[yt-svn] commit/yt: 2 new changesets
commits-noreply at bitbucket.org
commits-noreply at bitbucket.org
Wed Jul 30 13:55:50 PDT 2014
2 new commits in yt:
https://bitbucket.org/yt_analysis/yt/commits/80060cfc8861/
Changeset: 80060cfc8861
Branch: yt-3.0
User: ngoldbaum
Date: 2014-07-30 21:29:42
Summary: Fixing a number of small docs issues.
Affected #: 11 files
diff -r cde920145491deec2aeb335e7dbec901a51fd356 -r 80060cfc8861d59dc391419a24683d7b2a824c34 doc/source/analyzing/filtering.rst
--- a/doc/source/analyzing/filtering.rst
+++ b/doc/source/analyzing/filtering.rst
@@ -197,4 +197,3 @@
# Mark the center with a big X
prj.annotate_marker(center, 'x', plot_args={'s':100})
- prj.save()
diff -r cde920145491deec2aeb335e7dbec901a51fd356 -r 80060cfc8861d59dc391419a24683d7b2a824c34 doc/source/analyzing/particle_filter.ipynb
--- a/doc/source/analyzing/particle_filter.ipynb
+++ b/doc/source/analyzing/particle_filter.ipynb
@@ -1,6 +1,7 @@
{
"metadata": {
- "name": ""
+ "name": "",
+ "signature": "sha256:4d705a81671d5692ed6691b3402115edbe9c98af815af5bb160ddf551bf02c76"
},
"nbformat": 3,
"nbformat_minor": 0,
@@ -22,6 +23,8 @@
"collapsed": false,
"input": [
"import yt\n",
+ "import numpy as np\n",
+ "\n",
"ds = yt.load(\"TipsyGalaxy/galaxy.00300\")\n",
"for field in ds.derived_field_list:\n",
" if field[0] == 'Stars':\n",
@@ -29,80 +32,38 @@
],
"language": "python",
"metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "('Stars', 'Coordinates')\n",
- "('Stars', 'Epsilon')\n",
- "('Stars', 'FeMassFrac')\n",
- "('Stars', 'FormationTime')\n",
- "('Stars', 'Mass')\n",
- "('Stars', 'Metals')\n",
- "('Stars', 'Phi')\n",
- "('Stars', 'Velocities')\n",
- "('Stars', 'creation_time')\n",
- "('Stars', 'mesh_id')\n",
- "('Stars', 'metallicity')\n",
- "('Stars', 'particle_angular_momentum_magnitude')\n",
- "('Stars', 'particle_angular_momentum_x')\n",
- "('Stars', 'particle_angular_momentum_y')\n",
- "('Stars', 'particle_angular_momentum_z')\n",
- "('Stars', 'particle_mass')\n",
- "('Stars', 'particle_ones')\n",
- "('Stars', 'particle_phi_spherical')\n",
- "('Stars', 'particle_phi_velocity')\n",
- "('Stars', 'particle_position')\n",
- "('Stars', 'particle_position_x')\n",
- "('Stars', 'particle_position_y')\n",
- "('Stars', 'particle_position_z')\n",
- "('Stars', 'particle_radial_velocity')\n",
- "('Stars', 'particle_radius')\n",
- "('Stars', 'particle_radius_spherical')\n",
- "('Stars', 'particle_specific_angular_momentum')\n",
- "('Stars', 'particle_specific_angular_momentum_magnitude')\n",
- "('Stars', 'particle_specific_angular_momentum_x')\n",
- "('Stars', 'particle_specific_angular_momentum_y')\n",
- "('Stars', 'particle_specific_angular_momentum_z')\n",
- "('Stars', 'particle_theta_spherical')\n",
- "('Stars', 'particle_theta_velocity')\n",
- "('Stars', 'particle_velocity')\n",
- "('Stars', 'particle_velocity_magnitude')\n",
- "('Stars', 'particle_velocity_x')\n",
- "('Stars', 'particle_velocity_y')\n",
- "('Stars', 'particle_velocity_z')\n"
- ]
- }
- ],
- "prompt_number": 8
+ "outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "We will filter these into young stars and old stars by masking on the ('Stars', 'creation_time') field. \n",
- "In order to do this, we first make a function which applies our desired cut, assuming `data` is our input data object:"
+ "We will filter these into young stars and old stars by masking on the ('Stars', 'creation_time') field. \n",
+ "\n",
+ "In order to do this, we first make a function which applies our desired cut. This function must accept two arguments: `pfilter` and `data`. The second argument is a yt data container and is usually the only one used in a filter definition.\n",
+ "\n",
+ "Let's call \"young\" stars only those stars with ages less 5 million years. Since Tipsy assigns a very large `creation_time` for stars in the initial conditions, we need to also exclude stars with negative ages.\n",
+ "\n",
+ "Old stars either formed dynamically in the simulation (ages greater than 5 Myr) or were present in the initial conditions (negative ages)."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
- "def stars_under_100Myr(pfilter, data):\n",
- " age = data.pf.current_time - data[\"Stars\", \"creation_time\"]\n",
- " filter = age.in_units('Myr') <= 100\n",
+ "def young_stars(pfilter, data):\n",
+ " age = data.ds.current_time - data[\"Stars\", \"creation_time\"]\n",
+ " filter = np.logical_and(age.in_units('Myr') <= 5, age >= 0)\n",
" return filter\n",
"\n",
- "def stars_over_100Myr(pfilter, data):\n",
- " age = data.pf.current_time - data[\"Stars\", \"creation_time\"]\n",
- " filter = age.in_units('Myr') >= 100\n",
+ "def old_stars(pfilter, data):\n",
+ " age = data.ds.current_time - data[\"Stars\", \"creation_time\"]\n",
+ " filter = np.logical_or(age.in_units('Myr') >= 5, age < 0)\n",
" return filter"
],
"language": "python",
"metadata": {},
- "outputs": [],
- "prompt_number": 9
+ "outputs": []
},
{
