[yt-svn] commit/yt: 14 new changesets
commits-noreply at bitbucket.org
commits-noreply at bitbucket.org
Fri Sep 5 15:13:26 PDT 2014
14 new commits in yt:
https://bitbucket.org/yt_analysis/yt/commits/df4308cf7023/
Changeset: df4308cf7023
Branch: yt
User: chummels
Date: 2014-08-29 20:01:28
Summary: Correcting bug in thin_slice_projection recipe.
Affected #: 1 file
diff -r a7736c8f4158cb76b0453eca1a6f5046775ede01 -r df4308cf7023575e3246f0869afcad461bcb3e97 doc/source/cookbook/thin_slice_projection.py
--- a/doc/source/cookbook/thin_slice_projection.py
+++ b/doc/source/cookbook/thin_slice_projection.py
@@ -17,12 +17,12 @@
right_corner = ds.domain_right_edge
# Now adjust the size of the region along the line of sight (x axis).
-depth = ds.quan(10.0,'Mpc')
+depth = ds.quan(5.0,'Mpc')
left_corner[0] = center[0] - 0.5 * depth
-left_corner[0] = center[0] + 0.5 * depth
+right_corner[0] = center[0] + 0.5 * depth
# Create the region
-region = ds.region(center, left_corner, right_corner)
+region = ds.box(left_corner, right_corner)
# Create a density projection and supply the region we have just created.
# Only cells within the region will be included in the projection.
https://bitbucket.org/yt_analysis/yt/commits/2234e395a010/
Changeset: 2234e395a010
Branch: yt
User: chummels
Date: 2014-08-29 20:02:07
Summary: Correcting thin_slice_projection recipe.
Affected #: 1 file
diff -r df4308cf7023575e3246f0869afcad461bcb3e97 -r 2234e395a010392f83fdbe7291eb38fcf72804ad doc/source/cookbook/thin_slice_projection.py
--- a/doc/source/cookbook/thin_slice_projection.py
+++ b/doc/source/cookbook/thin_slice_projection.py
@@ -4,7 +4,7 @@
ds = yt.load("Enzo_64/DD0030/data0030")
# Make a projection that is the full width of the domain,
-# but only 10 Mpc in depth. This is done by creating a
+# but only 5 Mpc in depth. This is done by creating a
# region object with this exact geometry and providing it
# as a data_source for the projection.
https://bitbucket.org/yt_analysis/yt/commits/3c5333906e6e/
Changeset: 3c5333906e6e
Branch: yt
User: chummels
Date: 2014-08-29 20:02:29
Summary: Merging.
Affected #: 1 file
diff -r 2234e395a010392f83fdbe7291eb38fcf72804ad -r 3c5333906e6ec1383e09e518477f2e2551495019 yt/__init__.py
--- a/yt/__init__.py
+++ b/yt/__init__.py
@@ -150,7 +150,7 @@
TransferFunctionHelper
from yt.utilities.parallel_tools.parallel_analysis_interface import \
- parallel_objects, enable_parallelism
+ parallel_objects, enable_parallelism, communication_system
from yt.convenience import \
load, simulation
https://bitbucket.org/yt_analysis/yt/commits/d8e56e296288/
Changeset: d8e56e296288
Branch: yt
User: chummels
Date: 2014-09-03 03:12:32
Summary: Fixing a bug in the annotate_halos callback where you couldn't use it because the kwargs were set to None instead of empty dictionaries. Also added default annotation color to white and to use %g instead of %f for annotated text.
Affected #: 1 file
diff -r 3c5333906e6ec1383e09e518477f2e2551495019 -r d8e56e296288526a77230947154cf6c11d4dd9da yt/visualization/plot_modifications.py
--- a/yt/visualization/plot_modifications.py
+++ b/yt/visualization/plot_modifications.py
@@ -30,6 +30,7 @@
sec_per_kyr, sec_per_year, \
sec_per_day, sec_per_hr
from yt.units.yt_array import YTQuantity, YTArray
+from yt.units.unit_object import Unit
from yt.visualization.image_writer import apply_colormap
from yt.utilities.lib.geometry_utils import triangle_plane_intersect
@@ -906,9 +907,9 @@
class HaloCatalogCallback(PlotCallback):
"""
- annotate_halos(halo_catalog, circle_kwargs=None,
+ annotate_halos(halo_catalog, circle_kwargs={},
width = None, annotate_field=False,
- font_kwargs = None, factor = 1.0)
+ font_kwargs = {}, factor = 1.0)
Plots circles at the locations of all the halos
in a halo catalog with radii corresponding to the
@@ -935,17 +936,19 @@
region = None
_descriptor = None
- def __init__(self, halo_catalog, circle_kwargs = None,
+ def __init__(self, halo_catalog, circle_kwargs = {},
width = None, annotate_field = False,
- font_kwargs = None, factor = 1.0):
+ font_kwargs = {}, factor = 1.0):
PlotCallback.__init__(self)
self.halo_catalog = halo_catalog
self.width = width
self.annotate_field = annotate_field
+ if font_kwargs == {}:
+ font_kwargs = {'color':'white'}
self.font_kwargs = font_kwargs
self.factor = factor
- if circle_kwargs is None:
+ if circle_kwargs == {}:
circle_kwargs = {'edgecolor':'white', 'facecolor':'None'}
self.circle_kwargs = circle_kwargs
@@ -1005,7 +1008,7 @@
if self.annotate_field:
annotate_dat = halo_data[self.annotate_field]
- texts = ['{0}'.format(dat) for dat in annotate_dat]
+ texts = ['%g' % dat for dat in annotate_dat]
for pos_x, pos_y, t in zip(px, py, texts):
plot._axes.text(pos_x, pos_y, t, **self.font_kwargs)
https://bitbucket.org/yt_analysis/yt/commits/c738512f2cb6/
Changeset: c738512f2cb6
Branch: yt
User: chummels
Date: 2014-09-03 03:26:16
Summary: Making the docs for annotate_halos match the actual function. It was outdated.
Affected #: 1 file
diff -r d8e56e296288526a77230947154cf6c11d4dd9da -r c738512f2cb6389906397757472fdabab7a57d44 doc/source/visualizing/_cb_docstrings.inc
--- a/doc/source/visualizing/_cb_docstrings.inc
+++ b/doc/source/visualizing/_cb_docstrings.inc
@@ -151,19 +151,28 @@
Overplot Halo Annotations
~~~~~~~~~~~~~~~~~~~~~~~~~
-.. function:: annotate_halos(self, halo_catalog, col='white', alpha=1, \
- width=None):
+.. function:: annotate_halos(self, halo_catalog, circle_kwargs={}, width=None, \
+ annotate_field=False, font_kwargs={}, 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. If ``width`` is set to None (default) all halos are plotted.
- Otherwise, only halos that fall within a slab with width ``width`` centered
- on the center of the plot data. The color and transparency of the circles can
- be controlled with ``col`` and ``alpha`` respectively.
+ and plots a circle at the location of each halo with the radius of the
+ circle corresponding to the virial radius of the halo. 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). 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::
@@ -177,7 +186,7 @@
hc.create()
prj = yt.ProjectionPlot(data_ds, 'z', 'density')
- prj.annotate_halos(hc)
+ prj.annotate_halos(hc, annotate_field=particle_identifier)
prj.save()
Overplot a Straight Line
https://bitbucket.org/yt_analysis/yt/commits/f96bdad32d59/
Changeset: f96bdad32d59
Branch: yt
User: chummels
Date: 2014-09-03 04:11:48
Summary: Adding notes about the default quantities in the halo catalog docs.
Affected #: 1 file
diff -r c738512f2cb6389906397757472fdabab7a57d44 -r f96bdad32d599a173fffaa6550bd2168faa3f2d0 doc/source/analyzing/analysis_modules/halo_catalogs.rst
--- a/doc/source/analyzing/analysis_modules/halo_catalogs.rst
+++ b/doc/source/analyzing/analysis_modules/halo_catalogs.rst
@@ -129,7 +129,14 @@
are center_of_mass and bulk_velocity. Their definitions are available in
``yt/analysis_modules/halo_analysis/halo_quantities.py``. If you think that
your quantity may be of use to the general community, add it to
-``halo_quantities.py`` and issue a pull request.
+``halo_quantities.py`` and issue a pull request. Default halo quantities are:
+
+* ``particle_identifier`` -- Halo ID (e.g. 0 to N)
+* ``particle_mass`` -- Mass of halo
+* ``particle_position_x`` -- Location of halo
+* ``particle_position_y`` -- Location of halo
+* ``particle_position_z`` -- Location of halo
+* ``virial_radius`` -- Virial radius of halo
An example of adding a quantity:
https://bitbucket.org/yt_analysis/yt/commits/39a3e33941b0/
Changeset: 39a3e33941b0
Branch: yt
User: chummels
Date: 2014-09-03 04:12:17
Summary: Adding an example of using rockstar on a multi-particle-resolution dataset to the cookbook.
Affected #: 3 files
diff -r f96bdad32d599a173fffaa6550bd2168faa3f2d0 -r 39a3e33941b034d29421b477d525fb290eea5265 doc/source/analyzing/analysis_modules/halo_finders.rst
--- a/doc/source/analyzing/analysis_modules/halo_finders.rst
+++ b/doc/source/analyzing/analysis_modules/halo_finders.rst
@@ -75,7 +75,8 @@
mass. In simulations where the highest-resolution particles all have the
same mass (ie: zoom-in grid based simulations), one can set up a particle
filter to select the lowest mass particles and perform the halo finding
- only on those.
+ only on those. See the this cookbook recipe for an example:
+ :ref:`cookbook-rockstar-nested-grid`.
To run the Rockstar Halo finding, you must launch python with MPI and
parallelization enabled. While Rockstar itself does not require MPI to run,
diff -r f96bdad32d599a173fffaa6550bd2168faa3f2d0 -r 39a3e33941b034d29421b477d525fb290eea5265 doc/source/cookbook/cosmological_analysis.rst
--- a/doc/source/cookbook/cosmological_analysis.rst
+++ b/doc/source/cookbook/cosmological_analysis.rst
@@ -14,6 +14,22 @@
.. yt_cookbook:: halo_plotting.py
+.. _cookbook-rockstar-nested-grid:
+
+Running Rockstar to Find Halos on Multi-Resolution-Particle Datasets
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The version of Rockstar installed with yt does not have the capability
+to work on datasets with particles of different masses. Unfortunately,
+many simulations possess particles of different masses, notably cosmological
+zoom datasets. This recipe uses Rockstar in two different ways to generate a
+HaloCatalog from the highest resolution dark matter particles (the ones
+inside the zoom region). It then overlays some of those halos on a projection
+as a demonstration. See :ref:`halo-analysis` and :ref:`annotate-halos` for
+more information.
