class Fold2BlochNcfile(AbinitNcFile, Has_Header, Has_Structure, Has_ElectronBands, NotebookWriter):
"""
Netcdf file with output data produced by Fold2Bloch.
Usage example:
.. code-block:: python
with Fold2BlochNcfile("foo_FOLD2BLOCH.nc") as fb:
fb.plot_unfolded()
.. rubric:: Inheritance Diagram
.. inheritance-diagram:: Fold2BlochNcfile
"""
@classmethod
def from_wfkpath(cls, wfkpath, folds, workdir=None, manager=None, mpi_procs=1, verbose=0):
"""
Run fold2bloch in workdir.
Args:
wfkpath:
folds:
workdir: Working directory of the fake task used to compute the ibz. Use None for temporary dir.
manager: :class:`TaskManager` of the task. If None, the manager is initialized from the config file.
mpi_procs: Number of MPI processors to use.
verbose: Verbosity level.
"""
# Build a simple manager to run the job in a shell subprocess
from abipy import flowtk
manager = flowtk.TaskManager.as_manager(manager).to_shell_manager(mpi_procs=mpi_procs)
fold2bloch = flowtk.Fold2Bloch(manager=manager, verbose=verbose)
# Create temporary directory and link to the WFK file
import tempfile
workdir = tempfile.mkdtemp() if workdir is None else workdir
wfkpath = os.path.abspath(wfkpath)
link = os.path.join(workdir, os.path.basename(wfkpath))
os.symlink(wfkpath, link)
# Run fold2bloch
ncpath = fold2bloch.unfold(link, folds, workdir=workdir)
return cls(ncpath)
def __init__(self, filepath):
super(Fold2BlochNcfile, self).__init__(filepath)
self.reader = ElectronsReader(filepath)
# Initialize the electron bands from file.
# Spectral weights are dimensioned with `nss`
# Fortran arrays.
# nctkarr_t("reduced_coordinates_of_unfolded_kpoints", "dp", "number_of_reduced_dimensions, nk_unfolded")
# nctkarr_t("unfolded_eigenvalues", "dp", "max_number_of_states, nk_unfolded, number_of_spins")
# nctkarr_t("spectral_weights", "dp", "max_number_of_states, nk_unfolded, nsppol_times_nspinor")
self._ebands = self.reader.read_ebands()
self.nss = max(self.nsppol, self.nspinor)
self.fold_matrix = self.reader.read_value("fold_matrix")
# Compute direct lattice of the primitive cell from fold_matrix.
if is_diagonal(self.fold_matrix):
folds = np.diagonal(self.fold_matrix)
self.pc_lattice = Lattice((self.structure.lattice.matrix.T * (1.0 / folds)).T.copy())
else:
raise NotImplementedError("non diagonal fold_matrix: %s" % str(self.fold_matrix))
# Read fold2bloch output data.
self.uf_kfrac_coords = self.reader.read_value("reduced_coordinates_of_unfolded_kpoints")
self.uf_kpoints = KpointList(self.pc_lattice.reciprocal_lattice, self.uf_kfrac_coords)
self.uf_nkpt = len(self.uf_kpoints)
def __str__(self):
return self.to_string()
def to_string(self, verbose=0):
"""String representation."""
lines = []; app = lines.append
app(marquee("File Info", mark="="))
app(self.filestat(as_string=True))
app("")
app(self.structure.to_string(verbose=verbose, title="Structure"))
app("")
app(self.ebands.to_string(with_structure=False, title="Electronic Bands"))
app("Direct lattice of the primitive cell:")
to_s = lambda x: "%0.6f" % x
app("abc : " + " ".join([to_s(i).rjust(10) for i in self.pc_lattice.abc]))
app("angles: " + " ".join([to_s(i).rjust(10) for i in self.pc_lattice.angles]))
if is_diagonal(self.fold_matrix):
app("Diagonal folding: %s" % (str(np.diagonal(self.fold_matrix))))
else:
app("Folding matrix: %s" % str(self.fold_matrix))
if verbose:
app(self.uf_kpoints.to_string(verbose=verbose, title="Unfolded k-points"))
if verbose > 2:
app(self.hdr.to_string(verbose=verbose, title="Abinit Header"))
return "\n".join(lines)
@property
def ebands(self):
"""|ElectronBands| object with folded band energies."""
return self._ebands
@property
def structure(self):
"""|Structure| object defining the supercell."""
return self.ebands.structure
def close(self):
"""Close the file."""
self.reader.close()
@lazy_property
def params(self):
""":class:`OrderedDict` with parameters that might be subject to convergence studies."""
od = self.get_ebands_params()
return od
@lazy_property
def uf_eigens(self):
"""[nsppol, nk_unfolded, nband] |numpy-array| with unfolded eigenvalues in eV."""
