# coding: utf-8
"""
Object to analyze the results stored in the V1SYM.nc file (mainly for debugging purposes)
"""
import numpy as np
from collections import OrderedDict
from monty.string import marquee
from monty.functools import lazy_property
from abipy.tools.plotting import add_fig_kwargs, get_axarray_fig_plt
from abipy.core.mixins import AbinitNcFile, Has_Structure, NotebookWriter
from abipy.core.kpoints import KpointList, Kpoint
from abipy.iotools import ETSF_Reader
from abipy.tools import duck
[docs]class V1symFile(AbinitNcFile, Has_Structure, NotebookWriter):
def __init__(self, filepath):
super().__init__(filepath)
self.reader = r = ETSF_Reader(filepath)
# Read dimensions.
self.nfft = r.read_dimvalue("nfft")
self.nspden = r.read_dimvalue("nspden")
self.natom3 = len(self.structure) * 3
self.symv1scf = r.read_value("symv1scf")
# Read FFT mesh.
#self.ngfft = r.read_value("ngfft")
[docs] @lazy_property
def structure(self):
"""|Structure| object."""
return self.reader.read_structure()
[docs] @lazy_property
def pertsy_qpt(self):
"""
Determine the symmetrical perturbations. Meaning of pertsy:
0 for non-target perturbations.
1 for basis perturbations.
-1 for perturbations that can be found from basis perturbations.
"""
# Fortran array: nctkarr_t("pertsy_qpt", "int", "three, mpert, nqpt")))
return self.reader.read_value("pertsy_qpt")
[docs] def close(self):
self.reader.close()
[docs] @lazy_property
def params(self):
""":class:`OrderedDict` with parameters that might be subject to convergence studies."""
return {}
def __str__(self):
return self.to_string()
[docs] 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("symv1scf: %s" % self.symv1scf)
return "\n".join(lines)
[docs] @lazy_property
def qpoints(self):
return KpointList(self.structure.reciprocal_lattice, frac_coords=self.reader.read_value("qpts"))
def _find_iqpt_qpoint(self, qpoint):
if duck.is_intlike(qpoint):
iq = qpoint
qpoint = self.qpoints[iq]
else:
qpoint = Kpoint.as_kpoint(qpoint, self.structure.reciprocal_lattice)
iq = self.qpoints.index(qpoint)
return iq, qpoint
[docs] def read_v1_at_iq(self, key, iq, reshape_nfft_nspden=False):
# Fortran array ("two, nfft, nspden, natom3, nqpt")
v1 = self.reader.read_variable(key)[iq]
v1 = v1[..., 0] + 1j * v1[..., 1]
# reshape (nspden, nfft) dims because we are not interested in the spin dependence.
if reshape_nfft_nspden: v1 = np.reshape(v1, (self.natom3, self.nspden * self.nfft))
return v1
[docs] @add_fig_kwargs
def plot_diff_at_qpoint(self, qpoint=0, fontsize=8, **kwargs):
"""
Args:
qpoint:
ax: |matplotlib-Axes| or None if a new figure should be created.
fontsize: fontsize for legends and titles
Return: |matplotlib-Figure|
"""
iq, qpoint = self._find_iqpt_qpoint(qpoint)
# complex arrays with shape: (natom3, nspden * nfft)
origin_v1 = self.read_v1_at_iq("origin_v1scf", iq, reshape_nfft_nspden=True)
symm_v1 = self.read_v1_at_iq("recons_v1scf", iq, reshape_nfft_nspden=True)
num_plots, ncols, nrows = self.natom3, 3, self.natom3 // 3
ax_list, fig, plt = get_axarray_fig_plt(None, nrows=nrows, ncols=ncols,
sharex=False, sharey=False, squeeze=False)
for nu, ax in enumerate(ax_list.ravel()):
idir = nu % 3
ipert = (nu - idir) // 3
# l1_rerr(f1, f2) = \int |f1 - f2| dr / (\int |f2| dr
abs_diff = np.abs(origin_v1[nu] - symm_v1[nu])
l1_rerr = np.sum(abs_diff) / np.sum(np.abs(origin_v1[nu]))
stats = OrderedDict([
("max", abs_diff.max()),
("min", abs_diff.min()),
("mean", abs_diff.mean()),
("std", abs_diff.std()),
("L1_rerr", l1_rerr),
])
xs = np.arange(len(abs_diff))
ax.hist(abs_diff, facecolor='g', alpha=0.75)
ax.grid(True)
ax.set_title("idir: %d, iat: %d, pertsy: %d" % (idir, ipert, self.pertsy_qpt[iq, ipert, idir]),
fontsize=fontsize)
ax.axvline(stats["mean"], color='k', linestyle='dashed', linewidth=1)
_, max_ = ax.get_ylim()
ax.text(0.7, 0.7, "\n".join("%s = %.1E" % item for item in stats.items()),
fontsize=fontsize, horizontalalignment='center', verticalalignment='center',
transform=ax.transAxes)
fig.suptitle("qpoint: %s" % repr(qpoint))
return fig
[docs] @add_fig_kwargs
def plot_pots_at_qpoint(self, qpoint=0, fontsize=8, **kwargs):
"""
Args:
qpoint:
ax: |matplotlib-Axes| or None if a new figure should be created.
