def plot_elastic_properties(self, fontsize=10, **kwargs):
"""
Args:
fontsize: legend and label fontsize.
Returns: |matplotlib-Figure|
"""
df = self.get_elastic_dataframe(with_geo=False,
abspath=False,
with_params=False)
from pandas.api.types import is_numeric_dtype
keys = [k for k in df.keys() if is_numeric_dtype(df[k])]
i = keys.index("fitted_to_structure")
if i != -1:
keys.pop(i)
num_plots, ncols, nrows = len(keys), 1, 1
if num_plots > 1:
ncols = 3
nrows = (num_plots // ncols) + (num_plots % ncols)
ax_list, fig, plt = get_axarray_fig_plt(None,
nrows=nrows,
ncols=ncols,
sharex=False,
sharey=False,
squeeze=False)
ax_list = ax_list.ravel()
for ix, (key, ax) in enumerate(zip(keys, ax_list)):
irow, icol = divmod(ix, ncols)
xn = range(len(df.index))
ax.plot(xn, df[key], marker="o")
ax.grid(True)
ax.set_xticks(xn)
ax.set_ylabel(key, fontsize=fontsize)
ax.set_xticklabels([])
ax.set_xticklabels(self.keys(), fontsize=fontsize)
rotate_ticklabels(ax, 15)
if ix != len(ax_list) - 1:
for ix in range(ix + 1, len(ax_list)):
ax_list[ix].axis('off')
return fig
def plot_elastic_properties(self, fontsize=10, **kwargs):
"""
Args:
fontsize: legend and label fontsize.
Returns: |matplotlib-Figure|
"""
df = self.get_elastic_dataframe(with_geo=False, abspath=False, with_params=False)
from pandas.api.types import is_numeric_dtype
keys = [k for k in df.keys() if is_numeric_dtype(df[k])]
i = keys.index("fitted_to_structure")
if i != -1:
keys.pop(i)
num_plots, ncols, nrows = len(keys), 1, 1
if num_plots > 1:
ncols = 3
nrows = (num_plots // ncols) + (num_plots % ncols)
ax_list, fig, plt = get_axarray_fig_plt(None, nrows=nrows, ncols=ncols,
sharex=False, sharey=False, squeeze=False)
ax_list = ax_list.ravel()
for ix, (key, ax) in enumerate(zip(keys, ax_list)):
irow, icol = divmod(ix, ncols)
xn = range(len(df.index))
ax.plot(xn, df[key], marker="o")
ax.grid(True)
ax.set_xticks(xn)
ax.set_ylabel(key, fontsize=fontsize)
ax.set_xticklabels([])
ax.set_xticklabels(self.keys(), fontsize=fontsize)
rotate_ticklabels(ax, 15)
if ix != len(ax_list) -1:
for ix in range(ix + 1, len(ax_list)):
ax_list[ix].axis('off')
return fig
def plot_convergence_items(self,
items,
sortby=None,
hue=None,
fontsize=6,
**kwargs):
"""
Plot the convergence of a list of ``items`` wrt to the ``sortby`` parameter.
Values can optionally be grouped by ``hue``.
Args:
items: List of attributes (or callables) to be analyzed.
sortby: Define the convergence parameter, sort files and produce plot labels.
Can be None, string or function. If None, no sorting is performed.
If string and not empty it's assumed that the abifile has an attribute
with the same name and `getattr` is invoked.
If callable, the output of sortby(abifile) is used.
hue: Variable that define subsets of the data, which will be drawn on separate lines.
Accepts callable or string
If string, it's assumed that the abifile has an attribute with the same name and getattr is invoked.
Dot notation is also supported e.g. hue="structure.formula" --> abifile.structure.formula
If callable, the output of hue(abifile) is used.
fontsize: legend and label fontsize.
kwargs: keyword arguments are passed to ax.plot
Returns: |matplotlib-Figure|
"""