+
+.. yt_cookbook:: rockstar_nest.py
+
.. _cookbook-halo_finding:
Halo Profiling and Custom Analysis
diff -r f96bdad32d599a173fffaa6550bd2168faa3f2d0 -r 39a3e33941b034d29421b477d525fb290eea5265 doc/source/cookbook/rockstar_nest.py
--- /dev/null
+++ b/doc/source/cookbook/rockstar_nest.py
@@ -0,0 +1,70 @@
+# You must run this job in parallel.
+# There are several mpi flags which can be useful in order for it to work OK.
+# It requires at least 3 processors in order to run because of the way in which
+# rockstar divides up the work. Make sure you have mpi4py installed as per
+# http://yt-project.org/docs/dev/analyzing/parallel_computation.html#setting-up-parallel-yt
+
+# Usage: mpirun -np <num_procs> --mca btl ^openib python this_script.py
+
+import yt
+from yt.analysis_modules.halo_analysis.halo_catalog import HaloCatalog
+from yt.data_objects.particle_filters import add_particle_filter
+from yt.analysis_modules.halo_finding.rockstar.api import RockstarHaloFinder
+yt.enable_parallelism() # rockstar halofinding requires parallelism
+
+# Create a dark matter particle filter
+# This will be code dependent, but this function here is true for enzo
+
+def DarkMatter(pfilter, data):
+ filter = data[("all", "particle_type")] == 1 # DM = 1, Stars = 2
+ return filter
+
+add_particle_filter("dark_matter", function=DarkMatter, filtered_type='all', \
+ requires=["particle_type"])
+
+# Load the dataset and apply dark matter filter
+fn = "Enzo_64/DD0043/data0043"
+ds = yt.load(fn)
+ds.add_particle_filter('dark_matter')
+
+# Determine highest resolution DM particle mass in sim by looking
+# at the extrema of the dark_matter particle_mass field.
+ad = ds.all_data()
+min_dm_mass = ad.quantities.extrema(('dark_matter','particle_mass'))[0]
+
+# Define a new particle filter to isolate all highest resolution DM particles
+# and apply it to dataset
+def MaxResDarkMatter(pfilter, data):
+ return data["particle_mass"] <= 1.01 * min_dm_mass
+
+add_particle_filter("max_res_dark_matter", function=MaxResDarkMatter, \
+ filtered_type='dark_matter', requires=["particle_mass"])
+ds.add_particle_filter('max_res_dark_matter')
+
+# If desired, we can see the total number of DM and High-res DM particles
+#if yt.is_root():
+# print "Simulation has %d DM particles." % ad['dark_matter','particle_type'].shape
+# print "Simulation has %d Highest Res DM particles." % ad['max_res_dark_matter', 'particle_type'].shape
+
+# Run the halo catalog on the dataset only on the highest resolution dark matter
+# particles
+hc = HaloCatalog(data_ds=ds, finder_method='rockstar', \
+ finder_kwargs={'dm_only':True, 'particle_type':'max_res_dark_matter'})
+hc.create()
+
+# Or alternatively, just run the RockstarHaloFinder and later import the
+# output file as necessary. You can skip this step if you've already run it
+# once, but be careful since subsequent halo finds will overwrite this data.
+#rhf = RockstarHaloFinder(ds, particle_type="max_res_dark_matter")
+#rhf.run()
+# Load the halo list from a rockstar output for this dataset
+# Create a projection with the halos overplot on top
+#halos = yt.load('rockstar_halos/halos_0.0.bin')
+#hc = HaloCatalog(halos_ds=halos)
+#hc.load()
+
+# Regardless of your method of creating the halo catalog, use it to overplot the
+# halos on a projection.
+p = yt.ProjectionPlot(ds, "x", "density")
+p.annotate_halos(hc, annotate_field = 'particle_identifier', width=(10,'Mpc'), factor=2)
+p.save()
https://bitbucket.org/yt_analysis/yt/commits/41bb409eb3de/
Changeset: 41bb409eb3de
Branch: yt
User: chummels
Date: 2014-09-03 04:12:37
Summary: Merging.
Affected #: 60 files
diff -r 39a3e33941b034d29421b477d525fb290eea5265 -r 41bb409eb3de258c239f835a43686286467e2410 doc/install_script.sh
--- a/doc/install_script.sh
+++ b/doc/install_script.sh
@@ -500,13 +500,28 @@
fi
[ ! -e $LIB/extracted ] && tar xfz $LIB.tar.gz
touch $LIB/extracted
+ BUILD_ARGS=""
+ case $LIB in
+ *h5py*)
+ BUILD_ARGS="--hdf5=${HDF5_DIR}"
+ ;;
+ *numpy*)
+ if [ -e ${DEST_DIR}/lib/python2.7/site-packages/numpy/__init__.py ]
+ then
+ VER=$(${DEST_DIR}/bin/python -c 'from distutils.version import StrictVersion as SV; \
+ import numpy; print SV(numpy.__version__) < SV("1.8.0")')
+ if [ $VER == "True" ]
+ then
+ echo "Removing previous NumPy instance (see issue #889)"
+ rm -rf ${DEST_DIR}/lib/python2.7/site-packages/{numpy*,*.pth}
+ fi
+ fi
+ ;;
+ *)
+ ;;
+ esac
cd $LIB
- if [ ! -z `echo $LIB | grep h5py` ]
- then
- ( ${DEST_DIR}/bin/python2.7 setup.py build --hdf5=${HDF5_DIR} $* 2>&1 ) 1>> ${LOG_FILE} || do_exit
- else
- ( ${DEST_DIR}/bin/python2.7 setup.py build $* 2>&1 ) 1>> ${LOG_FILE} || do_exit
- fi
+ ( ${DEST_DIR}/bin/python2.7 setup.py build ${BUILD_ARGS} $* 2>&1 ) 1>> ${LOG_FILE} || do_exit
( ${DEST_DIR}/bin/python2.7 setup.py install 2>&1 ) 1>> ${LOG_FILE} || do_exit
touch done
cd ..
@@ -726,7 +741,7 @@
cd $FREETYPE_VER
( ./configure CFLAGS=-I${DEST_DIR}/include --prefix=${DEST_DIR}/ 2>&1 ) 1>> ${LOG_FILE} || do_exit
( make 2>&1 ) 1>> ${LOG_FILE} || do_exit
- ( make install 2>&1 ) 1>> ${LOG_FILE} || do_exit
+ ( make install 2>&1 ) 1>> ${LOG_FILE} || do_exit
( make clean 2>&1) 1>> ${LOG_FILE} || do_exit
touch done
cd ..
@@ -1022,7 +1037,7 @@
echo
echo "To get started with yt, check out the orientation:"
echo
- echo " http://yt-project.org/doc/bootcamp/"
+ echo " http://yt-project.org/doc/quickstart/"
echo
echo "The source for yt is located at:"
echo " $YT_DIR"
diff -r 39a3e33941b034d29421b477d525fb290eea5265 -r 41bb409eb3de258c239f835a43686286467e2410 doc/source/analyzing/units/index.rst
--- a/doc/source/analyzing/units/index.rst
+++ b/doc/source/analyzing/units/index.rst
@@ -37,7 +37,7 @@
.. note::
The notebooks use sample datasets that are available for download at
- http://yt-project.org/data. See :ref:`bootcamp-introduction` for more
+ http://yt-project.org/data. See :ref:`quickstart-introduction` for more
details.
