def plot(df, cut_name,
my_variables=False, save=False):
if my_variables is None:
my_variables = variables.get_variables()
df_reduced = variables.reduce_df(df,
my_variables)
print('%s: %d of %d events not shown out of plotting window' % (cut_name,
df['x'].count() - df_reduced['x'].count(),
df['x'].count()))
name = '%s Cut' % cut_name
df_reduced[name] = ['Pass' if x == True else 'Fail' for x in df_reduced[cut_name]]
number_passing = df_reduced[cut_name].sum()
total = df_reduced['x'].count()
if number_passing == total:
print('Not plotting, no removed events for', cut_name)
return
keys = list(my_variables.keys())
g = sns.PairGrid(df_reduced,
vars=keys,
diag_sharey=False,
hue_order=['Pass', 'Fail'],
palette=['green', 'red', ],
hue=name,
hue_kws={"cmap": ["Greens", "Reds", ],
"marker": ["o", "x"]})
g.map_lower(sns.kdeplot, shade=True,
n_levels=10,
shade_lowest=False, alpha=0.5, )
g.map_upper(plt.scatter, alpha=0.2)
g.map_diag(plt.hist, histtype="step", linewidth=3)
g.add_legend()
for i, ax in enumerate(g.axes.flat):
ax.set_xlim(my_variables[keys[i % len(my_variables)]]['range'])
ax.set_ylim(my_variables[keys[int(i / len(my_variables))]]['range'])
if save:
for extension in ['pdf', 'png', 'eps']:
plt.savefig('plots/%s_%d.%s' % (cut_name,
len(my_variables),
extension),
bbox_inches='tight')
else:
plt.show()
def plot_conv_qpgap(self, x_vars, **kwargs):
"""
Plot the convergence of the Quasi-particle gap.
kwargs are passed to :class:`seaborn.PairGrid`.
"""
import matplotlib.pyplot as plt
import seaborn.apionly as sns
data = self.get_qpgaps_dataframe()
grid = sns.PairGrid(data, x_vars=x_vars, y_vars="qpgap", **kwargs)
grid.map(plt.plot, marker="o")
grid.add_legend()
plt.show()
def _update_plot(self, axis, view):
if self.plot_type == 'regplot':
sns.regplot(x=view.x,
y=view.y,
data=view.data,
ax=axis,
**self.style)
elif self.plot_type == 'boxplot':
self.style.pop('return_type', None)
self.style.pop('figsize', None)
sns.boxplot(view.data[view.y],
view.data[view.x],
ax=axis,
**self.style)
elif self.plot_type == 'violinplot':
sns.violinplot(view.data[view.y],
view.data[view.x],
ax=axis,
**self.style)
elif self.plot_type == 'interact':
sns.interactplot(view.x,
view.x2,
view.y,
data=view.data,
ax=axis,
**self.style)
elif self.plot_type == 'corrplot':
sns.corrplot(view.data, ax=axis, **self.style)
elif self.plot_type == 'lmplot':
sns.lmplot(x=view.x,
y=view.y,
data=view.data,
ax=axis,
**self.style)
elif self.plot_type in ['pairplot', 'pairgrid', 'facetgrid']:
map_opts = [(k, self.style.pop(k)) for k in self.style.keys()
if 'map' in k]
if self.plot_type == 'pairplot':
g = sns.pairplot(view.data, **self.style)
elif self.plot_type == 'pairgrid':
g = sns.PairGrid(view.data, **self.style)
elif self.plot_type == 'facetgrid':
g = sns.FacetGrid(view.data, **self.style)
for opt, args in map_opts:
plot_fn = getattr(sns, args[0]) if hasattr(
sns, args[0]) else getattr(plt, args[0])
getattr(g, opt)(plot_fn, *args[1:])
plt.close(self.handles['fig'])
self.handles['fig'] = plt.gcf()
else:
super(SNSFramePlot, self)._update_plot(axis, view)
def _update_plot(self, axis, view):
