[yt-svn] commit/yt: 2 new changesets
commits-noreply at bitbucket.org
commits-noreply at bitbucket.org
Tue Sep 2 18:30:29 PDT 2014
2 new commits in yt:
https://bitbucket.org/yt_analysis/yt/commits/d771801e37dc/
Changeset: d771801e37dc
Branch: stable
User: MatthewTurk
Date: 2014-09-03 03:25:38
Summary: Merging from dev
Affected #: 109 files
diff -r 73a9f749157260c8949f05c07715305aafa06408 -r d771801e37dc81e648bc22a7ef9f4de6d8bfb0b9 doc/helper_scripts/show_fields.py
--- a/doc/helper_scripts/show_fields.py
+++ b/doc/helper_scripts/show_fields.py
@@ -186,9 +186,20 @@
this_f = getattr(frontends_module, frontend)
field_info_names = [fi for fi in dir(this_f) if "FieldInfo" in fi]
dataset_names = [dset for dset in dir(this_f) if "Dataset" in dset]
+
if frontend == "sph":
field_info_names = \
['TipsyFieldInfo' if 'Tipsy' in d else 'SPHFieldInfo' for d in dataset_names]
+ elif frontend == "boxlib":
+ field_info_names = []
+ for d in dataset_names:
+ if "Maestro" in d:
+ field_info_names.append("MaestroFieldInfo")
+ elif "Castro" in d:
+ field_info_names.append("CastroFieldInfo")
+ else:
+ field_info_names.append("BoxlibFieldInfo")
+
for dset_name, fi_name in zip(dataset_names, field_info_names):
fi = getattr(this_f, fi_name)
nfields = 0
diff -r 73a9f749157260c8949f05c07715305aafa06408 -r d771801e37dc81e648bc22a7ef9f4de6d8bfb0b9 doc/install_script.sh
--- a/doc/install_script.sh
+++ b/doc/install_script.sh
@@ -500,13 +500,28 @@
fi
[ ! -e $LIB/extracted ] && tar xfz $LIB.tar.gz
touch $LIB/extracted
+ BUILD_ARGS=""
+ case $LIB in
+ *h5py*)
+ BUILD_ARGS="--hdf5=${HDF5_DIR}"
+ ;;
+ *numpy*)
+ if [ -e ${DEST_DIR}/lib/python2.7/site-packages/numpy/__init__.py ]
+ then
+ VER=$(${DEST_DIR}/bin/python -c 'from distutils.version import StrictVersion as SV; \
+ import numpy; print SV(numpy.__version__) < SV("1.8.0")')
+ if [ $VER == "True" ]
+ then
+ echo "Removing previous NumPy instance (see issue #889)"
+ rm -rf ${DEST_DIR}/lib/python2.7/site-packages/{numpy*,*.pth}
+ fi
+ fi
+ ;;
+ *)
+ ;;
+ esac
cd $LIB
- if [ ! -z `echo $LIB | grep h5py` ]
- then
- ( ${DEST_DIR}/bin/python2.7 setup.py build --hdf5=${HDF5_DIR} $* 2>&1 ) 1>> ${LOG_FILE} || do_exit
- else
- ( ${DEST_DIR}/bin/python2.7 setup.py build $* 2>&1 ) 1>> ${LOG_FILE} || do_exit
- fi
+ ( ${DEST_DIR}/bin/python2.7 setup.py build ${BUILD_ARGS} $* 2>&1 ) 1>> ${LOG_FILE} || do_exit
( ${DEST_DIR}/bin/python2.7 setup.py install 2>&1 ) 1>> ${LOG_FILE} || do_exit
touch done
cd ..
@@ -580,56 +595,54 @@
mkdir -p ${DEST_DIR}/src
cd ${DEST_DIR}/src
-CYTHON='Cython-0.19.1'
-FORTHON='Forthon-0.8.11'
+CYTHON='Cython-0.20.2'
PYX='PyX-0.12.1'
-PYTHON='Python-2.7.6'
+PYTHON='Python-2.7.8'
BZLIB='bzip2-1.0.6'
FREETYPE_VER='freetype-2.4.12'
-H5PY='h5py-2.1.3'
+H5PY='h5py-2.3.1'
HDF5='hdf5-1.8.11'
-IPYTHON='ipython-2.1.0'
+IPYTHON='ipython-2.2.0'
LAPACK='lapack-3.4.2'
PNG=libpng-1.6.3
-MATPLOTLIB='matplotlib-1.3.0'
-MERCURIAL='mercurial-3.0'
-NOSE='nose-1.3.0'
-NUMPY='numpy-1.7.1'
+MATPLOTLIB='matplotlib-1.4.0'
+MERCURIAL='mercurial-3.1'
+NOSE='nose-1.3.4'
+NUMPY='numpy-1.8.2'
PYTHON_HGLIB='python-hglib-1.0'
-PYZMQ='pyzmq-13.1.0'
+PYZMQ='pyzmq-14.3.1'
ROCKSTAR='rockstar-0.99.6'
-SCIPY='scipy-0.12.0'
+SCIPY='scipy-0.14.0'
SQLITE='sqlite-autoconf-3071700'
-SYMPY='sympy-0.7.3'
-TORNADO='tornado-3.1'
-ZEROMQ='zeromq-3.2.4'
+SYMPY='sympy-0.7.5'
+TORNADO='tornado-4.0.1'
+ZEROMQ='zeromq-4.0.4'
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 '118e3ebd76f50bda8187b76654e65caab2c2c403df9b89da525c2c963dedc7b38d898ae0b92d44b278731d969a891eb3f7b5bcc138cfe3e037f175d4c87c29ec Cython-0.20.2.tar.gz' > Cython-0.20.2.tar.gz.sha512
echo '4941f5aa21aff3743546495fb073c10d2657ff42b2aff401903498638093d0e31e344cce778980f28a7170c6d29eab72ac074277b9d4088376e8692dc71e55c1 PyX-0.12.1.tar.gz' > PyX-0.12.1.tar.gz.sha512
-echo '3df0ba4b1cfef5f02fb27925de4c2ca414eca9000af6a3d475d39063720afe987287c3d51377e0a36b88015573ef699f700782e1749c7a357b8390971d858a79 Python-2.7.6.tgz' > Python-2.7.6.tgz.sha512
+echo '4b05f0a490ddee37e8fc7970403bb8b72c38e5d173703db40310e78140d9d5c5732789d69c68dbd5605a623e4582f5b9671f82b8239ecdb34ad4261019dace6a Python-2.7.8.tgz' > Python-2.7.8.tgz.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
echo 'a296dfcaef7e853e58eed4e24b37c4fa29cfc6ac688def048480f4bb384b9e37ca447faf96eec7b378fd764ba291713f03ac464581d62275e28eb2ec99110ab6 reason-js-20120623.zip' > reason-js-20120623.zip.sha512
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 'f0da1d2ac855c02fb828444d719a1b23a580adb049335f3e732ace67558a125ac8cd3b3a68ac6bf9d10aa3ab19e4672b814eb28cc8c66910750c62efb655d744 h5py-2.3.1.tar.gz' > h5py-2.3.1.tar.gz.sha512
echo 'e9db26baa297c8ed10f1ca4a3fcb12d6985c6542e34c18d48b2022db73014f054c8b8434f3df70dcf44631f38b016e8050701d52744953d0fced3272d7b6b3c1 hdf5-1.8.11.tar.gz' > hdf5-1.8.11.tar.gz.sha512
-echo '68c15f6402cacfd623f8e2b70c22d06541de3616fdb2d502ce93cd2fdb4e7507bb5b841a414a4123264221ee5ffb0ebefbb8541f79e647fcb9f73310b4c2d460 ipython-2.1.0.tar.gz' > ipython-2.1.0.tar.gz.sha512
+echo '4953bf5e9d6d5c6ad538d07d62b5b100fd86a37f6b861238501581c0059bd4655345ca05cf395e79709c38ce4cb9c6293f5d11ac0252a618ad8272b161140d13 ipython-2.2.0.tar.gz' > ipython-2.2.0.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 '8cd387ea0d74d5ed01b58d5ef8e3fb408d4b05f7deb45a02e34fbb931fd920aafbfcb3a9b52a027ebcdb562837198637a0e51f2121c94e0fcf7f7d8c016f5342 mercurial-3.0.tar.gz' > mercurial-3.0.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 '60aa386639dec17b4f579955df60f2aa7c8ccd589b3490bb9afeb2929ea418d5d1a36a0b02b8d4a6734293076e9069429956c56cf8bd099b756136f2657cf9d4 matplotlib-1.4.0.tar.gz' > matplotlib-1.4.0.tar.gz.sha512
+echo '1ee2fe7a241bf81087e55d9e4ee8fa986f41bb0655d4828d244322c18f3958a1f3111506e2df15aefcf86100b4fe530fcab2d4c041b5945599ed3b3a889d50f5 mercurial-3.1.tar.gz' > mercurial-3.1.tar.gz.sha512
+echo '19499ab08018229ea5195cdac739d6c7c247c5aa5b2c91b801cbd99bad12584ed84c5cfaaa6fa8b4893a46324571a2f8a1988a1381f4ddd58390e597bd7bdc24 nose-1.3.4.tar.gz' > nose-1.3.4.tar.gz.sha512
+echo '996e6b8e2d42f223e44660f56bf73eb8ab124f400d89218f8f5e4d7c9860ada44a4d7c54526137b0695c7a10f36e8834fbf0d42b7cb20bcdb5d5c245d673385c numpy-1.8.2.tar.gz' > numpy-1.8.2.tar.gz.sha512
echo '9c0a61299779aff613131aaabbc255c8648f0fa7ab1806af53f19fbdcece0c8a68ddca7880d25b926d67ff1b9201954b207919fb09f6a290acb078e8bbed7b68 python-hglib-1.0.tar.gz' > python-hglib-1.0.tar.gz.sha512
-echo 'c65013293dd4049af5db009fdf7b6890a3c6b1e12dd588b58fb5f5a5fef7286935851fb7a530e03ea16f28de48b964e50f48bbf87d34545fd23b80dd4380476b pyzmq-13.1.0.tar.gz' > pyzmq-13.1.0.tar.gz.sha512
-echo '80c8e137c3ccba86575d4263e144ba2c4684b94b5cd620e200f094c92d4e118ea6a631d27bdb259b0869771dfaeeae68c0fdd37fdd740b9027ee185026e921d4 scipy-0.12.0.tar.gz' > scipy-0.12.0.tar.gz.sha512
+echo '3d93a8fbd94fc3f1f90df68257cda548ba1adf3d7a819e7a17edc8681894003ac7ae6abd319473054340c11443a6a3817b931366fd7dae78e3807d549c544f8b pyzmq-14.3.1.tar.gz' > pyzmq-14.3.1.tar.gz.sha512
+echo 'ad1278740c1dc44c5e1b15335d61c4552b66c0439325ed6eeebc5872a1c0ba3fce1dd8509116b318d01e2d41da2ee49ec168da330a7fafd22511138b29f7235d scipy-0.14.0.tar.gz' > scipy-0.14.0.tar.gz.sha512
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 'd8eef84860bc5314b42a2cc210340572a9148e008ea65f7650844d0edbe457d6758785047c2770399607f69ba3b3a544db9775a5cdf961223f7e278ef7e0f5c6 zeromq-3.2.4.tar.gz' > zeromq-3.2.4.tar.gz.sha512
+echo '8a46e75abc3ed2388b5da9cb0e5874ae87580cf3612e2920b662d8f8eee8047efce5aa998eee96661d3565070b1a6b916c8bed74138b821f4e09115f14b6677d sympy-0.7.5.tar.gz' > sympy-0.7.5.tar.gz.sha512
+echo 'a4e0231e77ebbc2885bab648b292b842cb15c84d66a1972de18cb00fcc611eae2794b872f070ab7d5af32dd0c6c1773527fe1332bd382c1821e1f2d5d76808fb tornado-4.0.1.tar.gz' > tornado-4.0.1.tar.gz.sha512
+echo '7d70855d0537971841810a66b7a943a88304f6991ce445df19eea034aadc53dbce9d13be92bf44cfef1f3e19511a754eb01006a3968edc1ec3d1766ea4730cda zeromq-4.0.4.tar.gz' > zeromq-4.0.4.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
@@ -653,7 +666,6 @@
get_ytproject $H5PY.tar.gz
get_ytproject $CYTHON.tar.gz
get_ytproject reason-js-20120623.zip
-get_ytproject $FORTHON.tar.gz
get_ytproject $NOSE.tar.gz
get_ytproject $PYTHON_HGLIB.tar.gz
get_ytproject $SYMPY.tar.gz
@@ -729,7 +741,7 @@
cd $FREETYPE_VER
( ./configure CFLAGS=-I${DEST_DIR}/include --prefix=${DEST_DIR}/ 2>&1 ) 1>> ${LOG_FILE} || do_exit
( make 2>&1 ) 1>> ${LOG_FILE} || do_exit
- ( make install 2>&1 ) 1>> ${LOG_FILE} || do_exit
+ ( make install 2>&1 ) 1>> ${LOG_FILE} || do_exit
( make clean 2>&1) 1>> ${LOG_FILE} || do_exit
touch done
cd ..
@@ -932,7 +944,6 @@
do_setup_py $IPYTHON
do_setup_py $H5PY
do_setup_py $CYTHON
-do_setup_py $FORTHON
do_setup_py $NOSE
do_setup_py $PYTHON_HGLIB
do_setup_py $SYMPY
@@ -1026,7 +1037,7 @@
echo
echo "To get started with yt, check out the orientation:"
echo
- echo " http://yt-project.org/doc/bootcamp/"
+ echo " http://yt-project.org/doc/quickstart/"
echo
echo "The source for yt is located at:"
echo " $YT_DIR"
diff -r 73a9f749157260c8949f05c07715305aafa06408 -r d771801e37dc81e648bc22a7ef9f4de6d8bfb0b9 doc/source/_static/custom.css
--- a/doc/source/_static/custom.css
+++ b/doc/source/_static/custom.css
@@ -39,6 +39,13 @@
padding-top: 10px;
padding-bottom: 10px;
}
+ /* since 3.1.0 */
+ .navbar-collapse.collapse.in {
+ display: block!important;
+ }
+ .collapsing {
+ overflow: hidden!important;
+ }
}
/*
diff -r 73a9f749157260c8949f05c07715305aafa06408 -r d771801e37dc81e648bc22a7ef9f4de6d8bfb0b9 doc/source/analyzing/particle_filter.ipynb
--- a/doc/source/analyzing/particle_filter.ipynb
+++ b/doc/source/analyzing/particle_filter.ipynb
@@ -1,7 +1,7 @@
{
"metadata": {
"name": "",
- "signature": "sha256:4d705a81671d5692ed6691b3402115edbe9c98af815af5bb160ddf551bf02c76"
+ "signature": "sha256:427da1e1d02deb543246218dc8cce991268b518b25cfdd5944a4a436695f874b"
},
"nbformat": 3,
"nbformat_minor": 0,
@@ -40,11 +40,13 @@
"source": [
"We will filter these into young stars and old stars by masking on the ('Stars', 'creation_time') field. \n",
"\n",
- "In order to do this, we first make a function which applies our desired cut. This function must accept two arguments: `pfilter` and `data`. The second argument is a yt data container and is usually the only one used in a filter definition.\n",
+ "In order to do this, we first make a function which applies our desired cut. This function must accept two arguments: `pfilter` and `data`. The first argument is a `ParticleFilter` object that contains metadata about the filter its self. The second argument is a yt data container.\n",
"\n",
- "Let's call \"young\" stars only those stars with ages less 5 million years. Since Tipsy assigns a very large `creation_time` for stars in the initial conditions, we need to also exclude stars with negative ages.\n",
+ "Let's call \"young\" stars only those stars with ages less 5 million years. Since Tipsy assigns a very large `creation_time` for stars in the initial conditions, we need to also exclude stars with negative ages. \n",
"\n",
- "Old stars either formed dynamically in the simulation (ages greater than 5 Myr) or were present in the initial conditions (negative ages)."
+ "Conversely, let's define \"old\" stars as those stars formed dynamically in the simulation with ages greater than 5 Myr. We also include stars with negative ages, since these stars were included in the simulation initial conditions.\n",
+ "\n",
+ "We make use of `pfilter.filtered_type` so that the filter definition will use the same particle type as the one specified in the call to `add_particle_filter` below. This makes the filter definition usable for arbitrary particle types. Since we're only filtering the `\"Stars\"` particle type in this example, we could have also replaced `pfilter.filtered_type` with `\"Stars\"` and gotten the same result."
]
},
{
@@ -52,12 +54,12 @@
"collapsed": false,
"input": [
"def young_stars(pfilter, data):\n",
- " age = data.ds.current_time - data[\"Stars\", \"creation_time\"]\n",
+ " age = data.ds.current_time - data[pfilter.filtered_type, \"creation_time\"]\n",
" filter = np.logical_and(age.in_units('Myr') <= 5, age >= 0)\n",
" return filter\n",
"\n",
"def old_stars(pfilter, data):\n",
- " age = data.ds.current_time - data[\"Stars\", \"creation_time\"]\n",
+ " age = data.ds.current_time - data[pfilter.filtered_type, \"creation_time\"]\n",
" filter = np.logical_or(age.in_units('Myr') >= 5, age < 0)\n",
" return filter"
],
@@ -140,4 +142,4 @@
"metadata": {}
}
]
-}
+}
\ No newline at end of file
diff -r 73a9f749157260c8949f05c07715305aafa06408 -r d771801e37dc81e648bc22a7ef9f4de6d8bfb0b9 doc/source/analyzing/units/index.rst
--- a/doc/source/analyzing/units/index.rst
+++ b/doc/source/analyzing/units/index.rst
@@ -37,7 +37,7 @@
.. note::
The notebooks use sample datasets that are available for download at
- http://yt-project.org/data. See :ref:`bootcamp-introduction` for more
+ http://yt-project.org/data. See :ref:`quickstart-introduction` for more
details.
Let us know if you would like to contribute other example notebooks, or have
diff -r 73a9f749157260c8949f05c07715305aafa06408 -r d771801e37dc81e648bc22a7ef9f4de6d8bfb0b9 doc/source/bootcamp/1)_Introduction.ipynb
--- a/doc/source/bootcamp/1)_Introduction.ipynb
+++ /dev/null
@@ -1,72 +0,0 @@
-{
- "metadata": {
- "name": "",
- "signature": "sha256:39620670ce7751b23f30d2123fd3598de1c7843331f65de13e29f4ae9f759e0f"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
- {
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Welcome to the yt bootcamp!\n",
- "\n",
- "In this brief tutorial, we'll go over how to load up data, analyze things, inspect your data, and make some visualizations.\n",
- "\n",
- "Our documentation page can provide information on a variety of the commands that are used here, both in narrative documentation as well as recipes for specific functionality in our cookbook. The documentation exists at http://yt-project.org/doc/. If you encounter problems, look for help here: http://yt-project.org/doc/help/index.html.\n",
- "\n",
- "## Acquiring the datasets for this tutorial\n",
- "\n",
- "If you are executing these tutorials interactively, you need some sample datasets on which to run the code. You can download these datasets at http://yt-project.org/data/. The datasets necessary for each lesson are noted next to the corresponding tutorial.\n",
- "\n",
- "## What's Next?\n",
- "\n",
- "The Notebooks are meant to be explored in this order:\n",
- "\n",
- "1. Introduction\n",
- "2. Data Inspection (IsolatedGalaxy dataset)\n",
- "3. Simple Visualization (enzo_tiny_cosmology & Enzo_64 datasets)\n",
- "4. Data Objects and Time Series (IsolatedGalaxy dataset)\n",
- "5. Derived Fields and Profiles (IsolatedGalaxy dataset)\n",
- "6. Volume Rendering (IsolatedGalaxy dataset)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The following code will download the data needed for this tutorial automatically using `curl`. It may take some time so please wait when the kernel is busy. You will need to set `download_datasets` to True before using it."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "download_datasets = False\n",
- "if download_datasets:\n",
- " !curl -sSO http://yt-project.org/data/enzo_tiny_cosmology.tar\n",
- " print \"Got enzo_tiny_cosmology\"\n",
- " !tar xf enzo_tiny_cosmology.tar\n",
- " \n",
- " !curl -sSO http://yt-project.org/data/Enzo_64.tar\n",
- " print \"Got Enzo_64\"\n",
- " !tar xf Enzo_64.tar\n",
- " \n",
- " !curl -sSO http://yt-project.org/data/IsolatedGalaxy.tar\n",
- " print \"Got IsolatedGalaxy\"\n",
- " !tar xf IsolatedGalaxy.tar\n",
- " \n",
- " print \"All done!\""
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- }
- ],
- "metadata": {}
- }
- ]
-}
\ No newline at end of file
diff -r 73a9f749157260c8949f05c07715305aafa06408 -r d771801e37dc81e648bc22a7ef9f4de6d8bfb0b9 doc/source/bootcamp/2)_Data_Inspection.ipynb
--- a/doc/source/bootcamp/2)_Data_Inspection.ipynb
+++ /dev/null
@@ -1,384 +0,0 @@
-{
- "metadata": {
- "name": "",
- "signature": "sha256:a8fe78715c1f3900c37c675d84320fe65f0ba8734abba60fd12e74d957e5d8ee"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
- {
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Starting Out and Loading Data\n",
- "\n",
- "We're going to get started by loading up yt. This next command brings all of the libraries into memory and sets up our environment."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "import yt"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Now that we've loaded yt, we can load up some data. Let's load the `IsolatedGalaxy` dataset."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "ds = yt.load(\"IsolatedGalaxy/galaxy0030/galaxy0030\")"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Fields and Facts\n",
