[yt-svn] commit/yt: 4 new changesets
commits-noreply at bitbucket.org
commits-noreply at bitbucket.org
Wed Dec 11 08:04:39 PST 2013
4 new commits in yt:
https://bitbucket.org/yt_analysis/yt/commits/7acb7368e0b2/
Changeset: 7acb7368e0b2
Branch: yt-3.0
User: MatthewTurk
Date: 2013-12-07 00:49:55
Summary: Merging mainline yt dev into yt-3.0. Includes profile rewrites.
Affected #: 29 files
diff -r 09f43fdc818380523eb7445e8a3353f1e40ca1f6 -r 7acb7368e0b27ee1bae57c592dd0d6ce318aacb6 doc/install_script.sh
--- a/doc/install_script.sh
+++ b/doc/install_script.sh
@@ -264,28 +264,45 @@
echo "Alternatively, download the Xcode command line tools from"
echo "the Apple developer tools website."
echo
- echo "OS X 10.8.2: download Xcode 4.6.1 from the mac app store."
+ echo "OS X 10.8.4 and 10.9: download Xcode 5.02 from the mac app store."
echo "(search for Xcode)."
+ echo
echo "Additionally, you will have to manually install the Xcode"
- echo "command line tools, see:"
- echo "http://stackoverflow.com/questions/9353444"
- echo "Alternatively, download the Xcode command line tools from"
- echo "the Apple developer tools website."
+ echo "command line tools."
+ echo
+ echo "For OS X 10.8, see:"
+ echo "http://stackoverflow.com/questions/9353444"
echo
- echo "NOTE: It's possible that the installation will fail, if so,"
- echo "please set the following environment variables, remove any"
- echo "broken installation tree, and re-run this script verbatim."
- echo
- echo "$ export CC=gcc"
- echo "$ export CXX=g++"
- echo
- OSX_VERSION=`sw_vers -productVersion`
- if [ "${OSX_VERSION##10.8}" != "${OSX_VERSION}" ]
+ echo "For OS X 10.9, the command line tools can be installed"
+ echo "with the following command:"
+ echo " xcode-select --install"
+ echo
+ OSX_VERSION=`sw_vers -productVersion`
+ if [ "${OSX_VERSION##10.8}" != "${OSX_VERSION}" ]
then
MPL_SUPP_CFLAGS="${MPL_SUPP_CFLAGS} -mmacosx-version-min=10.7"
MPL_SUPP_CXXFLAGS="${MPL_SUPP_CXXFLAGS} -mmacosx-version-min=10.7"
fi
fi
+ if [ -f /etc/redhat-release ]
+ then
+ echo "Looks like you're on an Redhat-compatible machine."
+ echo
+ echo "You need to have these packages installed:"
+ echo
+ echo " * openssl-devel"
+ echo " * uuid-devel"
+ echo " * readline-devel"
+ echo " * ncurses-devel"
+ echo " * zip"
+ echo " * gcc-{,c++,gfortran}"
+ echo " * make"
+ echo " * patch"
+ echo
+ echo "You can accomplish this by executing:"
+ echo "$ sudo yum install gcc gcc-g++ gcc-gfortran make patch zip"
+ echo "$ sudo yum install ncurses-devel uuid-devel openssl-devel readline-devel"
+ fi
if [ -f /etc/SuSE-release ] && [ `grep --count SUSE /etc/SuSE-release` -gt 0 ]
then
echo "Looks like you're on an OpenSUSE-compatible machine."
@@ -566,16 +583,16 @@
CYTHON='Cython-0.19.1'
FORTHON='Forthon-0.8.11'
PYX='PyX-0.12.1'
-PYTHON='Python-2.7.5'
+PYTHON='Python-2.7.6'
BZLIB='bzip2-1.0.6'
FREETYPE_VER='freetype-2.4.12'
H5PY='h5py-2.1.3'
HDF5='hdf5-1.8.11'
-IPYTHON='ipython-1.0.0'
+IPYTHON='ipython-1.1.0'
LAPACK='lapack-3.4.2'
PNG=libpng-1.6.3
MATPLOTLIB='matplotlib-1.3.0'
-MERCURIAL='mercurial-2.7'
+MERCURIAL='mercurial-2.8'
NOSE='nose-1.3.0'
NUMPY='numpy-1.7.1'
PYTHON_HGLIB='python-hglib-1.0'
@@ -585,14 +602,14 @@
SQLITE='sqlite-autoconf-3071700'
SYMPY='sympy-0.7.3'
TORNADO='tornado-3.1'
-ZEROMQ='zeromq-3.2.3'
+ZEROMQ='zeromq-3.2.4'
ZLIB='zlib-1.2.8'
# Now we dump all our SHA512 files out.
echo '9dcdda5b2ee2e63c2d3755245b7b4ed2f4592455f40feb6f8e86503195d9474559094ed27e789ab1c086d09da0bb21c4fe844af0e32a7d47c81ff59979b18ca0 Cython-0.19.1.tar.gz' > Cython-0.19.1.tar.gz.sha512
echo '3f53d0b474bfd79fea2536d0a9197eaef6c0927e95f2f9fd52dbd6c1d46409d0e649c21ac418d8f7767a9f10fe6114b516e06f2be4b06aec3ab5bdebc8768220 Forthon-0.8.11.tar.gz' > Forthon-0.8.11.tar.gz.sha512
echo '4941f5aa21aff3743546495fb073c10d2657ff42b2aff401903498638093d0e31e344cce778980f28a7170c6d29eab72ac074277b9d4088376e8692dc71e55c1 PyX-0.12.1.tar.gz' > PyX-0.12.1.tar.gz.sha512
-echo 'd6580eb170b36ad50f3a30023fe6ca60234156af91ccb3971b0b0983119b86f3a9f6c717a515c3c6cb72b3dcbf1d02695c6d0b92745f460b46a3defd3ff6ef2f Python-2.7.5.tgz' > Python-2.7.5.tgz.sha512
+echo '3df0ba4b1cfef5f02fb27925de4c2ca414eca9000af6a3d475d39063720afe987287c3d51377e0a36b88015573ef699f700782e1749c7a357b8390971d858a79 Python-2.7.6.tgz' > Python-2.7.6.tgz.sha512
echo '172f2bc671145ebb0add2669c117863db35851fb3bdb192006cd710d4d038e0037497eb39a6d01091cb923f71a7e8982a77b6e80bf71d6275d5d83a363c8d7e5 rockstar-0.99.6.tar.gz' > rockstar-0.99.6.tar.gz.sha512
echo '276bd9c061ec9a27d478b33078a86f93164ee2da72210e12e2c9da71dcffeb64767e4460b93f257302b09328eda8655e93c4b9ae85e74472869afbeae35ca71e blas.tar.gz' > blas.tar.gz.sha512
echo '00ace5438cfa0c577e5f578d8a808613187eff5217c35164ffe044fbafdfec9e98f4192c02a7d67e01e5a5ccced630583ad1003c37697219b0f147343a3fdd12 bzip2-1.0.6.tar.gz' > bzip2-1.0.6.tar.gz.sha512
@@ -600,11 +617,11 @@
echo '609a68a3675087e0cc95268574f31e104549daa48efe15a25a33b8e269a93b4bd160f4c3e8178dca9c950ef5ca514b039d6fd1b45db6af57f25342464d0429ce freetype-2.4.12.tar.gz' > freetype-2.4.12.tar.gz.sha512
echo '2eb7030f8559ff5cb06333223d98fda5b3a663b6f4a026949d1c423aa9a869d824e612ed5e1851f3bf830d645eea1a768414f73731c23ab4d406da26014fe202 h5py-2.1.3.tar.gz' > h5py-2.1.3.tar.gz.sha512
echo 'e9db26baa297c8ed10f1ca4a3fcb12d6985c6542e34c18d48b2022db73014f054c8b8434f3df70dcf44631f38b016e8050701d52744953d0fced3272d7b6b3c1 hdf5-1.8.11.tar.gz' > hdf5-1.8.11.tar.gz.sha512
-echo '1b309c08009583e66d1725a2d2051e6de934db246129568fa6d5ba33ad6babd3b443e7c2782d817128d2b112e21bcdd71e66be34fbd528badd900f1d0ed3db56 ipython-1.0.0.tar.gz' > ipython-1.0.0.tar.gz.sha512
+echo '46b8ae25df2ced674b3b3629070aafac955ba3aa2a5e749f8e63ef1f459126e1c4a9a03661406151622590a90c73b527716ad71bc626f57f52b51abfae0f43ca ipython-1.1.0.tar.gz' > ipython-1.1.0.tar.gz.sha512
echo '8770214491e31f0a7a3efaade90eee7b0eb20a8a6ab635c5f854d78263f59a1849133c14ef5123d01023f0110cbb9fc6f818da053c01277914ae81473430a952 lapack-3.4.2.tar.gz' > lapack-3.4.2.tar.gz.sha512
echo '887582e5a22e4cde338aa8fec7a89f6dd31f2f02b8842735f00f970f64582333fa03401cea6d01704083403c7e8b7ebc26655468ce930165673b33efa4bcd586 libpng-1.6.3.tar.gz' > libpng-1.6.3.tar.gz.sha512
echo '990e3a155ca7a9d329c41a43b44a9625f717205e81157c668a8f3f2ad5459ed3fed8c9bd85e7f81c509e0628d2192a262d4aa30c8bfc348bb67ed60a0362505a matplotlib-1.3.0.tar.gz' > matplotlib-1.3.0.tar.gz.sha512
-echo 'e425778edb0f71c34e719e04561ee3de37feaa1be4d60b94c780aebdbe6d41f8f4ab15103a8bbe8894ebeb228c42f0e2cd41b8db840f8384e1cd7cd2d5b67b97 mercurial-2.7.tar.gz' > mercurial-2.7.tar.gz.sha512
+echo 'b08dcd746728d89f1f96036f39df1608fad0ff863ae48fe12424b1645936ebbf59b9068b93fe3c7cfd2036db046df3dc814119f89a827bd5f008d32f323d45a8 mercurial-2.8.tar.gz' > mercurial-2.8.tar.gz.sha512
echo 'a3b8060e415560a868599224449a3af636d24a060f1381990b175dcd12f30249edd181179d23aea06b0c755ff3dc821b7a15ed8840f7855530479587d4d814f4 nose-1.3.0.tar.gz' > nose-1.3.0.tar.gz.sha512
echo 'd58177f3971b6d07baf6f81a2088ba371c7e43ea64ee7ada261da97c6d725b4bd4927122ac373c55383254e4e31691939276dab08a79a238bfa55172a3eff684 numpy-1.7.1.tar.gz' > numpy-1.7.1.tar.gz.sha512
echo '9c0a61299779aff613131aaabbc255c8648f0fa7ab1806af53f19fbdcece0c8a68ddca7880d25b926d67ff1b9201954b207919fb09f6a290acb078e8bbed7b68 python-hglib-1.0.tar.gz' > python-hglib-1.0.tar.gz.sha512
@@ -614,7 +631,7 @@
echo '96f3e51b46741450bc6b63779c10ebb4a7066860fe544385d64d1eda52592e376a589ef282ace2e1df73df61c10eab1a0d793abbdaf770e60289494d4bf3bcb4 sqlite-autoconf-3071700.tar.gz' > sqlite-autoconf-3071700.tar.gz.sha512
echo '2992baa3edfb4e1842fb642abf0bf0fc0bf56fc183aab8fed6b3c42fbea928fa110ede7fdddea2d63fc5953e8d304b04da433dc811134fadefb1eecc326121b8 sympy-0.7.3.tar.gz' > sympy-0.7.3.tar.gz.sha512
echo '101544db6c97beeadc5a02b2ef79edefa0a07e129840ace2e4aa451f3976002a273606bcdc12d6cef5c22ff4c1c9dcf60abccfdee4cbef8e3f957cd25c0430cf tornado-3.1.tar.gz' > tornado-3.1.tar.gz.sha512
-echo '34ffb6aa645f62bd1158a8f2888bf92929ccf90917a6c50ed51ed1240732f498522e164d1536f26480c87ad5457fe614a93bf0e15f2f89b0b168e64a30de68ca zeromq-3.2.3.tar.gz' > zeromq-3.2.3.tar.gz.sha512
+echo 'd8eef84860bc5314b42a2cc210340572a9148e008ea65f7650844d0edbe457d6758785047c2770399607f69ba3b3a544db9775a5cdf961223f7e278ef7e0f5c6 zeromq-3.2.4.tar.gz' > zeromq-3.2.4.tar.gz.sha512
echo 'ece209d4c7ec0cb58ede791444dc754e0d10811cbbdebe3df61c0fd9f9f9867c1c3ccd5f1827f847c005e24eef34fb5bf87b5d3f894d75da04f1797538290e4a zlib-1.2.8.tar.gz' > zlib-1.2.8.tar.gz.sha512
# Individual processes
[ -z "$HDF5_DIR" ] && get_ytproject $HDF5.tar.gz
@@ -1006,10 +1023,7 @@
echo
echo "To get started with yt, check out the orientation:"
echo
- echo " http://yt-project.org/doc/orientation/"
- echo
- echo "or just activate your environment and run 'yt serve' to bring up the"
- echo "yt GUI."
+ echo " http://yt-project.org/doc/bootcamp/"
echo
echo "The source for yt is located at:"
echo " $YT_DIR"
diff -r 09f43fdc818380523eb7445e8a3353f1e40ca1f6 -r 7acb7368e0b27ee1bae57c592dd0d6ce318aacb6 log
--- /dev/null
+++ b/log
@@ -0,0 +1,146 @@
+/home/mturk/yt-conda/lib/python2.7/site-packages/matplotlib/__init__.py:727: UserWarning: Found matplotlib configuration in ~/.matplotlib/. To conform with the XDG base directory standard, this configuration location has been deprecated on Linux, and the new location is now '/home/mturk/.config'/matplotlib/. Please move your configuration there to ensure that matplotlib will continue to find it in the future.
+ _get_xdg_config_dir())
+yt : [DEBUG ] 2013-12-06 18:45:17,831 Set log level to 1
+yt : [DEBUG ] 2013-12-06 18:45:17,877 Registering: StaticOutput as <class 'yt.data_objects.static_output.StaticOutput'>
+yt : [DEBUG ] 2013-12-06 18:45:17,879 SIGUSR1 registered for traceback printing
+yt : [DEBUG ] 2013-12-06 18:45:17,879 SIGUSR2 registered for IPython Insertion
+yt : [DEBUG ] 2013-12-06 18:45:17,909 Registering: EnzoStaticOutput as <class 'yt.frontends.enzo.data_structures.EnzoStaticOutput'>
+yt : [DEBUG ] 2013-12-06 18:45:17,909 Registering: EnzoStaticOutputInMemory as <class 'yt.frontends.enzo.data_structures.EnzoStaticOutputInMemory'>
+yt : [DEBUG ] 2013-12-06 18:45:17,909 Registering simulation: Enzo as <class 'yt.frontends.enzo.simulation_handling.EnzoSimulation'>
+yt : [DEBUG ] 2013-12-06 18:45:17,916 Registering: BoxlibStaticOutput as <class 'yt.frontends.boxlib.data_structures.BoxlibStaticOutput'>
+yt : [DEBUG ] 2013-12-06 18:45:17,916 Registering: OrionStaticOutput as <class 'yt.frontends.boxlib.data_structures.OrionStaticOutput'>
+yt : [DEBUG ] 2013-12-06 18:45:17,916 Registering: CastroStaticOutput as <class 'yt.frontends.boxlib.data_structures.CastroStaticOutput'>
+yt : [DEBUG ] 2013-12-06 18:45:17,916 Registering: MaestroStaticOutput as <class 'yt.frontends.boxlib.data_structures.MaestroStaticOutput'>
+yt : [DEBUG ] 2013-12-06 18:45:17,916 Registering: NyxStaticOutput as <class 'yt.frontends.boxlib.data_structures.NyxStaticOutput'>
+yt : [DEBUG ] 2013-12-06 18:45:17,926 Registering: FLASHStaticOutput as <class 'yt.frontends.flash.data_structures.FLASHStaticOutput'>
+yt : [DEBUG ] 2013-12-06 18:45:17,930 Registering: ARTStaticOutput as <class 'yt.frontends.art.data_structures.ARTStaticOutput'>
+yt : [DEBUG ] 2013-12-06 18:45:17,932 Registering: ARTIOStaticOutput as <class 'yt.frontends.artio.data_structures.ARTIOStaticOutput'>
+yt : [DEBUG ] 2013-12-06 18:45:17,933 Registering: RAMSESStaticOutput as <class 'yt.frontends.ramses.data_structures.RAMSESStaticOutput'>
+yt : [DEBUG ] 2013-12-06 18:45:17,935 Registering: ChomboStaticOutput as <class 'yt.frontends.chombo.data_structures.ChomboStaticOutput'>
+yt : [DEBUG ] 2013-12-06 18:45:17,936 Registering: GDFStaticOutput as <class 'yt.frontends.gdf.data_structures.GDFStaticOutput'>
+yt : [DEBUG ] 2013-12-06 18:45:17,937 Registering: MoabHex8StaticOutput as <class 'yt.frontends.moab.data_structures.MoabHex8StaticOutput'>
+yt : [DEBUG ] 2013-12-06 18:45:17,937 Registering: PyneMoabHex8StaticOutput as <class 'yt.frontends.moab.data_structures.PyneMoabHex8StaticOutput'>
+yt : [DEBUG ] 2013-12-06 18:45:17,939 Registering: AthenaStaticOutput as <class 'yt.frontends.athena.data_structures.AthenaStaticOutput'>
+yt : [DEBUG ] 2013-12-06 18:45:17,940 Registering: PlutoStaticOutput as <class 'yt.frontends.pluto.data_structures.PlutoStaticOutput'>
+yt : [DEBUG ] 2013-12-06 18:45:17,969 Registering: ParticleStaticOutput as <class 'yt.frontends.sph.data_structures.ParticleStaticOutput'>
+yt : [DEBUG ] 2013-12-06 18:45:17,969 Registering: GadgetStaticOutput as <class 'yt.frontends.sph.data_structures.GadgetStaticOutput'>
+yt : [DEBUG ] 2013-12-06 18:45:17,970 Registering: GadgetHDF5StaticOutput as <class 'yt.frontends.sph.data_structures.GadgetHDF5StaticOutput'>
+yt : [DEBUG ] 2013-12-06 18:45:17,970 Registering: OWLSStaticOutput as <class 'yt.frontends.sph.data_structures.OWLSStaticOutput'>
+yt : [DEBUG ] 2013-12-06 18:45:17,970 Registering: TipsyStaticOutput as <class 'yt.frontends.sph.data_structures.TipsyStaticOutput'>
+yt : [DEBUG ] 2013-12-06 18:45:17,970 Registering: HTTPStreamStaticOutput as <class 'yt.frontends.sph.data_structures.HTTPStreamStaticOutput'>
+yt : [DEBUG ] 2013-12-06 18:45:17,971 Registering: StreamStaticOutput as <class 'yt.frontends.stream.data_structures.StreamStaticOutput'>
+yt : [DEBUG ] 2013-12-06 18:45:17,971 Registering: StreamParticlesStaticOutput as <class 'yt.frontends.stream.data_structures.StreamParticlesStaticOutput'>
+yt : [DEBUG ] 2013-12-06 18:45:17,971 Registering: StreamOctreeStaticOutput as <class 'yt.frontends.stream.data_structures.StreamOctreeStaticOutput'>
+yt : [DEBUG ] 2013-12-06 18:45:17,971 Registering: StreamHexahedralStaticOutput as <class 'yt.frontends.stream.data_structures.StreamHexahedralStaticOutput'>
+yt : [DEBUG ] 2013-12-06 18:45:17,976 The Fortran kD-Tree did not import correctly.
+yt : [INFO ] 2013-12-06 18:45:18,144 Loading plugins from /home/mturk/.yt/my_plugins.py
+......................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................E............................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................../home/mturk/yt-conda/lib/python2.7/site-packages/matplotlib/tight_layout.py:225: UserWarning: tight_layout : falling back to Agg renderer
+ warnings.warn("tight_layout : falling back to Agg renderer")
+......................................................
+======================================================================
+ERROR: Failure: TypeError ('NoneType' object is not iterable)
+----------------------------------------------------------------------
+Traceback (most recent call last):
+ File "/home/mturk/yt-conda/lib/python2.7/site-packages/nose/loader.py", line 253, in generate
+ for test in g():
+ File "/home/mturk/yt/yt-3.0/yt/data_objects/tests/test_profiles.py", line 92, in test_profiles
+ p1d.add_fields(["Ones", "Temperature"])
+ File "/home/mturk/yt/yt-3.0/yt/data_objects/profiles.py", line 760, in add_fields
+ for g in parallel_objects(self.data_source._grids):
+ File "/home/mturk/yt/yt-3.0/yt/utilities/parallel_tools/parallel_analysis_interface.py", line 433, in parallel_objects
+ oiter = itertools.islice(enumerate(objects), my_new_id, None, njobs)
+TypeError: 'NoneType' object is not iterable
+-------------------- >> begin captured logging << --------------------
+yt: INFO: Parameters: current_time = 0.0
+yt: INFO: Parameters: domain_dimensions = [64 64 64]
+yt: INFO: Parameters: domain_left_edge = [ 0. 0. 0.]
+yt: INFO: Parameters: domain_right_edge = [ 1. 1. 1.]
+yt: INFO: Parameters: cosmological_simulation = 0.0
+yt: DEBUG: Initializing data storage.
+yt: DEBUG: Setting up classes.
+yt: DEBUG: Setting up domain geometry.
+yt: DEBUG: Counting grids.
+yt: DEBUG: Initializing grid arrays.
+yt: DEBUG: Allocating arrays for 8 grids
+yt: DEBUG: Parsing hierarchy.
+yt: DEBUG: Copying reverse tree
+yt: DEBUG: Preparing grids
+yt: DEBUG: Prepared 0 / 8 grids
+yt: DEBUG: Prepared
+yt: DEBUG: Constructing grid objects.
+yt: DEBUG: Re-examining hierarchy
+yt: DEBUG: Initializing data grid data IO
+yt: DEBUG: Detecting fields.
+yt: DEBUG: Detecting fields in backup.
+yt: DEBUG: Adding unknown detected fields
+yt: DEBUG: Checking ['Dinosaurs', 'Density', 'Temperature', 'Tribbles']
+yt: INFO: Adding unknown field Dinosaurs to list of fields
+yt: INFO: Adding unknown field Density to list of fields
+yt: INFO: Adding unknown field Temperature to list of fields
+yt: INFO: Adding unknown field Tribbles to list of fields
+yt: DEBUG: Setting up derived fields
+yt: DEBUG: Raises <type 'exceptions.RuntimeError'> during field ('all', 'ParticleMass') detection.
+yt: DEBUG: Raises <type 'exceptions.RuntimeError'> during field ('all', 'particle_ones') detection.
+yt: DEBUG: Raises <type 'exceptions.RuntimeError'> during field ('all', 'ParticleMassMsun') detection.
+yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityRPGrowthMagnitude detection.
+yt: DEBUG: Raises <type 'exceptions.KeyError'> during field BaroclinicVorticityMagnitude detection.
+yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityGrowthTimescale detection.
+yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityStretchingY detection.
+yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityZ detection.
+yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityGrowthMagnitude detection.
+yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityGrowthX detection.
+yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityGrowthY detection.
+yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityGrowthZ detection.
+yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityStretchingX detection.
+yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityStretchingZ detection.
+yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityMagnitude detection.
+yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityRPGrowthMagnitudeABS detection.
+yt: DEBUG: Raises <type 'exceptions.KeyError'> during field gradDensityMagnitude detection.
+yt: DEBUG: Generating vorticity on defaultdict(<function <lambda> at 0xc6fcf50>, {'dz': array([ 1.]), 'dx': array([ 1.]), 'dy': array([ 1.])})
+yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticitySquared detection.
+yt: DEBUG: Raises <type 'exceptions.KeyError'> during field AbsDivV detection.
+yt: DEBUG: Raises <type 'exceptions.KeyError'> during field DivV detection.
+yt: DEBUG: Raises <type 'exceptions.KeyError'> during field Shear detection.
+yt: DEBUG: Raises <type 'exceptions.KeyError'> during field gradDensityZ detection.
+yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityRPGrowthX detection.
+yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityRPGrowthY detection.
+yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityRPGrowthZ detection.
+yt: DEBUG: Raises <type 'exceptions.KeyError'> during field gradDensityX detection.
+yt: DEBUG: Raises <type 'exceptions.KeyError'> during field gradDensityY detection.
+yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityGrowthMagnitudeABS detection.
+yt: DEBUG: Raises <type 'exceptions.KeyError'> during field ShearCriterion detection.
+yt: DEBUG: Raises <type 'exceptions.KeyError'> during field ShearMach detection.
+yt: DEBUG: Raises <type 'exceptions.KeyError'> during field gradPressureZ detection.
+yt: DEBUG: Raises <type 'exceptions.KeyError'> during field gradPressureX detection.
+yt: DEBUG: Raises <type 'exceptions.KeyError'> during field gradPressureY detection.
+yt: DEBUG: Raises <type 'exceptions.KeyError'> during field BaroclinicVorticityY detection.
+yt: DEBUG: Raises <type 'exceptions.KeyError'> during field BaroclinicVorticityX detection.
+yt: DEBUG: Raises <type 'exceptions.KeyError'> during field BaroclinicVorticityZ detection.
+yt: DEBUG: Raises <type 'exceptions.KeyError'> during field gradPressureMagnitude detection.
+yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityStretchingMagnitude detection.
+yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityRPGrowthTimescale detection.
+yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityY detection.
+yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityX detection.
+yt: DEBUG: Setting up particle fields
+yt: DEBUG: Don't know what to do with io
+yt: DEBUG: Checking derived fields again
+yt: DEBUG: Creating Particle Union 'all'
+yt: DEBUG: Don't know what to do with all
+yt: DEBUG: Checking ['Dinosaurs', 'Density', 'Temperature', 'Tribbles']
+yt: DEBUG: Adding known field Dinosaurs to list of fields
+yt: DEBUG: Adding known field Density to list of fields
+yt: DEBUG: Adding known field Temperature to list of fields
+yt: DEBUG: Adding known field Tribbles to list of fields
+yt: DEBUG: Appending object to UniformGridData (type: <class 'yt.geometry.geometry_handler.YTRegion'>)
+yt: DEBUG: Reading 262144 cells of ['Density'] fields in 8 blocks
+yt: DEBUG: Reading 262144 cells of ['Temperature'] fields in 8 blocks
+yt: DEBUG: Reading 262144 cells of ['Dinosaurs'] fields in 8 blocks
+yt: DEBUG: Reading 262144 cells of ['Density'] fields in 8 blocks
+yt: DEBUG: Reading 262144 cells of ['Temperature'] fields in 8 blocks
+yt: DEBUG: Reading 262144 cells of ['Dinosaurs'] fields in 8 blocks
+--------------------- >> end captured logging << ---------------------
+
+----------------------------------------------------------------------
+Ran 3787 tests in 263.236s
+
+FAILED (errors=1)
diff -r 09f43fdc818380523eb7445e8a3353f1e40ca1f6 -r 7acb7368e0b27ee1bae57c592dd0d6ce318aacb6 nosetests.xml
--- /dev/null
+++ b/nosetests.xml
@@ -0,0 +1,103 @@
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[ 1., 1., 1., ..., 1., 1., 1" name="]]]))" time="0.002"></testcase><testcase classname="yt.data_objects.tests.test_profiles" name="test_binned_profiles(262144.0, 262144)" time="0.000"></testcase><testcase classname="yt.data_objects.tests.test_profiles" name="test_binned_profiles(130952.21709330557, 130952.21709330491, 7)" time="0.000"></testcase><testcase classname="yt.data_objects.tests.test_profiles" name="test_binned_profiles(262144.0, 262144)" time="0.000"></testcase><testcase classname="yt.data_objects.tests.test_profiles" name="test_binned_profiles(130952.21709330557, 130952.21709330498, 7)" time="0.000"></testcase><testcase classname="yt.data_objects.tests.test_profiles" name="test_binned_profiles(262144.0, 262144)" time="0.000"></testcase><testcase classname="yt.data_objects.tests.test_profiles" name="test_binned_profiles(130952.21709330557, 130952.2170933048, 7)" time="0.000"></testcase><testcase classname="yt.data_objects.tests.test_profiles" name="test_binned_profiles(262144.0, 262144)" time="0.000"></testcase><testcase classname="yt.data_objects.tests.test_profiles" name="test_binned_profiles(130952.21709330557, 130952.21709330557, 7)" time="0.000"></testcase><testcase classname="yt.data_objects.tests.test_profiles" name="test_binned_profiles(262144.0, 262144)" time="0.000"></testcase><testcase classname="yt.data_objects.tests.test_profiles" name="test_binned_profiles(130952.21709330557, 130952.21709330495, 7)" time="0.000"></testcase><testcase classname="yt.data_objects.tests.test_profiles" name="test_binned_profiles(262144.0, 262144)" time="0.000"></testcase><testcase classname="yt.data_objects.tests.test_profiles" name="test_binned_profiles(130952.21709330557, 130952.21709330491, 7)" time="0.000"></testcase><testcase classname="yt.data_objects.tests.test_profiles" name="test_binned_profiles(262144.0, 262144)" time="0.000"></testcase><testcase classname="yt.data_objects.tests.test_profiles" name="test_binned_profiles(130952.21709330557, 130952.21709330501, 7)" time="0.000"></testcase><testcase classname="yt.data_objects.tests.test_profiles" name="test_binned_profiles(262144.0, 262144)" time="0.000"></testcase><testcase classname="yt.data_objects.tests.test_profiles" name="test_binned_profiles(130952.21709330557, 130952.21709330507, 7)" time="0.000"></testcase><testcase classname="yt.data_objects.tests.test_profiles" name="test_binned_profiles(262144.0, 262144)" time="0.000"></testcase><testcase classname="yt.data_objects.tests.test_profiles" name="test_binned_profiles(130952.21709330557, 130952.21709330603, 7)" time="0.000"></testcase><testcase classname="yt.data_objects.tests.test_profiles" name="test_binned_profiles(262144.0, 262144)" time="0.000"></testcase><testcase classname="yt.data_objects.tests.test_profiles" name="test_binned_profiles(130952.21709330557, 130952.21709330495, 7)" time="0.000"></testcase><testcase classname="yt.data_objects.tests.test_profiles" name="test_binned_profiles(262144.0, 262144)" time="0.000"></testcase><testcase classname="yt.data_objects.tests.test_profiles" name="test_binned_profiles(130952.21709330557, 130952.21709330498, 7)" time="0.000"></testcase><testcase classname="yt.data_objects.tests.test_profiles" name="test_binned_profiles(262144.0, 262144)" time="0.000"></testcase><testcase classname="yt.data_objects.tests.test_profiles" name="test_binned_profiles(130952.21709330557, 130952.21709330622, 7)" time="0.000"></testcase><testcase classname="yt.data_objects.tests.test_profiles.test_binned_profiles(array([ 4096., 4096., 4096., 4096., 4096., 4096., 4096., 4096.,
4096., 4096., 4096., 4096., 4096., 4096., 4096., 4096.,
4096., 4096., 4096., 4096., 4096., 4096., 4096., 4096.,
4096., 4096., 4096., 4096., 4096., 4096., 4096., 4096.,
4096., 4096., 4096., 4096., 4096., 4096., 4096., 4096.,
4096., 4096., 4096., 4096., 4096., 4096., 4096., 4096.,
4096., 4096., 4096., 4096., 4096., 4096., 4096., 4096.,
4096., 4096., 4096., 4096., 4096., 4096., 4096., 4096.]), array([ 4096., 4096., 4096., 4096., 4096., 4096., 4096., 4096.,
4096., 4096., 4096., 4096., 4096., 4096., 4096., 4096.,
4096., 4096., 4096., 4096., 4096., 4096., 4096., 4096.,
4096., 4096., 4096., 4096., 4096., 4096., 4096., 4096.,
4096., 4096., 4096., 4096., 4096., 4096., 4096., 4096.,
4096., 4096., 4096., 4096., 4096., 4096., 4096., 4096.,
4096., 4096., 4096., 4096., 4096., 4096., 4096., 4096.,
4096., 4096., 4096., 4096., 4096., 4096., 4096., 4096" name="]))" time="0.000"></testcase><testcase classname="yt.data_objects.tests.test_profiles.test_binned_profiles(array([ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]), array([ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1" name="]))" time="0.000"></testcase><testcase classname="yt.data_objects.tests.test_profiles.test_binned_profiles(array([[ 64., 64., 64., ..., 64., 64., 64.],
[ 64., 64., 64., ..., 64., 64., 64.],
[ 64., 64., 64., ..., 64., 64., 64.],
...,
[ 64., 64., 64., ..., 64., 64., 64.],
[ 64., 64., 64., ..., 64., 64., 64.],
[ 64., 64., 64., ..., 64., 64., 64.]]), array([[ 64., 64., 64., ..., 64., 64., 64.],
[ 64., 64., 64., ..., 64., 64., 64.],
[ 64., 64., 64., ..., 64., 64., 64.],
...,
[ 64., 64., 64., ..., 64., 64., 64.],
[ 64., 64., 64., ..., 64., 64., 64.],
[ 64., 64., 64., ..., 64., 64., 64" name="]]))" time="0.000"></testcase><testcase classname="yt.data_objects.tests.test_profiles.test_binned_profiles(array([[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.],
...,
[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.]]), array([[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.],
...,
[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1" name="]]))" time="0.000"></testcase><testcase classname="yt.data_objects.tests.test_profiles.test_binned_profiles(array([[[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.],
...,
[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.]],
[[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.],
...,
[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.]],
[[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.],
...,
[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.]],
...,
[[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.],
...,
[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.]],
[[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.],
...,
[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.]],
[[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.],
...,
[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.]]]), array([[[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.],
...,
[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.]],
[[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.],
...,
[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.]],
[[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.],
...,
[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.]],
...,
[[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.],
...,
[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.]],
[[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.],
...,
[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.]],
[[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.],
...,
[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1" name="]]]))" time="0.011"></testcase><testcase classname="yt.data_objects.tests.test_profiles.test_binned_profiles(array([[[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.],
...,
[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.]],
[[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.],
...,
[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.]],
[[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.],
...,
[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.]],
...,
[[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.],
...,
[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.]],
[[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.],
...,
[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.]],
[[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.],
...,
[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.]]]), array([[[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.],
...,
[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.]],
[[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.],
...,
[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.]],
[[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.],
...,
[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.]],
...,
[[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.],
...,
[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.]],
[[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.],
...,
[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.]],
[[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.],
...,
[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1.],
[ 1., 1., 1., ..., 1., 1., 1" name="]]]))" time="0.011"></testcase><testcase classname="nose.failure.Failure" name="runTest" time="0.000"><error type="exceptions.TypeError" message="'NoneType' object is not iterable
-------------------- >> begin captured logging << --------------------
yt: INFO: Parameters: current_time = 0.0
yt: INFO: Parameters: domain_dimensions = [64 64 64]
yt: INFO: Parameters: domain_left_edge = [ 0. 0. 0.]
