def load_data(resultspath):
    # Sort the data from different simulations
    dirs = FT.get_result_dirs(resultspath)
    resultsdir = FT.sort_by(dirs, "eps")

    number_simulations = FT.get_number_simulations(resultspath)

    ekindata = []
    epotdata = []
    axisdata = []

    iom = IOManager()

    for resultdir in resultsdir:
        resultsfile = FT.get_results_file(resultdir)

        print(" Reading " + resultsfile)

        iom.open_file(filename=resultsfile)
        parameters = iom.load_parameters()
        number_components = parameters["ncomponents"]
        axisdata.append(parameters["eps"])

        ekin, epot = iom.load_energy()
        ekindata.append(ekin)
        epotdata.append(epot)

    iom.finalize()

    return (axisdata, ekindata, epotdata, number_simulations, number_components)
示例#2
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def load_data(resultspath):
    # Sort the data from different simulations according to the filenames
    dirs = FT.get_result_dirs(resultspath)
    resultsdir = FT.sort_by(dirs, "eps")

    number_simulations = FT.get_number_simulations(resultspath)

    normdata = []
    axisdata = []

    iom = IOManager()

    for resultdir in resultsdir:
        resultsfile = FT.get_results_file(resultdir)

        print(" Reading " + resultsfile)

        iom.open_file(filename=resultsfile)
        parameters = iom.load_parameters()
        number_components = parameters["ncomponents"]
        axisdata.append(parameters["eps"])

        norms = iom.load_norm()
        normdata.append(norms)

    iom.finalize()

    return (axisdata, normdata, number_simulations, number_components)
示例#3
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def load_data(resultspath, which_norm="wf"):
    # Sort the data from different simulations
    ids = FT.get_result_dirs(resultspath)
    dirs_f = FT.gather_all(ids, "fourier")
    dirs_h = FT.gather_all(ids, "hagedorn")

    dirs_f = FT.sort_by(dirs_f, "eps")
    dirs_h = FT.sort_by(dirs_h, "eps")

    if len(dirs_f) != len(dirs_h):
        raise ValueError("Found different number of fourier and hagedorn simulations!")

    number_simulations = len(dirs_f)

    normdata = []
    axisdata = []

    iom_f = IOManager()
    iom_h = IOManager()

    # Loop over all simulations
    for dir_f, dir_h in zip(dirs_f, dirs_h):

        print("Comparing simulation " + dir_h + " with " + dir_f)

        # Load the simulation data files
        resultsfile_f = FT.get_results_file(dir_f)
        iom_f.open_file(filename=resultsfile_f)

        resultsfile_h = FT.get_results_file(dir_h)
        iom_h.open_file(filename=resultsfile_h)

        # Read the parameters
        parameters_f = iom_f.load_parameters()
        parameters_h = iom_h.load_parameters()

        number_components = parameters_f["ncomponents"]

        # Scalar parameter that discriminates the simulations
        axisdata.append(parameters_f["eps"])

        # Get the data
        grid = iom_f.load_grid(blockid="global")
        timesteps = iom_f.load_wavefunction_timegrid()
        data_f = iom_f.load_wavefunction()
        data_h = iom_h.load_wavefunction()

        # Compute the norm  || u_f - u_h ||_L2 for all timesteps
        data_diff = data_f - data_h

        WF = WaveFunction(parameters_f)
        WF.set_grid(grid)

        norms = []

        for i, step in enumerate(timesteps):
            if which_norm == "wf":
                WF.set_values([ data_diff[i,0,:] ])
                no = WF.get_norm()
            elif which_norm == "2":
                no = norm(data_diff[i,0,:])
            elif which_norm == "max":
                no = max(data_diff[i,0,:])

            norms.append(no)

        # Append norm values to global data structure
        norms = array(norms)
        normdata.append(norms)

    iom_f.finalize()
    iom_h.finalize()

    return (axisdata, normdata, number_simulations, number_components)
def load_data(resultspath, which_norm="wf"):
    # Group the data from different simulations
    ids = FT.get_result_dirs(resultspath)
    ids = FT.group_by(ids, "eps")

    nsims = FT.get_number_simulations(resultspath)

    groupdata = []
    axisdata = [ [] for i in xrange(nsims) ]
    normdata = [ [] for i in xrange(nsims) ]

    iom_f = IOManager()
    iom_h = IOManager()

    for index, sims in enumerate(ids):
        # Sorting based on file names
        dirs_f = FT.gather_all(sims, "fourier")
        dirs_h = FT.gather_all(sims, "hagedorn")

        if len(dirs_f) != len(dirs_h):
            raise ValueError("Found different number of fourier and hagedorn simulations!")

        dirs_f = FT.sort_by(dirs_f, "eps", as_string=True)
        dirs_h = FT.sort_by(dirs_h, "eps", as_string=True)

        # Loop over all simulations
        for dir_f, dir_h in zip(dirs_f, dirs_h):

            print("Comparing simulation " + dir_h + " with " + dir_f)

            resultsfile_f = FT.get_results_file(dir_f)
            iom_f.open_file(filename=resultsfile_f)

            resultsfile_h = FT.get_results_file(dir_h)
            iom_h.open_file(filename=resultsfile_h)

            # Read the parameters
            parameters_f = iom_f.load_parameters()
            parameters_h = iom_h.load_parameters()

            grid = iom_f.load_grid(blockid="global")

            # Precalculate eigenvectors for efficiency
            Potential = PotentialFactory().create_potential(parameters_f)
            eigenvectors = Potential.evaluate_eigenvectors_at(grid)

            # Get the data
            # Number of time steps we saved
            timesteps = iom_f.load_wavefunction_timegrid()

            # Scalar parameter that discriminates the simulations
            axisdata[index].append((parameters_f, timesteps))

            WF = WaveFunction(parameters_f)
            WF.set_grid(grid)

            norms = []

            for i, step in enumerate(timesteps):
                # Load the data that belong to the current timestep
                data_f = iom_f.load_wavefunction(timestep=step)
                data_h = iom_h.load_wavefunction(timestep=step)

                data_f = Potential.project_to_eigen(grid, data_f, eigenvectors)
                data_f = array(data_f)

                data_diff = data_f - data_h

                # Compute the norm  || u_f - u_h ||
                if which_norm == "wf":
                    # Rearrange data to fit the input of WF and handle over
                    WF.set_values([ data_diff[n,:] for n in xrange(parameters_f.ncomponents) ])
                    curnorm = WF.get_norm()

                    # More than one component? If yes, compute also the overall norm
                    if parameters_f.ncomponents > 1:
                        nosum = WF.get_norm(summed=True)
                        curnorm = list(curnorm) + [nosum]

                elif which_norm == "max":
                    curnorm = [ max( abs(data_diff[n,:]) ) for n in xrange(parameters_f.ncomponents) ]

                    # More than one component? If yes, compute also the overall norm
                    if parameters_f.ncomponents > 1:
                        nosum = max(curnorm)
                        curnorm = list(curnorm) + [nosum]

                print(" at time " + str(step*parameters_f.dt) + " the error norm is " + str(curnorm))
                norms.append(curnorm)

            # Append norm values to global data structure
            norms = array(norms)
            normdata[index].append(norms)

        # Scalar parameter of the different curves
        # We add this here because the simulation parameters are
        # already loaded but not overwritten yet be the next iteration
        # Remember: we need only a single epsilon out of each eps_group.
        groupdata.append(parameters_f.dt)

    iom_f.finalize()
    iom_h.finalize()

    return (groupdata, axisdata, normdata)