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 mp(nx,
ny,
colorbar_size=2,
size_x=None,
size_y=None,
ylabel_size=1.5,
right_margin=0,
xlabel_size=1,
title_size=0.5,
dpi=300,
debug=0):
"""Make *nx* by *ny* plots of size *size_x* by *size_y*, that share common title, xlabel, ylabel, and colorbar.
ylabel is for the y axis, whose real estate comes out of the x dimension. Vice-versa for x."""
total_x = 1.0 * (nx * size_x + ylabel_size + colorbar_size + right_margin)
total_y = 1.0 * (ny * size_y + xlabel_size + title_size)
if size_x is None:
size_x = 4
if size_y is None:
size_y = 4
fr_x = size_x / total_x
fr_y = size_y / total_y
if debug > 0:
print "Total", total_x, total_y, "pixels", total_x * dpi, total_y * dpi
fig = matplotlib.figure.Figure((total_x, total_y), dpi=dpi)
fig.set_canvas(FigureCanvasAgg(fig))
axes_list = []
counter = 0
for j in range(ny - 1, -1, -1):
counter += 1
axes_list.append([])
for i in range(nx):
left = (ylabel_size + i * size_x) / total_x
bottom = (xlabel_size + j * size_y) / total_y
ax_extent = [left, bottom, size_x / total_x, size_y / total_y]
ax = fig.add_axes(ax_extent)
axes_list[-1].append(ax)
if debug > 0:
print "new ax: (l,b,r,t) (%f, %f, %f, %f)" % tuple(ax_extent)
print "new axb: (x0,x1) (%f %f)" % (ax.bbox.x0, ax.bbox.x1)
cbar = None
if colorbar_size > 0:
left = (ylabel_size + nx * size_x) / total_x
bottom = 1.0 * xlabel_size / total_y
cbar_extents = [
left, bottom, (colorbar_size - ylabel_size) / total_x,
ny * size_y / total_y
]
if debug > 0:
print cbar_extents
cbar = fig.add_axes(cbar_extents)
return fig, axes_list, cbar
def save_annotated(self,
fname=None,
label_fmt=None,
text_annotate=None,
dpi=100,
sigma_clip=None):
r"""Saves the most recently rendered image of the Scene to disk,
including an image of the transfer function and and user-defined
text.
Once you have created a scene and rendered that scene to an image
array, this saves that image array to disk with an optional filename.
If an image has not yet been rendered for the current scene object,
it forces one and writes it out.
Parameters
----------
fname: string, optional
If specified, save the rendering as a bitmap to the file "fname".
If unspecified, it creates a default based on the dataset filename.
Default: None
sigma_clip: float, optional
Image values greater than this number times the standard deviation
plus the mean of the image will be clipped before saving. Useful
for enhancing images as it gets rid of rare high pixel values.
Default: None
floor(vals > std_dev*sigma_clip + mean)
dpi: integer, optional
By default, the resulting image will be the same size as the camera
parameters. If you supply a dpi, then the image will be scaled
accordingly (from the default 100 dpi)
label_fmt : str, optional
A format specifier (e.g., label_fmt="%.2g") to use in formatting
the data values that label the transfer function colorbar.
text_annotate : list of iterables
Any text that you wish to display on the image. This should be an
list containing a tuple of coordinates (in normalized figure
coordinates), the text to display, and, optionally, a dictionary of
keyword/value pairs to pass through to the matplotlib text()
function.
Each item in the main list is a separate string to write.
Returns
-------
Nothing
Examples
--------
>>> sc.save_annotated("fig.png",
... text_annotate=[[(0.05, 0.05),
... "t = {}".format(ds.current_time.d),
... dict(horizontalalignment="left")],
... [(0.5,0.95),
... "simulation title",
... dict(color="y", fontsize="24",
... horizontalalignment="center")]])
"""
from yt.visualization._mpl_imports import \
FigureCanvasAgg, FigureCanvasPdf, FigureCanvasPS
sources = list(itervalues(self.sources))
rensources = [s for s in sources if isinstance(s, RenderSource)]
if fname is None:
# if a volume source present, use its affiliated ds for fname
if len(rensources) > 0:
rs = rensources[0]
basename = rs.data_source.ds.basename
if isinstance(rs.field, string_types):
field = rs.field
else:
field = rs.field[-1]
fname = "%s_Render_%s.png" % (basename, field)
# if no volume source present, use a default filename
else:
fname = "Render_opaque.png"
suffix = get_image_suffix(fname)
if suffix == '':
suffix = '.png'
fname = '%s%s' % (fname, suffix)
self.render()
# which transfer function?
rs = rensources[0]
tf = rs.transfer_function
label = rs.data_source.ds._get_field_info(rs.field).get_label()
ax = self._show_mpl(self._last_render.swapaxes(0, 1),
sigma_clip=sigma_clip,
dpi=dpi)
self._annotate(ax.axes, tf, rs, label=label, label_fmt=label_fmt)
# any text?
if text_annotate is not None:
f = self._render_figure
for t in text_annotate:
xy = t[0]
string = t[1]
if len(t) == 3:
opt = t[2]
else:
opt = dict()
# sane default
if "color" not in opt:
opt["color"] = "w"
ax.axes.text(xy[0],
xy[1],
string,
transform=f.transFigure,
**opt)
suffix = get_image_suffix(fname)
if suffix == ".png":
canvas = FigureCanvasAgg(self._render_figure)
elif suffix == ".pdf":
canvas = FigureCanvasPdf(self._render_figure)
elif suffix in (".eps", ".ps"):
canvas = FigureCanvasPS(self._render_figure)
else:
mylog.warning("Unknown suffix %s, defaulting to Agg", suffix)
canvas = self.canvas
self._render_figure.canvas = canvas
self._render_figure.tight_layout()
self._render_figure.savefig(fname, facecolor='black', pad_inches=0)
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)
from yt.mods import *
import matplotlib
import pylab
from yt.visualization._mpl_imports import FigureCanvasAgg
#Figure parameters
total_width = 36
total_height = 6
n_plots = 4
fontsize = 30
fig = matplotlib.figure.Figure((total_width, total_height), dpi=300)
fig.set_canvas(FigureCanvasAgg(fig))
#numbers from this point on are relative to the total
m_left = 0.05 #left margin
m_right = 0.01
m_bottom = 0.2 #bottom margin
m_top = 0.05
width = (1. - m_right) / (n_plots) - m_left
height_main = (1 - m_bottom - m_top) * 0.66
height_dx = 1 - m_bottom - height_main - m_top
#ax_rho_d = fig.add_axes( [0.01, 0.01, 0.5, 0.5])
#ax_rho_1 = fig.add_axes( [m_left,m_bottom+height_dx, width, height_main])
ax_main = []
ax_diff = []
for n_plot in range(n_plots):
this_plot_left = m_left + n_plot * (m_left + width)
extents_density_diff = [this_plot_left, m_bottom, width, height_dx]
extents_density_main = [