yt: INFO: Parameters: domain_right_edge = [ 1. 1. 1.]
yt: INFO: Parameters: cosmological_simulation = 0.0
yt: DEBUG: Initializing data storage.
yt: DEBUG: Setting up classes.
yt: DEBUG: Setting up domain geometry.
yt: DEBUG: Counting grids.
yt: DEBUG: Initializing grid arrays.
yt: DEBUG: Allocating arrays for 8 grids
yt: DEBUG: Parsing hierarchy.
yt: DEBUG: Copying reverse tree
yt: DEBUG: Preparing grids
yt: DEBUG: Prepared 0 / 8 grids
yt: DEBUG: Prepared
yt: DEBUG: Constructing grid objects.
yt: DEBUG: Re-examining hierarchy
yt: DEBUG: Initializing data grid data IO
yt: DEBUG: Detecting fields.
yt: DEBUG: Detecting fields in backup.
yt: DEBUG: Adding unknown detected fields
yt: DEBUG: Checking ['Dinosaurs', 'Density', 'Temperature', 'Tribbles']
yt: INFO: Adding unknown field Dinosaurs to list of fields
yt: INFO: Adding unknown field Density to list of fields
yt: INFO: Adding unknown field Temperature to list of fields
yt: INFO: Adding unknown field Tribbles to list of fields
yt: DEBUG: Setting up derived fields
yt: DEBUG: Raises <type 'exceptions.RuntimeError'> during field ('all', 'ParticleMass') detection.
yt: DEBUG: Raises <type 'exceptions.RuntimeError'> during field ('all', 'particle_ones') detection.
yt: DEBUG: Raises <type 'exceptions.RuntimeError'> during field ('all', 'ParticleMassMsun') detection.
yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityRPGrowthMagnitude detection.
yt: DEBUG: Raises <type 'exceptions.KeyError'> during field BaroclinicVorticityMagnitude detection.
yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityGrowthTimescale detection.
yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityStretchingY detection.
yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityZ detection.
yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityGrowthMagnitude detection.
yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityGrowthX detection.
yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityGrowthY detection.
yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityGrowthZ detection.
yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityStretchingX detection.
yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityStretchingZ detection.
yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityMagnitude detection.
yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityRPGrowthMagnitudeABS detection.
yt: DEBUG: Raises <type 'exceptions.KeyError'> during field gradDensityMagnitude detection.
yt: DEBUG: Generating vorticity on defaultdict(<function <lambda> at 0xc6fcf50>, {'dz': array([ 1.]), 'dx': array([ 1.]), 'dy': array([ 1.])})
yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticitySquared detection.
yt: DEBUG: Raises <type 'exceptions.KeyError'> during field AbsDivV detection.
yt: DEBUG: Raises <type 'exceptions.KeyError'> during field DivV detection.
yt: DEBUG: Raises <type 'exceptions.KeyError'> during field Shear detection.
yt: DEBUG: Raises <type 'exceptions.KeyError'> during field gradDensityZ detection.
yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityRPGrowthX detection.
yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityRPGrowthY detection.
yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityRPGrowthZ detection.
yt: DEBUG: Raises <type 'exceptions.KeyError'> during field gradDensityX detection.
yt: DEBUG: Raises <type 'exceptions.KeyError'> during field gradDensityY detection.
yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityGrowthMagnitudeABS detection.
yt: DEBUG: Raises <type 'exceptions.KeyError'> during field ShearCriterion detection.
yt: DEBUG: Raises <type 'exceptions.KeyError'> during field ShearMach detection.
yt: DEBUG: Raises <type 'exceptions.KeyError'> during field gradPressureZ detection.
yt: DEBUG: Raises <type 'exceptions.KeyError'> during field gradPressureX detection.
yt: DEBUG: Raises <type 'exceptions.KeyError'> during field gradPressureY detection.
yt: DEBUG: Raises <type 'exceptions.KeyError'> during field BaroclinicVorticityY detection.
yt: DEBUG: Raises <type 'exceptions.KeyError'> during field BaroclinicVorticityX detection.
yt: DEBUG: Raises <type 'exceptions.KeyError'> during field BaroclinicVorticityZ detection.
yt: DEBUG: Raises <type 'exceptions.KeyError'> during field gradPressureMagnitude detection.
yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityStretchingMagnitude detection.
yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityRPGrowthTimescale detection.
yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityY detection.
yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityX detection.
yt: DEBUG: Setting up particle fields
yt: DEBUG: Don't know what to do with io
yt: DEBUG: Checking derived fields again
yt: DEBUG: Creating Particle Union 'all'
yt: DEBUG: Don't know what to do with all
yt: DEBUG: Checking ['Dinosaurs', 'Density', 'Temperature', 'Tribbles']
yt: DEBUG: Adding known field Dinosaurs to list of fields
yt: DEBUG: Adding known field Density to list of fields
yt: DEBUG: Adding known field Temperature to list of fields
yt: DEBUG: Adding known field Tribbles to list of fields
yt: DEBUG: Appending object to UniformGridData (type: <class 'yt.geometry.geometry_handler.YTRegion'>)
yt: DEBUG: Reading 262144 cells of ['Density'] fields in 8 blocks
yt: DEBUG: Reading 262144 cells of ['Temperature'] fields in 8 blocks
yt: DEBUG: Reading 262144 cells of ['Dinosaurs'] fields in 8 blocks
yt: DEBUG: Reading 262144 cells of ['Density'] fields in 8 blocks
yt: DEBUG: Reading 262144 cells of ['Temperature'] fields in 8 blocks
yt: DEBUG: Reading 262144 cells of ['Dinosaurs'] fields in 8 blocks
--------------------- >> end captured logging << ---------------------"><![CDATA[ File "/home/mturk/yt-conda/lib/python2.7/unittest/case.py", line 331, in run
+ testMethod()
+ File "/home/mturk/yt-conda/lib/python2.7/site-packages/nose/loader.py", line 253, in generate
+ for test in g():
+ File "/home/mturk/yt/yt-3.0/yt/data_objects/tests/test_profiles.py", line 92, in test_profiles
+ p1d.add_fields(["Ones", "Temperature"])
+ File "/home/mturk/yt/yt-3.0/yt/data_objects/profiles.py", line 760, in add_fields
+ for g in parallel_objects(self.data_source._grids):
+ File "/home/mturk/yt/yt-3.0/yt/utilities/parallel_tools/parallel_analysis_interface.py", line 433, in parallel_objects
+ oiter = itertools.islice(enumerate(objects), my_new_id, None, njobs)
+'NoneType' object is not iterable
+-------------------- >> begin captured logging << --------------------
+yt: INFO: Parameters: current_time = 0.0
+yt: INFO: Parameters: domain_dimensions = [64 64 64]
+yt: INFO: Parameters: domain_left_edge = [ 0. 0. 0.]
+yt: INFO: Parameters: domain_right_edge = [ 1. 1. 1.]
+yt: INFO: Parameters: cosmological_simulation = 0.0
+yt: DEBUG: Initializing data storage.
+yt: DEBUG: Setting up classes.
+yt: DEBUG: Setting up domain geometry.
+yt: DEBUG: Counting grids.
+yt: DEBUG: Initializing grid arrays.
+yt: DEBUG: Allocating arrays for 8 grids
+yt: DEBUG: Parsing hierarchy.
+yt: DEBUG: Copying reverse tree
+yt: DEBUG: Preparing grids
+yt: DEBUG: Prepared 0 / 8 grids
+yt: DEBUG: Prepared
+yt: DEBUG: Constructing grid objects.
+yt: DEBUG: Re-examining hierarchy
+yt: DEBUG: Initializing data grid data IO
+yt: DEBUG: Detecting fields.
+yt: DEBUG: Detecting fields in backup.
+yt: DEBUG: Adding unknown detected fields
+yt: DEBUG: Checking ['Dinosaurs', 'Density', 'Temperature', 'Tribbles']
+yt: INFO: Adding unknown field Dinosaurs to list of fields
+yt: INFO: Adding unknown field Density to list of fields
+yt: INFO: Adding unknown field Temperature to list of fields
+yt: INFO: Adding unknown field Tribbles to list of fields
+yt: DEBUG: Setting up derived fields
+yt: DEBUG: Raises <type 'exceptions.RuntimeError'> during field ('all', 'ParticleMass') detection.
+yt: DEBUG: Raises <type 'exceptions.RuntimeError'> during field ('all', 'particle_ones') detection.
+yt: DEBUG: Raises <type 'exceptions.RuntimeError'> during field ('all', 'ParticleMassMsun') detection.
+yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityRPGrowthMagnitude detection.
+yt: DEBUG: Raises <type 'exceptions.KeyError'> during field BaroclinicVorticityMagnitude detection.
+yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityGrowthTimescale detection.
+yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityStretchingY detection.
+yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityZ detection.
+yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityGrowthMagnitude detection.
+yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityGrowthX detection.
+yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityGrowthY detection.
+yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityGrowthZ detection.
+yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityStretchingX detection.
+yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityStretchingZ detection.
+yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityMagnitude detection.
+yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityRPGrowthMagnitudeABS detection.
+yt: DEBUG: Raises <type 'exceptions.KeyError'> during field gradDensityMagnitude detection.
+yt: DEBUG: Generating vorticity on defaultdict(<function <lambda> at 0xc6fcf50>, {'dz': array([ 1.]), 'dx': array([ 1.]), 'dy': array([ 1.])})
+yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticitySquared detection.
+yt: DEBUG: Raises <type 'exceptions.KeyError'> during field AbsDivV detection.
+yt: DEBUG: Raises <type 'exceptions.KeyError'> during field DivV detection.
+yt: DEBUG: Raises <type 'exceptions.KeyError'> during field Shear detection.
+yt: DEBUG: Raises <type 'exceptions.KeyError'> during field gradDensityZ detection.
+yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityRPGrowthX detection.
+yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityRPGrowthY detection.
+yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityRPGrowthZ detection.
+yt: DEBUG: Raises <type 'exceptions.KeyError'> during field gradDensityX detection.
+yt: DEBUG: Raises <type 'exceptions.KeyError'> during field gradDensityY detection.
+yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityGrowthMagnitudeABS detection.
+yt: DEBUG: Raises <type 'exceptions.KeyError'> during field ShearCriterion detection.
+yt: DEBUG: Raises <type 'exceptions.KeyError'> during field ShearMach detection.
+yt: DEBUG: Raises <type 'exceptions.KeyError'> during field gradPressureZ detection.
+yt: DEBUG: Raises <type 'exceptions.KeyError'> during field gradPressureX detection.
+yt: DEBUG: Raises <type 'exceptions.KeyError'> during field gradPressureY detection.
+yt: DEBUG: Raises <type 'exceptions.KeyError'> during field BaroclinicVorticityY detection.
+yt: DEBUG: Raises <type 'exceptions.KeyError'> during field BaroclinicVorticityX detection.
+yt: DEBUG: Raises <type 'exceptions.KeyError'> during field BaroclinicVorticityZ detection.
+yt: DEBUG: Raises <type 'exceptions.KeyError'> during field gradPressureMagnitude detection.
+yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityStretchingMagnitude detection.
+yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityRPGrowthTimescale detection.
+yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityY detection.
+yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityX detection.
+yt: DEBUG: Setting up particle fields
+yt: DEBUG: Don't know what to do with io
+yt: DEBUG: Checking derived fields again
+yt: DEBUG: Creating Particle Union 'all'
+yt: DEBUG: Don't know what to do with all
+yt: DEBUG: Checking ['Dinosaurs', 'Density', 'Temperature', 'Tribbles']
+yt: DEBUG: Adding known field Dinosaurs to list of fields
+yt: DEBUG: Adding known field Density to list of fields
+yt: DEBUG: Adding known field Temperature to list of fields
+yt: DEBUG: Adding known field Tribbles to list of fields
+yt: DEBUG: Appending object to UniformGridData (type: <class 'yt.geometry.geometry_handler.YTRegion'>)
+yt: DEBUG: Reading 262144 cells of ['Density'] fields in 8 blocks
+yt: DEBUG: Reading 262144 cells of ['Temperature'] fields in 8 blocks
+yt: DEBUG: Reading 262144 cells of ['Dinosaurs'] fields in 8 blocks
+yt: DEBUG: Reading 262144 cells of ['Density'] fields in 8 blocks
+yt: DEBUG: Reading 262144 cells of ['Temperature'] fields in 8 blocks
+yt: DEBUG: Reading 262144 cells of ['Dinosaurs'] fields in 8 blocks
+--------------------- >> end captured logging << ---------------------]]></error></testcase><testcase classname="yt.data_objects.tests.test_projection" name="test_projection(4096.0, 4096)" time="0.000"></testcase><testcase classname="yt.data_objects.tests.test_projection" name="test_projection(1.0, 1.0)" time="0.000"></testcase><testcase classname="yt.data_objects.tests.test_projection" name="test_projection(1.0, 1.0)" time="0.000"></testcase><testcase classname="yt.data_objects.tests.test_projection.test_projection(array([ 0.0078125, 0.0234375, 0.0390625, 0.0546875, 0.0703125,
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32, 64, 128, 256, 512,
1024, 2048, 4096, 8192, 16384,
32768, 65536, 131072, 262144, 524288,
1048576, 2097152, 4194304, 8388608, 16777216,
33554432, 67108864, 134217728, 268435456, 536870912,
1073741824], dtype=uint64), array([ 1, 2, 4, 8, 16,
32, 64, 128, 256, 512,
1024, 2048, 4096, 8192, 16384,
32768, 65536, 131072, 262144, 524288,
1048576, 2097152, 4194304, 8388608, 16777216,
33554432, 67108864, 134217728, 268435456, 536870912,
1073741824], dtype=uint64))" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(array([ 1, 2, 4, 8, 16,
32, 64, 128, 256, 512,
1024, 2048, 4096, 8192, 16384,
32768, 65536, 131072, 262144, 524288,
1048576, 2097152, 4194304, 8388608, 16777216,
33554432, 67108864, 134217728, 268435456, 536870912,
1073741824], dtype=uint64), array([ 1, 2, 4, 8, 16,
32, 64, 128, 256, 512,
1024, 2048, 4096, 8192, 16384,
32768, 65536, 131072, 262144, 524288,
1048576, 2097152, 4194304, 8388608, 16777216,
33554432, 67108864, 134217728, 268435456, 536870912,
1073741824], dtype=uint64))" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(0L, 0)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1L, 1)" time="0.001"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(2L, 2)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(3L, 3)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase 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274877906944, 549755813888, 1099511627776,
2199023255552, 4398046511104, 8796093022208,
17592186044416, 35184372088832, 70368744177664,
140737488355328, 281474976710656, 562949953421312,
1125899906842624, 2251799813685248, 4503599627370496,
9007199254740992, 18014398509481984, 36028797018963968,
72057594037927936, 144115188075855872, 288230376151711744,
576460752303423488, 1152921504606846976, 2305843009213693952,
4611686018427387904, 9223372036854775808], dtype=uint64), array([ 0, 1, 2,
4, 8, 16,
32, 64, 128,
256, 512, 1024,
2048, 4096, 8192,
16384, 32768, 65536,
131072, 262144, 524288,
1048576, 2097152, 4194304,
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67108864, 134217728, 268435456,
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4294967296, 8589934592, 17179869184,
34359738368, 68719476736, 137438953472,
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576460752303423488, 1152921504606846976, 2305843009213693952,
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2048, 4096, 8192,
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140737488355328, 281474976710656, 562949953421312,
1125899906842624, 2251799813685248, 4503599627370496,
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576460752303423488, 1152921504606846976, 2305843009213693952,
4611686018427387904, 9223372036854775808], dtype=uint64), array([ 0, 1, 2,
4, 8, 16,
32, 64, 128,
256, 512, 1024,
2048, 4096, 8192,
16384, 32768, 65536,
131072, 262144, 524288,
1048576, 2097152, 4194304,
8388608, 16777216, 33554432,
67108864, 134217728, 268435456,
536870912, 1073741824, 2147483648,
4294967296, 8589934592, 17179869184,
34359738368, 68719476736, 137438953472,
274877906944, 549755813888, 1099511627776,
2199023255552, 4398046511104, 8796093022208,
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140737488355328, 281474976710656, 562949953421312,
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72057594037927936, 144115188075855872, 288230376151711744,
576460752303423488, 1152921504606846976, 2305843009213693952,
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32, 64, 128,
256, 512, 1024,
2048, 4096, 8192,
16384, 32768, 65536,
131072, 262144, 524288,
1048576, 2097152, 4194304,
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67108864, 134217728, 268435456,
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4294967296, 8589934592, 17179869184,
34359738368, 68719476736, 137438953472,
274877906944, 549755813888, 1099511627776,
2199023255552, 4398046511104, 8796093022208,
17592186044416, 35184372088832, 70368744177664,
140737488355328, 281474976710656, 562949953421312,
1125899906842624, 2251799813685248, 4503599627370496,
9007199254740992, 18014398509481984, 36028797018963968,
72057594037927936, 144115188075855872, 288230376151711744,
576460752303423488, 1152921504606846976, 2305843009213693952,
4611686018427387904], dtype=uint64), array([ 0, 1, 2,
4, 8, 16,
32, 64, 128,
256, 512, 1024,
2048, 4096, 8192,
16384, 32768, 65536,
131072, 262144, 524288,
1048576, 2097152, 4194304,
8388608, 16777216, 33554432,
67108864, 134217728, 268435456,
536870912, 1073741824, 2147483648,
4294967296, 8589934592, 17179869184,
34359738368, 68719476736, 137438953472,
274877906944, 549755813888, 1099511627776,
2199023255552, 4398046511104, 8796093022208,
17592186044416, 35184372088832, 70368744177664,
140737488355328, 281474976710656, 562949953421312,
1125899906842624, 2251799813685248, 4503599627370496,
9007199254740992, 18014398509481984, 36028797018963968,
72057594037927936, 144115188075855872, 288230376151711744,
576460752303423488, 1152921504606846976, 2305843009213693952,
4611686018427387904], dtype=uint64))" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(array([ 0, 1, 2,
4, 8, 16,
32, 64, 128,
256, 512, 1024,
2048, 4096, 8192,
16384, 32768, 65536,
131072, 262144, 524288,
1048576, 2097152, 4194304,
8388608, 16777216, 33554432,
67108864, 134217728, 268435456,
536870912, 1073741824, 2147483648,
4294967296, 8589934592, 17179869184,
34359738368, 68719476736, 137438953472,
274877906944, 549755813888, 1099511627776,
2199023255552, 4398046511104, 8796093022208,
17592186044416, 35184372088832, 70368744177664,
140737488355328, 281474976710656, 562949953421312,
1125899906842624, 2251799813685248, 4503599627370496,
9007199254740992, 18014398509481984, 36028797018963968,
72057594037927936, 144115188075855872, 288230376151711744,
576460752303423488, 1152921504606846976, 2305843009213693952,
4611686018427387904], dtype=uint64), array([ 0, 1, 2,
4, 8, 16,
32, 64, 128,
256, 512, 1024,
2048, 4096, 8192,
16384, 32768, 65536,
131072, 262144, 524288,
1048576, 2097152, 4194304,
8388608, 16777216, 33554432,
67108864, 134217728, 268435456,
536870912, 1073741824, 2147483648,
4294967296, 8589934592, 17179869184,
34359738368, 68719476736, 137438953472,
274877906944, 549755813888, 1099511627776,
2199023255552, 4398046511104, 8796093022208,
17592186044416, 35184372088832, 70368744177664,
140737488355328, 281474976710656, 562949953421312,
1125899906842624, 2251799813685248, 4503599627370496,
9007199254740992, 18014398509481984, 36028797018963968,
72057594037927936, 144115188075855872, 288230376151711744,
576460752303423488, 1152921504606846976, 2305843009213693952,
4611686018427387904], dtype=uint64))" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(0L, 0)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1L, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(2L, 2)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(3L, 3)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(4L, 4)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(5L, 5)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(6L, 6)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(7L, 7)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(8L, 8)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(9L, 9)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(10L, 10)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(11L, 11)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(12L, 12)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(13L, 13)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(14L, 14)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(15L, 15)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(16L, 16)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(17L, 17)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(18L, 18)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(19L, 19)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(20L, 20)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(21L, 21)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(22L, 22)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(23L, 23)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(24L, 24)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(25L, 25)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(26L, 26)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(27L, 27)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(28L, 28)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(29L, 29)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(30L, 30)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(31L, 31)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(32L, 32)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(33L, 33)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(34L, 34)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(35L, 35)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(36L, 36)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(37L, 37)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(38L, 38)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(39L, 39)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(40L, 40)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(41L, 41)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(42L, 42)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(43L, 43)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(44L, 44)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(45L, 45)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(46L, 46)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(47L, 47)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(48L, 48)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(49L, 49)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(50L, 50)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(51L, 51)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(52L, 52)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(53L, 53)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(54L, 54)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(55L, 55)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(56L, 56)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(57L, 57)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(58L, 58)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(59L, 59)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(60L, 60)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" 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time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(66L, 66)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(67L, 67)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(68L, 68)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(69L, 69)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(70L, 70)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(71L, 71)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(72L, 72)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(73L, 73)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(74L, 74)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(75L, 75)" 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0.95032889, 0.95498737, 0.95498737, 0.96663355, 0.96663355,
0.97827974, 0.97827974, 0.97827974, 0.98992593, 0.98992593,
0.99574902, 0.99574902, 0.99924288, 1.00157211, 1.00157211,
1.00157211, 1.0132183 , 1.0132183 , 1.02486449, 1.02486449,
1.02486449, 1.02719373, 1.03068758, 1.03068758, 1.03651068,
1.03651068, 1.03651068, 1.04116915, 1.04815686, 1.04815686,
1.05514458, 1.05980305, 1.05980305, 1.06562615, 1.06912 ,
1.07144924, 1.07144924, 1.08309543, 1.08309543, 1.08746275,
1.09183007, 1.09474161, 1.09474161, 1.09619739, 1.09619739,
1.09707085, 1.09765316, 1.09910893, 1.10056471, 1.10056471,
1.10202048, 1.10202048, 1.10274837, 1.10274837, 1.1031851 ,
1.10347625, 1.10493203, 1.10493203, 1.1063878 , 1.1063878 ,
1.10667896, 1.10711569, 1.10784357, 1.10784357, 1.10842588,
1.10929935, 1.10929935, 1.11017281, 1.11075512, 1.11148301,
1.11191974, 1.1122109 , 1.11366667, 1.11366667, 1.11421258,
1.1147585 , 1.11512244, 1.11512244, 1.11530441, 1.11530441,
1.1154136 , 1.11548639, 1.11548639, 1.11566836, 1.11566836,
1.11585033, 1.11585033, 1.1160323 , 1.1160323 , 1.11612329,
1.11617788, 1.11621427, 1.11621427, 1.11639624, 1.11657822,
1.11661461, 1.1166692 , 1.1166692 , 1.11676019, 1.11683298,
1.11694216, 1.11694216, 1.11705134, 1.11712413, 1.11712413,
1.11721512, 1.11726971, 1.1173061 , 1.1173061 , 1.11748807,
1.11748807, 1.11755631, 1.11762455, 1.11767005, 1.11767005,
1.11769279, 1.11770644, 1.11771554, 1.11773828, 1.11773828,
1.11776103, 1.11776103, 1.11778378, 1.11778378, 1.11779515,
1.11779515, 1.11780197, 1.11780652, 1.11780652, 1.11782927,
1.11782927, 1.11785202, 1.11785202, 1.11785657, 1.11786339,
1.11786339, 1.11787476, 1.11787476, 1.11787476, 1.11788386,
1.11789751, 1.11789751, 1.11791116, 1.11792026, 1.11792026,
1.11793163, 1.11793845, 1.117943 , 1.11796575, 1.11796575,
1.11797428, 1.11798281, 1.1179885 , 1.11799134, 1.11799305,
1.11799418, 1.11799418, 1.11799703, 1.11799987, 1.11799987,
1.11800271, 1.11800271, 1.11800413, 1.11800413, 1.11800499,
1.11800556, 1.11800556, 1.11800556, 1.1180084 , 1.1180084 ,
1.11801124, 1.11801124, 1.11801181, 1.11801266, 1.11801409,
1.11801409, 1.11801522, 1.11801693, 1.11801693, 1.11801863,
1.11801977, 1.11802119, 1.11802205, 1.11802262, 1.11802546,
1.11802546, 1.11802546, 1.11802653, 1.11802759, 1.1180283 ,
1.1180283 , 1.11802866, 1.11802866, 1.11802887, 1.11802901,
1.11802901, 1.11802937, 1.11802972, 1.11802972, 1.11803008,
1.11803008, 1.11803026, 1.11803036, 1.11803043, 1.11803043,
1.11803079, 1.11803079, 1.11803115, 1.11803115, 1.11803122,
1.11803132, 1.1180315 , 1.1180315 , 1.11803164, 1.11803186,
1.11803186, 1.11803207, 1.11803221, 1.11803221, 1.11803239,
1.1180325 , 1.11803257, 1.11803257, 1.11803292, 1.11803292,
1.11803328, 1.11803328, 1.11803335, 1.11803346, 1.11803346,
1.11803363, 1.11803378, 1.11803399, 1.11803399, 1.11803399,
1.1180342 , 1.11803434, 1.11803434, 1.11803452, 1.11803463,
1.1180347 , 1.11803505, 1.11803541, 1.11803548, 1.11803559,
1.11803569, 1.11803577, 1.11803591, 1.11803612, 1.11803633,
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time="0.907"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_offaxis_projection_plot_1_testpng" time="0.847"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_offaxis_projection_plot_2_testeps" time="0.320"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_offaxis_projection_plot_3_testps" time="0.312"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_offaxis_projection_plot_4_testpdf" time="0.589"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_offaxis_slice_plot_0" time="0.570"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_offaxis_slice_plot_1_testpng" time="0.510"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_offaxis_slice_plot_2_testeps" time="0.326"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_offaxis_slice_plot_3_testps" time="0.325"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_offaxis_slice_plot_4_testpdf" time="0.551"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_projection_plot_0" time="0.662"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_projection_plot_1" time="0.395"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_projection_plot_10" time="0.447"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_projection_plot_11" time="0.397"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_projection_plot_12" time="0.309"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_projection_plot_13" time="0.309"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_projection_plot_14" time="0.485"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_projection_plot_2" time="0.306"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_projection_plot_3" time="0.307"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_projection_plot_4" time="0.483"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_projection_plot_5" time="0.445"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_projection_plot_6" time="0.394"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_projection_plot_7" time="0.307"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_projection_plot_8" time="0.308"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_projection_plot_9" time="0.482"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_projection_plot_ds_0" time="0.437"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_projection_plot_ds_1" time="0.437"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_projection_plot_ds_2" time="0.437"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_slice_plot_0" time="0.463"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_slice_plot_1" time="0.413"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_slice_plot_10" time="0.463"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_slice_plot_11" time="0.412"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_slice_plot_12" time="0.321"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_slice_plot_13" time="0.321"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_slice_plot_14" time="0.504"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_slice_plot_2" time="0.322"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_slice_plot_3" time="0.322"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_slice_plot_4" time="0.504"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_slice_plot_5" time="0.463"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_slice_plot_6" time="0.412"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_slice_plot_7" time="0.320"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_slice_plot_8" time="0.321"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_slice_plot_9" time="0.504"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestSetWidth" name="test_pair_of_tuples_neq" time="0.549"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestSetWidth" name="test_set_width_nonequal" time="0.808"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestSetWidth" name="test_set_width_one" time="0.426"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestSetWidth" name="test_tuple_eq" time="0.557"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestSetWidth" name="test_tuple_of_tuples_neq" time="0.554"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestSetWidth" name="test_tuple_of_tuples_neq2" time="0.559"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestSetWidth" name="test_twoargs_eq" time="0.562"></testcase></testsuite>
\ No newline at end of file
diff -r 09f43fdc818380523eb7445e8a3353f1e40ca1f6 -r 7acb7368e0b27ee1bae57c592dd0d6ce318aacb6 yt/analysis_modules/halo_profiler/multi_halo_profiler.py
--- a/yt/analysis_modules/halo_profiler/multi_halo_profiler.py
+++ b/yt/analysis_modules/halo_profiler/multi_halo_profiler.py
@@ -25,7 +25,7 @@
from yt.convenience import \
load
from yt.data_objects.profiles import \
- BinnedProfile1D, EmptyProfileData
+ BinnedProfile1D, YTEmptyProfileData
from yt.analysis_modules.halo_finding.api import *
from .halo_filters import \
VirialFilter
@@ -588,7 +588,7 @@
profile = BinnedProfile1D(sphere, self.n_profile_bins, "RadiusMpc",
r_min, halo['r_max'],
log_space=True, end_collect=True)
- except EmptyProfileData:
+ except YTEmptyProfileData:
mylog.error("Caught EmptyProfileData exception, returning None for this halo.")
return None
# Figure out which fields to add simultaneously
diff -r 09f43fdc818380523eb7445e8a3353f1e40ca1f6 -r 7acb7368e0b27ee1bae57c592dd0d6ce318aacb6 yt/analysis_modules/spectral_integrator/spectral_frequency_integrator.py
--- a/yt/analysis_modules/spectral_integrator/spectral_frequency_integrator.py
+++ b/yt/analysis_modules/spectral_integrator/spectral_frequency_integrator.py
@@ -19,7 +19,10 @@
import numpy as np
import os
-from yt.funcs import *
+from yt.funcs import \
+ download_file, \
+ mylog, \
+ only_on_root
from yt.data_objects.field_info_container import add_field
from yt.utilities.exceptions import YTException
@@ -31,6 +34,23 @@
xray_data_version = 1
+def _get_data_file():
+ data_file = "xray_emissivity.h5"
+ data_url = "http://yt-project.org/data"
+ if "YT_DEST" in os.environ and \
+ os.path.isdir(os.path.join(os.environ["YT_DEST"], "data")):
+ data_dir = os.path.join(os.environ["YT_DEST"], "data")
+ else:
+ data_dir = "."