- "\n",
- "When you call the `load` function, yt tries to do very little -- this is designed to be a fast operation, just setting up some information about the simulation. Now, the first time you access the \"index\" it will read and load the mesh and then determine where data is placed in the physical domain and on disk. Once it knows that, yt can tell you some statistics about the simulation:"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "ds.print_stats()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "yt can also tell you the fields it found on disk:"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "ds.field_list"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "And, all of the fields it thinks it knows how to generate:"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "ds.derived_field_list"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "yt can also transparently generate fields. However, we encourage you to examine exactly what yt is doing when it generates those fields. To see, you can ask for the source of a given field."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "print ds.field_info[\"gas\", \"vorticity_x\"].get_source()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "yt stores information about the domain of the simulation:"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "print ds.domain_width"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "yt can also convert this into various units:"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "print ds.domain_width.in_units(\"kpc\")\n",
- "print ds.domain_width.in_units(\"au\")\n",
- "print ds.domain_width.in_units(\"mile\")"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Mesh Structure\n",
- "\n",
- "If you're using a simulation type that has grids (for instance, here we're using an Enzo simulation) you can examine the structure of the mesh. For the most part, you probably won't have to use this unless you're debugging a simulation or examining in detail what is going on."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "print ds.index.grid_left_edge"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "But, you may have to access information about individual grid objects! Each grid object mediates accessing data from the disk and has a number of attributes that tell you about it. The index (`ds.index` here) has an attribute `grids` which is all of the grid objects."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "print ds.index.grids[1]"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "g = ds.index.grids[1]\n",
- "print g"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Grids have dimensions, extents, level, and even a list of Child grids."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "g.ActiveDimensions"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "g.LeftEdge, g.RightEdge"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "g.Level"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "g.Children"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Advanced Grid Inspection\n",
- "\n",
- "If we want to examine grids only at a given level, we can! Not only that, but we can load data and take a look at various fields.\n",
- "\n",
- "*This section can be skipped!*"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "gs = ds.index.select_grids(ds.index.max_level)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "g2 = gs[0]\n",
- "print g2\n",
- "print g2.Parent\n",
- "print g2.get_global_startindex()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "print g2[\"density\"][:,:,0]"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "print (g2.Parent.child_mask == 0).sum() * 8\n",
- "print g2.ActiveDimensions.prod()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "for f in ds.field_list:\n",
- " fv = g[f]\n",
- " if fv.size == 0: continue\n",
- " print f, fv.min(), fv.max()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Examining Data in Regions\n",
- "\n",
- "yt provides data object selectors. In subsequent notebooks we'll examine these in more detail, but we can select a sphere of data and perform a number of operations on it. yt makes it easy to operate on fluid fields in an object in *bulk*, but you can also examine individual field values.\n",
- "\n",
- "This creates a sphere selector positioned at the most dense point in the simulation that has a radius of 10 kpc."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "sp = ds.sphere(\"max\", (10, 'kpc'))"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "print sp"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We can calculate a bunch of bulk quantities. Here's that list, but there's a list in the docs, too!"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "print sp.quantities.keys()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Let's look at the total mass. This is how you call a given quantity. yt calls these \"Derived Quantities\". We'll talk about a few in a later notebook."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "print sp.quantities.total_mass()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- }
- ],
- "metadata": {}
- }
- ]
-}
\ No newline at end of file
diff -r 73a9f749157260c8949f05c07715305aafa06408 -r d771801e37dc81e648bc22a7ef9f4de6d8bfb0b9 doc/source/bootcamp/3)_Simple_Visualization.ipynb
--- a/doc/source/bootcamp/3)_Simple_Visualization.ipynb
+++ /dev/null
@@ -1,275 +0,0 @@
-{
- "metadata": {
- "name": "",
- "signature": "sha256:c00ba7fdbbd9ea957d06060ad70f06f629b1fd4ebf5379c1fdad2697ab0a4cd6"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
- {
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Simple Visualizations of Data\n",
- "\n",
- "Just like in our first notebook, we have to load yt and then some data."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "import yt"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "For this notebook, we'll load up a cosmology dataset."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "ds = yt.load(\"enzo_tiny_cosmology/DD0046/DD0046\")\n",
- "print \"Redshift =\", ds.current_redshift"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "In the terms that yt uses, a projection is a line integral through the domain. This can either be unweighted (in which case a column density is returned) or weighted, in which case an average value is returned. Projections are, like all other data objects in yt, full-fledged data objects that churn through data and present that to you. However, we also provide a simple method of creating Projections and plotting them in a single step. This is called a Plot Window, here specifically known as a `ProjectionPlot`. One thing to note is that in yt, we project all the way through the entire domain at a single time. This means that the first call to projecting can be somewhat time consuming, but panning, zooming and plotting are all quite fast.\n",
- "\n",
- "yt is designed to make it easy to make nice plots and straightforward to modify those plots directly. The cookbook in the documentation includes detailed examples of this."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "p = yt.ProjectionPlot(ds, \"y\", \"density\")\n",
- "p.show()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The `show` command simply sends the plot to the IPython notebook. You can also call `p.save()` which will save the plot to the file system. This function accepts an argument, which will be pre-prended to the filename and can be used to name it based on the width or to supply a location.\n",
- "\n",
- "Now we'll zoom and pan a bit."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "p.zoom(2.0)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "p.pan_rel((0.1, 0.0))"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "p.zoom(10.0)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "p.pan_rel((-0.25, -0.5))"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "p.zoom(0.1)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "If we specify multiple fields, each time we call `show` we get multiple plots back. Same for `save`!"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "p = yt.ProjectionPlot(ds, \"z\", [\"density\", \"temperature\"], weight_field=\"density\")\n",