Let us know if you would like to contribute other example notebooks, or have
diff -r 39a3e33941b034d29421b477d525fb290eea5265 -r 41bb409eb3de258c239f835a43686286467e2410 doc/source/bootcamp/1)_Introduction.ipynb
--- a/doc/source/bootcamp/1)_Introduction.ipynb
+++ /dev/null
@@ -1,72 +0,0 @@
-{
- "metadata": {
- "name": "",
- "signature": "sha256:39620670ce7751b23f30d2123fd3598de1c7843331f65de13e29f4ae9f759e0f"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
- {
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Welcome to the yt bootcamp!\n",
- "\n",
- "In this brief tutorial, we'll go over how to load up data, analyze things, inspect your data, and make some visualizations.\n",
- "\n",
- "Our documentation page can provide information on a variety of the commands that are used here, both in narrative documentation as well as recipes for specific functionality in our cookbook. The documentation exists at http://yt-project.org/doc/. If you encounter problems, look for help here: http://yt-project.org/doc/help/index.html.\n",
- "\n",
- "## Acquiring the datasets for this tutorial\n",
- "\n",
- "If you are executing these tutorials interactively, you need some sample datasets on which to run the code. You can download these datasets at http://yt-project.org/data/. The datasets necessary for each lesson are noted next to the corresponding tutorial.\n",
- "\n",
- "## What's Next?\n",
- "\n",
- "The Notebooks are meant to be explored in this order:\n",
- "\n",
- "1. Introduction\n",
- "2. Data Inspection (IsolatedGalaxy dataset)\n",
- "3. Simple Visualization (enzo_tiny_cosmology & Enzo_64 datasets)\n",
- "4. Data Objects and Time Series (IsolatedGalaxy dataset)\n",
- "5. Derived Fields and Profiles (IsolatedGalaxy dataset)\n",
- "6. Volume Rendering (IsolatedGalaxy dataset)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The following code will download the data needed for this tutorial automatically using `curl`. It may take some time so please wait when the kernel is busy. You will need to set `download_datasets` to True before using it."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "download_datasets = False\n",
- "if download_datasets:\n",
- " !curl -sSO http://yt-project.org/data/enzo_tiny_cosmology.tar\n",
- " print \"Got enzo_tiny_cosmology\"\n",
- " !tar xf enzo_tiny_cosmology.tar\n",
- " \n",
- " !curl -sSO http://yt-project.org/data/Enzo_64.tar\n",
- " print \"Got Enzo_64\"\n",
- " !tar xf Enzo_64.tar\n",
- " \n",
- " !curl -sSO http://yt-project.org/data/IsolatedGalaxy.tar\n",
- " print \"Got IsolatedGalaxy\"\n",
- " !tar xf IsolatedGalaxy.tar\n",
- " \n",
- " print \"All done!\""
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- }
- ],
- "metadata": {}
- }
- ]
-}
\ No newline at end of file
diff -r 39a3e33941b034d29421b477d525fb290eea5265 -r 41bb409eb3de258c239f835a43686286467e2410 doc/source/bootcamp/2)_Data_Inspection.ipynb
--- a/doc/source/bootcamp/2)_Data_Inspection.ipynb
+++ /dev/null
@@ -1,384 +0,0 @@
-{
- "metadata": {
- "name": "",
- "signature": "sha256:a8fe78715c1f3900c37c675d84320fe65f0ba8734abba60fd12e74d957e5d8ee"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
- {
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Starting Out and Loading Data\n",
- "\n",
- "We're going to get started by loading up yt. This next command brings all of the libraries into memory and sets up our environment."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "import yt"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Now that we've loaded yt, we can load up some data. Let's load the `IsolatedGalaxy` dataset."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "ds = yt.load(\"IsolatedGalaxy/galaxy0030/galaxy0030\")"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Fields and Facts\n",
- "\n",
- "When you call the `load` function, yt tries to do very little -- this is designed to be a fast operation, just setting up some information about the simulation. Now, the first time you access the \"index\" it will read and load the mesh and then determine where data is placed in the physical domain and on disk. Once it knows that, yt can tell you some statistics about the simulation:"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "ds.print_stats()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "yt can also tell you the fields it found on disk:"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "ds.field_list"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "And, all of the fields it thinks it knows how to generate:"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "ds.derived_field_list"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "yt can also transparently generate fields. However, we encourage you to examine exactly what yt is doing when it generates those fields. To see, you can ask for the source of a given field."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "print ds.field_info[\"gas\", \"vorticity_x\"].get_source()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "yt stores information about the domain of the simulation:"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "print ds.domain_width"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "yt can also convert this into various units:"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "print ds.domain_width.in_units(\"kpc\")\n",
- "print ds.domain_width.in_units(\"au\")\n",
- "print ds.domain_width.in_units(\"mile\")"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Mesh Structure\n",
- "\n",
- "If you're using a simulation type that has grids (for instance, here we're using an Enzo simulation) you can examine the structure of the mesh. For the most part, you probably won't have to use this unless you're debugging a simulation or examining in detail what is going on."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "print ds.index.grid_left_edge"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "But, you may have to access information about individual grid objects! Each grid object mediates accessing data from the disk and has a number of attributes that tell you about it. The index (`ds.index` here) has an attribute `grids` which is all of the grid objects."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "print ds.index.grids[1]"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "g = ds.index.grids[1]\n",
- "print g"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Grids have dimensions, extents, level, and even a list of Child grids."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "g.ActiveDimensions"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "g.LeftEdge, g.RightEdge"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "g.Level"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "g.Children"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Advanced Grid Inspection\n",
- "\n",
- "If we want to examine grids only at a given level, we can! Not only that, but we can load data and take a look at various fields.\n",
- "\n",
- "*This section can be skipped!*"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "gs = ds.index.select_grids(ds.index.max_level)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "g2 = gs[0]\n",
- "print g2\n",
- "print g2.Parent\n",
- "print g2.get_global_startindex()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "print g2[\"density\"][:,:,0]"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "print (g2.Parent.child_mask == 0).sum() * 8\n",
- "print g2.ActiveDimensions.prod()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "for f in ds.field_list:\n",
- " fv = g[f]\n",
- " if fv.size == 0: continue\n",
- " print f, fv.min(), fv.max()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Examining Data in Regions\n",
- "\n",
- "yt provides data object selectors. In subsequent notebooks we'll examine these in more detail, but we can select a sphere of data and perform a number of operations on it. yt makes it easy to operate on fluid fields in an object in *bulk*, but you can also examine individual field values.\n",
- "\n",
- "This creates a sphere selector positioned at the most dense point in the simulation that has a radius of 10 kpc."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "sp = ds.sphere(\"max\", (10, 'kpc'))"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "print sp"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We can calculate a bunch of bulk quantities. Here's that list, but there's a list in the docs, too!"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "print sp.quantities.keys()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Let's look at the total mass. This is how you call a given quantity. yt calls these \"Derived Quantities\". We'll talk about a few in a later notebook."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "print sp.quantities.total_mass()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- }
- ],
- "metadata": {}
- }
- ]
-}
\ No newline at end of file
diff -r 39a3e33941b034d29421b477d525fb290eea5265 -r 41bb409eb3de258c239f835a43686286467e2410 doc/source/bootcamp/3)_Simple_Visualization.ipynb
--- a/doc/source/bootcamp/3)_Simple_Visualization.ipynb
+++ /dev/null
@@ -1,275 +0,0 @@
-{
- "metadata": {
- "name": "",
- "signature": "sha256:c00ba7fdbbd9ea957d06060ad70f06f629b1fd4ebf5379c1fdad2697ab0a4cd6"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
- {
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Simple Visualizations of Data\n",
- "\n",
- "Just like in our first notebook, we have to load yt and then some data."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "import yt"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "For this notebook, we'll load up a cosmology dataset."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "ds = yt.load(\"enzo_tiny_cosmology/DD0046/DD0046\")\n",
- "print \"Redshift =\", ds.current_redshift"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "In the terms that yt uses, a projection is a line integral through the domain. This can either be unweighted (in which case a column density is returned) or weighted, in which case an average value is returned. Projections are, like all other data objects in yt, full-fledged data objects that churn through data and present that to you. However, we also provide a simple method of creating Projections and plotting them in a single step. This is called a Plot Window, here specifically known as a `ProjectionPlot`. One thing to note is that in yt, we project all the way through the entire domain at a single time. This means that the first call to projecting can be somewhat time consuming, but panning, zooming and plotting are all quite fast.\n",
- "\n",
- "yt is designed to make it easy to make nice plots and straightforward to modify those plots directly. The cookbook in the documentation includes detailed examples of this."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "p = yt.ProjectionPlot(ds, \"y\", \"density\")\n",
- "p.show()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The `show` command simply sends the plot to the IPython notebook. You can also call `p.save()` which will save the plot to the file system. This function accepts an argument, which will be pre-prended to the filename and can be used to name it based on the width or to supply a location.\n",
- "\n",
- "Now we'll zoom and pan a bit."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "p.zoom(2.0)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "p.pan_rel((0.1, 0.0))"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "p.zoom(10.0)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "p.pan_rel((-0.25, -0.5))"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "p.zoom(0.1)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "If we specify multiple fields, each time we call `show` we get multiple plots back. Same for `save`!"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "p = yt.ProjectionPlot(ds, \"z\", [\"density\", \"temperature\"], weight_field=\"density\")\n",
- "p.show()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We can adjust the colormap on a field-by-field basis."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "p.set_cmap(\"temperature\", \"hot\")"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "And, we can re-center the plot on different locations. One possible use of this would be to make a single `ProjectionPlot` which you move around to look at different regions in your simulation, saving at each one."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "v, c = ds.find_max(\"density\")\n",
- "p.set_center((c[0], c[1]))\n",
- "p.zoom(10)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Okay, let's load up a bigger simulation (from `Enzo_64` this time) and make a slice plot."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "ds = yt.load(\"Enzo_64/DD0043/data0043\")\n",
