[yt-svn] commit/yt: 7 new changesets
commits-noreply at bitbucket.org
commits-noreply at bitbucket.org
Sun Jul 20 07:17:51 PDT 2014
7 new commits in yt:
https://bitbucket.org/yt_analysis/yt/commits/cc5dc03971fb/
Changeset: cc5dc03971fb
Branch: yt-3.0
User: chummels
Date: 2014-07-15 01:55:58
Summary: Adding "projected" argument to PlotWindow class, in order to prepend all fields in ProjectionPlots with "Projected"
Affected #: 1 file
diff -r 50f4b65d410f0301a545468bdf7b92e660c415ca -r cc5dc03971fbde08bc9524d8a0fd1853c55b9184 yt/visualization/plot_window.py
--- a/yt/visualization/plot_window.py
+++ b/yt/visualization/plot_window.py
@@ -271,7 +271,7 @@
"""
frb = None
def __init__(self, data_source, bounds, buff_size=(800,800), antialias=True,
- periodic=True, origin='center-window', oblique=False,
+ periodic=True, origin='center-window', oblique=False, projected=False,
window_size=8.0, fields=None, fontsize=18, aspect=None, setup=False):
if not hasattr(self, "pf"):
self.pf = data_source.pf
@@ -282,6 +282,7 @@
self.center = None
self._periodic = periodic
self.oblique = oblique
+ self.projected = projected
self.buff_size = buff_size
self.antialias = antialias
self.aspect = aspect
@@ -915,6 +916,8 @@
label.set_fontproperties(fp)
colorbar_label = image.info['label']
+ if self.projected:
+ colorbar_label = "$\\rm{Projected }$ %s" % colorbar_label
# Determine the units of the data
units = Unit(self.frb[f].units, registry=self.pf.unit_registry)
@@ -1220,7 +1223,8 @@
center=center, data_source=data_source,
field_parameters = field_parameters, style = proj_style)
PWViewerMPL.__init__(self, proj, bounds, fields=fields, origin=origin,
- fontsize=fontsize, window_size=window_size, aspect=aspect)
+ fontsize=fontsize, window_size=window_size,
+ aspect=aspect, projected=True)
if axes_unit is None:
axes_unit = get_axes_unit(width, pf)
self.set_axes_unit(axes_unit)
@@ -1425,7 +1429,7 @@
# aren't well-defined for off-axis data objects
PWViewerMPL.__init__(
self, OffAxisProj, bounds, fields=fields, origin='center-window',
- periodic=False, oblique=True, fontsize=fontsize)
+ periodic=False, oblique=True, fontsize=fontsize, projected=True)
if axes_unit is None:
axes_unit = get_axes_unit(width, pf)
self.set_axes_unit(axes_unit)
https://bitbucket.org/yt_analysis/yt/commits/09f580d2a6a5/
Changeset: 09f580d2a6a5
Branch: yt-3.0
User: chummels
Date: 2014-07-15 02:18:35
Summary: Merging.
Affected #: 1 file
diff -r b2403b6960cf90515d3149868a0fb816d139b22f -r 09f580d2a6a59b05483a4179338fbd284474ff6f yt/visualization/plot_window.py
--- a/yt/visualization/plot_window.py
+++ b/yt/visualization/plot_window.py
@@ -271,7 +271,7 @@
"""
frb = None
def __init__(self, data_source, bounds, buff_size=(800,800), antialias=True,
- periodic=True, origin='center-window', oblique=False,
+ periodic=True, origin='center-window', oblique=False, projected=False,
window_size=8.0, fields=None, fontsize=18, aspect=None, setup=False):
if not hasattr(self, "pf"):
self.pf = data_source.pf
@@ -282,6 +282,7 @@
self.center = None
self._periodic = periodic
self.oblique = oblique
+ self.projected = projected
self.buff_size = buff_size
self.antialias = antialias
self.aspect = aspect
@@ -915,6 +916,8 @@
label.set_fontproperties(fp)
colorbar_label = image.info['label']
+ if self.projected:
+ colorbar_label = "$\\rm{Projected }$ %s" % colorbar_label
# Determine the units of the data
units = Unit(self.frb[f].units, registry=self.pf.unit_registry)
@@ -1220,7 +1223,8 @@
center=center, data_source=data_source,
field_parameters = field_parameters, style = proj_style)
PWViewerMPL.__init__(self, proj, bounds, fields=fields, origin=origin,
- fontsize=fontsize, window_size=window_size, aspect=aspect)
+ fontsize=fontsize, window_size=window_size,
+ aspect=aspect, projected=True)
if axes_unit is None:
axes_unit = get_axes_unit(width, pf)
self.set_axes_unit(axes_unit)
@@ -1425,7 +1429,7 @@
# aren't well-defined for off-axis data objects
PWViewerMPL.__init__(
self, OffAxisProj, bounds, fields=fields, origin='center-window',
- periodic=False, oblique=True, fontsize=fontsize)
+ periodic=False, oblique=True, fontsize=fontsize, projected=True)
if axes_unit is None:
axes_unit = get_axes_unit(width, pf)
self.set_axes_unit(axes_unit)
https://bitbucket.org/yt_analysis/yt/commits/3302a13d3964/
Changeset: 3302a13d3964
Branch: yt-3.0
User: chummels
Date: 2014-07-15 06:37:18
Summary: Making non-weighted projection plots change the colorbar's label name to "Projected <field>"
Affected #: 1 file
diff -r 09f580d2a6a59b05483a4179338fbd284474ff6f -r 3302a13d3964cda2a7592749c0ecedc6869f4136 yt/visualization/plot_window.py
--- a/yt/visualization/plot_window.py
+++ b/yt/visualization/plot_window.py
@@ -271,8 +271,9 @@
"""
frb = None
def __init__(self, data_source, bounds, buff_size=(800,800), antialias=True,
- periodic=True, origin='center-window', oblique=False, projected=False,
- window_size=8.0, fields=None, fontsize=18, aspect=None, setup=False):
+ periodic=True, origin='center-window', oblique=False,
+ window_size=8.0, fields=None, fontsize=18, aspect=None,
+ setup=False):
if not hasattr(self, "pf"):
self.pf = data_source.pf
ts = self._initialize_dataset(self.pf)
@@ -282,7 +283,6 @@
self.center = None
self._periodic = periodic
self.oblique = oblique
- self.projected = projected
self.buff_size = buff_size
self.antialias = antialias
self.aspect = aspect
@@ -916,7 +916,10 @@
label.set_fontproperties(fp)
colorbar_label = image.info['label']
- if self.projected:
+
+ # If we're creating a plot of a projection, change the displayed
+ # field name accordingly.
+ if hasattr(self, 'projected'):
colorbar_label = "$\\rm{Projected }$ %s" % colorbar_label
# Determine the units of the data
@@ -1217,6 +1220,9 @@
self.ts = ts
pf = self.pf = ts[0]
axis = fix_axis(axis, pf)
+ # If a non-weighted projection, assure field-label reflects that
+ if weight_field is None:
+ self.projected = True
(bounds, center) = get_window_parameters(axis, center, width, pf)
if field_parameters is None: field_parameters = {}
proj = pf.proj(fields, axis, weight_field=weight_field,
@@ -1224,7 +1230,7 @@
field_parameters = field_parameters, style = proj_style)
PWViewerMPL.__init__(self, proj, bounds, fields=fields, origin=origin,
fontsize=fontsize, window_size=window_size,
- aspect=aspect, projected=True)
+ aspect=aspect)
if axes_unit is None:
axes_unit = get_axes_unit(width, pf)
self.set_axes_unit(axes_unit)
@@ -1425,11 +1431,14 @@
center_rot, pf, normal, oap_width, fields, interpolated,
weight=weight_field, volume=volume, no_ghost=no_ghost,
le=le, re=re, north_vector=north_vector)
+ # If a non-weighted projection, assure field-label reflects that
+ if weight_field is None:
+ self.projected = True
# Hard-coding the origin keyword since the other two options
# aren't well-defined for off-axis data objects
PWViewerMPL.__init__(
self, OffAxisProj, bounds, fields=fields, origin='center-window',
- periodic=False, oblique=True, fontsize=fontsize, projected=True)
+ periodic=False, oblique=True, fontsize=fontsize)
if axes_unit is None:
axes_unit = get_axes_unit(width, pf)
self.set_axes_unit(axes_unit)
https://bitbucket.org/yt_analysis/yt/commits/317470ca82f5/
Changeset: 317470ca82f5
Branch: yt-3.0
User: chummels
Date: 2014-07-18 21:10:25
Summary: Updating "projected" to only work with projections that are line integrals.
Affected #: 1 file
diff -r 3302a13d3964cda2a7592749c0ecedc6869f4136 -r 317470ca82f580407f932c66cc6eb6ad964a68df yt/visualization/plot_window.py
--- a/yt/visualization/plot_window.py
+++ b/yt/visualization/plot_window.py
@@ -1220,8 +1220,8 @@
self.ts = ts
pf = self.pf = ts[0]
axis = fix_axis(axis, pf)
- # If a non-weighted projection, assure field-label reflects that
- if weight_field is None:
+ # If a non-weighted integral projection, assure field-label reflects that
+ if weight_field is None and proj_stype = "integrate":
self.projected = True
(bounds, center) = get_window_parameters(axis, center, width, pf)
if field_parameters is None: field_parameters = {}
@@ -1431,8 +1431,8 @@
center_rot, pf, normal, oap_width, fields, interpolated,
weight=weight_field, volume=volume, no_ghost=no_ghost,
le=le, re=re, north_vector=north_vector)
- # If a non-weighted projection, assure field-label reflects that
- if weight_field is None:
+ # If a non-weighted, integral projection, assure field-label reflects that
+ if weight_field is None and proj_stype = "integrate":
self.projected = True
# Hard-coding the origin keyword since the other two options
# aren't well-defined for off-axis data objects
https://bitbucket.org/yt_analysis/yt/commits/74f5a294c4dc/
Changeset: 74f5a294c4dc
Branch: yt-3.0
User: chummels
Date: 2014-07-20 03:47:21
Summary: Merging.