+ data_path = os.path.join(data_dir, data_file)
+ if not os.path.exists(data_path):
+ mylog.info("Attempting to download supplementary data from %s to %s." %
+ (data_url, data_dir))
+ fn = download_file(os.path.join(data_url, data_file), data_path)
+ if fn != data_path:
+ raise RuntimeError, "Failed to download supplementary data."
+ return data_path
+
class EnergyBoundsException(YTException):
def __init__(self, lower, upper):
self.lower = lower
@@ -65,8 +85,7 @@
default_filename = False
if filename is None:
- filename = os.path.join(os.environ["YT_DEST"],
- "data", "xray_emissivity.h5")
+ filename = _get_data_file()
default_filename = True
if not os.path.exists(filename):
diff -r 09f43fdc818380523eb7445e8a3353f1e40ca1f6 -r 7acb7368e0b27ee1bae57c592dd0d6ce318aacb6 yt/convenience.py
--- a/yt/convenience.py
+++ b/yt/convenience.py
@@ -130,6 +130,20 @@
if simulation_type not in simulation_time_series_registry:
raise YTSimulationNotIdentified(simulation_type)
+ if os.path.exists(parameter_filename):
+ valid_file = True
+ elif os.path.exists(os.path.join(ytcfg.get("yt", "test_data_dir"),
+ parameter_filename)):
+ parameter_filename = os.path.join(ytcfg.get("yt", "test_data_dir"),
+ parameter_filename)
+ valid_file = True
+ else:
+ valid_file = False
+
+ if not valid_file:
+ raise YTOutputNotIdentified((parameter_filename, simulation_type),
+ dict(find_outputs=find_outputs))
+
return simulation_time_series_registry[simulation_type](parameter_filename,
find_outputs=find_outputs)
diff -r 09f43fdc818380523eb7445e8a3353f1e40ca1f6 -r 7acb7368e0b27ee1bae57c592dd0d6ce318aacb6 yt/data_objects/api.py
--- a/yt/data_objects/api.py
+++ b/yt/data_objects/api.py
@@ -27,11 +27,12 @@
particle_handler_registry
from profiles import \
- EmptyProfileData, \
+ YTEmptyProfileData, \
BinnedProfile, \
BinnedProfile1D, \
BinnedProfile2D, \
- BinnedProfile3D
+ BinnedProfile3D, \
+ create_profile
from time_series import \
TimeSeriesData, \
diff -r 09f43fdc818380523eb7445e8a3353f1e40ca1f6 -r 7acb7368e0b27ee1bae57c592dd0d6ce318aacb6 yt/data_objects/field_info_container.py
--- a/yt/data_objects/field_info_container.py
+++ b/yt/data_objects/field_info_container.py
@@ -60,21 +60,21 @@
def _gradx(f, data):
grad = data[field][sl,1:-1,1:-1] - data[field][sr,1:-1,1:-1]
- grad /= 2.0*data["dx"].flat[0]
+ grad /= 2.0*data["dx"].flat[0]*data.pf.units["cm"]
g = np.zeros(data[field].shape, dtype='float64')
g[1:-1,1:-1,1:-1] = grad
return g
def _grady(f, data):
grad = data[field][1:-1,sl,1:-1] - data[field][1:-1,sr,1:-1]
- grad /= 2.0*data["dy"].flat[0]
+ grad /= 2.0*data["dy"].flat[0]*data.pf.units["cm"]
g = np.zeros(data[field].shape, dtype='float64')
g[1:-1,1:-1,1:-1] = grad
return g
def _gradz(f, data):
grad = data[field][1:-1,1:-1,sl] - data[field][1:-1,1:-1,sr]
- grad /= 2.0*data["dz"].flat[0]
+ grad /= 2.0*data["dz"].flat[0]*data.pf.units["cm"]
g = np.zeros(data[field].shape, dtype='float64')
g[1:-1,1:-1,1:-1] = grad
return g
diff -r 09f43fdc818380523eb7445e8a3353f1e40ca1f6 -r 7acb7368e0b27ee1bae57c592dd0d6ce318aacb6 yt/data_objects/profiles.py
--- a/yt/data_objects/profiles.py
+++ b/yt/data_objects/profiles.py
@@ -20,16 +20,10 @@
from yt.data_objects.data_containers import YTFieldData
from yt.utilities.lib import bin_profile1d, bin_profile2d, bin_profile3d
+from yt.utilities.lib import new_bin_profile1d, new_bin_profile2d, \
+ new_bin_profile3d
from yt.utilities.parallel_tools.parallel_analysis_interface import \
- ParallelAnalysisInterface
-
-_field_mapping = {
- "total_mass": ("CellMassMsun", "ParticleMassMsun"),
- "hybrid_radius": ("RadiusCode", "ParticleRadiusCode"),
- }
-
-class EmptyProfileData(Exception):
- pass
+ ParallelAnalysisInterface, parallel_objects
def preserve_source_parameters(func):
def save_state(*args, **kwargs):
@@ -55,7 +49,6 @@
self._data_source = data_source
self.pf = data_source.pf
self.field_data = YTFieldData()
- self._pdata = {}
@property
def hierarchy(self):
@@ -131,14 +124,11 @@
def __setitem__(self, key, value):
self.field_data[key] = value
- def _get_field(self, source, this_field, check_cut):
+ def _get_field(self, source, field, check_cut):
# This is where we will iterate to get all contributions to a field
# which is how we will implement hybrid particle/cell fields
# but... we default to just the field.
- data = []
- for field in _field_mapping.get(this_field, (this_field,)):
- data.append(source[field].astype('float64'))
- return np.concatenate(data, axis=0)
+ return source[field].astype('float64')
def _fix_pickle(self):
if isinstance(self._data_source, tuple):
@@ -225,7 +215,7 @@
def _get_bins(self, source, check_cut=False):
source_data = self._get_field(source, self.bin_field, check_cut)
if source_data.size == 0: # Nothing for us here.
- raise EmptyProfileData()
+ raise YTEmptyProfileData()
# Truncate at boundaries.
if self.end_collect:
mi = np.ones_like(source_data).astype('bool')
@@ -234,7 +224,7 @@
& (source_data < self._bins.max()))
sd = source_data[mi]
if sd.size == 0:
- raise EmptyProfileData()
+ raise YTEmptyProfileData()
# Stick the bins into our fixed bins, set at initialization
bin_indices = np.digitize(sd, self._bins)
if self.end_collect: #limit the range of values to 0 and n_bins-1
@@ -400,7 +390,7 @@
source_data_x = self._get_field(source, self.x_bin_field, check_cut)
source_data_y = self._get_field(source, self.y_bin_field, check_cut)
if source_data_x.size == 0:
- raise EmptyProfileData()
+ raise YTEmptyProfileData()
if self.end_collect:
mi = np.arange(source_data_x.size)
@@ -412,7 +402,7 @@
sd_x = source_data_x[mi]
sd_y = source_data_y[mi]
if sd_x.size == 0 or sd_y.size == 0:
- raise EmptyProfileData()
+ raise YTEmptyProfileData()
bin_indices_x = np.digitize(sd_x, self._x_bins) - 1
bin_indices_y = np.digitize(sd_y, self._y_bins) - 1
@@ -518,48 +508,6 @@
return np.log10(upper), np.log10(lower)
return upper, lower
-class BinnedProfile2DInlineCut(BinnedProfile2D):
- def __init__(self, data_source,
- x_n_bins, x_bin_field, x_lower_bound, x_upper_bound, x_log,
- y_n_bins, y_bin_field, y_lower_bound, y_upper_bound, y_log,
- end_collect=False):
- self.indices = data_source["Ones"].astype("bool")
- BinnedProfile2D.__init__(self, data_source,
- x_n_bins, x_bin_field, x_lower_bound, x_upper_bound, x_log,
- y_n_bins, y_bin_field, y_lower_bound, y_upper_bound, y_log,
- end_collect)
-
- @preserve_source_parameters
- def _bin_field(self, source, field, weight, accumulation,
- args, check_cut=False):
- source_data = self._get_field(source, field, check_cut)
- if weight: weight_data = self._get_field(source, weight, check_cut)
- else: weight_data = np.ones(source_data.shape, dtype='float64')
- self.total_stuff = source_data.sum()
- binned_field = self._get_empty_field()
- weight_field = self._get_empty_field()
- m_field = self._get_empty_field()
- q_field = self._get_empty_field()
- used_field = self._get_empty_field()
- mi = args[0]
- bin_indices_x = args[1][self.indices].ravel().astype('int64')
- bin_indices_y = args[2][self.indices].ravel().astype('int64')
- source_data = source_data[mi][self.indices]
- weight_data = weight_data[mi][self.indices]
- nx = bin_indices_x.size
- #mylog.debug("Binning %s / %s times", source_data.size, nx)
- bin_profile2d(bin_indices_x, bin_indices_y, weight_data, source_data,
- weight_field, binned_field, m_field, q_field, used_field)
- if accumulation: # Fix for laziness
- if not iterable(accumulation):
- raise SyntaxError("Accumulation needs to have length 2")
- if accumulation[0]:
- binned_field = np.add.accumulate(binned_field, axis=0)
- if accumulation[1]:
- binned_field = np.add.accumulate(binned_field, axis=1)
- return binned_field, weight_field, used_field.astype('bool')
-
-
class BinnedProfile3D(BinnedProfile):
"""
A 'Profile' produces either a weighted (or unweighted) average
@@ -658,7 +606,7 @@
source_data_y = self._get_field(source, self.y_bin_field, check_cut)
source_data_z = self._get_field(source, self.z_bin_field, check_cut)
if source_data_x.size == 0:
- raise EmptyProfileData()
+ raise YTEmptyProfileData()
if self.end_collect:
mi = np.arange(source_data_x.size)
else:
@@ -672,7 +620,7 @@
sd_y = source_data_y[mi]
sd_z = source_data_z[mi]
if sd_x.size == 0 or sd_y.size == 0 or sd_z.size == 0:
- raise EmptyProfileData()
+ raise YTEmptyProfileData()
bin_indices_x = np.digitize(sd_x, self._x_bins) - 1
bin_indices_y = np.digitize(sd_y, self._y_bins) - 1
@@ -790,25 +738,280 @@
self._data_source.hierarchy.save_data(values, "/Profiles", name,
set_attr, force=force)
-class StoredBinnedProfile3D(BinnedProfile3D):
- def __init__(self, pf, name):
- """
- Given a *pf* parameterfile and the *name* of a stored profile, retrieve
- it into a read-only data structure.
- """
+class ProfileFieldAccumulator(object):
+ def __init__(self, n_fields, size):
+ shape = size + (n_fields,)
+ self.values = np.zeros(shape, dtype="float64")
+ self.mvalues = np.zeros(shape, dtype="float64")
+ self.qvalues = np.zeros(shape, dtype="float64")
+ self.used = np.zeros(size, dtype='bool')
+ self.weight_values = np.zeros(size, dtype="float64")
+
+class ProfileND(ParallelAnalysisInterface):
+ def __init__(self, data_source, weight_field = None):
+ self.data_source = data_source
+ self.pf = data_source.pf
self.field_data = YTFieldData()
- prof_arr = pf.h.get_data("/Profiles", name)
- if prof_arr is None: raise KeyError("No such array")
- for ax in 'xyz':
- for base in ['%s_bin_field', '_%s_log']:
- setattr(self, base % ax, prof_arr.getAttr(base % ax))
- for ax in 'xyz':
- fn = getattr(self, '%s_bin_field' % ax)
- self.field_data[fn] = prof_arr.getAttr('%s_bin_values' % ax)
- shape = prof_arr.getAttr('shape')
- for fn, fd in zip(prof_arr.getAttr('field_order'),
- prof_arr.read().transpose()):
- self.field_data[fn] = fd.reshape(shape)
+ self.weight_field = weight_field
- def add_fields(self, *args, **kwargs):
- raise RuntimeError("Sorry, you can't add to a stored profile.")
+ def add_fields(self, fields):
+ fields = ensure_list(fields)
+ temp_storage = ProfileFieldAccumulator(len(fields), self.size)
+ for g in parallel_objects(self.data_source._grids):
+ self._bin_grid(g, fields, temp_storage)
+ self._finalize_storage(fields, temp_storage)
+
+ def _finalize_storage(self, fields, temp_storage):
+ # We use our main comm here
+ # This also will fill _field_data
+ # FIXME: Add parallelism and combining std stuff
+ if self.weight_field is not None:
+ temp_storage.values /= temp_storage.weight_values[...,None]
+ blank = ~temp_storage.used
+ for i, field in enumerate(fields):
+ self.field_data[field] = temp_storage.values[...,i]
+ self.field_data[field][blank] = 0.0
+
+ def _bin_grid(self, grid, fields, storage):
+ raise NotImplementedError
+
+ def _filter(self, bin_fields, cut_points):
+ # cut_points is initially just the points inside our region
+ # we also want to apply a filtering based on min/max
+ filter = np.zeros(bin_fields[0].shape, dtype='bool')
+ filter[cut_points] = True
+ for (mi, ma), data in zip(self.bounds, bin_fields):
+ filter &= (data > mi)
+ filter &= (data < ma)
+ return filter, [data[filter] for data in bin_fields]
+
+ def _get_data(self, grid, fields):
+ # Save the values in the grid beforehand.
+ old_params = grid.field_parameters
+ old_keys = grid.field_data.keys()
+ grid.field_parameters = self.data_source.field_parameters
+ # Now we ask our source which values to include
+ pointI = self.data_source._get_point_indices(grid)
+ bin_fields = [grid[bf] for bf in self.bin_fields]
+ # We want to make sure that our fields are within the bounds of the
+ # binning
+ filter, bin_fields = self._filter(bin_fields, pointI)
+ if not np.any(filter): return None
+ arr = np.zeros((bin_fields[0].size, len(fields)), dtype="float64")
+ for i, field in enumerate(fields):
+ arr[:,i] = grid[field][filter]
+ if self.weight_field is not None:
+ weight_data = grid[self.weight_field]
+ else:
+ weight_data = np.ones(grid.ActiveDimensions, dtype="float64")
+ weight_data = weight_data[filter]
+ # So that we can pass these into
+ grid.field_parameters = old_params
+ grid.field_data = YTFieldData( [(k, grid.field_data[k]) for k in old_keys] )
+ return arr, weight_data, bin_fields
+
+ def __getitem__(self, key):
+ return self.field_data[key]
+
+ def __iter__(self):
+ return sorted(self.field_data.items())
+
+ def _get_bins(self, mi, ma, n, take_log):
+ if take_log:
+ return np.logspace(np.log10(mi), np.log10(ma), n+1)
+ else:
+ return np.linspace(mi, ma, n+1)
+
+class Profile1D(ProfileND):
+ def __init__(self, data_source, x_field, x_n, x_min, x_max, x_log,
+ weight_field = None):
+ super(Profile1D, self).__init__(data_source, weight_field)
+ self.x_field = x_field
+ self.x_log = x_log
+ self.x_bins = self._get_bins(x_min, x_max, x_n, x_log)
+
+ self.size = (self.x_bins.size - 1,)
+ self.bin_fields = (self.x_field,)
+ self.bounds = ((self.x_bins[0], self.x_bins[-1]),)
+ self.x = self.x_bins
+
+ def _bin_grid(self, grid, fields, storage):
+ gd = self._get_data(grid, fields)
+ if gd is None: return
+ fdata, wdata, (bf_x,) = gd
+ bin_ind = np.digitize(bf_x, self.x_bins) - 1
+ new_bin_profile1d(bin_ind, wdata, fdata,
+ storage.weight_values, storage.values,
+ storage.mvalues, storage.qvalues,
+ storage.used)
+ # We've binned it!
+
+class Profile2D(ProfileND):
+ def __init__(self, data_source,
+ x_field, x_n, x_min, x_max, x_log,
+ y_field, y_n, y_min, y_max, y_log,
+ weight_field = None):
+ super(Profile2D, self).__init__(data_source, weight_field)
+ self.x_field = x_field
+ self.x_log = x_log
+ self.x_bins = self._get_bins(x_min, x_max, x_n, x_log)
+ self.y_field = y_field
+ self.y_log = y_log
+ self.y_bins = self._get_bins(y_min, y_max, y_n, y_log)
+
+ self.size = (self.x_bins.size - 1, self.y_bins.size - 1)
+
+ self.bin_fields = (self.x_field, self.y_field)
+ self.bounds = ((self.x_bins[0], self.x_bins[-1]),
+ (self.y_bins[0], self.y_bins[-1]))
+ self.x = self.x_bins
+ self.y = self.y_bins
+
+ def _bin_grid(self, grid, fields, storage):
+ rv = self._get_data(grid, fields)
+ if rv is None: return
+ fdata, wdata, (bf_x, bf_y) = rv
+ bin_ind_x = np.digitize(bf_x, self.x_bins) - 1
+ bin_ind_y = np.digitize(bf_y, self.y_bins) - 1
+ new_bin_profile2d(bin_ind_x, bin_ind_y, wdata, fdata,
+ storage.weight_values, storage.values,
+ storage.mvalues, storage.qvalues,
+ storage.used)
+ # We've binned it!
+
+class Profile3D(ProfileND):
+ def __init__(self, data_source,
+ x_field, x_n, x_min, x_max, x_log,
+ y_field, y_n, y_min, y_max, y_log,
+ z_field, z_n, z_min, z_max, z_log,
+ weight_field = None):
+ super(Profile3D, self).__init__(data_source, weight_field)
+ # X
+ self.x_field = x_field
+ self.x_log = x_log
+ self.x_bins = self._get_bins(x_min, x_max, x_n, x_log)
+ # Y
+ self.y_field = y_field
+ self.y_log = y_log
+ self.y_bins = self._get_bins(y_min, y_max, y_n, y_log)
+ # Z
+ self.z_field = z_field
+ self.z_log = z_log
+ self.z_bins = self._get_bins(z_min, z_max, z_n, z_log)
+
+ self.size = (self.x_bins.size - 1,
+ self.y_bins.size - 1,
+ self.z_bins.size - 1)
+
+ self.bin_fields = (self.x_field, self.y_field, self.z_field)
+ self.bounds = ((self.x_bins[0], self.x_bins[-1]),
+ (self.y_bins[0], self.y_bins[-1]),
+ (self.z_bins[0], self.z_bins[-1]))
+
+ self.x = self.x_bins
+ self.y = self.y_bins
+ self.z = self.z_bins
+
+ def _bin_grid(self, grid, fields, storage):
+ rv = self._get_data(grid, fields)
+ if rv is None: return
+ fdata, wdata, (bf_x, bf_y, bf_z) = rv
+ bin_ind_x = np.digitize(bf_x, self.x_bins) - 1
+ bin_ind_y = np.digitize(bf_y, self.y_bins) - 1
+ bin_ind_z = np.digitize(bf_z, self.z_bins) - 1
+ new_bin_profile3d(bin_ind_x, bin_ind_y, bin_ind_z, wdata, fdata,
+ storage.weight_values, storage.values,
+ storage.mvalues, storage.qvalues,
+ storage.used)
+ # We've binned it!
+
+def create_profile(data_source, bin_fields, n = 64,
+ weight_field = "CellMass", fields = None,
+ accumulation = False, fractional = False):
+ r"""
+ Create a 1, 2, or 3D profile object.
+
+ The dimensionality of the profile object is chosen by the number of
+ fields given in the bin_fields argument.
+
+ Parameters
+ ----------
+ data_source : AMR3DData Object
+ The data object to be profiled.
+ bin_fields : list of strings
+ List of the binning fields for profiling.
+ n : int or list of ints
+ The number of bins in each dimension. If None, 64 bins for
+ each bin are used for each bin field.
+ Default: 64.
+ weight_field : str
+ The weight field for computing weighted average for the profile
+ values. If None, the profile values are sums of the data in
+ each bin.
+ fields : list of strings
+ The fields to be profiled.
+ accumulation : bool or list of bools
+ If True, the profile values for a bin n are the cumulative sum of
+ all the values from bin 0 to n. If -True, the sum is reversed so
+ that the value for bin n is the cumulative sum from bin N (total bins)
+ to n. If the profile is 2D or 3D, a list of values can be given to
+ control the summation in each dimension independently.
+ Default: False.
+ fractional : If True the profile values are divided by the sum of all
+ the profile data such that the profile represents a probability
+ distribution function.
+
+ Examples
+ --------
+
+ Create a 1d profile. Access bin field from profile.x and field
+ data from profile.field_data.
+
+ >>> pf = load("DD0046/DD0046")
+ >>> ad = pf.h.all_data()
+ >>> profile = create_profile(ad, ["Density"],
+ ... fields=["Temperature", "x-velocity"]))
+ >>> print profile.x
+ >>> print profile.field_data["Temperature"]
+
+ """
+ if len(bin_fields) == 1:
+ cls = Profile1D
+ elif len(bin_fields) == 2:
+ cls = Profile2D
+ elif len(bin_fields) == 3:
+ cls = Profile3D
+ else:
+ raise NotImplementedError
+ if not iterable(n):
+ n = [n] * len(bin_fields)
+ if not iterable(accumulation):
+ accumulation = [accumulation] * len(bin_fields)
+ logs = [data_source.pf.field_info[f].take_log for f in bin_fields]
+ ex = [data_source.quantities["Extrema"](f, non_zero=l)[0] \
+ for f, l in zip(bin_fields, logs)]
+ args = [data_source]
+ for f, n, (mi, ma), l in zip(bin_fields, n, ex, logs):
+ args += [f, n, mi, ma, l]
+ obj = cls(*args, weight_field = weight_field)
+ setattr(obj, "accumulation", accumulation)
+ setattr(obj, "fractional", fractional)
+ if fields is not None:
+ obj.add_fields(fields)
+ for field in fields:
+ if fractional:
+ obj.field_data[field] /= obj.field_data[field].sum()
+ for axis, acc in enumerate(accumulation):
+ if not acc: continue
+ temp = obj.field_data[field]
+ temp = np.rollaxis(temp, axis)
+ if acc < 0:
+ temp = temp[::-1]
+ temp = temp.cumsum(axis=0)
+ if acc < 0:
+ temp = temp[::-1]
+ temp = np.rollaxis(temp, axis)
+ obj.field_data[field] = temp
+
+ return obj
+
diff -r 09f43fdc818380523eb7445e8a3353f1e40ca1f6 -r 7acb7368e0b27ee1bae57c592dd0d6ce318aacb6 yt/data_objects/tests/test_profiles.py
--- a/yt/data_objects/tests/test_profiles.py
+++ b/yt/data_objects/tests/test_profiles.py
@@ -1,10 +1,11 @@
from yt.testing import *
from yt.data_objects.profiles import \
- BinnedProfile1D, BinnedProfile2D, BinnedProfile3D
+ BinnedProfile1D, BinnedProfile2D, BinnedProfile3D, \
+ Profile1D, Profile2D, Profile3D
_fields = ("Density", "Temperature", "Dinosaurs", "Tribbles")
-def test_profiles():
+def test_binned_profiles():
pf = fake_random_pf(64, nprocs = 8, fields = _fields)
nv = pf.domain_dimensions.prod()
dd = pf.h.all_data()
@@ -71,3 +72,83 @@
p3d.add_fields(["Ones"], weight="Temperature")
yield assert_equal, p3d["Ones"][:-1,:-1,:-1], np.ones((nb,nb,nb))
+def test_profiles():
+ pf = fake_random_pf(64, nprocs = 8, fields = _fields)
+ nv = pf.domain_dimensions.prod()
+ dd = pf.h.all_data()
+ (rmi, rma), (tmi, tma), (dmi, dma) = dd.quantities["Extrema"](
+ ["Density", "Temperature", "Dinosaurs"])
+ rt, tt, dt = dd.quantities["TotalQuantity"](
+ ["Density", "Temperature", "Dinosaurs"])
+ # First we look at the
+ e1, e2 = 0.9, 1.1
+ for nb in [8, 16, 32, 64]:
+ # We log all the fields or don't log 'em all. No need to do them
+ # individually.
+ for lf in [True, False]:
+ p1d = Profile1D(dd,
+ "Density", nb, rmi*e1, rma*e2, lf,
+ weight_field = None)
+ p1d.add_fields(["Ones", "Temperature"])
+ yield assert_equal, p1d["Ones"].sum(), nv
+ yield assert_rel_equal, tt, p1d["Temperature"].sum(), 7
+
+ p2d = Profile2D(dd,
+ "Density", nb, rmi*e1, rma*e2, lf,
+ "Temperature", nb, tmi*e1, tma*e2, lf,
+ weight_field = None)
+ p2d.add_fields(["Ones", "Temperature"])
+ yield assert_equal, p2d["Ones"].sum(), nv
+ yield assert_rel_equal, tt, p2d["Temperature"].sum(), 7
+
+ p3d = Profile3D(dd,
+ "Density", nb, rmi*e1, rma*e2, lf,
+ "Temperature", nb, tmi*e1, tma*e2, lf,
+ "Dinosaurs", nb, dmi*e1, dma*e2, lf,
+ weight_field = None)
+ p3d.add_fields(["Ones", "Temperature"])
+ yield assert_equal, p3d["Ones"].sum(), nv
+ yield assert_rel_equal, tt, p3d["Temperature"].sum(), 7
+
+ p1d = Profile1D(dd, "x", nb, 0.0, 1.0, False,
+ weight_field = None)
+ p1d.add_fields("Ones")
+ av = nv / nb
+ yield assert_equal, p1d["Ones"], np.ones(nb)*av
+
+ # We re-bin ones with a weight now
+ p1d = Profile1D(dd, "x", nb, 0.0, 1.0, False,
+ weight_field = "Temperature")
+ p1d.add_fields(["Ones"])
+ yield assert_equal, p1d["Ones"], np.ones(nb)
+
+ p2d = Profile2D(dd, "x", nb, 0.0, 1.0, False,
+ "y", nb, 0.0, 1.0, False,
+ weight_field = None)
+ p2d.add_fields("Ones")
+ av = nv / nb**2
+ yield assert_equal, p2d["Ones"], np.ones((nb, nb))*av
+
+ # We re-bin ones with a weight now
+ p2d = Profile2D(dd, "x", nb, 0.0, 1.0, False,
+ "y", nb, 0.0, 1.0, False,
+ weight_field = "Temperature")
+ p2d.add_fields(["Ones"])
+ yield assert_equal, p2d["Ones"], np.ones((nb, nb))
+
+ p3d = Profile3D(dd, "x", nb, 0.0, 1.0, False,
+ "y", nb, 0.0, 1.0, False,
+ "z", nb, 0.0, 1.0, False,
+ weight_field = None)
+ p3d.add_fields("Ones")
+ av = nv / nb**3
+ yield assert_equal, p3d["Ones"], np.ones((nb, nb, nb))*av
+
+ # We re-bin ones with a weight now
+ p3d = Profile3D(dd, "x", nb, 0.0, 1.0, False,
+ "y", nb, 0.0, 1.0, False,
+ "z", nb, 0.0, 1.0, False,
+ weight_field = "Temperature")
+ p3d.add_fields(["Ones"])
+ yield assert_equal, p3d["Ones"], np.ones((nb,nb,nb))
+
diff -r 09f43fdc818380523eb7445e8a3353f1e40ca1f6 -r 7acb7368e0b27ee1bae57c592dd0d6ce318aacb6 yt/frontends/athena/data_structures.py
--- a/yt/frontends/athena/data_structures.py
+++ b/yt/frontends/athena/data_structures.py
@@ -266,7 +266,7 @@
# know the extent of all the grids.
glis = np.round((glis - self.parameter_file.domain_left_edge)/gdds).astype('int')
new_dre = np.max(gres,axis=0)
- self.parameter_file.domain_right_edge = np.round(new_dre, decimals=6)
+ self.parameter_file.domain_right_edge = np.round(new_dre, decimals=12)
self.parameter_file.domain_width = \
(self.parameter_file.domain_right_edge -
self.parameter_file.domain_left_edge)
@@ -294,9 +294,9 @@
dxs.append(dx)
dx = np.array(dxs)
- self.grid_left_edge = np.round(self.parameter_file.domain_left_edge + dx*glis, decimals=6)
+ self.grid_left_edge = np.round(self.parameter_file.domain_left_edge + dx*glis, decimals=12)
self.grid_dimensions = gdims.astype("int32")
- self.grid_right_edge = np.round(self.grid_left_edge + dx*self.grid_dimensions, decimals=6)
+ self.grid_right_edge = np.round(self.grid_left_edge + dx*self.grid_dimensions, decimals=12)
if self.parameter_file.dimensionality <= 2:
self.grid_right_edge[:,2] = self.parameter_file.domain_right_edge[2]
if self.parameter_file.dimensionality == 1:
diff -r 09f43fdc818380523eb7445e8a3353f1e40ca1f6 -r 7acb7368e0b27ee1bae57c592dd0d6ce318aacb6 yt/frontends/athena/fields.py
--- a/yt/frontends/athena/fields.py
+++ b/yt/frontends/athena/fields.py
@@ -78,7 +78,7 @@
def _convertDensity(data) :
return data.convert("Density")
def _density(field, data) :
- return data["density"]
+ return data["density"].copy()
add_field("Density", function=_density, take_log=False,
units=r"\rm{g}/\rm{cm}^3", projected_units=r"\rm{g}/\rm{cm}^2",
convert_function=_convertDensity)
@@ -87,21 +87,21 @@
return data.convert("x-velocity")
def _xvelocity(field, data):
if "velocity_x" in data.pf.field_info:
- return data["velocity_x"]
+ return data["velocity_x"].copy()
else:
return data["momentum_x"]/data["density"]
add_field("x-velocity", function=_xvelocity, take_log=False,
units=r"\rm{cm}/\rm{s}", convert_function=_convertVelocity)
def _yvelocity(field, data):
if "velocity_y" in data.pf.field_info:
- return data["velocity_y"]
+ return data["velocity_y"].copy()
else:
return data["momentum_y"]/data["density"]
add_field("y-velocity", function=_yvelocity, take_log=False,
units=r"\rm{cm}/\rm{s}", convert_function=_convertVelocity)
def _zvelocity(field, data):
if "velocity_z" in data.pf.field_info:
- return data["velocity_z"]
+ return data["velocity_z"].copy()
else:
return data["momentum_z"]/data["density"]
add_field("z-velocity", function=_zvelocity, take_log=False,
@@ -128,7 +128,7 @@
return data.convert("Density")*data.convert("x-velocity")**2
def _pressure(field, data) :
if "pressure" in data.pf.field_info:
- return data["pressure"]
+ return data["pressure"].copy()
else:
eint = data["total_energy"] - 0.5*(data["momentum_x"]**2 +
data["momentum_y"]**2 +
@@ -154,17 +154,17 @@
def _convertBfield(data):
return np.sqrt(4*np.pi*data.convert("Density")*data.convert("x-velocity")**2)
def _Bx(field, data):
- return data['cell_centered_B_x']
+ return data['cell_centered_B_x'].copy()
add_field("Bx", function=_Bx, take_log=False,
units=r"\rm{Gauss}", display_name=r"B_x",
convert_function=_convertBfield)
def _By(field, data):
- return data['cell_centered_B_y']
+ return data['cell_centered_B_y'].copy()
add_field("By", function=_By, take_log=False,
units=r"\rm{Gauss}", display_name=r"B_y",
convert_function=_convertBfield)
def _Bz(field, data):
- return data['cell_centered_B_z']
+ return data['cell_centered_B_z'].copy()
add_field("Bz", function=_Bz, take_log=False,
units=r"\rm{Gauss}", display_name=r"B_z",
convert_function=_convertBfield)
diff -r 09f43fdc818380523eb7445e8a3353f1e40ca1f6 -r 7acb7368e0b27ee1bae57c592dd0d6ce318aacb6 yt/funcs.py
--- a/yt/funcs.py
+++ b/yt/funcs.py
@@ -538,6 +538,16 @@
contents = None
return contents
+def download_file(url, filename):
+ import urllib
+ class MyURLopener(urllib.FancyURLopener):
+ def http_error_default(self, url, fp, errcode, errmsg, headers):
+ raise RuntimeError, \
+ "Attempt to download file from %s failed with error %s: %s." % \
+ (url, errcode, errmsg)
+ fn, h = MyURLopener().retrieve(url, filename)
+ return fn
+
# This code snippet is modified from Georg Brandl
def bb_apicall(endpoint, data, use_pass = True):
import urllib, urllib2
diff -r 09f43fdc818380523eb7445e8a3353f1e40ca1f6 -r 7acb7368e0b27ee1bae57c592dd0d6ce318aacb6 yt/mods.py
--- a/yt/mods.py
+++ b/yt/mods.py
@@ -54,7 +54,7 @@
ValidateParameter, ValidateDataField, ValidateProperty, \
ValidateSpatial, ValidateGridType, \
TimeSeriesData, AnalysisTask, analysis_task, \
- ImageArray, particle_filter
+ ImageArray, particle_filter, create_profile
from yt.data_objects.derived_quantities import \
add_quantity, quantity_info
@@ -141,7 +141,7 @@
apply_colormap, scale_image, write_projection, \
SlicePlot, AxisAlignedSlicePlot, OffAxisSlicePlot, \
ProjectionPlot, OffAxisProjectionPlot, \
- show_colormaps
+ show_colormaps, ProfilePlot, PhasePlot
from yt.visualization.volume_rendering.api import \
ColorTransferFunction, PlanckTransferFunction, ProjectionTransferFunction, \
diff -r 09f43fdc818380523eb7445e8a3353f1e40ca1f6 -r 7acb7368e0b27ee1bae57c592dd0d6ce318aacb6 yt/utilities/exceptions.py
--- a/yt/utilities/exceptions.py
+++ b/yt/utilities/exceptions.py
@@ -13,6 +13,7 @@
# The full license is in the file COPYING.txt, distributed with this software.
#-----------------------------------------------------------------------------
+
# We don't need to import 'exceptions'
#import exceptions
import os.path
@@ -317,3 +318,19 @@
return "This FITS header is not recognizable in its current form.\n" + \
"If you would like to force loading, specify: \n" + \
"ignore_unit_names = True"
+
+class YTEmptyProfileData(Exception):
+ pass
+
+class YTDuplicateFieldInProfile(Exception):
+ def __init__(self, field, new_spec, old_spec):
+ self.field = field
+ self.new_spec = new_spec
+ self.old_spec = old_spec
+
+ def __str__(self):
+ r = """Field %s already exists with field spec:
+ %s
+ But being asked to add it with:
+ %s""" % (self.field, self.old_spec, self.new_spec)
+ return r
diff -r 09f43fdc818380523eb7445e8a3353f1e40ca1f6 -r 7acb7368e0b27ee1bae57c592dd0d6ce318aacb6 yt/utilities/lib/misc_utilities.pyx
--- a/yt/utilities/lib/misc_utilities.pyx
+++ b/yt/utilities/lib/misc_utilities.pyx
@@ -25,6 +25,108 @@
@cython.boundscheck(False)
@cython.wraparound(False)
@cython.cdivision(True)
+def new_bin_profile1d(np.ndarray[np.int64_t, ndim=1] bins_x,
+ np.ndarray[np.float64_t, ndim=1] wsource,
+ np.ndarray[np.float64_t, ndim=2] bsource,
+ np.ndarray[np.float64_t, ndim=1] wresult,
+ np.ndarray[np.float64_t, ndim=2] bresult,
+ np.ndarray[np.float64_t, ndim=2] mresult,
+ np.ndarray[np.float64_t, ndim=2] qresult,
+ np.ndarray[np.uint8_t, ndim=1, cast=True] used):
+ cdef int n, fi, bin
+ cdef np.float64_t wval, bval, oldwr
+ cdef int nb = bins_x.shape[0]
+ cdef int nf = bsource.shape[1]
+ for n in range(nb):
+ bin = bins_x[n]
+ wval = wsource[n]
+ oldwr = wresult[bin]
+ wresult[bin] += wval
+ for fi in range(nf):
+ bval = bsource[n,fi]
+ # qresult has to have the previous wresult
+ qresult[bin,fi] += (oldwr * wval * (bval - mresult[bin,fi])**2) / \
+ (oldwr + wval)
+ bresult[bin,fi] += wval*bval
+ # mresult needs the new wresult
+ mresult[bin,fi] += wval * (bval - mresult[bin,fi]) / wresult[bin]
+ used[bin] = 1
+ return
+
+ at cython.boundscheck(False)
+ at cython.wraparound(False)
+ at cython.cdivision(True)
+def new_bin_profile2d(np.ndarray[np.int64_t, ndim=1] bins_x,
+ np.ndarray[np.int64_t, ndim=1] bins_y,
+ np.ndarray[np.float64_t, ndim=1] wsource,
+ np.ndarray[np.float64_t, ndim=2] bsource,
+ np.ndarray[np.float64_t, ndim=2] wresult,
+ np.ndarray[np.float64_t, ndim=3] bresult,
+ np.ndarray[np.float64_t, ndim=3] mresult,
+ np.ndarray[np.float64_t, ndim=3] qresult,
+ np.ndarray[np.uint8_t, ndim=2, cast=True] used):
+ cdef int n, fi, bin_x, bin_y
+ cdef np.float64_t wval, bval, oldwr
+ cdef int nb = bins_x.shape[0]
+ cdef int nf = bsource.shape[1]
+ for n in range(nb):
+ bin_x = bins_x[n]
+ bin_y = bins_y[n]
+ wval = wsource[n]
+ oldwr = wresult[bin_x, bin_y]
+ wresult[bin_x,bin_y] += wval
+ for fi in range(nf):
+ bval = bsource[n,fi]
+ # qresult has to have the previous wresult
+ qresult[bin_x,bin_y,fi] += (oldwr * wval * (bval - mresult[bin_x,bin_y,fi])**2) / \
+ (oldwr + wval)
+ bresult[bin_x,bin_y,fi] += wval*bval
+ # mresult needs the new wresult
+ mresult[bin_x,bin_y,fi] += wval * (bval - mresult[bin_x,bin_y,fi]) / wresult[bin_x,bin_y]
+ used[bin_x,bin_y] = 1
+ return
+
+ at cython.boundscheck(False)
+ at cython.wraparound(False)
+ at cython.cdivision(True)
+def new_bin_profile3d(np.ndarray[np.int64_t, ndim=1] bins_x,
+ np.ndarray[np.int64_t, ndim=1] bins_y,
+ np.ndarray[np.int64_t, ndim=1] bins_z,
+ np.ndarray[np.float64_t, ndim=1] wsource,
+ np.ndarray[np.float64_t, ndim=2] bsource,
+ np.ndarray[np.float64_t, ndim=3] wresult,
+ np.ndarray[np.float64_t, ndim=4] bresult,
+ np.ndarray[np.float64_t, ndim=4] mresult,
+ np.ndarray[np.float64_t, ndim=4] qresult,
+ np.ndarray[np.uint8_t, ndim=3, cast=True] used):
+ cdef int n, fi, bin_x, bin_y, bin_z
+ cdef np.float64_t wval, bval, oldwr
+ cdef int nb = bins_x.shape[0]
+ cdef int nf = bsource.shape[1]
+ for n in range(nb):
+ bin_x = bins_x[n]
+ bin_y = bins_y[n]
+ bin_z = bins_z[n]
+ wval = wsource[n]
+ oldwr = wresult[bin_x, bin_y, bin_z]
+ wresult[bin_x,bin_y,bin_z] += wval
+ for fi in range(nf):
+ bval = bsource[n,fi]
+ # qresult has to have the previous wresult
+ qresult[bin_x,bin_y,bin_z,fi] += \
+ (oldwr * wval * (bval - mresult[bin_x,bin_y,bin_z,fi])**2) / \
+ (oldwr + wval)
+ bresult[bin_x,bin_y,bin_z,fi] += wval*bval
+ # mresult needs the new wresult
+ mresult[bin_x,bin_y,bin_z,fi] += wval * \
+ (bval - mresult[bin_x,bin_y,bin_z,fi]) / \
+ wresult[bin_x,bin_y,bin_z]
+ used[bin_x,bin_y,bin_z] = 1
+ return
+
+ at cython.boundscheck(False)
+ at cython.wraparound(False)
+ at cython.cdivision(True)
def bin_profile1d(np.ndarray[np.int64_t, ndim=1] bins_x,
np.ndarray[np.float64_t, ndim=1] wsource,
np.ndarray[np.float64_t, ndim=1] bsource,
diff -r 09f43fdc818380523eb7445e8a3353f1e40ca1f6 -r 7acb7368e0b27ee1bae57c592dd0d6ce318aacb6 yt/visualization/api.py
--- a/yt/visualization/api.py
+++ b/yt/visualization/api.py
@@ -53,6 +53,10 @@
ProjectionPlot, \
OffAxisProjectionPlot
+from profile_plotter import \
+ ProfilePlot, \
+ PhasePlot
+
from base_plot_types import \
get_multi_plot
diff -r 09f43fdc818380523eb7445e8a3353f1e40ca1f6 -r 7acb7368e0b27ee1bae57c592dd0d6ce318aacb6 yt/visualization/base_plot_types.py
--- a/yt/visualization/base_plot_types.py
+++ b/yt/visualization/base_plot_types.py
@@ -17,9 +17,28 @@
from ._mpl_imports import \
FigureCanvasAgg, FigureCanvasPdf, FigureCanvasPS
from yt.funcs import \
- get_image_suffix, mylog
+ get_image_suffix, mylog, x_dict, y_dict
-
+class CallbackWrapper(object):
+ def __init__(self, viewer, window_plot, frb, field):
+ self.frb = frb
+ self.data = frb.data_source
+ self._axes = window_plot.axes
+ self._figure = window_plot.figure
+ if len(self._axes.images) > 0:
+ self.image = self._axes.images[0]
+ if frb.axis < 3:
+ DD = frb.pf.domain_width
+ xax = x_dict[frb.axis]
+ yax = y_dict[frb.axis]
+ self._period = (DD[xax], DD[yax])
+ self.pf = frb.pf
+ self.xlim = viewer.xlim
+ self.ylim = viewer.ylim
+ if 'OffAxisSlice' in viewer._plot_type:
+ self._type_name = "CuttingPlane"
+ else:
+ self._type_name = viewer._plot_type
class PlotMPL(object):
"""A base class for all yt plots made using matplotlib.
@@ -36,6 +55,7 @@
self.axes = self.figure.add_axes(axrect)
else:
axes.cla()
+ axes.set_position(axrect)
self.axes = axes
self.canvas = FigureCanvasAgg(self.figure)
diff -r 09f43fdc818380523eb7445e8a3353f1e40ca1f6 -r 7acb7368e0b27ee1bae57c592dd0d6ce318aacb6 yt/visualization/eps_writer.py
--- a/yt/visualization/eps_writer.py
+++ b/yt/visualization/eps_writer.py
@@ -26,7 +26,9 @@
from .plot_types import \
VMPlot, \
ProfilePlot
+from .plot_collection import PlotCollection
from .plot_window import PlotWindow
+from .profile_plotter import PhasePlot
from .plot_modifications import get_smallest_appropriate_unit
class DualEPS(object):
@@ -57,6 +59,14 @@
#=============================================================================
+ def return_field(self, plot):
+ if isinstance(plot, PlotWindow) or isinstance(plot, PhasePlot):
+ return plot.plots.keys()[0]
+ else:
+ return None
+
+#=============================================================================
+
def axis_box(self, xrange=(0,1), yrange=(0,1), xlabel="", ylabel="",
xlog=False, ylog=False, xdata=None, ydata=None,
tickcolor=None, bare_axes=False,
@@ -275,18 +285,17 @@
>>> d.axis_box_yt(p)
>>> d.save_fig()
"""
- if isinstance(plot, PlotWindow):
+ if isinstance(plot, (PlotWindow, PhasePlot)):
plot.refresh()
- width = plot.width[0]
else:
plot._redraw_image()
- if isinstance(plot, VMPlot):
- width = plot.width
- if isinstance(plot, VMPlot) or isinstance(plot, PlotWindow):
+ if isinstance(plot, (VMPlot, PlotWindow)):
if isinstance(plot, PlotWindow):
data = plot._frb
+ width = plot.width[0]
else:
data = plot.data
+ width = plot.width
if units == None:
units = get_smallest_appropriate_unit(width, plot.pf)
_xrange = (0, width * plot.pf[units])
@@ -314,6 +323,26 @@
_ylabel = 'Image y (%s)' % (units)
if tickcolor == None:
_tickcolor = pyx.color.cmyk.white
+ elif isinstance(plot, PhasePlot):
+ k = plot.plots.keys()[0]
+ _xrange = plot[k].axes.get_xlim()
+ _yrange = plot[k].axes.get_ylim()
+ _xlog = plot.profile.x_log
+ _ylog = plot.profile.y_log
+ if bare_axes:
+ _xlabel = ""
+ _ylabel = ""
+ else:
+ if xlabel != None:
+ _xlabel = xlabel
+ else:
+ _xlabel = plot[k].axes.get_xlabel()
+ if ylabel != None:
+ _ylabel = ylabel
+ else:
+ _ylabel = plot[k].axes.get_ylabel()
+ if tickcolor == None:
+ _tickcolor = None
else:
_xrange = plot._axes.get_xlim()
_yrange = plot._axes.get_ylim()
@@ -412,7 +441,7 @@
plot.colorbar = None
plot._redraw_image()
_p1 = plot._figure
- elif isinstance(plot, PlotWindow):
+ elif isinstance(plot, (PlotWindow, PhasePlot)):
self.field = field
if self.field == None:
self.field = plot.plots.keys()[0]
@@ -424,18 +453,22 @@
plot.refresh()
_p1 = plot.plots[self.field].figure
# hack to account for non-square display ratios (not sure why)
- shift = 12.0 / 340
+ if isinstance(plot, PlotWindow):
+ shift = 12.0 / 340
elif isinstance(plot, ProfilePlot):
plot._redraw_image()
# Remove colorbar
_p1 = plot._figure
_p1.delaxes(_p1.axes[1])
+ else:
+ raise RuntimeError("Unknown plot type")
+
_p1.axes[0].set_axis_off() # remove axes
_p1.axes[0].set_position([-shift,0,1,1]) # rescale figure
_p1.set_facecolor('w') # set background color
figure_canvas = FigureCanvasAgg(_p1)
figure_canvas.draw()
- size = _p1.get_size_inches() * _p1.dpi
+ size = (_p1.get_size_inches() * _p1.dpi).astype('int')
image = pyx.bitmap.image(size[0], size[1], "RGB",
figure_canvas.tostring_rgb())
#figure_canvas.print_png('test.png')
@@ -567,7 +600,7 @@
#=============================================================================
- def colorbar_yt(self, plot, **kwargs):
+ def colorbar_yt(self, plot, field=None, **kwargs):
r"""Wrapper around DualEPS.colorbar to take information from a yt plot.
Accepts all parameters that DualEPS.colorbar takes.
@@ -587,7 +620,9 @@
>>> d.save_fig()
"""
_cmap = None
- if isinstance(plot, PlotWindow):
+ if field != None:
+ self.field = field
+ if isinstance(plot, (PlotWindow, PhasePlot)):
_cmap = plot._colormaps[self.field]
else:
if plot.cmap != None:
@@ -601,10 +636,13 @@
_zlabel = _zlabel.replace("_","\;")
_zlog = plot.log_field
_zrange = (plot.norm.vmin, plot.norm.vmax)
- elif isinstance(plot, PlotWindow):
+ elif isinstance(plot, (PlotWindow, PhasePlot)):
proj = plot._plot_type.endswith("Projection") and \
plot.data_source.weight_field == None
- _zlabel = plot.pf.field_info[self.field].get_label(proj)
+ if isinstance(plot, PlotWindow):
+ _zlabel = plot.pf.field_info[self.field].get_label(proj)
+ else:
+ _zlabel = plot.data_source.pf.field_info[self.field].get_label(proj)
_zlabel = _zlabel.replace("_","\;")
_zlog = plot.get_log(self.field)[self.field]
if plot.plots[self.field].zmin == None:
@@ -783,13 +821,13 @@
#=============================================================================
#=============================================================================
-def multiplot(ncol, nrow, yt_plots=None, images=None, xranges=None,
- yranges=None, xlabels=None, ylabels=None,
+def multiplot(ncol, nrow, yt_plots=None, fields=None, images=None,
+ xranges=None, yranges=None, xlabels=None, ylabels=None,
xdata=None, ydata=None, colorbars=None,
shrink_cb=0.95, figsize=(8,8), margins=(0,0), titles=None,
savefig=None, format="eps", yt_nocbar=False, bare_axes=False,
xaxis_flags=None, yaxis_flags=None,
- cb_flags=None):
+ cb_flags=None, cb_location=None, plot_collection=False):
r"""Convenience routine to create a multi-panel figure from yt plots or
JPEGs. The images are first placed from the origin, and then
bottom-to-top and left-to-right.
@@ -834,6 +872,11 @@
axes.
cb_flags : list of booleans
Flags for each plot to have a colorbar or not.
+ cb_location : list of strings
+ Strings to control the location of the colorbar (left, right,
+ top, bottom)
+ plot_collection : boolean
+ Set to true to yt_plots is a PlotCollection
Examples
--------
@@ -858,8 +901,9 @@
yt plots.
"""
# Error check
+ npanels = ncol*nrow
if images != None:
- if len(images) != ncol*nrow:
+ if len(images) != npanels:
raise RuntimeError("Number of images (%d) doesn't match nrow(%d)"\
" x ncol(%d)." % (len(images), nrow, ncol))
return
@@ -872,21 +916,23 @@
_yt = True
else:
_yt = False
+ if fields == None:
+ fields = [None] * npanels
# If no ranges or labels given and given only images, fill them in.
if not _yt:
if xranges is None:
xranges = []
- for i in range(nrow*ncol): xranges.append((0,1))
+ for i in range(npanels): xranges.append((0,1))
if yranges is None:
yranges = []
- for i in range(nrow*ncol): yranges.append((0,1))
+ for i in range(npanels): yranges.append((0,1))
if xlabels is None:
xlabels = []
- for i in range(nrow*ncol): xlabels.append("")
+ for i in range(npanels): xlabels.append("")
if ylabels is None:
ylabels = []
- for i in range(nrow*ncol): ylabels.append("")
+ for i in range(npanels): ylabels.append("")
d = DualEPS(figsize=figsize)
count = 0
@@ -930,7 +976,8 @@
ylabel = ylabels[j]
else:
ylabel = None
- d.insert_image_yt(yt_plots[index], pos=(xpos, ypos))
+ d.insert_image_yt(yt_plots[index], pos=(xpos, ypos),
+ field=fields[index])
d.axis_box_yt(yt_plots[index], pos=(xpos, ypos),
bare_axes=bare_axes, xaxis_side=xaxis,
yaxis_side=yaxis,
@@ -964,32 +1011,49 @@
if cb_flags != None:
if cb_flags[index] == False:
continue
- if ncol == 1:
- orientation = "right"
+ if cb_location == None:
+ if ncol == 1:
+ orientation = "right"
+ elif i == 0:
+ orientation = "left"
+ elif i+1 == ncol:
+ orientation = "right"
+ elif j == 0:
+ orientation = "bottom"
+ elif j+1 == nrow:
+ orientation = "top"
+ else:
+ orientation = None # Marker for interior plot
+ else:
+ if fields[index] not in cb_location.keys():
+ raise RuntimeError("%s not found in cb_location dict" %
+ fields[index])
+ return
+ orientation = cb_location[fields[index]]
+ if orientation == "right":
xpos = bbox[1]
ypos = ypos0
- elif j == 0:
- orientation = "bottom"
+ elif orientation == "left":
+ xpos = bbox[0]
+ ypos = ypos0
+ elif orientation == "bottom":
ypos = bbox[2]
xpos = xpos0
- elif i == 0:
- orientation = "left"
- xpos = bbox[0]
- ypos = ypos0
- elif i+1 == ncol:
- orientation = "right"
- xpos = bbox[1]
- ypos = ypos0
- elif j+1 == nrow:
- orientation = "top"
+ elif orientation == "top":
ypos = bbox[3]
xpos = xpos0
else:
+ mylog.warning("Unknown colorbar location %s. "
+ "No colorbar displayed." % orientation)
orientation = None # Marker for interior plot
if orientation != None:
if _yt:
+ # Set field if undefined
+ if fields[index] == None:
+ fields[index] = d.return_field(yt_plots[index])
d.colorbar_yt(yt_plots[index],
+ field=fields[index],
pos=[xpos,ypos],
shrink=shrink_cb,
orientation=orientation)
@@ -1009,8 +1073,8 @@
#=============================================================================
-def multiplot_yt(ncol, nrow, plot_col, **kwargs):
- r"""Wrapper for multiplot that takes a yt PlotCollection.
+def multiplot_yt(ncol, nrow, plots, fields=None, **kwargs):
+ r"""Wrapper for multiplot that takes a yt PlotWindow or PlotCollection.
Accepts all parameters used in multiplot.
@@ -1020,8 +1084,8 @@
Number of columns in the figure.
nrow : integer
Number of rows in the figure.
- plot_col : `PlotCollection`
- yt PlotCollection that has the plots to be used.
+ plots : `PlotCollection` or `PlotWindow`
+ yt PlotCollection or PlotWindow that has the plots to be used.
Examples
--------
@@ -1040,18 +1104,42 @@
>>> mp = multiplot_yt(2,2,pc,savefig="yt",shrink_cb=0.9, bare_axes=False,
>>> yt_nocbar=False, margins=(0.5,0.5))
"""
- if len(plot_col.plots) < nrow*ncol:
- raise RuntimeError("Number of plots in PlotCollection is less "\
- "than nrow(%d) x ncol(%d)." % \
- (len(plot_col.plots), nrow, ncol))
+ # Determine whether the plots are organized in a PlotCollection,
+ # PlotWindow, or list of PlotWindows
+ if isinstance(plots, PlotCollection):
+ if len(plots.plots) < nrow*ncol:
+ raise RuntimeError("Number of plots in PlotCollection is less "\
+ "than nrow(%d) x ncol(%d)." % \
+ (len(plots.plots), nrow, ncol))
+ return
+ figure = multiplot(ncol, nrow, yt_plots=plots.plots,
+ plot_collection=True, **kwargs)
+ elif isinstance(plots, PlotWindow):
+ if fields == None:
+ fields = plots.fields
+ if len(fields) < nrow*ncol:
+ raise RuntimeError("Number of plots is less "\
+ "than nrow(%d) x ncol(%d)." % \
+ (len(fields), nrow, ncol))
+ return
+ figure = multiplot(ncol, nrow, yt_plots=plots, fields=fields, **kwargs)
+ elif isinstance(plots, list) and isinstance(plots[0], PlotWindow):
+ if len(plots) < nrow*ncol:
+ raise RuntimeError("Number of plots is less "\
+ "than nrow(%d) x ncol(%d)." % \
+ (len(fields), nrow, ncol))
+ return
+ figure = multiplot(ncol, nrow, yt_plots=plots, fields=fields, **kwargs)
+ else:
+ raise RuntimeError("Unknown plot type in multiplot_yt")
return
- figure = multiplot(ncol, nrow, yt_plots=plot_col.plots, **kwargs)
return figure
#=============================================================================
-def single_plot(plot, figsize=(12,12), cb_orient="right", bare_axes=False,
- savefig=None, colorbar=True, file_format='eps', **kwargs):
+def single_plot(plot, field=None, figsize=(12,12), cb_orient="right",
+ bare_axes=False, savefig=None, colorbar=True,
+ file_format='eps', **kwargs):
r"""Wrapper for DualEPS routines to create a figure directy from a yt
plot. Calls insert_image_yt, axis_box_yt, and colorbar_yt.
@@ -1080,7 +1168,7 @@
>>> single_plot(p, savefig="figure1")
"""
d = DualEPS(figsize=figsize)
- d.insert_image_yt(plot)
+ d.insert_image_yt(plot, field=field)
d.axis_box_yt(plot, bare_axes=bare_axes, **kwargs)
if colorbar:
d.colorbar_yt(plot, orientation=cb_orient)
This diff is so big that we needed to truncate the remainder.
https://bitbucket.org/yt_analysis/yt/commits/71268689acc5/
Changeset: 71268689acc5
Branch: yt-3.0
User: MatthewTurk
Date: 2013-12-07 00:54:26
Summary: Removing accidentally added files.
Affected #: 2 files
diff -r 7acb7368e0b27ee1bae57c592dd0d6ce318aacb6 -r 71268689acc5e7a7a8eec59313256aaec57f89c8 log
--- a/log
+++ /dev/null
@@ -1,146 +0,0 @@
-/home/mturk/yt-conda/lib/python2.7/site-packages/matplotlib/__init__.py:727: UserWarning: Found matplotlib configuration in ~/.matplotlib/. To conform with the XDG base directory standard, this configuration location has been deprecated on Linux, and the new location is now '/home/mturk/.config'/matplotlib/. Please move your configuration there to ensure that matplotlib will continue to find it in the future.
- _get_xdg_config_dir())
-yt : [DEBUG ] 2013-12-06 18:45:17,831 Set log level to 1
-yt : [DEBUG ] 2013-12-06 18:45:17,877 Registering: StaticOutput as <class 'yt.data_objects.static_output.StaticOutput'>
-yt : [DEBUG ] 2013-12-06 18:45:17,879 SIGUSR1 registered for traceback printing
-yt : [DEBUG ] 2013-12-06 18:45:17,879 SIGUSR2 registered for IPython Insertion
-yt : [DEBUG ] 2013-12-06 18:45:17,909 Registering: EnzoStaticOutput as <class 'yt.frontends.enzo.data_structures.EnzoStaticOutput'>
-yt : [DEBUG ] 2013-12-06 18:45:17,909 Registering: EnzoStaticOutputInMemory as <class 'yt.frontends.enzo.data_structures.EnzoStaticOutputInMemory'>
-yt : [DEBUG ] 2013-12-06 18:45:17,909 Registering simulation: Enzo as <class 'yt.frontends.enzo.simulation_handling.EnzoSimulation'>
-yt : [DEBUG ] 2013-12-06 18:45:17,916 Registering: BoxlibStaticOutput as <class 'yt.frontends.boxlib.data_structures.BoxlibStaticOutput'>
-yt : [DEBUG ] 2013-12-06 18:45:17,916 Registering: OrionStaticOutput as <class 'yt.frontends.boxlib.data_structures.OrionStaticOutput'>
-yt : [DEBUG ] 2013-12-06 18:45:17,916 Registering: CastroStaticOutput as <class 'yt.frontends.boxlib.data_structures.CastroStaticOutput'>
-yt : [DEBUG ] 2013-12-06 18:45:17,916 Registering: MaestroStaticOutput as <class 'yt.frontends.boxlib.data_structures.MaestroStaticOutput'>
-yt : [DEBUG ] 2013-12-06 18:45:17,916 Registering: NyxStaticOutput as <class 'yt.frontends.boxlib.data_structures.NyxStaticOutput'>
-yt : [DEBUG ] 2013-12-06 18:45:17,926 Registering: FLASHStaticOutput as <class 'yt.frontends.flash.data_structures.FLASHStaticOutput'>
-yt : [DEBUG ] 2013-12-06 18:45:17,930 Registering: ARTStaticOutput as <class 'yt.frontends.art.data_structures.ARTStaticOutput'>
-yt : [DEBUG ] 2013-12-06 18:45:17,932 Registering: ARTIOStaticOutput as <class 'yt.frontends.artio.data_structures.ARTIOStaticOutput'>
-yt : [DEBUG ] 2013-12-06 18:45:17,933 Registering: RAMSESStaticOutput as <class 'yt.frontends.ramses.data_structures.RAMSESStaticOutput'>
-yt : [DEBUG ] 2013-12-06 18:45:17,935 Registering: ChomboStaticOutput as <class 'yt.frontends.chombo.data_structures.ChomboStaticOutput'>
-yt : [DEBUG ] 2013-12-06 18:45:17,936 Registering: GDFStaticOutput as <class 'yt.frontends.gdf.data_structures.GDFStaticOutput'>
-yt : [DEBUG ] 2013-12-06 18:45:17,937 Registering: MoabHex8StaticOutput as <class 'yt.frontends.moab.data_structures.MoabHex8StaticOutput'>
-yt : [DEBUG ] 2013-12-06 18:45:17,937 Registering: PyneMoabHex8StaticOutput as <class 'yt.frontends.moab.data_structures.PyneMoabHex8StaticOutput'>
-yt : [DEBUG ] 2013-12-06 18:45:17,939 Registering: AthenaStaticOutput as <class 'yt.frontends.athena.data_structures.AthenaStaticOutput'>
-yt : [DEBUG ] 2013-12-06 18:45:17,940 Registering: PlutoStaticOutput as <class 'yt.frontends.pluto.data_structures.PlutoStaticOutput'>
-yt : [DEBUG ] 2013-12-06 18:45:17,969 Registering: ParticleStaticOutput as <class 'yt.frontends.sph.data_structures.ParticleStaticOutput'>
-yt : [DEBUG ] 2013-12-06 18:45:17,969 Registering: GadgetStaticOutput as <class 'yt.frontends.sph.data_structures.GadgetStaticOutput'>
-yt : [DEBUG ] 2013-12-06 18:45:17,970 Registering: GadgetHDF5StaticOutput as <class 'yt.frontends.sph.data_structures.GadgetHDF5StaticOutput'>
-yt : [DEBUG ] 2013-12-06 18:45:17,970 Registering: OWLSStaticOutput as <class 'yt.frontends.sph.data_structures.OWLSStaticOutput'>
-yt : [DEBUG ] 2013-12-06 18:45:17,970 Registering: TipsyStaticOutput as <class 'yt.frontends.sph.data_structures.TipsyStaticOutput'>
-yt : [DEBUG ] 2013-12-06 18:45:17,970 Registering: HTTPStreamStaticOutput as <class 'yt.frontends.sph.data_structures.HTTPStreamStaticOutput'>
-yt : [DEBUG ] 2013-12-06 18:45:17,971 Registering: StreamStaticOutput as <class 'yt.frontends.stream.data_structures.StreamStaticOutput'>
-yt : [DEBUG ] 2013-12-06 18:45:17,971 Registering: StreamParticlesStaticOutput as <class 'yt.frontends.stream.data_structures.StreamParticlesStaticOutput'>
-yt : [DEBUG ] 2013-12-06 18:45:17,971 Registering: StreamOctreeStaticOutput as <class 'yt.frontends.stream.data_structures.StreamOctreeStaticOutput'>
-yt : [DEBUG ] 2013-12-06 18:45:17,971 Registering: StreamHexahedralStaticOutput as <class 'yt.frontends.stream.data_structures.StreamHexahedralStaticOutput'>
-yt : [DEBUG ] 2013-12-06 18:45:17,976 The Fortran kD-Tree did not import correctly.
-yt : [INFO ] 2013-12-06 18:45:18,144 Loading plugins from /home/mturk/.yt/my_plugins.py
-......................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................E............................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................../home/mturk/yt-conda/lib/python2.7/site-packages/matplotlib/tight_layout.py:225: UserWarning: tight_layout : falling back to Agg renderer
- warnings.warn("tight_layout : falling back to Agg renderer")
-......................................................
