.. _callbacks:
Plot Modifications: Overplotting Contours, Velocities, Particles, and More
==========================================================================
Adding callbacks to plots
-------------------------
After a plot is generated using the standard tools (e.g. SlicePlot,
ProjectionPlot, etc.), it can be annotated with any number of ``callbacks``
before being saved to disk. These callbacks can modify the plots by adding
lines, text, markers, streamlines, velocity vectors, contours, and more.
Callbacks can be applied to plots created with
:class:`~yt.visualization.plot_window.SlicePlot`,
:class:`~yt.visualization.plot_window.ProjectionPlot`,
:class:`~yt.visualization.plot_window.AxisAlignedSlicePlot`,
:class:`~yt.visualization.plot_window.AxisAlignedProjectionPlot`,
:class:`~yt.visualization.plot_window.OffAxisSlicePlot`, or
:class:`~yt.visualization.plot_window.OffAxisProjectionPlot`, by calling
one of the ``annotate_`` methods that hang off of the plot object.
The ``annotate_`` methods are dynamically generated based on the list
of available callbacks. For example:
.. code-block:: python
slc = SlicePlot(ds, "x", ("gas", "density"))
slc.annotate_title("This is a Density plot")
would add the :func:`~yt.visualization.plot_modifications.TitleCallback` to
the plot object. All of the callbacks listed below are available via
similar ``annotate_`` functions.
To clear one or more annotations from an existing plot, see the
:ref:`clear_annotations function <clear-annotations>`.
For a brief demonstration of a few of these callbacks in action together,
see the cookbook recipe: :ref:`annotations-recipe`.
Also note that new ``annotate_`` methods can be defined without modifying yt's
source code, see :ref:`extend-annotations`.
Coordinate Systems in Callbacks
-------------------------------
Many of the callbacks (e.g.
:class:`~yt.visualization.plot_modifications.TextLabelCallback`) are specified
to occur at user-defined coordinate locations (like where to place a marker
or text on the plot). There are several different coordinate systems used
to identify these locations. These coordinate systems can be specified with
the ``coord_system`` keyword in the relevant callback, which is by default
set to ``data``. The valid coordinate systems are:
``data`` – the 3D dataset coordinates
``plot`` – the 2D coordinates defined by the actual plot limits
``axis`` – the MPL axis coordinates: (0,0) is lower left; (1,1) is upper right
``figure`` – the MPL figure coordinates: (0,0) is lower left, (1,1) is upper right
Here we will demonstrate these different coordinate systems for an projection
of the x-plane (i.e. with axes in the y and z directions):
.. python-script::
import yt
ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030")
s = yt.SlicePlot(ds, "x", ("gas", "density"))
s.set_axes_unit("kpc")
# Plot marker and text in data coords
s.annotate_marker((0.2, 0.5, 0.9), coord_system="data")
s.annotate_text((0.2, 0.5, 0.9), "data: (0.2, 0.5, 0.9)", coord_system="data")
# Plot marker and text in plot coords
s.annotate_marker((200, -300), coord_system="plot")
s.annotate_text((200, -300), "plot: (200, -300)", coord_system="plot")
# Plot marker and text in axis coords
s.annotate_marker((0.1, 0.2), coord_system="axis")
s.annotate_text((0.1, 0.2), "axis: (0.1, 0.2)", coord_system="axis")
# Plot marker and text in figure coords
# N.B. marker will not render outside of axis bounds
s.annotate_marker((0.1, 0.2), coord_system="figure", color="black")
s.annotate_text(
(0.1, 0.2),
"figure: (0.1, 0.2)",
coord_system="figure",
text_args={"color": "black"},
)
s.save()
Note that for non-cartesian geometries and ``coord_system="data"``, the coordinates
are still interpreted in the corresponding cartesian system. For instance using a polar
dataset from AMRVAC :
.. python-script::
import yt
ds = yt.load("amrvac/bw_polar_2D0000.dat")
s = yt.plot_2d(ds, ("gas", "density"))
s.set_background_color("density", "black")
# Plot marker and text in data coords
s.annotate_marker((0.2, 0.5, 0.9), coord_system="data")
s.annotate_text((0.2, 0.5, 0.9), "data: (0.2, 0.5, 0.9)", coord_system="data")
# Plot marker and text in plot coords
s.annotate_marker((0.4, -0.5), coord_system="plot")
s.annotate_text((0.4, -0.5), "plot: (0.4, -0.5)", coord_system="plot")
# Plot marker and text in axis coords
s.annotate_marker((0.1, 0.2), coord_system="axis")
s.annotate_text((0.1, 0.2), "axis: (0.1, 0.2)", coord_system="axis")
# Plot marker and text in figure coords
# N.B. marker will not render outside of axis bounds
s.annotate_marker((0.6, 0.2), coord_system="figure")
s.annotate_text((0.6, 0.2), "figure: (0.6, 0.2)", coord_system="figure")
s.save()
Available Callbacks
-------------------
The underlying functions are more thoroughly documented in :ref:`callback-api`.