# Note: in principle one could call plot_convergence inside a loop but
# this one is faster as sorting is done only once.
# Build grid plot.
nrows, ncols = len(items), 1
ax_list, fig, plt = get_axarray_fig_plt(None,
nrows=nrows,
ncols=ncols,
sharex=True,
sharey=False,
squeeze=False)
ax_list = ax_list.ravel()
# Sort and group files if hue.
if hue is None:
labels, ncfiles, params = self.sortby(sortby, unpack=True)
else:
groups = self.group_and_sortby(hue, sortby)
marker = kwargs.pop("marker", "o")
for i, (ax, item) in enumerate(zip(ax_list, items)):
if hue is None:
# Extract data.
if callable(item):
yvals = [float(item(gsr)) for gsr in self.abifiles]
else:
yvals = [getattrd(gsr, item) for gsr in self.abifiles]
if not is_string(params[0]):
ax.plot(params, yvals, marker=marker, **kwargs)
else:
# Must handle list of strings in a different way.
xn = range(len(params))
ax.plot(xn, yvals, marker=marker, **kwargs)
ax.set_xticks(xn)
ax.set_xticklabels(params, fontsize=fontsize)
else:
for g in groups:
# Extract data.
if callable(item):
yvals = [float(item(gsr)) for gsr in g.abifiles]
else:
yvals = [getattrd(gsr, item) for gsr in g.abifiles]
label = "%s: %s" % (self._get_label(hue), g.hvalue)
ax.plot(g.xvalues,
yvals,
label=label,
marker=marker,
**kwargs)
ax.grid(True)
ax.set_ylabel(self._get_label(item))
if i == len(items) - 1:
ax.set_xlabel("%s" % self._get_label(sortby))
if sortby is None: rotate_ticklabels(ax, 15)
if i == 0 and hue is not None:
ax.legend(loc="best", fontsize=fontsize, shadow=True)
return fig
def plot_convergence(self,
item,
sortby=None,
hue=None,
ax=None,
fontsize=12,
**kwargs):
"""
Plot the convergence of ``item`` wrt the ``sortby`` parameter.
Values can optionally be grouped by ``hue``.
Args:
item: Define the quantity to plot. Accepts callable or string
If string, it's assumed that the abifile has an attribute with the same name and `getattr` is invoked.
Dot notation is also supported e.g. hue="structure.formula" --> abifile.structure.formula
If callable, the output of item(abifile) is used.
sortby: Define the convergence parameter, sort files and produce plot labels.
Can be None, string or function. If None, no sorting is performed.
If string and not empty it's assumed that the abifile has an attribute
with the same name and `getattr` is invoked.
If callable, the output of sortby(abifile) is used.
hue: Variable that define subsets of the data, which will be drawn on separate lines.
Accepts callable or string
If string, it's assumed that the abifile has an attribute with the same name and getattr is invoked.
If callable, the output of hue(abifile) is used.
ax: |matplotlib-Axes| or None if a new figure should be created.
fontsize: legend and label fontsize.
kwargs: keyword arguments passed to matplotlib plot method.
Returns: |matplotlib-Figure|
Example:
robot.plot_convergence("energy")
robot.plot_convergence("energy", sortby="nkpt")
robot.plot_convergence("pressure", sortby="nkpt", hue="tsmear")
"""
ax, fig, plt = get_ax_fig_plt(ax=ax)
if "marker" not in kwargs:
kwargs["marker"] = "o"
def get_yvalues(abifiles):
if callable(item):
return [float(item(a)) for a in abifiles]
else:
return [float(getattr(a, item)) for a in abifiles]
if hue is None:
labels, abifiles, params = self.sortby(sortby, unpack=True)
yvals = get_yvalues(abifiles)
#print("params", params, "\nyvals", yvals)
ax.plot(params, yvals, **kwargs)
else:
groups = self.group_and_sortby(hue, sortby)
for g in groups:
yvals = get_yvalues(g.abifiles)
label = "%s: %s" % (self._get_label(hue), g.hvalue)
ax.plot(g.xvalues, yvals, label=label, **kwargs)
ax.grid(True)
ax.set_xlabel("%s" % self._get_label(sortby))
if sortby is None: rotate_ticklabels(ax, 15)
ax.set_ylabel("%s" % self._get_label(item))
if hue is not None:
ax.legend(loc="best", fontsize=fontsize, shadow=True)
return fig
def plot_convergence_items(self, items, sortby=None, hue=None, fontsize=6, **kwargs):
"""
Plot the convergence of a list of ``items`` wrt to the ``sortby`` parameter.
Values can optionally be grouped by ``hue``.
Args:
items: List of attributes (or callables) to be analyzed.
sortby: Define the convergence parameter, sort files and produce plot labels.
Can be None, string or function. If None, no sorting is performed.
If string and not empty it's assumed that the abifile has an attribute
with the same name and `getattr` is invoked.
If callable, the output of sortby(abifile) is used.
hue: Variable that define subsets of the data, which will be drawn on separate lines.
Accepts callable or string
If string, it's assumed that the abifile has an attribute with the same name and getattr is invoked.
