Corning whitepaper on MFD/EA: https://www.corning.com/microsites/coc/oem/documents/specialty-fiber/WP7071-Mode-Field-Diam-and-Eff-Area.pdf
 
+RamanFiber
+~~~~~~~~~~
+
+The RamanFiber can be used to simulate Raman amplification through dedicated Raman pumps. The Raman pumps must be listed
+in the key ``raman_pumps`` within the RamanFiber ``operational`` dictionary. The description of each Raman pump must
+contain the following:
+
++---------------------------+-----------+------------------------------------------------------------+
+| field                     | type      | description                                                |
++===========================+===========+============================================================+
+| ``power``                 | (number)  | Total pump power in :math:`W`                              |
+|                           |           | considering a depolarized pump                             |
++---------------------------+-----------+------------------------------------------------------------+
+| ``frequency``             | (number)  | Pump central frequency in :math:`Hz`                       |
++---------------------------+-----------+------------------------------------------------------------+
+| ``propagation_direction`` | (number)  | The pumps can propagate in the same or opposite direction  |
+|                           |           | with respect the signal. Valid choices are ``coprop`` and  |
+|                           |           | ``counterprop``, respectively                              |
++---------------------------+-----------+------------------------------------------------------------+
+
+Beside the list of Raman pumps, the RamanFiber ``operational`` dictionary must include the ``temperature`` that affects
+the amplified spontaneous emission noise generated by the Raman amplification.
+As the loss coefficient significantly varies outside the C-band, where the Raman pumps are usually placed,
+it is suggested to include an estimation of the loss coefficient for the Raman pump central frequencies within
+a dictionary-like definition of the ``RamanFiber.params.loss_coef``
+(e.g. ``loss_coef = {"value": [0.18, 0.18, 0.20, 0.20], "frequency": [191e12, 196e12, 200e12, 210e12]}``).
+
 Transceiver
 ~~~~~~~~~~~
 
@@ -191,45 +218,57 @@ For amplifiers defined in the topology JSON input but whose ``gain = 0`` (placeh
 The file ``sim_params.json`` contains the tuning parameters used within both the ``gnpy.science_utils.RamanSolver`` and
 the ``gnpy.science_utils.NliSolver`` for the evaluation of the Raman profile and the NLI generation, respectively.
 
-+-----------------------------------------+-----------+---------------------------------------------+
-| field                                   |   type    | description                                 |
-+=========================================+===========+=============================================+
-| ``raman_params.flag``                   | (boolean) | Enable/Disable the Raman effect that        |
-|                                         |           | produces a power transfer from higher to    |
-|                                         |           | lower frequencies.                          |
-|                                         |           | In general, considering the Raman effect    |
-|                                         |           | provides more accurate results. It is       |
-|                                         |           | mandatory when Raman amplification is       |
-|                                         |           | included in the simulation                  |
-+-----------------------------------------+-----------+---------------------------------------------+
-| ``raman_params.space_resolution``       | (number)  | Spatial resolution of the output            |
-|                                         |           | Raman profile along the entire fiber span.  |
-|                                         |           | This affects the accuracy and the           |
-|                                         |           | computational time of the NLI               |
-|                                         |           | calculation when the GGN method is used:    |
-|                                         |           | smaller the space resolution higher both    |
-|                                         |           | the accuracy and the computational time.    |
-|                                         |           | In C-band simulations, with input power per |
-|                                         |           | channel around 0 dBm, a suggested value of  |
-|                                         |           | space resolution is 10e3 m                  |
-+-----------------------------------------+-----------+---------------------------------------------+
-| ``nli_params.method``                   | (string)  | Model used for the NLI evaluation. Valid    |
-|                                         |           | choices are ``gn_model_analytic`` (see      |
-|                                         |           | eq. 120 from `arXiv:1209.0394               |
-|                                         |           | <https://arxiv.org/abs/1209.0394>`_) and    |
-|                                         |           | ``ggn_spectrally_separated`` (see eq. 21    |
-|                                         |           | from `arXiv:1710.02225                      |
-|                                         |           | <https://arxiv.org/abs/1710.02225>`_).      |
-+-----------------------------------------+-----------+---------------------------------------------+
-| ``nli_params.computed_channels``        | (number)  | The channels on which the NLI is            |
-|                                         |           | explicitly evaluated.                       |
-|                                         |           | The NLI of the other channels is            |
-|                                         |           | interpolated using ``numpy.interp``.        |
-|                                         |           | In a C-band simulation with 96 channels in  |
-|                                         |           | a 50 GHz spacing fix-grid we recommend at   |
-|                                         |           | one computed channel every 20 channels.     |
-+-----------------------------------------+-----------+---------------------------------------------+
-
++---------------------------------------------+-----------+---------------------------------------------+
+| field                                       |   type    | description                                 |
++=============================================+===========+=============================================+
+| ``raman_params.flag``                       | (boolean) | Enable/Disable the Raman effect that        |
+|                                             |           | produces a power transfer from higher to    |
+|                                             |           | lower frequencies.                          |
+|                                             |           | In general, considering the Raman effect    |
+|                                             |           | provides more accurate results. It is       |
+|                                             |           | mandatory when Raman amplification is       |
+|                                             |           | included in the simulation                  |
++---------------------------------------------+-----------+---------------------------------------------+
+| ``raman_params.result_spatial_resolution``  | (number)  | Spatial resolution of the output            |
+|                                             |           | Raman profile along the entire fiber span.  |
+|                                             |           | This affects the accuracy and the           |
+|                                             |           | computational time of the NLI               |
+|                                             |           | calculation when the GGN method is used:    |
+|                                             |           | smaller the spatial resolution higher both  |
+|                                             |           | the accuracy and the computational time.    |
+|                                             |           | In C-band simulations, with input power per |
+|                                             |           | channel around 0 dBm, a suggested value of  |
+|                                             |           | spatial resolution is 10e3 m                |
++---------------------------------------------+-----------+---------------------------------------------+
+| ``raman_params.solver_spatial_resolution``  | (number)  | Spatial step for the iterative solution     |
+|                                             |           | of the first order differential equation    |
+|                                             |           | used to calculate the Raman profile         |
+|                                             |           | along the entire fiber span.                |
+|                                             |           | This affects the accuracy and the           |
+|                                             |           | computational time of the evaluated         |
+|                                             |           | Raman profile:                              |
+|                                             |           | smaller the spatial resolution higher both  |
+|                                             |           | the accuracy and the computational time.    |
+|                                             |           | In C-band simulations, with input power per |
+|                                             |           | channel around 0 dBm, a suggested value of  |
+|                                             |           | spatial resolution is 100 m                 |
++---------------------------------------------+-----------+---------------------------------------------+
+| ``nli_params.method``                       | (string)  | Model used for the NLI evaluation. Valid    |
+|                                             |           | choices are ``gn_model_analytic`` (see      |
+|                                             |           | eq. 120 from `arXiv:1209.0394               |
+|                                             |           | <https://arxiv.org/abs/1209.0394>`_) and    |
+|                                             |           | ``ggn_spectrally_separated`` (see eq. 21    |
+|                                             |           | from `arXiv:1710.02225                      |
+|                                             |           | <https://arxiv.org/abs/1710.02225>`_).      |
++---------------------------------------------+-----------+---------------------------------------------+
+| ``nli_params.computed_channels``            | (number)  | The channels on which the NLI is            |
+|                                             |           | explicitly evaluated.                       |
+|                                             |           | The NLI of the other channels is            |
+|                                             |           | interpolated using ``numpy.interp``.        |
+|                                             |           | In a C-band simulation with 96 channels in  |
+|                                             |           | a 50 GHz spacing fix-grid we recommend at   |
+|                                             |           | one computed channel every 20 channels.     |
++---------------------------------------------+-----------+---------------------------------------------+
 
 Span
 ~~~~
diff --git a/gnpy/core/elements.py b/gnpy/core/elements.py
index 1e8ca232..7452da0b 100644
--- a/gnpy/core/elements.py
+++ b/gnpy/core/elements.py
@@ -20,8 +20,8 @@ unique identifier and a printable name, and provide the :py:meth:`__call__` meth
 instance as a result.
 """
 
