<filename>tutorials/basics/g_code_listing_01.py
r"""
Basic workflow
==============

This examples demonstrates a basic workflow using the `py-fmas` library code.

.. codeauthor:: <NAME> <<EMAIL>>
"""

###############################################################################
# We start by simply importing the required `fmas` into the current namespace.
#

import fmas

###############################################################################
# If an adequate input file is located within the current working directory,
# the function `read_h5`, located in module `data_io`, can be used to read-in
# the propagation setting stored in the input file `input_file.h5`:

glob = fmas.data_io.read_h5('input_file.h5')

###############################################################################
# Next, the problem specific data structures, given by the computational grid 
# and the propagation model, can be initialized: 

grid = fmas.grid.Grid(
    t_max = glob.t_max,
    t_num = glob.t_num,
    z_max = glob.z_max,
    z_num = glob.z_num)

model = fmas.models.FMAS_S_R(
    w = grid.w,
    beta_w = glob.beta_w,
    n2 = glob.n2,
    fR = glob.fR,
    tau1 = glob.tau1,
    tau2 = glob.tau2)

###############################################################################
# The provided initial condition, which represents the real-valued optical
# field can be converted to the complex-valued analytic signal as shown below:

ic = fmas.analytic_signal.AS(glob.E_0t)

###############################################################################
# Below we implement a user-action function that can be passed to the
# propagation algorithm. Upon propagation it will evaluated at every
# :math:`z`-step

import numpy as np

def Cp(i, zi, w, uw):
    Iw = np.abs(uw)**2
    return np.sum(Iw[w>0]/w[w>0])

###############################################################################
# Next, we initialzize the  :math:`z`-propagation algorithm, given by the
# `Runge-Kutta in the interaction picture` (RK4IP) method, set the initial
# condition, and perform :math:`z`-propagation:

solver = fmas.solver.IFM_RK4IP(
    model.Lw, model.Nw,
    user_action = Cp)

solver.set_initial_condition(
    grid.w, ic.w_rep)

solver.propagate(
    z_range = glob.z_max,
    n_steps = glob.z_num,
    n_skip = glob.z_skip)

###############################################################################
# After the propagation algorithm has terminated, the generated simulation data
# can be stored within an output file in HDF5-format. Therefore, the data is
# organized as dictionary with custom keys for the stored data objects, which
# is then passed to the function `save_h5` implemented in module `data_io`:

res = {
    "t": grid.t,
    "z": solver.z,
    "w": solver.w,
    "u": solver.utz,
    "Cp": solver.ua_vals}

fmas.data_io.save_h5('out_file.h5', **res)

###############################################################################
# A simple plot of the generated data can be obtained using convenience functions
# implemented in module `tools`:

fmas.tools.plot_evolution(
    solver.z, grid.t, solver.utz,
    t_lim = (-500,2200), w_lim = (1.,4.))

<filename>tutorials/basics/g_code_listing_01.py
r"""
Basic workflow
==============

This examples demonstrates a basic workflow using the `py-fmas` library code.

.. codeauthor:: <NAME> <<EMAIL>>
"""

###############################################################################
# We start by simply importing the required `fmas` into the current namespace.
#

import fmas

###############################################################################
# If an adequate input file is located within the current working directory,
# the function `read_h5`, located in module `data_io`, can be used to read-in
# the propagation setting stored in the input file `input_file.h5`:

glob = fmas.data_io.read_h5('input_file.h5')

###############################################################################
# Next, the problem specific data structures, given by the computational grid 
# and the propagation model, can be initialized: 

grid = fmas.grid.Grid(
    t_max = glob.t_max,
    t_num = glob.t_num,
    z_max = glob.z_max,
    z_num = glob.z_num)

model = fmas.models.FMAS_S_R(
    w = grid.w,
    beta_w = glob.beta_w,
    n2 = glob.n2,
    fR = glob.fR,
    tau1 = glob.tau1,
    tau2 = glob.tau2)

###############################################################################
# The provided initial condition, which represents the real-valued optical
# field can be converted to the complex-valued analytic signal as shown below:

ic = fmas.analytic_signal.AS(glob.E_0t)

###############################################################################
# Below we implement a user-action function that can be passed to the
# propagation algorithm. Upon propagation it will evaluated at every
# :math:`z`-step

import numpy as np

def Cp(i, zi, w, uw):
    Iw = np.abs(uw)**2
    return np.sum(Iw[w>0]/w[w>0])

###############################################################################
# Next, we initialzize the  :math:`z`-propagation algorithm, given by the
# `Runge-Kutta in the interaction picture` (RK4IP) method, set the initial
# condition, and perform :math:`z`-propagation:

solver = fmas.solver.IFM_RK4IP(
    model.Lw, model.Nw,
    user_action = Cp)

solver.set_initial_condition(
    grid.w, ic.w_rep)

solver.propagate(
    z_range = glob.z_max,
    n_steps = glob.z_num,
    n_skip = glob.z_skip)

###############################################################################
# After the propagation algorithm has terminated, the generated simulation data
# can be stored within an output file in HDF5-format. Therefore, the data is
# organized as dictionary with custom keys for the stored data objects, which
# is then passed to the function `save_h5` implemented in module `data_io`:

res = {
    "t": grid.t,
    "z": solver.z,
    "w": solver.w,
    "u": solver.utz,
    "Cp": solver.ua_vals}

fmas.data_io.save_h5('out_file.h5', **res)

###############################################################################
# A simple plot of the generated data can be obtained using convenience functions
# implemented in module `tools`:

fmas.tools.plot_evolution(
    solver.z, grid.t, solver.utz,
    t_lim = (-500,2200), w_lim = (1.,4.))

Basic workflow ==============  This examples demonstrates a basic workflow using the `py-fmas` library code.  .. codeauthor:: <NAME> <<EMAIL>> ############################################################################### # We start by simply importing the required `fmas` into the current namespace. # ############################################################################### # If an adequate input file is located within the current working directory, # the function `read_h5`, located in module `data_io`, can be used to read-in # the propagation setting stored in the input file `input_file.h5`: ############################################################################### # Next, the problem specific data structures, given by the computational grid # and the propagation model, can be initialized: ############################################################################### # The provided initial condition, which represents the real-valued optical # field can be converted to the complex-valued analytic signal as shown below: ############################################################################### # Below we implement a user-action function that can be passed to the # propagation algorithm. Upon propagation it will evaluated at every # :math:`z`-step ############################################################################### # Next, we initialzize the  :math:`z`-propagation algorithm, given by the # `Runge-Kutta in the interaction picture` (RK4IP) method, set the initial # condition, and perform :math:`z`-propagation: ############################################################################### # After the propagation algorithm has terminated, the generated simulation data # can be stored within an output file in HDF5-format. Therefore, the data is # organized as dictionary with custom keys for the stored data objects, which # is then passed to the function `save_h5` implemented in module `data_io`: ############################################################################### # A simple plot of the generated data can be obtained using convenience functions # implemented in module `tools`: