def plot_gg(self, cplx_mode="abs", wpos=None, **kwargs):
r"""
Use matplotlib imshow to plot :math:`W_{GG'}` matrix
Args:
cplx_mode: string defining the data to print.
Possible choices are (case-insensitive): ``re`` for the real part
``im`` for the imaginary part, ``abs`` for the absolute value.
``angle`` will display the phase of the complex number in radians.
wpos: List of frequency indices to plot. If None, the first frequency is used (usually w=0).
If wpos == "all" all frequencies are shown (use it carefully)
Other possible values: "real" if only real frequencies are wanted.
"imag" for imaginary frequencies only.
Returns: |matplotlib-Figure|
"""
# Get wpos indices.
choice_wpos = {None: [0], "all": range(self.nw),
"real": range(self.nrew), "imag": range(self.nrew, self.nw)}
if any(wpos == k for k in choice_wpos):
wpos = choice_wpos[wpos]
else:
if duck.is_intlike(wpos): wpos = [int(wpos)]
wpos = np.array(wpos)
# Build plotter.
plotter = ArrayPlotter()
for iw in wpos:
label = r"%s $\omega=%s$" % (self.latex_label(cplx_mode), self.wpoints[iw])
data = data_from_cplx_mode(cplx_mode, self.wggmat[iw])
plotter.add_array(label, data)
return plotter.plot(show=False, **kwargs)
def plot_emacro(self, cplx_mode="re-im", ax=None, xlims=None, fontsize=12, **kwargs):
r"""
Plot the macroscopic dielectric function with local-field effects.
Args:
cplx_mode: string defining the data to print.
Possible choices are (case-insensitive):
"re" for the real part. "im" for the imaginary part.
"abs" for the absolute value.
"angle" will display the phase of the complex number in radians.
Options can be concatenated with ``-`` e.g. ``re-im``.
xlims: Set the data limits for the x-axis in eV. Accept tuple e.g. (left, right)
or scalar e.g. left. If left (right) is None, default values are used.
ax: |matplotlib-Axes| or None if a new figure should be created.
fontsize: Legend and title fontsize.
Returns:
|matplotlib-Figure|
"""
emlf = self.reader.read_emacro_lf()
xx, yy = emlf.mesh * pmgu.Ha_to_eV, emlf.values
ax, fig, plt = get_ax_fig_plt(ax=ax)
for c in cplx_mode.lower().split("-"):
ax.plot(xx, data_from_cplx_mode(c, yy),
color=_COLOR_CMODE[c], linewidth=kwargs.get("linewidth", 2),
linestyle=kwargs.get("linestyle", "solid"),
label=_latex_symbol_cplxmode(r"\varepsilon_{M}", c))
set_axlims(ax, xlims, "x")
ax.grid(True)
ax.set_xlabel(r"$\omega$ (eV)")
ax.legend(loc="best", shadow=True, fontsize=fontsize)
return fig
def plot_gg(self, cplx_mode="abs", wpos=None, **kwargs):
r"""
Use matplotlib imshow to plot :math:`W_{GG'}` matrix
Args:
cplx_mode: string defining the data to print.
Possible choices are (case-insensitive): ``re`` for the real part
``im`` for the imaginary part, ``abs`` for the absolute value.
``angle`` will display the phase of the complex number in radians.
wpos: List of frequency indices to plot. If None, the first frequency is used (usually w=0).
If wpos == "all" all frequencies are shown (use it carefully)
Other possible values: "real" if only real frequencies are wanted.
"imag" for imaginary frequencies only.
Returns: |matplotlib-Figure|
"""
# Get wpos indices.
choice_wpos = {None: [0], "all": range(self.nw),
"real": range(self.nrew), "imag": range(self.nrew, self.nw)}
if any(wpos == k for k in choice_wpos):
wpos = choice_wpos[wpos]
else:
if duck.is_intlike(wpos): wpos = [int(wpos)]
wpos = np.array(wpos)
# Build plotter.
plotter = ArrayPlotter()
for iw in wpos:
label = r"%s $\omega=%s$" % (self.latex_label(cplx_mode), self.wpoints[iw])
data = data_from_cplx_mode(cplx_mode, self.wggmat[iw])
plotter.add_array(label, data)
return plotter.plot(show=False, **kwargs)
def plot_emacro(self, cplx_mode="re-im", ax=None, xlims=None, fontsize=12, **kwargs):
r"""
Plot the macroscopic dielectric function with local-field effects.
Args:
cplx_mode: string defining the data to print.
Possible choices are (case-insensitive):
"re" for the real part. "im" for the imaginary part.
"abs" for the absolute value.
"angle" will display the phase of the complex number in radians.
Options can be concatenated with ``-`` e.g. ``re-im``.
xlims: Set the data limits for the x-axis in eV. Accept tuple e.g. (left, right)
or scalar e.g. left. If left (right) is None, default values are used.
ax: |matplotlib-Axes| or None if a new figure should be created.
fontsize: Legend and title fontsize.
Returns:
|matplotlib-Figure|
"""
emlf = self.reader.read_emacro_lf()
xx, yy = emlf.mesh * pmgu.Ha_to_eV, emlf.values
ax, fig, plt = get_ax_fig_plt(ax=ax)
for c in cplx_mode.lower().split("-"):
ax.plot(xx, data_from_cplx_mode(c, yy),
color=_COLOR_CMODE[c], linewidth=kwargs.get("linewidth", 2),
linestyle=kwargs.get("linestyle", "solid"),
label=_latex_symbol_cplxmode(r"\varepsilon_{M}", c))
set_axlims(ax, xlims, "x")
ax.grid(True)
ax.set_xlabel(r"$\omega$ (eV)")
ax.legend(loc="best", shadow=True, fontsize=fontsize)
return fig
def plot_chi2(self,
key,
components="all",
what="abs",
itemp=0,
decompose=False,
ax=None,
xlims=None,
with_xlabel=True,
label=None,
fontsize=12,
**kwargs):
"""
Low-level function to plot chi2 tensor.
Args:
key:
components: List of cartesian tensor components to plot e.g. ["xxx", "xyz"].
"all" if all components available on file should be plotted on the same ax.
what: quantity to plot. "re" for real part, "im" for imaginary, Accepts also "abs", "angle".
itemp: Temperature index.
decompose: True to plot individual contributions.
ax: |matplotlib-Axes| or None if a new figure should be created.
xlims: Set the data limits for the x-axis. Accept tuple e.g. ``(left, right)``
or scalar e.g. ``left``. If left (right) is None, default values are used.
with_xlabel: True to add x-label.
label: True to add legend label to each curve.
fontsize: Legend and label fontsize.
Returns: |matplotlib-Figure|
"""
if not self.reader.computed_components[key]: return None
comp2terms = self.reader.read_tensor3_terms(key,
components,
itemp=itemp)
ax, fig, plt = get_ax_fig_plt(ax=ax)
for comp, terms in comp2terms.items():
for name, values in terms.items():
if not decompose and not name.endswith("tot"): continue
values = data_from_cplx_mode(what, values)
ax.plot(
self.wmesh[1:],
values[1:],
label=self.get_chi2_latex_label(key, what, comp)
if label is None else label,
)
ax.grid(True)
if with_xlabel: ax.set_xlabel('Photon Energy [eV]')
set_axlims(ax, xlims, "x")
ax.legend(loc="best", fontsize=fontsize, shadow=True)
return fig
def plot_freq(self, gvec1, gvec2=None, waxis="real", cplx_mode="re-im", ax=None, fontsize=12, **kwargs):
r"""
Plot the frequency dependence of :math:`W_{G1, G2}(\omega)`
Args:
gvec1, gvec2:
waxis: ``real`` to plot along the real axis, ``imag`` for the imaginary axis.
cplx_mode: string defining the data to print.
Possible choices are (case-insensitive): ``re`` for the real part
``im`` for the imaginary part, ``abs`` for the absolute value.
``angle`` will display the phase of the complex number in radians.
Options can be concatenated with ``-`` e.g. ``re-im``.
ax: |matplotlib-Axes| or None if a new figure should be created.
fontsize: legend and label fontsize.
Returns: |matplotlib-Figure|
"""
# Select data to plot
ig1 = self.gindex(gvec1)
ig2 = ig1 if gvec2 is None else self.gindex(gvec2)
ax, fig, plt = get_ax_fig_plt(ax=ax)
if waxis == "real":
if self.nrew == 0: return fig
xx = self.real_wpoints.real * pmgu.Ha_to_eV
yy = self.wggmat_realw[:, ig1, ig2]
elif waxis == "imag":
if self.nimw == 0: return fig
xx = self.imag_wpoints.imag * pmgu.Ha_to_eV
yy = self.wggmat_imagw[:, ig1, ig2]
else:
raise ValueError("Wrong value for waxis: %s" % str(waxis))
linewidth = kwargs.pop("linewidth", 2)
linestyle = kwargs.pop("linestyle", "solid")
lines = []
for c in cplx_mode.lower().split("-"):
l, = ax.plot(xx, data_from_cplx_mode(c, yy),
color=_COLOR_CMODE[c], linewidth=linewidth, linestyle=linestyle,
label=self.latex_label(c))
lines.append(l)
ax.grid(True)
ax.set_xlabel(r"$\omega$ (eV)")
ax.set_title("%s, kpoint: %s" % (self.netcdf_name, self.kpoint), fontsize=fontsize)
ax.legend(loc="best", fontsize=fontsize, shadow=True)
return fig
def plot_freq(self, gvec1, gvec2=None, waxis="real", cplx_mode="re-im", ax=None, fontsize=12, **kwargs):
r"""
Plot the frequency dependence of :math:`W_{G1, G2}(\omega)`
Args:
gvec1, gvec2:
waxis: ``real`` to plot along the real axis, ``imag`` for the imaginary axis.
cplx_mode: string defining the data to print.
Possible choices are (case-insensitive): ``re`` for the real part
``im`` for the imaginary part, ``abs`` for the absolute value.
``angle`` will display the phase of the complex number in radians.
Options can be concatenated with ``-`` e.g. ``re-im``.
ax: |matplotlib-Axes| or None if a new figure should be created.
fontsize: legend and label fontsize.
Returns: |matplotlib-Figure|
"""
# Select data to plot
ig1 = self.gindex(gvec1)
ig2 = ig1 if gvec2 is None else self.gindex(gvec2)
ax, fig, plt = get_ax_fig_plt(ax=ax)
if waxis == "real":
if self.nrew == 0: return fig
xx = self.real_wpoints.real * pmgu.Ha_to_eV
yy = self.wggmat_realw[:, ig1, ig2]
elif waxis == "imag":
if self.nimw == 0: return fig
xx = self.imag_wpoints.imag * pmgu.Ha_to_eV
yy = self.wggmat_imagw[:, ig1, ig2]
else:
raise ValueError("Wrong value for waxis: %s" % str(waxis))
linewidth = kwargs.pop("linewidth", 2)
linestyle = kwargs.pop("linestyle", "solid")
lines = []
for c in cplx_mode.lower().split("-"):
l, = ax.plot(xx, data_from_cplx_mode(c, yy),
color=_COLOR_CMODE[c], linewidth=linewidth, linestyle=linestyle,
label=self.latex_label(c))
lines.append(l)
ax.grid(True)
ax.set_xlabel(r"$\omega$ (eV)")
ax.set_title("%s, kpoint: %s" % (self.netcdf_name, self.kpoint), fontsize=fontsize)
ax.legend(loc="best", fontsize=fontsize, shadow=True)
return fig
def plot_ax(self,
ax,
exchange_xy=False,
xfactor=1,
yfactor=1,
*args,
**kwargs):
"""
Helper function to plot self on axis ax.
Args:
ax: |matplotlib-Axes|.
exchange_xy: True to exchange the axis in the plot.
args: Positional arguments passed to ax.plot
xfactor, yfactor: xvalues and yvalues are multiplied by this factor before plotting.
kwargs: Keyword arguments passed to ``matplotlib``. Accepts
============== ===============================================================
kwargs Meaning
============== ===============================================================
cplx_mode string defining the data to print.
Possible choices are (case-insensitive): `re` for the real part
"im" for the imaginary part, "abs" for the absolute value.
"angle" to display the phase of the complex number in radians.
Options can be concatenated with "-"
============== ===============================================================
Returns:
List of lines added.
"""
if self.iscomplexobj:
cplx_mode = kwargs.pop("cplx_mode", "re-im")
else:
cplx_mode = kwargs.pop("cplx_mode", "re")
lines = []
for c in cplx_mode.lower().split("-"):
xx, yy = self.mesh, data_from_cplx_mode(c, self.values)
if xfactor != 1: xx = xx * xfactor
if yfactor != 1: yy = yy * yfactor
if exchange_xy:
xx, yy = yy, xx
lines.extend(ax.plot(xx, yy, *args, **kwargs))
return lines
def plot_ax(self, ax, exchange_xy=False, xfactor=1, yfactor=1, *args, **kwargs):
"""
Helper function to plot self on axis ax.
Args:
ax: |matplotlib-Axes|.
exchange_xy: True to exchange the axis in the plot.
args: Positional arguments passed to ax.plot
xfactor, yfactor: xvalues and yvalues are multiplied by this factor before plotting.
kwargs: Keyword arguments passed to ``matplotlib``. Accepts
============== ===============================================================
kwargs Meaning
============== ===============================================================
cplx_mode string defining the data to print.
Possible choices are (case-insensitive): `re` for the real part
"im" for the imaginary part, "abs" for the absolute value.
"angle" to display the phase of the complex number in radians.
Options can be concatenated with "-"
============== ===============================================================
Returns:
List of lines added.
"""
if self.iscomplexobj:
cplx_mode = kwargs.pop("cplx_mode", "re-im")
else:
cplx_mode = kwargs.pop("cplx_mode", "re")
lines = []
for c in cplx_mode.lower().split("-"):
xx, yy = self.mesh, data_from_cplx_mode(c, self.values)
if xfactor != 1: xx = xx * xfactor
if yfactor != 1: yy = yy * yfactor
if exchange_xy:
xx, yy = yy, xx
lines.extend(ax.plot(xx, yy, *args, **kwargs))
return lines
def plot_chi2(self, key, components="all", what="abs", itemp=0, decompose=False,
ax=None, xlims=None, with_xlabel=True, label=None, fontsize=12, **kwargs):
"""
Low-level function to plot chi2 tensor.
Args:
key:
components: List of cartesian tensor components to plot e.g. ["xxx", "xyz"].
"all" if all components available on file should be plotted on the same ax.
what: quantity to plot. "re" for real part, "im" for imaginary, Accepts also "abs", "angle".
itemp: Temperature index.
decompose: True to plot individual contributions.
ax: |matplotlib-Axes| or None if a new figure should be created.
xlims: Set the data limits for the x-axis. Accept tuple e.g. ``(left, right)``
or scalar e.g. ``left``. If left (right) is None, default values are used.
with_xlabel: True to add x-label.
label: True to add legend label to each curve.
fontsize: Legend and label fontsize.
Returns: |matplotlib-Figure|
"""
if not self.reader.computed_components[key]: return None
comp2terms = self.reader.read_tensor3_terms(key, components, itemp=itemp)
ax, fig, plt = get_ax_fig_plt(ax=ax)
for comp, terms in comp2terms.items():
for name, values in terms.items():
if not decompose and not name.endswith("tot"): continue
values = data_from_cplx_mode(what, values)
ax.plot(self.wmesh[1:], values[1:],
label=self.get_chi2_latex_label(key, what, comp) if label is None else label,
)
ax.grid(True)
if with_xlabel: ax.set_xlabel('Photon Energy (eV)')
set_axlims(ax, xlims, "x")
ax.legend(loc="best", fontsize=fontsize, shadow=True)
return fig
def plot_ggmat(self, cplx_mode="abs", wpos=None, **kwargs):
"""
Use imshow for plotting W_GG'.
Args:
cplx_mode:
wpos: List of frequency indices to plot. If None, the first
frequency is used (usually w=0). if wpos == "all"
all frequencies are shown (use it carefully)
Other possible values: "real" if only real frequencies are wanted.
"imag" for imaginary frequencies only.
Returns:
`matplotlib` figure.
"""
# Get wpos indices.
choice_wpos = {
None: [0],
"all": range(self.nw),
"real": range(self.nrew),
"imag": range(self.nrew, self.nw)
}
if any(wpos == k for k in choice_wpos):
wpos = choice_wpos[wpos]
else:
if isinstance(wpos, int): wpos = [wpos]
wpos = np.array(wpos)
# Build plotter.
plotter = ArrayPlotter()
for iw in wpos:
label = cplx_mode + r" $\omega = %s" % self.wpts[iw]
data = data_from_cplx_mode(cplx_mode, self.wggmat[iw, :, :])
plotter.add_array(label, data)
return plotter.plot(**kwargs)
def plot_w(self,
gvec1,
gvec2=None,
waxis="real",
cplx_mode="re-im",
ax=None,
**kwargs):
"""
Plot the frequency dependence of W_{g1, g2}
Args:
gvec1, gvec2:
waxis: "real" to plot along the real axis, "imag" for the imaginary axis.
cplx_mode: string defining the data to print.
Possible choices are (case-insensitive): `re` for the real part
"im" for the imaginary part, "abs" for the absolute value.
"angle" will display the phase of the complex number in radians.
Options can be concatenated with "-" e.g. "re-im"
ax: matplotlib :class:`Axes` or None if a new figure should be created.
Returns:
matplotlib figure.
"""
# Select data to plot
ig1 = self.gindex(gvec1)
ig2 = ig1 if gvec2 is None else self.gindex(gvec2)
ax, fig, plt = get_ax_fig_plt(ax)
if waxis == "real":
if self.nrew == 0: return fig
xx = (self.real_wpts * Ha_to_eV).real
yy = self.wggmat_realw[:, ig1, ig2]
elif waxis == "imag":
if self.nimw == 0: return fig
xx = (self.imag_wpts * Ha_to_eV).imag
yy = self.wggmat_imagw[:, ig1, ig2]
else:
raise ValueError("Wrong value for waxis: %s" % waxis)
color_cmode = dict(re="red", im="blue", abs="black", angle="green")
linewidth = kwargs.pop("linewidth", 2)
linestyle = kwargs.pop("linestyle", "solid")
lines = []
for c in cplx_mode.lower().split("-"):
l, = ax.plot(xx,
data_from_cplx_mode(c, yy),
color=color_cmode[c],
linewidth=linewidth,
linestyle=linestyle,
label=self.latex_label(c))
lines.append(l)
ax.grid(True)
ax.set_xlabel(r"$\omega$ [eV]")
ax.set_title("%s, qpoint: %s" % (self.etsf_name, self.qpoint))
#ax.legend(loc="best")
ax.legend(loc="upper right")
return fig
