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)
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)
if __name__ == "__main__":
iom_o = IOManager()
iom_s = IOManager()
# NOTE
#
# first cmd-line data file is spawning data
# second cmd-line data file is reference data
# Read file with new simulation data
try:
iom_s.open_file(filename=sys.argv[1])
except IndexError:
iom_s.open_file()
# Read file with original reference simulation data
try:
iom_o.open_file(filename=sys.argv[2])
except IndexError:
iom_o.open_file()
# The axes rectangle that is plotted
view = [-8.5, 8.5]
gids = iom_s.get_group_ids(exclude=["global"])
for gid in gids:
def load_data(resultsdir, evaluation_times, which_norm="wf"):
"""This script assumes filename specification: something_eps=..._dt=..._[h|f]_other_things.
We group the simulations first by eps and then by dt.
"""
iom_f = IOManager()
iom_h = IOManager()
# Group the data from different simulations according to epsilon
ids = get_result_dirs(resultsdir)
eps_groups = group_by(ids, "eps")
# Data structures for results
epsdata = [ None for i in xrange(len(eps_groups)) ]
axisdata = [ [] for i in xrange(len(eps_groups)) ]
normdata = [ [ [] for i in xrange(len(eps_groups)) ] for t in xrange(len(evaluation_times)) ]
# Loop over all simulations, grouped by same eps value
for index, eps_group in enumerate(eps_groups):
# Partition into fourier and hagedorn simulations
dirs_f = gather_all(eps_group, "algorithm=fourier")
dirs_h = gather_all(eps_group, "algorithm=hagedorn")
if len(dirs_f) != len(dirs_h):
raise ValueError("Found different number of fourier and hagedorn simulations!")
# And sort by dt value
dirs_f = sort_by(dirs_f, "dt")
dirs_h = sort_by(dirs_h, "dt")
# Loop over all simulations with same eps values sorted by size of dt
for dir_f, dir_h in zip(dirs_f, dirs_h):
print("Comparing simulation " + dir_h + " with " + dir_f)
resultsfile_f = get_results_file(dir_f)
iom_f.open_file(filename=resultsfile_f)
resultsfile_h = 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()
# Scalar parameter of the x axis
axisdata[index].append(parameters_f["dt"])
# Get the data
grid = iom_f.load_grid(blockid="global")
WF = WaveFunction(parameters_f)
WF.set_grid(grid)
# Convert times to timesteps using the time manager
tm = parameters_f.get_timemanager()
# Loop over all times
for i, time in enumerate(evaluation_times):
print(" at time T: " + str(time))
step = tm.compute_timestep(time)
data_f = iom_f.load_wavefunction(timestep=step)
data_h = iom_h.load_wavefunction(timestep=step)
# Compute the norm || u_f - u_h || for all timesteps
data_diff = data_f - data_h
if which_norm == "wf":
WF.set_values( [ data_diff[0,...] ] )
no = WF.get_norm(summed=True)
elif which_norm == "2":
no = norm( data_diff[0,...] )
elif which_norm == "max":
no = max( data_diff[0,...] )
# Append norm values to global data structure
normdata[i][index].append(no)
# 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.
epsdata[index] = parameters_f["eps"]
iom_f.finalize()
iom_h.finalize()
# Convert lists to arrays
epsdata = array(epsdata)
axisdata = [ array(item) for item in axisdata ]
return (times, epsdata, axisdata, normdata)
if fill:
ax.fill(grid, ew, facecolor="blue", alpha=0.25)
ax.plot(grid, ew, label=r"$\lambda_"+str(index)+r"$")
ax.ticklabel_format(style="sci", scilimits=(0,0), axis="y")
ax.grid(True)
ax.set_xlabel(r"$x$")
ax.set_ylabel(r"$\lambda_i\left(x\right)$")
legend(loc="outer right")
ax.set_title(r"The eigenvalues $\lambda_i$ of the potential $V\left(x\right)$")
fig.savefig("potential"+GD.output_format)
close(fig)
if __name__ == "__main__":
iom = IOManager()
# Read file with simulation data
try:
iom.open_file(filename=sys.argv[1])
except IndexError:
iom.open_file()
parameters = iom.load_parameters()
potential = PotentialFactory().create_potential(parameters)
grid = iom.load_grid(blockid="global")
plot_potential(grid, potential, fill=False)
iom.finalize()
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)