def _repr_html_(self):
ret = ''
for k, v in self.plots.iteritems():
canvas = FigureCanvasAgg(v)
f = StringIO()
canvas.print_figure(f)
f.seek(0)
img = base64.b64encode(f.read())
ret += '<img src="data:image/png;base64,%s"><br>' % img
return ret
def _repr_html_(self):
ret = ''
for k, v in self.plots.items():
canvas = FigureCanvasAgg(v)
f = IO()
canvas.print_figure(f)
f.seek(0)
img = base64.b64encode(f.read()).decode()
ret += r'<img style="max-width:100%%;max-height:100%%;" ' \
r'src="data:image/png;base64,%s"><br>' % img
return ret
def _repr_html_(self):
ret = ''
for k, v in self.plots.items():
canvas = FigureCanvasAgg(v)
f = IO()
canvas.print_figure(f)
f.seek(0)
img = base64.b64encode(f.read()).decode()
ret += r'<img style="max-width:100%%;max-height:100%%;" ' \
r'src="data:image/png;base64,%s"><br>' % img
return ret
def _repr_html_(self):
from yt.visualization._mpl_imports import FigureCanvasAgg
ret = ""
for k, v in self.plots.items():
canvas = FigureCanvasAgg(v)
f = BytesIO()
canvas.print_figure(f)
f.seek(0)
img = base64.b64encode(f.read()).decode()
ret += (r'<img style="max-width:100%%;max-height:100%%;" '
r'src="data:image/png;base64,%s"><br>' % img)
return ret
def plot(self, fn=None, profile_field=None, profile_weight=None):
"""
Save the current transfer function to a bitmap, or display
it inline.
Parameters
----------
fn: string, optional
Filename to save the image to. If None, the returns an image
to an IPython session.
Returns
-------
If fn is None, will return an image to an IPython notebook.
"""
from yt.visualization._mpl_imports import FigureCanvasAgg
from matplotlib.figure import Figure
if self.tf is None:
self.build_transfer_function()
self.setup_default()
tf = self.tf
if self.log:
xfunc = np.logspace
xmi, xma = np.log10(self.bounds[0]), np.log10(self.bounds[1])
else:
xfunc = np.linspace
# Need to strip units off of the bounds to avoid a recursion error
# in matplotlib 1.3.1
xmi, xma = [np.float64(b) for b in self.bounds]
x = xfunc(xmi, xma, tf.nbins)
y = tf.funcs[3].y
w = np.append(x[1:] - x[:-1], x[-1] - x[-2])
colors = np.array(
[tf.funcs[0].y, tf.funcs[1].y, tf.funcs[2].y,
np.ones_like(x)]).T
fig = Figure(figsize=[6, 3])
canvas = FigureCanvasAgg(fig)
ax = fig.add_axes([0.2, 0.2, 0.75, 0.75])
ax.bar(x,
tf.funcs[3].y,
w,
edgecolor=[0.0, 0.0, 0.0, 0.0],
log=self.log,
color=colors,
bottom=[0])
if profile_field is not None:
try:
prof = self.profiles[self.field]
except KeyError:
self.setup_profile(profile_field, profile_weight)
prof = self.profiles[self.field]
try:
prof[profile_field]
except KeyError:
prof.add_fields([profile_field])
# Strip units, if any, for matplotlib 1.3.1
xplot = np.array(prof.x)
yplot = np.array(prof[profile_field] * tf.funcs[3].y.max() /
prof[profile_field].max())
ax.plot(xplot, yplot, color='w', linewidth=3)
ax.plot(xplot, yplot, color='k')
ax.set_xscale({True: 'log', False: 'linear'}[self.log])
ax.set_xlim(x.min(), x.max())
ax.set_xlabel(self.ds._get_field_info(self.field).get_label())
ax.set_ylabel(r'$\mathrm{alpha}$')
ax.set_ylim(y.max() * 1.0e-3, y.max() * 2)
if fn is None:
from IPython.core.display import Image
f = BytesIO()
canvas.print_figure(f)
f.seek(0)
img = f.read()
return Image(img)
else:
fig.savefig(fn)
def plot(self, fn=None, profile_field=None, profile_weight=None):
"""
Save the current transfer function to a bitmap, or display
it inline.
Parameters
----------
fn: string, optional
Filename to save the image to. If None, the returns an image
to an IPython session.
Returns
-------
If fn is None, will return an image to an IPython notebook.
"""
if self.tf is None:
self.build_transfer_function()
tf = self.tf
if self.log:
xfunc = np.logspace
xmi, xma = np.log10(self.bounds[0]), np.log10(self.bounds[1])
else:
xfunc = np.linspace
# Need to strip units off of the bounds to avoid a recursion error
# in matplotlib 1.3.1
xmi, xma = [np.float64(b) for b in self.bounds]
x = xfunc(xmi, xma, tf.nbins)
y = tf.funcs[3].y
w = np.append(x[1:] - x[:-1], x[-1] - x[-2])
colors = np.array([tf.funcs[0].y, tf.funcs[1].y, tf.funcs[2].y, np.ones_like(x)]).T
fig = Figure(figsize=[6, 3])
canvas = FigureCanvasAgg(fig)
ax = fig.add_axes([0.2, 0.2, 0.75, 0.75])
ax.bar(x, tf.funcs[3].y, w, edgecolor=[0.0, 0.0, 0.0, 0.0], log=self.log, color=colors, bottom=[0])
if profile_field is not None:
try:
prof = self.profiles[self.field]
except KeyError:
self.setup_profile(profile_field, profile_weight)
prof = self.profiles[self.field]
if profile_field not in prof.keys():
prof.add_fields([profile_field], fractional=False, weight=profile_weight)
# Strip units, if any, for matplotlib 1.3.1
xplot = np.array(prof[self.field])
yplot = np.array(prof[profile_field] * tf.funcs[3].y.max() / prof[profile_field].max())
ax.plot(xplot, yplot, color="w", linewidth=3)
ax.plot(xplot, yplot, color="k")
ax.set_xscale({True: "log", False: "linear"}[self.log])
ax.set_xlim(x.min(), x.max())
ax.set_xlabel(self.ds._get_field_info(self.field).get_label())
ax.set_ylabel(r"$\mathrm{alpha}$")
ax.set_ylim(y.max() * 1.0e-3, y.max() * 2)
if fn is None:
from IPython.core.display import Image
f = StringIO()
canvas.print_figure(f)
f.seek(0)
img = f.read()
return Image(img)
else:
fig.savefig(fn)