-from numpy import abs, array, divide, errstate, interp, mean, pi, polyfit, polyval, sum, sqrt, log10, exp,\
-    asarray, full, squeeze
+from numpy import abs, array, divide, errstate, ones, interp, mean, pi, polyfit, polyval, sum, sqrt, log10, exp,\
+    asarray, full, squeeze, zeros, append, flip, outer
 from scipy.constants import h, c
 from scipy.interpolate import interp1d
 from collections import namedtuple
@@ -30,7 +30,7 @@ from gnpy.core.utils import lin2db, db2lin, arrange_frequencies, snr_sum
 from gnpy.core.parameters import FiberParams, PumpParams
 from gnpy.core.science_utils import NliSolver, RamanSolver
 from gnpy.core.info import SpectralInformation
-from gnpy.core.exceptions import SpectrumError
+from gnpy.core.exceptions import NetworkTopologyError, SpectrumError
 
 
 class Location(namedtuple('Location', 'latitude longitude city region')):
@@ -313,9 +313,20 @@ class Fiber(_Node):
             params = {}
         super().__init__(*args, params=FiberParams(**params), **kwargs)
         self.pch_out_db = None
-        self.nli_solver = NliSolver(self)
         self.passive = True
 
+        # Raman efficiency matrix function of the delta frequency constructed such that each row is related to a
+        # fixed frequency: positive elements represent a gain (from higher frequency) and negative elements represent
+        # a loss (to lower frequency)
+        if self.params.raman_efficiency:
+            frequency_offset = self.params.raman_efficiency['frequency_offset']
+            frequency_offset = append(-flip(frequency_offset[1:]), frequency_offset)
+            cr = self.params.raman_efficiency['cr']
+            cr = append(- flip(cr[1:]), cr)
+            self._cr_function = lambda frequency: interp(frequency, frequency_offset, cr)
+        else:
+            self._cr_function = lambda frequency: zeros(squeeze(frequency).shape)
+
     @property
     def to_json(self):
         return {'uid': self.uid,
@@ -376,9 +387,6 @@ class Fiber(_Node):
         return self.loss_coef_func(self.params.ref_frequency) * self.params.length + \
             self.params.con_in + self.params.con_out + self.params.att_in
 
-    def lin_attenuation(self, frequency):
-        return 1 / db2lin(self.params.length * self.loss_coef_func(frequency))
-
     def alpha(self, frequency):
         """Returns the linear exponent attenuation coefficient such that
         :math: `lin_attenuation = e^{- alpha length}`
@@ -388,6 +396,17 @@ class Fiber(_Node):
         """
         return self.loss_coef_func(frequency) / (10 * log10(exp(1)))
 
+    def cr(self, frequency):
+        """Returns the raman efficiency matrix including the vibrational loss
+
+        :param frequency: the frequency at which cr is computed [Hz]
+        :return: cr: raman efficiency matrix [1 / (W m)]
+        """
+        df = outer(ones(frequency.shape), frequency) - outer(frequency, ones(frequency.shape))
+        cr = self._cr_function(df)
+        vibrational_loss = outer(frequency, ones(frequency.shape)) / outer(ones(frequency.shape), frequency)
+        return cr * (cr >= 0) + cr * (cr < 0) * vibrational_loss  # Raman efficiency [1/(W m)]
+
     def chromatic_dispersion(self, freq=None):
         """Returns accumulated chromatic dispersion (CD).
 
@@ -412,21 +431,25 @@ class Fiber(_Node):
         """Modifies the spectral information computing the attenuation, the non-linear interference generation,
         the CD and PMD accumulation.
         """
-
+        # apply the attenuation due to the input connector loss
         attenuation_in_db = self.params.con_in + self.params.att_in
         spectral_info.apply_attenuation_db(attenuation_in_db)
 
+        # inter channels Raman effect
+        stimulated_raman_scattering = RamanSolver.calculate_stimulated_raman_scattering(spectral_info, self)
+
         # NLI noise evaluated at the fiber input
-        nli = self.nli_solver.compute_nli(spectral_info)
-        spectral_info.nli += nli
+        spectral_info.nli += NliSolver.compute_nli(spectral_info, stimulated_raman_scattering, self)
 
         # chromatic dispersion and pmd variations
         spectral_info.chromatic_dispersion += self.chromatic_dispersion(spectral_info.frequency)
         spectral_info.pmd = sqrt(spectral_info.pmd ** 2 + self.pmd ** 2)
 
         # apply the attenuation due to the fiber losses
-        spectral_info.apply_attenuation_lin(self.lin_attenuation(spectral_info.frequency))
+        attenuation_fiber = stimulated_raman_scattering.loss_profile[:, -1]
+        spectral_info.apply_attenuation_lin(attenuation_fiber)
 
+        # apply the attenuation due to the output connector loss
         attenuation_out_db = self.params.con_out
         spectral_info.apply_attenuation_db(attenuation_out_db)
 
@@ -451,44 +474,50 @@ class Fiber(_Node):
 class RamanFiber(Fiber):
     def __init__(self, *args, params=None, **kwargs):
         super().__init__(*args, params=params, **kwargs)
-        if self.operational and 'raman_pumps' in self.operational:
-            self.raman_pumps = tuple(PumpParams(p['power'], p['frequency'], p['propagation_direction'])
-                                     for p in self.operational['raman_pumps'])
-        else:
-            self.raman_pumps = None
-        self.raman_solver = RamanSolver(self)
+        if not self.operational:
+            raise NetworkTopologyError(f'Fiber element uid:{self.uid} '
+                                       'defined as RamanFiber without operational parameters')
 
-    @property
-    def to_json(self):
-        return dict(super().to_json, operational=self.operational)
+        if 'raman_pumps' not in self.operational:
+            raise NetworkTopologyError(f'Fiber element uid:{self.uid} '
+                                       'defined as RamanFiber without raman pumps description in operational')
+
+        if 'temperature' not in self.operational:
+            raise NetworkTopologyError(f'Fiber element uid:{self.uid} '
+                                       'defined as RamanFiber without temperature in operational')
+
+        pump_loss = db2lin(self.params.con_out)
+        self.raman_pumps = tuple(PumpParams(p['power'] / pump_loss, p['frequency'], p['propagation_direction'])
+                                 for p in self.operational['raman_pumps'])
+        self.temperature = self.operational['temperature']
 
     def propagate(self, spectral_info: SpectralInformation):
         """Modifies the spectral information computing the attenuation, the non-linear interference generation,
         the CD and PMD accumulation.
         """
+        # apply the attenuation due to the input connector loss
         attenuation_in_db = self.params.con_in + self.params.att_in
         spectral_info.apply_attenuation_db(attenuation_in_db)
 
-        self.raman_solver.carriers = spectral_info.carriers
-        self.raman_solver.raman_pumps = self.raman_pumps
-        self.nli_solver.stimulated_raman_scattering = self.raman_solver.stimulated_raman_scattering
-        raman_ase = self.raman_solver.spontaneous_raman_scattering.power[:spectral_info.number_of_channels, -1]
+        # Raman pumps and inter channel Raman effect
+        stimulated_raman_scattering = RamanSolver.calculate_stimulated_raman_scattering(spectral_info, self)
+        spontaneous_raman_scattering = \
+            RamanSolver.calculate_spontaneous_raman_scattering(spectral_info, stimulated_raman_scattering, self)
 
-        # NLI noise evaluated at the fiber input
-        nli = self.nli_solver.compute_nli(spectral_info)
-        spectral_info.nli += nli
+        # nli and ase noise evaluated at the fiber input
+        spectral_info.nli += NliSolver.compute_nli(spectral_info, stimulated_raman_scattering, self)
+        spectral_info.ase += spontaneous_raman_scattering
 
         # chromatic dispersion and pmd variations
         spectral_info.chromatic_dispersion += self.chromatic_dispersion(spectral_info.frequency)
         spectral_info.pmd = sqrt(spectral_info.pmd ** 2 + self.pmd ** 2)
 
