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

commits-noreply at bitbucket.org commits-noreply at bitbucket.org
Wed Nov 6 06:42:56 PST 2013


2 new commits in yt-doc:

https://bitbucket.org/yt_analysis/yt-doc/commits/b38e97e488e6/
Changeset:   b38e97e488e6
User:        brittonsmith
Date:        2013-11-05 16:09:52
Summary:     Updating docs for cut_region.
Affected #:  1 file

diff -r aa482e4881748677e642ab64369bc601b2ad442e -r b38e97e488e6275e23a8871c41bead01bb255c1a source/analyzing/objects.rst
--- a/source/analyzing/objects.rst
+++ b/source/analyzing/objects.rst
@@ -233,49 +233,44 @@
 
 .. _field_cuts:
 
-Querying and Subselecting Objects
----------------------------------
+Cutting Objects by Field Values
+-------------------------------
 
-Often when analyzing astrophysical objects, only regions that posses certain
-properties are of interest.  For instance, in a global galactic disk
-simulation, perhaps you want to examine only the cold gas, or only the hot gas.
-These criteria can be applied to selections of data in yt using one of two
-mechanisms: the "field cuts" mechanism or the "extract region" mechanism.  
-"Field cuts" are more suited to specifying extractions based on easily
-described properties of the gas, whereas "extracted regions" are usually suited
-for when the gas properties of interest require explicit examination.
+Data objects can be cut by their field values using the ``cut_region`` 
+method.  For example, this could be used to compute the total mass within 
+a certain temperature range, as in the following example.
 
-Objects can be selected using field cuts by specifying one or more criteria to
-the ``cut_field`` method on a data object.  These criteria should be of the
-form ``grid["FieldName"] < SOMETHING`` or similar; they will be evaluated with
-respect to a grid.  The operation will then supply a new objects composed of
-all the cells that satisfy this.  For instance, if I wanted to take a sphere
-and only examine inflowing gas:
+.. notebook-cell::
 
-.. code-block:: python
+   from yt.mods import *
+   pf = load("enzo_tiny_cosmology/DD0046/DD0046")
+   ad = pf.h.all_data()
+   total_mass = ad.quantities["TotalQuantity"]("CellMassMsun")
+   # now select only gas with 1e5 K < T < 1e7 K.
+   new_region = ad.cut_region(['grid["Temperature"] > 1e5',
+                               'grid["Temperature"] < 1e7'])
+   cut_mass = new_region.quantities["TotalQuantity"]("CellMassMsun")
+   print "The fraction of mass in this temperature range is %f." % \
+     (cut_mass[0] / total_mass[0])
 
-   sp = pf.h.sphere("max", (100.0, 'au'))
-   inflow = sp.cut_region( [ "grid['RadialVelocity'] < 0" ])
+The ``cut_region`` function generates a new object containing only the cells 
+that meet all of the specified criteria.  The sole argument to ``cut_region`` 
+is a list of strings, where each string is evaluated with an ``eval`` 
+statement.  ``eval`` is a native Python function that evaluates a string as 
+a Python expression.  Any type of data object can be cut with ``cut_region``.  
+Objects generated with ``cut_region`` can be used in the same way as all 
+other data objects.  For example, a cut region can be visualized by giving 
+it as a data_source to a projection.
 
-The new returned object, ``inflow``, can be analyzed as any other data object.
-For instance we could examine its angular momentum vector:
+.. python-script::
 
-.. code-block:: python
-
-   L = inflow.quantities["AngularMomentumVector"]()
-
-There are two things to note about the arguments to ``cut_region``: they are a
-list, where each argument is an independent criterion for inclusion, and they
-are evaluated on each grid object.  Only the cells where all arguments are
-evaluated as ``True`` will be included in the resulting object.  For instance,
-to select only dense, cold gas:
-
-.. code-block:: python
-
-   dense = sp.cut_region([ "grid['dens'] > 1e5", "grid['temp'] < 200" ])
-
-Each of the criteria will be evaluated independently and the resulting
-intersection will be returned.
+   from yt.mods import *
+   pf = load("enzo_tiny_cosmology/DD0046/DD0046")
+   ad = pf.h.all_data()
+   new_region = ad.cut_region(['grid["Density"] > 1e-29'])
+   plot = ProjectionPlot(pf, "x", "Density", weight_field="Density",
+                         data_source=new_region)
+   plot.save()
 