Dot notation is also supported e.g. hue="structure.formula" --> abifile.structure.formula
If callable, the output of hue(abifile) is used.
fontsize: legend and label fontsize.
kwargs: keyword arguments are passed to ax.plot
Returns: |matplotlib-Figure|
"""
# Note: in principle one could call plot_convergence inside a loop but
# this one is faster as sorting is done only once.
# Build grid plot.
nrows, ncols = len(items), 1
ax_list, fig, plt = get_axarray_fig_plt(None, nrows=nrows, ncols=ncols,
sharex=True, sharey=False, squeeze=False)
ax_list = ax_list.ravel()
# Sort and group files if hue.
if hue is None:
labels, ncfiles, params = self.sortby(sortby, unpack=True)
else:
groups = self.group_and_sortby(hue, sortby)
marker = kwargs.pop("marker", "o")
for i, (ax, item) in enumerate(zip(ax_list, items)):
if hue is None:
# Extract data.
if callable(item):
yvals = [float(item(gsr)) for gsr in self.abifiles]
else:
yvals = [getattrd(gsr, item) for gsr in self.abifiles]
if not is_string(params[0]):
ax.plot(params, yvals, marker=marker, **kwargs)
else:
# Must handle list of strings in a different way.
xn = range(len(params))
ax.plot(xn, yvals, marker=marker, **kwargs)
ax.set_xticks(xn)
ax.set_xticklabels(params, fontsize=fontsize)
else:
for g in groups:
# Extract data.
if callable(item):
yvals = [float(item(gsr)) for gsr in g.abifiles]
else:
yvals = [getattrd(gsr, item) for gsr in g.abifiles]
label = "%s: %s" % (self._get_label(hue), g.hvalue)
ax.plot(g.xvalues, yvals, label=label, marker=marker, **kwargs)
ax.grid(True)
ax.set_ylabel(self._get_label(item))
if i == len(items) - 1:
ax.set_xlabel("%s" % self._get_label(sortby))
if sortby is None: rotate_ticklabels(ax, 15)
if i == 0 and hue is not None:
ax.legend(loc="best", fontsize=fontsize, shadow=True)
return fig
def plot_convergence(self, item, sortby=None, hue=None, ax=None, fontsize=12, **kwargs):
"""
Plot the convergence of ``item`` wrt the ``sortby`` parameter.
Values can optionally be grouped by ``hue``.
Args:
item: Define the quantity to plot. Accepts callable or string
If string, it's assumed that the abifile has an attribute with the same name and `getattr` is invoked.
Dot notation is also supported e.g. hue="structure.formula" --> abifile.structure.formula
If callable, the output of item(abifile) is used.
sortby: Define the convergence parameter, sort files and produce plot labels.
Can be None, string or function. If None, no sorting is performed.
If string and not empty it's assumed that the abifile has an attribute
with the same name and `getattr` is invoked.
If callable, the output of sortby(abifile) is used.
hue: Variable that define subsets of the data, which will be drawn on separate lines.
Accepts callable or string
If string, it's assumed that the abifile has an attribute with the same name and getattr is invoked.
If callable, the output of hue(abifile) is used.
ax: |matplotlib-Axes| or None if a new figure should be created.
fontsize: legend and label fontsize.
kwargs: keyword arguments passed to matplotlib plot method.
Returns: |matplotlib-Figure|
Example:
robot.plot_convergence("energy")
robot.plot_convergence("energy", sortby="nkpt")
robot.plot_convergence("pressure", sortby="nkpt", hue="tsmear")
"""
ax, fig, plt = get_ax_fig_plt(ax=ax)
if "marker" not in kwargs:
kwargs["marker"] = "o"
def get_yvalues(abifiles):
if callable(item):
return [float(item(a)) for a in abifiles]
else:
return [float(getattr(a, item)) for a in abifiles]
if hue is None:
labels, abifiles, params = self.sortby(sortby, unpack=True)
yvals = get_yvalues(abifiles)
#print("params", params, "\nyvals", yvals)
ax.plot(params, yvals, **kwargs)
else:
groups = self.group_and_sortby(hue, sortby)
for g in groups:
yvals = get_yvalues(g.abifiles)
label = "%s: %s" % (self._get_label(hue), g.hvalue)
ax.plot(g.xvalues, yvals, label=label, **kwargs)
ax.grid(True)
ax.set_xlabel("%s" % self._get_label(sortby))
if sortby is None: rotate_ticklabels(ax, 15)
ax.set_ylabel("%s" % self._get_label(item))
if hue is not None:
ax.legend(loc="best", fontsize=fontsize, shadow=True)
return fig