         # apply the attenuation due to the fiber losses
-        attenuation_fiber = \
-            self.raman_solver.stimulated_raman_scattering.rho[:spectral_info.number_of_channels, -1] ** 2
+        attenuation_fiber = stimulated_raman_scattering.loss_profile[:spectral_info.number_of_channels, -1]
+
         spectral_info.apply_attenuation_lin(attenuation_fiber)
 
-        spectral_info.ase += raman_ase
-
+        # apply the attenuation due to the output connector loss
         attenuation_out_db = self.params.con_out
         spectral_info.apply_attenuation_db(attenuation_out_db)
 
diff --git a/gnpy/core/parameters.py b/gnpy/core/parameters.py
index 7db9435c..59b78ba8 100644
--- a/gnpy/core/parameters.py
+++ b/gnpy/core/parameters.py
@@ -34,15 +34,15 @@ class PumpParams(Parameters):
 
 
 class RamanParams(Parameters):
-    def __init__(self, flag=False, space_resolution=10e3, tolerance=None):
+    def __init__(self, flag=False, result_spatial_resolution=10e3, solver_spatial_resolution=50):
         """ Simulation parameters used within the Raman Solver
-        :params flag: boolean for enabling/disable the evaluation of the Raman Power profile in frequency and position
-        :params space_resolution: spatial resolution of the evaluated Raman Power profile
-        :params tolerance: tuning parameter for scipy.integrate.solve_bvp solution
+        :params flag: boolean for enabling/disable the evaluation of the Raman power profile in frequency and position
+        :params result_spatial_resolution: spatial resolution of the evaluated Raman power profile
+        :params solver_spatial_resolution: spatial step for the iterative solution of the first order ode
         """
         self.flag = flag
-        self.space_resolution = space_resolution  # [m]
-        self.tolerance = tolerance
+        self.result_spatial_resolution = result_spatial_resolution  # [m]
+        self.solver_spatial_resolution = solver_spatial_resolution  # [m]
 
 
 class NLIParams(Parameters):
@@ -156,7 +156,6 @@ class FiberParams(Parameters):
             else:
                 self._loss_coef = asarray(kwargs['loss_coef']) * 1e-3  # lineic loss dB/m
                 self._f_loss_ref = asarray(self._ref_frequency)  # Hz
-            self._pumps_loss_coef = kwargs.get('pumps_loss_coef')
         except KeyError as e:
             raise ParametersError(f'Fiber configurations json must include {e}. Configuration: {kwargs}')
 
@@ -237,12 +236,10 @@ class FiberParams(Parameters):
     def raman_efficiency(self):
         return self._raman_efficiency
 
-    @property
-    def pumps_loss_coef(self):
-        return self._pumps_loss_coef
-
     def asdict(self):
         dictionary = super().asdict()
         dictionary['loss_coef'] = self.loss_coef * 1e3
         dictionary['length_units'] = 'm'
+        if not self.raman_efficiency:
+            dictionary.pop('raman_efficiency')
         return dictionary
diff --git a/gnpy/core/science_utils.py b/gnpy/core/science_utils.py
index f7d94ec0..20c4a065 100644
--- a/gnpy/core/science_utils.py
+++ b/gnpy/core/science_utils.py
@@ -10,15 +10,11 @@ Solver definitions to calculate the Raman effect and the nonlinear interference
 The solvers take as input instances of the spectral information, the fiber and the simulation parameters
 """
 
-from numpy import interp, pi, zeros, shape, where, cos, reshape, array, append, ones, argsort, nan, exp, arange, sqrt, \
-    empty, vstack, trapz, arcsinh, clip, abs, sum, outer, diag
-from operator import attrgetter
+from numpy import interp, pi, zeros, shape, where, cos, array, append, ones, exp, arange, sqrt, empty, trapz, arcsinh, \
+    clip, abs, sum, concatenate, flip, outer, inner, transpose, max, format_float_scientific, diag
 from logging import getLogger
-import scipy.constants as ph
-from scipy.integrate import solve_bvp
-from scipy.integrate import cumtrapz
+from scipy.constants import k, h
 from scipy.interpolate import interp1d
-from scipy.optimize import OptimizeResult
 from math import isclose
 
 from gnpy.core.utils import db2lin, lin2db
@@ -29,10 +25,10 @@ from gnpy.core.info import SpectralInformation
 logger = getLogger(__name__)
 sim_params = SimParams.get()
 
-
 def raised_cosine_comb(f, *carriers):
     """ Returns an array storing the PSD of a WDM comb of raised cosine shaped
     channels at the input frequencies defined in array f
+
     :param f: numpy array of frequencies in Hz
     :param carriers: namedtuple describing the WDM comb
     :return: PSD of the WDM comb evaluated over f
@@ -54,277 +50,199 @@ def raised_cosine_comb(f, *carriers):
     return psd
 
 
-class SpontaneousRamanScattering:
-    def __init__(self, frequency, z, power):
-        self.frequency = frequency
-        self.z = z
-        self.power = power
-
-
 class StimulatedRamanScattering:
-    def __init__(self, frequency, z, rho, power):
+    def __init__(self, power_profile, loss_profile, frequency, z):
+        """
+        :params power_profile: power profile matrix along frequency and z [W]
+        :params loss_profile: power profile matrix along frequency and z [linear units]
+        :params frequency: channels frequencies array [Hz]
+        :params z: positions array [m]
+        """
+        self.power_profile = power_profile
+        self.loss_profile = loss_profile
+        # Field loss profile matrix along frequency and z
+        self.rho = sqrt(loss_profile)
         self.frequency = frequency
         self.z = z
-        self.rho = rho
-        self.power = power
 
 
 class RamanSolver:
-    def __init__(self, fiber=None):
-        """ Initialize the Raman solver object.
-        :param fiber: instance of elements.py/Fiber.
-        :param carriers: tuple of carrier objects
-        :param raman_pumps: tuple containing pumps characteristics
-        """
-        self._fiber = fiber
-        self._carriers = None
-        self._raman_pumps = None
-        self._stimulated_raman_scattering = None
-        self._spontaneous_raman_scattering = None
-
-    @property
-    def fiber(self):
-        return self._fiber
-
-    @property
-    def carriers(self):
-        return self._carriers
-
-    @carriers.setter
-    def carriers(self, carriers):
-        self._carriers = carriers
-        self._spontaneous_raman_scattering = None
-        self._stimulated_raman_scattering = None
-
-    @property
-    def raman_pumps(self):
-        return self._raman_pumps
-
-    @raman_pumps.setter
-    def raman_pumps(self, raman_pumps):
-        self._raman_pumps = raman_pumps
-        self._stimulated_raman_scattering = None
-
-    @property
-    def stimulated_raman_scattering(self):
-        if self._stimulated_raman_scattering is None:
-            self.calculate_stimulated_raman_scattering(self.carriers, self.raman_pumps)
-        return self._stimulated_raman_scattering
-
-    @property
-    def spontaneous_raman_scattering(self):
-        if self._spontaneous_raman_scattering is None:
-            self.calculate_spontaneous_raman_scattering(self.carriers, self.raman_pumps)
-        return self._spontaneous_raman_scattering
-
-    def calculate_spontaneous_raman_scattering(self, carriers, raman_pumps):
-        raman_efficiency = self.fiber.params.raman_efficiency
-        temperature = self.fiber.operational['temperature']
-
-        logger.debug('Start computing fiber Spontaneous Raman Scattering')
-        power_spectrum, freq_array, prop_direct, bn_array = self._compute_power_spectrum(carriers, raman_pumps)
-
-        alphap_fiber = self.fiber.alpha(freq_array)
-
-        freq_diff = abs(freq_array - reshape(freq_array, (len(freq_array), 1)))
-        interp_cr = interp1d(raman_efficiency['frequency_offset'], raman_efficiency['cr'])
-        cr = interp_cr(freq_diff)
-
-        # z propagation axis
-        z_array = self.stimulated_raman_scattering.z
-        ase_bc = zeros(freq_array.shape)
-
-        # calculate ase power
-        int_spontaneous_raman = self._int_spontaneous_raman(z_array, self._stimulated_raman_scattering.power,
-                                                            alphap_fiber, freq_array, cr, freq_diff, ase_bc,
-                                                            bn_array, temperature)
-
-        spontaneous_raman_scattering = SpontaneousRamanScattering(freq_array, z_array, int_spontaneous_raman.x)
-        logger.debug("Spontaneous Raman Scattering evaluated successfully")
-        self._spontaneous_raman_scattering = spontaneous_raman_scattering
+    """This class contains the methods to calculate the Raman scattering effect."""
 