.. _clear-annotations:
Clear Callbacks (Some or All)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. function:: clear_annotations(index=None)
This function will clear previous annotations (callbacks) in the plot.
If no index is provided, it will clear all annotations to the plot.
If an index is provided, it will clear only the Nth annotation
to the plot. Note that the index goes from 0..N, and you can
specify the index of the last added annotation as -1.
(This is a proxy for
:func:`~yt.visualization.plot_window.clear_annotations`.)
.. python-script::
import yt
ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030")
p = yt.SlicePlot(ds, "z", ("gas", "density"), center="c", width=(20, "kpc"))
p.annotate_scale()
p.annotate_timestamp()
# Oops, I didn't want any of that.
p.clear_annotations()
p.save()
.. _annotate-list:
List Currently Applied Callbacks
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. function:: list_annotations()
This function will print a list of each of the currently applied
callbacks together with their index. The index can be used with
:ref:`clear_annotations() function <clear-annotations>` to remove a
specific callback.
(This is a proxy for
:func:`~yt.visualization.plot_window.list_annotations`.)
.. python-script::
import yt
ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030")
p = yt.SlicePlot(ds, "z", ("gas", "density"), center="c", width=(20, "kpc"))
p.annotate_scale()
p.annotate_timestamp()
p.list_annotations()
.. _annotate-arrow:
Overplot Arrow
~~~~~~~~~~~~~~
.. function:: annotate_arrow(self, pos, length=0.03, coord_system='data', **kwargs)
(This is a proxy for
:class:`~yt.visualization.plot_modifications.ArrowCallback`.)
Overplot an arrow pointing at a position for highlighting a specific
feature. Arrow points from lower left to the designated position with
arrow length "length".
.. python-script::
import yt
ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030")
slc = yt.SlicePlot(ds, "z", ("gas", "density"), width=(10, "kpc"), center="c")
slc.annotate_arrow((0.5, 0.5, 0.5), length=0.06, color="blue")
slc.save()
.. _annotate-clumps:
Clump Finder Callback
~~~~~~~~~~~~~~~~~~~~~
.. function:: annotate_clumps(self, clumps, **kwargs)
(This is a proxy for
:class:`~yt.visualization.plot_modifications.ClumpContourCallback`.)
Take a list of ``clumps`` and plot them as a set of
contours.
.. python-script::
import numpy as np
import yt
from yt.data_objects.level_sets.api import Clump, find_clumps
ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030")
data_source = ds.disk([0.5, 0.5, 0.5], [0.0, 0.0, 1.0], (8.0, "kpc"), (1.0, "kpc"))
c_min = 10 ** np.floor(np.log10(data_source["gas", "density"]).min())
c_max = 10 ** np.floor(np.log10(data_source["gas", "density"]).max() + 1)
master_clump = Clump(data_source, ("gas", "density"))
master_clump.add_validator("min_cells", 20)
find_clumps(master_clump, c_min, c_max, 2.0)
leaf_clumps = master_clump.leaves
prj = yt.ProjectionPlot(ds, "z", ("gas", "density"), center="c", width=(20, "kpc"))
prj.annotate_clumps(leaf_clumps)
prj.save("clumps")
.. _annotate-contours:
Overplot Contours
~~~~~~~~~~~~~~~~~
.. function:: annotate_contour(self, field, levels=5, factor=4, take_log=False,\
clim=None, plot_args=None, label=False, \
text_args=None, data_source=None)
(This is a proxy for
:class:`~yt.visualization.plot_modifications.ContourCallback`.)