Affected #: 394 files
diff -r 317470ca82f580407f932c66cc6eb6ad964a68df -r 74f5a294c4dcc113b0941ea30ee4ba81f4f6998d doc/cheatsheet.tex
--- a/doc/cheatsheet.tex
+++ b/doc/cheatsheet.tex
@@ -208,38 +208,38 @@
After that, simulation data is generally accessed in yt using {\it Data Containers} which are Python objects
that define a region of simulation space from which data should be selected.
\settowidth{\MyLen}{\texttt{multicol} }
-\texttt{pf = load(}{\it dataset}\texttt{)} \textemdash\ Reference a single snapshot.\\
-\texttt{dd = pf.h.all\_data()} \textemdash\ Select the entire volume.\\
+\texttt{ds = load(}{\it dataset}\texttt{)} \textemdash\ Reference a single snapshot.\\
+\texttt{dd = ds.all\_data()} \textemdash\ Select the entire volume.\\
\texttt{a = dd[}{\it field\_name}\texttt{]} \textemdash\ Saves the contents of {\it field} into the
numpy array \texttt{a}. Similarly for other data containers.\\
-\texttt{pf.h.field\_list} \textemdash\ A list of available fields in the snapshot. \\
-\texttt{pf.h.derived\_field\_list} \textemdash\ A list of available derived fields
+\texttt{ds.field\_list} \textemdash\ A list of available fields in the snapshot. \\
+\texttt{ds.derived\_field\_list} \textemdash\ A list of available derived fields
in the snapshot. \\
-\texttt{val, loc = pf.h.find\_max("Density")} \textemdash\ Find the \texttt{val}ue of
+\texttt{val, loc = ds.find\_max("Density")} \textemdash\ Find the \texttt{val}ue of
the maximum of the field \texttt{Density} and its \texttt{loc}ation. \\
-\texttt{sp = pf.sphere(}{\it cen}\texttt{,}{\it radius}\texttt{)} \textemdash\ Create a spherical data
+\texttt{sp = ds.sphere(}{\it cen}\texttt{,}{\it radius}\texttt{)} \textemdash\ Create a spherical data
container. {\it cen} may be a coordinate, or ``max'' which
centers on the max density point. {\it radius} may be a float in
code units or a tuple of ({\it length, unit}).\\
-\texttt{re = pf.region({\it cen}, {\it left edge}, {\it right edge})} \textemdash\ Create a
+\texttt{re = ds.region({\it cen}, {\it left edge}, {\it right edge})} \textemdash\ Create a
rectilinear data container. {\it cen} is required but not used.
{\it left} and {\it right edge} are coordinate values that define the region.
-\texttt{di = pf.disk({\it cen}, {\it normal}, {\it radius}, {\it height})} \textemdash\
+\texttt{di = ds.disk({\it cen}, {\it normal}, {\it radius}, {\it height})} \textemdash\
Create a cylindrical data container centered at {\it cen} along the
direction set by {\it normal},with total length
2$\times${\it height} and with radius {\it radius}. \\
- \texttt{bl = pf.boolean({\it constructor})} \textemdash\ Create a boolean data
+ \texttt{bl = ds.boolean({\it constructor})} \textemdash\ Create a boolean data
container. {\it constructor} is a list of pre-defined non-boolean
data containers with nested boolean logic using the
``AND'', ``NOT'', or ``OR'' operators. E.g. {\it constructor=}
{\it [sp, ``NOT'', (di, ``OR'', re)]} gives a volume defined
by {\it sp} minus the patches covered by {\it di} and {\it re}.\\
-\texttt{pf.h.save\_object(sp, {\it ``sp\_for\_later''})} \textemdash\ Save an object (\texttt{sp}) for later use.\\
-\texttt{sp = pf.h.load\_object({\it ``sp\_for\_later''})} \textemdash\ Recover a saved object.\\
+\texttt{ds.save\_object(sp, {\it ``sp\_for\_later''})} \textemdash\ Save an object (\texttt{sp}) for later use.\\
+\texttt{sp = ds.load\_object({\it ``sp\_for\_later''})} \textemdash\ Recover a saved object.\\
\subsection{Defining New Fields \& Quantities}
@@ -261,15 +261,15 @@
\subsection{Slices and Projections}
\settowidth{\MyLen}{\texttt{multicol} }
-\texttt{slc = SlicePlot(pf, {\it axis}, {\it field}, {\it center=}, {\it width=}, {\it weight\_field=}, {\it additional parameters})} \textemdash\ Make a slice plot
+\texttt{slc = SlicePlot(ds, {\it axis}, {\it field}, {\it center=}, {\it width=}, {\it weight\_field=}, {\it additional parameters})} \textemdash\ Make a slice plot
perpendicular to {\it axis} of {\it field} weighted by {\it weight\_field} at (code-units) {\it center} with
{\it width} in code units or a (value, unit) tuple. Hint: try {\it SlicePlot?} in IPython to see additional parameters.\\
\texttt{slc.save({\it file\_prefix})} \textemdash\ Save the slice to a png with name prefix {\it file\_prefix}.
\texttt{.save()} works similarly for the commands below.\\
-\texttt{prj = ProjectionPlot(pf, {\it axis}, {\it field}, {\it addit. params})} \textemdash\ Make a projection. \\
-\texttt{prj = OffAxisSlicePlot(pf, {\it normal}, {\it fields}, {\it center=}, {\it width=}, {\it depth=},{\it north\_vector=},{\it weight\_field=})} \textemdash Make an off-axis slice. Note this takes an array of fields. \\
-\texttt{prj = OffAxisProjectionPlot(pf, {\it normal}, {\it fields}, {\it center=}, {\it width=}, {\it depth=},{\it north\_vector=},{\it weight\_field=})} \textemdash Make an off axis projection. Note this takes an array of fields. \\
+\texttt{prj = ProjectionPlot(ds, {\it axis}, {\it field}, {\it addit. params})} \textemdash\ Make a projection. \\
+\texttt{prj = OffAxisSlicePlot(ds, {\it normal}, {\it fields}, {\it center=}, {\it width=}, {\it depth=},{\it north\_vector=},{\it weight\_field=})} \textemdash Make an off-axis slice. Note this takes an array of fields. \\
+\texttt{prj = OffAxisProjectionPlot(ds, {\it normal}, {\it fields}, {\it center=}, {\it width=}, {\it depth=},{\it north\_vector=},{\it weight\_field=})} \textemdash Make an off axis projection. Note this takes an array of fields. \\
\subsection{Plot Annotations}
\settowidth{\MyLen}{\texttt{multicol} }
@@ -365,8 +365,8 @@
\subsection{FAQ}
\settowidth{\MyLen}{\texttt{multicol}}
-\texttt{pf.field\_info[`field'].take\_log = False} \textemdash\ When plotting \texttt{field}, do not take log.
-Must enter \texttt{pf.h} before this command. \\
+\texttt{ds.field\_info[`field'].take\_log = False} \textemdash\ When plotting \texttt{field}, do not take log.
+Must enter \texttt{ds.index} before this command. \\
%\rule{0.3\linewidth}{0.25pt}
diff -r 317470ca82f580407f932c66cc6eb6ad964a68df -r 74f5a294c4dcc113b0941ea30ee4ba81f4f6998d doc/coding_styleguide.txt
--- a/doc/coding_styleguide.txt
+++ b/doc/coding_styleguide.txt
@@ -49,7 +49,7 @@
* Don't create a new class to replicate the functionality of an old class --
replace the old class. Too many options makes for a confusing user
experience.
- * Parameter files are a last resort.
+ * Parameter files external to yt are a last resort.
* The usage of the **kwargs construction should be avoided. If they cannot
be avoided, they must be explained, even if they are only to be passed on to
a nested function.
@@ -61,7 +61,7 @@
* Hard-coding parameter names that are the same as those in Enzo. The
following translation table should be of some help. Note that the
parameters are now properties on a Dataset subclass: you access them
- like pf.refine_by .
+ like ds.refine_by .
* RefineBy => refine_by
* TopGridRank => dimensionality
* TopGridDimensions => domain_dimensions
diff -r 317470ca82f580407f932c66cc6eb6ad964a68df -r 74f5a294c4dcc113b0941ea30ee4ba81f4f6998d doc/docstring_example.txt
--- a/doc/docstring_example.txt
+++ b/doc/docstring_example.txt
@@ -73,7 +73,7 @@
Examples
--------
These are written in doctest format, and should illustrate how to
- use the function. Use the variables 'pf' for the parameter file, 'pc' for
+ use the function. Use the variables 'ds' for the dataset, 'pc' for
a plot collection, 'c' for a center, and 'L' for a vector.