     @staticmethod
-    def _compute_power_spectrum(carriers, raman_pumps=None):
+    def calculate_attenuation_profile(spectral_info: SpectralInformation, fiber):
+        """Evaluates the attenuation profile along the z axis for all the frequency propagating in the
+        fiber without considering the stimulated Raman scattering.
         """
-        Rearrangement of spectral and Raman pump information to make them compatible with Raman solver
-        :param carriers: a tuple of namedtuples describing the transmitted channels
-        :param raman_pumps: a namedtuple describing the Raman pumps
-        :return:
+        # z array definition
+        z = array([0, fiber.params.length])
+        frequency = spectral_info.frequency
+        alpha = fiber.alpha(frequency)
+        loss_profile = exp(- outer(alpha, z))
+        power_profile = outer(spectral_info.signal, ones(z.size)) * loss_profile
+        stimulated_raman_scattering = StimulatedRamanScattering(power_profile, loss_profile, frequency, z)
+        return stimulated_raman_scattering
+
+    @staticmethod
+    def calculate_stimulated_raman_scattering(spectral_info: SpectralInformation, fiber):
+        """Evaluates the Raman profile along the z axis for all the frequency propagated in the fiber
+        including the Raman pumps co- and counter-propagating
         """
-
-        # Signal power spectrum
-        pow_array = array([])
-        f_array = array([])
-        noise_bandwidth_array = array([])
-        for carrier in sorted(carriers, key=attrgetter('frequency')):
-            f_array = append(f_array, carrier.frequency)
-            pow_array = append(pow_array, carrier.power.signal)
-            ref_bw = carrier.baud_rate
-            noise_bandwidth_array = append(noise_bandwidth_array, ref_bw)
-
-        propagation_direction = ones(len(f_array))
-
-        # Raman pump power spectrum
-        if raman_pumps:
-            for pump in raman_pumps:
-                pow_array = append(pow_array, pump.power)
-                f_array = append(f_array, pump.frequency)
-                direction = +1 if pump.propagation_direction == 'coprop' else -1
-                propagation_direction = append(propagation_direction, direction)
-                noise_bandwidth_array = append(noise_bandwidth_array, ref_bw)
-
-        # Final sorting
-        ind = argsort(f_array)
-        f_array = f_array[ind]
-        pow_array = pow_array[ind]
-        propagation_direction = propagation_direction[ind]
-
-        return pow_array, f_array, propagation_direction, noise_bandwidth_array
-
-    def _int_spontaneous_raman(self, z_array, raman_matrix, alphap_fiber, freq_array,
-                               cr_raman_matrix, freq_diff, ase_bc, bn_array, temperature):
-        spontaneous_raman_scattering = OptimizeResult()
-
-        dx = sim_params.raman_params.space_resolution
-        h = ph.value('Planck constant')
-        kb = ph.value('Boltzmann constant')
-
-        power_ase = nan * ones(raman_matrix.shape)
-        int_pump = cumtrapz(raman_matrix, z_array, dx=dx, axis=1, initial=0)
-
-        for f_ind, f_ase in enumerate(freq_array):
-            cr_raman = cr_raman_matrix[f_ind, :]
-            vibrational_loss = f_ase / freq_array[:f_ind]
-            eta = 1 / (exp((h * freq_diff[f_ind, f_ind + 1:]) / (kb * temperature)) - 1)
-
-            int_fiber_loss = -alphap_fiber[f_ind] * z_array
-            int_raman_loss = sum((cr_raman[:f_ind] * vibrational_loss * int_pump[:f_ind, :].transpose()).transpose(),
-                                    axis=0)
-            int_raman_gain = sum((cr_raman[f_ind + 1:] * int_pump[f_ind + 1:, :].transpose()).transpose(), axis=0)
-
-            int_gain_loss = int_fiber_loss + int_raman_gain + int_raman_loss
-
-            new_ase = sum((cr_raman[f_ind + 1:] * (1 + eta) * raman_matrix[f_ind + 1:, :].transpose()).transpose()
-                             * h * f_ase * bn_array[f_ind], axis=0)
-
-            bc_evolution = ase_bc[f_ind] * exp(int_gain_loss)
-            ase_evolution = exp(int_gain_loss) * cumtrapz(new_ase * exp(-int_gain_loss), z_array, dx=dx, initial=0)
-
-            power_ase[f_ind, :] = bc_evolution + ase_evolution
-
-        spontaneous_raman_scattering.x = 2 * power_ase
-        return spontaneous_raman_scattering
-
-    def calculate_stimulated_raman_scattering(self, carriers, raman_pumps):
-        """ Returns stimulated Raman scattering solution including
-        fiber gain/loss profile.
-        :return: None
-        """
-        # fiber parameters
-        fiber_length = self.fiber.params.length
-        raman_efficiency = self.fiber.params.raman_efficiency
-
-        if not sim_params.raman_params.flag:
-            raman_efficiency['cr'] = zeros(len(raman_efficiency['cr']))
-        # raman solver parameters
-        z_resolution = sim_params.raman_params.space_resolution
-        tolerance = sim_params.raman_params.tolerance
-
         logger.debug('Start computing fiber Stimulated Raman Scattering')
 
-        power_spectrum, freq_array, prop_direct, _ = self._compute_power_spectrum(carriers, raman_pumps)
+        # Raman parameters
+        z_resolution = sim_params.raman_params.result_spatial_resolution
+        z_step = sim_params.raman_params.solver_spatial_resolution
+        z = append(arange(0, fiber.params.length, z_step), fiber.params.length)
+        z_final = append(arange(0, fiber.params.length, z_resolution), fiber.params.length)
 
-        alphap_fiber = self.fiber.alpha(freq_array)
+        if sim_params.raman_params.flag:
+            if hasattr(fiber, 'raman_pumps'):
+                # TODO: verify co-propagating pumps computation and in general unsorted frequency
+                # Co-propagating spectrum definition
+                co_raman_pump_power = array([pump.power for pump in fiber.raman_pumps
+                                             if pump.propagation_direction == 'coprop'])
+                co_raman_pump_frequency = array([pump.frequency for pump in fiber.raman_pumps
+                                                 if pump.propagation_direction == 'coprop'])
 
-        freq_diff = abs(freq_array - reshape(freq_array, (len(freq_array), 1)))
-        interp_cr = interp1d(raman_efficiency['frequency_offset'], raman_efficiency['cr'])
-        cr = interp_cr(freq_diff)
+                co_power = concatenate((spectral_info.signal, co_raman_pump_power))
+                co_frequency = concatenate((spectral_info.frequency, co_raman_pump_frequency))
 
-        # z propagation axis
-        z = append(arange(0, fiber_length, z_resolution), fiber_length)
-
-        def ode_function(z, p):
-            return self._ode_stimulated_raman(z, p, alphap_fiber, freq_array, cr, prop_direct)
-
-        def boundary_residual(ya, yb):
-            return self._residuals_stimulated_raman(ya, yb, power_spectrum, prop_direct)
-
-        initial_guess_conditions = self._initial_guess_stimulated_raman(z, power_spectrum, alphap_fiber, prop_direct)
-
-        # ODE SOLVER
-        bvp_solution = solve_bvp(ode_function, boundary_residual, z, initial_guess_conditions, tol=tolerance)
-
-        rho = (bvp_solution.y.transpose() / power_spectrum).transpose()
-        rho = sqrt(rho)    # From power attenuation to field attenuation
-        stimulated_raman_scattering = StimulatedRamanScattering(freq_array, bvp_solution.x, rho, bvp_solution.y)
-
-        self._stimulated_raman_scattering = stimulated_raman_scattering
-
-    def _residuals_stimulated_raman(self, ya, yb, power_spectrum, prop_direct):
-
-        computed_boundary_value = zeros(ya.size)
-
-        for index, direction in enumerate(prop_direct):
-            if direction == +1:
-                computed_boundary_value[index] = ya[index]
+                # Counter-propagating spectrum definition
+                cnt_power = array([pump.power for pump in fiber.raman_pumps
+                                   if pump.propagation_direction == 'counterprop'])
+                cnt_frequency = array([pump.frequency for pump in fiber.raman_pumps
+                                       if pump.propagation_direction == 'counterprop'])
+                # Co-propagating profile initialization
+                co_power_profile = empty([co_frequency.size, z.size])
+                if co_frequency.size:
+                    co_cr = fiber.cr(co_frequency)
+                    co_alpha = fiber.alpha(co_frequency)
+                    co_power_profile = \
+                        RamanSolver.first_order_derivative_solution(co_power, co_alpha, co_cr, z)
+                # Counter-propagating profile initialization
+                cnt_power_profile = empty([co_frequency.size, z.size])
+                if cnt_frequency.size:
+                    cnt_cr = fiber.cr(cnt_frequency)
+                    cnt_alpha = fiber.alpha(cnt_frequency)
+                    cnt_power_profile = \
+                        flip(RamanSolver.first_order_derivative_solution(cnt_power, cnt_alpha, cnt_cr, z[-1] - flip(z)))
+                # Co-propagating and Counter-propagating Profile Computation
+                if co_frequency.size and cnt_frequency.size:
+                    co_power_profile, cnt_power_profile = \
+                        RamanSolver.iterative_algorithm(co_power_profile, cnt_power_profile, co_frequency, cnt_frequency,
+                                                        z, fiber)
+                # Complete Power Profile
+                power_profile = concatenate((co_power_profile, cnt_power_profile), axis=0)
+                # Complete Loss Profile
+                co_loss_profile = co_power_profile / outer(co_power, ones(z.size))
+                cnt_loss_profile = cnt_power_profile / outer(cnt_power, ones(z.size))
+                loss_profile = concatenate((co_loss_profile, cnt_loss_profile), axis=0)
+                # Complete frequency
+                frequency = concatenate((co_frequency, cnt_frequency))
             else:
-                computed_boundary_value[index] = yb[index]
+                # Without Raman pumps
+                alpha = fiber.alpha(spectral_info.frequency)
+                cr = fiber.cr(spectral_info.frequency)
+                # Power profile
+                power_profile = \
+                    RamanSolver.first_order_derivative_solution(spectral_info.signal, alpha, cr, z)
+                # Loss profile
+                loss_profile = power_profile / outer(spectral_info.signal, ones(z.size))
+                frequency = spectral_info.frequency
+            power_profile = interp1d(z, power_profile, axis=1)(z_final)
+            loss_profile = interp1d(z, loss_profile, axis=1)(z_final)
+            stimulated_raman_scattering = StimulatedRamanScattering(power_profile, loss_profile, frequency, z_final)
+        else:
+            stimulated_raman_scattering = RamanSolver.calculate_attenuation_profile(spectral_info, fiber)
+        return stimulated_raman_scattering
 
-        return power_spectrum - computed_boundary_value
-
-    def _initial_guess_stimulated_raman(self, z, power_spectrum, alphap_fiber, prop_direct):
-        """ Computes the initial guess knowing the boundary conditions
-        :param z: patial axis [m]. numpy array
-        :param power_spectrum: power in each frequency slice [W].
-        Frequency axis is defined by freq_array. numpy array
-        :param alphap_fiber: frequency dependent fiber attenuation of signal power [1/m].
-        Frequency defined by freq_array. numpy array
-        :param prop_direct: indicates the propagation direction of each power slice in power_spectrum:
-        +1 for forward propagation and -1 for backward propagation. Frequency defined by freq_array. numpy array
-        :return: power_guess: guess on the initial conditions [W].
-        The first ndarray index identifies the frequency slice,
-        the second ndarray index identifies the step in z. ndarray
+    @staticmethod
+    def calculate_spontaneous_raman_scattering(spectral_info: SpectralInformation, srs: StimulatedRamanScattering,
+                                               fiber):
+        """Evaluates the Raman profile along the z axis for all the frequency propagated in the fiber
+        including the Raman pumps co- and counter-propagating.
         """
+        logger.debug('Start computing fiber Spontaneous Raman Scattering')
+        z = srs.z
+        baud_rate = spectral_info.baud_rate
+        frequency = spectral_info.frequency
+        channels_loss = srs.loss_profile[:spectral_info.number_of_channels, :]
 
-        power_guess = empty((power_spectrum.size, z.size))
-        for f_index, power_slice in enumerate(power_spectrum):
-            if prop_direct[f_index] == +1:
-                power_guess[f_index, :] = exp(-alphap_fiber[f_index] * z) * power_slice
-            else:
-                power_guess[f_index, :] = exp(-alphap_fiber[f_index] * z[::-1]) * power_slice
+        # calculate ase power
+        ase = zeros(spectral_info.number_of_channels)
+        for i, pump in enumerate(fiber.raman_pumps):
+            pump_power = srs.power_profile[spectral_info.number_of_channels + i, :]
+            df = pump.frequency - frequency
+            eta = - 1 / (1 - exp(h * df / (k * fiber.temperature)))
+            cr = fiber._cr_function(df)
+            integral = trapz(pump_power / channels_loss, z, axis=1)
+            ase += 2 * h * baud_rate * frequency * (1 + eta) * cr * (df > 0) * integral  # 2 factor for double pol
+        return ase
 
-        return power_guess
+    @staticmethod
+    def first_order_derivative_solution(power_in, alpha, cr, z):
+        """Solves the Raman first order derivative equation
 
-    def _ode_stimulated_raman(self, z, power_spectrum, alphap_fiber, freq_array, cr_raman_matrix, prop_direct):
-        """ Aim of ode_raman is to implement the set of ordinary differential equations (ODEs)
-        describing the Raman effect.
-        :param z: spatial axis (unused).
-        :param power_spectrum: power in each frequency slice [W].
-        Frequency axis is defined by freq_array. numpy array. Size n
-        :param alphap_fiber: frequency dependent fiber attenuation of signal power [1/m].
-        Frequency defined by freq_array. numpy array. Size n
-        :param freq_array: reference frequency axis [Hz]. numpy array. Size n
-        :param cr_raman: Cr(f) Raman gain efficiency variation in frequency [1/W/m].
-        Frequency defined by freq_array. numpy ndarray. Size nxn
-        :param prop_direct: indicates the propagation direction of each power slice in power_spectrum:
-        +1 for forward propagation and -1 for backward propagation.
-        Frequency defined by freq_array. numpy array. Size n
-        :return: dP/dz: the power variation in dz [W/m]. numpy array. Size n
+        :param power_in: launch power array
+        :param alpha: loss coefficient array
+        :param cr: Raman efficiency coefficients matrix
+        :param z: z position array
+        :return: power profile matrix
         """
+        dz = z[1:] - z[:-1]
+        power = outer(power_in, ones(z.size))
+        for i in range(1, z.size):
+            power[:, i] = power[:, i - 1] * (1 + (- alpha + sum(cr * power[:, i - 1], 1)) * dz[i - 1])
+        return power
 
-        dpdz = nan * ones(power_spectrum.shape)
-        for f_ind, power in enumerate(power_spectrum):
-            cr_raman = cr_raman_matrix[f_ind, :]
-            vibrational_loss = freq_array[f_ind] / freq_array[:f_ind]
+    @staticmethod
+    def iterative_algorithm(co_initial_guess_power, cnt_initial_guess_power, co_frequency, cnt_frequency, z, fiber):
+        """Solves the Raman first order derivative equation in case of both co- and counter-propagating
+        frequencies
 
