[yt-svn] commit/yt-doc: 2 new changesets

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2 new commits in yt-doc:

https://bitbucket.org/yt_analysis/yt-doc/commits/7a552a149fc8/
Changeset:   7a552a149fc8
User:        ngoldbaum
Date:        2013-05-18 00:22:56
Summary:     Correcting an image link.
Affected #:  1 file

diff -r 26462bde1cd6c6eda7c422a2edb499dbeaf765a4 -r 7a552a149fc8e99941ace5f6b847eb3fd1620a05 source/visualizing/volume_rendering.rst
--- a/source/visualizing/volume_rendering.rst
+++ b/source/visualizing/volume_rendering.rst
@@ -281,7 +281,7 @@
 
 This produces an image like this:
 
-.. image:: _images/vr_sample.jpg
+.. image:: _images/allsky.png
    :width: 512
 
 However, below we describe a longer, build-it-yourself method.  To actually


https://bitbucket.org/yt_analysis/yt-doc/commits/64aed6437ca2/
Changeset:   64aed6437ca2
User:        ngoldbaum
Date:        2013-05-18 00:23:28
Summary:     Merging with tip.
Affected #:  4 files

diff -r 7a552a149fc8e99941ace5f6b847eb3fd1620a05 -r 64aed6437ca24173153ef36dde7ca7f27aa315ac source/analyzing/creating_derived_fields.rst
--- a/source/analyzing/creating_derived_fields.rst
+++ b/source/analyzing/creating_derived_fields.rst
@@ -188,6 +188,37 @@
 
 .. _derived-field-options:
 
+Saving Derived Fields
+---------------------
+
+Complex fields can be time-consuming to generate, especially on large datasets. To mitigate this, yt provides a mechanism for saving fields to a backup file using the Grid Data Format. The next time you start yt, it will check this file and your field will be treated as native if present. 
+
+The code below creates a new derived field called "Entr" and saves it to disk:
+
+.. code-block:: python
+
+    from yt.mods import *
+    from yt.utilities.grid_data_format import writer
+
+    def _Entropy(field, data) :
+        return data["Temperature"]*data["Density"]**(-2./3.)
+    add_field("Entr", function=_Entropy)
+
+    pf = load('GasSloshing/sloshing_nomag2_hdf5_plt_cnt_0100')
+    writer.save_field(pf, "Entr")
+
+This creates a "_backup.gdf" file next to your datadump. If you load up the dataset again:
+
+.. code-block:: python
+
+    from yt.mods import *
+
+    pf = load('GasSloshing/sloshing_nomag2_hdf5_plt_cnt_0100')
+    data = pf.h.all_data()
+    print data["Entr"]
+
+you can work with the field exactly as before, without having to recompute it.
+
 Field Options
 -------------
 

diff -r 7a552a149fc8e99941ace5f6b847eb3fd1620a05 -r 64aed6437ca24173153ef36dde7ca7f27aa315ac source/analyzing/generating_processed_data.rst
--- a/source/analyzing/generating_processed_data.rst
+++ b/source/analyzing/generating_processed_data.rst
@@ -141,6 +141,22 @@
 
 .. _generating-line-queries:
 
+Calculating the Variance of Profiled Fields
++++++++++++++++++++++++++++++++++++++++++++
+
+See :ref:`cookbook-profile-variance` for an example of the following.  
+When calculating average 1D and 2D profiles (when the *weight* keyword is not 
+None), the variance within each bin is calculated automatically.  A practical 
+application for this would be calculating velocity dispersion by profiling the 
+average velocity magnitude.  The variance values for 1D and 2D profiles are 
+accessible as the name of the profiled field followed by ``_std``.  For the 
+above examples, this is done with
+
+.. code-block:: python
+
+   print profile["Temperature_std"]
+   print prof2d["Temperature_std"]
+
 Line Queries and Planar Integrals
 ---------------------------------
 

diff -r 7a552a149fc8e99941ace5f6b847eb3fd1620a05 -r 64aed6437ca24173153ef36dde7ca7f27aa315ac source/cookbook/profile_with_variance.py
--- /dev/null
+++ b/source/cookbook/profile_with_variance.py
@@ -0,0 +1,30 @@
+from matplotlib import pyplot
+
+from yt.mods import *
+
+# Load the dataset.
+pf = load("IsolatedGalaxy/galaxy0030/galaxy0030")
+
+# Create a sphere of radius 1000 kpc centered on the max density.
+sphere = pf.h.sphere("max", (1000, "kpc"))
+
+# Calculate and store the bulk velocity for the sphere.
+bulk_velocity = sphere.quantities['BulkVelocity']()
+sphere.set_field_parameter('bulk_velocity', bulk_velocity)
+
+# Create a 1D profile object for profiles over radius
+# and add a velocity profile.
+profile = BinnedProfile1D(sphere, 100, "Radiuskpc", 0.1, 1000.)
+profile.add_fields('VelocityMagnitude')
+
+# Plot the average velocity magnitude.
+pyplot.loglog(profile['Radiuskpc'], profile['VelocityMagnitude'],
+              label='mean')
+# Plot the variance of the velocity madnitude.
+pyplot.loglog(profile['Radiuskpc'], profile['VelocityMagnitude_std'],
+              label='std')
+pyplot.xlabel('r [kpc]')
+pyplot.ylabel('v [cm/s]')
+pyplot.legend()
+
+pyplot.savefig('velocity_profiles.png')

diff -r 7a552a149fc8e99941ace5f6b847eb3fd1620a05 -r 64aed6437ca24173153ef36dde7ca7f27aa315ac source/cookbook/simple_plots.rst
--- a/source/cookbook/simple_plots.rst
+++ b/source/cookbook/simple_plots.rst
@@ -55,6 +55,17 @@
 
 .. yt_cookbook:: simple_radial_profile.py
 
+.. _cookbook-profile-variance:
+
+Profiles with Variance Values
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+This shows how to plot the variance for a 1D profile.  In this example, we 
+manually create a 1D profile object, which gives us access to the variance 
+data.
+
+.. yt_cookbook:: profile_with_variance.py
+
 Making Plots of Multiple Fields Simultaneously
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Repository URL: https://bitbucket.org/yt_analysis/yt-doc/

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