.. _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.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,0,'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:`annotate_clear() function <annotate-clear>`.

For a brief demonstration of a few of these callbacks in action together,
see the cookbook recipe: :ref:`annotations-recipe`.

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', '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',
                    plot_args={'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.plot2d(ds, '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`.

.. _annotate-clear:

Clear Callbacks (Some or All)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. function:: annotate_clear(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.

.. python-script::

    import yt
    ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030")
    p = yt.SlicePlot(ds, 'z', 'density', center='c', width=(20, 'kpc'))
    p.annotate_scale()
    p.annotate_timestamp()

    # Oops, I didn't want any of that.
    p.annotate_clear()
    p.save()

.. _annotate-arrow:

Overplot Arrow
~~~~~~~~~~~~~~

.. function:: annotate_arrow(self, pos, length=0.03, coord_system='data', \
                             plot_args=None)

   (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', 'density', width=(10,'kpc'), center='c')
   slc.annotate_arrow((0.5, 0.5, 0.5), length=0.06, plot_args={'color':'blue'})
   slc.save()

.. _annotate-clumps:

Clump Finder Callback
~~~~~~~~~~~~~~~~~~~~~

.. function:: annotate_clumps(self, clumps, plot_args=None)

   (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 yt
   import numpy as np
   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., 1.],
                         (8., 'kpc'), (1., 'kpc'))

   c_min = 10**np.floor(np.log10(data_source['density']).min()  )
   c_max = 10**np.floor(np.log10(data_source['density']).max()+1)

   master_clump = Clump(data_source, '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, 2, 'density', center='c', width=(20,'kpc'))
   prj.annotate_clumps(leaf_clumps)
   prj.save('clumps')

.. _annotate-contours:

Overplot Contours
~~~~~~~~~~~~~~~~~

.. function:: annotate_contour(self, field, ncont=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.  ``ncont`` 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 (upper, lower) limits for contouring.

.. python-script::

   import yt
   ds = yt.load("Enzo_64/DD0043/data0043")
   s = yt.SlicePlot(ds, "x", "density", center="max")
   s.annotate_contour("temperature")
   s.save()

.. _annotate-quivers:

Overplot Quivers
~~~~~~~~~~~~~~~~

Axis-Aligned Data Sources
^^^^^^^^^^^^^^^^^^^^^^^^^

.. function:: annotate_quiver(self, field_x, field_y, factor=16, scale=None, \
                              scale_units=None, normalize=False, plot_args=None)

   (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`` 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("IsolatedGalaxy/galaxy0030/galaxy0030")
   p = yt.ProjectionPlot(ds, 'z', 'density', center=[0.5, 0.5, 0.5],
                         weight_field='density', width=(20, 'kpc'))
   p.annotate_quiver('velocity_x', 'velocity_y', factor=16, 
                     plot_args={"color": "purple"})
   p.save()

Off-Axis Data Sources
^^^^^^^^^^^^^^^^^^^^^

.. function:: annotate_cquiver(self, field_x, field_y, factor=16, scale=None, \
                               scale_units=None, normalize=False, plot_args=None)

   (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], ["density"], center="c")
   s.annotate_cquiver('cutting_plane_velocity_x', 'cutting_plane_velocity_y', 
                      factor=10, plot_args={'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', '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', 'density', width=(10,'kpc'), center='max')
   slc.annotate_cell_edges()
   slc.save()

.. _annotate-halos:

Overplot Halo Annotations
~~~~~~~~~~~~~~~~~~~~~~~~~

.. function:: annotate_halos(self, halo_catalog, circle_args=None, \
                             width=None, annotate_field=None, \
                             radius_field='virial_radius', \
                             center_field_prefix="particle_position", \
                             text_args=None, factor=1.0)

   (This is a proxy for
   :class:`~yt.visualization.plot_modifications.HaloCatalogCallback`.)

   Accepts a :class:`~yt.analysis_modules.halo_analysis.halo_catalog.HaloCatalog`
   and plots a circle at the location of each halo with the radius of the
   circle corresponding to the virial radius of the halo. Also accepts a
   :ref:`loaded halo catalog dataset <halo-catalog-data>` or a data
   container from a halo catalog dataset. If ``width`` is set
   to None (default) all halos are plotted, otherwise it accepts a tuple in
   the form (1.0, ‘Mpc’) to only display halos that fall within a slab with
   width ``width`` centered on the center of the plot data.  The appearance of
   the circles can be changed with the circle_kwargs dictionary, which is
   supplied to the Matplotlib patch Circle.  One can label each of the halos
   with the annotate_field, which accepts a field contained in the halo catalog
   to add text to the plot near the halo (example: ``annotate_field=
   'particle_mass'`` will write the halo mass next to each halo, whereas
   ``'particle_identifier'`` shows the halo number). The size of the circles is
   found from the field ``radius_field`` which is ``'virial_radius'`` by
   default. If another radius has been found as part of your halo analysis
   workflow, you can save that field and use it as the ``radius_field`` to
   change the size of the halos. The position of each halo is determined using
   ``center_field_prefix`` in the following way. If ``'particle_position'``
   is the value of ``center_field_prefix`` as is the default, the x value of
   the halo position is stored in the field ``'particle_position_x'``, y is
   ``'particle_position_y'``, and z is ``'particle_position_z'``. If you have
   stored another set of coordinates for each halo as part of your halo
   analysis as fields such as ``'halo_position_x'``, you can use these fields
   to determine halo position by passing ``'halo_position'`` to
   ``center_field_prefix``. font_kwargs contains the arguments controlling the
   text appearance of the annotated field. Factor is the number the virial
   radius is multiplied by for plotting the circles. Ex: ``factor=2.0`` will
   plot circles with twice the radius of each halo virial radius.

.. python-script::

   import yt

   data_ds = yt.load('Enzo_64/RD0006/RedshiftOutput0006')
   halos_ds = yt.load('rockstar_halos/halos_0.0.bin')

   prj = yt.ProjectionPlot(data_ds, 'z', 'density')
   prj.annotate_halos(halos_ds, annotate_field='particle_identifier')
   prj.save()

.. _annotate-image-line:

Overplot a Straight Line
~~~~~~~~~~~~~~~~~~~~~~~~

.. function:: annotate_line(self, p1, p2, coord_system='data', plot_args=None)

   (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', '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, \
                                      plot_args=None)

   (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",
                parameters={"time_unit":(1, 'Myr'), "length_unit":(1, 'Mpc'),
                            "mass_unit":(1e17, 'Msun')})
   p = yt.ProjectionPlot(ds, 'z', 'density', center='c', width=(300, 'kpc'))
   p.annotate_magnetic_field(plot_args={"headlength": 3})
   p.save()

.. _annotate-marker:

Annotate a Point With a Marker
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. function:: annotate_marker(self, pos, marker='x', coord_system='data', \
                              plot_args=None)

    (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', 'density', center='c', width=(10, 'kpc'))
   s.annotate_marker((-2,-2), coord_system='plot',
                     plot_args={'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", "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", "density", center='m', width=(10, 'Mpc'))
   sp = ds.sphere([0.5,0.5,0.5],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', '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, factor=16, \
                                   density=1, display_threshold=None, \
                                   plot_args=None)

   (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.

.. python-script::

   import yt
   ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030")
   s = yt.SlicePlot(ds, 'z', 'density', center='c', width=(20, 'kpc'))
   s.annotate_streamlines('velocity_x', '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', 'density', center='c', width=(20, 'kpc'))
   s.annotate_line_integral_convolution('velocity_x', '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', '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', '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, plot_args=None)

   (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', 'density', center='m', width=(10, 'kpc'))
   p.annotate_velocity(plot_args={"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', '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', 'density', center='c', width=(20, 'kpc'))
   p.annotate_scale()
   p.save()

.. _annotate-triangle-facets:

Annotate Triangle Facets Callback
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. function:: annotate_triangle_facets(triangle_vertices, plot_args=None)

   (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 h5py
   import os
   import yt

   # Load data file
   pf = yt.load("MoabTest/fng_usrbin22.h5m")

   # Create the desired slice plot
   s = yt.SlicePlot(pf, '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, "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, plot_args={"colors": 'black'})
   s.save()

.. _annotate-mesh-lines:

Annotate Mesh Lines Callback
~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. function:: annotate_mesh_lines(plot_args=None)

   (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, 2, ('connect1', 'nodal_aux'))
   sl.annotate_mesh_lines(plot_args={'color':'black'})
   sl.save()

.. _annotate-ray:

Overplot the Path of a Ray
~~~~~~~~~~~~~~~~~~~~~~~~~~

.. function:: annotate_ray(ray, plot_args=None)

   (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_data_containers.YTOrthoRay`,
    :class:`~yt.data_objects.selection_data_containers.YTRay`, or a
    :class:`~yt.analysis_modules.cosmological_observation.light_ray.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', 'density')
   p.annotate_ray(oray)
   p.annotate_ray(ray)
   p.save()