>>> a=[1,2,3]
diff -r 317470ca82f580407f932c66cc6eb6ad964a68df -r 74f5a294c4dcc113b0941ea30ee4ba81f4f6998d doc/docstring_idioms.txt
--- a/doc/docstring_idioms.txt
+++ b/doc/docstring_idioms.txt
@@ -19,7 +19,7 @@
useful variable names that correspond to specific instances that the user is
presupposed to have created.
- * `pf`: a parameter file, loaded successfully
+ * `ds`: a dataset, loaded successfully
* `sp`: a sphere
* `c`: a 3-component "center"
* `L`: a 3-component vector that corresponds to either angular momentum or a
diff -r 317470ca82f580407f932c66cc6eb6ad964a68df -r 74f5a294c4dcc113b0941ea30ee4ba81f4f6998d doc/helper_scripts/parse_cb_list.py
--- a/doc/helper_scripts/parse_cb_list.py
+++ b/doc/helper_scripts/parse_cb_list.py
@@ -2,7 +2,7 @@
import inspect
from textwrap import TextWrapper
-pf = load("RD0005-mine/RedshiftOutput0005")
+ds = load("RD0005-mine/RedshiftOutput0005")
output = open("source/visualizing/_cb_docstrings.inc", "w")
diff -r 317470ca82f580407f932c66cc6eb6ad964a68df -r 74f5a294c4dcc113b0941ea30ee4ba81f4f6998d doc/helper_scripts/parse_dq_list.py
--- a/doc/helper_scripts/parse_dq_list.py
+++ b/doc/helper_scripts/parse_dq_list.py
@@ -2,7 +2,7 @@
import inspect
from textwrap import TextWrapper
-pf = load("RD0005-mine/RedshiftOutput0005")
+ds = load("RD0005-mine/RedshiftOutput0005")
output = open("source/analyzing/_dq_docstrings.inc", "w")
@@ -29,7 +29,7 @@
docstring = docstring))
#docstring = "\n".join(tw.wrap(docstring))))
-dd = pf.h.all_data()
+dd = ds.all_data()
for n,func in sorted(dd.quantities.functions.items()):
print n, func
write_docstring(output, n, func[1])
diff -r 317470ca82f580407f932c66cc6eb6ad964a68df -r 74f5a294c4dcc113b0941ea30ee4ba81f4f6998d doc/helper_scripts/parse_object_list.py
--- a/doc/helper_scripts/parse_object_list.py
+++ b/doc/helper_scripts/parse_object_list.py
@@ -2,7 +2,7 @@
import inspect
from textwrap import TextWrapper
-pf = load("RD0005-mine/RedshiftOutput0005")
+ds = load("RD0005-mine/RedshiftOutput0005")
output = open("source/analyzing/_obj_docstrings.inc", "w")
@@ -27,7 +27,7 @@
f.write(template % dict(clsname = clsname, sig = sig, clsproxy=clsproxy,
docstring = 'physical-object-api'))
-for n,c in sorted(pf.h.__dict__.items()):
+for n,c in sorted(ds.__dict__.items()):
if hasattr(c, '_con_args'):
print n
write_docstring(output, n, c)
diff -r 317470ca82f580407f932c66cc6eb6ad964a68df -r 74f5a294c4dcc113b0941ea30ee4ba81f4f6998d doc/helper_scripts/show_fields.py
--- a/doc/helper_scripts/show_fields.py
+++ b/doc/helper_scripts/show_fields.py
@@ -17,15 +17,15 @@
everywhere, "Enzo" fields in Enzo datasets, "Orion" fields in Orion datasets,
and so on.
-Try using the ``pf.field_list`` and ``pf.derived_field_list`` to view the
+Try using the ``ds.field_list`` and ``ds.derived_field_list`` to view the
native and derived fields available for your dataset respectively. For example
to display the native fields in alphabetical order:
.. notebook-cell::
from yt.mods import *
- pf = load("Enzo_64/DD0043/data0043")
- for i in sorted(pf.field_list):
+ ds = load("Enzo_64/DD0043/data0043")
+ for i in sorted(ds.field_list):
print i
.. note:: Universal fields will be overridden by a code-specific field.
diff -r 317470ca82f580407f932c66cc6eb6ad964a68df -r 74f5a294c4dcc113b0941ea30ee4ba81f4f6998d doc/source/analyzing/_obj_docstrings.inc
--- a/doc/source/analyzing/_obj_docstrings.inc
+++ b/doc/source/analyzing/_obj_docstrings.inc
@@ -1,12 +1,12 @@
-.. class:: boolean(self, regions, fields=None, pf=None, **field_parameters):
+.. class:: boolean(self, regions, fields=None, ds=None, **field_parameters):
For more information, see :ref:`physical-object-api`
(This is a proxy for :class:`~yt.data_objects.data_containers.AMRBooleanRegionBase`.)
-.. class:: covering_grid(self, level, left_edge, dims, fields=None, pf=None, num_ghost_zones=0, use_pbar=True, **field_parameters):
+.. class:: covering_grid(self, level, left_edge, dims, fields=None, ds=None, num_ghost_zones=0, use_pbar=True, **field_parameters):
For more information, see :ref:`physical-object-api`
(This is a proxy for :class:`~yt.data_objects.data_containers.AMRCoveringGridBase`.)
@@ -24,13 +24,13 @@
(This is a proxy for :class:`~yt.data_objects.data_containers.AMRCuttingPlaneBase`.)
-.. class:: disk(self, center, normal, radius, height, fields=None, pf=None, **field_parameters):
+.. class:: disk(self, center, normal, radius, height, fields=None, ds=None, **field_parameters):
For more information, see :ref:`physical-object-api`
(This is a proxy for :class:`~yt.data_objects.data_containers.AMRCylinderBase`.)
-.. class:: ellipsoid(self, center, A, B, C, e0, tilt, fields=None, pf=None, **field_parameters):
+.. class:: ellipsoid(self, center, A, B, C, e0, tilt, fields=None, ds=None, **field_parameters):
For more information, see :ref:`physical-object-api`
(This is a proxy for :class:`~yt.data_objects.data_containers.AMREllipsoidBase`.)
@@ -48,79 +48,79 @@
(This is a proxy for :class:`~yt.data_objects.data_containers.AMRFixedResCuttingPlaneBase`.)
-.. class:: fixed_res_proj(self, axis, level, left_edge, dims, fields=None, pf=None, **field_parameters):
+.. class:: fixed_res_proj(self, axis, level, left_edge, dims, fields=None, ds=None, **field_parameters):
For more information, see :ref:`physical-object-api`
(This is a proxy for :class:`~yt.data_objects.data_containers.AMRFixedResProjectionBase`.)
-.. class:: grid_collection(self, center, grid_list, fields=None, pf=None, **field_parameters):
+.. class:: grid_collection(self, center, grid_list, fields=None, ds=None, **field_parameters):
For more information, see :ref:`physical-object-api`
(This is a proxy for :class:`~yt.data_objects.data_containers.AMRGridCollectionBase`.)
-.. class:: grid_collection_max_level(self, center, max_level, fields=None, pf=None, **field_parameters):
+.. class:: grid_collection_max_level(self, center, max_level, fields=None, ds=None, **field_parameters):
For more information, see :ref:`physical-object-api`
(This is a proxy for :class:`~yt.data_objects.data_containers.AMRMaxLevelCollectionBase`.)
-.. class:: inclined_box(self, origin, box_vectors, fields=None, pf=None, **field_parameters):
+.. class:: inclined_box(self, origin, box_vectors, fields=None, ds=None, **field_parameters):
For more information, see :ref:`physical-object-api`
(This is a proxy for :class:`~yt.data_objects.data_containers.AMRInclinedBoxBase`.)
-.. class:: ortho_ray(self, axis, coords, fields=None, pf=None, **field_parameters):
+.. class:: ortho_ray(self, axis, coords, fields=None, ds=None, **field_parameters):
For more information, see :ref:`physical-object-api`
(This is a proxy for :class:`~yt.data_objects.data_containers.AMROrthoRayBase`.)
-.. class:: overlap_proj(self, axis, field, weight_field=None, max_level=None, center=None, pf=None, source=None, node_name=None, field_cuts=None, preload_style='level', serialize=True, **field_parameters):
+.. class:: overlap_proj(self, axis, field, weight_field=None, max_level=None, center=None, ds=None, source=None, node_name=None, field_cuts=None, preload_style='level', serialize=True, **field_parameters):
For more information, see :ref:`physical-object-api`
(This is a proxy for :class:`~yt.data_objects.data_containers.AMRProjBase`.)
-.. class:: periodic_region(self, center, left_edge, right_edge, fields=None, pf=None, **field_parameters):
+.. class:: periodic_region(self, center, left_edge, right_edge, fields=None, ds=None, **field_parameters):
For more information, see :ref:`physical-object-api`
(This is a proxy for :class:`~yt.data_objects.data_containers.AMRPeriodicRegionBase`.)
-.. class:: periodic_region_strict(self, center, left_edge, right_edge, fields=None, pf=None, **field_parameters):
+.. class:: periodic_region_strict(self, center, left_edge, right_edge, fields=None, ds=None, **field_parameters):
For more information, see :ref:`physical-object-api`
(This is a proxy for :class:`~yt.data_objects.data_containers.AMRPeriodicRegionStrictBase`.)