"cell_type": "markdown",
@@ -118,13 +79,12 @@
"input": [
"from yt.data_objects.particle_filters import add_particle_filter\n",
"\n",
- "add_particle_filter(\"young_stars\", function=stars_under_100Myr, filtered_type='Stars', requires=[\"creation_time\"])\n",
- "add_particle_filter(\"old_stars\", function=stars_over_100Myr, filtered_type='Stars', requires=[\"creation_time\"])"
+ "add_particle_filter(\"young_stars\", function=young_stars, filtered_type='Stars', requires=[\"creation_time\"])\n",
+ "add_particle_filter(\"old_stars\", function=old_stars, filtered_type='Stars', requires=[\"creation_time\"])"
],
"language": "python",
"metadata": {},
- "outputs": [],
- "prompt_number": 10
+ "outputs": []
},
{
"cell_type": "markdown",
@@ -148,57 +108,7 @@
],
"language": "python",
"metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "('deposit', 'young_stars_cic')\n",
- "('deposit', 'young_stars_count')\n",
- "('deposit', 'young_stars_density')\n",
- "('deposit', 'young_stars_mass')\n",
- "('young_stars', 'Coordinates')\n",
- "('young_stars', 'Epsilon')\n",
- "('young_stars', 'FeMassFrac')\n",
- "('young_stars', 'FormationTime')\n",
- "('young_stars', 'Mass')\n",
- "('young_stars', 'Metals')\n",
- "('young_stars', 'Phi')\n",
- "('young_stars', 'Velocities')\n",
- "('young_stars', 'creation_time')\n",
- "('young_stars', 'mesh_id')\n",
- "('young_stars', 'metallicity')\n",
- "('young_stars', 'particle_angular_momentum_magnitude')\n",
- "('young_stars', 'particle_angular_momentum_x')\n",
- "('young_stars', 'particle_angular_momentum_y')\n",
- "('young_stars', 'particle_angular_momentum_z')\n",
- "('young_stars', 'particle_mass')\n",
- "('young_stars', 'particle_ones')\n",
- "('young_stars', 'particle_phi_spherical')\n",
- "('young_stars', 'particle_phi_velocity')\n",
- "('young_stars', 'particle_position')\n",
- "('young_stars', 'particle_position_x')\n",
- "('young_stars', 'particle_position_y')\n",
- "('young_stars', 'particle_position_z')\n",
- "('young_stars', 'particle_radial_velocity')\n",
- "('young_stars', 'particle_radius')\n",
- "('young_stars', 'particle_radius_spherical')\n",
- "('young_stars', 'particle_specific_angular_momentum')\n",
- "('young_stars', 'particle_specific_angular_momentum_magnitude')\n",
- "('young_stars', 'particle_specific_angular_momentum_x')\n",
- "('young_stars', 'particle_specific_angular_momentum_y')\n",
- "('young_stars', 'particle_specific_angular_momentum_z')\n",
- "('young_stars', 'particle_theta_spherical')\n",
- "('young_stars', 'particle_theta_velocity')\n",
- "('young_stars', 'particle_velocity')\n",
- "('young_stars', 'particle_velocity_magnitude')\n",
- "('young_stars', 'particle_velocity_x')\n",
- "('young_stars', 'particle_velocity_y')\n",
- "('young_stars', 'particle_velocity_z')\n"
- ]
- }
- ],
- "prompt_number": 11
+ "outputs": []
},
{
"cell_type": "markdown",
@@ -211,30 +121,19 @@
"cell_type": "code",
"collapsed": false,
"input": [
- "p = yt.ProjectionPlot(ds, 'z', [('deposit', 'young_stars_cic'), ('deposit', 'old_stars_cic')])\n",
+ "p = yt.ProjectionPlot(ds, 'z', [('deposit', 'young_stars_cic'), ('deposit', 'old_stars_cic')], width=(40, 'kpc'), center='m')\n",
+ "p.set_figure_size(5)\n",
"p.show()"
],
"language": "python",
"metadata": {},
- "outputs": [
- {
- "html": [
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\"><br><img 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\"><br>"
- ],
- "metadata": {},
- "output_type": "display_data",
- "text": [
- "<yt.visualization.plot_window.ProjectionPlot at 0x10b5bfbd0>"
- ]
- }
- ],
- "prompt_number": 16
+ "outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "In this idealized simulation, the old stars exist in the bulge and there is more recent star formation occurring elsewhere in the simulation volume."
+ "We see that young stars are concentrated in regions of active star formation, while old stars are more spatially extended."
]
}
],
diff -r cde920145491deec2aeb335e7dbec901a51fd356 -r 80060cfc8861d59dc391419a24683d7b2a824c34 doc/source/bootcamp/index.rst
--- a/doc/source/bootcamp/index.rst
+++ b/doc/source/bootcamp/index.rst
@@ -3,19 +3,18 @@
yt Bootcamp
===========
-yt Bootcamp is a series of worked examples of how to use much of the
-funtionality of yt. These are not meant to be detailed walkthroughs but simple,
-short introductions to give you a taste of what the code can do.
+The bootcamp is a series of worked examples of how to use much of the
+funtionality of yt. These are simple, short introductions to give you a taste
+of what the code can do and are not meant to be detailed walkthroughs.
There are two ways in which you can go through the bootcamp: interactively and
non-interactively. We recommend the interactive method, but if you're pressed
-on time, you can non-interactively go through the following pages and view the
+on time, you can non-interactively go through the linked pages below and view the
worked examples.