-            for z_ind, power_sample in enumerate(power):
-                raman_gain = sum(cr_raman[f_ind + 1:] * power_spectrum[f_ind + 1:, z_ind])
-                raman_loss = sum(vibrational_loss * cr_raman[:f_ind] * power_spectrum[:f_ind, z_ind])
+        :param co_initial_guess_power: co-propagationg Raman first order derivative equation solution
+        :param cnt_initial_guess_power: counter-propagationg Raman first order derivative equation solution
+        :param co_frequency: co-propagationg frequencies
+        :param cnt_frequency: counter-propagationg frequencies
+        :param z: z position array
+        :param fiber: instance of gnpy.core.elements.Fiber or gnpy.core.elements.RamanFiber
+        :return: co- and counter-propagatng power profile matrix
+        """
+        logger.debug('  Start iterative algorithm')
+        residue = 1
+        residue_tol = 1e-6
+        accuracy = 1
+        accuracy_tol = 1e-3
+        iteration = 0
+        num_max_iter = 1000
+        prev_power = concatenate((co_initial_guess_power, cnt_initial_guess_power))
+        frequency = concatenate((co_frequency, cnt_frequency))
+        dz = z[1:] - z[:-1]
+        cr = fiber.cr(frequency)
+        alpha = fiber.alpha(frequency)
+        next_power = array(prev_power)
+        while residue > residue_tol and accuracy > accuracy_tol and iteration < num_max_iter:
+            iteration += 1
+            for i in range(1, z.size):
+                dpdz = - alpha + sum(cr * next_power[:, i - 1], 1)
+                next_power[:co_frequency.size, i] = \
+                    next_power[:co_frequency.size, i - 1] * (1 + dpdz[:co_frequency.size] * dz[i - 1])
+            for i in range(1, z.size):
+                dpdz = - alpha + sum(cr * next_power[:, -i], 1)
+                next_power[co_frequency.size:, -i - 1] = \
+                    next_power[co_frequency.size:, -i] * (1 + dpdz[co_frequency.size:] * dz[-i])
 
-                dpdz_element = prop_direct[f_ind] * (-alphap_fiber[f_ind] + raman_gain - raman_loss) * power_sample
-                dpdz[f_ind][z_ind] = dpdz_element
+            dpdz_num = (next_power[:co_frequency.size, 1:] - next_power[:co_frequency.size, :-1]) / dz
+            dpdz_exp = next_power[:co_frequency.size, :-1] * \
+                (- outer(alpha, ones(z.size)) + inner(cr, transpose(next_power)))[:co_frequency.size, :-1]
+
+            residue = max(abs((next_power - prev_power) / next_power))
+            accuracy = max(abs((dpdz_exp - dpdz_num) / dpdz_exp))
+            prev_power = array(next_power)
+            logger.debug(f'     Iteration: {iteration}  Accuracy: {format_float_scientific(accuracy, precision=3)}')
+        return next_power[:co_frequency.size, :], next_power[co_frequency.size:, :]
 
-        return vstack(dpdz)
 
 class NliSolver:
     """ This class implements the NLI models.
@@ -334,21 +252,6 @@ class NliSolver:
         'ggn_spectrally_separated': eq. 21 from arXiv: 1710.02225 spectrally separated
     """
 
-    def __init__(self, fiber=None):
-        """ Initialize the Nli solver object.
-        :param fiber: instance of elements.py/Fiber.
-        """
-        self._fiber = fiber
-        self._stimulated_raman_scattering = None
-
-    @property
-    def stimulated_raman_scattering(self):
-        return self._stimulated_raman_scattering
-
-    @stimulated_raman_scattering.setter
-    def stimulated_raman_scattering(self, stimulated_raman_scattering):
-        self._stimulated_raman_scattering = stimulated_raman_scattering
-
     @staticmethod
     def effective_length(alpha, length):
         """The effective length identify the region in which the NLI has a significant contribution to
@@ -356,15 +259,13 @@ class NliSolver:
         """
         return (1 - exp(- alpha * length)) / alpha
 
-    def compute_nli(self, spectral_info: SpectralInformation):
+    @staticmethod
+    def compute_nli(spectral_info: SpectralInformation, srs: StimulatedRamanScattering, fiber):
         """ Compute NLI power generated by the WDM comb `*carriers` on the channel under test `carrier`
         at the end of the fiber span.
         """
         logger.debug('Start computing fiber NLI noise')
         # Physical fiber parameters
-        fiber = self._fiber
-        srs = self._stimulated_raman_scattering
-
         alpha = fiber.alpha(spectral_info.frequency)
         beta2 = fiber.params.beta2
         beta3 = fiber.params.beta3
@@ -429,7 +330,7 @@ class NliSolver:
         dispersion_tolerance = sim_params.nli_params.dispersion_tolerance
         phase_shift_tolerance = sim_params.nli_params.phase_shift_tolerance
         slot_width = max(spectral_info.slot_width)
-        delta_z = sim_params.raman_params.space_resolution
+        delta_z = sim_params.raman_params.result_spatial_resolution
         spm_weight = (16.0 / 27.0) * gamma ** 2
         xpm_weight = 2 * (16.0 / 27.0) * gamma ** 2
         cuts = [carrier for carrier in spectral_info.carriers if carrier.channel_number
diff --git a/gnpy/example-data/raman_edfa_example_network.json b/gnpy/example-data/raman_edfa_example_network.json
index 4974238f..9b014b7e 100644
--- a/gnpy/example-data/raman_edfa_example_network.json
+++ b/gnpy/example-data/raman_edfa_example_network.json
@@ -20,12 +20,12 @@
         "temperature": 283,
         "raman_pumps": [
           {
-            "power": 200e-3,
+            "power": 224.403e-3,
             "frequency": 205e12,
             "propagation_direction": "counterprop"
           },
           {
-            "power": 206e-3,
+            "power": 231.135e-3,
             "frequency": 201e12,
             "propagation_direction": "counterprop"
           }
@@ -49,6 +49,21 @@
         }
       }
     },
+    {
+      "uid": "Fused1",
+      "type": "Fused",
+      "params": {
+        "loss": 0
+      },
+      "metadata": {
+        "location": {
+          "latitude": 1.5,
+          "longitude": 0,
+          "city": null,
+          "region": ""
+        }
+      }
+    },
     {
       "uid": "Edfa1",
       "type": "Edfa",
@@ -88,6 +103,10 @@
     },
     {
       "from_node": "Span1",
+      "to_node": "Fused1"
+    },
+    {
+      "from_node": "Fused1",
       "to_node": "Edfa1"
     },
     {
diff --git a/gnpy/example-data/sim_params.json b/gnpy/example-data/sim_params.json
index 35c91dfd..7a76b295 100644
--- a/gnpy/example-data/sim_params.json
+++ b/gnpy/example-data/sim_params.json
@@ -1,8 +1,8 @@
 {
   "raman_params": {
     "flag": true,
-    "space_resolution": 10e3,
-    "tolerance": 1e-8
+    "result_spatial_resolution": 10e3,
+    "solver_spatial_resolution": 50
   },
   "nli_params": {
     "method": "ggn_spectrally_separated",
diff --git a/tests/data/sim_params.json b/tests/data/sim_params.json
index 35c91dfd..7a76b295 100644
--- a/tests/data/sim_params.json
+++ b/tests/data/sim_params.json
@@ -1,8 +1,8 @@
 {
   "raman_params": {
     "flag": true,
-    "space_resolution": 10e3,
-    "tolerance": 1e-8
+    "result_spatial_resolution": 10e3,
+    "solver_spatial_resolution": 50
   },
   "nli_params": {
     "method": "ggn_spectrally_separated",
diff --git a/tests/data/test_raman_fiber_expected_results.csv b/tests/data/test_raman_fiber_expected_results.csv
index 6171b269..832a890b 100644
--- a/tests/data/test_raman_fiber_expected_results.csv
+++ b/tests/data/test_raman_fiber_expected_results.csv
@@ -1,97 +1,97 @@
 ,signal,ase,nli
-0,0.0002869472910749756,3.829244288314411e-08,2.1570435023738975e-07
-1,0.0002844264441819097,3.810807396068084e-08,2.1799950841473497e-07
-2,0.00028192866252406385,3.792544000755193e-08,2.2023841125047751e-07
-3,0.0002794537215642667,3.7744517714620316e-08,2.2242189941355056e-07
-4,0.00027562432957345563,3.739256592350871e-08,2.2343448272115905e-07
-5,0.0002718482755003939,3.7044482870002475e-08,2.2437826192962336e-07
-6,0.00026812479793132313,3.670020704375223e-08,2.2525495466693408e-07
-7,0.000264450700138397,3.635954085714981e-08,2.2606415187873477e-07
-8,0.0002608253488030976,3.602242835595967e-08,2.2680748521505387e-07
-9,0.0002569046888856947,3.564392097524325e-08,2.2718285844823122e-07
-10,0.0002530414048172964,3.52696660940159e-08,2.2749429758474536e-07
-11,0.0002492279873569917,3.489974200864255e-08,2.277374766527899e-07
-12,0.00024546394589921574,3.453407358954537e-08,2.2791414400785136e-07
-13,0.00024174879169001578,3.4172586853993816e-08,2.280260208417818e-07
-14,0.00023798746912554602,3.3802283179520985e-08,2.2798420759778034e-07
-15,0.00023427697848580554,3.343627022987542e-08,2.2788101592695744e-07
-16,0.0002306167836320285,3.307447309241581e-08,2.2771816297650914e-07
-17,0.00022700656967539738,3.2716831574363364e-08,2.274975560288182e-07
-18,0.00022344579480967338,3.236327278261661e-08,2.2361822442592406e-07
-19,0.00021953361935365365,3.195819964288877e-08,2.1939761734541424e-07
-20,0.000215683131390894,3.155821693631402e-08,2.152494588710531e-07
-21,0.0002118936126056039,3.116322947665684e-08,2.1117277567387026e-07
-22,0.00020816423698459974,3.0773146233359933e-08,2.0716649124095414e-07
-23,0.000204494186708796,3.0387877710694614e-08,2.0322954179937734e-07
-24,0.0002011608152067422,3.0044038268833097e-08,1.9963693210325328e-07
-25,0.0001978756946189507,2.9704204306604607e-08,1.9610141536963302e-07
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diff --git a/tests/data/test_science_utils_fiber_config.json b/tests/data/test_science_utils_fiber_config.json
index 28656998..49ad249c 100644
--- a/tests/data/test_science_utils_fiber_config.json
+++ b/tests/data/test_science_utils_fiber_config.json
@@ -13,20 +13,20 @@
     "pmd_coef": 1.265e-15
   },
   "operational": {
-    "temperature": 283,
-    "raman_pumps": [
-      {
-        "power": 0.2,
-        "frequency": 205000000000000,
-        "propagation_direction": "counterprop"
+        "temperature": 283,
+        "raman_pumps": [
+          {
+            "power": 224.403e-3,
+            "frequency": 205e12,
+            "propagation_direction": "counterprop"
+          },
+          {
+            "power": 231.135e-3,
+            "frequency": 201e12,
+            "propagation_direction": "counterprop"
+          }
+        ]
       },
-      {
-        "power": 0.206,
-        "frequency": 201000000000000,
-        "propagation_direction": "counterprop"
-      }
-    ]
-  },
   "metadata": {
     "location": {
       "latitude": 1,
diff --git a/tests/invocation/transmission_main_example__raman b/tests/invocation/transmission_main_example__raman
index 9c04651d..fa7f9ca0 100644
--- a/tests/invocation/transmission_main_example__raman
+++ b/tests/invocation/transmission_main_example__raman
@@ -21,109 +21,111 @@ RamanFiber          Span1
   total loss (dB):             17.00
   (includes conn loss (dB) in: 0.50 out: 0.50)
   (conn loss out includes EOL margin defined in eqpt_config.json)
-  pch out (dBm): -7.74
+  pch out (dBm): -7.71
+Fused Fused1
+  loss (dB): 0.00
 Edfa Edfa1
   type_variety:           std_low_gain
-  effective gain(dB):     5.74
+  effective gain(dB):     5.71
   (before att_in and before output VOA)
-  noise figure (dB):      13.26
+  noise figure (dB):      13.29
   (including att_in)
-  pad att_in (dB):        2.26
-  Power In (dBm):         11.07
+  pad att_in (dB):        2.29
+  Power In (dBm):         11.11
   Power Out (dBm):        16.82
   Delta_P (dB):           -2.00
   target pch (dBm):       -2.00
   effective pch (dBm):    -2.00
   output VOA (dB):        0.00
 Transceiver Site_B
-  GSNR (0.1nm, dB):          31.43
-  GSNR (signal bw, dB):      27.35
-  OSNR ASE (0.1nm, dB):      34.18
-  OSNR ASE (signal bw, dB):  30.10
+  GSNR (0.1nm, dB):          31.44
+  GSNR (signal bw, dB):      27.36
+  OSNR ASE (0.1nm, dB):      34.22
+  OSNR ASE (signal bw, dB):  30.14
   CD (ps/nm):                1336.00
   PMD (ps):                  0.36
 