-.. class:: proj(self, axis, field, weight_field=None, max_level=None, center=None, pf=None, source=None, node_name=None, field_cuts=None, preload_style=None, serialize=True, style='integrate', **field_parameters):
+.. class:: proj(self, axis, field, weight_field=None, max_level=None, center=None, ds=None, source=None, node_name=None, field_cuts=None, preload_style=None, serialize=True, style='integrate', **field_parameters):
For more information, see :ref:`physical-object-api`
(This is a proxy for :class:`~yt.data_objects.data_containers.AMRQuadTreeProjBase`.)
-.. class:: ray(self, start_point, end_point, fields=None, pf=None, **field_parameters):
+.. class:: ray(self, start_point, end_point, fields=None, ds=None, **field_parameters):
For more information, see :ref:`physical-object-api`
(This is a proxy for :class:`~yt.data_objects.data_containers.AMRRayBase`.)
-.. class:: region(self, center, left_edge, right_edge, fields=None, pf=None, **field_parameters):
+.. class:: region(self, center, left_edge, right_edge, fields=None, ds=None, **field_parameters):
For more information, see :ref:`physical-object-api`
(This is a proxy for :class:`~yt.data_objects.data_containers.AMRRegionBase`.)
-.. class:: region_strict(self, center, left_edge, right_edge, fields=None, pf=None, **field_parameters):
+.. class:: region_strict(self, center, left_edge, right_edge, fields=None, ds=None, **field_parameters):
For more information, see :ref:`physical-object-api`
(This is a proxy for :class:`~yt.data_objects.data_containers.AMRRegionStrictBase`.)
-.. class:: slice(self, axis, coord, fields=None, center=None, pf=None, node_name=False, **field_parameters):
+.. class:: slice(self, axis, coord, fields=None, center=None, ds=None, node_name=False, **field_parameters):
For more information, see :ref:`physical-object-api`
(This is a proxy for :class:`~yt.data_objects.data_containers.AMRSliceBase`.)
@@ -132,13 +132,13 @@
(This is a proxy for :class:`~yt.data_objects.data_containers.AMRSmoothedCoveringGridBase`.)
-.. class:: sphere(self, center, radius, fields=None, pf=None, **field_parameters):
+.. class:: sphere(self, center, radius, fields=None, ds=None, **field_parameters):
For more information, see :ref:`physical-object-api`
(This is a proxy for :class:`~yt.data_objects.data_containers.AMRSphereBase`.)
-.. class:: streamline(self, positions, length=1.0, fields=None, pf=None, **field_parameters):
+.. class:: streamline(self, positions, length=1.0, fields=None, ds=None, **field_parameters):
For more information, see :ref:`physical-object-api`
(This is a proxy for :class:`~yt.data_objects.data_containers.AMRStreamlineBase`.)
diff -r 317470ca82f580407f932c66cc6eb6ad964a68df -r 74f5a294c4dcc113b0941ea30ee4ba81f4f6998d doc/source/analyzing/analysis_modules/Halo_Analysis.ipynb
--- a/doc/source/analyzing/analysis_modules/Halo_Analysis.ipynb
+++ b/doc/source/analyzing/analysis_modules/Halo_Analysis.ipynb
@@ -44,7 +44,7 @@
"tmpdir = tempfile.mkdtemp()\n",
"\n",
"# Load the data set with the full simulation information\n",
- "data_pf = load('Enzo_64/RD0006/RedshiftOutput0006')"
+ "data_ds = load('Enzo_64/RD0006/RedshiftOutput0006')"
],
"language": "python",
"metadata": {},
@@ -62,7 +62,7 @@
"collapsed": false,
"input": [
"# Load the rockstar data files\n",
- "halos_pf = load('rockstar_halos/halos_0.0.bin')"
+ "halos_ds = load('rockstar_halos/halos_0.0.bin')"
],
"language": "python",
"metadata": {},
@@ -80,7 +80,7 @@
"collapsed": false,
"input": [
"# Instantiate a catalog using those two paramter files\n",
- "hc = HaloCatalog(data_pf=data_pf, halos_pf=halos_pf, \n",
+ "hc = HaloCatalog(data_ds=data_ds, halos_ds=halos_ds, \n",
" output_dir=os.path.join(tmpdir, 'halo_catalog'))"
],
"language": "python",
@@ -295,9 +295,9 @@
"cell_type": "code",
"collapsed": false,
"input": [
- "halos_pf = load(os.path.join(tmpdir, 'halo_catalog/halo_catalog.0.h5'))\n",
+ "halos_ds = load(os.path.join(tmpdir, 'halo_catalog/halo_catalog.0.h5'))\n",
"\n",
- "hc_reloaded = HaloCatalog(halos_pf=halos_pf,\n",
+ "hc_reloaded = HaloCatalog(halos_ds=halos_ds,\n",
" output_dir=os.path.join(tmpdir, 'halo_catalog'))"
],
"language": "python",
@@ -407,4 +407,4 @@
"metadata": {}
}
]
-}
\ No newline at end of file
+}
diff -r 317470ca82f580407f932c66cc6eb6ad964a68df -r 74f5a294c4dcc113b0941ea30ee4ba81f4f6998d doc/source/analyzing/analysis_modules/PPVCube.ipynb
--- a/doc/source/analyzing/analysis_modules/PPVCube.ipynb
+++ b/doc/source/analyzing/analysis_modules/PPVCube.ipynb
@@ -1,7 +1,7 @@
{
"metadata": {
"name": "",
- "signature": "sha256:3f810954006851303837edb8fd85ee6583a883122b0f4867903562546c4f19d2"
+ "signature": "sha256:ba8b6a53571695ae1d0c236ad43875823746e979a329a9d35ab0a8b899cebbba"
},
"nbformat": 3,
"nbformat_minor": 0,
@@ -21,7 +21,7 @@
"input": [
"%matplotlib inline\n",
"from yt.mods import *\n",
- "from yt.analysis_modules.api import PPVCube"
+ "from yt.analysis_modules.ppv_cube.api import PPVCube"
],
"language": "python",
"metadata": {},
@@ -222,7 +222,7 @@
"cell_type": "code",
"collapsed": false,
"input": [
- "pf = load(\"cube.fits\")"
+ "ds = load(\"cube.fits\")"
],
"language": "python",
"metadata": {},
@@ -233,7 +233,7 @@
"collapsed": false,
"input": [
"# Specifying no center gives us the center slice\n",
- "slc = SlicePlot(pf, \"z\", [\"density\"])\n",
+ "slc = SlicePlot(ds, \"z\", [\"density\"])\n",
"slc.show()"
],
"language": "python",
@@ -246,9 +246,9 @@
"input": [
"import yt.units as u\n",
"# Picking different velocities for the slices\n",
- "new_center = pf.domain_center\n",
- "new_center[2] = pf.spec2pixel(-1.0*u.km/u.s)\n",
- "slc = SlicePlot(pf, \"z\", [\"density\"], center=new_center)\n",
+ "new_center = ds.domain_center\n",
+ "new_center[2] = ds.spec2pixel(-1.0*u.km/u.s)\n",
+ "slc = SlicePlot(ds, \"z\", [\"density\"], center=new_center)\n",
"slc.show()"
],
"language": "python",
@@ -259,8 +259,8 @@
"cell_type": "code",
"collapsed": false,
"input": [
- "new_center[2] = pf.spec2pixel(0.7*u.km/u.s)\n",
- "slc = SlicePlot(pf, \"z\", [\"density\"], center=new_center)\n",
+ "new_center[2] = ds.spec2pixel(0.7*u.km/u.s)\n",
+ "slc = SlicePlot(ds, \"z\", [\"density\"], center=new_center)\n",
"slc.show()"
],
"language": "python",
@@ -271,8 +271,8 @@
"cell_type": "code",
"collapsed": false,
"input": [
- "new_center[2] = pf.spec2pixel(-0.3*u.km/u.s)\n",
- "slc = SlicePlot(pf, \"z\", [\"density\"], center=new_center)\n",
+ "new_center[2] = ds.spec2pixel(-0.3*u.km/u.s)\n",
+ "slc = SlicePlot(ds, \"z\", [\"density\"], center=new_center)\n",
"slc.show()"
],
"language": "python",
@@ -290,7 +290,7 @@
"cell_type": "code",
"collapsed": false,
"input": [
- "prj = ProjectionPlot(pf, \"z\", [\"density\"], proj_style=\"sum\")\n",
+ "prj = ProjectionPlot(ds, \"z\", [\"density\"], proj_style=\"sum\")\n",
"prj.set_log(\"density\", True)\n",
"prj.set_zlim(\"density\", 1.0e-3, 0.2)\n",
"prj.show()"
@@ -303,4 +303,4 @@
"metadata": {}
}
]
-}
\ No newline at end of file
+}
diff -r 317470ca82f580407f932c66cc6eb6ad964a68df -r 74f5a294c4dcc113b0941ea30ee4ba81f4f6998d doc/source/analyzing/analysis_modules/SZ_projections.ipynb
--- a/doc/source/analyzing/analysis_modules/SZ_projections.ipynb
+++ b/doc/source/analyzing/analysis_modules/SZ_projections.ipynb
@@ -1,7 +1,7 @@
{
"metadata": {
"name": "",
- "signature": "sha256:7fc053480ba7896bfa5905bd69f7b3dd326364fbab324975b76f79640f2e0adf"
+ "signature": "sha256:4745a15abb6512547b50280b92c22567f89255189fd968ca706ef7c39d48024f"
},
"nbformat": 3,
"nbformat_minor": 0,
@@ -91,7 +91,7 @@
"input": [
"%matplotlib inline\n",
"from yt.mods import *\n",
- "from yt.analysis_modules.api import SZProjection\n",