To execute the bootcamp interactively, you need to download the repository and
start the IPython notebook. If you do not already have the yt repository, the
-easiest way to get the repository is to use your already-installed mercurial
-program to grab it:
+easiest way to get the repository is to clone it using mercurial:
.. code-block:: bash
diff -r cde920145491deec2aeb335e7dbec901a51fd356 -r 80060cfc8861d59dc391419a24683d7b2a824c34 doc/source/cookbook/complex_plots.rst
--- a/doc/source/cookbook/complex_plots.rst
+++ b/doc/source/cookbook/complex_plots.rst
@@ -46,6 +46,8 @@
.. yt_cookbook:: multi_plot_slice_and_proj.py
+.. _advanced-multi-panel:
+
Advanced Multi-Plot Multi-Panel
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
diff -r cde920145491deec2aeb335e7dbec901a51fd356 -r 80060cfc8861d59dc391419a24683d7b2a824c34 doc/source/reference/api/api.rst
--- a/doc/source/reference/api/api.rst
+++ b/doc/source/reference/api/api.rst
@@ -18,6 +18,7 @@
~yt.visualization.plot_window.ProjectionPlot
~yt.visualization.plot_window.OffAxisProjectionPlot
~yt.visualization.plot_window.WindowPlotMPL
+ ~yt.visualization.plot_window.PlotWindow
ProfilePlot and PhasePlot
^^^^^^^^^^^^^^^^^^^^^^^^^
diff -r cde920145491deec2aeb335e7dbec901a51fd356 -r 80060cfc8861d59dc391419a24683d7b2a824c34 doc/source/visualizing/TransferFunctionHelper_Tutorial.ipynb
--- a/doc/source/visualizing/TransferFunctionHelper_Tutorial.ipynb
+++ b/doc/source/visualizing/TransferFunctionHelper_Tutorial.ipynb
@@ -1,7 +1,7 @@
{
"metadata": {
"name": "",
- "signature": "sha256:0b3811c163a3c9d35de8d103a38328e5f4d3ae481327542d2ed178ddcc718f5e"
+ "signature": "sha256:f3e6416e4807e008a016ad8c7dfc8e78cab0d7519498458660554a4c88549c23"
},
"nbformat": 3,
"nbformat_minor": 0,
@@ -12,8 +12,6 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "# `TransferFunctionHelper`: Building Beautiful Transfer Functions\n",
- "\n",
"Here, we explain how to use TransferFunctionHelper to visualize and interpret `yt` volume rendering transfer functions. TransferFunctionHelper is a utility class that makes it easy to visualize he probability density functions of yt fields that you might want to volume render. This makes it easier to choose a nice transfer function that highlights interesting physical regimes.\n",
"\n",
"First, we set up our namespace and define a convenience function to display volume renderings inline in the notebook. Using `%matplotlib inline` makes it so matplotlib plots display inline in the notebook."
diff -r cde920145491deec2aeb335e7dbec901a51fd356 -r 80060cfc8861d59dc391419a24683d7b2a824c34 doc/source/visualizing/_cb_docstrings.inc
--- a/doc/source/visualizing/_cb_docstrings.inc
+++ b/doc/source/visualizing/_cb_docstrings.inc
@@ -81,7 +81,7 @@
Axis-Aligned data sources
^^^^^^^^^^^^^^^^^^^^^^^^^
-.. function:: annotate_quiver(self, field_x, field_y, factor, scale=None,
+.. function:: annotate_quiver(self, field_x, field_y, factor, scale=None, \
scale_units=None, normalize=False):
(This is a proxy for
@@ -124,7 +124,7 @@
Overplot grids
~~~~~~~~~~~~~~
-.. function:: annotate_grids(self, alpha=1.0, min_pix=1, annotate=False,
+.. function:: annotate_grids(self, alpha=1.0, min_pix=1, annotate=False, \
periodic=True):
(This is a proxy for
@@ -145,7 +145,7 @@
Overplot Halo Annotations
~~~~~~~~~~~~~~~~~~~~~~~~~
-.. function:: annotate_halos(self, halo_catalog, col='white', alpha =1,
+.. function:: annotate_halos(self, halo_catalog, col='white', alpha=1, \
width=None):
(This is a proxy for
@@ -176,8 +176,7 @@
Overplot a Straight Line
~~~~~~~~~~~~~~~~~~~~~~~~
-.. function:: annotate_image_line(self, p1, p2, data_coords=False,
- plot_args=None):
+.. function:: annotate_image_line(self, p1, p2, data_coords=False, plot_args=None):
(This is a proxy for
:class:`~yt.visualization.plot_modifications.ImageLineCallback`.)
@@ -218,7 +217,7 @@
Overplot Magnetic Field Quivers
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-.. function:: annotate_magnetic_field(self, factor=16, scale=None,
+.. function:: annotate_magnetic_field(self, factor=16, scale=None, \
scale_units=None, normalize=False):
(This is a proxy for
@@ -264,9 +263,8 @@
Overplotting Particle Positions
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-.. function:: annotate_particles(self, width, p_size=1.0, col='k', marker='o',
- stride=1.0, ptype=None, stars_only=False,
- dm_only=False, minimum_mass=None):
+.. function:: annotate_particles(self, width, p_size=1.0, col='k', marker='o', \
+ stride=1.0, ptype=None, minimum_mass=None):
(This is a proxy for
:class:`~yt.visualization.plot_modifications.ParticleCallback`.)
@@ -308,7 +306,7 @@
Overplot a circle on a plot
~~~~~~~~~~~~~~~~~~~~~~~~~~~
-.. function:: annotate_sphere(self, center, radius, circle_args=None, text=None,
+.. function:: annotate_sphere(self, center, radius, circle_args=None, text=None, \
text_args=None):
(This is a proxy for
@@ -329,9 +327,9 @@
Overplot streamlines
~~~~~~~~~~~~~~~~~~~~
-.. function:: annotate_streamlines(self, field_x, field_y, factor=6.0, nx=16,
- ny=16, xstart=(0, 1), ystart=(0, 1),
- nsample=256, start_at_xedge=False,
+.. function:: annotate_streamlines(self, field_x, field_y, factor=6.0, nx=16, \
+ ny=16, xstart=(0, 1), ystart=(0, 1), \
+ nsample=256, start_at_xedge=False, \
start_at_yedge=False, plot_args=None):
(This is a proxy for
@@ -394,7 +392,7 @@
Overplot quivers for the velocity field
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-.. function:: annotate_velocity(self, factor=16, scale=None, scale_units=None,
+.. function:: annotate_velocity(self, factor=16, scale=None, scale_units=None, \
normalize=False):
(This is a proxy for
@@ -419,7 +417,7 @@
Add a Timestamp Inset Box
~~~~~~~~~~~~~~~~~~~~~~~~~
-.. function:: annotate_timestamp(x, y, units=None, format="{time:.3G} {units}",
+.. function:: annotate_timestamp(x, y, units=None, format="{time:.3G} {units}", \
**kwargs, normalized=False, bbox_dict=None)
(This is a proxy for
@@ -481,4 +479,4 @@
# Annotate slice-triangle intersection contours to the plot
s.annotate_triangle_facets(points, plot_args={"colors": 'black'})
- s.save()
\ No newline at end of file
+ s.save()
diff -r cde920145491deec2aeb335e7dbec901a51fd356 -r 80060cfc8861d59dc391419a24683d7b2a824c34 doc/source/visualizing/manual_plotting.rst
--- a/doc/source/visualizing/manual_plotting.rst
+++ b/doc/source/visualizing/manual_plotting.rst
@@ -49,8 +49,12 @@
P.imshow(np.array(frb['density']))
P.savefig('my_perfect_figure.png')
+Note that in the above example the axes tick marks indicate pixel indices. If you
+want to represent physical distances on your plot axes, you will need to use the
+``extent`` keyword of the ``imshow`` function.
+
The FRB is a very small object that can be deleted and recreated quickly (in
-fact, this is how the reason GUI works when you pan and scan). Furthermore, you
+fact, this is how ``PlotWindow`` plots work behind the scenes). Furthermore, you
can add new fields in the same "window", and each of them can be plotted with
their own zlimit. This is quite useful for creating a mosaic of the same region
in space with Density, Temperature, and x-velocity, for example. Each of these
@@ -58,7 +62,7 @@
A more complex example, showing a few ``yt`` helper functions that can make
setting up multiple axes with colorbars easier than it would be using only
-matplotlib can be found in the cookbook.
+matplotlib can be found in the :ref:`advanced-multi-panel` cookbook recipe.
.. _manual-line-plots:
diff -r cde920145491deec2aeb335e7dbec901a51fd356 -r 80060cfc8861d59dc391419a24683d7b2a824c34 doc/source/visualizing/plots.rst
--- a/doc/source/visualizing/plots.rst
+++ b/doc/source/visualizing/plots.rst
@@ -910,18 +910,17 @@
IPython notebook you can connect to.
If you want to see yt plots inline inside your notebook, you need only create a
-plot and then call ``.show()``:
+plot and then call ``.show()`` and the image will appear inline:
.. notebook-cell::
import yt
ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030")
- p = yt.ProjectionPlot(ds, "x", "density", center='m', width=(10,'kpc'),
+ p = yt.ProjectionPlot(ds, "z", "density", center='m', width=(10,'kpc'),
weight_field='density')
+ p.set_figure_size(5)
p.show()
-The image will appear inline.
-
.. _eps-writer:
Publication-ready Figures
diff -r cde920145491deec2aeb335e7dbec901a51fd356 -r 80060cfc8861d59dc391419a24683d7b2a824c34 doc/source/visualizing/volume_rendering.rst
--- a/doc/source/visualizing/volume_rendering.rst
+++ b/doc/source/visualizing/volume_rendering.rst
@@ -19,7 +19,7 @@
for transitioning the rendering to the GPU. In addition, this allows users to create
volume renderings on traditional supercomputing platforms that may not have access to GPUs.
-As of yt 2.4, this code is threaded using OpenMP. Many of the commands
+The volume renderer is also threaded using OpenMP. Many of the commands
(including `snapshot`) will accept a `num_threads` option.
Tutorial
@@ -34,7 +34,7 @@
direction
#. Take a snapshot and save the image.
-Here is a working example for the IsolatedGalaxy dataset from the 2012 yt workshop.
+Here is a working example for the IsolatedGalaxy dataset.
.. python-script::
@@ -83,7 +83,7 @@
------
Direct ray casting through a volume enables the generation of new types of
-visualizations and images describing a simulation. ``yt`` now has the facility
+visualizations and images describing a simulation. ``yt`` has the facility
to generate volume renderings by a direct ray casting method. However, the
ability to create volume renderings informed by analysis by other mechanisms --
for instance, halo location, angular momentum, spectral energy distributions --
@@ -96,7 +96,7 @@
These can be functions of any field variable, weighted by independent
fields, and even weighted by other evaluated transfer functions. (See
`transfer_functions`.)
-#. Partition all grids into non-overlapping, fully domain-tiling "bricks."
+#. Partition all chunks into non-overlapping, fully domain-tiling "bricks."
Each of these "bricks" contains the finest available data at any location.
#. Generate vertex-centered data for all grids in the volume rendered domain.
#. Order the bricks from back-to-front.
diff -r cde920145491deec2aeb335e7dbec901a51fd356 -r 80060cfc8861d59dc391419a24683d7b2a824c34 yt/visualization/plot_modifications.py
--- a/yt/visualization/plot_modifications.py
+++ b/yt/visualization/plot_modifications.py
@@ -1000,8 +1000,7 @@
class ParticleCallback(PlotCallback):
"""
annotate_particles(width, p_size=1.0, col='k', marker='o', stride=1.0,
- ptype=None, stars_only=False, dm_only=False,
- minimum_mass=None, alpha=1.0)
+ ptype=None, minimum_mass=None, alpha=1.0)
Adds particle positions, based on a thick slab along *axis* with a
*width* along the line of sight. *p_size* controls the number of
https://bitbucket.org/yt_analysis/yt/commits/21df009356a6/
Changeset: 21df009356a6
Branch: yt-3.0
User: mzingale
Date: 2014-07-30 22:55:43
Summary: Merged in ngoldbaum/yt/yt-3.0 (pull request #1108)
Fixing a number of small docs issues.