fontsize: fontsize for legends and titles
Return: |matplotlib-Figure|
"""
iq, qpoint = self._find_iqpt_qpoint(qpoint)
# complex arrays with shape: (natom3, nspden * nfft)
origin_v1 = self.read_v1_at_iq("origin_v1scf", iq, reshape_nfft_nspden=True)
symm_v1 = self.read_v1_at_iq("recons_v1scf", iq, reshape_nfft_nspden=True)
num_plots, ncols, nrows = self.natom3, 3, self.natom3 // 3
ax_list, fig, plt = get_axarray_fig_plt(None, nrows=nrows, ncols=ncols,
sharex=False, sharey=False, squeeze=False)
natom = len(self.structure)
xs = np.arange(self.nspden * self.nfft)
for nu, ax in enumerate(ax_list.ravel()):
idir = nu % 3
ipert = (nu - idir) // 3
# l1_rerr(f1, f2) = \int |f1 - f2| dr / (\int |f2| dr
abs_diff = np.abs(origin_v1[nu] - symm_v1[nu])
l1_rerr = np.sum(abs_diff) / np.sum(np.abs(origin_v1[nu]))
stats = OrderedDict([
("max", abs_diff.max()),
("min", abs_diff.min()),
("mean", abs_diff.mean()),
("std", abs_diff.std()),
("L1_rerr", l1_rerr),
])
ax.grid(True)
ax.set_title("idir: %d, iat: %d, pertsy: %d" % (idir, ipert, self.pertsy_qpt[iq, ipert, idir]),
fontsize=fontsize)
# Plot absolute error
#ax.plot(xs, abs_diff, linestyle="-", color="red", alpha=1.0, label="Abs diff" if nu == 0 else None)
# Plot absolute values
#ax.plot(xs, np.abs(origin_v1[nu]), linestyle="--", color="red", alpha=0.4, label="Origin" if nu == 0 else None)
#ax.plot(xs, -np.abs(symm_v1[nu]), linestyle="--", color="blue", alpha=0.4, label="-Symm" if nu == 0 else None)
# Plot real and imag
#ax.plot(xs, origin_v1[nu].real, linestyle="--", color="red", alpha=0.4, label="Re Origin" if nu == 0 else None)
#ax.plot(xs, -symm_v1[nu].real, linestyle="--", color="blue", alpha=0.4, label="Re Symm" if nu == 0 else None)
data = np.angle(origin_v1[nu], deg=True) - np.angle(symm_v1[nu], deg=True)
#data = data[abs_diff > stats["mean"]]
data = data[np.abs(origin_v1[nu]) > 1e-5]
ax.plot(np.arange(len(data)), data,
linestyle="--", color="red", alpha=0.4, label="diff angle degrees" if nu == 0 else None)
#ax.plot(xs, origin_v1[nu].real, linestyle="--", color="red", alpha=0.4, label="Re Origin" if nu == 0 else None)
#ax.plot(xs, -symm_v1[nu].real, linestyle="--", color="blue", alpha=0.4, label="Re Symm" if nu == 0 else None)
#ax.plot(xs, origin_v1[nu].real - symm_v1[nu].real, linestyle="--", color="red", alpha=0.4,
# label="Re Origin" if nu == 0 else None)
#ax.plot(xs, origin_v1[nu].imag, linestyle=":", color="red", alpha=0.4, label="Imag Origin" if nu == 0 else None)
#ax.plot(xs, -symm_v1[nu].imag, linestyle=":", color="blue", alpha=0.4, label="Imag Symm" if nu == 0 else None)
#ax.plot(xs, origin_v1[nu].imag - symm_v1[nu].imag, linestyle="--", color="blue", alpha=0.4,
# label="Re Origin" if nu == 0 else None)
if nu == 0:
ax.set_ylabel(r"Abs diff")
ax.legend(loc="best", fontsize=fontsize, shadow=True)
if ipert == natom - 1:
ax.set_xlabel(r"FFT index")
#ax.axvline(stats["mean"], color='k', linestyle='dashed', linewidth=1)
_, max_ = ax.get_ylim()
ax.text(0.7, 0.7, "\n".join("%s = %.1E" % item for item in stats.items()),
fontsize=fontsize, horizontalalignment='center', verticalalignment='center',
transform=ax.transAxes)
#ax2 = ax.twinx()
#rerr = 100 * abs_diff / np.abs(origin_v1[nu])
#ax2.plot(xs, rerr, linestyle="--", color="blue", alpha=0.4,
# label=r"|V_{\mathrm{origin}}|" if nu == 0 else None)
fig.suptitle("qpoint: %s" % repr(qpoint))
return fig
[docs] def yield_figs(self, **kwargs): # pragma: no cover
"""
This function *generates* a predefined list of matplotlib figures with minimal input from the user.
"""
maxnq = 3
for iq, qpoint in enumerate(self.qpoints):
if iq > maxnq:
print("Only the first %d q-points are show..." % maxnq)
break
#yield self.plot_diff_at_qpoint(qpoint=iq, **kwargs, show=False)
yield self.plot_pots_at_qpoint(qpoint=iq, **kwargs, show=False)
[docs] 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("ncfile = abilab.abiopen('%s')" % self.filepath),
nbv.new_code_cell("print(ncfile)"),
])
for iq, qpoint in enumerate(self.qpoints):
nb.cells.append(nbv.new_code_cell("ncfile.plot_diff_at_qpoint(qpoint=%d);" % iq))
#nb.cells.append(nbv.new_code_cell("ncfile.plot_diff_at_qpoint(qpoint=%d);" % iq))
return self._write_nb_nbpath(nb, nbpath)