Add contours in ``field`` to the plot. ``levels`` governs the number of
contours generated, ``factor`` governs the number of points used in the
interpolation, ``take_log`` governs how it is contoured and ``clim`` gives
the (lower, upper) limits for contouring.
.. python-script::
import yt
ds = yt.load("Enzo_64/DD0043/data0043")
s = yt.SlicePlot(ds, "x", ("gas", "density"), center="max")
s.annotate_contour(("gas", "temperature"))
s.save()
.. _annotate-quivers:
Overplot Quivers
~~~~~~~~~~~~~~~~
Axis-Aligned Data Sources
^^^^^^^^^^^^^^^^^^^^^^^^^
.. function:: annotate_quiver(self, field_x, field_y, field_c=None, *, factor=16, scale=None, \
scale_units=None, normalize=False, **kwargs)
(This is a proxy for
:class:`~yt.visualization.plot_modifications.QuiverCallback`.)
Adds a 'quiver' plot to any plot, using the ``field_x`` and ``field_y`` from
the associated data, skipping every ``factor`` pixels in the
discretization. A third field, ``field_c``, can be used as color; which is the
counterpart of ``matplotlib.axes.Axes.quiver``'s final positional argument ``C``.
``scale`` is the data units per arrow length unit using
``scale_units``. If ``normalize`` is ``True``, the fields will be scaled by
their local (in-plane) length, allowing morphological features to be more
clearly seen for fields with substantial variation in field strength.
All additional keyword arguments are passed down to ``matplotlib.Axes.axes.quiver``.
Example using a constant color
.. python-script::
import yt
ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030")
p = yt.ProjectionPlot(
ds,
"z",
("gas", "density"),
center=[0.5, 0.5, 0.5],
weight_field="density",
width=(20, "kpc"),
)
p.annotate_quiver(
("gas", "velocity_x"),
("gas", "velocity_y"),
factor=16,
color="purple",
)
p.save()
And now using a continuous colormap
.. python-script::
import yt
ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030")
p = yt.ProjectionPlot(
ds,
"z",
("gas", "density"),
center=[0.5, 0.5, 0.5],
weight_field="density",
width=(20, "kpc"),
)
p.annotate_quiver(
("gas", "velocity_x"),
("gas", "velocity_y"),
("gas", "vorticity_z"),
factor=16,
cmap="inferno_r",
)
p.save()
Off-Axis Data Sources
^^^^^^^^^^^^^^^^^^^^^
.. function:: annotate_cquiver(self, field_x, field_y, field_c=None, *, factor=16, scale=None, \
scale_units=None, normalize=False, **kwargs)
(This is a proxy for
:class:`~yt.visualization.plot_modifications.CuttingQuiverCallback`.)
Get a quiver plot on top of a cutting plane, using the ``field_x`` and
``field_y`` from the associated data, skipping every ``factor`` datapoints in
the discretization. ``scale`` is the data units per arrow length unit using
``scale_units``. If ``normalize`` is ``True``, the fields will be scaled by
their local (in-plane) length, allowing morphological features to be more
clearly seen for fields with substantial variation in field strength.
Additional arguments can be passed to the ``plot_args`` dictionary, see
matplotlib.axes.Axes.quiver for more info.
.. python-script::
import yt
ds = yt.load("Enzo_64/DD0043/data0043")
s = yt.OffAxisSlicePlot(ds, [1, 1, 0], [("gas", "density")], center="c")
s.annotate_cquiver(
("gas", "cutting_plane_velocity_x"),
("gas", "cutting_plane_velocity_y"),
factor=10,
color="orange",
)
s.zoom(1.5)
s.save()
.. _annotate-grids:
Overplot Grids
~~~~~~~~~~~~~~
.. function:: annotate_grids(self, alpha=0.7, min_pix=1, min_pix_ids=20, \
draw_ids=False, id_loc="lower left", \
periodic=True, min_level=None, \
max_level=None, cmap='B-W Linear_r', \
edgecolors=None, linewidth=1.0)
(This is a proxy for
:class:`~yt.visualization.plot_modifications.GridBoundaryCallback`.)
Adds grid boundaries to a plot, optionally with alpha-blending via the
``alpha`` keyword. Cuttoff for display is at ``min_pix`` wide. ``draw_ids``
puts the grid id in the ``id_loc`` corner of the grid. (``id_loc`` can be
upper/lower left/right. ``draw_ids`` is not so great in projections...)