Affected #: 11 files
diff -r 0a2aeff69c6a8e35860deeac6c65439598d9ec20 -r 21df009356a6da013a5e8a736612d9203ef8966e doc/source/analyzing/filtering.rst
--- a/doc/source/analyzing/filtering.rst
+++ b/doc/source/analyzing/filtering.rst
@@ -197,4 +197,3 @@
# Mark the center with a big X
prj.annotate_marker(center, 'x', plot_args={'s':100})
- prj.save()
diff -r 0a2aeff69c6a8e35860deeac6c65439598d9ec20 -r 21df009356a6da013a5e8a736612d9203ef8966e doc/source/analyzing/particle_filter.ipynb
--- a/doc/source/analyzing/particle_filter.ipynb
+++ b/doc/source/analyzing/particle_filter.ipynb
@@ -1,6 +1,7 @@
{
"metadata": {
- "name": ""
+ "name": "",
+ "signature": "sha256:4d705a81671d5692ed6691b3402115edbe9c98af815af5bb160ddf551bf02c76"
},
"nbformat": 3,
"nbformat_minor": 0,
@@ -22,6 +23,8 @@
"collapsed": false,
"input": [
"import yt\n",
+ "import numpy as np\n",
+ "\n",
"ds = yt.load(\"TipsyGalaxy/galaxy.00300\")\n",
"for field in ds.derived_field_list:\n",
" if field[0] == 'Stars':\n",
@@ -29,80 +32,38 @@
],
"language": "python",
"metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "('Stars', 'Coordinates')\n",
- "('Stars', 'Epsilon')\n",
- "('Stars', 'FeMassFrac')\n",
- "('Stars', 'FormationTime')\n",
- "('Stars', 'Mass')\n",
- "('Stars', 'Metals')\n",
- "('Stars', 'Phi')\n",
- "('Stars', 'Velocities')\n",
- "('Stars', 'creation_time')\n",
- "('Stars', 'mesh_id')\n",
- "('Stars', 'metallicity')\n",
- "('Stars', 'particle_angular_momentum_magnitude')\n",
- "('Stars', 'particle_angular_momentum_x')\n",
- "('Stars', 'particle_angular_momentum_y')\n",
- "('Stars', 'particle_angular_momentum_z')\n",
- "('Stars', 'particle_mass')\n",
- "('Stars', 'particle_ones')\n",
- "('Stars', 'particle_phi_spherical')\n",
- "('Stars', 'particle_phi_velocity')\n",
- "('Stars', 'particle_position')\n",
- "('Stars', 'particle_position_x')\n",
- "('Stars', 'particle_position_y')\n",
- "('Stars', 'particle_position_z')\n",
- "('Stars', 'particle_radial_velocity')\n",
- "('Stars', 'particle_radius')\n",
- "('Stars', 'particle_radius_spherical')\n",
- "('Stars', 'particle_specific_angular_momentum')\n",
- "('Stars', 'particle_specific_angular_momentum_magnitude')\n",
- "('Stars', 'particle_specific_angular_momentum_x')\n",
- "('Stars', 'particle_specific_angular_momentum_y')\n",
- "('Stars', 'particle_specific_angular_momentum_z')\n",
- "('Stars', 'particle_theta_spherical')\n",
- "('Stars', 'particle_theta_velocity')\n",
- "('Stars', 'particle_velocity')\n",
- "('Stars', 'particle_velocity_magnitude')\n",
- "('Stars', 'particle_velocity_x')\n",
- "('Stars', 'particle_velocity_y')\n",
- "('Stars', 'particle_velocity_z')\n"
- ]
- }
- ],
- "prompt_number": 8
+ "outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "We will filter these into young stars and old stars by masking on the ('Stars', 'creation_time') field. \n",
- "In order to do this, we first make a function which applies our desired cut, assuming `data` is our input data object:"
+ "We will filter these into young stars and old stars by masking on the ('Stars', 'creation_time') field. \n",
+ "\n",
+ "In order to do this, we first make a function which applies our desired cut. This function must accept two arguments: `pfilter` and `data`. The second argument is a yt data container and is usually the only one used in a filter definition.\n",
+ "\n",
+ "Let's call \"young\" stars only those stars with ages less 5 million years. Since Tipsy assigns a very large `creation_time` for stars in the initial conditions, we need to also exclude stars with negative ages.\n",
+ "\n",
+ "Old stars either formed dynamically in the simulation (ages greater than 5 Myr) or were present in the initial conditions (negative ages)."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
- "def stars_under_100Myr(pfilter, data):\n",
- " age = data.pf.current_time - data[\"Stars\", \"creation_time\"]\n",
- " filter = age.in_units('Myr') <= 100\n",
+ "def young_stars(pfilter, data):\n",
+ " age = data.ds.current_time - data[\"Stars\", \"creation_time\"]\n",
+ " filter = np.logical_and(age.in_units('Myr') <= 5, age >= 0)\n",
" return filter\n",
"\n",
- "def stars_over_100Myr(pfilter, data):\n",
- " age = data.pf.current_time - data[\"Stars\", \"creation_time\"]\n",
- " filter = age.in_units('Myr') >= 100\n",
+ "def old_stars(pfilter, data):\n",
+ " age = data.ds.current_time - data[\"Stars\", \"creation_time\"]\n",
+ " filter = np.logical_or(age.in_units('Myr') >= 5, age < 0)\n",
" return filter"
],
"language": "python",
"metadata": {},
- "outputs": [],
- "prompt_number": 9
+ "outputs": []
},
{
"cell_type": "markdown",
@@ -118,13 +79,12 @@
"input": [
"from yt.data_objects.particle_filters import add_particle_filter\n",
"\n",
- "add_particle_filter(\"young_stars\", function=stars_under_100Myr, filtered_type='Stars', requires=[\"creation_time\"])\n",
- "add_particle_filter(\"old_stars\", function=stars_over_100Myr, filtered_type='Stars', requires=[\"creation_time\"])"
+ "add_particle_filter(\"young_stars\", function=young_stars, filtered_type='Stars', requires=[\"creation_time\"])\n",
+ "add_particle_filter(\"old_stars\", function=old_stars, filtered_type='Stars', requires=[\"creation_time\"])"
],
"language": "python",
"metadata": {},
- "outputs": [],
- "prompt_number": 10
+ "outputs": []
},
{
"cell_type": "markdown",
@@ -148,57 +108,7 @@
],
"language": "python",
"metadata": {},
- "outputs": [
- {
- "output_type": "stream",
- "stream": "stdout",
- "text": [
- "('deposit', 'young_stars_cic')\n",
- "('deposit', 'young_stars_count')\n",
- "('deposit', 'young_stars_density')\n",
- "('deposit', 'young_stars_mass')\n",
- "('young_stars', 'Coordinates')\n",
- "('young_stars', 'Epsilon')\n",
- "('young_stars', 'FeMassFrac')\n",
- "('young_stars', 'FormationTime')\n",
- "('young_stars', 'Mass')\n",
- "('young_stars', 'Metals')\n",
- "('young_stars', 'Phi')\n",
- "('young_stars', 'Velocities')\n",
- "('young_stars', 'creation_time')\n",
- "('young_stars', 'mesh_id')\n",
- "('young_stars', 'metallicity')\n",
- "('young_stars', 'particle_angular_momentum_magnitude')\n",
- "('young_stars', 'particle_angular_momentum_x')\n",
- "('young_stars', 'particle_angular_momentum_y')\n",
- "('young_stars', 'particle_angular_momentum_z')\n",
- "('young_stars', 'particle_mass')\n",
- "('young_stars', 'particle_ones')\n",
- "('young_stars', 'particle_phi_spherical')\n",
- "('young_stars', 'particle_phi_velocity')\n",
- "('young_stars', 'particle_position')\n",
- "('young_stars', 'particle_position_x')\n",
- "('young_stars', 'particle_position_y')\n",
- "('young_stars', 'particle_position_z')\n",
- "('young_stars', 'particle_radial_velocity')\n",
- "('young_stars', 'particle_radius')\n",
- "('young_stars', 'particle_radius_spherical')\n",
- "('young_stars', 'particle_specific_angular_momentum')\n",
- "('young_stars', 'particle_specific_angular_momentum_magnitude')\n",
- "('young_stars', 'particle_specific_angular_momentum_x')\n",
- "('young_stars', 'particle_specific_angular_momentum_y')\n",
- "('young_stars', 'particle_specific_angular_momentum_z')\n",
- "('young_stars', 'particle_theta_spherical')\n",
- "('young_stars', 'particle_theta_velocity')\n",
- "('young_stars', 'particle_velocity')\n",
- "('young_stars', 'particle_velocity_magnitude')\n",
- "('young_stars', 'particle_velocity_x')\n",
- "('young_stars', 'particle_velocity_y')\n",
- "('young_stars', 'particle_velocity_z')\n"
- ]
- }
- ],
- "prompt_number": 11
+ "outputs": []
},
{
"cell_type": "markdown",
@@ -211,30 +121,19 @@
"cell_type": "code",
"collapsed": false,
"input": [
- "p = yt.ProjectionPlot(ds, 'z', [('deposit', 'young_stars_cic'), ('deposit', 'old_stars_cic')])\n",
+ "p = yt.ProjectionPlot(ds, 'z', [('deposit', 'young_stars_cic'), ('deposit', 'old_stars_cic')], width=(40, 'kpc'), center='m')\n",
+ "p.set_figure_size(5)\n",
"p.show()"
],
"language": "python",
"metadata": {},
- "outputs": [
- {
- "html": [
- "<img 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\"><br><img 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\"><br>"
- ],
- "metadata": {},
- "output_type": "display_data",
- "text": [
- "<yt.visualization.plot_window.ProjectionPlot at 0x10b5bfbd0>"
- ]
- }
- ],
- "prompt_number": 16
+ "outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "In this idealized simulation, the old stars exist in the bulge and there is more recent star formation occurring elsewhere in the simulation volume."
+ "We see that young stars are concentrated in regions of active star formation, while old stars are more spatially extended."
]
}
],
diff -r 0a2aeff69c6a8e35860deeac6c65439598d9ec20 -r 21df009356a6da013a5e8a736612d9203ef8966e doc/source/bootcamp/index.rst
--- a/doc/source/bootcamp/index.rst
+++ b/doc/source/bootcamp/index.rst
@@ -3,19 +3,18 @@
yt Bootcamp
===========
-yt Bootcamp is a series of worked examples of how to use much of the
-funtionality of yt. These are not meant to be detailed walkthroughs but simple,
-short introductions to give you a taste of what the code can do.
+The bootcamp is a series of worked examples of how to use much of the
+funtionality of yt. These are simple, short introductions to give you a taste
+of what the code can do and are not meant to be detailed walkthroughs.
There are two ways in which you can go through the bootcamp: interactively and
non-interactively. We recommend the interactive method, but if you're pressed
-on time, you can non-interactively go through the following pages and view the
+on time, you can non-interactively go through the linked pages below and view the
worked examples.
To execute the bootcamp interactively, you need to download the repository and
start the IPython notebook. If you do not already have the yt repository, the
-easiest way to get the repository is to use your already-installed mercurial
-program to grab it:
+easiest way to get the repository is to clone it using mercurial:
.. code-block:: bash
diff -r 0a2aeff69c6a8e35860deeac6c65439598d9ec20 -r 21df009356a6da013a5e8a736612d9203ef8966e doc/source/cookbook/complex_plots.rst
--- a/doc/source/cookbook/complex_plots.rst
+++ b/doc/source/cookbook/complex_plots.rst
@@ -46,6 +46,8 @@
.. yt_cookbook:: multi_plot_slice_and_proj.py
+.. _advanced-multi-panel:
+
Advanced Multi-Plot Multi-Panel
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
diff -r 0a2aeff69c6a8e35860deeac6c65439598d9ec20 -r 21df009356a6da013a5e8a736612d9203ef8966e doc/source/reference/api/api.rst
--- a/doc/source/reference/api/api.rst
+++ b/doc/source/reference/api/api.rst
@@ -18,6 +18,7 @@
~yt.visualization.plot_window.ProjectionPlot
~yt.visualization.plot_window.OffAxisProjectionPlot
~yt.visualization.plot_window.WindowPlotMPL
+ ~yt.visualization.plot_window.PlotWindow
ProfilePlot and PhasePlot
^^^^^^^^^^^^^^^^^^^^^^^^^
diff -r 0a2aeff69c6a8e35860deeac6c65439598d9ec20 -r 21df009356a6da013a5e8a736612d9203ef8966e doc/source/visualizing/TransferFunctionHelper_Tutorial.ipynb
--- a/doc/source/visualizing/TransferFunctionHelper_Tutorial.ipynb
+++ b/doc/source/visualizing/TransferFunctionHelper_Tutorial.ipynb
@@ -1,7 +1,7 @@
{
"metadata": {
"name": "",
- "signature": "sha256:0b3811c163a3c9d35de8d103a38328e5f4d3ae481327542d2ed178ddcc718f5e"
+ "signature": "sha256:f3e6416e4807e008a016ad8c7dfc8e78cab0d7519498458660554a4c88549c23"
},
"nbformat": 3,
"nbformat_minor": 0,
@@ -12,8 +12,6 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "# `TransferFunctionHelper`: Building Beautiful Transfer Functions\n",
- "\n",
"Here, we explain how to use TransferFunctionHelper to visualize and interpret `yt` volume rendering transfer functions. TransferFunctionHelper is a utility class that makes it easy to visualize he probability density functions of yt fields that you might want to volume render. This makes it easier to choose a nice transfer function that highlights interesting physical regimes.\n",
"\n",
"First, we set up our namespace and define a convenience function to display volume renderings inline in the notebook. Using `%matplotlib inline` makes it so matplotlib plots display inline in the notebook."
diff -r 0a2aeff69c6a8e35860deeac6c65439598d9ec20 -r 21df009356a6da013a5e8a736612d9203ef8966e doc/source/visualizing/_cb_docstrings.inc
--- a/doc/source/visualizing/_cb_docstrings.inc
+++ b/doc/source/visualizing/_cb_docstrings.inc
@@ -81,7 +81,7 @@
Axis-Aligned data sources
^^^^^^^^^^^^^^^^^^^^^^^^^
-.. function:: annotate_quiver(self, field_x, field_y, factor, scale=None,
+.. function:: annotate_quiver(self, field_x, field_y, factor, scale=None, \
scale_units=None, normalize=False):
(This is a proxy for
@@ -124,7 +124,7 @@
Overplot grids
~~~~~~~~~~~~~~
-.. function:: annotate_grids(self, alpha=1.0, min_pix=1, annotate=False,
+.. function:: annotate_grids(self, alpha=1.0, min_pix=1, annotate=False, \
periodic=True):
(This is a proxy for
@@ -145,7 +145,7 @@
Overplot Halo Annotations
~~~~~~~~~~~~~~~~~~~~~~~~~
-.. function:: annotate_halos(self, halo_catalog, col='white', alpha =1,
+.. function:: annotate_halos(self, halo_catalog, col='white', alpha=1, \
width=None):
(This is a proxy for
@@ -176,8 +176,7 @@
Overplot a Straight Line
~~~~~~~~~~~~~~~~~~~~~~~~
-.. function:: annotate_image_line(self, p1, p2, data_coords=False,
- plot_args=None):
+.. function:: annotate_image_line(self, p1, p2, data_coords=False, plot_args=None):
(This is a proxy for
:class:`~yt.visualization.plot_modifications.ImageLineCallback`.)
@@ -218,7 +217,7 @@
Overplot Magnetic Field Quivers
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-.. function:: annotate_magnetic_field(self, factor=16, scale=None,
+.. function:: annotate_magnetic_field(self, factor=16, scale=None, \
scale_units=None, normalize=False):
(This is a proxy for
@@ -264,9 +263,8 @@
Overplotting Particle Positions
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-.. function:: annotate_particles(self, width, p_size=1.0, col='k', marker='o',
- stride=1.0, ptype=None, stars_only=False,
- dm_only=False, minimum_mass=None):
+.. function:: annotate_particles(self, width, p_size=1.0, col='k', marker='o', \
+ stride=1.0, ptype=None, minimum_mass=None):
(This is a proxy for
:class:`~yt.visualization.plot_modifications.ParticleCallback`.)