- "p.show()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We can adjust the colormap on a field-by-field basis."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "p.set_cmap(\"temperature\", \"hot\")"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "And, we can re-center the plot on different locations. One possible use of this would be to make a single `ProjectionPlot` which you move around to look at different regions in your simulation, saving at each one."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "v, c = ds.find_max(\"density\")\n",
- "p.set_center((c[0], c[1]))\n",
- "p.zoom(10)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Okay, let's load up a bigger simulation (from `Enzo_64` this time) and make a slice plot."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "ds = yt.load(\"Enzo_64/DD0043/data0043\")\n",
- "s = yt.SlicePlot(ds, \"z\", [\"density\", \"velocity_magnitude\"], center=\"max\")\n",
- "s.set_cmap(\"velocity_magnitude\", \"kamae\")\n",
- "s.zoom(10.0)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We can adjust the logging of various fields:"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "s.set_log(\"velocity_magnitude\", True)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "yt provides many different annotations for your plots. You can see all of these in the documentation, or if you type `s.annotate_` and press tab, a list will show up here. We'll annotate with velocity arrows."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "s.annotate_velocity()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Contours can also be overlaid:"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "s = yt.SlicePlot(ds, \"x\", [\"density\"], center=\"max\")\n",
- "s.annotate_contour(\"temperature\")\n",
- "s.zoom(2.5)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Finally, we can save out to the file system."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "s.save()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- }
- ],
- "metadata": {}
- }
- ]
-}
\ No newline at end of file
diff -r 73a9f749157260c8949f05c07715305aafa06408 -r d771801e37dc81e648bc22a7ef9f4de6d8bfb0b9 doc/source/bootcamp/4)_Data_Objects_and_Time_Series.ipynb
--- a/doc/source/bootcamp/4)_Data_Objects_and_Time_Series.ipynb
+++ /dev/null
@@ -1,382 +0,0 @@
-{
- "metadata": {
- "name": "",
- "signature": "sha256:a46e1baa90d32045c2b524100f28bad41b3665249612c9a275ee0375a6f4be20"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
- {
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Data Objects and Time Series Data\n",
- "\n",
- "Just like before, we will load up yt. Since we'll be using pylab to plot some data in this notebook, we additionally tell matplotlib to place plots inline inside the notebook."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "%matplotlib inline\n",
- "import yt\n",
- "import numpy as np\n",
- "from matplotlib import pylab\n",
- "from yt.analysis_modules.halo_finding.api import HaloFinder"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Time Series Data\n",
- "\n",
- "Unlike before, instead of loading a single dataset, this time we'll load a bunch which we'll examine in sequence. This command creates a `DatasetSeries` object, which can be iterated over (including in parallel, which is outside the scope of this bootcamp) and analyzed. There are some other helpful operations it can provide, but we'll stick to the basics here.\n",
- "\n",
- "Note that you can specify either a list of filenames, or a glob (i.e., asterisk) pattern in this."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "ts = yt.DatasetSeries(\"enzo_tiny_cosmology/*/*.hierarchy\")"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Example 1: Simple Time Series\n",
- "\n",
- "As a simple example of how we can use this functionality, let's find the min and max of the density as a function of time in this simulation. To do this we use the construction `for ds in ts` where `ds` means \"Dataset\" and `ts` is the \"Time Series\" we just loaded up. For each dataset, we'll create an object (`dd`) that covers the entire domain. (`all_data` is a shorthand function for this.) We'll then call the `extrema` Derived Quantity, and append the min and max to our extrema outputs."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "rho_ex = []\n",
- "times = []\n",
- "for ds in ts:\n",
- " dd = ds.all_data()\n",
- " rho_ex.append(dd.quantities.extrema(\"density\"))\n",
- " times.append(ds.current_time.in_units(\"Gyr\"))\n",
- "rho_ex = np.array(rho_ex)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Now we plot the minimum and the maximum:"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "pylab.semilogy(times, rho_ex[:,0], '-xk', label='Minimum')\n",
- "pylab.semilogy(times, rho_ex[:,1], '-xr', label='Maximum')\n",
- "pylab.ylabel(\"Density ($g/cm^3$)\")\n",
- "pylab.xlabel(\"Time (Gyr)\")\n",
- "pylab.legend()\n",
- "pylab.ylim(1e-32, 1e-21)\n",
- "pylab.show()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Example 2: Advanced Time Series\n",
- "\n",
- "Let's do something a bit different. Let's calculate the total mass inside halos and outside halos.\n",
- "\n",
- "This actually touches a lot of different pieces of machinery in yt. For every dataset, we will run the halo finder HOP. Then, we calculate the total mass in the domain. Then, for each halo, we calculate the sum of the baryon mass in that halo. We'll keep running tallies of these two things."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "from yt.units import Msun\n",
- "\n",
- "mass = []\n",
- "zs = []\n",
- "for ds in ts:\n",
- " halos = HaloFinder(ds)\n",
- " dd = ds.all_data()\n",
- " total_mass = dd.quantities.total_quantity(\"cell_mass\").in_units(\"Msun\")\n",
- " total_in_baryons = 0.0*Msun\n",
- " for halo in halos:\n",
- " sp = halo.get_sphere()\n",
- " total_in_baryons += sp.quantities.total_quantity(\"cell_mass\").in_units(\"Msun\")\n",
- " mass.append(total_in_baryons/total_mass)\n",
- " zs.append(ds.current_redshift)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Now let's plot them!"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "pylab.semilogx(zs, mass, '-xb')\n",
- "pylab.xlabel(\"Redshift\")\n",
- "pylab.ylabel(\"Mass in halos / Total mass\")\n",
- "pylab.xlim(max(zs), min(zs))\n",
- "pylab.ylim(-0.01, .18)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Data Objects\n",
- "\n",
- "Time series data have many applications, but most of them rely on examining the underlying data in some way. Below, we'll see how to use and manipulate data objects.\n",
- "\n",
- "### Ray Queries\n",
- "\n",
- "yt provides the ability to examine rays, or lines, through the domain. Note that these are not periodic, unlike most other data objects. We create a ray object and can then examine quantities of it. Rays have the special fields `t` and `dts`, which correspond to the time the ray enters a given cell and the distance it travels through that cell.\n",
- "\n",
- "To create a ray, we specify the start and end points.\n",
- "\n",
- "Note that we need to convert these arrays to numpy arrays due to a bug in matplotlib 1.3.1."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "ray = ds.ray([0.1, 0.2, 0.3], [0.9, 0.8, 0.7])\n",
- "pylab.semilogy(np.array(ray[\"t\"]), np.array(ray[\"density\"]))"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "print ray[\"dts\"]"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "print ray[\"t\"]"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "print ray[\"x\"]"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Slice Queries\n",
- "\n",