-======================================================================
-ERROR: Failure: TypeError ('NoneType' object is not iterable)
-----------------------------------------------------------------------
-Traceback (most recent call last):
- File "/home/mturk/yt-conda/lib/python2.7/site-packages/nose/loader.py", line 253, in generate
- for test in g():
- File "/home/mturk/yt/yt-3.0/yt/data_objects/tests/test_profiles.py", line 92, in test_profiles
- p1d.add_fields(["Ones", "Temperature"])
- File "/home/mturk/yt/yt-3.0/yt/data_objects/profiles.py", line 760, in add_fields
- for g in parallel_objects(self.data_source._grids):
- File "/home/mturk/yt/yt-3.0/yt/utilities/parallel_tools/parallel_analysis_interface.py", line 433, in parallel_objects
- oiter = itertools.islice(enumerate(objects), my_new_id, None, njobs)
-TypeError: 'NoneType' object is not iterable
--------------------- >> begin captured logging << --------------------
-yt: INFO: Parameters: current_time = 0.0
-yt: INFO: Parameters: domain_dimensions = [64 64 64]
-yt: INFO: Parameters: domain_left_edge = [ 0. 0. 0.]
-yt: INFO: Parameters: domain_right_edge = [ 1. 1. 1.]
-yt: INFO: Parameters: cosmological_simulation = 0.0
-yt: DEBUG: Initializing data storage.
-yt: DEBUG: Setting up classes.
-yt: DEBUG: Setting up domain geometry.
-yt: DEBUG: Counting grids.
-yt: DEBUG: Initializing grid arrays.
-yt: DEBUG: Allocating arrays for 8 grids
-yt: DEBUG: Parsing hierarchy.
-yt: DEBUG: Copying reverse tree
-yt: DEBUG: Preparing grids
-yt: DEBUG: Prepared 0 / 8 grids
-yt: DEBUG: Prepared
-yt: DEBUG: Constructing grid objects.
-yt: DEBUG: Re-examining hierarchy
-yt: DEBUG: Initializing data grid data IO
-yt: DEBUG: Detecting fields.
-yt: DEBUG: Detecting fields in backup.
-yt: DEBUG: Adding unknown detected fields
-yt: DEBUG: Checking ['Dinosaurs', 'Density', 'Temperature', 'Tribbles']
-yt: INFO: Adding unknown field Dinosaurs to list of fields
-yt: INFO: Adding unknown field Density to list of fields
-yt: INFO: Adding unknown field Temperature to list of fields
-yt: INFO: Adding unknown field Tribbles to list of fields
-yt: DEBUG: Setting up derived fields
-yt: DEBUG: Raises <type 'exceptions.RuntimeError'> during field ('all', 'ParticleMass') detection.
-yt: DEBUG: Raises <type 'exceptions.RuntimeError'> during field ('all', 'particle_ones') detection.
-yt: DEBUG: Raises <type 'exceptions.RuntimeError'> during field ('all', 'ParticleMassMsun') detection.
-yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityRPGrowthMagnitude detection.
-yt: DEBUG: Raises <type 'exceptions.KeyError'> during field BaroclinicVorticityMagnitude detection.
-yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityGrowthTimescale detection.
-yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityStretchingY detection.
-yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityZ detection.
-yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityGrowthMagnitude detection.
-yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityGrowthX detection.
-yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityGrowthY detection.
-yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityGrowthZ detection.
-yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityStretchingX detection.
-yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityStretchingZ detection.
-yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityMagnitude detection.
-yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityRPGrowthMagnitudeABS detection.
-yt: DEBUG: Raises <type 'exceptions.KeyError'> during field gradDensityMagnitude detection.
-yt: DEBUG: Generating vorticity on defaultdict(<function <lambda> at 0xc6fcf50>, {'dz': array([ 1.]), 'dx': array([ 1.]), 'dy': array([ 1.])})
-yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticitySquared detection.
-yt: DEBUG: Raises <type 'exceptions.KeyError'> during field AbsDivV detection.
-yt: DEBUG: Raises <type 'exceptions.KeyError'> during field DivV detection.
-yt: DEBUG: Raises <type 'exceptions.KeyError'> during field Shear detection.
-yt: DEBUG: Raises <type 'exceptions.KeyError'> during field gradDensityZ detection.
-yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityRPGrowthX detection.
-yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityRPGrowthY detection.
-yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityRPGrowthZ detection.
-yt: DEBUG: Raises <type 'exceptions.KeyError'> during field gradDensityX detection.
-yt: DEBUG: Raises <type 'exceptions.KeyError'> during field gradDensityY detection.
-yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityGrowthMagnitudeABS detection.
-yt: DEBUG: Raises <type 'exceptions.KeyError'> during field ShearCriterion detection.
-yt: DEBUG: Raises <type 'exceptions.KeyError'> during field ShearMach detection.
-yt: DEBUG: Raises <type 'exceptions.KeyError'> during field gradPressureZ detection.
-yt: DEBUG: Raises <type 'exceptions.KeyError'> during field gradPressureX detection.
-yt: DEBUG: Raises <type 'exceptions.KeyError'> during field gradPressureY detection.
-yt: DEBUG: Raises <type 'exceptions.KeyError'> during field BaroclinicVorticityY detection.
-yt: DEBUG: Raises <type 'exceptions.KeyError'> during field BaroclinicVorticityX detection.
-yt: DEBUG: Raises <type 'exceptions.KeyError'> during field BaroclinicVorticityZ detection.
-yt: DEBUG: Raises <type 'exceptions.KeyError'> during field gradPressureMagnitude detection.
-yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityStretchingMagnitude detection.
-yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityRPGrowthTimescale detection.
-yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityY detection.
-yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityX detection.
-yt: DEBUG: Setting up particle fields
-yt: DEBUG: Don't know what to do with io
-yt: DEBUG: Checking derived fields again
-yt: DEBUG: Creating Particle Union 'all'
-yt: DEBUG: Don't know what to do with all
-yt: DEBUG: Checking ['Dinosaurs', 'Density', 'Temperature', 'Tribbles']
-yt: DEBUG: Adding known field Dinosaurs to list of fields
-yt: DEBUG: Adding known field Density to list of fields
-yt: DEBUG: Adding known field Temperature to list of fields
-yt: DEBUG: Adding known field Tribbles to list of fields
-yt: DEBUG: Appending object to UniformGridData (type: <class 'yt.geometry.geometry_handler.YTRegion'>)
-yt: DEBUG: Reading 262144 cells of ['Density'] fields in 8 blocks
-yt: DEBUG: Reading 262144 cells of ['Temperature'] fields in 8 blocks
-yt: DEBUG: Reading 262144 cells of ['Dinosaurs'] fields in 8 blocks
-yt: DEBUG: Reading 262144 cells of ['Density'] fields in 8 blocks
-yt: DEBUG: Reading 262144 cells of ['Temperature'] fields in 8 blocks
-yt: DEBUG: Reading 262144 cells of ['Dinosaurs'] fields in 8 blocks
---------------------- >> end captured logging << ---------------------
-
-----------------------------------------------------------------------
-Ran 3787 tests in 263.236s
-
-FAILED (errors=1)
diff -r 7acb7368e0b27ee1bae57c592dd0d6ce318aacb6 -r 71268689acc5e7a7a8eec59313256aaec57f89c8 nosetests.xml
--- a/nosetests.xml
+++ /dev/null
@@ -1,103 +0,0 @@
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[ 1., 1., 1., ..., 1., 1., 1" name="]]]))" time="0.011"></testcase><testcase classname="nose.failure.Failure" name="runTest" time="0.000"><error type="exceptions.TypeError" message="'NoneType' object is not iterable
-------------------- >> begin captured logging << --------------------
yt: INFO: Parameters: current_time = 0.0
yt: INFO: Parameters: domain_dimensions = [64 64 64]
yt: INFO: Parameters: domain_left_edge = [ 0. 0. 0.]
yt: INFO: Parameters: domain_right_edge = [ 1. 1. 1.]
yt: INFO: Parameters: cosmological_simulation = 0.0
yt: DEBUG: Initializing data storage.
yt: DEBUG: Setting up classes.
yt: DEBUG: Setting up domain geometry.
yt: DEBUG: Counting grids.
yt: DEBUG: Initializing grid arrays.
yt: DEBUG: Allocating arrays for 8 grids
yt: DEBUG: Parsing hierarchy.
yt: DEBUG: Copying reverse tree
yt: DEBUG: Preparing grids
yt: DEBUG: Prepared 0 / 8 grids
yt: DEBUG: Prepared
yt: DEBUG: Constructing grid objects.
yt: DEBUG: Re-examining hierarchy
yt: DEBUG: Initializing data grid data IO
yt: DEBUG: Detecting fields.
yt: DEBUG: Detecting fields in backup.
yt: DEBUG: Adding unknown detected fields
yt: DEBUG: Checking ['Dinosaurs', 'Density', 'Temperature', 'Tribbles']
yt: INFO: Adding unknown field Dinosaurs to list of fields
yt: INFO: Adding unknown field Density to list of fields
yt: INFO: Adding unknown field Temperature to list of fields
yt: INFO: Adding unknown field Tribbles to list of fields
yt: DEBUG: Setting up derived fields
yt: DEBUG: Raises <type 'exceptions.RuntimeError'> during field ('all', 'ParticleMass') detection.
yt: DEBUG: Raises <type 'exceptions.RuntimeError'> during field ('all', 'particle_ones') detection.
yt: DEBUG: Raises <type 'exceptions.RuntimeError'> during field ('all', 'ParticleMassMsun') detection.
yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityRPGrowthMagnitude detection.
yt: DEBUG: Raises <type 'exceptions.KeyError'> during field BaroclinicVorticityMagnitude detection.
yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityGrowthTimescale detection.
yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityStretchingY detection.
yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityZ detection.
yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityGrowthMagnitude detection.
yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityGrowthX detection.
yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityGrowthY detection.
yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityGrowthZ detection.
yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityStretchingX detection.
yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityStretchingZ detection.
yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityMagnitude detection.
yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityRPGrowthMagnitudeABS detection.
yt: DEBUG: Raises <type 'exceptions.KeyError'> during field gradDensityMagnitude detection.
yt: DEBUG: Generating vorticity on defaultdict(<function <lambda> at 0xc6fcf50>, {'dz': array([ 1.]), 'dx': array([ 1.]), 'dy': array([ 1.])})
yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticitySquared detection.
yt: DEBUG: Raises <type 'exceptions.KeyError'> during field AbsDivV detection.
yt: DEBUG: Raises <type 'exceptions.KeyError'> during field DivV detection.
yt: DEBUG: Raises <type 'exceptions.KeyError'> during field Shear detection.
yt: DEBUG: Raises <type 'exceptions.KeyError'> during field gradDensityZ detection.
yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityRPGrowthX detection.
yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityRPGrowthY detection.
yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityRPGrowthZ detection.
yt: DEBUG: Raises <type 'exceptions.KeyError'> during field gradDensityX detection.
yt: DEBUG: Raises <type 'exceptions.KeyError'> during field gradDensityY detection.
yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityGrowthMagnitudeABS detection.
yt: DEBUG: Raises <type 'exceptions.KeyError'> during field ShearCriterion detection.
yt: DEBUG: Raises <type 'exceptions.KeyError'> during field ShearMach detection.
yt: DEBUG: Raises <type 'exceptions.KeyError'> during field gradPressureZ detection.
yt: DEBUG: Raises <type 'exceptions.KeyError'> during field gradPressureX detection.
yt: DEBUG: Raises <type 'exceptions.KeyError'> during field gradPressureY detection.
yt: DEBUG: Raises <type 'exceptions.KeyError'> during field BaroclinicVorticityY detection.
yt: DEBUG: Raises <type 'exceptions.KeyError'> during field BaroclinicVorticityX detection.
yt: DEBUG: Raises <type 'exceptions.KeyError'> during field BaroclinicVorticityZ detection.
yt: DEBUG: Raises <type 'exceptions.KeyError'> during field gradPressureMagnitude detection.
yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityStretchingMagnitude detection.
yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityRPGrowthTimescale detection.
yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityY detection.
yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityX detection.
yt: DEBUG: Setting up particle fields
yt: DEBUG: Don't know what to do with io
yt: DEBUG: Checking derived fields again
yt: DEBUG: Creating Particle Union 'all'
yt: DEBUG: Don't know what to do with all
yt: DEBUG: Checking ['Dinosaurs', 'Density', 'Temperature', 'Tribbles']
yt: DEBUG: Adding known field Dinosaurs to list of fields
yt: DEBUG: Adding known field Density to list of fields
yt: DEBUG: Adding known field Temperature to list of fields
yt: DEBUG: Adding known field Tribbles to list of fields
yt: DEBUG: Appending object to UniformGridData (type: <class 'yt.geometry.geometry_handler.YTRegion'>)
yt: DEBUG: Reading 262144 cells of ['Density'] fields in 8 blocks
yt: DEBUG: Reading 262144 cells of ['Temperature'] fields in 8 blocks
yt: DEBUG: Reading 262144 cells of ['Dinosaurs'] fields in 8 blocks
yt: DEBUG: Reading 262144 cells of ['Density'] fields in 8 blocks
yt: DEBUG: Reading 262144 cells of ['Temperature'] fields in 8 blocks
yt: DEBUG: Reading 262144 cells of ['Dinosaurs'] fields in 8 blocks
--------------------- >> end captured logging << ---------------------"><![CDATA[ File "/home/mturk/yt-conda/lib/python2.7/unittest/case.py", line 331, in run
- testMethod()
- File "/home/mturk/yt-conda/lib/python2.7/site-packages/nose/loader.py", line 253, in generate
- for test in g():
- File "/home/mturk/yt/yt-3.0/yt/data_objects/tests/test_profiles.py", line 92, in test_profiles
- p1d.add_fields(["Ones", "Temperature"])
- File "/home/mturk/yt/yt-3.0/yt/data_objects/profiles.py", line 760, in add_fields
- for g in parallel_objects(self.data_source._grids):
- File "/home/mturk/yt/yt-3.0/yt/utilities/parallel_tools/parallel_analysis_interface.py", line 433, in parallel_objects
- oiter = itertools.islice(enumerate(objects), my_new_id, None, njobs)
-'NoneType' object is not iterable
--------------------- >> begin captured logging << --------------------
-yt: INFO: Parameters: current_time = 0.0
-yt: INFO: Parameters: domain_dimensions = [64 64 64]
-yt: INFO: Parameters: domain_left_edge = [ 0. 0. 0.]
-yt: INFO: Parameters: domain_right_edge = [ 1. 1. 1.]
-yt: INFO: Parameters: cosmological_simulation = 0.0
-yt: DEBUG: Initializing data storage.
-yt: DEBUG: Setting up classes.
-yt: DEBUG: Setting up domain geometry.
-yt: DEBUG: Counting grids.
-yt: DEBUG: Initializing grid arrays.
-yt: DEBUG: Allocating arrays for 8 grids
-yt: DEBUG: Parsing hierarchy.
-yt: DEBUG: Copying reverse tree
-yt: DEBUG: Preparing grids
-yt: DEBUG: Prepared 0 / 8 grids
-yt: DEBUG: Prepared
-yt: DEBUG: Constructing grid objects.
-yt: DEBUG: Re-examining hierarchy
-yt: DEBUG: Initializing data grid data IO
-yt: DEBUG: Detecting fields.
-yt: DEBUG: Detecting fields in backup.
-yt: DEBUG: Adding unknown detected fields
-yt: DEBUG: Checking ['Dinosaurs', 'Density', 'Temperature', 'Tribbles']
-yt: INFO: Adding unknown field Dinosaurs to list of fields
-yt: INFO: Adding unknown field Density to list of fields
-yt: INFO: Adding unknown field Temperature to list of fields
-yt: INFO: Adding unknown field Tribbles to list of fields
-yt: DEBUG: Setting up derived fields
-yt: DEBUG: Raises <type 'exceptions.RuntimeError'> during field ('all', 'ParticleMass') detection.
-yt: DEBUG: Raises <type 'exceptions.RuntimeError'> during field ('all', 'particle_ones') detection.
-yt: DEBUG: Raises <type 'exceptions.RuntimeError'> during field ('all', 'ParticleMassMsun') detection.
-yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityRPGrowthMagnitude detection.
-yt: DEBUG: Raises <type 'exceptions.KeyError'> during field BaroclinicVorticityMagnitude detection.
-yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityGrowthTimescale detection.
-yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityStretchingY detection.
-yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityZ detection.
-yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityGrowthMagnitude detection.
-yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityGrowthX detection.
-yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityGrowthY detection.
-yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityGrowthZ detection.
-yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityStretchingX detection.
-yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityStretchingZ detection.
-yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityMagnitude detection.
-yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityRPGrowthMagnitudeABS detection.
-yt: DEBUG: Raises <type 'exceptions.KeyError'> during field gradDensityMagnitude detection.
-yt: DEBUG: Generating vorticity on defaultdict(<function <lambda> at 0xc6fcf50>, {'dz': array([ 1.]), 'dx': array([ 1.]), 'dy': array([ 1.])})
-yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticitySquared detection.
-yt: DEBUG: Raises <type 'exceptions.KeyError'> during field AbsDivV detection.
-yt: DEBUG: Raises <type 'exceptions.KeyError'> during field DivV detection.
-yt: DEBUG: Raises <type 'exceptions.KeyError'> during field Shear detection.
-yt: DEBUG: Raises <type 'exceptions.KeyError'> during field gradDensityZ detection.
-yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityRPGrowthX detection.
-yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityRPGrowthY detection.
-yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityRPGrowthZ detection.
-yt: DEBUG: Raises <type 'exceptions.KeyError'> during field gradDensityX detection.
-yt: DEBUG: Raises <type 'exceptions.KeyError'> during field gradDensityY detection.
-yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityGrowthMagnitudeABS detection.
-yt: DEBUG: Raises <type 'exceptions.KeyError'> during field ShearCriterion detection.
-yt: DEBUG: Raises <type 'exceptions.KeyError'> during field ShearMach detection.
-yt: DEBUG: Raises <type 'exceptions.KeyError'> during field gradPressureZ detection.
-yt: DEBUG: Raises <type 'exceptions.KeyError'> during field gradPressureX detection.
-yt: DEBUG: Raises <type 'exceptions.KeyError'> during field gradPressureY detection.
-yt: DEBUG: Raises <type 'exceptions.KeyError'> during field BaroclinicVorticityY detection.
-yt: DEBUG: Raises <type 'exceptions.KeyError'> during field BaroclinicVorticityX detection.
-yt: DEBUG: Raises <type 'exceptions.KeyError'> during field BaroclinicVorticityZ detection.
-yt: DEBUG: Raises <type 'exceptions.KeyError'> during field gradPressureMagnitude detection.
-yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityStretchingMagnitude detection.
-yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityRPGrowthTimescale detection.
-yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityY detection.
-yt: DEBUG: Raises <type 'exceptions.KeyError'> during field VorticityX detection.
-yt: DEBUG: Setting up particle fields
-yt: DEBUG: Don't know what to do with io
-yt: DEBUG: Checking derived fields again
-yt: DEBUG: Creating Particle Union 'all'
-yt: DEBUG: Don't know what to do with all
-yt: DEBUG: Checking ['Dinosaurs', 'Density', 'Temperature', 'Tribbles']
-yt: DEBUG: Adding known field Dinosaurs to list of fields
-yt: DEBUG: Adding known field Density to list of fields
-yt: DEBUG: Adding known field Temperature to list of fields
-yt: DEBUG: Adding known field Tribbles to list of fields
-yt: DEBUG: Appending object to UniformGridData (type: <class 'yt.geometry.geometry_handler.YTRegion'>)
-yt: DEBUG: Reading 262144 cells of ['Density'] fields in 8 blocks
-yt: DEBUG: Reading 262144 cells of ['Temperature'] fields in 8 blocks
-yt: DEBUG: Reading 262144 cells of ['Dinosaurs'] fields in 8 blocks
-yt: DEBUG: Reading 262144 cells of ['Density'] fields in 8 blocks
-yt: DEBUG: Reading 262144 cells of ['Temperature'] fields in 8 blocks
-yt: DEBUG: Reading 262144 cells of ['Dinosaurs'] fields in 8 blocks
---------------------- >> end captured logging << ---------------------]]></error></testcase><testcase classname="yt.data_objects.tests.test_projection" name="test_projection(4096.0, 4096)" time="0.000"></testcase><testcase classname="yt.data_objects.tests.test_projection" name="test_projection(1.0, 1.0)" time="0.000"></testcase><testcase classname="yt.data_objects.tests.test_projection" name="test_projection(1.0, 1.0)" time="0.000"></testcase><testcase classname="yt.data_objects.tests.test_projection.test_projection(array([ 0.0078125, 0.0234375, 0.0390625, 0.0546875, 0.0703125,
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name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(28L, 28)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(29L, 29)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(30L, 30)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(array([ 1, 2, 4, 8, 16,
32, 64, 128, 256, 512,
1024, 2048, 4096, 8192, 16384,
32768, 65536, 131072, 262144, 524288,
1048576, 2097152, 4194304, 8388608, 16777216,
33554432, 67108864, 134217728, 268435456, 536870912,
1073741824], dtype=uint64), array([ 1, 2, 4, 8, 16,
32, 64, 128, 256, 512,
1024, 2048, 4096, 8192, 16384,
32768, 65536, 131072, 262144, 524288,
1048576, 2097152, 4194304, 8388608, 16777216,
33554432, 67108864, 134217728, 268435456, 536870912,
1073741824], dtype=uint64))" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(array([ 1, 2, 4, 8, 16,
32, 64, 128, 256, 512,
1024, 2048, 4096, 8192, 16384,
32768, 65536, 131072, 262144, 524288,
1048576, 2097152, 4194304, 8388608, 16777216,
33554432, 67108864, 134217728, 268435456, 536870912,
1073741824], dtype=uint64), array([ 1, 2, 4, 8, 16,
32, 64, 128, 256, 512,
1024, 2048, 4096, 8192, 16384,
32768, 65536, 131072, 262144, 524288,
1048576, 2097152, 4194304, 8388608, 16777216,
33554432, 67108864, 134217728, 268435456, 536870912,
1073741824], dtype=uint64))" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(0L, 0)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1L, 1)" time="0.001"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(2L, 2)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(3L, 3)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase 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2048, 4096, 8192,
16384, 32768, 65536,
131072, 262144, 524288,
1048576, 2097152, 4194304,
8388608, 16777216, 33554432,
67108864, 134217728, 268435456,
536870912, 1073741824, 2147483648,
4294967296, 8589934592, 17179869184,
34359738368, 68719476736, 137438953472,
274877906944, 549755813888, 1099511627776,
2199023255552, 4398046511104, 8796093022208,
17592186044416, 35184372088832, 70368744177664,
140737488355328, 281474976710656, 562949953421312,
1125899906842624, 2251799813685248, 4503599627370496,
9007199254740992, 18014398509481984, 36028797018963968,
72057594037927936, 144115188075855872, 288230376151711744,
576460752303423488, 1152921504606846976, 2305843009213693952,
4611686018427387904, 9223372036854775808], dtype=uint64), array([ 0, 1, 2,
4, 8, 16,
32, 64, 128,
256, 512, 1024,
2048, 4096, 8192,
16384, 32768, 65536,
131072, 262144, 524288,
1048576, 2097152, 4194304,
8388608, 16777216, 33554432,
67108864, 134217728, 268435456,
536870912, 1073741824, 2147483648,
4294967296, 8589934592, 17179869184,
34359738368, 68719476736, 137438953472,
274877906944, 549755813888, 1099511627776,
2199023255552, 4398046511104, 8796093022208,
17592186044416, 35184372088832, 70368744177664,
140737488355328, 281474976710656, 562949953421312,
1125899906842624, 2251799813685248, 4503599627370496,
9007199254740992, 18014398509481984, 36028797018963968,
72057594037927936, 144115188075855872, 288230376151711744,
576460752303423488, 1152921504606846976, 2305843009213693952,
4611686018427387904, 9223372036854775808], dtype=uint64))" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(array([ 0, 1, 2,
4, 8, 16,
32, 64, 128,
256, 512, 1024,
2048, 4096, 8192,
16384, 32768, 65536,
131072, 262144, 524288,
1048576, 2097152, 4194304,
8388608, 16777216, 33554432,
67108864, 134217728, 268435456,
536870912, 1073741824, 2147483648,
4294967296, 8589934592, 17179869184,
34359738368, 68719476736, 137438953472,
274877906944, 549755813888, 1099511627776,
2199023255552, 4398046511104, 8796093022208,
17592186044416, 35184372088832, 70368744177664,
140737488355328, 281474976710656, 562949953421312,
1125899906842624, 2251799813685248, 4503599627370496,
9007199254740992, 18014398509481984, 36028797018963968,
72057594037927936, 144115188075855872, 288230376151711744,
576460752303423488, 1152921504606846976, 2305843009213693952,
4611686018427387904], dtype=uint64), array([ 0, 1, 2,
4, 8, 16,
32, 64, 128,
256, 512, 1024,
2048, 4096, 8192,
16384, 32768, 65536,
131072, 262144, 524288,
1048576, 2097152, 4194304,
8388608, 16777216, 33554432,
67108864, 134217728, 268435456,
536870912, 1073741824, 2147483648,
4294967296, 8589934592, 17179869184,
34359738368, 68719476736, 137438953472,
274877906944, 549755813888, 1099511627776,
2199023255552, 4398046511104, 8796093022208,
17592186044416, 35184372088832, 70368744177664,
140737488355328, 281474976710656, 562949953421312,
1125899906842624, 2251799813685248, 4503599627370496,
9007199254740992, 18014398509481984, 36028797018963968,
72057594037927936, 144115188075855872, 288230376151711744,
576460752303423488, 1152921504606846976, 2305843009213693952,
4611686018427387904], dtype=uint64))" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(array([ 0, 1, 2,
4, 8, 16,
32, 64, 128,
256, 512, 1024,
2048, 4096, 8192,
16384, 32768, 65536,
131072, 262144, 524288,
1048576, 2097152, 4194304,
8388608, 16777216, 33554432,
67108864, 134217728, 268435456,
536870912, 1073741824, 2147483648,
4294967296, 8589934592, 17179869184,
34359738368, 68719476736, 137438953472,
274877906944, 549755813888, 1099511627776,
2199023255552, 4398046511104, 8796093022208,
17592186044416, 35184372088832, 70368744177664,
140737488355328, 281474976710656, 562949953421312,
1125899906842624, 2251799813685248, 4503599627370496,
9007199254740992, 18014398509481984, 36028797018963968,
72057594037927936, 144115188075855872, 288230376151711744,
576460752303423488, 1152921504606846976, 2305843009213693952,
4611686018427387904], dtype=uint64), array([ 0, 1, 2,
4, 8, 16,
32, 64, 128,
256, 512, 1024,
2048, 4096, 8192,
16384, 32768, 65536,
131072, 262144, 524288,
1048576, 2097152, 4194304,
8388608, 16777216, 33554432,
67108864, 134217728, 268435456,
536870912, 1073741824, 2147483648,
4294967296, 8589934592, 17179869184,
34359738368, 68719476736, 137438953472,
274877906944, 549755813888, 1099511627776,
2199023255552, 4398046511104, 8796093022208,
17592186044416, 35184372088832, 70368744177664,
140737488355328, 281474976710656, 562949953421312,
1125899906842624, 2251799813685248, 4503599627370496,
9007199254740992, 18014398509481984, 36028797018963968,
72057594037927936, 144115188075855872, 288230376151711744,
576460752303423488, 1152921504606846976, 2305843009213693952,
4611686018427387904], dtype=uint64))" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(0L, 0)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1L, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(2L, 2)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(3L, 3)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(4L, 4)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(5L, 5)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(6L, 6)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(7L, 7)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(8L, 8)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(9L, 9)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(10L, 10)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(11L, 11)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(12L, 12)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(13L, 13)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(14L, 14)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(15L, 15)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(16L, 16)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(17L, 17)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(18L, 18)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(19L, 19)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(20L, 20)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(21L, 21)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(22L, 22)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(23L, 23)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(24L, 24)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(25L, 25)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(26L, 26)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(27L, 27)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(28L, 28)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(29L, 29)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(30L, 30)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(31L, 31)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(32L, 32)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(33L, 33)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(34L, 34)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(35L, 35)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(36L, 36)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(37L, 37)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(38L, 38)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(39L, 39)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(40L, 40)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(41L, 41)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(42L, 42)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(43L, 43)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(44L, 44)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(45L, 45)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(46L, 46)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" 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name="test_particle_regions(61L, 61)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(62L, 62)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(63L, 63)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(64L, 64)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(65L, 65)" time="0.000"></testcase><testcase classname="test_particle_octree" name="test_particle_regions(1, 1)" 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0.49999968, 0.49999968, 0.49999971, 0.49999976, 0.49999976,