+ "from yt.analysis_modules.sunyaev_zeldovich.api import SZProjection\n",
"\n",
"ds = load(\"enzo_tiny_cosmology/DD0046/DD0046\")\n",
"\n",
diff -r 317470ca82f580407f932c66cc6eb6ad964a68df -r 74f5a294c4dcc113b0941ea30ee4ba81f4f6998d doc/source/analyzing/analysis_modules/absorption_spectrum.rst
--- a/doc/source/analyzing/analysis_modules/absorption_spectrum.rst
+++ b/doc/source/analyzing/analysis_modules/absorption_spectrum.rst
@@ -35,7 +35,7 @@
.. code-block:: python
- from yt.analysis_modules.api import AbsorptionSpectrum
+ from yt.analysis_modules.absorption_spectrum.api import AbsorptionSpectrum
sp = AbsorptionSpectrum(900.0, 1800.0, 10000)
diff -r 317470ca82f580407f932c66cc6eb6ad964a68df -r 74f5a294c4dcc113b0941ea30ee4ba81f4f6998d doc/source/analyzing/analysis_modules/clump_finding.rst
--- a/doc/source/analyzing/analysis_modules/clump_finding.rst
+++ b/doc/source/analyzing/analysis_modules/clump_finding.rst
@@ -84,8 +84,8 @@
from yt.mods import *
- pf = load("DD0000")
- sp = pf.sphere([0.5, 0.5, 0.5], radius=0.1)
+ ds = load("DD0000")
+ sp = ds.sphere([0.5, 0.5, 0.5], radius=0.1)
ratio = sp.quantities["IsBound"](truncate=False, include_thermal_energy=True,
treecode=True, opening_angle=2.0)
@@ -97,8 +97,8 @@
from yt.mods import *
- pf = load("DD0000")
- sp = pf.sphere([0.5, 0.5, 0.5], radius=0.1)
+ ds = load("DD0000")
+ sp = ds.sphere([0.5, 0.5, 0.5], radius=0.1)
ratio = sp.quantities["IsBound"](truncate=False, include_thermal_energy=True,
treecode=False)
diff -r 317470ca82f580407f932c66cc6eb6ad964a68df -r 74f5a294c4dcc113b0941ea30ee4ba81f4f6998d doc/source/analyzing/analysis_modules/ellipsoid_analysis.rst
--- a/doc/source/analyzing/analysis_modules/ellipsoid_analysis.rst
+++ b/doc/source/analyzing/analysis_modules/ellipsoid_analysis.rst
@@ -58,8 +58,8 @@
from yt.mods import *
from yt.analysis_modules.halo_finding.api import *
- pf=load('Enzo_64/RD0006/RedshiftOutput0006')
- halo_list = parallelHF(pf)
+ ds=load('Enzo_64/RD0006/RedshiftOutput0006')
+ halo_list = parallelHF(ds)
halo_list.dump('MyHaloList')
Ellipsoid Parameters
@@ -69,8 +69,8 @@
from yt.mods import *
from yt.analysis_modules.halo_finding.api import *
- pf=load('Enzo_64/RD0006/RedshiftOutput0006')
- haloes = LoadHaloes(pf, 'MyHaloList')
+ ds=load('Enzo_64/RD0006/RedshiftOutput0006')
+ haloes = LoadHaloes(ds, 'MyHaloList')
Once the halo information is saved you can load it into the data
object "haloes", you can get loop over the list of haloes and do
@@ -107,7 +107,7 @@
.. code-block:: python
- ell = pf.ellipsoid(ell_param[0],
+ ell = ds.ellipsoid(ell_param[0],
ell_param[1],
ell_param[2],
ell_param[3],
diff -r 317470ca82f580407f932c66cc6eb6ad964a68df -r 74f5a294c4dcc113b0941ea30ee4ba81f4f6998d doc/source/analyzing/analysis_modules/halo_analysis.rst
--- a/doc/source/analyzing/analysis_modules/halo_analysis.rst
+++ b/doc/source/analyzing/analysis_modules/halo_analysis.rst
@@ -8,6 +8,7 @@
:maxdepth: 1
halo_catalogs
+ halo_transition
halo_finding
halo_mass_function
halo_analysis_example
diff -r 317470ca82f580407f932c66cc6eb6ad964a68df -r 74f5a294c4dcc113b0941ea30ee4ba81f4f6998d doc/source/analyzing/analysis_modules/halo_catalogs.rst
--- a/doc/source/analyzing/analysis_modules/halo_catalogs.rst
+++ b/doc/source/analyzing/analysis_modules/halo_catalogs.rst
@@ -7,9 +7,11 @@
together into a single framework. This framework is substantially
different from the limited framework included in yt-2.x and is only
backwards compatible in that output from old halo finders may be loaded.
+For a direct translation of various halo analysis tasks using yt-2.x
+to yt-3.0 please see :ref:`halo_transition`.
A catalog of halos can be created from any initial dataset given to halo
-catalog through data_pf. These halos can be found using friends-of-friends,
+catalog through data_ds. These halos can be found using friends-of-friends,
HOP, and Rockstar. The finder_method keyword dictates which halo finder to
use. The available arguments are 'fof', 'hop', and'rockstar'. For more
details on the relative differences between these halo finders see
@@ -19,32 +21,32 @@
from yt.mods import *
from yt.analysis_modules.halo_analysis.api import HaloCatalog
- data_pf = load('Enzo_64/RD0006/RedshiftOutput0006')
- hc = HaloCatalog(data_pf=data_pf, finder_method='hop')
+ data_ds = load('Enzo_64/RD0006/RedshiftOutput0006')
+ hc = HaloCatalog(data_ds=data_ds, finder_method='hop')
A halo catalog may also be created from already run rockstar outputs.
This method is not implemented for previously run friends-of-friends or
HOP finders. Even though rockstar creates one file per processor,
specifying any one file allows the full catalog to be loaded. Here we
only specify the file output by the processor with ID 0. Note that the
-argument for supplying a rockstar output is `halos_pf`, not `data_pf`.
+argument for supplying a rockstar output is `halos_ds`, not `data_ds`.
.. code-block:: python
- halos_pf = load(path+'rockstar_halos/halos_0.0.bin')
- hc = HaloCatalog(halos_pf=halos_pf)
+ halos_ds = load(path+'rockstar_halos/halos_0.0.bin')
+ hc = HaloCatalog(halos_ds=halos_ds)
Although supplying only the binary output of the rockstar halo finder
is sufficient for creating a halo catalog, it is not possible to find
any new information about the identified halos. To associate the halos
with the dataset from which they were found, supply arguments to both
-halos_pf and data_pf.
+halos_ds and data_ds.
.. code-block:: python
- halos_pf = load(path+'rockstar_halos/halos_0.0.bin')
- data_pf = load('Enzo_64/RD0006/RedshiftOutput0006')
- hc = HaloCatalog(data_pf=data_pf, halos_pf=halos_pf)
+ halos_ds = load(path+'rockstar_halos/halos_0.0.bin')
+ data_ds = load('Enzo_64/RD0006/RedshiftOutput0006')
+ hc = HaloCatalog(data_ds=data_ds, halos_ds=halos_ds)
A data container can also be supplied via keyword data_source,
associated with either dataset, to control the spatial region in
@@ -215,8 +217,8 @@
.. code-block:: python
- hpf = load(path+"halo_catalogs/catalog_0046/catalog_0046.0.h5")
- hc = HaloCatalog(halos_pf=hpf,
+ hds = load(path+"halo_catalogs/catalog_0046/catalog_0046.0.h5")
+ hc = HaloCatalog(halos_ds=hds,
output_dir="halo_catalogs/catalog_0046")
hc.add_callback("load_profiles", output_dir="profiles",
filename="virial_profiles")
diff -r 317470ca82f580407f932c66cc6eb6ad964a68df -r 74f5a294c4dcc113b0941ea30ee4ba81f4f6998d doc/source/analyzing/analysis_modules/halo_finders.rst
--- /dev/null
+++ b/doc/source/analyzing/analysis_modules/halo_finders.rst
@@ -0,0 +1,192 @@
+.. _halo_finding:
+
+Halo Finding
+============
+
+There are four methods of finding particle haloes in yt. The
+recommended and default method is called HOP, a method described
+in `Eisenstein and Hut (1998)
+<http://adsabs.harvard.edu/abs/1998ApJ...498..137E>`_. A basic
+friends-of-friends (e.g. `Efstathiou et al. (1985)
+<http://adsabs.harvard.edu/abs/1985ApJS...57..241E>`_) halo
+finder is also implemented. Finally Rockstar (`Behroozi et a.
+(2011) <http://adsabs.harvard.edu/abs/2011arXiv1110.4372B>`_) is
+a 6D-phase space halo finder developed by Peter Behroozi that
+excels in finding subhalos and substrcture, but does not allow
+multiple particle masses.
+
+HOP
+---
+
+The version of HOP used in yt is an upgraded version of the
+`publicly available HOP code
+<http://cmb.as.arizona.edu/~eisenste/hop/hop.html>`_. Support
+for 64-bit floats and integers has been added, as well as
+parallel analysis through spatial decomposition. HOP builds
+groups in this fashion:
+
+ 1. Estimates the local density at each particle using a
+ smoothing kernel.