.. python-script::
import yt
ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030")
slc = yt.SlicePlot(ds, "z", ("gas", "density"), width=(10, "kpc"), center="max")
slc.annotate_grids()
slc.save()
.. _annotate-cell-edges:
Overplot Cell Edges
~~~~~~~~~~~~~~~~~~~
.. function:: annotate_cell_edges(line_width=0.002, alpha=1.0, color='black')
(This is a proxy for
:class:`~yt.visualization.plot_modifications.CellEdgesCallback`.)
Annotate the edges of cells, where the ``line_width`` relative to size of
the longest plot axis is specified. The ``alpha`` of the overlaid image and
the ``color`` of the lines are also specifiable. Note that because the
lines are drawn from both sides of a cell, the image sometimes has the
effect of doubling the line width. Color here is a matplotlib color name or
a 3-tuple of RGB float values.
.. python-script::
import yt
ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030")
slc = yt.SlicePlot(ds, "z", ("gas", "density"), width=(10, "kpc"), center="max")
slc.annotate_cell_edges()
slc.save()
.. _annotate-image-line:
Overplot a Straight Line
~~~~~~~~~~~~~~~~~~~~~~~~
.. function:: annotate_line(self, p1, p2, *, coord_system='data', **kwargs)
(This is a proxy for
:class:`~yt.visualization.plot_modifications.LinePlotCallback`.)
Overplot a line with endpoints at p1 and p2. p1 and p2
should be 2D or 3D coordinates consistent with the coordinate
system denoted in the "coord_system" keyword.
.. python-script::
import yt
ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030")
p = yt.ProjectionPlot(ds, "z", ("gas", "density"), center="m", width=(10, "kpc"))
p.annotate_line((0.3, 0.4), (0.8, 0.9), coord_system="axis")
p.save()
.. _annotate-magnetic-field:
Overplot Magnetic Field Quivers
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. function:: annotate_magnetic_field(self, factor=16, *, scale=None, \
scale_units=None, normalize=False, \
**kwargs)
(This is a proxy for
:class:`~yt.visualization.plot_modifications.MagFieldCallback`.)
Adds a 'quiver' plot of magnetic field to the plot, skipping every ``factor``
datapoints in the discretization. ``scale`` is the data units per arrow
length unit using ``scale_units``. If ``normalize`` is ``True``, the
magnetic fields will be scaled by their local (in-plane) length, allowing
morphological features to be more clearly seen for fields with substantial
variation in field strength. Additional arguments can be passed to the
``plot_args`` dictionary, see matplotlib.axes.Axes.quiver for more info.
.. python-script::
import yt
ds = yt.load(
"MHDSloshing/virgo_low_res.0054.vtk",
units_override={
"time_unit": (1, "Myr"),
"length_unit": (1, "Mpc"),
"mass_unit": (1e17, "Msun"),
},
)
p = yt.ProjectionPlot(ds, "z", ("gas", "density"), center="c", width=(300, "kpc"))
p.annotate_magnetic_field(headlength=3)
p.save()
.. _annotate-marker:
Annotate a Point With a Marker
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. function:: annotate_marker(self, pos, marker='x', *, coord_system='data', **kwargs)
(This is a proxy for
:class:`~yt.visualization.plot_modifications.MarkerAnnotateCallback`.)
Overplot a marker on a position for highlighting specific features.
.. python-script::
import yt
ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030")
s = yt.SlicePlot(ds, "z", ("gas", "density"), center="c", width=(10, "kpc"))
s.annotate_marker((-2, -2), coord_system="plot", color="blue", s=500)
s.save()
.. _annotate-particles:
Overplotting Particle Positions
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. function:: annotate_particles(self, width, p_size=1.0, col='k', marker='o',\
stride=1, ptype='all', alpha=1.0, data_source=None)
(This is a proxy for
:class:`~yt.visualization.plot_modifications.ParticleCallback`.)
Adds particle positions, based on a thick slab along ``axis`` with a
``width`` along the line of sight. ``p_size`` controls the number of pixels
per particle, and ``col`` governs the color. ``ptype`` will restrict plotted
particles to only those that are of a given type. ``data_source`` will only
plot particles contained within the data_source object.
WARNING: if ``data_source`` is a :class:`yt.data_objects.selection_data_containers.YTCutRegion`
then the ``width`` parameter is ignored.