0.49999984, 0.4999999 , 0.5 , 0.5 , 0.5 ,
0.5000001 , 0.50000016, 0.50000016, 0.50000024, 0.50000029,
0.50000032, 0.50000048, 0.50000064, 0.50000067, 0.50000072,
0.50000076, 0.50000079, 0.50000086, 0.50000095, 0.50000105,
0.50000111, 0.50000119, 0.50000119, 0.50000124, 0.50000127,
0.50000127, 0.50000143, 0.50000143, 0.50000167, 0.50000167,
0.50000191, 0.50000191, 0.50000229, 0.50000254, 0.50000254,
0.50000381, 0.50000381, 0.50000509, 0.50000509, 0.50000509,
0.50000534, 0.50000572, 0.50000572, 0.5000061 , 0.50000636,
0.50000636, 0.50000687, 0.50000763, 0.50000763, 0.50000839,
0.5000089 , 0.50000954, 0.50000954, 0.50000992, 0.50001017,
0.50001017, 0.50001144, 0.50001144, 0.50001335, 0.50001335,
0.50001526, 0.50001526, 0.50001831, 0.50002035, 0.50002035,
0.50003052, 0.50003052, 0.50004069, 0.50004069, 0.50004272,
0.50004578, 0.50004578, 0.50004883, 0.50005086, 0.50005493,
0.50006104, 0.50006104, 0.50006714, 0.50007121, 0.50007629,
0.50007935, 0.50008138, 0.50008138, 0.50009155, 0.50010681,
0.50012207, 0.50012207, 0.50014648, 0.50016276, 0.50016276,
0.50016276, 0.50024414, 0.50032552, 0.50032552, 0.5003418 ,
0.50036621, 0.50036621, 0.50039062, 0.5004069 , 0.50043945,
0.50048828, 0.50048828, 0.50048828, 0.50053711, 0.50056966,
0.50061035, 0.50061035, 0.50063477, 0.50065104, 0.50065104,
0.50073242, 0.50085449, 0.50085449, 0.50097656, 0.50097656,
0.50117187, 0.50130208, 0.50130208, 0.50195312, 0.50260417,
0.50273437, 0.50292969, 0.50292969, 0.503125 , 0.50325521,
0.50351562, 0.50390625, 0.50390625, 0.50429687, 0.50455729,
0.50488281, 0.50488281, 0.50507812, 0.50520833, 0.50520833,
0.50585938, 0.50585938, 0.50683594, 0.50683594, 0.5078125 ,
0.5078125 , 0.509375 , 0.51041667, 0.51041667, 0.515625 ,
0.515625 , 0.52083333, 0.52083333, 0.52083333, 0.521875 ,
0.5234375 , 0.525 , 0.52604167, 0.52604167, 0.528125 ,
0.53125 , 0.53125 , 0.534375 , 0.53645833, 0.5390625 ,
0.5390625 , 0.540625 , 0.54166667, 0.546875 , 0.546875 ,
0.5546875 , 0.5546875 , 0.5625 , 0.5625 , 0.575 ,
0.58333333, 0.58333333, 0.625 , 0.625 , 0.625 ,
0.66666667, 0.66666667, 0.675 , 0.6875 , 0.6875 ,
0.70833333, 0.70833333, 0.70833333, 0.725 , 0.75 ,
0.75 , 0.75 , 0.775 , 0.79166667, 0.8125 ,
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0.186339 , 0.186339 , 0.2795085 , 0.372678 , 0.372678 ,
0.3913119 , 0.41926275, 0.4658475 , 0.4658475 , 0.50311529,
0.55901699, 0.55901699, 0.61491869, 0.65218649, 0.65218649,
0.65218649, 0.69877124, 0.72672209, 0.74535599, 0.74535599,
0.83852549, 0.83852549, 0.83852549, 0.87346405, 0.90840262,
0.93169499, 0.93169499, 0.93169499, 0.94334118, 0.94334118,
0.95032889, 0.95498737, 0.95498737, 0.96663355, 0.96663355,
0.97827974, 0.97827974, 0.97827974, 0.98992593, 0.98992593,
0.99574902, 0.99574902, 0.99924288, 1.00157211, 1.00157211,
1.00157211, 1.0132183 , 1.0132183 , 1.02486449, 1.02486449,
1.02486449, 1.02719373, 1.03068758, 1.03068758, 1.03651068,
1.03651068, 1.03651068, 1.04116915, 1.04815686, 1.04815686,
1.05514458, 1.05980305, 1.05980305, 1.06562615, 1.06912 ,
1.07144924, 1.07144924, 1.08309543, 1.08309543, 1.08746275,
1.09183007, 1.09474161, 1.09474161, 1.09619739, 1.09619739,
1.09707085, 1.09765316, 1.09910893, 1.10056471, 1.10056471,
1.10202048, 1.10202048, 1.10274837, 1.10274837, 1.1031851 ,
1.10347625, 1.10493203, 1.10493203, 1.1063878 , 1.1063878 ,
1.10667896, 1.10711569, 1.10784357, 1.10784357, 1.10842588,
1.10929935, 1.10929935, 1.11017281, 1.11075512, 1.11148301,
1.11191974, 1.1122109 , 1.11366667, 1.11366667, 1.11421258,
1.1147585 , 1.11512244, 1.11512244, 1.11530441, 1.11530441,
1.1154136 , 1.11548639, 1.11548639, 1.11566836, 1.11566836,
1.11585033, 1.11585033, 1.1160323 , 1.1160323 , 1.11612329,
1.11617788, 1.11621427, 1.11621427, 1.11639624, 1.11657822,
1.11661461, 1.1166692 , 1.1166692 , 1.11676019, 1.11683298,
1.11694216, 1.11694216, 1.11705134, 1.11712413, 1.11712413,
1.11721512, 1.11726971, 1.1173061 , 1.1173061 , 1.11748807,
1.11748807, 1.11755631, 1.11762455, 1.11767005, 1.11767005,
1.11769279, 1.11770644, 1.11771554, 1.11773828, 1.11773828,
1.11776103, 1.11776103, 1.11778378, 1.11778378, 1.11779515,
1.11779515, 1.11780197, 1.11780652, 1.11780652, 1.11782927,
1.11782927, 1.11785202, 1.11785202, 1.11785657, 1.11786339,
1.11786339, 1.11787476, 1.11787476, 1.11787476, 1.11788386,
1.11789751, 1.11789751, 1.11791116, 1.11792026, 1.11792026,
1.11793163, 1.11793845, 1.117943 , 1.11796575, 1.11796575,
1.11797428, 1.11798281, 1.1179885 , 1.11799134, 1.11799305,
1.11799418, 1.11799418, 1.11799703, 1.11799987, 1.11799987,
1.11800271, 1.11800271, 1.11800413, 1.11800413, 1.11800499,
1.11800556, 1.11800556, 1.11800556, 1.1180084 , 1.1180084 ,
1.11801124, 1.11801124, 1.11801181, 1.11801266, 1.11801409,
1.11801409, 1.11801522, 1.11801693, 1.11801693, 1.11801863,
1.11801977, 1.11802119, 1.11802205, 1.11802262, 1.11802546,
1.11802546, 1.11802546, 1.11802653, 1.11802759, 1.1180283 ,
1.1180283 , 1.11802866, 1.11802866, 1.11802887, 1.11802901,
1.11802901, 1.11802937, 1.11802972, 1.11802972, 1.11803008,
1.11803008, 1.11803026, 1.11803036, 1.11803043, 1.11803043,
1.11803079, 1.11803079, 1.11803115, 1.11803115, 1.11803122,
1.11803132, 1.1180315 , 1.1180315 , 1.11803164, 1.11803186,
1.11803186, 1.11803207, 1.11803221, 1.11803221, 1.11803239,
1.1180325 , 1.11803257, 1.11803257, 1.11803292, 1.11803292,
1.11803328, 1.11803328, 1.11803335, 1.11803346, 1.11803346,
1.11803363, 1.11803378, 1.11803399, 1.11803399, 1.11803399,
1.1180342 , 1.11803434, 1.11803434, 1.11803452, 1.11803463,
1.1180347 , 1.11803505, 1.11803541, 1.11803548, 1.11803559,
1.11803569, 1.11803577, 1.11803591, 1.11803612, 1.11803633,
1.11803648, 1.11803665, 1.11803665, 1.11803676, 1.11803683,
1.11803683, 1.11803719, 1.11803719, 1.11803772, 1.11803772,
1.11803825, 1.11803825, 1.11803911, 1.11803968, 1.11803968,
1.11804252, 1.11804252, 1.11804536, 1.11804536, 1.11804536,
1.11804593, 1.11804678, 1.11804678, 1.11804764, 1.11804821,
1.11804821, 1.11804934, 1.11805105, 1.11805105, 1.11805275,
1.11805389, 1.11805531, 1.11805531, 1.11805617, 1.11805674,
1.11805674, 1.11805958, 1.11805958, 1.11806384, 1.11806384,
1.11806811, 1.11806811, 1.11807493, 1.11807948, 1.11807948,
1.11810223, 1.11810223, 1.11812497, 1.11812497, 1.11812952,
1.11813635, 1.11813635, 1.11814317, 1.11814772, 1.11815682,
1.11817047, 1.11817047, 1.11818412, 1.11819321, 1.11820459,
1.11821141, 1.11821596, 1.11821596, 1.11823871, 1.11827283,
1.11830695, 1.11830695, 1.11836154, 1.11839793, 1.11839793,
1.11839793, 1.1185799 , 1.11876188, 1.11876188, 1.11879827,
1.11885286, 1.11885286, 1.11890745, 1.11894385, 1.11901664,
1.11912582, 1.11912582, 1.11912582, 1.119235 , 1.11930779,
1.11939878, 1.11939878, 1.11945337, 1.11948976, 1.11948976,
1.11967173, 1.11994469, 1.11994469, 1.12021765, 1.12021765,
1.12065438, 1.12094554, 1.12094554, 1.12240131, 1.12385708,
1.12414824, 1.12458497, 1.12458497, 1.1250217 , 1.12531286,
1.12589517, 1.12676863, 1.12676863, 1.12764209, 1.1282244 ,
1.12895229, 1.12895229, 1.12938902, 1.12968018, 1.12968018,
1.13113595, 1.13113595, 1.13331961, 1.13331961, 1.13550327,
1.13550327, 1.13899713, 1.14132636, 1.14132636, 1.15297255,
1.15297255, 1.16461874, 1.16461874, 1.16461874, 1.16694798,
1.17044183, 1.17393569, 1.17626493, 1.17626493, 1.1809234 ,
1.18791111, 1.18791111, 1.19489883, 1.1995573 , 1.20538039,
1.20538039, 1.20887425, 1.21120349, 1.22284968, 1.22284968,
1.24031896, 1.24031896, 1.25778824, 1.25778824, 1.28573909,
1.30437299, 1.30437299, 1.39754249, 1.39754249, 1.39754249,
1.49071198, 1.49071198, 1.50934588, 1.53729673, 1.53729673,
1.58388148, 1.58388148, 1.58388148, 1.62114928, 1.67705098,
1.67705098, 1.67705098, 1.73295268, 1.77022048, 1.81680523,
1.81680523, 1.84475608, 1.95655948, 1.95655948, 2.09631373,
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7.81250000e-02, 7.81250000e-02, 7.50000000e-02,
7.29166667e-02, 7.29166667e-02, 6.77083333e-02,
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6.25000000e-02, 5.72916667e-02, 5.72916667e-02,
5.46875000e-02, 5.46875000e-02, 5.31250000e-02,
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4.68750000e-02, 4.68750000e-02, 4.16666667e-02,
4.16666667e-02, 4.16666667e-02, 4.06250000e-02,
3.90625000e-02, 3.90625000e-02, 3.64583333e-02,
3.64583333e-02, 3.64583333e-02, 3.43750000e-02,
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7.81250000e-03, 7.16145833e-03, 7.16145833e-03,
6.83593750e-03, 6.83593750e-03, 6.64062500e-03,
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4.88281250e-03, 4.55729167e-03, 4.55729167e-03,
4.29687500e-03, 3.90625000e-03, 3.90625000e-03,
3.51562500e-03, 3.25520833e-03, 2.92968750e-03,
2.73437500e-03, 2.60416667e-03, 1.95312500e-03,
1.95312500e-03, 1.70898437e-03, 1.46484375e-03,
1.30208333e-03, 1.30208333e-03, 1.22070313e-03,
1.22070312e-03, 1.17187500e-03, 1.13932292e-03,
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9.76562500e-04, 9.76562500e-04, 8.95182292e-04,
8.95182292e-04, 8.54492188e-04, 8.30078125e-04,
8.13802083e-04, 8.13802083e-04, 7.32421875e-04,
6.51041667e-04, 6.34765625e-04, 6.10351562e-04,
6.10351562e-04, 5.69661458e-04, 5.37109375e-04,
4.88281250e-04, 4.88281250e-04, 4.39453125e-04,
4.06901042e-04, 4.06901042e-04, 3.66210937e-04,
3.41796875e-04, 3.25520833e-04, 3.25520833e-04,
2.44140625e-04, 2.44140625e-04, 2.13623047e-04,
1.83105469e-04, 1.62760417e-04, 1.62760417e-04,
1.52587891e-04, 1.46484375e-04, 1.42415365e-04,
1.32242839e-04, 1.32242839e-04, 1.22070313e-04,
1.22070312e-04, 1.11897786e-04, 1.11897786e-04,
1.06811523e-04, 1.06811523e-04, 1.03759766e-04,
1.01725260e-04, 1.01725260e-04, 9.15527344e-05,
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7.93457031e-05, 7.62939453e-05, 7.62939453e-05,
7.12076823e-05, 7.12076823e-05, 7.12076823e-05,
6.71386719e-05, 6.10351563e-05, 6.10351562e-05,
5.49316406e-05, 5.08626302e-05, 5.08626302e-05,
4.57763672e-05, 4.27246094e-05, 4.06901042e-05,
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2.28881836e-06, 2.22524007e-06, 2.22524007e-06,
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1.74840291e-06, 1.74840291e-06, 1.66893005e-06,
1.62124634e-06, 1.58945719e-06, 1.58945719e-06,
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1.11262004e-06, 1.11262004e-06, 1.04904175e-06,
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8.90096029e-06, 9.53674316e-06, 9.53674316e-06,
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1.14440918e-05, 1.14440918e-05, 1.33514404e-05,
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1.95312500e-03, 1.95312500e-03, 1.95312500e-03,
1.93986421e-03, 1.70898438e-03, 1.70898438e-03,
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2.17178847e-04, 2.13623047e-04, 2.13623047e-04,
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1.39372822e-04, 1.22070313e-04, 1.22070312e-04,
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0.24966079, 0.24966443, 0.24970623, 0.24970896, 0.25 ,
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0.31168831, 0.31190019, 0.31225033, 0.31229773, 0.31238095,
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0.37378641, 0.37452471, 0.37459807, 0.3747646 , 0.3748056 ,
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0.41025641, 0.41071429, 0.41176471, 0.4125 , 0.41304348,
0.4137931 , 0.41428571, 0.41666667, 0.41860465, 0.41891892,
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0.43243243, 0.43478261, 0.43548387, 0.43617021, 0.43630573,
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0.43729904, 0.43733509, 0.43733681, 0.43738318, 0.43741765,
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0.4516129 , 0.45180723, 0.45238095, 0.453125 , 0.45408163,
0.45454545, 0.45762712, 0.45774648, 0.45833333, 0.46 ,
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0.47260274, 0.47297297, 0.475 , 0.47540984, 0.4765625 ,
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0.48360656, 0.484375 , 0.485 , 0.48529412, 0.48611111,
0.48648649, 0.48666667, 0.48863636, 0.48876404, 0.48958333,
0.48979592, 0.49019608, 0.49180328, 0.49193548, 0.4921875 ,
0.49316406, 0.49324324, 0.49333333, 0.49414062, 0.49511719,
0.49609375, 0.49707031, 0.49804688, 0.49902344, 0.49914551,
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0.50000119, 0.50000143, 0.50000167, 0.50000191, 0.50000381,
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0.50585938, 0.50649351, 0.50657895, 0.50683594, 0.5078125 ,
0.50793651, 0.51 , 0.51315789, 0.51351351, 0.515625 ,
0.51587302, 0.51948052, 0.51960784, 0.52 , 0.52083333,
0.5234375 , 0.52631579, 0.52941176, 0.53125 , 0.53846154,
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0.56217617, 0.56220096, 0.56238532, 0.5625 , 0.56259542,
0.56268882, 0.56274256, 0.56284658, 0.56289308, 0.56308411,
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0.57692308, 0.62322275, 0.62345679, 0.62380952, 0.62383178,
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0.81278539, 0.81306991, 0.81336406, 0.81422018, 0.87037037,
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0.50079239, 0.50515464, 0.51020408, 0.62105263, 0.62237762,
0.62337662, 0.62380038, 0.62450067, 0.62459547, 0.6247619 ,
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0.74757282, 0.74904943, 0.74919614, 0.74952919, 0.7496112 ,
0.74966711, 0.74967062, 0.74971165, 0.74971429, 0.75 ,
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0.7804878 , 0.7826087 , 0.79310345, 0.79411765, 0.79487179,
0.79545455, 0.8 , 0.80487805, 0.81395349, 0.81818182,
0.82051282, 0.82142857, 0.82352941, 0.825 , 0.82608696,
0.82758621, 0.82857143, 0.83333333, 0.8372093 , 0.83783784,
0.84210526, 0.84375 , 0.84615385, 0.84848485, 0.85 ,
0.85185185, 0.85365854, 0.85714286, 0.86111111, 0.86363636,
0.86486486, 0.86956522, 0.87096774, 0.87234043, 0.87261146,
0.87378641, 0.87382298, 0.87384615, 0.8742236 , 0.87439614,
0.87459807, 0.87467018, 0.87467363, 0.87476636, 0.87483531,
0.875 , 0.8751926 , 0.87542469, 0.87542857, 0.8754717 ,
0.8755832 , 0.87581699, 0.87619048, 0.87804878, 0.88235294,
0.88461538, 0.88571429, 0.88888889, 0.890625 , 0.89156627,
0.89285714, 0.89583333, 0.9 , 0.90140845, 0.90277778,
0.90322581, 0.90361446, 0.9047619 , 0.90625 , 0.90816327,
0.90909091, 0.91525424, 0.91549296, 0.91666667, 0.92 ,
0.921875 , 0.92307692, 0.92857143, 0.92941176, 0.93055556,
0.93150685, 0.93220339, 0.93333333, 0.9375 , 0.93877551,
0.94 , 0.94117647, 0.94186047, 0.94252874, 0.94444444,
0.94520548, 0.94594595, 0.95 , 0.95081967, 0.953125 ,
0.95348837, 0.95833333, 0.96 , 0.96551724, 0.96590909,
0.96721311, 0.96875 , 0.97 , 0.97058824, 0.97222222,
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0.97959184, 0.98039216, 0.98360656, 0.98387097, 0.984375 ,
0.98632812, 0.98648649, 0.98666667, 0.98828125, 0.99023438,
0.9921875 , 0.99414062, 0.99609375, 0.99804688, 0.99829102,
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0.99975586, 0.99978638, 0.99981689, 0.99984741, 0.99987793,
0.99990845, 0.99993896, 0.99996948, 0.9999733 , 0.99997711,
0.99998093, 0.99998474, 0.99998856, 0.99999237, 0.99999619,
0.99999666, 0.99999714, 0.99999762, 0.99999809, 0.99999857,
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1.00000238, 1.00000286, 1.00000334, 1.00000381, 1.00000763,
1.00001144, 1.00001526, 1.00001907, 1.00002289, 1.0000267 ,
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1.00018311, 1.00021362, 1.00024414, 1.00048828, 1.00073242,
1.00097656, 1.0012207 , 1.00146484, 1.00170898, 1.00195312,
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1.01171875, 1.01298701, 1.01315789, 1.01367188, 1.015625 ,
1.01587302, 1.02 , 1.02631579, 1.02702703, 1.03125 ,
1.03174603, 1.03896104, 1.03921569, 1.04 , 1.04166667,
1.046875 , 1.05263158, 1.05882353, 1.0625 , 1.07692308,
1.078125 , 1.07894737, 1.08 , 1.08333333, 1.09375 ,
1.109375 , 1.12322275, 1.12380952, 1.12405446, 1.12407407,
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1.12537764, 1.12548512, 1.12569316, 1.12578616, 1.12616822,
1.12618297, 1.12637363, 1.12735849, 1.12772586, 1.12857143,
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1.24909091, 1.24954128, 1.24962179, 1.25 , 1.25045372,
1.25075529, 1.25077399, 1.25091575, 1.25233645, 1.25352113,
1.25471698, 1.37209302, 1.37308869, 1.37345679, 1.37383178,
1.37423313, 1.37454545, 1.374613 , 1.375 , 1.37558685,
1.37568058, 1.37614679, 1.37735849, 1.49767442, 1.49847095,
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1.62037037, 1.62272727, 1.62385321, 1.62424242, 1.625 ,
1.62557078, 1.62613982, 1.62672811, 1.62844037, 1.74074074,
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0" name="8984375]))" time="0.000"></testcase><testcase classname="yt.utilities.tests.test_units" name="test_no_conflicting_symbols" time="0.000"></testcase><testcase classname="yt.utilities.tests.test_units" name="test_dimensionless" time="0.002"></testcase><testcase classname="yt.utilities.tests.test_units" name="test_create_from_string" time="0.005"></testcase><testcase classname="yt.utilities.tests.test_units" name="test_create_from_expr" time="0.002"></testcase><testcase classname="yt.utilities.tests.test_units" name="test_create_with_duplicate_dimensions" time="0.006"><system-out><![CDATA[km/(Mpc*s)
-1/(time)
-3.24077674937129e-20
-]]></system-out></testcase><testcase classname="yt.utilities.tests.test_units" name="test_create_new_symbol" time="0.001"></testcase><testcase classname="yt.utilities.tests.test_units" name="test_create_fail_on_unknown_symbol" time="0.000"></testcase><testcase classname="yt.utilities.tests.test_units" name="test_create_fail_on_bad_symbol_type" time="0.000"></testcase><testcase classname="yt.utilities.tests.test_units" name="test_create_fail_on_bad_dimensions_type" time="0.001"></testcase><testcase classname="yt.utilities.tests.test_units" name="test_create_fail_on_dimensions_content" time="0.001"></testcase><testcase classname="yt.utilities.tests.test_units" name="test_create_fail_on_cgs_value_type" time="0.000"></testcase><testcase classname="yt.utilities.tests.test_units" name="test_string_representation" time="0.003"></testcase><testcase classname="yt.utilities.tests.test_units" name="test_multiplication" time="0.005"></testcase><testcase classname="yt.utilities.tests.test_units" name="test_division" time="0.003"></testcase><testcase classname="yt.utilities.tests.test_units" name="test_power" time="0.044"></testcase><testcase classname="yt.utilities.tests.test_units" name="test_equality" time="0.003"></testcase><testcase classname="yt.utilities.tests.test_units" name="test_cgs_equivalent" time="0.008"></testcase><testcase classname="yt.visualization.volume_rendering.tests.test_vr_cameras.CameraTest" name="test_camera" time="0.262"></testcase><testcase classname="yt.visualization.volume_rendering.tests.test_vr_cameras.CameraTest" name="test_camera_movement" time="0.383"></testcase><testcase classname="yt.visualization.volume_rendering.tests.test_vr_cameras.CameraTest" name="test_data_source_camera" time="0.290"></testcase><testcase classname="yt.visualization.volume_rendering.tests.test_vr_cameras.CameraTest" name="test_interactive_camera" time="0.257"></testcase><testcase classname="yt.visualization.volume_rendering.tests.test_vr_cameras.CameraTest" name="test_perspective_camera" time="0.485"></testcase><testcase classname="yt.visualization.volume_rendering.tests.test_vr_cameras.CameraTest" name="test_projection_camera" time="0.249"></testcase><testcase classname="yt.visualization.volume_rendering.tests.test_vr_cameras.CameraTest" name="test_stereo_camera" time="0.291"></testcase><testcase classname="yt.visualization.tests.test_offaxisprojection" name="test_write_projection(True, True)" time="0.000"></testcase><testcase classname="yt.visualization.tests.test_offaxisprojection" name="test_write_projection(True, True)" time="0.000"></testcase><testcase classname="yt.visualization.tests.test_offaxisprojection" name="test_write_projection(True, True)" time="0.000"></testcase><testcase classname="yt.visualization.tests.test_offaxisprojection" name="test_write_projection(True, True)" time="0.000"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_offaxis_projection_plot_0" time="0.907"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_offaxis_projection_plot_1_testpng" time="0.847"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_offaxis_projection_plot_2_testeps" time="0.320"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_offaxis_projection_plot_3_testps" time="0.312"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_offaxis_projection_plot_4_testpdf" time="0.589"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_offaxis_slice_plot_0" time="0.570"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_offaxis_slice_plot_1_testpng" time="0.510"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_offaxis_slice_plot_2_testeps" time="0.326"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_offaxis_slice_plot_3_testps" time="0.325"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_offaxis_slice_plot_4_testpdf" time="0.551"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_projection_plot_0" time="0.662"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_projection_plot_1" time="0.395"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_projection_plot_10" time="0.447"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_projection_plot_11" time="0.397"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_projection_plot_12" time="0.309"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_projection_plot_13" time="0.309"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_projection_plot_14" time="0.485"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_projection_plot_2" time="0.306"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_projection_plot_3" time="0.307"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_projection_plot_4" time="0.483"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_projection_plot_5" time="0.445"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_projection_plot_6" time="0.394"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_projection_plot_7" time="0.307"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_projection_plot_8" time="0.308"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_projection_plot_9" time="0.482"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_projection_plot_ds_0" time="0.437"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_projection_plot_ds_1" time="0.437"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_projection_plot_ds_2" time="0.437"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_slice_plot_0" time="0.463"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_slice_plot_1" time="0.413"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_slice_plot_10" time="0.463"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_slice_plot_11" time="0.412"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_slice_plot_12" time="0.321"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_slice_plot_13" time="0.321"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_slice_plot_14" time="0.504"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_slice_plot_2" time="0.322"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_slice_plot_3" time="0.322"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_slice_plot_4" time="0.504"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_slice_plot_5" time="0.463"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_slice_plot_6" time="0.412"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_slice_plot_7" time="0.320"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_slice_plot_8" time="0.321"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestPlotWindowSave" name="test_slice_plot_9" time="0.504"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestSetWidth" name="test_pair_of_tuples_neq" time="0.549"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestSetWidth" name="test_set_width_nonequal" time="0.808"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestSetWidth" name="test_set_width_one" time="0.426"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestSetWidth" name="test_tuple_eq" time="0.557"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestSetWidth" name="test_tuple_of_tuples_neq" time="0.554"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestSetWidth" name="test_tuple_of_tuples_neq2" time="0.559"></testcase><testcase classname="yt.visualization.tests.test_plotwindow.TestSetWidth" name="test_twoargs_eq" time="0.562"></testcase></testsuite>
\ No newline at end of file
https://bitbucket.org/yt_analysis/yt/commits/58413f892e30/
Changeset: 58413f892e30
Branch: yt-3.0
User: MatthewTurk
Date: 2013-12-07 17:18:18
Summary: Fixing docstring.
Affected #: 1 file
diff -r 71268689acc5e7a7a8eec59313256aaec57f89c8 -r 58413f892e30b0072af5ed44b4d4e8e3f0d2868c yt/visualization/plot_window.py
--- a/yt/visualization/plot_window.py
+++ b/yt/visualization/plot_window.py
@@ -902,7 +902,7 @@
or the axis name itself
fields : string
The name of the field(s) to be plotted.
- center : two or three-element vector of sequence floats, 'c', or 'center',
+ center : two or three-element vector of sequence floats, 'c',
'center', 'max' or 'm'. The coordinate of the center of the image.
If set to 'c', 'center' or left blank, the plot is centered on the
middle of the domain. If set to 'max' or 'm', the center will be at
https://bitbucket.org/yt_analysis/yt/commits/7026b9ddfc20/
Changeset: 7026b9ddfc20
Branch: yt-3.0
User: MatthewTurk
Date: 2013-12-11 17:03:11
Summary: Merged in MatthewTurk/yt/yt-3.0 (pull request #674)
Merging from mainline yt into yt-3.0, including profiles
Affected #: 27 files
diff -r 7159d0816ffe9e933fdfa0301ac612ac4eedbf63 -r 7026b9ddfc20c9f9f05e654fdbf84c89a42007d1 doc/install_script.sh
--- a/doc/install_script.sh
+++ b/doc/install_script.sh
@@ -264,28 +264,45 @@
echo "Alternatively, download the Xcode command line tools from"
echo "the Apple developer tools website."
echo
- echo "OS X 10.8.2: download Xcode 4.6.1 from the mac app store."
+ echo "OS X 10.8.4 and 10.9: download Xcode 5.02 from the mac app store."
echo "(search for Xcode)."
+ echo
echo "Additionally, you will have to manually install the Xcode"
- echo "command line tools, see:"
- echo "http://stackoverflow.com/questions/9353444"
- echo "Alternatively, download the Xcode command line tools from"
- echo "the Apple developer tools website."
+ echo "command line tools."
+ echo
+ echo "For OS X 10.8, see:"
+ echo "http://stackoverflow.com/questions/9353444"
echo
- echo "NOTE: It's possible that the installation will fail, if so,"
- echo "please set the following environment variables, remove any"
- echo "broken installation tree, and re-run this script verbatim."
- echo
- echo "$ export CC=gcc"
- echo "$ export CXX=g++"
- echo
- OSX_VERSION=`sw_vers -productVersion`
- if [ "${OSX_VERSION##10.8}" != "${OSX_VERSION}" ]
+ echo "For OS X 10.9, the command line tools can be installed"
+ echo "with the following command:"
+ echo " xcode-select --install"
+ echo
+ OSX_VERSION=`sw_vers -productVersion`
+ if [ "${OSX_VERSION##10.8}" != "${OSX_VERSION}" ]
then
MPL_SUPP_CFLAGS="${MPL_SUPP_CFLAGS} -mmacosx-version-min=10.7"
MPL_SUPP_CXXFLAGS="${MPL_SUPP_CXXFLAGS} -mmacosx-version-min=10.7"
fi
fi
+ if [ -f /etc/redhat-release ]
+ then
+ echo "Looks like you're on an Redhat-compatible machine."
+ echo
+ echo "You need to have these packages installed:"
+ echo
+ echo " * openssl-devel"
+ echo " * uuid-devel"
+ echo " * readline-devel"
+ echo " * ncurses-devel"
+ echo " * zip"
+ echo " * gcc-{,c++,gfortran}"
+ echo " * make"
+ echo " * patch"
+ echo
+ echo "You can accomplish this by executing:"
+ echo "$ sudo yum install gcc gcc-g++ gcc-gfortran make patch zip"
+ echo "$ sudo yum install ncurses-devel uuid-devel openssl-devel readline-devel"
+ fi
if [ -f /etc/SuSE-release ] && [ `grep --count SUSE /etc/SuSE-release` -gt 0 ]
then
echo "Looks like you're on an OpenSUSE-compatible machine."