- "s = yt.SlicePlot(ds, \"z\", [\"density\", \"velocity_magnitude\"], center=\"max\")\n",
- "s.set_cmap(\"velocity_magnitude\", \"kamae\")\n",
- "s.zoom(10.0)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We can adjust the logging of various fields:"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "s.set_log(\"velocity_magnitude\", True)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "yt provides many different annotations for your plots. You can see all of these in the documentation, or if you type `s.annotate_` and press tab, a list will show up here. We'll annotate with velocity arrows."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "s.annotate_velocity()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Contours can also be overlaid:"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "s = yt.SlicePlot(ds, \"x\", [\"density\"], center=\"max\")\n",
- "s.annotate_contour(\"temperature\")\n",
- "s.zoom(2.5)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Finally, we can save out to the file system."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "s.save()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- }
- ],
- "metadata": {}
- }
- ]
-}
\ No newline at end of file
diff -r 39a3e33941b034d29421b477d525fb290eea5265 -r 41bb409eb3de258c239f835a43686286467e2410 doc/source/bootcamp/4)_Data_Objects_and_Time_Series.ipynb
--- a/doc/source/bootcamp/4)_Data_Objects_and_Time_Series.ipynb
+++ /dev/null
@@ -1,382 +0,0 @@
-{
- "metadata": {
- "name": "",
- "signature": "sha256:a46e1baa90d32045c2b524100f28bad41b3665249612c9a275ee0375a6f4be20"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
- {
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Data Objects and Time Series Data\n",
- "\n",
- "Just like before, we will load up yt. Since we'll be using pylab to plot some data in this notebook, we additionally tell matplotlib to place plots inline inside the notebook."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "%matplotlib inline\n",
- "import yt\n",
- "import numpy as np\n",
- "from matplotlib import pylab\n",
- "from yt.analysis_modules.halo_finding.api import HaloFinder"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Time Series Data\n",
- "\n",
- "Unlike before, instead of loading a single dataset, this time we'll load a bunch which we'll examine in sequence. This command creates a `DatasetSeries` object, which can be iterated over (including in parallel, which is outside the scope of this bootcamp) and analyzed. There are some other helpful operations it can provide, but we'll stick to the basics here.\n",
- "\n",
- "Note that you can specify either a list of filenames, or a glob (i.e., asterisk) pattern in this."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "ts = yt.DatasetSeries(\"enzo_tiny_cosmology/*/*.hierarchy\")"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Example 1: Simple Time Series\n",
- "\n",
- "As a simple example of how we can use this functionality, let's find the min and max of the density as a function of time in this simulation. To do this we use the construction `for ds in ts` where `ds` means \"Dataset\" and `ts` is the \"Time Series\" we just loaded up. For each dataset, we'll create an object (`dd`) that covers the entire domain. (`all_data` is a shorthand function for this.) We'll then call the `extrema` Derived Quantity, and append the min and max to our extrema outputs."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "rho_ex = []\n",
- "times = []\n",
- "for ds in ts:\n",
- " dd = ds.all_data()\n",
- " rho_ex.append(dd.quantities.extrema(\"density\"))\n",
- " times.append(ds.current_time.in_units(\"Gyr\"))\n",
- "rho_ex = np.array(rho_ex)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Now we plot the minimum and the maximum:"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "pylab.semilogy(times, rho_ex[:,0], '-xk', label='Minimum')\n",
- "pylab.semilogy(times, rho_ex[:,1], '-xr', label='Maximum')\n",
- "pylab.ylabel(\"Density ($g/cm^3$)\")\n",
- "pylab.xlabel(\"Time (Gyr)\")\n",
- "pylab.legend()\n",
- "pylab.ylim(1e-32, 1e-21)\n",
- "pylab.show()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Example 2: Advanced Time Series\n",
- "\n",
- "Let's do something a bit different. Let's calculate the total mass inside halos and outside halos.\n",
- "\n",
- "This actually touches a lot of different pieces of machinery in yt. For every dataset, we will run the halo finder HOP. Then, we calculate the total mass in the domain. Then, for each halo, we calculate the sum of the baryon mass in that halo. We'll keep running tallies of these two things."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "from yt.units import Msun\n",
- "\n",
- "mass = []\n",
- "zs = []\n",
- "for ds in ts:\n",
- " halos = HaloFinder(ds)\n",
- " dd = ds.all_data()\n",
- " total_mass = dd.quantities.total_quantity(\"cell_mass\").in_units(\"Msun\")\n",
- " total_in_baryons = 0.0*Msun\n",
- " for halo in halos:\n",
- " sp = halo.get_sphere()\n",
- " total_in_baryons += sp.quantities.total_quantity(\"cell_mass\").in_units(\"Msun\")\n",
- " mass.append(total_in_baryons/total_mass)\n",
- " zs.append(ds.current_redshift)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Now let's plot them!"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "pylab.semilogx(zs, mass, '-xb')\n",
- "pylab.xlabel(\"Redshift\")\n",
- "pylab.ylabel(\"Mass in halos / Total mass\")\n",
- "pylab.xlim(max(zs), min(zs))\n",
- "pylab.ylim(-0.01, .18)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Data Objects\n",
- "\n",
- "Time series data have many applications, but most of them rely on examining the underlying data in some way. Below, we'll see how to use and manipulate data objects.\n",
- "\n",
- "### Ray Queries\n",
- "\n",
- "yt provides the ability to examine rays, or lines, through the domain. Note that these are not periodic, unlike most other data objects. We create a ray object and can then examine quantities of it. Rays have the special fields `t` and `dts`, which correspond to the time the ray enters a given cell and the distance it travels through that cell.\n",
- "\n",
- "To create a ray, we specify the start and end points.\n",
- "\n",
- "Note that we need to convert these arrays to numpy arrays due to a bug in matplotlib 1.3.1."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "ray = ds.ray([0.1, 0.2, 0.3], [0.9, 0.8, 0.7])\n",
- "pylab.semilogy(np.array(ray[\"t\"]), np.array(ray[\"density\"]))"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "print ray[\"dts\"]"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "print ray[\"t\"]"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "print ray[\"x\"]"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Slice Queries\n",
- "\n",
- "While slices are often used for visualization, they can be useful for other operations as well. yt regards slices as multi-resolution objects. They are an array of cells that are not all the same size; it only returns the cells at the highest resolution that it intersects. (This is true for all yt data objects.) Slices and projections have the special fields `px`, `py`, `pdx` and `pdy`, which correspond to the coordinates and half-widths in the pixel plane."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "ds = yt.load(\"IsolatedGalaxy/galaxy0030/galaxy0030\")\n",
- "v, c = ds.find_max(\"density\")\n",
- "sl = ds.slice(0, c[0])\n",
- "print sl[\"index\", \"x\"]\n",
- "print sl[\"index\", \"z\"]\n",
- "print sl[\"pdx\"]\n",
- "print sl[\"gas\", \"density\"].shape"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "If we want to do something interesting with a `Slice`, we can turn it into a `FixedResolutionBuffer`. This object can be queried and will return a 2D array of values."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "frb = sl.to_frb((50.0, 'kpc'), 1024)\n",
- "print frb[\"gas\", \"density\"].shape"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "yt provides a few functions for writing arrays to disk, particularly in image form. Here we'll write out the log of `density`, and then use IPython to display it back here. Note that for the most part, you will probably want to use a `PlotWindow` for this, but in the case that it is useful you can directly manipulate the data."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "yt.write_image(np.log10(frb[\"gas\", \"density\"]), \"temp.png\")\n",
- "from IPython.display import Image\n",
- "Image(filename = \"temp.png\")"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Off-Axis Slices\n",
- "\n",
- "yt provides not only slices, but off-axis slices that are sometimes called \"cutting planes.\" These are specified by (in order) a normal vector and a center. Here we've set the normal vector to `[0.2, 0.3, 0.5]` and the center to be the point of maximum density.\n",
- "\n",
- "We can then turn these directly into plot windows using `to_pw`. Note that the `to_pw` and `to_frb` methods are available on slices, off-axis slices, and projections, and can be used on any of them."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "cp = ds.cutting([0.2, 0.3, 0.5], \"max\")\n",
- "pw = cp.to_pw(fields = [(\"gas\", \"density\")])"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Once we have our plot window from our cutting plane, we can show it here."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "pw.show()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We can, as noted above, do the same with our slice:"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "pws = sl.to_pw(fields=[\"density\"])\n",
- "#pws.show()\n",
- "print pws.plots.keys()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Covering Grids\n",
- "\n",
- "If we want to access a 3D array of data that spans multiple resolutions in our simulation, we can use a covering grid. This will return a 3D array of data, drawing from up to the resolution level specified when creating the data. For example, if you create a covering grid that spans two child grids of a single parent grid, it will fill those zones covered by a zone of a child grid with the data from that child grid. Where it is covered only by the parent grid, the cells from the parent grid will be duplicated (appropriately) to fill the covering grid.\n",
- "\n",
- "There are two different types of covering grids: unsmoothed and smoothed. Smoothed grids will be filled through a cascading interpolation process; they will be filled at level 0, interpolated to level 1, filled at level 1, interpolated to level 2, filled at level 2, etc. This will help to reduce edge effects. Unsmoothed covering grids will not be interpolated, but rather values will be duplicated multiple times.\n",
- "\n",
- "Here we create an unsmoothed covering grid at level 2, with the left edge at `[0.0, 0.0, 0.0]` and with dimensions equal to those that would cover the entire domain at level 2. We can then ask for the Density field, which will be a 3D array."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "cg = ds.covering_grid(2, [0.0, 0.0, 0.0], ds.domain_dimensions * 2**2)\n",
- "print cg[\"density\"].shape"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "In this example, we do exactly the same thing: except we ask for a *smoothed* covering grid, which will reduce edge effects."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "scg = ds.smoothed_covering_grid(2, [0.0, 0.0, 0.0], ds.domain_dimensions * 2**2)\n",
- "print scg[\"density\"].shape"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- }
- ],
- "metadata": {}
- }
- ]
-}
\ No newline at end of file
diff -r 39a3e33941b034d29421b477d525fb290eea5265 -r 41bb409eb3de258c239f835a43686286467e2410 doc/source/bootcamp/5)_Derived_Fields_and_Profiles.ipynb
--- a/doc/source/bootcamp/5)_Derived_Fields_and_Profiles.ipynb
+++ /dev/null
@@ -1,254 +0,0 @@
-{
- "metadata": {
- "name": "",
- "signature": "sha256:eca573e749829cacda0a8c07c6d5d11d07a5de657563a44b8c4ffff8f735caed"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
- {
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Derived Fields and Profiles\n",
- "\n",
- "One of the most powerful features in yt is the ability to create derived fields that act and look exactly like fields that exist on disk. This means that they will be generated on demand and can be used anywhere a field that exists on disk would be used. Additionally, you can create them by just writing python functions."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "%matplotlib inline\n",
- "import yt\n",
- "import numpy as np\n",
- "from yt import derived_field\n",
- "from matplotlib import pylab"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Derived Fields\n",
- "\n",