.. python-script::
import yt
ds = yt.load("Enzo_64/DD0043/data0043")
p = yt.ProjectionPlot(ds, "x", ("gas", "density"), center="m", width=(10, "Mpc"))
p.annotate_particles((10, "Mpc"))
p.save()
To plot only the central particles
.. python-script::
import yt
ds = yt.load("Enzo_64/DD0043/data0043")
p = yt.ProjectionPlot(ds, "x", ("gas", "density"), center="m", width=(10, "Mpc"))
sp = ds.sphere(p.data_source.center, ds.quan(1, "Mpc"))
p.annotate_particles((10, "Mpc"), data_source=sp)
p.save()
.. _annotate-sphere:
Overplot a Circle on a Plot
~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. function:: annotate_sphere(self, center, radius, circle_args=None, \
coord_system='data', text=None, text_args=None)
(This is a proxy for
:class:`~yt.visualization.plot_modifications.SphereCallback`.)
Overplot a circle with designated center and radius with optional text.
.. python-script::
import yt
ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030")
p = yt.ProjectionPlot(ds, "z", ("gas", "density"), center="c", width=(20, "kpc"))
p.annotate_sphere([0.5, 0.5, 0.5], radius=(2, "kpc"), circle_args={"color": "black"})
p.save()
.. _annotate-streamlines:
Overplot Streamlines
~~~~~~~~~~~~~~~~~~~~
.. function:: annotate_streamlines(self, field_x, field_y, *, linewidth=1.0, linewidth_upscaling=1.0, \
color=None, color_threshold=float('-inf'), factor=16, **kwargs)
(This is a proxy for
:class:`~yt.visualization.plot_modifications.StreamlineCallback`.)
Add streamlines to any plot, using the ``field_x`` and ``field_y`` from the
associated data, using ``nx`` and ``ny`` starting points that are bounded by
``xstart`` and ``ystart``. To begin streamlines from the left edge of the
plot, set ``start_at_xedge`` to ``True``; for the bottom edge, use
``start_at_yedge``. A line with the qmean vector magnitude will cover
1.0/``factor`` of the image.
Additional keyword arguments are passed down to
`matplotlib.axes.Axes.streamplot <https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.streamplot.html>`_
.. python-script::
import yt
ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030")
s = yt.SlicePlot(ds, "z", ("gas", "density"), center="c", width=(20, "kpc"))
s.annotate_streamlines(("gas", "velocity_x"), ("gas", "velocity_y"))
s.save()
.. _annotate-line-integral-convolution:
Overplot Line Integral Convolution
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. function:: annotate_line_integral_convolution(self, field_x, field_y, \
texture=None, kernellen=50., \
lim=(0.5,0.6), cmap='binary', \
alpha=0.8, const_alpha=False)
(This is a proxy for
:class:`~yt.visualization.plot_modifications.LineIntegralConvolutionCallback`.)
Add line integral convolution to any plot, using the ``field_x`` and ``field_y``
from the associated data. A white noise background will be used for ``texture``
as default. Adjust the bounds of ``lim`` in the range of ``[0, 1]`` which applies
upper and lower bounds to the values of line integral convolution and enhance
the visibility of plots. When ``const_alpha=False``, alpha will be weighted
spatially by the values of line integral convolution; otherwise a constant value
of the given alpha is used.
.. python-script::
import yt
ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030")
s = yt.SlicePlot(ds, "z", ("gas", "density"), center="c", width=(20, "kpc"))
s.annotate_line_integral_convolution(("gas", "velocity_x"), ("gas", "velocity_y"), lim=(0.5, 0.65))
s.save()
.. _annotate-text:
Overplot Text
~~~~~~~~~~~~~
.. function:: annotate_text(self, pos, text, coord_system='data', \
text_args=None, inset_box_args=None)
(This is a proxy for
:class:`~yt.visualization.plot_modifications.TextLabelCallback`.)
Overplot text on the plot at a specified position. If you desire an inset
box around your text, set one with the inset_box_args dictionary
keyword.
.. python-script::
import yt
ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030")
s = yt.SlicePlot(ds, "z", ("gas", "density"), center="max", width=(10, "kpc"))
s.annotate_text((2, 2), "Galaxy!", coord_system="plot")
s.save()
.. _annotate-title:
Add a Title
~~~~~~~~~~~
.. function:: annotate_title(self, title='Plot')
(This is a proxy for
:class:`~yt.visualization.plot_modifications.TitleCallback`.)