+ 2. Builds chains of linked particles by 'hopping' from one
+ particle to its densest neighbor. A particle which is
+ its own densest neighbor is the end of the chain.
+ 3. All chains that share the same densest particle are
+ grouped together.
+ 4. Groups are included, linked together, or discarded
+ depending on the user-supplied over density
+ threshold parameter. The default is 160.0.
+
+Please see the `HOP method paper
+<http://adsabs.harvard.edu/abs/1998ApJ...498..137E>`_ for
+full details.
+
+.. warning:: The FoF halo finder in yt is not thoroughly tested!
+ It is probably fine to use, but you are strongly encouraged
+ to check your results against the data for errors.
+
+Rockstar Halo Finding
+---------------------
+
+Rockstar uses an adaptive hierarchical refinement of friends-of-friends
+groups in six phase-space dimensions and one time dimension, which
+allows for robust (grid-independent, shape-independent, and noise-
+resilient) tracking of substructure. The code is prepackaged with yt,
+but also `separately available <http://code.google.com/p/rockstar>`_. The lead
+developer is Peter Behroozi, and the methods are described in `Behroozi
+et al. 2011 <http://rockstar.googlecode.com/files/rockstar_ap101911.pdf>`_.
+
+.. note:: At the moment, Rockstar does not support multiple particle masses,
+ instead using a fixed particle mass. This will not affect most dark matter
+ simulations, but does make it less useful for finding halos from the stellar
+ mass. In simulations where the highest-resolution particles all have the
+ same mass (ie: zoom-in grid based simulations), one can set up a particle
+ filter to select the lowest mass particles and perform the halo finding
+ only on those.
+
+To run the Rockstar Halo finding, you must launch python with MPI and
+parallelization enabled. While Rockstar itself does not require MPI to run,
+the MPI libraries allow yt to distribute particle information across multiple
+nodes.
+
+.. warning:: At the moment, running Rockstar inside of yt on multiple compute nodes
+ connected by an Infiniband network can be problematic. Therefore, for now
+ we recommend forcing the use of the non-Infiniband network (e.g. Ethernet)
+ using this flag: ``--mca btl ^openib``.
+ For example, here is how Rockstar might be called using 24 cores:
+ ``mpirun -n 24 --mca btl ^openib python ./run_rockstar.py --parallel``.
+
+The script above configures the Halo finder, launches a server process which
+disseminates run information and coordinates writer-reader processes.
+Afterwards, it launches reader and writer tasks, filling the available MPI
+slots, which alternately read particle information and analyze for halo
+content.
+
+The RockstarHaloFinder class has these options that can be supplied to the
+halo catalog through the ``finder_kwargs`` argument:
+
+ * ``dm_type``, the index of the dark matter particle. Default is 1.
+ * ``outbase``, This is where the out*list files that Rockstar makes should be
+ placed. Default is 'rockstar_halos'.
+ * ``num_readers``, the number of reader tasks (which are idle most of the
+ time.) Default is 1.
+ * ``num_writers``, the number of writer tasks (which are fed particles and
+ do most of the analysis). Default is MPI_TASKS-num_readers-1.
+ If left undefined, the above options are automatically
+ configured from the number of available MPI tasks.
+ * ``force_res``, the resolution that Rockstar uses for various calculations
+ and smoothing lengths. This is in units of Mpc/h.
+ If no value is provided, this parameter is automatically set to
+ the width of the smallest grid element in the simulation from the
+ last data snapshot (i.e. the one where time has evolved the
+ longest) in the time series:
+ ``ds_last.index.get_smallest_dx() * ds_last['mpch']``.
+ * ``total_particles``, if supplied, this is a pre-calculated
+ total number of dark matter
+ particles present in the simulation. For example, this is useful
+ when analyzing a series of snapshots where the number of dark
+ matter particles should not change and this will save some disk
+ access time. If left unspecified, it will
+ be calculated automatically. Default: ``None``.
+ * ``dm_only``, if set to ``True``, it will be assumed that there are
+ only dark matter particles present in the simulation.
+ This option does not modify the halos found by Rockstar, however
+ this option can save disk access time if there are no star particles
+ (or other non-dark matter particles) in the simulation. Default: ``False``.
+
+Rockstar dumps halo information in a series of text (halo*list and
+out*list) and binary (halo*bin) files inside the ``outbase`` directory.
+We use the halo list classes to recover the information.
+
+Inside the ``outbase`` directory there is a text file named ``datasets.txt``
+that records the connection between ds names and the Rockstar file names.
+
+Parallel HOP and FOF
+--------------------
+
+Both the HOP and FoF halo finders can run in parallel using simple
+spatial decomposition. In order to run them in parallel it is helpful
+to understand how it works. Below in the first plot (i) is a simplified
+depiction of three haloes labeled 1,2 and 3:
+
+.. image:: _images/ParallelHaloFinder.png
+ :width: 500
+
+Halo 3 is twice reflected around the periodic boundary conditions.
+
+In (ii), the volume has been sub-divided into four equal subregions,
+A,B,C and D, shown with dotted lines. Notice that halo 2 is now in
+two different subregions, C and D, and that halo 3 is now in three,
+A, B and D. If the halo finder is run on these four separate subregions,
+halo 1 is be identified as a single halo, but haloes 2 and 3 are split
+up into multiple haloes, which is incorrect. The solution is to give
+each subregion padding to oversample into neighboring regions.
+
+In (iii), subregion C has oversampled into the other three regions,
+with the periodic boundary conditions taken into account, shown by
+dot-dashed lines. The other subregions oversample in a similar way.
+
+The halo finder is then run on each padded subregion independently
+and simultaneously. By oversampling like this, haloes 2 and 3 will
+both be enclosed fully in at least one subregion and identified
+completely.
+
+Haloes identified with centers of mass inside the padded part of a
+subregion are thrown out, eliminating the problem of halo duplication.
+The centers for the three haloes are shown with stars. Halo 1 will
+belong to subregion A, 2 to C and 3 to B.
+
+To run with parallel halo finding, you must supply a value for
+padding in the finder_kwargs argument. The ``padding`` parameter
+is in simulation units and defaults to 0.02. This parameter is how
+much padding is added to each of the six sides of a subregion.
+This value should be 2x-3x larger than the largest expected halo
+in the simulation. It is unlikely, of course, that the largest
+object in the simulation will be on a subregion boundary, but there
+is no way of knowing before the halo finder is run.
+
+.. code-block:: python
+
+ from yt.mods import *
+ from yt.analysis_modules.halo_analysis.api import *
+ ds = load("data0001")
+ hc= HaloCatalog(data_ds =ds,finder_method='hop'
+ finder_kwargs={'padding':0.02})
+ # --or--
+ hc= HaloCatalog(data_ds =ds,finder_method='fof'
+ finder_kwargs={'padding':0.02})
+
+
+In general, a little bit of padding goes a long way, and too much
+just slows down the analysis and doesn't improve the answer (but
+doesn't change it). It may be worth your time to run the parallel
+halo finder at a few paddings to find the right amount, especially
+if you're analyzing many similar datasets.
+
+Rockstar Installation
+=====================
+
+The Rockstar is slightly patched and modified to run as a library inside of
+yt. By default it will be built with yt using the ``install_script.sh``.
+If it wasn't installed, please make sure that the installation setting
+``INST_ROCKSTAR=1`` is defined in the ``install_script.sh`` and re-run
+the installation script.
diff -r 317470ca82f580407f932c66cc6eb6ad964a68df -r 74f5a294c4dcc113b0941ea30ee4ba81f4f6998d doc/source/analyzing/analysis_modules/halo_mass_function.rst
--- a/doc/source/analyzing/analysis_modules/halo_mass_function.rst
+++ b/doc/source/analyzing/analysis_modules/halo_mass_function.rst
@@ -60,8 +60,8 @@
from yt.mods import *
from yt.analysis_modules.halo_mass_function.api import *
- pf = load("data0030")
- hmf = HaloMassFcn(pf, halo_file="FilteredQuantities.out", num_sigma_bins=200,
+ ds = load("data0030")
+ hmf = HaloMassFcn(ds, halo_file="FilteredQuantities.out", num_sigma_bins=200,
mass_column=5)
Attached to ``hmf`` is the convenience function ``write_out``, which saves
@@ -102,8 +102,8 @@
from yt.mods import *
from yt.analysis_modules.halo_mass_function.api import *
- pf = load("data0030")
- hmf = HaloMassFcn(pf, halo_file="FilteredQuantities.out",
+ ds = load("data0030")
+ hmf = HaloMassFcn(ds, halo_file="FilteredQuantities.out",
sigma8input=0.9, primordial_index=1., omega_baryon0=0.06,
fitting_function=4)
hmf.write_out(prefix='hmf')
diff -r 317470ca82f580407f932c66cc6eb6ad964a68df -r 74f5a294c4dcc113b0941ea30ee4ba81f4f6998d doc/source/analyzing/analysis_modules/halo_profiling.rst
--- a/doc/source/analyzing/analysis_modules/halo_profiling.rst
+++ /dev/null
@@ -1,451 +0,0 @@
-.. _halo_profiling:
-
-Halo Profiling
-==============
-.. sectionauthor:: Britton Smith <brittonsmith at gmail.com>,
- Stephen Skory <s at skory.us>
-
-The ``HaloProfiler`` provides a means of performing analysis on multiple halos
-in a parallel-safe way.