 .. _extracting-connected-sets:
 


https://bitbucket.org/yt_analysis/yt-doc/commits/42f94049fb5d/
Changeset:   42f94049fb5d
User:        samskillman
Date:        2013-11-06 15:42:54
Summary:     Merged in brittonsmith/yt-doc (pull request #113)

Updating docs for cut_region.
Affected #:  1 file

diff -r 53e9fd3ef9ae3089c0322d2220fdb0c27eca7bb1 -r 42f94049fb5d2a8f22d00915ae24e7a4d9f644fa source/analyzing/objects.rst
--- a/source/analyzing/objects.rst
+++ b/source/analyzing/objects.rst
@@ -233,49 +233,44 @@
 
 .. _field_cuts:
 
-Querying and Subselecting Objects
----------------------------------
+Cutting Objects by Field Values
+-------------------------------
 
-Often when analyzing astrophysical objects, only regions that posses certain
-properties are of interest.  For instance, in a global galactic disk
-simulation, perhaps you want to examine only the cold gas, or only the hot gas.
-These criteria can be applied to selections of data in yt using one of two
-mechanisms: the "field cuts" mechanism or the "extract region" mechanism.  
-"Field cuts" are more suited to specifying extractions based on easily
-described properties of the gas, whereas "extracted regions" are usually suited
-for when the gas properties of interest require explicit examination.
+Data objects can be cut by their field values using the ``cut_region`` 
+method.  For example, this could be used to compute the total mass within 
+a certain temperature range, as in the following example.
 
-Objects can be selected using field cuts by specifying one or more criteria to
-the ``cut_field`` method on a data object.  These criteria should be of the
-form ``grid["FieldName"] < SOMETHING`` or similar; they will be evaluated with
-respect to a grid.  The operation will then supply a new objects composed of
-all the cells that satisfy this.  For instance, if I wanted to take a sphere
-and only examine inflowing gas:
+.. notebook-cell::
 
-.. code-block:: python
+   from yt.mods import *
+   pf = load("enzo_tiny_cosmology/DD0046/DD0046")
+   ad = pf.h.all_data()
+   total_mass = ad.quantities["TotalQuantity"]("CellMassMsun")
+   # now select only gas with 1e5 K < T < 1e7 K.
+   new_region = ad.cut_region(['grid["Temperature"] > 1e5',
+                               'grid["Temperature"] < 1e7'])
+   cut_mass = new_region.quantities["TotalQuantity"]("CellMassMsun")
+   print "The fraction of mass in this temperature range is %f." % \
+     (cut_mass[0] / total_mass[0])
 
-   sp = pf.h.sphere("max", (100.0, 'au'))
-   inflow = sp.cut_region( [ "grid['RadialVelocity'] < 0" ])
+The ``cut_region`` function generates a new object containing only the cells 
+that meet all of the specified criteria.  The sole argument to ``cut_region`` 
+is a list of strings, where each string is evaluated with an ``eval`` 
+statement.  ``eval`` is a native Python function that evaluates a string as 
+a Python expression.  Any type of data object can be cut with ``cut_region``.  
+Objects generated with ``cut_region`` can be used in the same way as all 
+other data objects.  For example, a cut region can be visualized by giving 
+it as a data_source to a projection.
 
-The new returned object, ``inflow``, can be analyzed as any other data object.
-For instance we could examine its angular momentum vector:
+.. python-script::
 
-.. code-block:: python
-
-   L = inflow.quantities["AngularMomentumVector"]()
-
-There are two things to note about the arguments to ``cut_region``: they are a
-list, where each argument is an independent criterion for inclusion, and they
-are evaluated on each grid object.  Only the cells where all arguments are
-evaluated as ``True`` will be included in the resulting object.  For instance,
-to select only dense, cold gas:
-
-.. code-block:: python
-
-   dense = sp.cut_region([ "grid['dens'] > 1e5", "grid['temp'] < 200" ])
-
-Each of the criteria will be evaluated independently and the resulting
-intersection will be returned.
+   from yt.mods import *
+   pf = load("enzo_tiny_cosmology/DD0046/DD0046")
+   ad = pf.h.all_data()
+   new_region = ad.cut_region(['grid["Density"] > 1e-29'])
+   plot = ProjectionPlot(pf, "x", "Density", weight_field="Density",
+                         data_source=new_region)
+   plot.save()
 
 .. _extracting-connected-sets:

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

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