style = self._process_style(self.style[self.cyclic_index])
if self.plot_type == 'factorplot':
opts = dict(style, **({'hue': view.x2} if view.x2 else {}))
sns.factorplot(x=view.x, y=view.y, data=view.data, **opts)
elif self.plot_type == 'regplot':
sns.regplot(x=view.x, y=view.y, data=view.data, ax=axis, **style)
elif self.plot_type == 'boxplot':
style.pop('return_type', None)
style.pop('figsize', None)
sns.boxplot(view.data[view.y], view.data[view.x], ax=axis, **style)
elif self.plot_type == 'violinplot':
if view.x:
sns.violinplot(view.data[view.y],
view.data[view.x],
ax=axis,
**style)
else:
sns.violinplot(view.data, ax=axis, **style)
elif self.plot_type == 'interact':
sns.interactplot(view.x,
view.x2,
view.y,
data=view.data,
ax=axis,
**style)
elif self.plot_type == 'corrplot':
sns.corrplot(view.data, ax=axis, **style)
elif self.plot_type == 'lmplot':
sns.lmplot(x=view.x, y=view.y, data=view.data, ax=axis, **style)
elif self.plot_type in ['pairplot', 'pairgrid', 'facetgrid']:
style_keys = list(style.keys())
map_opts = [(k, style.pop(k)) for k in style_keys if 'map' in k]
if self.plot_type == 'pairplot':
g = sns.pairplot(view.data, **style)
elif self.plot_type == 'pairgrid':
g = sns.PairGrid(view.data, **style)
elif self.plot_type == 'facetgrid':
g = sns.FacetGrid(view.data, **style)
for opt, args in map_opts:
plot_fn = getattr(sns, args[0]) if hasattr(
sns, args[0]) else getattr(plt, args[0])
getattr(g, opt)(plot_fn, *args[1:])
plt.close(self.handles['fig'])
self.handles['fig'] = plt.gcf()
else:
super(SNSFramePlot, self)._update_plot(axis, view)
def pairplot(self, data=None, getter="get_dataframe", map_kws=None, show=True, **kwargs):
# TODO: Remove
import matplotlib.pyplot as plt
import seaborn.apionly as sns
if data is None:
data = getattr(self, getter)()
#grid = sns.PairGrid(data, x_vars="nkpt", y_vars=["a", "volume"]) #, hue="tsmear")
grid = sns.PairGrid(data, **kwargs)
if map_kws is None:
grid.map(plt.plot, marker="o")
else:
func = map_kws.pop("func", plt.plot)
grid.map(func, **map_kws)
grid.add_legend()
if show: plt.show()
return grid
def plot_conv_phfreqs_qpoint(self, x_vars, qpoint=None, **kwargs):
"""
Plot the convergence of the phonon frequencies.
kwargs are passed to :class:`seaborn.PairGrid`.
"""
import matplotlib.pyplot as plt
import seaborn.apionly as sns
# Get the dataframe for this q-point.
data = self.get_dataframe_at_qpoint(qpoint=qpoint)
y_vars = sorted([k for k in data if k.startswith("mode")])
#print(y_vars)
# Call seaborn.
grid = sns.PairGrid(data, x_vars=x_vars, y_vars=y_vars, **kwargs)
grid.map(plt.plot, marker="o")
grid.add_legend()
plt.show()
def plot_synapse_pairs(X, zh, output='synapse_pairs.pdf'):
sns.set_style({"font.size": 10, "axes.labelsize": 30})
d = {'z': zh}
n, p = X.shape
for i in range(p):
d[r'$x_{%i}$' % (i + 1)] = X[:, i]
#d[r'$x_{%i}$'%(i+1)] = X[:,i]/(10**5)
df = pd.DataFrame(data=d)
g = sns.PairGrid(df,
hue="z",
vars=[r'$x_{%i}$' % (i + 1) for i in range(p)])
def scatter_fake_diag(x, y, *a, **kw):
if x.equals(y):
kw["color"] = (0, 0, 0, 0)
plt.scatter(x, y, s=3, *a, **kw)
g.map(scatter_fake_diag)
g.map_diag(plt.hist)
g.savefig(output)