- "While slices are often used for visualization, they can be useful for other operations as well. yt regards slices as multi-resolution objects. They are an array of cells that are not all the same size; it only returns the cells at the highest resolution that it intersects. (This is true for all yt data objects.) Slices and projections have the special fields `px`, `py`, `pdx` and `pdy`, which correspond to the coordinates and half-widths in the pixel plane."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "ds = yt.load(\"IsolatedGalaxy/galaxy0030/galaxy0030\")\n",
- "v, c = ds.find_max(\"density\")\n",
- "sl = ds.slice(0, c[0])\n",
- "print sl[\"index\", \"x\"]\n",
- "print sl[\"index\", \"z\"]\n",
- "print sl[\"pdx\"]\n",
- "print sl[\"gas\", \"density\"].shape"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "If we want to do something interesting with a `Slice`, we can turn it into a `FixedResolutionBuffer`. This object can be queried and will return a 2D array of values."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "frb = sl.to_frb((50.0, 'kpc'), 1024)\n",
- "print frb[\"gas\", \"density\"].shape"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "yt provides a few functions for writing arrays to disk, particularly in image form. Here we'll write out the log of `density`, and then use IPython to display it back here. Note that for the most part, you will probably want to use a `PlotWindow` for this, but in the case that it is useful you can directly manipulate the data."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "yt.write_image(np.log10(frb[\"gas\", \"density\"]), \"temp.png\")\n",
- "from IPython.display import Image\n",
- "Image(filename = \"temp.png\")"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Off-Axis Slices\n",
- "\n",
- "yt provides not only slices, but off-axis slices that are sometimes called \"cutting planes.\" These are specified by (in order) a normal vector and a center. Here we've set the normal vector to `[0.2, 0.3, 0.5]` and the center to be the point of maximum density.\n",
- "\n",
- "We can then turn these directly into plot windows using `to_pw`. Note that the `to_pw` and `to_frb` methods are available on slices, off-axis slices, and projections, and can be used on any of them."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "cp = ds.cutting([0.2, 0.3, 0.5], \"max\")\n",
- "pw = cp.to_pw(fields = [(\"gas\", \"density\")])"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Once we have our plot window from our cutting plane, we can show it here."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "pw.show()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We can, as noted above, do the same with our slice:"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "pws = sl.to_pw(fields=[\"density\"])\n",
- "#pws.show()\n",
- "print pws.plots.keys()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Covering Grids\n",
- "\n",
- "If we want to access a 3D array of data that spans multiple resolutions in our simulation, we can use a covering grid. This will return a 3D array of data, drawing from up to the resolution level specified when creating the data. For example, if you create a covering grid that spans two child grids of a single parent grid, it will fill those zones covered by a zone of a child grid with the data from that child grid. Where it is covered only by the parent grid, the cells from the parent grid will be duplicated (appropriately) to fill the covering grid.\n",
- "\n",
- "There are two different types of covering grids: unsmoothed and smoothed. Smoothed grids will be filled through a cascading interpolation process; they will be filled at level 0, interpolated to level 1, filled at level 1, interpolated to level 2, filled at level 2, etc. This will help to reduce edge effects. Unsmoothed covering grids will not be interpolated, but rather values will be duplicated multiple times.\n",
- "\n",
- "Here we create an unsmoothed covering grid at level 2, with the left edge at `[0.0, 0.0, 0.0]` and with dimensions equal to those that would cover the entire domain at level 2. We can then ask for the Density field, which will be a 3D array."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "cg = ds.covering_grid(2, [0.0, 0.0, 0.0], ds.domain_dimensions * 2**2)\n",
- "print cg[\"density\"].shape"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "In this example, we do exactly the same thing: except we ask for a *smoothed* covering grid, which will reduce edge effects."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "scg = ds.smoothed_covering_grid(2, [0.0, 0.0, 0.0], ds.domain_dimensions * 2**2)\n",
- "print scg[\"density\"].shape"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- }
- ],
- "metadata": {}
- }
- ]
-}
\ No newline at end of file
diff -r 73a9f749157260c8949f05c07715305aafa06408 -r d771801e37dc81e648bc22a7ef9f4de6d8bfb0b9 doc/source/bootcamp/5)_Derived_Fields_and_Profiles.ipynb
--- a/doc/source/bootcamp/5)_Derived_Fields_and_Profiles.ipynb
+++ /dev/null
@@ -1,254 +0,0 @@
-{
- "metadata": {
- "name": "",
- "signature": "sha256:eca573e749829cacda0a8c07c6d5d11d07a5de657563a44b8c4ffff8f735caed"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
- {
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Derived Fields and Profiles\n",
- "\n",
- "One of the most powerful features in yt is the ability to create derived fields that act and look exactly like fields that exist on disk. This means that they will be generated on demand and can be used anywhere a field that exists on disk would be used. Additionally, you can create them by just writing python functions."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "%matplotlib inline\n",
- "import yt\n",
- "import numpy as np\n",
- "from yt import derived_field\n",
- "from matplotlib import pylab"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Derived Fields\n",
- "\n",
- "This is an example of the simplest possible way to create a derived field. All derived fields are defined by a function and some metadata; that metadata can include units, LaTeX-friendly names, conversion factors, and so on. Fields can be defined in the way in the next cell. What this does is create a function which accepts two arguments and then provide the units for that field. In this case, our field is `dinosaurs` and our units are `K*cm/s`. The function itself can access any fields that are in the simulation, and it does so by requesting data from the object called `data`."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "@derived_field(name = \"dinosaurs\", units = \"K * cm/s\")\n",
- "def _dinos(field, data):\n",
- " return data[\"temperature\"] * data[\"velocity_magnitude\"]"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "One important thing to note is that derived fields must be defined *before* any datasets are loaded. Let's load up our data and take a look at some quantities."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "ds = yt.load(\"IsolatedGalaxy/galaxy0030/galaxy0030\")\n",
- "dd = ds.all_data()\n",
- "print dd.quantities.keys()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "One interesting question is, what are the minimum and maximum values of dinosaur production rates in our isolated galaxy? We can do that by examining the `extrema` quantity -- the exact same way that we would for density, temperature, and so on."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "print dd.quantities.extrema(\"dinosaurs\")"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We can do the same for the average quantities as well."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "print dd.quantities.weighted_average_quantity(\"dinosaurs\", weight=\"temperature\")"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## A Few Other Quantities\n",
- "\n",
- "We can ask other quantities of our data, as well. For instance, this sequence of operations will find the most dense point, center a sphere on it, calculate the bulk velocity of that sphere, calculate the baryonic angular momentum vector, and then the density extrema. All of this is done in a memory conservative way: if you have an absolutely enormous dataset, yt will split that dataset into pieces, apply intermediate reductions and then a final reduction to calculate your quantity."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "sp = ds.sphere(\"max\", (10.0, 'kpc'))\n",
- "bv = sp.quantities.bulk_velocity()\n",
- "L = sp.quantities.angular_momentum_vector()\n",
- "rho_min, rho_max = sp.quantities.extrema(\"density\")\n",
- "print bv\n",
- "print L\n",
- "print rho_min, rho_max"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Profiles\n",
- "\n",
- "yt provides the ability to bin in 1, 2 and 3 dimensions. This means discretizing in one or more dimensions of phase space (density, temperature, etc) and then calculating either the total value of a field in each bin or the average value of a field in each bin.\n",
- "\n",
- "We do this using the objects `Profile1D`, `Profile2D`, and `Profile3D`. The first two are the most common since they are the easiest to visualize.\n",
- "\n",
- "This first set of commands manually creates a profile object the sphere we created earlier, binned in 32 bins according to density between `rho_min` and `rho_max`, and then takes the density-weighted average of the fields `temperature` and (previously-defined) `dinosaurs`. We then plot it in a loglog plot."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "prof = yt.Profile1D(sp, \"density\", 32, rho_min, rho_max, True, weight_field=\"cell_mass\")\n",
- "prof.add_fields([\"temperature\",\"dinosaurs\"])\n",
- "pylab.loglog(np.array(prof.x), np.array(prof[\"temperature\"]), \"-x\")\n",
- "pylab.xlabel('Density $(g/cm^3)$')\n",
- "pylab.ylabel('Temperature $(K)$')"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Now we plot the `dinosaurs` field."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "pylab.loglog(np.array(prof.x), np.array(prof[\"dinosaurs\"]), '-x')\n",
- "pylab.xlabel('Density $(g/cm^3)$')\n",
- "pylab.ylabel('Dinosaurs $(K cm / s)$')"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "If we want to see the total mass in every bin, we profile the `cell_mass` field with no weight. Specifying `weight=None` will simply take the total value in every bin and add that up."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "prof = yt.Profile1D(sp, \"density\", 32, rho_min, rho_max, True, weight_field=None)\n",
- "prof.add_fields([\"cell_mass\"])\n",
- "pylab.loglog(np.array(prof.x), np.array(prof[\"cell_mass\"].in_units(\"Msun\")), '-x')\n",
- "pylab.xlabel('Density $(g/cm^3)$')\n",
- "pylab.ylabel('Cell mass $(M_\\odot)$')"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "In addition to the low-level `ProfileND` interface, it's also quite straightforward to quickly create plots of profiles using the `ProfilePlot` class. Let's redo the last plot using `ProfilePlot`"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "prof = yt.ProfilePlot(sp, 'density', 'cell_mass', weight_field=None)\n",
- "prof.set_unit('cell_mass', 'Msun')\n",
- "prof.show()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Field Parameters\n",
- "\n",
- "Field parameters are a method of passing information to derived fields. For instance, you might pass in information about a vector you want to use as a basis for a coordinate transformation. yt often uses things like `bulk_velocity` to identify velocities that should be subtracted off. Here we show how that works:"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "sp_small = ds.sphere(\"max\", (50.0, 'kpc'))\n",
- "bv = sp_small.quantities.bulk_velocity()\n",
- "\n",
- "sp = ds.sphere(\"max\", (0.1, 'Mpc'))\n",
- "rv1 = sp.quantities.extrema(\"radial_velocity\")\n",
- "\n",
- "sp.clear_data()\n",
- "sp.set_field_parameter(\"bulk_velocity\", bv)\n",
- "rv2 = sp.quantities.extrema(\"radial_velocity\")\n",
- "\n",
- "print bv\n",
- "print rv1\n",
- "print rv2"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- }
- ],
- "metadata": {}
- }
- ]
-}
diff -r 73a9f749157260c8949f05c07715305aafa06408 -r d771801e37dc81e648bc22a7ef9f4de6d8bfb0b9 doc/source/bootcamp/6)_Volume_Rendering.ipynb
--- a/doc/source/bootcamp/6)_Volume_Rendering.ipynb
+++ /dev/null
@@ -1,96 +0,0 @@
-{
- "metadata": {
- "name": "",
- "signature": "sha256:2a24bbe82955f9d948b39cbd1b1302968ff57f62f73afb2c7a5c4953393d00ae"
- },
- "nbformat": 3,
- "nbformat_minor": 0,
- "worksheets": [
- {
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# A Brief Demo of Volume Rendering\n",
- "\n",
- "This shows a small amount of volume rendering. Really, just enough to get your feet wet!"
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "import yt\n",
- "ds = yt.load(\"IsolatedGalaxy/galaxy0030/galaxy0030\")"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "To create a volume rendering, we need a camera and a transfer function. We'll use the `ColorTransferFunction`, which accepts (in log space) the minimum and maximum bounds of our transfer function. This means behavior for data outside these values is undefined.\n",
- "\n",
- "We then add on \"layers\" like an onion. This function can accept a width (here specified) in data units, and also a color map. Here we add on four layers.\n",
- "\n",
- "Finally, we create a camera. The focal point is `[0.5, 0.5, 0.5]`, the width is 20 kpc (including front-to-back integration) and we specify a transfer function. Once we've done that, we call `show` to actually cast our rays and display them inline."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "tf = yt.ColorTransferFunction((-28, -24))\n",
- "tf.add_layers(4, w=0.01)\n",
- "cam = ds.camera([0.5, 0.5, 0.5], [1.0, 1.0, 1.0], (20, 'kpc'), 512, tf, fields=[\"density\"])\n",
- "cam.show()"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "If we want to apply a clipping, we can specify the `clip_ratio`. This will clip the upper bounds to this value times the standard deviation of the values in the image array."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "cam.show(clip_ratio=4)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "There are several other options we can specify. Note that here we have turned on the use of ghost zones, shortened the data interval for the transfer function, and widened our gaussian layers."
- ]
- },
- {
- "cell_type": "code",
- "collapsed": false,
- "input": [
- "tf = yt.ColorTransferFunction((-28, -25))\n",
- "tf.add_layers(4, w=0.03)\n",
- "cam = ds.camera([0.5, 0.5, 0.5], [1.0, 1.0, 1.0], (20.0, 'kpc'), 512, tf, no_ghost=False)\n",
- "cam.show(clip_ratio=4.0)"
- ],
- "language": "python",
- "metadata": {},
- "outputs": []
- }
- ],
- "metadata": {}
- }
- ]
-}
\ No newline at end of file
diff -r 73a9f749157260c8949f05c07715305aafa06408 -r d771801e37dc81e648bc22a7ef9f4de6d8bfb0b9 doc/source/bootcamp/data_inspection.rst
--- a/doc/source/bootcamp/data_inspection.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-.. _data_inspection:
-
-Data Inspection
----------------
-
-.. notebook:: 2)_Data_Inspection.ipynb
diff -r 73a9f749157260c8949f05c07715305aafa06408 -r d771801e37dc81e648bc22a7ef9f4de6d8bfb0b9 doc/source/bootcamp/data_objects_and_time_series.rst
--- a/doc/source/bootcamp/data_objects_and_time_series.rst
+++ /dev/null
@@ -1,4 +0,0 @@
-Data Objects and Time Series
-----------------------------
-
-.. notebook:: 4)_Data_Objects_and_Time_Series.ipynb
diff -r 73a9f749157260c8949f05c07715305aafa06408 -r d771801e37dc81e648bc22a7ef9f4de6d8bfb0b9 doc/source/bootcamp/derived_fields_and_profiles.rst
--- a/doc/source/bootcamp/derived_fields_and_profiles.rst
+++ /dev/null
@@ -1,4 +0,0 @@
-Derived Fields and Profiles
----------------------------
-
-.. notebook:: 5)_Derived_Fields_and_Profiles.ipynb
diff -r 73a9f749157260c8949f05c07715305aafa06408 -r d771801e37dc81e648bc22a7ef9f4de6d8bfb0b9 doc/source/bootcamp/index.rst
--- a/doc/source/bootcamp/index.rst
+++ /dev/null
@@ -1,59 +0,0 @@
-.. _bootcamp:
-
-yt Bootcamp
-===========
-
-The bootcamp is a series of worked examples of how to use much of the
-funtionality of yt. These are simple, short introductions to give you a taste
-of what the code can do and are not meant to be detailed walkthroughs.