Accepts a ``title`` and adds it to the plot.
.. python-script::
import yt
ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030")
p = yt.ProjectionPlot(ds, "z", ("gas", "density"), center="c", width=(20, "kpc"))
p.annotate_title("Density Plot")
p.save()
.. _annotate-velocity:
Overplot Quivers for the Velocity Field
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. function:: annotate_velocity(self, factor=16, *, scale=None, scale_units=None, \
normalize=False, **kwargs)
(This is a proxy for
:class:`~yt.visualization.plot_modifications.VelocityCallback`.)
Adds a 'quiver' plot of velocity to the plot, skipping every ``factor``
datapoints in the discretization. ``scale`` is the data units per arrow
length unit using ``scale_units``. If ``normalize`` is ``True``, the
velocity fields will be scaled by their local (in-plane) length, allowing
morphological features to be more clearly seen for fields with substantial
variation in field strength. Additional arguments can be passed to the
``plot_args`` dictionary, see matplotlib.axes.Axes.quiver for more info.
.. python-script::
import yt
ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030")
p = yt.SlicePlot(ds, "z", ("gas", "density"), center="m", width=(10, "kpc"))
p.annotate_velocity(headwidth=4)
p.save()
.. _annotate-timestamp:
Add the Current Time and/or Redshift
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. function:: annotate_timestamp(x_pos=None, y_pos=None, corner='lower_left',\
time=True, redshift=False, \
time_format='t = {time:.1f} {units}', \
time_unit=None, time_offset=None, \
redshift_format='z = {redshift:.2f}', \
draw_inset_box=False, coord_system='axis', \
text_args=None, inset_box_args=None)
(This is a proxy for
:class:`~yt.visualization.plot_modifications.TimestampCallback`.)
Annotates the timestamp and/or redshift of the data output at a specified
location in the image (either in a present corner, or by specifying (x,y)
image coordinates with the x_pos, y_pos arguments. If no time_units are
specified, it will automatically choose appropriate units. It allows for
custom formatting of the time and redshift information, the specification
of an inset box around the text, and changing the value of the timestamp
via a constant offset.
.. python-script::
import yt
ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030")
p = yt.SlicePlot(ds, "z", ("gas", "density"), center="c", width=(20, "kpc"))
p.annotate_timestamp()
p.save()
.. _annotate-scale:
Add a Physical Scale Bar
~~~~~~~~~~~~~~~~~~~~~~~~
.. function:: annotate_scale(corner='lower_right', coeff=None, \
unit=None, pos=None, \
scale_text_format="{scale} {units}", \
max_frac=0.16, min_frac=0.015, \
coord_system='axis', text_args=None, \
size_bar_args=None, draw_inset_box=False, \
inset_box_args=None)
(This is a proxy for
:class:`~yt.visualization.plot_modifications.ScaleCallback`.)
Annotates the scale of the plot at a specified location in the image
(either in a preset corner, or by specifying (x,y) image coordinates with
the pos argument. Coeff and units (e.g. 1 Mpc or 100 kpc) refer to the
distance scale you desire to show on the plot. If no coeff and units are
specified, an appropriate pair will be determined such that your scale bar
is never smaller than min_frac or greater than max_frac of your plottable
axis length. Additional customization of the scale bar is possible by
adjusting the text_args and size_bar_args dictionaries. The text_args
dictionary accepts matplotlib's font_properties arguments to override
the default font_properties for the current plot. The size_bar_args
dictionary accepts keyword arguments for the AnchoredSizeBar class in
matplotlib's axes_grid toolkit. Finally, the format of the scale bar text
can be adjusted using the scale_text_format keyword argument.
.. python-script::
import yt
ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030")
p = yt.SlicePlot(ds, "z", ("gas", "density"), center="c", width=(20, "kpc"))
p.annotate_scale()
p.save()
.. _annotate-triangle-facets:
Annotate Triangle Facets Callback
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. function:: annotate_triangle_facets(triangle_vertices, **kwargs)
(This is a proxy for
:class:`~yt.visualization.plot_modifications.TriangleFacetsCallback`.)
This add a line collection of a SlicePlot's plane-intersection
with the triangles to the plot. This callback is ideal for a
dataset representing a geometric model of triangular facets.