# nctkarr_t("unfolded_eigenvalues", "dp", "max_number_of_states, nk_unfolded, number_of_spins")
return self.reader.read_value("unfolded_eigenvalues") * units.Ha_to_eV
@lazy_property
def uf_weights(self):
"""[nss, nk_unfolded, nband] array with spectral weights. nss = max(nspinor, nsppol)."""
# nctkarr_t("spectral_weights", "dp", "max_number_of_states, nk_unfolded, nsppol_times_nspinor")
return self.reader.read_value("spectral_weights")
def get_spectral_functions(self, step=0.01, width=0.02):
"""
Args:
step: Energy step (eV) of the linear mesh.
width: Standard deviation (eV) of the gaussian.
Return:
mesh, sfw, int_sfw
"""
# Compute linear mesh.
epad = 3.0 * width
e_min = self.uf_eigens.min() - epad
e_max = self.uf_eigens.max() + epad
nw = int(1 + (e_max - e_min) / step)
mesh, step = np.linspace(e_min, e_max, num=nw, endpoint=True, retstep=True)
sfw = np.zeros((self.nss, self.uf_nkpt, nw))
for spin in range(self.nss):
for ik in range(self.uf_nkpt):
for band in range(self.nband):
e = self.uf_eigens[spin, ik, band]
sfw[spin, ik] += self.uf_weights[spin, ik, band] * gaussian(mesh, width, center=e)
from scipy.integrate import cumtrapz
int_sfw = cumtrapz(sfw, x=mesh, initial=0.0)
return dict2namedtuple(mesh=mesh, sfw=sfw, int_sfw=int_sfw)
@add_fig_kwargs
def plot_unfolded(self, kbounds, klabels, ylims=None, dist_tol=1e-12, verbose=0,
colormap="afmhot", facecolor="black", ax=None, fontsize=12, **kwargs):
r"""
Plot unfolded band structure with spectral weights.
Args:
klabels: dictionary whose keys are tuple with the reduced coordinates of the k-points.
The values are the labels. e.g. ``klabels = {(0.0,0.0,0.0): "$\Gamma$", (0.5,0,0): "L"}``.
ylims: Set the data limits for the y-axis. Accept tuple e.g. ``(left, right)``
or scalar e.g. ``left``. If left (right) is None, default values are used
dist_tol: A point is considered to be on the path if its distance from the line
is less than dist_tol.
verbose: Verbosity level.
colormap: Have a look at the colormaps here and decide which one you like:
http://matplotlib.sourceforge.net/examples/pylab_examples/show_colormaps.html
facecolor:
ax: |matplotlib-Axes| or None if a new figure should be created.
fontsize: Legend and title fontsize.