@@ -566,16 +583,16 @@
CYTHON='Cython-0.19.1'
FORTHON='Forthon-0.8.11'
PYX='PyX-0.12.1'
-PYTHON='Python-2.7.5'
+PYTHON='Python-2.7.6'
BZLIB='bzip2-1.0.6'
FREETYPE_VER='freetype-2.4.12'
H5PY='h5py-2.1.3'
HDF5='hdf5-1.8.11'
-IPYTHON='ipython-1.0.0'
+IPYTHON='ipython-1.1.0'
LAPACK='lapack-3.4.2'
PNG=libpng-1.6.3
MATPLOTLIB='matplotlib-1.3.0'
-MERCURIAL='mercurial-2.7'
+MERCURIAL='mercurial-2.8'
NOSE='nose-1.3.0'
NUMPY='numpy-1.7.1'
PYTHON_HGLIB='python-hglib-1.0'
@@ -585,14 +602,14 @@
SQLITE='sqlite-autoconf-3071700'
SYMPY='sympy-0.7.3'
TORNADO='tornado-3.1'
-ZEROMQ='zeromq-3.2.3'
+ZEROMQ='zeromq-3.2.4'
ZLIB='zlib-1.2.8'
# Now we dump all our SHA512 files out.
echo '9dcdda5b2ee2e63c2d3755245b7b4ed2f4592455f40feb6f8e86503195d9474559094ed27e789ab1c086d09da0bb21c4fe844af0e32a7d47c81ff59979b18ca0 Cython-0.19.1.tar.gz' > Cython-0.19.1.tar.gz.sha512
echo '3f53d0b474bfd79fea2536d0a9197eaef6c0927e95f2f9fd52dbd6c1d46409d0e649c21ac418d8f7767a9f10fe6114b516e06f2be4b06aec3ab5bdebc8768220 Forthon-0.8.11.tar.gz' > Forthon-0.8.11.tar.gz.sha512
echo '4941f5aa21aff3743546495fb073c10d2657ff42b2aff401903498638093d0e31e344cce778980f28a7170c6d29eab72ac074277b9d4088376e8692dc71e55c1 PyX-0.12.1.tar.gz' > PyX-0.12.1.tar.gz.sha512
-echo 'd6580eb170b36ad50f3a30023fe6ca60234156af91ccb3971b0b0983119b86f3a9f6c717a515c3c6cb72b3dcbf1d02695c6d0b92745f460b46a3defd3ff6ef2f Python-2.7.5.tgz' > Python-2.7.5.tgz.sha512
+echo '3df0ba4b1cfef5f02fb27925de4c2ca414eca9000af6a3d475d39063720afe987287c3d51377e0a36b88015573ef699f700782e1749c7a357b8390971d858a79 Python-2.7.6.tgz' > Python-2.7.6.tgz.sha512
echo '172f2bc671145ebb0add2669c117863db35851fb3bdb192006cd710d4d038e0037497eb39a6d01091cb923f71a7e8982a77b6e80bf71d6275d5d83a363c8d7e5 rockstar-0.99.6.tar.gz' > rockstar-0.99.6.tar.gz.sha512
echo '276bd9c061ec9a27d478b33078a86f93164ee2da72210e12e2c9da71dcffeb64767e4460b93f257302b09328eda8655e93c4b9ae85e74472869afbeae35ca71e blas.tar.gz' > blas.tar.gz.sha512
echo '00ace5438cfa0c577e5f578d8a808613187eff5217c35164ffe044fbafdfec9e98f4192c02a7d67e01e5a5ccced630583ad1003c37697219b0f147343a3fdd12 bzip2-1.0.6.tar.gz' > bzip2-1.0.6.tar.gz.sha512
@@ -600,11 +617,11 @@
echo '609a68a3675087e0cc95268574f31e104549daa48efe15a25a33b8e269a93b4bd160f4c3e8178dca9c950ef5ca514b039d6fd1b45db6af57f25342464d0429ce freetype-2.4.12.tar.gz' > freetype-2.4.12.tar.gz.sha512
echo '2eb7030f8559ff5cb06333223d98fda5b3a663b6f4a026949d1c423aa9a869d824e612ed5e1851f3bf830d645eea1a768414f73731c23ab4d406da26014fe202 h5py-2.1.3.tar.gz' > h5py-2.1.3.tar.gz.sha512
echo 'e9db26baa297c8ed10f1ca4a3fcb12d6985c6542e34c18d48b2022db73014f054c8b8434f3df70dcf44631f38b016e8050701d52744953d0fced3272d7b6b3c1 hdf5-1.8.11.tar.gz' > hdf5-1.8.11.tar.gz.sha512
-echo '1b309c08009583e66d1725a2d2051e6de934db246129568fa6d5ba33ad6babd3b443e7c2782d817128d2b112e21bcdd71e66be34fbd528badd900f1d0ed3db56 ipython-1.0.0.tar.gz' > ipython-1.0.0.tar.gz.sha512
+echo '46b8ae25df2ced674b3b3629070aafac955ba3aa2a5e749f8e63ef1f459126e1c4a9a03661406151622590a90c73b527716ad71bc626f57f52b51abfae0f43ca ipython-1.1.0.tar.gz' > ipython-1.1.0.tar.gz.sha512
echo '8770214491e31f0a7a3efaade90eee7b0eb20a8a6ab635c5f854d78263f59a1849133c14ef5123d01023f0110cbb9fc6f818da053c01277914ae81473430a952 lapack-3.4.2.tar.gz' > lapack-3.4.2.tar.gz.sha512
echo '887582e5a22e4cde338aa8fec7a89f6dd31f2f02b8842735f00f970f64582333fa03401cea6d01704083403c7e8b7ebc26655468ce930165673b33efa4bcd586 libpng-1.6.3.tar.gz' > libpng-1.6.3.tar.gz.sha512
echo '990e3a155ca7a9d329c41a43b44a9625f717205e81157c668a8f3f2ad5459ed3fed8c9bd85e7f81c509e0628d2192a262d4aa30c8bfc348bb67ed60a0362505a matplotlib-1.3.0.tar.gz' > matplotlib-1.3.0.tar.gz.sha512
-echo 'e425778edb0f71c34e719e04561ee3de37feaa1be4d60b94c780aebdbe6d41f8f4ab15103a8bbe8894ebeb228c42f0e2cd41b8db840f8384e1cd7cd2d5b67b97 mercurial-2.7.tar.gz' > mercurial-2.7.tar.gz.sha512
+echo 'b08dcd746728d89f1f96036f39df1608fad0ff863ae48fe12424b1645936ebbf59b9068b93fe3c7cfd2036db046df3dc814119f89a827bd5f008d32f323d45a8 mercurial-2.8.tar.gz' > mercurial-2.8.tar.gz.sha512
echo 'a3b8060e415560a868599224449a3af636d24a060f1381990b175dcd12f30249edd181179d23aea06b0c755ff3dc821b7a15ed8840f7855530479587d4d814f4 nose-1.3.0.tar.gz' > nose-1.3.0.tar.gz.sha512
echo 'd58177f3971b6d07baf6f81a2088ba371c7e43ea64ee7ada261da97c6d725b4bd4927122ac373c55383254e4e31691939276dab08a79a238bfa55172a3eff684 numpy-1.7.1.tar.gz' > numpy-1.7.1.tar.gz.sha512
echo '9c0a61299779aff613131aaabbc255c8648f0fa7ab1806af53f19fbdcece0c8a68ddca7880d25b926d67ff1b9201954b207919fb09f6a290acb078e8bbed7b68 python-hglib-1.0.tar.gz' > python-hglib-1.0.tar.gz.sha512
@@ -614,7 +631,7 @@
echo '96f3e51b46741450bc6b63779c10ebb4a7066860fe544385d64d1eda52592e376a589ef282ace2e1df73df61c10eab1a0d793abbdaf770e60289494d4bf3bcb4 sqlite-autoconf-3071700.tar.gz' > sqlite-autoconf-3071700.tar.gz.sha512
echo '2992baa3edfb4e1842fb642abf0bf0fc0bf56fc183aab8fed6b3c42fbea928fa110ede7fdddea2d63fc5953e8d304b04da433dc811134fadefb1eecc326121b8 sympy-0.7.3.tar.gz' > sympy-0.7.3.tar.gz.sha512
echo '101544db6c97beeadc5a02b2ef79edefa0a07e129840ace2e4aa451f3976002a273606bcdc12d6cef5c22ff4c1c9dcf60abccfdee4cbef8e3f957cd25c0430cf tornado-3.1.tar.gz' > tornado-3.1.tar.gz.sha512
-echo '34ffb6aa645f62bd1158a8f2888bf92929ccf90917a6c50ed51ed1240732f498522e164d1536f26480c87ad5457fe614a93bf0e15f2f89b0b168e64a30de68ca zeromq-3.2.3.tar.gz' > zeromq-3.2.3.tar.gz.sha512
+echo 'd8eef84860bc5314b42a2cc210340572a9148e008ea65f7650844d0edbe457d6758785047c2770399607f69ba3b3a544db9775a5cdf961223f7e278ef7e0f5c6 zeromq-3.2.4.tar.gz' > zeromq-3.2.4.tar.gz.sha512
echo 'ece209d4c7ec0cb58ede791444dc754e0d10811cbbdebe3df61c0fd9f9f9867c1c3ccd5f1827f847c005e24eef34fb5bf87b5d3f894d75da04f1797538290e4a zlib-1.2.8.tar.gz' > zlib-1.2.8.tar.gz.sha512
# Individual processes
[ -z "$HDF5_DIR" ] && get_ytproject $HDF5.tar.gz
@@ -1006,10 +1023,7 @@
echo
echo "To get started with yt, check out the orientation:"
echo
- echo " http://yt-project.org/doc/orientation/"
- echo
- echo "or just activate your environment and run 'yt serve' to bring up the"
- echo "yt GUI."
+ echo " http://yt-project.org/doc/bootcamp/"
echo
echo "The source for yt is located at:"
echo " $YT_DIR"
diff -r 7159d0816ffe9e933fdfa0301ac612ac4eedbf63 -r 7026b9ddfc20c9f9f05e654fdbf84c89a42007d1 yt/analysis_modules/halo_profiler/multi_halo_profiler.py
--- a/yt/analysis_modules/halo_profiler/multi_halo_profiler.py
+++ b/yt/analysis_modules/halo_profiler/multi_halo_profiler.py
@@ -25,7 +25,7 @@
from yt.convenience import \
load
from yt.data_objects.profiles import \
- BinnedProfile1D, EmptyProfileData
+ BinnedProfile1D, YTEmptyProfileData
from yt.analysis_modules.halo_finding.api import *
from .halo_filters import \
VirialFilter
@@ -588,7 +588,7 @@
profile = BinnedProfile1D(sphere, self.n_profile_bins, "RadiusMpc",
r_min, halo['r_max'],
log_space=True, end_collect=True)
- except EmptyProfileData:
+ except YTEmptyProfileData:
mylog.error("Caught EmptyProfileData exception, returning None for this halo.")
return None
# Figure out which fields to add simultaneously
diff -r 7159d0816ffe9e933fdfa0301ac612ac4eedbf63 -r 7026b9ddfc20c9f9f05e654fdbf84c89a42007d1 yt/analysis_modules/spectral_integrator/spectral_frequency_integrator.py
--- a/yt/analysis_modules/spectral_integrator/spectral_frequency_integrator.py
+++ b/yt/analysis_modules/spectral_integrator/spectral_frequency_integrator.py
@@ -19,7 +19,10 @@
import numpy as np
import os
-from yt.funcs import *
+from yt.funcs import \
+ download_file, \
+ mylog, \
+ only_on_root
from yt.data_objects.field_info_container import add_field
from yt.utilities.exceptions import YTException
@@ -31,6 +34,23 @@
xray_data_version = 1
+def _get_data_file():
+ data_file = "xray_emissivity.h5"
+ data_url = "http://yt-project.org/data"
+ if "YT_DEST" in os.environ and \
+ os.path.isdir(os.path.join(os.environ["YT_DEST"], "data")):
+ data_dir = os.path.join(os.environ["YT_DEST"], "data")
+ else:
+ data_dir = "."
+ data_path = os.path.join(data_dir, data_file)
+ if not os.path.exists(data_path):
+ mylog.info("Attempting to download supplementary data from %s to %s." %
+ (data_url, data_dir))
+ fn = download_file(os.path.join(data_url, data_file), data_path)
+ if fn != data_path:
+ raise RuntimeError, "Failed to download supplementary data."
+ return data_path
+
class EnergyBoundsException(YTException):
def __init__(self, lower, upper):
self.lower = lower
@@ -65,8 +85,7 @@
default_filename = False
if filename is None:
- filename = os.path.join(os.environ["YT_DEST"],
- "data", "xray_emissivity.h5")
+ filename = _get_data_file()
default_filename = True
if not os.path.exists(filename):
diff -r 7159d0816ffe9e933fdfa0301ac612ac4eedbf63 -r 7026b9ddfc20c9f9f05e654fdbf84c89a42007d1 yt/convenience.py
--- a/yt/convenience.py
+++ b/yt/convenience.py
@@ -130,6 +130,20 @@
if simulation_type not in simulation_time_series_registry:
raise YTSimulationNotIdentified(simulation_type)
+ if os.path.exists(parameter_filename):
+ valid_file = True
+ elif os.path.exists(os.path.join(ytcfg.get("yt", "test_data_dir"),
+ parameter_filename)):
+ parameter_filename = os.path.join(ytcfg.get("yt", "test_data_dir"),
+ parameter_filename)
+ valid_file = True
+ else:
+ valid_file = False
+
+ if not valid_file:
+ raise YTOutputNotIdentified((parameter_filename, simulation_type),
+ dict(find_outputs=find_outputs))
+
return simulation_time_series_registry[simulation_type](parameter_filename,
find_outputs=find_outputs)
diff -r 7159d0816ffe9e933fdfa0301ac612ac4eedbf63 -r 7026b9ddfc20c9f9f05e654fdbf84c89a42007d1 yt/data_objects/api.py
--- a/yt/data_objects/api.py
+++ b/yt/data_objects/api.py
@@ -27,11 +27,12 @@
particle_handler_registry
from profiles import \
- EmptyProfileData, \
+ YTEmptyProfileData, \
BinnedProfile, \
BinnedProfile1D, \
BinnedProfile2D, \
- BinnedProfile3D
+ BinnedProfile3D, \
+ create_profile
from time_series import \
TimeSeriesData, \
diff -r 7159d0816ffe9e933fdfa0301ac612ac4eedbf63 -r 7026b9ddfc20c9f9f05e654fdbf84c89a42007d1 yt/data_objects/field_info_container.py
--- a/yt/data_objects/field_info_container.py
+++ b/yt/data_objects/field_info_container.py
@@ -60,21 +60,21 @@
def _gradx(f, data):
grad = data[field][sl,1:-1,1:-1] - data[field][sr,1:-1,1:-1]
- grad /= 2.0*data["dx"].flat[0]
+ grad /= 2.0*data["dx"].flat[0]*data.pf.units["cm"]
g = np.zeros(data[field].shape, dtype='float64')
g[1:-1,1:-1,1:-1] = grad
return g
def _grady(f, data):
grad = data[field][1:-1,sl,1:-1] - data[field][1:-1,sr,1:-1]
- grad /= 2.0*data["dy"].flat[0]
+ grad /= 2.0*data["dy"].flat[0]*data.pf.units["cm"]
g = np.zeros(data[field].shape, dtype='float64')
g[1:-1,1:-1,1:-1] = grad
return g
def _gradz(f, data):
grad = data[field][1:-1,1:-1,sl] - data[field][1:-1,1:-1,sr]
- grad /= 2.0*data["dz"].flat[0]
+ grad /= 2.0*data["dz"].flat[0]*data.pf.units["cm"]
g = np.zeros(data[field].shape, dtype='float64')
g[1:-1,1:-1,1:-1] = grad
return g
diff -r 7159d0816ffe9e933fdfa0301ac612ac4eedbf63 -r 7026b9ddfc20c9f9f05e654fdbf84c89a42007d1 yt/data_objects/profiles.py
--- a/yt/data_objects/profiles.py
+++ b/yt/data_objects/profiles.py
@@ -20,16 +20,10 @@
from yt.data_objects.data_containers import YTFieldData
from yt.utilities.lib import bin_profile1d, bin_profile2d, bin_profile3d
+from yt.utilities.lib import new_bin_profile1d, new_bin_profile2d, \
+ new_bin_profile3d
from yt.utilities.parallel_tools.parallel_analysis_interface import \
- ParallelAnalysisInterface
-
-_field_mapping = {
- "total_mass": ("CellMassMsun", "ParticleMassMsun"),
- "hybrid_radius": ("RadiusCode", "ParticleRadiusCode"),
- }
-
-class EmptyProfileData(Exception):
- pass
+ ParallelAnalysisInterface, parallel_objects
def preserve_source_parameters(func):
def save_state(*args, **kwargs):
@@ -55,7 +49,6 @@
self._data_source = data_source
self.pf = data_source.pf
self.field_data = YTFieldData()
- self._pdata = {}
@property
def hierarchy(self):
@@ -131,14 +124,11 @@
def __setitem__(self, key, value):
self.field_data[key] = value
- def _get_field(self, source, this_field, check_cut):
+ def _get_field(self, source, field, check_cut):
# This is where we will iterate to get all contributions to a field
# which is how we will implement hybrid particle/cell fields
# but... we default to just the field.
- data = []
- for field in _field_mapping.get(this_field, (this_field,)):
- data.append(source[field].astype('float64'))
- return np.concatenate(data, axis=0)
+ return source[field].astype('float64')
def _fix_pickle(self):
if isinstance(self._data_source, tuple):
@@ -225,7 +215,7 @@
def _get_bins(self, source, check_cut=False):
source_data = self._get_field(source, self.bin_field, check_cut)
if source_data.size == 0: # Nothing for us here.
- raise EmptyProfileData()
+ raise YTEmptyProfileData()
# Truncate at boundaries.
if self.end_collect:
mi = np.ones_like(source_data).astype('bool')
@@ -234,7 +224,7 @@
& (source_data < self._bins.max()))
sd = source_data[mi]
if sd.size == 0:
- raise EmptyProfileData()
+ raise YTEmptyProfileData()
# Stick the bins into our fixed bins, set at initialization
bin_indices = np.digitize(sd, self._bins)
if self.end_collect: #limit the range of values to 0 and n_bins-1
@@ -400,7 +390,7 @@
source_data_x = self._get_field(source, self.x_bin_field, check_cut)
source_data_y = self._get_field(source, self.y_bin_field, check_cut)
if source_data_x.size == 0:
- raise EmptyProfileData()
+ raise YTEmptyProfileData()
if self.end_collect:
mi = np.arange(source_data_x.size)
@@ -412,7 +402,7 @@
sd_x = source_data_x[mi]
sd_y = source_data_y[mi]
if sd_x.size == 0 or sd_y.size == 0:
- raise EmptyProfileData()
+ raise YTEmptyProfileData()
bin_indices_x = np.digitize(sd_x, self._x_bins) - 1
bin_indices_y = np.digitize(sd_y, self._y_bins) - 1
@@ -518,48 +508,6 @@
return np.log10(upper), np.log10(lower)
return upper, lower
-class BinnedProfile2DInlineCut(BinnedProfile2D):
- def __init__(self, data_source,
- x_n_bins, x_bin_field, x_lower_bound, x_upper_bound, x_log,
- y_n_bins, y_bin_field, y_lower_bound, y_upper_bound, y_log,
- end_collect=False):
- self.indices = data_source["Ones"].astype("bool")
- BinnedProfile2D.__init__(self, data_source,
- x_n_bins, x_bin_field, x_lower_bound, x_upper_bound, x_log,
- y_n_bins, y_bin_field, y_lower_bound, y_upper_bound, y_log,
- end_collect)
-
- @preserve_source_parameters
- def _bin_field(self, source, field, weight, accumulation,
- args, check_cut=False):
- source_data = self._get_field(source, field, check_cut)
- if weight: weight_data = self._get_field(source, weight, check_cut)
- else: weight_data = np.ones(source_data.shape, dtype='float64')
- self.total_stuff = source_data.sum()
- binned_field = self._get_empty_field()
- weight_field = self._get_empty_field()
- m_field = self._get_empty_field()
- q_field = self._get_empty_field()
- used_field = self._get_empty_field()
- mi = args[0]
- bin_indices_x = args[1][self.indices].ravel().astype('int64')
- bin_indices_y = args[2][self.indices].ravel().astype('int64')
- source_data = source_data[mi][self.indices]
- weight_data = weight_data[mi][self.indices]
- nx = bin_indices_x.size
- #mylog.debug("Binning %s / %s times", source_data.size, nx)
- bin_profile2d(bin_indices_x, bin_indices_y, weight_data, source_data,
- weight_field, binned_field, m_field, q_field, used_field)
- if accumulation: # Fix for laziness
- if not iterable(accumulation):
- raise SyntaxError("Accumulation needs to have length 2")
- if accumulation[0]:
- binned_field = np.add.accumulate(binned_field, axis=0)
- if accumulation[1]:
- binned_field = np.add.accumulate(binned_field, axis=1)
- return binned_field, weight_field, used_field.astype('bool')
-
-
class BinnedProfile3D(BinnedProfile):
"""
A 'Profile' produces either a weighted (or unweighted) average
@@ -658,7 +606,7 @@
source_data_y = self._get_field(source, self.y_bin_field, check_cut)
source_data_z = self._get_field(source, self.z_bin_field, check_cut)
if source_data_x.size == 0:
- raise EmptyProfileData()
+ raise YTEmptyProfileData()
if self.end_collect:
mi = np.arange(source_data_x.size)
else:
@@ -672,7 +620,7 @@
sd_y = source_data_y[mi]
sd_z = source_data_z[mi]
if sd_x.size == 0 or sd_y.size == 0 or sd_z.size == 0:
- raise EmptyProfileData()
+ raise YTEmptyProfileData()
bin_indices_x = np.digitize(sd_x, self._x_bins) - 1
bin_indices_y = np.digitize(sd_y, self._y_bins) - 1
@@ -790,25 +738,280 @@
self._data_source.hierarchy.save_data(values, "/Profiles", name,
set_attr, force=force)
-class StoredBinnedProfile3D(BinnedProfile3D):
- def __init__(self, pf, name):
- """
- Given a *pf* parameterfile and the *name* of a stored profile, retrieve
- it into a read-only data structure.
- """
+class ProfileFieldAccumulator(object):
+ def __init__(self, n_fields, size):
+ shape = size + (n_fields,)
+ self.values = np.zeros(shape, dtype="float64")
+ self.mvalues = np.zeros(shape, dtype="float64")
+ self.qvalues = np.zeros(shape, dtype="float64")
+ self.used = np.zeros(size, dtype='bool')
+ self.weight_values = np.zeros(size, dtype="float64")
+
+class ProfileND(ParallelAnalysisInterface):
+ def __init__(self, data_source, weight_field = None):
+ self.data_source = data_source
+ self.pf = data_source.pf
self.field_data = YTFieldData()
- prof_arr = pf.h.get_data("/Profiles", name)
- if prof_arr is None: raise KeyError("No such array")
- for ax in 'xyz':
- for base in ['%s_bin_field', '_%s_log']:
- setattr(self, base % ax, prof_arr.getAttr(base % ax))
- for ax in 'xyz':
- fn = getattr(self, '%s_bin_field' % ax)
- self.field_data[fn] = prof_arr.getAttr('%s_bin_values' % ax)
- shape = prof_arr.getAttr('shape')
- for fn, fd in zip(prof_arr.getAttr('field_order'),
- prof_arr.read().transpose()):
- self.field_data[fn] = fd.reshape(shape)
+ self.weight_field = weight_field
- def add_fields(self, *args, **kwargs):
- raise RuntimeError("Sorry, you can't add to a stored profile.")
+ def add_fields(self, fields):
+ fields = ensure_list(fields)
+ temp_storage = ProfileFieldAccumulator(len(fields), self.size)
+ for g in parallel_objects(self.data_source._grids):
+ self._bin_grid(g, fields, temp_storage)
+ self._finalize_storage(fields, temp_storage)
+
+ def _finalize_storage(self, fields, temp_storage):
+ # We use our main comm here
+ # This also will fill _field_data
+ # FIXME: Add parallelism and combining std stuff
+ if self.weight_field is not None:
+ temp_storage.values /= temp_storage.weight_values[...,None]
+ blank = ~temp_storage.used
+ for i, field in enumerate(fields):
+ self.field_data[field] = temp_storage.values[...,i]
+ self.field_data[field][blank] = 0.0
+
+ def _bin_grid(self, grid, fields, storage):
+ raise NotImplementedError
+
+ def _filter(self, bin_fields, cut_points):
+ # cut_points is initially just the points inside our region
+ # we also want to apply a filtering based on min/max
+ filter = np.zeros(bin_fields[0].shape, dtype='bool')
+ filter[cut_points] = True
+ for (mi, ma), data in zip(self.bounds, bin_fields):
+ filter &= (data > mi)
+ filter &= (data < ma)
+ return filter, [data[filter] for data in bin_fields]
+
+ def _get_data(self, grid, fields):
+ # Save the values in the grid beforehand.
+ old_params = grid.field_parameters
+ old_keys = grid.field_data.keys()
+ grid.field_parameters = self.data_source.field_parameters
+ # Now we ask our source which values to include
+ pointI = self.data_source._get_point_indices(grid)
+ bin_fields = [grid[bf] for bf in self.bin_fields]
+ # We want to make sure that our fields are within the bounds of the
+ # binning
+ filter, bin_fields = self._filter(bin_fields, pointI)
+ if not np.any(filter): return None
+ arr = np.zeros((bin_fields[0].size, len(fields)), dtype="float64")
+ for i, field in enumerate(fields):
+ arr[:,i] = grid[field][filter]
+ if self.weight_field is not None:
+ weight_data = grid[self.weight_field]
+ else:
+ weight_data = np.ones(grid.ActiveDimensions, dtype="float64")
+ weight_data = weight_data[filter]
+ # So that we can pass these into
+ grid.field_parameters = old_params
+ grid.field_data = YTFieldData( [(k, grid.field_data[k]) for k in old_keys] )
+ return arr, weight_data, bin_fields
+
+ def __getitem__(self, key):
+ return self.field_data[key]
+
+ def __iter__(self):
+ return sorted(self.field_data.items())
+
+ def _get_bins(self, mi, ma, n, take_log):
+ if take_log:
+ return np.logspace(np.log10(mi), np.log10(ma), n+1)
+ else:
+ return np.linspace(mi, ma, n+1)
+
+class Profile1D(ProfileND):
+ def __init__(self, data_source, x_field, x_n, x_min, x_max, x_log,
+ weight_field = None):
+ super(Profile1D, self).__init__(data_source, weight_field)
+ self.x_field = x_field
+ self.x_log = x_log
+ self.x_bins = self._get_bins(x_min, x_max, x_n, x_log)
+
+ self.size = (self.x_bins.size - 1,)
+ self.bin_fields = (self.x_field,)
+ self.bounds = ((self.x_bins[0], self.x_bins[-1]),)
+ self.x = self.x_bins
+
+ def _bin_grid(self, grid, fields, storage):
+ gd = self._get_data(grid, fields)
+ if gd is None: return
+ fdata, wdata, (bf_x,) = gd
+ bin_ind = np.digitize(bf_x, self.x_bins) - 1
+ new_bin_profile1d(bin_ind, wdata, fdata,
+ storage.weight_values, storage.values,
+ storage.mvalues, storage.qvalues,
+ storage.used)
+ # We've binned it!
+
+class Profile2D(ProfileND):
+ def __init__(self, data_source,
+ x_field, x_n, x_min, x_max, x_log,
+ y_field, y_n, y_min, y_max, y_log,
+ weight_field = None):
+ super(Profile2D, self).__init__(data_source, weight_field)
+ self.x_field = x_field
+ self.x_log = x_log
+ self.x_bins = self._get_bins(x_min, x_max, x_n, x_log)
+ self.y_field = y_field
+ self.y_log = y_log
+ self.y_bins = self._get_bins(y_min, y_max, y_n, y_log)
+
+ self.size = (self.x_bins.size - 1, self.y_bins.size - 1)
+
+ self.bin_fields = (self.x_field, self.y_field)
+ self.bounds = ((self.x_bins[0], self.x_bins[-1]),
+ (self.y_bins[0], self.y_bins[-1]))
+ self.x = self.x_bins
+ self.y = self.y_bins
+
+ def _bin_grid(self, grid, fields, storage):
+ rv = self._get_data(grid, fields)
+ if rv is None: return
+ fdata, wdata, (bf_x, bf_y) = rv
+ bin_ind_x = np.digitize(bf_x, self.x_bins) - 1
+ bin_ind_y = np.digitize(bf_y, self.y_bins) - 1
+ new_bin_profile2d(bin_ind_x, bin_ind_y, wdata, fdata,
+ storage.weight_values, storage.values,
+ storage.mvalues, storage.qvalues,
+ storage.used)
+ # We've binned it!
+
+class Profile3D(ProfileND):
+ def __init__(self, data_source,
+ x_field, x_n, x_min, x_max, x_log,
+ y_field, y_n, y_min, y_max, y_log,
+ z_field, z_n, z_min, z_max, z_log,
+ weight_field = None):
+ super(Profile3D, self).__init__(data_source, weight_field)
+ # X
+ self.x_field = x_field
+ self.x_log = x_log
+ self.x_bins = self._get_bins(x_min, x_max, x_n, x_log)
+ # Y
+ self.y_field = y_field
+ self.y_log = y_log
+ self.y_bins = self._get_bins(y_min, y_max, y_n, y_log)
+ # Z
+ self.z_field = z_field
+ self.z_log = z_log
+ self.z_bins = self._get_bins(z_min, z_max, z_n, z_log)
+
+ self.size = (self.x_bins.size - 1,
+ self.y_bins.size - 1,
+ self.z_bins.size - 1)
+
+ self.bin_fields = (self.x_field, self.y_field, self.z_field)
+ self.bounds = ((self.x_bins[0], self.x_bins[-1]),
+ (self.y_bins[0], self.y_bins[-1]),
+ (self.z_bins[0], self.z_bins[-1]))
+
+ self.x = self.x_bins
+ self.y = self.y_bins
+ self.z = self.z_bins
+
+ def _bin_grid(self, grid, fields, storage):
+ rv = self._get_data(grid, fields)
+ if rv is None: return
+ fdata, wdata, (bf_x, bf_y, bf_z) = rv
+ bin_ind_x = np.digitize(bf_x, self.x_bins) - 1
+ bin_ind_y = np.digitize(bf_y, self.y_bins) - 1
+ bin_ind_z = np.digitize(bf_z, self.z_bins) - 1
+ new_bin_profile3d(bin_ind_x, bin_ind_y, bin_ind_z, wdata, fdata,
+ storage.weight_values, storage.values,
+ storage.mvalues, storage.qvalues,
+ storage.used)
+ # We've binned it!
+
+def create_profile(data_source, bin_fields, n = 64,
+ weight_field = "CellMass", fields = None,
+ accumulation = False, fractional = False):
+ r"""
+ Create a 1, 2, or 3D profile object.
+
+ The dimensionality of the profile object is chosen by the number of
+ fields given in the bin_fields argument.
+
+ Parameters
+ ----------
+ data_source : AMR3DData Object
+ The data object to be profiled.
+ bin_fields : list of strings
+ List of the binning fields for profiling.
+ n : int or list of ints
+ The number of bins in each dimension. If None, 64 bins for
+ each bin are used for each bin field.
+ Default: 64.
+ weight_field : str
+ The weight field for computing weighted average for the profile
+ values. If None, the profile values are sums of the data in
+ each bin.
+ fields : list of strings
+ The fields to be profiled.
+ accumulation : bool or list of bools
+ If True, the profile values for a bin n are the cumulative sum of
+ all the values from bin 0 to n. If -True, the sum is reversed so
+ that the value for bin n is the cumulative sum from bin N (total bins)
+ to n. If the profile is 2D or 3D, a list of values can be given to
+ control the summation in each dimension independently.
+ Default: False.
+ fractional : If True the profile values are divided by the sum of all
+ the profile data such that the profile represents a probability
+ distribution function.
+
+ Examples
+ --------
+
+ Create a 1d profile. Access bin field from profile.x and field
+ data from profile.field_data.
+
+ >>> pf = load("DD0046/DD0046")
+ >>> ad = pf.h.all_data()
+ >>> profile = create_profile(ad, ["Density"],
+ ... fields=["Temperature", "x-velocity"]))
+ >>> print profile.x
+ >>> print profile.field_data["Temperature"]
+
+ """
+ if len(bin_fields) == 1:
+ cls = Profile1D
+ elif len(bin_fields) == 2:
+ cls = Profile2D
+ elif len(bin_fields) == 3:
+ cls = Profile3D
+ else:
+ raise NotImplementedError
+ if not iterable(n):
+ n = [n] * len(bin_fields)
+ if not iterable(accumulation):
+ accumulation = [accumulation] * len(bin_fields)
+ logs = [data_source.pf.field_info[f].take_log for f in bin_fields]
+ ex = [data_source.quantities["Extrema"](f, non_zero=l)[0] \
+ for f, l in zip(bin_fields, logs)]
+ args = [data_source]
+ for f, n, (mi, ma), l in zip(bin_fields, n, ex, logs):
+ args += [f, n, mi, ma, l]
+ obj = cls(*args, weight_field = weight_field)
+ setattr(obj, "accumulation", accumulation)
+ setattr(obj, "fractional", fractional)
+ if fields is not None:
+ obj.add_fields(fields)
+ for field in fields:
+ if fractional:
+ obj.field_data[field] /= obj.field_data[field].sum()
+ for axis, acc in enumerate(accumulation):
+ if not acc: continue
+ temp = obj.field_data[field]
+ temp = np.rollaxis(temp, axis)
+ if acc < 0:
+ temp = temp[::-1]
+ temp = temp.cumsum(axis=0)
+ if acc < 0:
+ temp = temp[::-1]
+ temp = np.rollaxis(temp, axis)
+ obj.field_data[field] = temp
+
+ return obj
+
diff -r 7159d0816ffe9e933fdfa0301ac612ac4eedbf63 -r 7026b9ddfc20c9f9f05e654fdbf84c89a42007d1 yt/data_objects/tests/test_profiles.py
--- a/yt/data_objects/tests/test_profiles.py
+++ b/yt/data_objects/tests/test_profiles.py
@@ -1,10 +1,11 @@
from yt.testing import *
from yt.data_objects.profiles import \
- BinnedProfile1D, BinnedProfile2D, BinnedProfile3D
+ BinnedProfile1D, BinnedProfile2D, BinnedProfile3D, \
+ Profile1D, Profile2D, Profile3D
_fields = ("Density", "Temperature", "Dinosaurs", "Tribbles")
-def test_profiles():
+def test_binned_profiles():
pf = fake_random_pf(64, nprocs = 8, fields = _fields)
nv = pf.domain_dimensions.prod()
dd = pf.h.all_data()
@@ -71,3 +72,83 @@
p3d.add_fields(["Ones"], weight="Temperature")
yield assert_equal, p3d["Ones"][:-1,:-1,:-1], np.ones((nb,nb,nb))
+def test_profiles():
+ pf = fake_random_pf(64, nprocs = 8, fields = _fields)
+ nv = pf.domain_dimensions.prod()
+ dd = pf.h.all_data()
+ (rmi, rma), (tmi, tma), (dmi, dma) = dd.quantities["Extrema"](
+ ["Density", "Temperature", "Dinosaurs"])
+ rt, tt, dt = dd.quantities["TotalQuantity"](
+ ["Density", "Temperature", "Dinosaurs"])
+ # First we look at the
+ e1, e2 = 0.9, 1.1
+ for nb in [8, 16, 32, 64]:
+ # We log all the fields or don't log 'em all. No need to do them
+ # individually.