.. python-script::
import os
import h5py
import yt
# Load data file
ds = yt.load("MoabTest/fng_usrbin22.h5m")
# Create the desired slice plot
s = yt.SlicePlot(ds, "z", ("moab", "TALLY_TAG"))
# get triangle vertices from file (in this case hdf5)
# setup file path for yt test directory
filename = os.path.join(
yt.config.ytcfg.get("yt", "test_data_dir"), "MoabTest/mcnp_n_impr_fluka.h5m"
)
f = h5py.File(filename, mode="r")
coords = f["/tstt/nodes/coordinates"][:]
conn = f["/tstt/elements/Tri3/connectivity"][:]
points = coords[conn - 1]
# Annotate slice-triangle intersection contours to the plot
s.annotate_triangle_facets(points, colors="black")
s.save()
.. _annotate-mesh-lines:
Annotate Mesh Lines Callback
~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. function:: annotate_mesh_lines(**kwargs)
(This is a proxy for
:class:`~yt.visualization.plot_modifications.MeshLinesCallback`.)
This draws the mesh line boundaries over a plot using a Matplotlib
line collection. This callback is only useful for unstructured or
semi-structured mesh datasets.
.. python-script::
import yt
ds = yt.load("MOOSE_sample_data/out.e")
sl = yt.SlicePlot(ds, "z", ("connect1", "nodal_aux"))
sl.annotate_mesh_lines(color="black")
sl.save()
.. _annotate-ray:
Overplot the Path of a Ray
~~~~~~~~~~~~~~~~~~~~~~~~~~
.. function:: annotate_ray(ray, *, arrow=False, **kwargs)
(This is a proxy for
:class:`~yt.visualization.plot_modifications.RayCallback`.)
Adds a line representing the projected path of a ray across the plot. The
ray can be either a
:class:`~yt.data_objects.selection_objects.ray.YTOrthoRay`,
:class:`~yt.data_objects.selection_objects.ray.YTRay`, or a
Trident :class:`~trident.light_ray.LightRay`
object. annotate_ray() will properly account for periodic rays across the
volume.
.. python-script::
import yt
ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030")
oray = ds.ortho_ray(0, (0.3, 0.4))
ray = ds.ray((0.1, 0.2, 0.3), (0.6, 0.7, 0.8))
p = yt.ProjectionPlot(ds, "z", ("gas", "density"))
p.annotate_ray(oray)
p.annotate_ray(ray)
p.save()
Applying filters on the final image
-----------------------------------
It is also possible to operate on the plotted image directly by using
one of the fixed resolution buffer filter as described in
:ref:`frb-filters`.
Note that it is necessary to call the plot object's ``refresh`` method
to apply filters.
.. python-script::
import yt
ds = yt.load('IsolatedGalaxy/galaxy0030/galaxy0030')
p = yt.SlicePlot(ds, 'z', 'density')
p.frb.apply_gauss_beam(sigma=30)
p.refresh()
p.save()
.. _extend-annotations:
Extending annotations methods
-----------------------------
New ``annotate_`` methods can be added to plot objects at runtime (i.e., without
modifying yt's source code) by subclassing the base ``PlotCallback`` class.
This is the recommended way to add custom and unique annotations to yt plots,
as it can be done through local plugins, individual scripts, or even external packages.
Here's a minimal example:
.. python-script::
import yt
from yt.visualization.api import PlotCallback
class TextToPositionCallback(PlotCallback):
# bind a new `annotate_text_to_position` plot method
_type_name = "text_to_position"
def __init__(self, text, x, y):
# this method can have arbitrary arguments
# and should store them without alteration,
# but not run expensive computations
self.text = text
self.position = (x, y)
def __call__(self, plot):
# this method's signature is required
# this is where we perform potentially expensive operations
# the plot argument exposes matplotlib objects:
# - plot._axes is a matplotlib.axes.Axes object
# - plot._figure is a matplotlib.figure.Figure object
plot._axes.annotate(
self.text,
xy=self.position,
xycoords="data",
xytext=(0.2, 0.6),
textcoords="axes fraction",
color="white",
fontsize=30,
arrowprops=dict(facecolor="black", shrink=0.05),
)
ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030")
p = yt.SlicePlot(ds, "z", "density")
p.annotate_text_to_position("Galactic center !", x=0, y=0)
p.save()