-
-The halo profiler performs three primary functions: radial profiles,
-projections, and custom analysis. See the cookbook for a recipe demonstrating
-all of these features.
-
-Configuring the Halo Profiler
------------------------------
-
-The only argument required to create a ``HaloProfiler`` object is the path
-to the dataset.
-
-.. code-block:: python
-
- from yt.analysis_modules.halo_profiler.api import *
- hp = HaloProfiler("enzo_tiny_cosmology/DD0046/DD0046")
-
-Most of the halo profiler's options are configured with additional keyword
-arguments:
-
- * **output_dir** (*str*): if specified, all output will be put into this path
- instead of in the dataset directories. Default: None.
-
- * **halos** (*str*): "multiple" for profiling more than one halo. In this mode
- halos are read in from a list or identified with a
- `halo finder <../cookbook/running_halofinder.html>`_. In "single" mode, the
- one and only halo center is identified automatically as the location of the
- peak in the density field. Default: "multiple".
-
- * **halo_list_file** (*str*): name of file containing the list of halos.
- The halo profiler will look for this file in the data directory.
- Default: "HopAnalysis.out".
-
- * **halo_list_format** (*str* or *dict*): the format of the halo list file.
- "yt_hop" for the format given by yt's halo finders. "enzo_hop" for the
- format written by enzo_hop. This keyword can also be given in the form of a
- dictionary specifying the column in which various properties can be found.
- For example, {"id": 0, "center": [1, 2, 3], "mass": 4, "radius": 5}.
- Default: "yt_hop".
-
- * **halo_finder_function** (*function*): If halos is set to multiple and the
- file given by halo_list_file does not exit, the halo finding function
- specified here will be called. Default: HaloFinder (yt_hop).
-
- * **halo_finder_args** (*tuple*): args given with call to halo finder function.
- Default: None.
-
- * **halo_finder_kwargs** (*dict*): kwargs given with call to halo finder
- function. Default: None.
-
- * **recenter** (*string* or function name): The name of a function
- that will be used to move the center of the halo for the purposes of
- analysis. See explanation and examples, below. Default: None, which
- is equivalent to the center of mass of the halo as output by the halo
- finder.
-
- * **halo_radius** (*float*): if no halo radii are provided in the halo list
- file, this parameter is used to specify the radius out to which radial
- profiles will be made. This keyword is also used when halos is set to
- single. Default: 0.1.
-
- * **radius_units** (*str*): the units of **halo_radius**.
- Default: "1" (code units).
-
- * **n_profile_bins** (*int*): the number of bins in the radial profiles.
- Default: 50.
-
- * **profile_output_dir** (*str*): the subdirectory, inside the data directory,
- in which radial profile output files will be created. The directory will be
- created if it does not exist. Default: "radial_profiles".
-
- * **projection_output_dir** (*str*): the subdirectory, inside the data
- directory, in which projection output files will be created. The directory
- will be created if it does not exist. Default: "projections".
-
- * **projection_width** (*float*): the width of halo projections.
- Default: 8.0.
-
- * **projection_width_units** (*str*): the units of projection_width.
- Default: "mpc".
-
- * **project_at_level** (*int* or "max"): the maximum refinement level to be
- included in projections. Default: "max" (maximum level within the dataset).
-
- * **velocity_center** (*list*): the method in which the halo bulk velocity is
- calculated (used for calculation of radial and tangential velocities. Valid
- options are:
- - ["bulk", "halo"] (Default): the velocity provided in the halo list
- - ["bulk", "sphere"]: the bulk velocity of the sphere centered on the halo center.
- - ["max", field]: the velocity of the cell that is the location of the maximum of the field specified.
-
- * **filter_quantities** (*list*): quantities from the original halo list
- file to be written out in the filtered list file. Default: ['id','center'].
-
- * **use_critical_density** (*bool*): if True, the definition of overdensity
- for virial quantities is calculated with respect to the critical
- density. If False, overdensity is with respect to mean matter density,
- which is lower by a factor of Omega_M. Default: False.
-
-Profiles
---------
-
-Once the halo profiler object has been instantiated, fields can be added for
-profiling with the :meth:`add_profile` method:
-
-.. code-block:: python
-
- hp.add_profile('cell_volume', weight_field=None, accumulation=True)
- hp.add_profile('TotalMassMsun', weight_field=None, accumulation=True)
- hp.add_profile('density', weight_field=None, accumulation=False)
- hp.add_profile('temperature', weight_field='cell_mass', accumulation=False)
- hp.make_profiles(njobs=-1, prefilters=["halo['mass'] > 1e13"],
- filename='VirialQuantities.h5')
-
-The :meth:`make_profiles` method will begin the profiling. Use the
-**njobs** keyword to control the number of jobs over which the
-profiling is divided. Setting to -1 results in a single processor per
-halo. Setting to 1 results in all available processors working on the
-same halo. The prefilters keyword tells the profiler to skip all halos with
-masses (as loaded from the halo finder) less than a given amount. See below
-for more information. Additional keyword arguments are:
-
- * **filename** (*str*): If set, a file will be written with all of the
- filtered halos and the quantities returned by the filter functions.
- Default: None.
-
- * **prefilters** (*list*): A single dataset can contain thousands or tens of
- thousands of halos. Significant time can be saved by not profiling halos
- that are certain to not pass any filter functions in place. Simple filters
- based on quantities provided in the initial halo list can be used to filter
- out unwanted halos using this parameter. Default: None.
-
- * **njobs** (*int*): The number of jobs over which to split the profiling.
- Set to -1 so that each halo is done by a single processor. Default: -1.
-
- * **dynamic** (*bool*): If True, distribute halos using a task queue. If
- False, distribute halos evenly over all jobs. Default: False.
-
- * **profile_format** (*str*): The file format for the radial profiles,
- 'ascii' or 'hdf5'. Default: 'ascii'.
-
-.. image:: _images/profiles.png
- :width: 500
-
-Radial profiles of Overdensity (left) and Temperature (right) for five halos.
-
-Projections
------------
-
-The process of making projections is similar to that of profiles:
-
-.. code-block:: python
-
- hp.add_projection('density', weight_field=None)
- hp.add_projection('temperature', weight_field='density')
- hp.add_projection('metallicity', weight_field='density')
- hp.make_projections(axes=[0, 1, 2], save_cube=True, save_images=True,
- halo_list="filtered", njobs=-1)
-
-If **save_cube** is set to True, the projection data
-will be written to a set of hdf5 files
-in the directory given by **projection_output_dir**.
-The keyword, **halo_list**, can be
-used to select between the full list of halos ("all"),
-the filtered list ("filtered"), or
-an entirely new list given in the form of a file name.
-See :ref:`filter_functions` for a
-discussion of filtering halos. Use the **njobs** keyword to control
-the number of jobs over which the profiling is divided. Setting to -1
-results in a single processor per halo. Setting to 1 results in all
-available processors working on the same halo. The keyword arguments are:
-
- * **axes** (*list*): A list of the axes to project along, using the usual
- 0,1,2 convention. Default=[0,1,2].
-
- * **halo_list** (*str*) {'filtered', 'all'}: Which set of halos to make
- profiles of, either ones passed by the halo filters (if enabled/added), or
- all halos. Default='filtered'.
-
- * **save_images** (*bool*): Whether or not to save images of the projections.
- Default=False.
-
- * **save_cube** (*bool*): Whether or not to save the HDF5 files of the halo
- projections. Default=True.
-
- * **njobs** (*int*): The number of jobs over which to split the projections.
- Set to -1 so that each halo is done by a single processor. Default: -1.
-
- * **dynamic** (*bool*): If True, distribute halos using a task queue. If
- False, distribute halos evenly over all jobs. Default: False.
-
-.. image:: _images/projections.png
- :width: 500
-
-Projections of Density (top) and Temperature,
-weighted by Density (bottom), in the x (left),
-y (middle), and z (right) directions for a single halo with a width of 8 Mpc.
-
-Halo Filters
-------------
-
-Filters can be added to create a refined list of
-halos based on their profiles or to avoid
-profiling halos altogether based on information
-given in the halo list file.
-
-.. _filter_functions:
-
-Filter Functions
-^^^^^^^^^^^^^^^^
-
-It is often the case that one is looking to
-identify halos with a specific set of
-properties. This can be accomplished through the creation
-of filter functions. A filter
-function can take as many args and kwargs as you like,
-as long as the first argument is a
-profile object, or at least a dictionary which contains
-the profile arrays for each field.
-Filter functions must return a list of two things.
-The first is a True or False indicating
-whether the halo passed the filter.
-The second is a dictionary containing quantities
-calculated for that halo that will be written to a
-file if the halo passes the filter.
-A sample filter function based on virial quantities can be found in
-``yt/analysis_modules/halo_profiler/halo_filters.py``.
-
-Halo filtering takes place during the call to :meth:`make_profiles`.
-The :meth:`add_halo_filter` method is used to add a filter to be used
-during the profiling:
-
-.. code-block:: python
-
- hp.add_halo_filter(HP.VirialFilter, must_be_virialized=True,
- overdensity_field='ActualOverdensity',
- virial_overdensity=200,
- virial_filters=[['TotalMassMsun','>=','1e14']],
- virial_quantities=['TotalMassMsun','RadiusMpc'],
- use_log=True)
-
-The addition above will calculate and return virial quantities,
-mass and radius, for an
-overdensity of 200. In order to pass the filter, at least one
-point in the profile must be
-above the specified overdensity and the virial mass must be at
-least 1e14 solar masses. The **use_log** keyword indicates that interpolation
-should be done in log space. If
-the VirialFilter function has been added to the filter list,
-the halo profiler will make
-sure that the fields necessary for calculating virial quantities are added.