+ for lf in [True, False]:
+ p1d = Profile1D(dd,
+ "Density", nb, rmi*e1, rma*e2, lf,
+ weight_field = None)
+ p1d.add_fields(["Ones", "Temperature"])
+ yield assert_equal, p1d["Ones"].sum(), nv
+ yield assert_rel_equal, tt, p1d["Temperature"].sum(), 7
+
+ p2d = Profile2D(dd,
+ "Density", nb, rmi*e1, rma*e2, lf,
+ "Temperature", nb, tmi*e1, tma*e2, lf,
+ weight_field = None)
+ p2d.add_fields(["Ones", "Temperature"])
+ yield assert_equal, p2d["Ones"].sum(), nv
+ yield assert_rel_equal, tt, p2d["Temperature"].sum(), 7
+
+ p3d = Profile3D(dd,
+ "Density", nb, rmi*e1, rma*e2, lf,
+ "Temperature", nb, tmi*e1, tma*e2, lf,
+ "Dinosaurs", nb, dmi*e1, dma*e2, lf,
+ weight_field = None)
+ p3d.add_fields(["Ones", "Temperature"])
+ yield assert_equal, p3d["Ones"].sum(), nv
+ yield assert_rel_equal, tt, p3d["Temperature"].sum(), 7
+
+ p1d = Profile1D(dd, "x", nb, 0.0, 1.0, False,
+ weight_field = None)
+ p1d.add_fields("Ones")
+ av = nv / nb
+ yield assert_equal, p1d["Ones"], np.ones(nb)*av
+
+ # We re-bin ones with a weight now
+ p1d = Profile1D(dd, "x", nb, 0.0, 1.0, False,
+ weight_field = "Temperature")
+ p1d.add_fields(["Ones"])
+ yield assert_equal, p1d["Ones"], np.ones(nb)
+
+ p2d = Profile2D(dd, "x", nb, 0.0, 1.0, False,
+ "y", nb, 0.0, 1.0, False,
+ weight_field = None)
+ p2d.add_fields("Ones")
+ av = nv / nb**2
+ yield assert_equal, p2d["Ones"], np.ones((nb, nb))*av
+
+ # We re-bin ones with a weight now
+ p2d = Profile2D(dd, "x", nb, 0.0, 1.0, False,
+ "y", nb, 0.0, 1.0, False,
+ weight_field = "Temperature")
+ p2d.add_fields(["Ones"])
+ yield assert_equal, p2d["Ones"], np.ones((nb, nb))
+
+ p3d = Profile3D(dd, "x", nb, 0.0, 1.0, False,
+ "y", nb, 0.0, 1.0, False,
+ "z", nb, 0.0, 1.0, False,
+ weight_field = None)
+ p3d.add_fields("Ones")
+ av = nv / nb**3
+ yield assert_equal, p3d["Ones"], np.ones((nb, nb, nb))*av
+
+ # We re-bin ones with a weight now
+ p3d = Profile3D(dd, "x", nb, 0.0, 1.0, False,
+ "y", nb, 0.0, 1.0, False,
+ "z", nb, 0.0, 1.0, False,
+ weight_field = "Temperature")
+ p3d.add_fields(["Ones"])
+ yield assert_equal, p3d["Ones"], np.ones((nb,nb,nb))
+
diff -r 7159d0816ffe9e933fdfa0301ac612ac4eedbf63 -r 7026b9ddfc20c9f9f05e654fdbf84c89a42007d1 yt/frontends/athena/data_structures.py
--- a/yt/frontends/athena/data_structures.py
+++ b/yt/frontends/athena/data_structures.py
@@ -266,7 +266,7 @@
# know the extent of all the grids.
glis = np.round((glis - self.parameter_file.domain_left_edge)/gdds).astype('int')
new_dre = np.max(gres,axis=0)
- self.parameter_file.domain_right_edge = np.round(new_dre, decimals=6)
+ self.parameter_file.domain_right_edge = np.round(new_dre, decimals=12)
self.parameter_file.domain_width = \
(self.parameter_file.domain_right_edge -
self.parameter_file.domain_left_edge)
@@ -294,9 +294,9 @@
dxs.append(dx)
dx = np.array(dxs)
- self.grid_left_edge = np.round(self.parameter_file.domain_left_edge + dx*glis, decimals=6)
+ self.grid_left_edge = np.round(self.parameter_file.domain_left_edge + dx*glis, decimals=12)
self.grid_dimensions = gdims.astype("int32")
- self.grid_right_edge = np.round(self.grid_left_edge + dx*self.grid_dimensions, decimals=6)
+ self.grid_right_edge = np.round(self.grid_left_edge + dx*self.grid_dimensions, decimals=12)
if self.parameter_file.dimensionality <= 2:
self.grid_right_edge[:,2] = self.parameter_file.domain_right_edge[2]
if self.parameter_file.dimensionality == 1:
diff -r 7159d0816ffe9e933fdfa0301ac612ac4eedbf63 -r 7026b9ddfc20c9f9f05e654fdbf84c89a42007d1 yt/frontends/athena/fields.py
--- a/yt/frontends/athena/fields.py
+++ b/yt/frontends/athena/fields.py
@@ -78,7 +78,7 @@
def _convertDensity(data) :
return data.convert("Density")
def _density(field, data) :
- return data["density"]
+ return data["density"].copy()
add_field("Density", function=_density, take_log=False,
units=r"\rm{g}/\rm{cm}^3", projected_units=r"\rm{g}/\rm{cm}^2",
convert_function=_convertDensity)
@@ -87,21 +87,21 @@
return data.convert("x-velocity")
def _xvelocity(field, data):
if "velocity_x" in data.pf.field_info:
- return data["velocity_x"]
+ return data["velocity_x"].copy()
else:
return data["momentum_x"]/data["density"]
add_field("x-velocity", function=_xvelocity, take_log=False,
units=r"\rm{cm}/\rm{s}", convert_function=_convertVelocity)
def _yvelocity(field, data):
if "velocity_y" in data.pf.field_info:
- return data["velocity_y"]
+ return data["velocity_y"].copy()
else:
return data["momentum_y"]/data["density"]
add_field("y-velocity", function=_yvelocity, take_log=False,
units=r"\rm{cm}/\rm{s}", convert_function=_convertVelocity)
def _zvelocity(field, data):
if "velocity_z" in data.pf.field_info:
- return data["velocity_z"]
+ return data["velocity_z"].copy()
else:
return data["momentum_z"]/data["density"]
add_field("z-velocity", function=_zvelocity, take_log=False,
@@ -128,7 +128,7 @@
return data.convert("Density")*data.convert("x-velocity")**2
def _pressure(field, data) :
if "pressure" in data.pf.field_info:
- return data["pressure"]
+ return data["pressure"].copy()
else:
eint = data["total_energy"] - 0.5*(data["momentum_x"]**2 +
data["momentum_y"]**2 +
@@ -154,17 +154,17 @@
def _convertBfield(data):
return np.sqrt(4*np.pi*data.convert("Density")*data.convert("x-velocity")**2)
def _Bx(field, data):
- return data['cell_centered_B_x']
+ return data['cell_centered_B_x'].copy()
add_field("Bx", function=_Bx, take_log=False,
units=r"\rm{Gauss}", display_name=r"B_x",
convert_function=_convertBfield)
def _By(field, data):
- return data['cell_centered_B_y']
+ return data['cell_centered_B_y'].copy()
add_field("By", function=_By, take_log=False,
units=r"\rm{Gauss}", display_name=r"B_y",
convert_function=_convertBfield)
def _Bz(field, data):
- return data['cell_centered_B_z']
+ return data['cell_centered_B_z'].copy()
add_field("Bz", function=_Bz, take_log=False,
units=r"\rm{Gauss}", display_name=r"B_z",
convert_function=_convertBfield)
diff -r 7159d0816ffe9e933fdfa0301ac612ac4eedbf63 -r 7026b9ddfc20c9f9f05e654fdbf84c89a42007d1 yt/funcs.py
--- a/yt/funcs.py
+++ b/yt/funcs.py
@@ -538,6 +538,16 @@
contents = None
return contents
+def download_file(url, filename):
+ import urllib
+ class MyURLopener(urllib.FancyURLopener):
+ def http_error_default(self, url, fp, errcode, errmsg, headers):
+ raise RuntimeError, \
+ "Attempt to download file from %s failed with error %s: %s." % \
+ (url, errcode, errmsg)
+ fn, h = MyURLopener().retrieve(url, filename)
+ return fn
+
# This code snippet is modified from Georg Brandl
def bb_apicall(endpoint, data, use_pass = True):
import urllib, urllib2
diff -r 7159d0816ffe9e933fdfa0301ac612ac4eedbf63 -r 7026b9ddfc20c9f9f05e654fdbf84c89a42007d1 yt/mods.py
--- a/yt/mods.py
+++ b/yt/mods.py
@@ -54,7 +54,7 @@
ValidateParameter, ValidateDataField, ValidateProperty, \
ValidateSpatial, ValidateGridType, \
TimeSeriesData, AnalysisTask, analysis_task, \
- ImageArray, particle_filter
+ ImageArray, particle_filter, create_profile
from yt.data_objects.derived_quantities import \
add_quantity, quantity_info
@@ -141,7 +141,7 @@
apply_colormap, scale_image, write_projection, \
SlicePlot, AxisAlignedSlicePlot, OffAxisSlicePlot, \
ProjectionPlot, OffAxisProjectionPlot, \
- show_colormaps
+ show_colormaps, ProfilePlot, PhasePlot
from yt.visualization.volume_rendering.api import \
ColorTransferFunction, PlanckTransferFunction, ProjectionTransferFunction, \
diff -r 7159d0816ffe9e933fdfa0301ac612ac4eedbf63 -r 7026b9ddfc20c9f9f05e654fdbf84c89a42007d1 yt/utilities/exceptions.py
--- a/yt/utilities/exceptions.py
+++ b/yt/utilities/exceptions.py
@@ -13,6 +13,7 @@
# The full license is in the file COPYING.txt, distributed with this software.
#-----------------------------------------------------------------------------
+
# We don't need to import 'exceptions'
#import exceptions
import os.path
@@ -317,3 +318,19 @@
return "This FITS header is not recognizable in its current form.\n" + \
"If you would like to force loading, specify: \n" + \
"ignore_unit_names = True"
+
+class YTEmptyProfileData(Exception):
+ pass
+
+class YTDuplicateFieldInProfile(Exception):
+ def __init__(self, field, new_spec, old_spec):
+ self.field = field
+ self.new_spec = new_spec
+ self.old_spec = old_spec
+
+ def __str__(self):
+ r = """Field %s already exists with field spec:
+ %s
+ But being asked to add it with:
+ %s""" % (self.field, self.old_spec, self.new_spec)
+ return r
diff -r 7159d0816ffe9e933fdfa0301ac612ac4eedbf63 -r 7026b9ddfc20c9f9f05e654fdbf84c89a42007d1 yt/utilities/lib/misc_utilities.pyx
--- a/yt/utilities/lib/misc_utilities.pyx
+++ b/yt/utilities/lib/misc_utilities.pyx
@@ -25,6 +25,108 @@
@cython.boundscheck(False)
@cython.wraparound(False)
@cython.cdivision(True)
+def new_bin_profile1d(np.ndarray[np.int64_t, ndim=1] bins_x,
+ np.ndarray[np.float64_t, ndim=1] wsource,
+ np.ndarray[np.float64_t, ndim=2] bsource,
+ np.ndarray[np.float64_t, ndim=1] wresult,
+ np.ndarray[np.float64_t, ndim=2] bresult,
+ np.ndarray[np.float64_t, ndim=2] mresult,
+ np.ndarray[np.float64_t, ndim=2] qresult,
+ np.ndarray[np.uint8_t, ndim=1, cast=True] used):
+ cdef int n, fi, bin
+ cdef np.float64_t wval, bval, oldwr
+ cdef int nb = bins_x.shape[0]
+ cdef int nf = bsource.shape[1]
+ for n in range(nb):
+ bin = bins_x[n]
+ wval = wsource[n]
+ oldwr = wresult[bin]
+ wresult[bin] += wval
+ for fi in range(nf):
+ bval = bsource[n,fi]
+ # qresult has to have the previous wresult
+ qresult[bin,fi] += (oldwr * wval * (bval - mresult[bin,fi])**2) / \
+ (oldwr + wval)
+ bresult[bin,fi] += wval*bval
+ # mresult needs the new wresult
+ mresult[bin,fi] += wval * (bval - mresult[bin,fi]) / wresult[bin]
+ used[bin] = 1
+ return
+
+ at cython.boundscheck(False)
+ at cython.wraparound(False)
+ at cython.cdivision(True)
+def new_bin_profile2d(np.ndarray[np.int64_t, ndim=1] bins_x,
+ np.ndarray[np.int64_t, ndim=1] bins_y,
+ np.ndarray[np.float64_t, ndim=1] wsource,
+ np.ndarray[np.float64_t, ndim=2] bsource,
+ np.ndarray[np.float64_t, ndim=2] wresult,
+ np.ndarray[np.float64_t, ndim=3] bresult,
+ np.ndarray[np.float64_t, ndim=3] mresult,
+ np.ndarray[np.float64_t, ndim=3] qresult,
+ np.ndarray[np.uint8_t, ndim=2, cast=True] used):
+ cdef int n, fi, bin_x, bin_y
+ cdef np.float64_t wval, bval, oldwr
+ cdef int nb = bins_x.shape[0]
+ cdef int nf = bsource.shape[1]
+ for n in range(nb):
+ bin_x = bins_x[n]
+ bin_y = bins_y[n]
+ wval = wsource[n]
+ oldwr = wresult[bin_x, bin_y]
+ wresult[bin_x,bin_y] += wval
+ for fi in range(nf):
+ bval = bsource[n,fi]
+ # qresult has to have the previous wresult
+ qresult[bin_x,bin_y,fi] += (oldwr * wval * (bval - mresult[bin_x,bin_y,fi])**2) / \
+ (oldwr + wval)
+ bresult[bin_x,bin_y,fi] += wval*bval
+ # mresult needs the new wresult
+ mresult[bin_x,bin_y,fi] += wval * (bval - mresult[bin_x,bin_y,fi]) / wresult[bin_x,bin_y]
+ used[bin_x,bin_y] = 1
+ return
+
+ at cython.boundscheck(False)
+ at cython.wraparound(False)
+ at cython.cdivision(True)
+def new_bin_profile3d(np.ndarray[np.int64_t, ndim=1] bins_x,
+ np.ndarray[np.int64_t, ndim=1] bins_y,
+ np.ndarray[np.int64_t, ndim=1] bins_z,
+ np.ndarray[np.float64_t, ndim=1] wsource,
+ np.ndarray[np.float64_t, ndim=2] bsource,
+ np.ndarray[np.float64_t, ndim=3] wresult,
+ np.ndarray[np.float64_t, ndim=4] bresult,
+ np.ndarray[np.float64_t, ndim=4] mresult,
+ np.ndarray[np.float64_t, ndim=4] qresult,
+ np.ndarray[np.uint8_t, ndim=3, cast=True] used):
+ cdef int n, fi, bin_x, bin_y, bin_z
+ cdef np.float64_t wval, bval, oldwr
+ cdef int nb = bins_x.shape[0]
+ cdef int nf = bsource.shape[1]
+ for n in range(nb):
+ bin_x = bins_x[n]
+ bin_y = bins_y[n]
+ bin_z = bins_z[n]
+ wval = wsource[n]
+ oldwr = wresult[bin_x, bin_y, bin_z]
+ wresult[bin_x,bin_y,bin_z] += wval
+ for fi in range(nf):
+ bval = bsource[n,fi]
+ # qresult has to have the previous wresult
+ qresult[bin_x,bin_y,bin_z,fi] += \
+ (oldwr * wval * (bval - mresult[bin_x,bin_y,bin_z,fi])**2) / \
+ (oldwr + wval)
+ bresult[bin_x,bin_y,bin_z,fi] += wval*bval
+ # mresult needs the new wresult
+ mresult[bin_x,bin_y,bin_z,fi] += wval * \
+ (bval - mresult[bin_x,bin_y,bin_z,fi]) / \
+ wresult[bin_x,bin_y,bin_z]
+ used[bin_x,bin_y,bin_z] = 1
+ return
+
+ at cython.boundscheck(False)
+ at cython.wraparound(False)
+ at cython.cdivision(True)
def bin_profile1d(np.ndarray[np.int64_t, ndim=1] bins_x,
np.ndarray[np.float64_t, ndim=1] wsource,
np.ndarray[np.float64_t, ndim=1] bsource,
diff -r 7159d0816ffe9e933fdfa0301ac612ac4eedbf63 -r 7026b9ddfc20c9f9f05e654fdbf84c89a42007d1 yt/visualization/api.py
--- a/yt/visualization/api.py
+++ b/yt/visualization/api.py
@@ -53,6 +53,10 @@
ProjectionPlot, \
OffAxisProjectionPlot
+from profile_plotter import \
+ ProfilePlot, \
+ PhasePlot
+
from base_plot_types import \
get_multi_plot
diff -r 7159d0816ffe9e933fdfa0301ac612ac4eedbf63 -r 7026b9ddfc20c9f9f05e654fdbf84c89a42007d1 yt/visualization/base_plot_types.py
--- a/yt/visualization/base_plot_types.py
+++ b/yt/visualization/base_plot_types.py
@@ -17,9 +17,28 @@
from ._mpl_imports import \
FigureCanvasAgg, FigureCanvasPdf, FigureCanvasPS
from yt.funcs import \
- get_image_suffix, mylog
+ get_image_suffix, mylog, x_dict, y_dict
-
+class CallbackWrapper(object):
+ def __init__(self, viewer, window_plot, frb, field):
+ self.frb = frb
+ self.data = frb.data_source
+ self._axes = window_plot.axes
+ self._figure = window_plot.figure
+ if len(self._axes.images) > 0:
+ self.image = self._axes.images[0]
+ if frb.axis < 3:
+ DD = frb.pf.domain_width
+ xax = x_dict[frb.axis]
+ yax = y_dict[frb.axis]
+ self._period = (DD[xax], DD[yax])
+ self.pf = frb.pf
+ self.xlim = viewer.xlim
+ self.ylim = viewer.ylim
+ if 'OffAxisSlice' in viewer._plot_type:
+ self._type_name = "CuttingPlane"
+ else:
+ self._type_name = viewer._plot_type
class PlotMPL(object):
"""A base class for all yt plots made using matplotlib.
@@ -36,6 +55,7 @@
self.axes = self.figure.add_axes(axrect)
else:
axes.cla()
+ axes.set_position(axrect)
self.axes = axes
self.canvas = FigureCanvasAgg(self.figure)
diff -r 7159d0816ffe9e933fdfa0301ac612ac4eedbf63 -r 7026b9ddfc20c9f9f05e654fdbf84c89a42007d1 yt/visualization/eps_writer.py
--- a/yt/visualization/eps_writer.py
+++ b/yt/visualization/eps_writer.py
@@ -26,7 +26,9 @@
from .plot_types import \
VMPlot, \
ProfilePlot
+from .plot_collection import PlotCollection
from .plot_window import PlotWindow
+from .profile_plotter import PhasePlot
from .plot_modifications import get_smallest_appropriate_unit
class DualEPS(object):
@@ -57,6 +59,14 @@
#=============================================================================
+ def return_field(self, plot):
+ if isinstance(plot, PlotWindow) or isinstance(plot, PhasePlot):
+ return plot.plots.keys()[0]
+ else:
+ return None
+
+#=============================================================================
+
def axis_box(self, xrange=(0,1), yrange=(0,1), xlabel="", ylabel="",
xlog=False, ylog=False, xdata=None, ydata=None,
tickcolor=None, bare_axes=False,
@@ -275,18 +285,17 @@
>>> d.axis_box_yt(p)
>>> d.save_fig()
"""
- if isinstance(plot, PlotWindow):
+ if isinstance(plot, (PlotWindow, PhasePlot)):
plot.refresh()
- width = plot.width[0]
else:
plot._redraw_image()
- if isinstance(plot, VMPlot):
- width = plot.width
- if isinstance(plot, VMPlot) or isinstance(plot, PlotWindow):
+ if isinstance(plot, (VMPlot, PlotWindow)):
if isinstance(plot, PlotWindow):
data = plot._frb
+ width = plot.width[0]
else:
data = plot.data
+ width = plot.width
if units == None:
units = get_smallest_appropriate_unit(width, plot.pf)
_xrange = (0, width * plot.pf[units])
@@ -314,6 +323,26 @@
_ylabel = 'Image y (%s)' % (units)
if tickcolor == None:
_tickcolor = pyx.color.cmyk.white
+ elif isinstance(plot, PhasePlot):
+ k = plot.plots.keys()[0]
+ _xrange = plot[k].axes.get_xlim()
+ _yrange = plot[k].axes.get_ylim()
+ _xlog = plot.profile.x_log
+ _ylog = plot.profile.y_log
+ if bare_axes:
+ _xlabel = ""
+ _ylabel = ""
+ else:
+ if xlabel != None:
+ _xlabel = xlabel
+ else:
+ _xlabel = plot[k].axes.get_xlabel()
+ if ylabel != None:
+ _ylabel = ylabel
+ else:
+ _ylabel = plot[k].axes.get_ylabel()
+ if tickcolor == None:
+ _tickcolor = None
else:
_xrange = plot._axes.get_xlim()
_yrange = plot._axes.get_ylim()
@@ -412,7 +441,7 @@
plot.colorbar = None
plot._redraw_image()
_p1 = plot._figure
- elif isinstance(plot, PlotWindow):
+ elif isinstance(plot, (PlotWindow, PhasePlot)):
self.field = field
if self.field == None:
self.field = plot.plots.keys()[0]
@@ -424,18 +453,22 @@
plot.refresh()
_p1 = plot.plots[self.field].figure
# hack to account for non-square display ratios (not sure why)
- shift = 12.0 / 340
+ if isinstance(plot, PlotWindow):
+ shift = 12.0 / 340
elif isinstance(plot, ProfilePlot):
plot._redraw_image()
# Remove colorbar
_p1 = plot._figure
_p1.delaxes(_p1.axes[1])
+ else:
+ raise RuntimeError("Unknown plot type")
+
_p1.axes[0].set_axis_off() # remove axes
_p1.axes[0].set_position([-shift,0,1,1]) # rescale figure
_p1.set_facecolor('w') # set background color
figure_canvas = FigureCanvasAgg(_p1)
figure_canvas.draw()
- size = _p1.get_size_inches() * _p1.dpi
+ size = (_p1.get_size_inches() * _p1.dpi).astype('int')
image = pyx.bitmap.image(size[0], size[1], "RGB",
figure_canvas.tostring_rgb())
#figure_canvas.print_png('test.png')
@@ -567,7 +600,7 @@
#=============================================================================
- def colorbar_yt(self, plot, **kwargs):
+ def colorbar_yt(self, plot, field=None, **kwargs):
r"""Wrapper around DualEPS.colorbar to take information from a yt plot.
Accepts all parameters that DualEPS.colorbar takes.
@@ -587,7 +620,9 @@
>>> d.save_fig()
"""
_cmap = None
- if isinstance(plot, PlotWindow):
+ if field != None:
+ self.field = field
+ if isinstance(plot, (PlotWindow, PhasePlot)):
_cmap = plot._colormaps[self.field]
else:
if plot.cmap != None:
@@ -601,10 +636,13 @@
_zlabel = _zlabel.replace("_","\;")
_zlog = plot.log_field
_zrange = (plot.norm.vmin, plot.norm.vmax)
- elif isinstance(plot, PlotWindow):
+ elif isinstance(plot, (PlotWindow, PhasePlot)):
proj = plot._plot_type.endswith("Projection") and \
plot.data_source.weight_field == None
- _zlabel = plot.pf.field_info[self.field].get_label(proj)
+ if isinstance(plot, PlotWindow):
+ _zlabel = plot.pf.field_info[self.field].get_label(proj)
+ else:
+ _zlabel = plot.data_source.pf.field_info[self.field].get_label(proj)
_zlabel = _zlabel.replace("_","\;")
_zlog = plot.get_log(self.field)[self.field]
if plot.plots[self.field].zmin == None:
@@ -783,13 +821,13 @@
#=============================================================================
#=============================================================================
-def multiplot(ncol, nrow, yt_plots=None, images=None, xranges=None,
- yranges=None, xlabels=None, ylabels=None,
+def multiplot(ncol, nrow, yt_plots=None, fields=None, images=None,
+ xranges=None, yranges=None, xlabels=None, ylabels=None,
xdata=None, ydata=None, colorbars=None,
shrink_cb=0.95, figsize=(8,8), margins=(0,0), titles=None,
savefig=None, format="eps", yt_nocbar=False, bare_axes=False,
xaxis_flags=None, yaxis_flags=None,
- cb_flags=None):
+ cb_flags=None, cb_location=None, plot_collection=False):
r"""Convenience routine to create a multi-panel figure from yt plots or
JPEGs. The images are first placed from the origin, and then
bottom-to-top and left-to-right.
@@ -834,6 +872,11 @@
axes.
cb_flags : list of booleans
Flags for each plot to have a colorbar or not.
+ cb_location : list of strings
+ Strings to control the location of the colorbar (left, right,
+ top, bottom)
+ plot_collection : boolean
+ Set to true to yt_plots is a PlotCollection
Examples
--------
@@ -858,8 +901,9 @@
yt plots.
"""
# Error check
+ npanels = ncol*nrow
if images != None:
- if len(images) != ncol*nrow:
+ if len(images) != npanels:
raise RuntimeError("Number of images (%d) doesn't match nrow(%d)"\
" x ncol(%d)." % (len(images), nrow, ncol))
return
@@ -872,21 +916,23 @@
_yt = True
else:
_yt = False
+ if fields == None:
+ fields = [None] * npanels
# If no ranges or labels given and given only images, fill them in.
if not _yt:
if xranges is None:
xranges = []
- for i in range(nrow*ncol): xranges.append((0,1))
+ for i in range(npanels): xranges.append((0,1))
if yranges is None:
yranges = []
- for i in range(nrow*ncol): yranges.append((0,1))
+ for i in range(npanels): yranges.append((0,1))
if xlabels is None:
xlabels = []
- for i in range(nrow*ncol): xlabels.append("")
+ for i in range(npanels): xlabels.append("")
if ylabels is None:
ylabels = []
- for i in range(nrow*ncol): ylabels.append("")
+ for i in range(npanels): ylabels.append("")
d = DualEPS(figsize=figsize)
count = 0
@@ -930,7 +976,8 @@
ylabel = ylabels[j]
else:
ylabel = None
- d.insert_image_yt(yt_plots[index], pos=(xpos, ypos))
+ d.insert_image_yt(yt_plots[index], pos=(xpos, ypos),
+ field=fields[index])
d.axis_box_yt(yt_plots[index], pos=(xpos, ypos),
bare_axes=bare_axes, xaxis_side=xaxis,
yaxis_side=yaxis,
@@ -964,32 +1011,49 @@
if cb_flags != None:
if cb_flags[index] == False:
continue
- if ncol == 1:
- orientation = "right"
+ if cb_location == None:
+ if ncol == 1:
+ orientation = "right"
+ elif i == 0:
+ orientation = "left"
+ elif i+1 == ncol:
+ orientation = "right"
+ elif j == 0:
+ orientation = "bottom"
+ elif j+1 == nrow:
+ orientation = "top"
+ else:
+ orientation = None # Marker for interior plot
+ else:
+ if fields[index] not in cb_location.keys():
+ raise RuntimeError("%s not found in cb_location dict" %
+ fields[index])
+ return
+ orientation = cb_location[fields[index]]
+ if orientation == "right":
xpos = bbox[1]
ypos = ypos0
- elif j == 0:
- orientation = "bottom"
+ elif orientation == "left":
+ xpos = bbox[0]
+ ypos = ypos0
+ elif orientation == "bottom":
ypos = bbox[2]
xpos = xpos0
- elif i == 0:
- orientation = "left"
- xpos = bbox[0]
- ypos = ypos0
- elif i+1 == ncol:
- orientation = "right"
- xpos = bbox[1]
- ypos = ypos0
- elif j+1 == nrow:
- orientation = "top"
+ elif orientation == "top":
ypos = bbox[3]
xpos = xpos0
else:
+ mylog.warning("Unknown colorbar location %s. "
+ "No colorbar displayed." % orientation)
orientation = None # Marker for interior plot
if orientation != None:
if _yt:
+ # Set field if undefined
+ if fields[index] == None:
+ fields[index] = d.return_field(yt_plots[index])
d.colorbar_yt(yt_plots[index],
+ field=fields[index],
pos=[xpos,ypos],
shrink=shrink_cb,
orientation=orientation)
@@ -1009,8 +1073,8 @@
#=============================================================================
-def multiplot_yt(ncol, nrow, plot_col, **kwargs):
- r"""Wrapper for multiplot that takes a yt PlotCollection.
+def multiplot_yt(ncol, nrow, plots, fields=None, **kwargs):
+ r"""Wrapper for multiplot that takes a yt PlotWindow or PlotCollection.
Accepts all parameters used in multiplot.
@@ -1020,8 +1084,8 @@
Number of columns in the figure.
nrow : integer
Number of rows in the figure.
- plot_col : `PlotCollection`
- yt PlotCollection that has the plots to be used.
+ plots : `PlotCollection` or `PlotWindow`
+ yt PlotCollection or PlotWindow that has the plots to be used.
Examples
--------
@@ -1040,18 +1104,42 @@
>>> mp = multiplot_yt(2,2,pc,savefig="yt",shrink_cb=0.9, bare_axes=False,
>>> yt_nocbar=False, margins=(0.5,0.5))
"""
- if len(plot_col.plots) < nrow*ncol:
- raise RuntimeError("Number of plots in PlotCollection is less "\
- "than nrow(%d) x ncol(%d)." % \
- (len(plot_col.plots), nrow, ncol))
+ # Determine whether the plots are organized in a PlotCollection,
+ # PlotWindow, or list of PlotWindows
+ if isinstance(plots, PlotCollection):
+ if len(plots.plots) < nrow*ncol:
+ raise RuntimeError("Number of plots in PlotCollection is less "\
+ "than nrow(%d) x ncol(%d)." % \
+ (len(plots.plots), nrow, ncol))
+ return
+ figure = multiplot(ncol, nrow, yt_plots=plots.plots,
+ plot_collection=True, **kwargs)
+ elif isinstance(plots, PlotWindow):
+ if fields == None:
+ fields = plots.fields
+ if len(fields) < nrow*ncol:
+ raise RuntimeError("Number of plots is less "\
+ "than nrow(%d) x ncol(%d)." % \
+ (len(fields), nrow, ncol))
+ return
+ figure = multiplot(ncol, nrow, yt_plots=plots, fields=fields, **kwargs)
+ elif isinstance(plots, list) and isinstance(plots[0], PlotWindow):
+ if len(plots) < nrow*ncol:
+ raise RuntimeError("Number of plots is less "\
+ "than nrow(%d) x ncol(%d)." % \
+ (len(fields), nrow, ncol))
+ return
+ figure = multiplot(ncol, nrow, yt_plots=plots, fields=fields, **kwargs)
+ else:
+ raise RuntimeError("Unknown plot type in multiplot_yt")
return
- figure = multiplot(ncol, nrow, yt_plots=plot_col.plots, **kwargs)
return figure
#=============================================================================
-def single_plot(plot, figsize=(12,12), cb_orient="right", bare_axes=False,
- savefig=None, colorbar=True, file_format='eps', **kwargs):
+def single_plot(plot, field=None, figsize=(12,12), cb_orient="right",
+ bare_axes=False, savefig=None, colorbar=True,
+ file_format='eps', **kwargs):
r"""Wrapper for DualEPS routines to create a figure directy from a yt
plot. Calls insert_image_yt, axis_box_yt, and colorbar_yt.
@@ -1080,7 +1168,7 @@
>>> single_plot(p, savefig="figure1")
"""
d = DualEPS(figsize=figsize)
- d.insert_image_yt(plot)
+ d.insert_image_yt(plot, field=field)
d.axis_box_yt(plot, bare_axes=bare_axes, **kwargs)
if colorbar:
d.colorbar_yt(plot, orientation=cb_orient)
This diff is so big that we needed to truncate the remainder.
Repository URL: https://bitbucket.org/yt_analysis/yt/
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