-
-There are two ways in which you can go through the bootcamp: interactively and
-non-interactively. We recommend the interactive method, but if you're pressed
-on time, you can non-interactively go through the linked pages below and view the
-worked examples.
-
-To execute the bootcamp interactively, you need to download the repository and
-start the IPython notebook. If you do not already have the yt repository, the
-easiest way to get the repository is to clone it using mercurial:
-
-.. code-block:: bash
-
- hg clone https://bitbucket.org/yt_analysis/yt
-
-Now start the IPython notebook from within the repository:
-
-.. code-block:: bash
-
- cd yt/doc/source/bootcamp
- yt notebook
-
-This command will give you information about the notebook server and how to
-access it. You will basically just pick a password (for security reasons) and then
-redirect your web browser to point to the notebook server.
-Once you have done so, choose "Introduction" from the list of
-notebooks, which includes an introduction and information about how to download
-the sample data.
-
-.. warning:: The pre-filled out notebooks are *far* less fun than running them
- yourselves! Check out the repo and give it a try.
-
-Here are the notebooks, which have been filled in for inspection:
-
-.. toctree::
- :maxdepth: 1
-
- introduction
- data_inspection
- simple_visualization
- data_objects_and_time_series
- derived_fields_and_profiles
- volume_rendering
-
-.. note::
-
- The notebooks use sample datasets that are available for download at
- http://yt-project.org/data. See :ref:`bootcamp-introduction` for more
- details.
-
-Let us know if you would like to contribute other example notebooks, or have
-any suggestions for how these can be improved.
diff -r 73a9f749157260c8949f05c07715305aafa06408 -r d771801e37dc81e648bc22a7ef9f4de6d8bfb0b9 doc/source/bootcamp/introduction.rst
--- a/doc/source/bootcamp/introduction.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-.. _bootcamp-introduction:
-
-Introduction
-------------
-
-.. notebook:: 1)_Introduction.ipynb
diff -r 73a9f749157260c8949f05c07715305aafa06408 -r d771801e37dc81e648bc22a7ef9f4de6d8bfb0b9 doc/source/bootcamp/simple_visualization.rst
--- a/doc/source/bootcamp/simple_visualization.rst
+++ /dev/null
@@ -1,4 +0,0 @@
-Simple Visualization
---------------------
-
-.. notebook:: 3)_Simple_Visualization.ipynb
diff -r 73a9f749157260c8949f05c07715305aafa06408 -r d771801e37dc81e648bc22a7ef9f4de6d8bfb0b9 doc/source/bootcamp/volume_rendering.rst
--- a/doc/source/bootcamp/volume_rendering.rst
+++ /dev/null
@@ -1,4 +0,0 @@
-Volume Rendering
-----------------
-
-.. notebook:: 6)_Volume_Rendering.ipynb
diff -r 73a9f749157260c8949f05c07715305aafa06408 -r d771801e37dc81e648bc22a7ef9f4de6d8bfb0b9 doc/source/conf.py
--- a/doc/source/conf.py
+++ b/doc/source/conf.py
@@ -67,9 +67,9 @@
# built documents.
#
# The short X.Y version.
-version = '3.0'
+version = '3.0.1'
# The full version, including alpha/beta/rc tags.
-release = '3.0'
+release = '3.0.1'
# The language for content autogenerated by Sphinx. Refer to documentation
# for a list of supported languages.
@@ -122,7 +122,7 @@
bootswatch_theme = "readable",
navbar_links = [
("How to get help", "help/index"),
- ("Bootcamp notebooks", "bootcamp/index"),
+ ("Quickstart notebooks", "quickstart/index"),
("Cookbook", "cookbook/index"),
],
navbar_sidebarrel = False,
diff -r 73a9f749157260c8949f05c07715305aafa06408 -r d771801e37dc81e648bc22a7ef9f4de6d8bfb0b9 doc/source/cookbook/calculating_information.rst
--- a/doc/source/cookbook/calculating_information.rst
+++ b/doc/source/cookbook/calculating_information.rst
@@ -90,3 +90,14 @@
See :ref:`filtering-particles` for more information.
.. yt_cookbook:: particle_filter_sfr.py
+
+Making a Turbulent Kinetic Energy Power Spectrum
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+This recipe shows how to use `yt` to read data and put it on a uniform
+grid to interface with the NumPy FFT routines and create a turbulent
+kinetic energy power spectrum. (Note: the dataset used here is of low
+resolution, so the turbulence is not very well-developed. The spike
+at high wavenumbers is due to non-periodicity in the z-direction).
+
+.. yt_cookbook:: power_spectrum_example.py
diff -r 73a9f749157260c8949f05c07715305aafa06408 -r d771801e37dc81e648bc22a7ef9f4de6d8bfb0b9 doc/source/cookbook/custom_colorbar_tickmarks.rst
--- a/doc/source/cookbook/custom_colorbar_tickmarks.rst
+++ b/doc/source/cookbook/custom_colorbar_tickmarks.rst
@@ -1,4 +1,4 @@
-Custom Colorabar Tickmarks
---------------------------
+Custom Colorbar Tickmarks
+-------------------------
.. notebook:: custom_colorbar_tickmarks.ipynb
This diff is so big that we needed to truncate the remainder.
https://bitbucket.org/yt_analysis/yt/commits/0cf350f11a55/
Changeset: 0cf350f11a55
Branch: stable
User: MatthewTurk
Date: 2014-09-03 03:28:35
Summary: Merging to kill a head
Affected #: 1 file
diff -r d771801e37dc81e648bc22a7ef9f4de6d8bfb0b9 -r 0cf350f11a551f5a5b4039a70e9ff6d98342d1da .hgtags
--- a/.hgtags
+++ b/.hgtags
@@ -5177,3 +5177,4 @@
f1e22ef9f3a225f818c43262e6ce9644e05ffa21 yt-2.6.2
816186f16396a16853810ac9ebcde5057d8d5b1a yt-2.6.3
f327552a6ede406b82711fb800ebcd5fe692d1cb yt-3.0a4
+73a9f749157260c8949f05c07715305aafa06408 yt-3.0.0
Repository URL: https://bitbucket.org/yt_analysis/yt/
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