- "This is an example of the simplest possible way to create a derived field. All derived fields are defined by a function and some metadata; that metadata can include units, LaTeX-friendly names, conversion factors, and so on. Fields can be defined in the way in the next cell. What this does is create a function which accepts two arguments and then provide the units for that field. In this case, our field is `dinosaurs` and our units are `K*cm/s`. The function itself can access any fields that are in the simulation, and it does so by requesting data from the object called `data`."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "@derived_field(name = \"dinosaurs\", units = \"K * cm/s\")\n",
- "def _dinos(field, data):\n",
- " return data[\"temperature\"] * data[\"velocity_magnitude\"]"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "One important thing to note is that derived fields must be defined *before* any datasets are loaded. Let's load up our data and take a look at some quantities."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "ds = yt.load(\"IsolatedGalaxy/galaxy0030/galaxy0030\")\n",
- "dd = ds.all_data()\n",
- "print dd.quantities.keys()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "One interesting question is, what are the minimum and maximum values of dinosaur production rates in our isolated galaxy? We can do that by examining the `extrema` quantity -- the exact same way that we would for density, temperature, and so on."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "print dd.quantities.extrema(\"dinosaurs\")"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We can do the same for the average quantities as well."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "print dd.quantities.weighted_average_quantity(\"dinosaurs\", weight=\"temperature\")"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## A Few Other Quantities\n",
- "\n",
- "We can ask other quantities of our data, as well. For instance, this sequence of operations will find the most dense point, center a sphere on it, calculate the bulk velocity of that sphere, calculate the baryonic angular momentum vector, and then the density extrema. All of this is done in a memory conservative way: if you have an absolutely enormous dataset, yt will split that dataset into pieces, apply intermediate reductions and then a final reduction to calculate your quantity."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "sp = ds.sphere(\"max\", (10.0, 'kpc'))\n",
- "bv = sp.quantities.bulk_velocity()\n",
- "L = sp.quantities.angular_momentum_vector()\n",
- "rho_min, rho_max = sp.quantities.extrema(\"density\")\n",
- "print bv\n",
- "print L\n",
- "print rho_min, rho_max"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Profiles\n",
- "\n",
- "yt provides the ability to bin in 1, 2 and 3 dimensions. This means discretizing in one or more dimensions of phase space (density, temperature, etc) and then calculating either the total value of a field in each bin or the average value of a field in each bin.\n",
- "\n",
- "We do this using the objects `Profile1D`, `Profile2D`, and `Profile3D`. The first two are the most common since they are the easiest to visualize.\n",
- "\n",
- "This first set of commands manually creates a profile object the sphere we created earlier, binned in 32 bins according to density between `rho_min` and `rho_max`, and then takes the density-weighted average of the fields `temperature` and (previously-defined) `dinosaurs`. We then plot it in a loglog plot."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "prof = yt.Profile1D(sp, \"density\", 32, rho_min, rho_max, True, weight_field=\"cell_mass\")\n",
- "prof.add_fields([\"temperature\",\"dinosaurs\"])\n",
- "pylab.loglog(np.array(prof.x), np.array(prof[\"temperature\"]), \"-x\")\n",
- "pylab.xlabel('Density $(g/cm^3)$')\n",
- "pylab.ylabel('Temperature $(K)$')"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Now we plot the `dinosaurs` field."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "pylab.loglog(np.array(prof.x), np.array(prof[\"dinosaurs\"]), '-x')\n",
- "pylab.xlabel('Density $(g/cm^3)$')\n",
- "pylab.ylabel('Dinosaurs $(K cm / s)$')"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "If we want to see the total mass in every bin, we profile the `cell_mass` field with no weight. Specifying `weight=None` will simply take the total value in every bin and add that up."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "prof = yt.Profile1D(sp, \"density\", 32, rho_min, rho_max, True, weight_field=None)\n",
- "prof.add_fields([\"cell_mass\"])\n",
- "pylab.loglog(np.array(prof.x), np.array(prof[\"cell_mass\"].in_units(\"Msun\")), '-x')\n",
- "pylab.xlabel('Density $(g/cm^3)$')\n",
- "pylab.ylabel('Cell mass $(M_\\odot)$')"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "In addition to the low-level `ProfileND` interface, it's also quite straightforward to quickly create plots of profiles using the `ProfilePlot` class. Let's redo the last plot using `ProfilePlot`"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "prof = yt.ProfilePlot(sp, 'density', 'cell_mass', weight_field=None)\n",
- "prof.set_unit('cell_mass', 'Msun')\n",
- "prof.show()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Field Parameters\n",
- "\n",
- "Field parameters are a method of passing information to derived fields. For instance, you might pass in information about a vector you want to use as a basis for a coordinate transformation. yt often uses things like `bulk_velocity` to identify velocities that should be subtracted off. Here we show how that works:"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "sp_small = ds.sphere(\"max\", (50.0, 'kpc'))\n",
- "bv = sp_small.quantities.bulk_velocity()\n",
- "\n",
- "sp = ds.sphere(\"max\", (0.1, 'Mpc'))\n",
- "rv1 = sp.quantities.extrema(\"radial_velocity\")\n",
- "\n",
- "sp.clear_data()\n",
- "sp.set_field_parameter(\"bulk_velocity\", bv)\n",
- "rv2 = sp.quantities.extrema(\"radial_velocity\")\n",
- "\n",
- "print bv\n",
- "print rv1\n",
- "print rv2"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- }
- ],
- "metadata": {}
- }
- ]
-}
diff -r 39a3e33941b034d29421b477d525fb290eea5265 -r 41bb409eb3de258c239f835a43686286467e2410 doc/source/bootcamp/6)_Volume_Rendering.ipynb
--- a/doc/source/bootcamp/6)_Volume_Rendering.ipynb
+++ /dev/null
@@ -1,96 +0,0 @@
-{
- "metadata": {
- "name": "",
- "signature": "sha256:2a24bbe82955f9d948b39cbd1b1302968ff57f62f73afb2c7a5c4953393d00ae"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
- {
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# A Brief Demo of Volume Rendering\n",
- "\n",
- "This shows a small amount of volume rendering. Really, just enough to get your feet wet!"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "import yt\n",
- "ds = yt.load(\"IsolatedGalaxy/galaxy0030/galaxy0030\")"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "To create a volume rendering, we need a camera and a transfer function. We'll use the `ColorTransferFunction`, which accepts (in log space) the minimum and maximum bounds of our transfer function. This means behavior for data outside these values is undefined.\n",
- "\n",
- "We then add on \"layers\" like an onion. This function can accept a width (here specified) in data units, and also a color map. Here we add on four layers.\n",
- "\n",
- "Finally, we create a camera. The focal point is `[0.5, 0.5, 0.5]`, the width is 20 kpc (including front-to-back integration) and we specify a transfer function. Once we've done that, we call `show` to actually cast our rays and display them inline."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "tf = yt.ColorTransferFunction((-28, -24))\n",
- "tf.add_layers(4, w=0.01)\n",
- "cam = ds.camera([0.5, 0.5, 0.5], [1.0, 1.0, 1.0], (20, 'kpc'), 512, tf, fields=[\"density\"])\n",
- "cam.show()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "If we want to apply a clipping, we can specify the `clip_ratio`. This will clip the upper bounds to this value times the standard deviation of the values in the image array."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "cam.show(clip_ratio=4)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "There are several other options we can specify. Note that here we have turned on the use of ghost zones, shortened the data interval for the transfer function, and widened our gaussian layers."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "tf = yt.ColorTransferFunction((-28, -25))\n",
- "tf.add_layers(4, w=0.03)\n",
- "cam = ds.camera([0.5, 0.5, 0.5], [1.0, 1.0, 1.0], (20.0, 'kpc'), 512, tf, no_ghost=False)\n",
- "cam.show(clip_ratio=4.0)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- }
- ],
- "metadata": {}
- }
- ]
-}
\ No newline at end of file
diff -r 39a3e33941b034d29421b477d525fb290eea5265 -r 41bb409eb3de258c239f835a43686286467e2410 doc/source/bootcamp/data_inspection.rst
--- a/doc/source/bootcamp/data_inspection.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-.. _data_inspection:
-
-Data Inspection
----------------
-
-.. notebook:: 2)_Data_Inspection.ipynb
diff -r 39a3e33941b034d29421b477d525fb290eea5265 -r 41bb409eb3de258c239f835a43686286467e2410 doc/source/bootcamp/data_objects_and_time_series.rst
--- a/doc/source/bootcamp/data_objects_and_time_series.rst
+++ /dev/null
@@ -1,4 +0,0 @@
-Data Objects and Time Series
-----------------------------
-
-.. notebook:: 4)_Data_Objects_and_Time_Series.ipynb
diff -r 39a3e33941b034d29421b477d525fb290eea5265 -r 41bb409eb3de258c239f835a43686286467e2410 doc/source/bootcamp/derived_fields_and_profiles.rst
--- a/doc/source/bootcamp/derived_fields_and_profiles.rst
+++ /dev/null
@@ -1,4 +0,0 @@
-Derived Fields and Profiles
----------------------------
-
-.. notebook:: 5)_Derived_Fields_and_Profiles.ipynb
diff -r 39a3e33941b034d29421b477d525fb290eea5265 -r 41bb409eb3de258c239f835a43686286467e2410 doc/source/bootcamp/index.rst
--- a/doc/source/bootcamp/index.rst
+++ /dev/null
@@ -1,59 +0,0 @@
-.. _bootcamp:
-
-yt Bootcamp
-===========
-
-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 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 clone it using mercurial:
-
-.. code-block:: bash
-
- hg clone https://bitbucket.org/yt_analysis/yt
-
-Now start the IPython notebook from within the repository:
-
-.. code-block:: bash
-
- cd yt/doc/source/bootcamp
- yt notebook
-
-This command will give you information about the notebook server and how to
-access it. You will basically just pick a password (for security reasons) and then
-redirect your web browser to point to the notebook server.
-Once you have done so, choose "Introduction" from the list of
-notebooks, which includes an introduction and information about how to download
-the sample data.
-
-.. warning:: The pre-filled out notebooks are *far* less fun than running them
- yourselves! Check out the repo and give it a try.
-
-Here are the notebooks, which have been filled in for inspection:
-
-.. toctree::
- :maxdepth: 1
-
- introduction
- data_inspection
- simple_visualization
- data_objects_and_time_series
- derived_fields_and_profiles
- volume_rendering
-
-.. note::
-
- The notebooks use sample datasets that are available for download at
- http://yt-project.org/data. See :ref:`bootcamp-introduction` for more
- details.
-
-Let us know if you would like to contribute other example notebooks, or have
-any suggestions for how these can be improved.
diff -r 39a3e33941b034d29421b477d525fb290eea5265 -r 41bb409eb3de258c239f835a43686286467e2410 doc/source/bootcamp/introduction.rst
--- a/doc/source/bootcamp/introduction.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-.. _bootcamp-introduction:
-
-Introduction
-------------
-
-.. notebook:: 1)_Introduction.ipynb
diff -r 39a3e33941b034d29421b477d525fb290eea5265 -r 41bb409eb3de258c239f835a43686286467e2410 doc/source/bootcamp/simple_visualization.rst
--- a/doc/source/bootcamp/simple_visualization.rst
+++ /dev/null
@@ -1,4 +0,0 @@
-Simple Visualization
---------------------
-
-.. notebook:: 3)_Simple_Visualization.ipynb
diff -r 39a3e33941b034d29421b477d525fb290eea5265 -r 41bb409eb3de258c239f835a43686286467e2410 doc/source/bootcamp/volume_rendering.rst
--- a/doc/source/bootcamp/volume_rendering.rst
+++ /dev/null
@@ -1,4 +0,0 @@
-Volume Rendering
-----------------
-
-.. notebook:: 6)_Volume_Rendering.ipynb
diff -r 39a3e33941b034d29421b477d525fb290eea5265 -r 41bb409eb3de258c239f835a43686286467e2410 doc/source/conf.py
--- a/doc/source/conf.py
+++ b/doc/source/conf.py
@@ -122,7 +122,7 @@
bootswatch_theme = "readable",
navbar_links = [
("How to get help", "help/index"),
- ("Bootcamp notebooks", "bootcamp/index"),
+ ("Quickstart notebooks", "quickstart/index"),
("Cookbook", "cookbook/index"),
],
navbar_sidebarrel = False,
diff -r 39a3e33941b034d29421b477d525fb290eea5265 -r 41bb409eb3de258c239f835a43686286467e2410 doc/source/cookbook/calculating_information.rst
--- a/doc/source/cookbook/calculating_information.rst
+++ b/doc/source/cookbook/calculating_information.rst
@@ -90,3 +90,14 @@
See :ref:`filtering-particles` for more information.
.. yt_cookbook:: particle_filter_sfr.py
+
+Making a Turbulent Kinetic Energy Power Spectrum
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+This recipe shows how to use `yt` to read data and put it on a uniform
+grid to interface with the NumPy FFT routines and create a turbulent
+kinetic energy power spectrum. (Note: the dataset used here is of low
+resolution, so the turbulence is not very well-developed. The spike
+at high wavenumbers is due to non-periodicity in the z-direction).
+
+.. yt_cookbook:: power_spectrum_example.py
diff -r 39a3e33941b034d29421b477d525fb290eea5265 -r 41bb409eb3de258c239f835a43686286467e2410 doc/source/cookbook/custom_colorbar_tickmarks.rst
--- a/doc/source/cookbook/custom_colorbar_tickmarks.rst
+++ b/doc/source/cookbook/custom_colorbar_tickmarks.rst
@@ -1,4 +1,4 @@
-Custom Colorabar Tickmarks
---------------------------
+Custom Colorbar Tickmarks
+-------------------------
.. notebook:: custom_colorbar_tickmarks.ipynb
diff -r 39a3e33941b034d29421b477d525fb290eea5265 -r 41bb409eb3de258c239f835a43686286467e2410 doc/source/cookbook/power_spectrum_example.py
--- /dev/null
+++ b/doc/source/cookbook/power_spectrum_example.py
@@ -0,0 +1,118 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import yt
+
+"""
+Make a turbulent KE power spectrum. Since we are stratified, we use
+a rho**(1/3) scaling to the velocity to get something that would
+look Kolmogorov (if the turbulence were fully developed).
+
+Ultimately, we aim to compute:
+
+ 1 ^ ^*
+ E(k) = integral - V(k) . V(k) dS
+ 2
+
+ n ^
+where V = rho U is the density-weighted velocity field, and V is the
+FFT of V.
+
+(Note: sometimes we normalize by 1/volume to get a spectral
+energy density spectrum).
+
+
+"""
+
+
+def doit(ds):
+
+ # a FFT operates on uniformly gridded data. We'll use the yt
+ # covering grid for this.
+
+ max_level = ds.index.max_level
+
+ ref = int(np.product(ds.ref_factors[0:max_level]))
+
+ low = ds.domain_left_edge
+ dims = ds.domain_dimensions*ref
+
+ nx, ny, nz = dims
+
+ nindex_rho = 1./3.
+
+ Kk = np.zeros( (nx/2+1, ny/2+1, nz/2+1))
+
+ for vel in [("gas", "velocity_x"), ("gas", "velocity_y"),
+ ("gas", "velocity_z")]:
+
+ Kk += 0.5*fft_comp(ds, ("gas", "density"), vel,
+ nindex_rho, max_level, low, dims)
+
+ # wavenumbers
+ L = (ds.domain_right_edge - ds.domain_left_edge).d
+
+ kx = np.fft.rfftfreq(nx)*nx/L[0]
+ ky = np.fft.rfftfreq(ny)*ny/L[1]
+ kz = np.fft.rfftfreq(nz)*nz/L[2]
+
+ # physical limits to the wavenumbers
+ kmin = np.min(1.0/L)
+ kmax = np.max(0.5*dims/L)
+
+ kbins = np.arange(kmin, kmax, kmin)
+ N = len(kbins)
+
+ # bin the Fourier KE into radial kbins
+ kx3d, ky3d, kz3d = np.meshgrid(kx, ky, kz, indexing="ij")
+ k = np.sqrt(kx3d**2 + ky3d**2 + kz3d**2)
+
+ whichbin = np.digitize(k.flat, kbins)
+ ncount = np.bincount(whichbin)
+
+ E_spectrum = np.zeros(len(ncount)-1)
+
+ for n in range(1,len(ncount)):
+ E_spectrum[n-1] = np.sum(Kk.flat[whichbin==n])
+
+ k = 0.5*(kbins[0:N-1] + kbins[1:N])
+ E_spectrum = E_spectrum[1:N]
+
+ index = np.argmax(E_spectrum)
+ kmax = k[index]
+ Emax = E_spectrum[index]
+
+ plt.loglog(k, E_spectrum)
+ plt.loglog(k, Emax*(k/kmax)**(-5./3.), ls=":", color="0.5")
+
+ plt.xlabel(r"$k$")
+ plt.ylabel(r"$E(k)dk$")
+
+ plt.savefig("spectrum.png")
+
+
+def fft_comp(ds, irho, iu, nindex_rho, level, low, delta ):
+
+ cube = ds.covering_grid(level, left_edge=low,
+ dims=delta,
+ fields=[irho, iu])
+
+ rho = cube[irho].d
+ u = cube[iu].d
+
+ nx, ny, nz = rho.shape
+
+ # do the FFTs -- note that since our data is real, there will be
+ # too much information here. fftn puts the positive freq terms in
+ # the first half of the axes -- that's what we keep. Our
+ # normalization has an '8' to account for this clipping to one
+ # octant.
+ ru = np.fft.fftn(rho**nindex_rho * u)[0:nx/2+1,0:ny/2+1,0:nz/2+1]
+ ru = 8.0*ru/(nx*ny*nz)
+
+ return np.abs(ru)**2
+
+
+if __name__ == "__main__":
+
+ ds = yt.load("maestro_xrb_lores_23437")
+ doit(ds)
diff -r 39a3e33941b034d29421b477d525fb290eea5265 -r 41bb409eb3de258c239f835a43686286467e2410 doc/source/developing/building_the_docs.rst
--- a/doc/source/developing/building_the_docs.rst
+++ b/doc/source/developing/building_the_docs.rst
@@ -28,7 +28,7 @@
* Analyzing
* Examining
* Cookbook
-* Bootcamp
+* Quickstart
* Developing
* Reference
* Help
diff -r 39a3e33941b034d29421b477d525fb290eea5265 -r 41bb409eb3de258c239f835a43686286467e2410 doc/source/examining/low_level_inspection.rst
--- a/doc/source/examining/low_level_inspection.rst
+++ b/doc/source/examining/low_level_inspection.rst
@@ -12,7 +12,7 @@
based simulations. For now, these are represented as patches, with
the attendant properties.
-For a more basic introduction, see :ref:`bootcamp` and more specifically
+For a more basic introduction, see :ref:`quickstart` and more specifically
:ref:`data_inspection`.
.. _examining-grid-hierarchies:
diff -r 39a3e33941b034d29421b477d525fb290eea5265 -r 41bb409eb3de258c239f835a43686286467e2410 doc/source/index.rst
--- a/doc/source/index.rst
+++ b/doc/source/index.rst
@@ -34,7 +34,7 @@
<tr valign="top"><td width="25%"><p>
- <a href="bootcamp/index.html">yt Bootcamp</a>
+ <a href="quickstart/index.html">yt Quickstart</a></p></td><td width="75%">
@@ -127,7 +127,7 @@
:hidden:
installing
- yt Bootcamp <bootcamp/index>
+ yt Quickstart <quickstart/index>
yt3differences
cookbook/index
visualizing/index
diff -r 39a3e33941b034d29421b477d525fb290eea5265 -r 41bb409eb3de258c239f835a43686286467e2410 doc/source/quickstart/1)_Introduction.ipynb
--- /dev/null
+++ b/doc/source/quickstart/1)_Introduction.ipynb
@@ -0,0 +1,72 @@
+{
+ "metadata": {
+ "name": "",
+ "signature": "sha256:7c68cdd34ce71c042fa3c4badc4587693f1cc1b6aa0b3c99a4a63a1db6fe57f9"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+ {
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Welcome to the yt quickstart!\n",
+ "\n",
+ "In this brief tutorial, we'll go over how to load up data, analyze things, inspect your data, and make some visualizations.\n",
+ "\n",
+ "Our documentation page can provide information on a variety of the commands that are used here, both in narrative documentation as well as recipes for specific functionality in our cookbook. The documentation exists at http://yt-project.org/doc/. If you encounter problems, look for help here: http://yt-project.org/doc/help/index.html.\n",
+ "\n",
+ "## Acquiring the datasets for this tutorial\n",
+ "\n",
+ "If you are executing these tutorials interactively, you need some sample datasets on which to run the code. You can download these datasets at http://yt-project.org/data/. The datasets necessary for each lesson are noted next to the corresponding tutorial.\n",
+ "\n",
+ "## What's Next?\n",
+ "\n",
+ "The Notebooks are meant to be explored in this order:\n",
+ "\n",
+ "1. Introduction\n",
+ "2. Data Inspection (IsolatedGalaxy dataset)\n",
+ "3. Simple Visualization (enzo_tiny_cosmology & Enzo_64 datasets)\n",
+ "4. Data Objects and Time Series (IsolatedGalaxy dataset)\n",
+ "5. Derived Fields and Profiles (IsolatedGalaxy dataset)\n",
+ "6. Volume Rendering (IsolatedGalaxy dataset)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The following code will download the data needed for this tutorial automatically using `curl`. It may take some time so please wait when the kernel is busy. You will need to set `download_datasets` to True before using it."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "download_datasets = False\n",
+ "if download_datasets:\n",
+ " !curl -sSO http://yt-project.org/data/enzo_tiny_cosmology.tar\n",
+ " print \"Got enzo_tiny_cosmology\"\n",
+ " !tar xf enzo_tiny_cosmology.tar\n",
+ " \n",
+ " !curl -sSO http://yt-project.org/data/Enzo_64.tar\n",
+ " print \"Got Enzo_64\"\n",
+ " !tar xf Enzo_64.tar\n",
+ " \n",
+ " !curl -sSO http://yt-project.org/data/IsolatedGalaxy.tar\n",
+ " print \"Got IsolatedGalaxy\"\n",
+ " !tar xf IsolatedGalaxy.tar\n",
+ " \n",
+ " print \"All done!\""
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": []
+ }
+ ],
+ "metadata": {}
+ }
+ ]
+}
\ No newline at end of file
This diff is so big that we needed to truncate the remainder.
https://bitbucket.org/yt_analysis/yt/commits/0fa3234805b6/
Changeset: 0fa3234805b6
Branch: yt
User: chummels
Date: 2014-09-03 04:16:11
Summary: Oops. Didn't need to leave this import in plot_mods.
Affected #: 1 file
diff -r 41bb409eb3de258c239f835a43686286467e2410 -r 0fa3234805b606c49b829ac705fcd591b4c28104 yt/visualization/plot_modifications.py
--- a/yt/visualization/plot_modifications.py
+++ b/yt/visualization/plot_modifications.py
@@ -30,7 +30,6 @@
sec_per_kyr, sec_per_year, \
sec_per_day, sec_per_hr
from yt.units.yt_array import YTQuantity, YTArray
-from yt.units.unit_object import Unit
from yt.visualization.image_writer import apply_colormap
from yt.utilities.lib.geometry_utils import triangle_plane_intersect
https://bitbucket.org/yt_analysis/yt/commits/58e0d1b911d8/
Changeset: 58e0d1b911d8
Branch: yt
User: chummels
Date: 2014-09-03 05:26:53
Summary: Removing empty dictionaries from argument values and replacing with Nones to avoid weird iterative errors.
Affected #: 2 files
diff -r 0fa3234805b606c49b829ac705fcd591b4c28104 -r 58e0d1b911d8aedb84b876963b07e0e834af6f41 doc/source/visualizing/_cb_docstrings.inc
--- a/doc/source/visualizing/_cb_docstrings.inc
+++ b/doc/source/visualizing/_cb_docstrings.inc
@@ -151,8 +151,8 @@
Overplot Halo Annotations
~~~~~~~~~~~~~~~~~~~~~~~~~
-.. function:: annotate_halos(self, halo_catalog, circle_kwargs={}, width=None, \
- annotate_field=False, font_kwargs={}, factor=1.0):
+.. function:: annotate_halos(self, halo_catalog, circle_kwargs=None, width=None, \
+ annotate_field=False, font_kwargs=None, factor=1.0):
(This is a proxy for
:class:`~yt.visualization.plot_modifications.HaloCatalogCallback`.)
diff -r 0fa3234805b606c49b829ac705fcd591b4c28104 -r 58e0d1b911d8aedb84b876963b07e0e834af6f41 yt/visualization/plot_modifications.py
--- a/yt/visualization/plot_modifications.py
+++ b/yt/visualization/plot_modifications.py
@@ -906,9 +906,9 @@
class HaloCatalogCallback(PlotCallback):
"""
- annotate_halos(halo_catalog, circle_kwargs={},
+ annotate_halos(halo_catalog, circle_kwargs=None,
width = None, annotate_field=False,
- font_kwargs = {}, factor = 1.0)
+ font_kwargs = None, factor = 1.0)
Plots circles at the locations of all the halos
in a halo catalog with radii corresponding to the
@@ -935,19 +935,19 @@
region = None
_descriptor = None
- def __init__(self, halo_catalog, circle_kwargs = {},
+ def __init__(self, halo_catalog, circle_kwargs = None,
width = None, annotate_field = False,
- font_kwargs = {}, factor = 1.0):
+ font_kwargs = None, factor = 1.0):
PlotCallback.__init__(self)
self.halo_catalog = halo_catalog
self.width = width
self.annotate_field = annotate_field
- if font_kwargs == {}:
+ if font_kwargs == None:
font_kwargs = {'color':'white'}
self.font_kwargs = font_kwargs
self.factor = factor
- if circle_kwargs == {}:
+ if circle_kwargs == None:
circle_kwargs = {'edgecolor':'white', 'facecolor':'None'}
self.circle_kwargs = circle_kwargs
https://bitbucket.org/yt_analysis/yt/commits/8d4dc9a6b0ea/
Changeset: 8d4dc9a6b0ea
Branch: yt
User: chummels
Date: 2014-09-03 05:44:20
Summary: Changing "== None" to "is None"
Affected #: 1 file
diff -r 58e0d1b911d8aedb84b876963b07e0e834af6f41 -r 8d4dc9a6b0ea54b6650307eb37eb7d320098ff83 yt/visualization/plot_modifications.py
--- a/yt/visualization/plot_modifications.py
+++ b/yt/visualization/plot_modifications.py
@@ -943,11 +943,11 @@
self.halo_catalog = halo_catalog
self.width = width
self.annotate_field = annotate_field
- if font_kwargs == None:
+ if font_kwargs is None:
font_kwargs = {'color':'white'}
self.font_kwargs = font_kwargs
self.factor = factor
- if circle_kwargs == None:
+ if circle_kwargs is None:
circle_kwargs = {'edgecolor':'white', 'facecolor':'None'}
self.circle_kwargs = circle_kwargs
https://bitbucket.org/yt_analysis/yt/commits/bd0db2d1944b/
Changeset: bd0db2d1944b
Branch: yt
User: chummels
Date: 2014-09-03 22:57:31
Summary: Incorporating changes suggested in PR for assuring the rockstar_nest script is run with >=3 MPI procs and a string formatting issue.
Affected #: 2 files
diff -r 8d4dc9a6b0ea54b6650307eb37eb7d320098ff83 -r bd0db2d1944b7a75ee142fc170451ef782d2b884 doc/source/cookbook/rockstar_nest.py
--- a/doc/source/cookbook/rockstar_nest.py
+++ b/doc/source/cookbook/rockstar_nest.py
@@ -22,6 +22,10 @@
add_particle_filter("dark_matter", function=DarkMatter, filtered_type='all', \
requires=["particle_type"])
+# First, we make sure that this script is being run using mpirun with
+# at least 3 processors as indicated in the comments above.
+assert(yt.communication_system.communicators[-1].size >= 3)
+
# Load the dataset and apply dark matter filter
fn = "Enzo_64/DD0043/data0043"
ds = yt.load(fn)
diff -r 8d4dc9a6b0ea54b6650307eb37eb7d320098ff83 -r bd0db2d1944b7a75ee142fc170451ef782d2b884 yt/visualization/plot_modifications.py
--- a/yt/visualization/plot_modifications.py
+++ b/yt/visualization/plot_modifications.py
@@ -906,7 +906,7 @@
class HaloCatalogCallback(PlotCallback):
"""
- annotate_halos(halo_catalog, circle_kwargs=None,
+ annotate_halos(halo_catalog, circle_kwargs = None,
width = None, annotate_field=False,
font_kwargs = None, factor = 1.0)
@@ -1007,7 +1007,7 @@
if self.annotate_field:
annotate_dat = halo_data[self.annotate_field]
- texts = ['%g' % dat for dat in annotate_dat]
+ texts = ['{:g}'.format(float(dat))for dat in annotate_dat]
for pos_x, pos_y, t in zip(px, py, texts):
plot._axes.text(pos_x, pos_y, t, **self.font_kwargs)
https://bitbucket.org/yt_analysis/yt/commits/febfd1548c8b/
Changeset: febfd1548c8b
Branch: yt
User: chummels
Date: 2014-09-03 23:15:50
Summary: Fixing a formatting issue according to pep8
Affected #: 1 file
diff -r bd0db2d1944b7a75ee142fc170451ef782d2b884 -r febfd1548c8b8c24a31992e36ea54224fa297439 yt/visualization/plot_modifications.py
--- a/yt/visualization/plot_modifications.py
+++ b/yt/visualization/plot_modifications.py
@@ -906,9 +906,9 @@
class HaloCatalogCallback(PlotCallback):
"""
- annotate_halos(halo_catalog, circle_kwargs = None,
- width = None, annotate_field=False,
- font_kwargs = None, factor = 1.0)
+ annotate_halos(halo_catalog, circle_kwargs=None,
+ width = None, annotate_field = False,
+ font_kwargs=None, factor = 1.0)
Plots circles at the locations of all the halos
in a halo catalog with radii corresponding to the
@@ -935,9 +935,9 @@
region = None
_descriptor = None
- def __init__(self, halo_catalog, circle_kwargs = None,
+ def __init__(self, halo_catalog, circle_kwargs=None,
width = None, annotate_field = False,
- font_kwargs = None, factor = 1.0):
+ font_kwargs=None, factor = 1.0):
PlotCallback.__init__(self)
self.halo_catalog = halo_catalog
https://bitbucket.org/yt_analysis/yt/commits/0abeb6afd3bc/
Changeset: 0abeb6afd3bc
Branch: yt
User: xarthisius
Date: 2014-09-06 00:13:19
Summary: Merged in chummels/yt (pull request #1184)
A couple of bug fixes and a new cookbook recipe for running rockstar on nested cosmo runs
Affected #: 7 files
diff -r 1ae07010694a53b29095fa7300a5c4f457cb4b90 -r 0abeb6afd3bc2b563c023b4a72be222447d843af doc/source/analyzing/analysis_modules/halo_catalogs.rst
--- a/doc/source/analyzing/analysis_modules/halo_catalogs.rst
+++ b/doc/source/analyzing/analysis_modules/halo_catalogs.rst
@@ -129,7 +129,14 @@
are center_of_mass and bulk_velocity. Their definitions are available in
``yt/analysis_modules/halo_analysis/halo_quantities.py``. If you think that
your quantity may be of use to the general community, add it to
-``halo_quantities.py`` and issue a pull request.
+``halo_quantities.py`` and issue a pull request. Default halo quantities are:
+
+* ``particle_identifier`` -- Halo ID (e.g. 0 to N)
+* ``particle_mass`` -- Mass of halo
+* ``particle_position_x`` -- Location of halo
+* ``particle_position_y`` -- Location of halo
+* ``particle_position_z`` -- Location of halo
+* ``virial_radius`` -- Virial radius of halo
An example of adding a quantity:
diff -r 1ae07010694a53b29095fa7300a5c4f457cb4b90 -r 0abeb6afd3bc2b563c023b4a72be222447d843af doc/source/analyzing/analysis_modules/halo_finders.rst
--- a/doc/source/analyzing/analysis_modules/halo_finders.rst
+++ b/doc/source/analyzing/analysis_modules/halo_finders.rst
@@ -75,7 +75,8 @@
mass. In simulations where the highest-resolution particles all have the
same mass (ie: zoom-in grid based simulations), one can set up a particle
filter to select the lowest mass particles and perform the halo finding
- only on those.
+ only on those. See the this cookbook recipe for an example:
+ :ref:`cookbook-rockstar-nested-grid`.
To run the Rockstar Halo finding, you must launch python with MPI and
parallelization enabled. While Rockstar itself does not require MPI to run,
diff -r 1ae07010694a53b29095fa7300a5c4f457cb4b90 -r 0abeb6afd3bc2b563c023b4a72be222447d843af doc/source/cookbook/cosmological_analysis.rst
--- a/doc/source/cookbook/cosmological_analysis.rst
+++ b/doc/source/cookbook/cosmological_analysis.rst
@@ -14,6 +14,22 @@
.. yt_cookbook:: halo_plotting.py
+.. _cookbook-rockstar-nested-grid:
+
+Running Rockstar to Find Halos on Multi-Resolution-Particle Datasets
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The version of Rockstar installed with yt does not have the capability
+to work on datasets with particles of different masses. Unfortunately,
+many simulations possess particles of different masses, notably cosmological
+zoom datasets. This recipe uses Rockstar in two different ways to generate a
+HaloCatalog from the highest resolution dark matter particles (the ones
+inside the zoom region). It then overlays some of those halos on a projection
+as a demonstration. See :ref:`halo-analysis` and :ref:`annotate-halos` for
+more information.
+
+.. yt_cookbook:: rockstar_nest.py
+
.. _cookbook-halo_finding:
Halo Profiling and Custom Analysis
diff -r 1ae07010694a53b29095fa7300a5c4f457cb4b90 -r 0abeb6afd3bc2b563c023b4a72be222447d843af doc/source/cookbook/rockstar_nest.py
--- /dev/null
+++ b/doc/source/cookbook/rockstar_nest.py
@@ -0,0 +1,74 @@
+# You must run this job in parallel.
+# There are several mpi flags which can be useful in order for it to work OK.
+# It requires at least 3 processors in order to run because of the way in which
+# rockstar divides up the work. Make sure you have mpi4py installed as per
+# http://yt-project.org/docs/dev/analyzing/parallel_computation.html#setting-up-parallel-yt
+
+# Usage: mpirun -np <num_procs> --mca btl ^openib python this_script.py
+
+import yt
+from yt.analysis_modules.halo_analysis.halo_catalog import HaloCatalog
+from yt.data_objects.particle_filters import add_particle_filter
+from yt.analysis_modules.halo_finding.rockstar.api import RockstarHaloFinder
+yt.enable_parallelism() # rockstar halofinding requires parallelism
+
+# Create a dark matter particle filter
+# This will be code dependent, but this function here is true for enzo
+
+def DarkMatter(pfilter, data):
+ filter = data[("all", "particle_type")] == 1 # DM = 1, Stars = 2
+ return filter
+
+add_particle_filter("dark_matter", function=DarkMatter, filtered_type='all', \
+ requires=["particle_type"])
+
+# First, we make sure that this script is being run using mpirun with
+# at least 3 processors as indicated in the comments above.
+assert(yt.communication_system.communicators[-1].size >= 3)
+
+# Load the dataset and apply dark matter filter
+fn = "Enzo_64/DD0043/data0043"
+ds = yt.load(fn)
+ds.add_particle_filter('dark_matter')
+
+# Determine highest resolution DM particle mass in sim by looking
+# at the extrema of the dark_matter particle_mass field.
+ad = ds.all_data()
+min_dm_mass = ad.quantities.extrema(('dark_matter','particle_mass'))[0]
+
+# Define a new particle filter to isolate all highest resolution DM particles
+# and apply it to dataset
+def MaxResDarkMatter(pfilter, data):
+ return data["particle_mass"] <= 1.01 * min_dm_mass
+
+add_particle_filter("max_res_dark_matter", function=MaxResDarkMatter, \
+ filtered_type='dark_matter', requires=["particle_mass"])
+ds.add_particle_filter('max_res_dark_matter')
+
+# If desired, we can see the total number of DM and High-res DM particles
+#if yt.is_root():
+# print "Simulation has %d DM particles." % ad['dark_matter','particle_type'].shape
+# print "Simulation has %d Highest Res DM particles." % ad['max_res_dark_matter', 'particle_type'].shape
+
+# Run the halo catalog on the dataset only on the highest resolution dark matter
+# particles
+hc = HaloCatalog(data_ds=ds, finder_method='rockstar', \
+ finder_kwargs={'dm_only':True, 'particle_type':'max_res_dark_matter'})
+hc.create()
+
+# Or alternatively, just run the RockstarHaloFinder and later import the
+# output file as necessary. You can skip this step if you've already run it
+# once, but be careful since subsequent halo finds will overwrite this data.
+#rhf = RockstarHaloFinder(ds, particle_type="max_res_dark_matter")
+#rhf.run()
+# Load the halo list from a rockstar output for this dataset
+# Create a projection with the halos overplot on top
+#halos = yt.load('rockstar_halos/halos_0.0.bin')
+#hc = HaloCatalog(halos_ds=halos)
+#hc.load()
+
+# Regardless of your method of creating the halo catalog, use it to overplot the
+# halos on a projection.
+p = yt.ProjectionPlot(ds, "x", "density")
+p.annotate_halos(hc, annotate_field = 'particle_identifier', width=(10,'Mpc'), factor=2)
+p.save()
diff -r 1ae07010694a53b29095fa7300a5c4f457cb4b90 -r 0abeb6afd3bc2b563c023b4a72be222447d843af doc/source/cookbook/thin_slice_projection.py
--- a/doc/source/cookbook/thin_slice_projection.py
+++ b/doc/source/cookbook/thin_slice_projection.py
@@ -4,7 +4,7 @@
ds = yt.load("Enzo_64/DD0030/data0030")
# Make a projection that is the full width of the domain,
-# but only 10 Mpc in depth. This is done by creating a
+# but only 5 Mpc in depth. This is done by creating a
# region object with this exact geometry and providing it
# as a data_source for the projection.
@@ -17,12 +17,12 @@
right_corner = ds.domain_right_edge
# Now adjust the size of the region along the line of sight (x axis).
-depth = ds.quan(10.0,'Mpc')
+depth = ds.quan(5.0,'Mpc')
left_corner[0] = center[0] - 0.5 * depth
-left_corner[0] = center[0] + 0.5 * depth
+right_corner[0] = center[0] + 0.5 * depth
# Create the region
-region = ds.region(center, left_corner, right_corner)
+region = ds.box(left_corner, right_corner)
# Create a density projection and supply the region we have just created.
# Only cells within the region will be included in the projection.
diff -r 1ae07010694a53b29095fa7300a5c4f457cb4b90 -r 0abeb6afd3bc2b563c023b4a72be222447d843af doc/source/visualizing/_cb_docstrings.inc
--- a/doc/source/visualizing/_cb_docstrings.inc
+++ b/doc/source/visualizing/_cb_docstrings.inc
@@ -151,19 +151,28 @@
Overplot Halo Annotations
~~~~~~~~~~~~~~~~~~~~~~~~~
-.. function:: annotate_halos(self, halo_catalog, col='white', alpha=1, \
- width=None):
+.. function:: annotate_halos(self, halo_catalog, circle_kwargs=None, width=None, \
+ annotate_field=False, font_kwargs=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. If ``width`` is set to None (default) all halos are plotted.
- Otherwise, only halos that fall within a slab with width ``width`` centered
- on the center of the plot data. The color and transparency of the circles can
- be controlled with ``col`` and ``alpha`` respectively.
+ and plots a circle at the location of each halo with the radius of the
+ circle corresponding to the virial radius of the halo. 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). 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::
@@ -177,7 +186,7 @@
hc.create()
prj = yt.ProjectionPlot(data_ds, 'z', 'density')
- prj.annotate_halos(hc)
+ prj.annotate_halos(hc, annotate_field=particle_identifier)
prj.save()
Overplot a Straight Line
diff -r 1ae07010694a53b29095fa7300a5c4f457cb4b90 -r 0abeb6afd3bc2b563c023b4a72be222447d843af yt/visualization/plot_modifications.py
--- a/yt/visualization/plot_modifications.py
+++ b/yt/visualization/plot_modifications.py
@@ -907,8 +907,8 @@
class HaloCatalogCallback(PlotCallback):
"""
annotate_halos(halo_catalog, circle_kwargs=None,
- width = None, annotate_field=False,
- font_kwargs = None, factor = 1.0)
+ width = None, annotate_field = False,
+ font_kwargs=None, factor = 1.0)
Plots circles at the locations of all the halos
in a halo catalog with radii corresponding to the
@@ -935,14 +935,16 @@
region = None
_descriptor = None
- def __init__(self, halo_catalog, circle_kwargs = None,
+ def __init__(self, halo_catalog, circle_kwargs=None,
width = None, annotate_field = False,
- font_kwargs = None, factor = 1.0):
+ font_kwargs=None, factor = 1.0):
PlotCallback.__init__(self)
self.halo_catalog = halo_catalog
self.width = width
self.annotate_field = annotate_field
+ if font_kwargs is None:
+ font_kwargs = {'color':'white'}
self.font_kwargs = font_kwargs
self.factor = factor
if circle_kwargs is None:
@@ -1005,7 +1007,7 @@
if self.annotate_field:
annotate_dat = halo_data[self.annotate_field]
- texts = ['{0}'.format(dat) for dat in annotate_dat]
+ texts = ['{:g}'.format(float(dat))for dat in annotate_dat]
for pos_x, pos_y, t in zip(px, py, texts):
plot._axes.text(pos_x, pos_y, t, **self.font_kwargs)
Repository URL: https://bitbucket.org/yt_analysis/yt/
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