 Transmission result for input power = 0.00 dBm:
-  Final GSNR (0.1 nm): 31.43 dB
+  Final GSNR (0.1 nm): 31.44 dB
 
 The GSNR per channel at the end of the line is:
 Ch. #   Channel frequency (THz)       Channel power (dBm)    OSNR ASE (signal bw, dB)     SNR NLI (signal bw, dB)        GSNR (signal bw, dB)
-    1                    191.35                      0.21                       31.56                       31.47                       28.50
-    2                    191.40                      0.17                       31.54                       31.38                       28.45
-    3                    191.45                      0.14                       31.52                       31.30                       28.40
-    4                    191.50                      0.10                       31.50                       31.22                       28.34
-    5                    191.55                      0.04                       31.47                       31.14                       28.29
-    6                    191.60                     -0.02                       31.44                       31.06                       28.23
-    7                    191.65                     -0.08                       31.41                       30.98                       28.18
-    8                    191.70                     -0.14                       31.37                       30.90                       28.12
-    9                    191.75                     -0.20                       31.34                       30.83                       28.07
-   10                    191.80                     -0.26                       31.31                       30.75                       28.01
-   11                    191.85                     -0.33                       31.27                       30.68                       27.96
-   12                    191.90                     -0.39                       31.24                       30.61                       27.90
-   13                    191.95                     -0.46                       31.20                       30.54                       27.85
-   14                    192.00                     -0.52                       31.17                       30.47                       27.79
-   15                    192.05                     -0.59                       31.13                       30.40                       27.74
-   16                    192.10                     -0.66                       31.10                       30.33                       27.69
-   17                    192.15                     -0.72                       31.06                       30.27                       27.63
-   18                    192.20                     -0.79                       31.02                       30.20                       27.58
-   19                    192.25                     -0.86                       30.98                       30.21                       27.57
-   20                    192.30                     -0.94                       30.94                       30.21                       27.55
-   21                    192.35                     -1.01                       30.90                       30.22                       27.54
-   22                    192.40                     -1.09                       30.86                       30.23                       27.52
-   23                    192.45                     -1.16                       30.81                       30.23                       27.50
-   24                    192.50                     -1.24                       30.77                       30.24                       27.49
-   25                    192.55                     -1.31                       30.73                       30.25                       27.47
-   26                    192.60                     -1.38                       30.69                       30.26                       27.46
-   27                    192.65                     -1.45                       30.65                       30.26                       27.44
-   28                    192.70                     -1.52                       30.61                       30.27                       27.42
-   29                    192.75                     -1.59                       30.56                       30.28                       27.41
-   30                    192.80                     -1.66                       30.52                       30.28                       27.39
-   31                    192.85                     -1.73                       30.48                       30.29                       27.37
-   32                    192.90                     -1.80                       30.44                       30.30                       27.36
-   33                    192.95                     -1.87                       30.39                       30.31                       27.34
-   34                    193.00                     -1.94                       30.35                       30.31                       27.32
-   35                    193.05                     -2.01                       30.31                       30.32                       27.30
-   36                    193.10                     -2.08                       30.27                       30.33                       27.29
-   37                    193.15                     -2.15                       30.22                       30.33                       27.27
-   38                    193.20                     -2.22                       30.18                       30.35                       27.26
-   39                    193.25                     -2.29                       30.14                       30.37                       27.24
-   40                    193.30                     -2.36                       30.09                       30.39                       27.23
-   41                    193.35                     -2.43                       30.05                       30.40                       27.21
-   42                    193.40                     -2.49                       30.01                       30.42                       27.20
-   43                    193.45                     -2.56                       29.96                       30.44                       27.18
-   44                    193.50                     -2.63                       29.92                       30.46                       27.17
-   45                    193.55                     -2.70                       29.87                       30.47                       27.15
-   46                    193.60                     -2.78                       29.83                       30.49                       27.14
-   47                    193.65                     -2.85                       29.78                       30.51                       27.12
-   48                    193.70                     -2.92                       29.73                       30.53                       27.10
-   49                    193.75                     -2.99                       29.68                       30.55                       27.08
-   50                    193.80                     -3.06                       29.64                       30.56                       27.06
-   51                    193.85                     -3.14                       29.59                       30.58                       27.05
-   52                    193.90                     -3.21                       29.54                       30.60                       27.03
-   53                    193.95                     -3.28                       29.49                       30.62                       27.01
-   54                    194.00                     -3.35                       29.44                       30.64                       26.99
-   55                    194.05                     -3.42                       29.39                       30.66                       26.97
-   56                    194.10                     -3.50                       29.34                       30.67                       26.95
-   57                    194.15                     -3.57                       29.29                       30.73                       26.94
-   58                    194.20                     -3.64                       29.24                       30.79                       26.94
-   59                    194.25                     -3.72                       29.19                       30.85                       26.93
-   60                    194.30                     -3.79                       29.14                       30.91                       26.93
-   61                    194.35                     -3.86                       29.09                       30.97                       26.92
-   62                    194.40                     -3.93                       29.04                       31.03                       26.91
-   63                    194.45                     -4.01                       28.99                       31.09                       26.90
-   64                    194.50                     -4.08                       28.94                       31.15                       26.90
-   65                    194.55                     -4.14                       28.89                       31.22                       26.89
-   66                    194.60                     -4.21                       28.85                       31.28                       26.89
-   67                    194.65                     -4.28                       28.80                       31.35                       26.88
-   68                    194.70                     -4.34                       28.75                       31.42                       26.87
-   69                    194.75                     -4.41                       28.70                       31.48                       26.86
-   70                    194.80                     -4.47                       28.66                       31.55                       26.86
-   71                    194.85                     -4.54                       28.61                       31.62                       26.85
-   72                    194.90                     -4.60                       28.56                       31.69                       26.84
-   73                    194.95                     -4.67                       28.51                       31.77                       26.83
-   74                    195.00                     -4.73                       28.47                       31.84                       26.82
-   75                    195.05                     -4.80                       28.42                       31.92                       26.81
-   76                    195.10                     -4.86                       28.37                       31.92                       26.78
+    1                    191.35                      0.18                       31.61                       31.43                       28.51
+    2                    191.40                      0.14                       31.59                       31.34                       28.45
+    3                    191.45                      0.11                       31.57                       31.26                       28.40
+    4                    191.50                      0.07                       31.55                       31.17                       28.35
+    5                    191.55                      0.02                       31.52                       31.09                       28.29
+    6                    191.60                     -0.04                       31.49                       31.02                       28.24
+    7                    191.65                     -0.10                       31.46                       30.94                       28.18
+    8                    191.70                     -0.16                       31.43                       30.86                       28.13
+    9                    191.75                     -0.21                       31.40                       30.79                       28.07
+   10                    191.80                     -0.28                       31.36                       30.71                       28.02
+   11                    191.85                     -0.34                       31.33                       30.64                       27.96
+   12                    191.90                     -0.40                       31.29                       30.57                       27.91
+   13                    191.95                     -0.47                       31.26                       30.50                       27.85
+   14                    192.00                     -0.53                       31.22                       30.43                       27.80
+   15                    192.05                     -0.60                       31.18                       30.36                       27.74
+   16                    192.10                     -0.66                       31.15                       30.30                       27.69
+   17                    192.15                     -0.73                       31.11                       30.23                       27.64
+   18                    192.20                     -0.79                       31.07                       30.16                       27.58
+   19                    192.25                     -0.86                       31.04                       30.17                       27.57
+   20                    192.30                     -0.94                       30.99                       30.18                       27.56
+   21                    192.35                     -1.01                       30.95                       30.19                       27.54
+   22                    192.40                     -1.08                       30.91                       30.20                       27.53
+   23                    192.45                     -1.16                       30.86                       30.20                       27.51
+   24                    192.50                     -1.23                       30.82                       30.21                       27.49
+   25                    192.55                     -1.30                       30.78                       30.22                       27.48
+   26                    192.60                     -1.37                       30.74                       30.23                       27.46
+   27                    192.65                     -1.44                       30.70                       30.23                       27.45
+   28                    192.70                     -1.51                       30.65                       30.24                       27.43
+   29                    192.75                     -1.58                       30.61                       30.25                       27.42
+   30                    192.80                     -1.65                       30.57                       30.26                       27.40
+   31                    192.85                     -1.72                       30.53                       30.27                       27.38
+   32                    192.90                     -1.79                       30.48                       30.27                       27.37
+   33                    192.95                     -1.86                       30.44                       30.28                       27.35
+   34                    193.00                     -1.93                       30.40                       30.29                       27.33
+   35                    193.05                     -2.00                       30.35                       30.30                       27.32
+   36                    193.10                     -2.07                       30.31                       30.30                       27.30
+   37                    193.15                     -2.14                       30.27                       30.31                       27.28
+   38                    193.20                     -2.20                       30.22                       30.33                       27.27
+   39                    193.25                     -2.27                       30.18                       30.35                       27.25
+   40                    193.30                     -2.34                       30.14                       30.37                       27.24
+   41                    193.35                     -2.41                       30.09                       30.38                       27.23
+   42                    193.40                     -2.48                       30.05                       30.40                       27.21
+   43                    193.45                     -2.55                       30.00                       30.42                       27.20
+   44                    193.50                     -2.61                       29.96                       30.44                       27.18
+   45                    193.55                     -2.69                       29.91                       30.46                       27.16
+   46                    193.60                     -2.76                       29.86                       30.47                       27.15
+   47                    193.65                     -2.83                       29.82                       30.49                       27.13
+   48                    193.70                     -2.90                       29.77                       30.51                       27.11
+   49                    193.75                     -2.98                       29.72                       30.53                       27.10
+   50                    193.80                     -3.05                       29.67                       30.55                       27.08
+   51                    193.85                     -3.12                       29.62                       30.57                       27.06
+   52                    193.90                     -3.19                       29.58                       30.58                       27.04
+   53                    193.95                     -3.26                       29.53                       30.60                       27.02
+   54                    194.00                     -3.33                       29.48                       30.62                       27.00
+   55                    194.05                     -3.41                       29.43                       30.64                       26.98
+   56                    194.10                     -3.48                       29.38                       30.66                       26.96
+   57                    194.15                     -3.55                       29.33                       30.72                       26.96
+   58                    194.20                     -3.63                       29.28                       30.78                       26.95
+   59                    194.25                     -3.70                       29.23                       30.83                       26.95
+   60                    194.30                     -3.77                       29.18                       30.89                       26.94
+   61                    194.35                     -3.85                       29.12                       30.96                       26.93
+   62                    194.40                     -3.92                       29.07                       31.02                       26.93
+   63                    194.45                     -3.99                       29.02                       31.08                       26.92
+   64                    194.50                     -4.06                       28.97                       31.14                       26.91
+   65                    194.55                     -4.13                       28.92                       31.21                       26.91
+   66                    194.60                     -4.19                       28.88                       31.27                       26.90
+   67                    194.65                     -4.26                       28.83                       31.34                       26.89
+   68                    194.70                     -4.33                       28.78                       31.41                       26.89
+   69                    194.75                     -4.39                       28.73                       31.47                       26.88
+   70                    194.80                     -4.46                       28.68                       31.54                       26.87
+   71                    194.85                     -4.52                       28.64                       31.61                       26.86
+   72                    194.90                     -4.59                       28.59                       31.68                       26.86
+   73                    194.95                     -4.65                       28.54                       31.76                       26.85
+   74                    195.00                     -4.72                       28.49                       31.83                       26.84
+   75                    195.05                     -4.78                       28.44                       31.91                       26.83
+   76                    195.10                     -4.85                       28.39                       31.91                       26.79
 
 (No source node specified: picked Site_A)