@@ -308,7 +306,7 @@
Overplot a circle on a plot
~~~~~~~~~~~~~~~~~~~~~~~~~~~
-.. function:: annotate_sphere(self, center, radius, circle_args=None, text=None,
+.. function:: annotate_sphere(self, center, radius, circle_args=None, text=None, \
text_args=None):
(This is a proxy for
@@ -329,9 +327,9 @@
Overplot streamlines
~~~~~~~~~~~~~~~~~~~~
-.. function:: annotate_streamlines(self, field_x, field_y, factor=6.0, nx=16,
- ny=16, xstart=(0, 1), ystart=(0, 1),
- nsample=256, start_at_xedge=False,
+.. function:: annotate_streamlines(self, field_x, field_y, factor=6.0, nx=16, \
+ ny=16, xstart=(0, 1), ystart=(0, 1), \
+ nsample=256, start_at_xedge=False, \
start_at_yedge=False, plot_args=None):
(This is a proxy for
@@ -394,7 +392,7 @@
Overplot quivers for the velocity field
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-.. function:: annotate_velocity(self, factor=16, scale=None, scale_units=None,
+.. function:: annotate_velocity(self, factor=16, scale=None, scale_units=None, \
normalize=False):
(This is a proxy for
@@ -419,7 +417,7 @@
Add a Timestamp Inset Box
~~~~~~~~~~~~~~~~~~~~~~~~~
-.. function:: annotate_timestamp(x, y, units=None, format="{time:.3G} {units}",
+.. function:: annotate_timestamp(x, y, units=None, format="{time:.3G} {units}", \
**kwargs, normalized=False, bbox_dict=None)
(This is a proxy for
@@ -481,4 +479,4 @@
# Annotate slice-triangle intersection contours to the plot
s.annotate_triangle_facets(points, plot_args={"colors": 'black'})
- s.save()
\ No newline at end of file
+ s.save()
diff -r 0a2aeff69c6a8e35860deeac6c65439598d9ec20 -r 21df009356a6da013a5e8a736612d9203ef8966e doc/source/visualizing/manual_plotting.rst
--- a/doc/source/visualizing/manual_plotting.rst
+++ b/doc/source/visualizing/manual_plotting.rst
@@ -49,8 +49,12 @@
P.imshow(np.array(frb['density']))
P.savefig('my_perfect_figure.png')
+Note that in the above example the axes tick marks indicate pixel indices. If you
+want to represent physical distances on your plot axes, you will need to use the
+``extent`` keyword of the ``imshow`` function.
+
The FRB is a very small object that can be deleted and recreated quickly (in
-fact, this is how the reason GUI works when you pan and scan). Furthermore, you
+fact, this is how ``PlotWindow`` plots work behind the scenes). Furthermore, you
can add new fields in the same "window", and each of them can be plotted with
their own zlimit. This is quite useful for creating a mosaic of the same region
in space with Density, Temperature, and x-velocity, for example. Each of these
@@ -58,7 +62,7 @@
A more complex example, showing a few ``yt`` helper functions that can make
setting up multiple axes with colorbars easier than it would be using only
-matplotlib can be found in the cookbook.
+matplotlib can be found in the :ref:`advanced-multi-panel` cookbook recipe.
.. _manual-line-plots:
diff -r 0a2aeff69c6a8e35860deeac6c65439598d9ec20 -r 21df009356a6da013a5e8a736612d9203ef8966e doc/source/visualizing/plots.rst
--- a/doc/source/visualizing/plots.rst
+++ b/doc/source/visualizing/plots.rst
@@ -910,18 +910,17 @@
IPython notebook you can connect to.
If you want to see yt plots inline inside your notebook, you need only create a
-plot and then call ``.show()``:
+plot and then call ``.show()`` and the image will appear inline:
.. notebook-cell::
import yt
ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030")
- p = yt.ProjectionPlot(ds, "x", "density", center='m', width=(10,'kpc'),
+ p = yt.ProjectionPlot(ds, "z", "density", center='m', width=(10,'kpc'),
weight_field='density')
+ p.set_figure_size(5)
p.show()
-The image will appear inline.
-
.. _eps-writer:
Publication-ready Figures
diff -r 0a2aeff69c6a8e35860deeac6c65439598d9ec20 -r 21df009356a6da013a5e8a736612d9203ef8966e doc/source/visualizing/volume_rendering.rst
--- a/doc/source/visualizing/volume_rendering.rst
+++ b/doc/source/visualizing/volume_rendering.rst
@@ -19,7 +19,7 @@
for transitioning the rendering to the GPU. In addition, this allows users to create
volume renderings on traditional supercomputing platforms that may not have access to GPUs.
-As of yt 2.4, this code is threaded using OpenMP. Many of the commands
+The volume renderer is also threaded using OpenMP. Many of the commands
(including `snapshot`) will accept a `num_threads` option.
Tutorial
@@ -34,7 +34,7 @@
direction
#. Take a snapshot and save the image.
-Here is a working example for the IsolatedGalaxy dataset from the 2012 yt workshop.
+Here is a working example for the IsolatedGalaxy dataset.
.. python-script::
@@ -83,7 +83,7 @@
------
Direct ray casting through a volume enables the generation of new types of
-visualizations and images describing a simulation. ``yt`` now has the facility
+visualizations and images describing a simulation. ``yt`` has the facility
to generate volume renderings by a direct ray casting method. However, the
ability to create volume renderings informed by analysis by other mechanisms --
for instance, halo location, angular momentum, spectral energy distributions --
@@ -96,7 +96,7 @@
These can be functions of any field variable, weighted by independent
fields, and even weighted by other evaluated transfer functions. (See
`transfer_functions`.)
-#. Partition all grids into non-overlapping, fully domain-tiling "bricks."
+#. Partition all chunks into non-overlapping, fully domain-tiling "bricks."
Each of these "bricks" contains the finest available data at any location.
#. Generate vertex-centered data for all grids in the volume rendered domain.
#. Order the bricks from back-to-front.
diff -r 0a2aeff69c6a8e35860deeac6c65439598d9ec20 -r 21df009356a6da013a5e8a736612d9203ef8966e yt/visualization/plot_modifications.py
--- a/yt/visualization/plot_modifications.py
+++ b/yt/visualization/plot_modifications.py
@@ -1000,8 +1000,7 @@
class ParticleCallback(PlotCallback):
"""
annotate_particles(width, p_size=1.0, col='k', marker='o', stride=1.0,
- ptype=None, stars_only=False, dm_only=False,
- minimum_mass=None, alpha=1.0)
+ ptype=None, minimum_mass=None, alpha=1.0)
Adds particle positions, based on a thick slab along *axis* with a
*width* along the line of sight. *p_size* controls the number of
Repository URL: https://bitbucket.org/yt_analysis/yt/
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