Returns: |matplotlib-Figure|
"""
cart_bounds = [self.pc_lattice.reciprocal_lattice.get_cartesian_coords(c)
for c in np.reshape(kbounds, (-1, 3))]
uf_cart = self.uf_kpoints.get_cart_coords()
p = find_points_along_path(cart_bounds, uf_cart, dist_tol)
if len(p.ikfound) == 0:
cprint("Warning: find_points_along_path returned zero points along the path. Try to increase dist_tol.", "yellow")
return None
if verbose:
uf_frac_coords = np.reshape([k.frac_coords for k in self.uf_kpoints], (-1, 3))
fcoords = uf_frac_coords[p.ikfound]
print("Found %d points along input k-path" % len(fcoords))
print("k-points of path in reduced coordinates:")
print(fcoords)
fact = 8.0
e0 = self.ebands.fermie
ax, fig, plt = get_ax_fig_plt(ax=ax)
ax.set_facecolor(facecolor)
xs = np.tile(p.dist_list, self.nband)
marker_spin = {0: "^", 1: "v"} if self.nss == 2 else {0: "o"}
for spin in range(self.nss):
ys = self.uf_eigens[spin, p.ikfound, :] - e0
ws = self.uf_weights[spin, p.ikfound, :]
s = ax.scatter(xs, ys.T, s=fact * ws.T, c=ws.T,
marker=marker_spin[spin], label=None if self.nss == 1 else "spin %s" % spin,
linewidth=1, edgecolors='none', cmap=plt.get_cmap(colormap))
plt.colorbar(s, ax=ax, orientation='vertical')
ax.set_xticks(p.path_ticks, minor=False)
ax.set_xticklabels(klabels, fontdict=None, minor=False, size=kwargs.pop("klabel_size", "large"))
ax.grid(True)
ax.set_ylabel('Energy (eV)')
set_axlims(ax, ylims, "y")
if self.nss == 2: ax.legend(loc="best", fontsize=fontsize, shadow=True)
return fig
def yield_figs(self, **kwargs): # pragma: no cover
"""
This function *generates* a predefined list of matplotlib figures with minimal input from the user.
"""
print("TODO: Add call to plot_unfolded")
yield self.ebands.plot(show=False)
def write_notebook(self, nbpath=None):
"""
Write a jupyter_ notebook to nbpath. If `nbpath` is None, a temporay file in the current
working directory is created. Return path to the notebook.
"""
nbformat, nbv, nb = self.get_nbformat_nbv_nb(title=None)
nb.cells.extend([
nbv.new_code_cell("f2b = abilab.abiopen('%s')" % self.filepath),
nbv.new_code_cell("print(f2b)"),
nbv.new_code_cell("f2b.ebands.plot();"),
nbv.new_code_cell("#f2b.unfolded_kpoints.plot();"),
nbv.new_code_cell(r"""\
# kbounds = [0, 1/2, 0, 0, 0, 0, 0, 0, 1/2]
# klabels = ["Y", "$\Gamma$", "X"]
# f2b.plot_unfolded(kbounds, klabels);"""),
])
return self._write_nb_nbpath(nb, nbpath)
class Fold2BlochNcfile(AbinitNcFile, Has_Header, Has_Structure,
Has_ElectronBands, NotebookWriter):
"""
Netcdf file with output data produced by Fold2Bloch.
Usage example:
.. code-block:: python
with Fold2BlochNcfile("foo_FOLD2BLOCH.nc") as fb:
fb.plot_unfolded()
.. rubric:: Inheritance Diagram
.. inheritance-diagram:: Fold2BlochNcfile
"""
@classmethod
def from_wfkpath(cls,
wfkpath,
folds,
workdir=None,
manager=None,
mpi_procs=1,
verbose=0):
"""
Run fold2bloch in workdir.
Args:
wfkpath:
folds:
workdir: Working directory of the fake task used to compute the ibz. Use None for temporary dir.
manager: :class:`TaskManager` of the task. If None, the manager is initialized from the config file.
mpi_procs: Number of MPI processors to use.
verbose: Verbosity level.
"""
# Build a simple manager to run the job in a shell subprocess
from abipy import flowtk
manager = flowtk.TaskManager.as_manager(manager).to_shell_manager(
mpi_procs=mpi_procs)
fold2bloch = flowtk.Fold2Bloch(manager=manager, verbose=verbose)
# Create temporary directory and link to the WFK file
import tempfile
workdir = tempfile.mkdtemp() if workdir is None else workdir
wfkpath = os.path.abspath(wfkpath)
link = os.path.join(workdir, os.path.basename(wfkpath))
os.symlink(wfkpath, link)
# Run fold2bloch
ncpath = fold2bloch.unfold(link, folds, workdir=workdir)
return cls(ncpath)
def __init__(self, filepath):
super(Fold2BlochNcfile, self).__init__(filepath)
self.reader = ElectronsReader(filepath)
# Initialize the electron bands from file.
# Spectral weights are dimensioned with `nss`
# Fortran arrays.
# nctkarr_t("reduced_coordinates_of_unfolded_kpoints", "dp", "number_of_reduced_dimensions, nk_unfolded")
# nctkarr_t("unfolded_eigenvalues", "dp", "max_number_of_states, nk_unfolded, number_of_spins")
# nctkarr_t("spectral_weights", "dp", "max_number_of_states, nk_unfolded, nsppol_times_nspinor")
self._ebands = self.reader.read_ebands()
self.nss = max(self.nsppol, self.nspinor)
self.fold_matrix = self.reader.read_value("fold_matrix")
# Compute direct lattice of the primitive cell from fold_matrix.
if is_diagonal(self.fold_matrix):
folds = np.diagonal(self.fold_matrix)
self.pc_lattice = Lattice(
(self.structure.lattice.matrix.T * (1.0 / folds)).T.copy())
else:
raise NotImplementedError("non diagonal fold_matrix: %s" %
str(self.fold_matrix))
# Read fold2bloch output data.
self.uf_kfrac_coords = self.reader.read_value(
"reduced_coordinates_of_unfolded_kpoints")
self.uf_kpoints = KpointList(self.pc_lattice.reciprocal_lattice,
self.uf_kfrac_coords)
self.uf_nkpt = len(self.uf_kpoints)
def __str__(self):
return self.to_string()
def to_string(self, verbose=0):
"""String representation."""
lines = []
app = lines.append
app(marquee("File Info", mark="="))
app(self.filestat(as_string=True))
app("")
app(self.structure.to_string(verbose=verbose, title="Structure"))
app("")
app(
self.ebands.to_string(with_structure=False,
title="Electronic Bands"))
app("Direct lattice of the primitive cell:")
to_s = lambda x: "%0.6f" % x
app("abc : " +
" ".join([to_s(i).rjust(10) for i in self.pc_lattice.abc]))
app("angles: " +
" ".join([to_s(i).rjust(10) for i in self.pc_lattice.angles]))
if is_diagonal(self.fold_matrix):
app("Diagonal folding: %s" % (str(np.diagonal(self.fold_matrix))))
else:
app("Folding matrix: %s" % str(self.fold_matrix))
if verbose:
app(
self.uf_kpoints.to_string(verbose=verbose,
title="Unfolded k-points"))
if verbose > 2:
app(self.hdr.to_string(verbose=verbose, title="Abinit Header"))
return "\n".join(lines)
@property
def ebands(self):
"""|ElectronBands| object with folded band energies."""
return self._ebands
@property
def structure(self):
"""|Structure| object defining the supercell."""
return self.ebands.structure
def close(self):
"""Close the file."""
self.reader.close()
@lazy_property
def params(self):
""":class:`OrderedDict` with parameters that might be subject to convergence studies."""
od = self.get_ebands_params()
return od
@lazy_property
def uf_eigens(self):
"""[nsppol, nk_unfolded, nband] |numpy-array| with unfolded eigenvalues in eV."""
# nctkarr_t("unfolded_eigenvalues", "dp", "max_number_of_states, nk_unfolded, number_of_spins")
return self.reader.read_value("unfolded_eigenvalues") * units.Ha_to_eV
@lazy_property
def uf_weights(self):
"""[nss, nk_unfolded, nband] array with spectral weights. nss = max(nspinor, nsppol)."""
# nctkarr_t("spectral_weights", "dp", "max_number_of_states, nk_unfolded, nsppol_times_nspinor")
return self.reader.read_value("spectral_weights")
def get_spectral_functions(self, step=0.01, width=0.02):
"""
Args:
step: Energy step (eV) of the linear mesh.
width: Standard deviation (eV) of the gaussian.
Return:
mesh, sfw, int_sfw
"""
# Compute linear mesh.
epad = 3.0 * width
e_min = self.uf_eigens.min() - epad
e_max = self.uf_eigens.max() + epad
nw = int(1 + (e_max - e_min) / step)
mesh, step = np.linspace(e_min,
e_max,
num=nw,
endpoint=True,
retstep=True)
sfw = np.zeros((self.nss, self.uf_nkpt, nw))
for spin in range(self.nss):
for ik in range(self.uf_nkpt):
for band in range(self.nband):
e = self.uf_eigens[spin, ik, band]
sfw[spin,
ik] += self.uf_weights[spin, ik, band] * gaussian(
mesh, width, center=e)
from scipy.integrate import cumtrapz
int_sfw = cumtrapz(sfw, x=mesh, initial=0.0)
return dict2namedtuple(mesh=mesh, sfw=sfw, int_sfw=int_sfw)
@add_fig_kwargs
def plot_unfolded(self,
kbounds,
klabels,
ylims=None,
dist_tol=1e-12,
verbose=0,
colormap="afmhot",
facecolor="black",
ax=None,
fontsize=12,
**kwargs):
r"""
Plot unfolded band structure with spectral weights.
Args:
klabels: dictionary whose keys are tuple with the reduced coordinates of the k-points.
The values are the labels. e.g. ``klabels = {(0.0,0.0,0.0): "$\Gamma$", (0.5,0,0): "L"}``.
ylims: Set the data limits for the y-axis. Accept tuple e.g. ``(left, right)``
or scalar e.g. ``left``. If left (right) is None, default values are used
dist_tol: A point is considered to be on the path if its distance from the line
is less than dist_tol.
verbose: Verbosity level.
colormap: Have a look at the colormaps here and decide which one you like:
http://matplotlib.sourceforge.net/examples/pylab_examples/show_colormaps.html
facecolor:
ax: |matplotlib-Axes| or None if a new figure should be created.
fontsize: Legend and title fontsize.
Returns: |matplotlib-Figure|
"""
cart_bounds = [
self.pc_lattice.reciprocal_lattice.get_cartesian_coords(c)
for c in np.reshape(kbounds, (-1, 3))
]
uf_cart = self.uf_kpoints.get_cart_coords()
p = find_points_along_path(cart_bounds, uf_cart, dist_tol)
if len(p.ikfound) == 0:
cprint(
"Warning: find_points_along_path returned zero points along the path. Try to increase dist_tol.",
"yellow")
return None
if verbose:
uf_frac_coords = np.reshape(
[k.frac_coords for k in self.uf_kpoints], (-1, 3))
fcoords = uf_frac_coords[p.ikfound]
print("Found %d points along input k-path" % len(fcoords))
print("k-points of path in reduced coordinates:")
print(fcoords)
fact = 8.0
e0 = self.ebands.fermie
ax, fig, plt = get_ax_fig_plt(ax=ax)
ax.set_facecolor(facecolor)
xs = np.tile(p.dist_list, self.nband)
marker_spin = {0: "^", 1: "v"} if self.nss == 2 else {0: "o"}
for spin in range(self.nss):
ys = self.uf_eigens[spin, p.ikfound, :] - e0
ws = self.uf_weights[spin, p.ikfound, :]
s = ax.scatter(xs,
ys.T,
s=fact * ws.T,
c=ws.T,
marker=marker_spin[spin],
label=None if self.nss == 1 else "spin %s" % spin,
linewidth=1,
edgecolors='none',
cmap=plt.get_cmap(colormap))
plt.colorbar(s, ax=ax, orientation='vertical')
ax.set_xticks(p.path_ticks, minor=False)
ax.set_xticklabels(klabels,
fontdict=None,
minor=False,
size=kwargs.pop("klabel_size", "large"))
ax.grid(True)
ax.set_ylabel('Energy (eV)')
set_axlims(ax, ylims, "y")
if self.nss == 2: ax.legend(loc="best", fontsize=fontsize, shadow=True)
return fig
def yield_figs(self, **kwargs): # pragma: no cover
"""
This function *generates* a predefined list of matplotlib figures with minimal input from the user.
"""
print("TODO: Add call to plot_unfolded")
yield self.ebands.plot(show=False)
def write_notebook(self, nbpath=None):
"""
Write a jupyter_ notebook to nbpath. If `nbpath` is None, a temporay file in the current
working directory is created. Return path to the notebook.
"""
nbformat, nbv, nb = self.get_nbformat_nbv_nb(title=None)
nb.cells.extend([
nbv.new_code_cell("f2b = abilab.abiopen('%s')" % self.filepath),
nbv.new_code_cell("print(f2b)"),
nbv.new_code_cell("f2b.ebands.plot();"),
nbv.new_code_cell("#f2b.unfolded_kpoints.plot();"),
nbv.new_code_cell(r"""\
# kbounds = [0, 1/2, 0, 0, 0, 0, 0, 0, 1/2]
# klabels = ["Y", "$\Gamma$", "X"]
# f2b.plot_unfolded(kbounds, klabels);"""),
])
return self._write_nb_nbpath(nb, nbpath)