-As many filters as desired can be added. If filters have been added,
-the next call to :meth:`make_profiles` will filter by all of
-the added filter functions:
-
-.. code-block:: python
-
- hp.make_profiles(filename="FilteredQuantities.out")
-
-If the **filename** keyword is set, a file will be written with all of the
-filtered halos and the quantities returned by the filter functions.
-
-.. note:: If the profiles have already been run, the halo profiler will read
- in the previously created output files instead of re-running the profiles.
- The halo profiler will check to make sure the output file contains all of
- the requested halo fields. If not, the profile will be made again from
- scratch.
-
-.. _halo_profiler_pre_filters:
-
-Pre-filters
-^^^^^^^^^^^
-
-A single dataset can contain thousands or tens of thousands of halos.
-Significant time can
-be saved by not profiling halos that are certain to not pass any filter
-functions in place.
-Simple filters based on quantities provided in the initial halo list
-can be used to filter
-out unwanted halos using the **prefilters** keyword:
-
-.. code-block:: python
-
- hp.make_profiles(filename="FilteredQuantities.out",
- prefilters=["halo['mass'] > 1e13"])
-
-Arguments provided with the **prefilters** keyword should be given
-as a list of strings.
-Each string in the list will be evaluated with an *eval*.
-
-.. note:: If a VirialFilter function has been added with a filter based
- on mass (as in the example above), a prefilter will be automatically
- added to filter out halos with masses greater or less than (depending
- on the conditional of the filter) a factor of ten of the specified
- virial mass.
-
-Recentering the Halo For Analysis
----------------------------------
-
-It is possible to move the center of the halo to a new point using an
-arbitrary function for making profiles.
-By default, the center is provided by the halo finder,
-which outputs the center of mass of the particles. For the purposes of
-analysis, it may be important to recenter onto a gas density maximum,
-or a temperature minimum.
-
-There are a number of built-in functions to do this, listed below.
-Each of the functions uses mass-weighted fields for the calculations
-of new center points.
-To use
-them, supply the HaloProfiler with the ``recenter`` option and
-the name of the function, as in the example below.
-
-.. code-block:: python
-
- hp = HaloProfiler("enzo_tiny_cosmology/DD0046/DD0046",
- recenter="Max_Dark_Matter_Density")
-
-Additional options are:
-
- * *Min_Dark_Matter_Density* - Recenter on the point of minimum dark matter
- density in the halo.
-
- * *Max_Dark_Matter_Density* - Recenter on the point of maximum dark matter
- density in the halo.
-
- * *CoM_Dark_Matter_Density* - Recenter on the center of mass of the dark
- matter density field. This will be very similar to what the halo finder
- provides, but not precisely similar.
-
- * *Min_Gas_Density* - Recenter on the point of minimum gas density in the
- halo.
-
- * *Max_Gas_Density* - Recenter on the point of maximum gas density in the
- halo.
-
- * *CoM_Gas_Density* - Recenter on the center of mass of the gas density field
- in the halo.
-
- * *Min_Total_Density* - Recenter on the point of minimum total (gas + dark
- matter) density in the halo.
-
- * *Max_Total_Density* - Recenter on the point of maximum total density in the
- halo.
-
- * *CoM_Total_Density* - Recenter on the center of mass for the total density
- in the halo.
-
- * *Min_Temperature* - Recenter on the point of minimum temperature in the
- halo.
-
- * *Max_Temperature* - Recenter on the point of maximum temperature in the
- halo.
-
-It is also possible to supply a user-defined function to the HaloProfiler.
-This can be used if the pre-defined functions above are not sufficient.
-The function takes a single argument, a data container for the halo,
-which is a sphere. The function returns a 3-list with the new center.
-
-In this example below, a function is used such that the halos will be
-re-centered on the point of absolute minimum temperature, that is not
-mass weighted.
-
-.. code-block:: python
-
- from yt.mods import *
-
- def find_min_temp(sphere):
- ma, mini, mx, my, mz, mg = sphere.quantities['MinLocation']('temperature')
- return [mx,my,mz]
-
- hp = HaloProfiler("enzo_tiny_cosmology/DD0046/DD0046", recenter=find_min_temp)
-
-It is possible to make more complicated functions. This example below extends
-the example above to include a distance control that prevents the center from
-being moved too far. If the recenter moves too far, ``[-1, -1, -1]`` is
-returned which will prevent the halo from being profiled.
-Any triplet of values less than the ``domain_left_edge`` will suffice.
-There will be a note made in the output (stderr) showing which halos were
-skipped.
-
-.. code-block:: python
-
- from yt.mods import *
- from yt.utilities.math_utils import periodic_dist
-
- def find_min_temp_dist(sphere):
- old = sphere.center
- ma, mini, mx, my, mz, mg = sphere.quantities['MinLocation']('temperature')
- d = sphere.pf['kpc'] * periodic_dist(old, [mx, my, mz],
- sphere.pf.domain_right_edge - sphere.pf.domain_left_edge)
- # If new center farther than 5 kpc away, don't recenter
- if d > 5.: return [-1, -1, -1]
- return [mx,my,mz]
-
- hp = HaloProfiler("enzo_tiny_cosmology/DD0046/DD0046",
- recenter=find_min_temp_dist)
-
-Custom Halo Analysis
---------------------
-
-Besides radial profiles and projections, the halo profiler has the
-ability to run custom analysis functions on each halo. Custom halo
-analysis functions take two arguments: a halo dictionary containing
-the id, center, etc; and a sphere object. The example function shown
-below creates a 2D profile of the total mass in bins of density and
-temperature for a given halo.
-
-.. code-block:: python
-
- from yt.mods import *
- from yt.data_objects.profiles import BinnedProfile2D
-
- def halo_2D_profile(halo, sphere):
- "Make a 2D profile for a halo."
- my_profile = BinnedProfile2D(sphere,
- 128, 'density', 1e-30, 1e-24, True,
- 128, 'temperature', 1e2, 1e7, True,
- end_collect=False)
- my_profile.add_fields('cell_mass', weight=None, fractional=False)
- my_filename = os.path.join(sphere.pf.fullpath, '2D_profiles',
- 'Halo_%04d.h5' % halo['id'])
- my_profile.write_out_h5(my_filename)
-
-Using the :meth:`analyze_halo_spheres` function, the halo profiler
-will create a sphere centered on each halo, and perform the analysis
-from the custom routine.
-
-.. code-block:: python
-
- hp.analyze_halo_sphere(halo_2D_profile, halo_list='filtered',
- analysis_output_dir='2D_profiles',
- njobs=-1, dynamic=False)
-
-Just like with the :meth:`make_projections` function, the keyword,
-**halo_list**, can be used to select between the full list of halos
-("all"), the filtered list ("filtered"), or an entirely new list given
-in the form of a file name. If the **analysis_output_dir** keyword is
-set, the halo profiler will make sure the desired directory exists in
-a parallel-safe manner. Use the **njobs** keyword to control the
-number of jobs over which the profiling is divided. Setting to -1
-results in a single processor per halo. Setting to 1 results in all
-available processors working on the same halo.
diff -r 317470ca82f580407f932c66cc6eb6ad964a68df -r 74f5a294c4dcc113b0941ea30ee4ba81f4f6998d doc/source/analyzing/analysis_modules/halo_transition.rst
--- /dev/null
+++ b/doc/source/analyzing/analysis_modules/halo_transition.rst
@@ -0,0 +1,106 @@
+
+Getting up to Speed with Halo Analysis in yt-3.0
+================================================
+
+If you're used to halo analysis in yt-2.x, heres a guide to
+how to update your analysis pipeline to take advantage of
+the new halo catalog infrastructure.
+
+Finding Halos
+-------------
+
+Previously, halos were found using calls to ``HaloFinder``,
+``FOFHaloFinder`` and ``RockstarHaloFinder``. Now it is
+encouraged that you find the halos upon creation of the halo catalog
+by supplying a value to the ``finder_method`` keyword when calling
+``HaloCatalog``. Currently, only halos found using rockstar or a
+previous instance of a halo catalog are able to be loaded
+using the ``halos_ds`` keyword.
+
+To pass additional arguments to the halo finders
+themselves, supply a dictionary to ``finder_kwargs`` where
+each key in the dictionary is a keyword of the halo finder
+and the corresponding value is the value to be passed for
+that keyword.
+
+Getting Halo Information
+------------------------
+All quantities that used to be present in a ``halo_list`` are
+still able to be found but are not necessarily included by default.
+Every halo will by default have the following properties:
+
+* particle_position_i (where i can be x,y,z)
+* particle_mass
+* virial_radius
+* particle_identifier
+
+If other quantities are desired, they can be included by adding
+the corresponding quantity before the catalog is created. See
+the full halo catalog documentation for further information about
+how to add these quantities and what quantities are available.
+
+You no longer have to iteratre over halos in the ``halo_list``.
+Now a halo dataset can be treated as a regular dataset and
+all quantities are available by accessing ``all_data``.
+Specifically, all quantities can be accessed as shown:
+
+.. code-block:: python
+ from yt.mods import *
+ from yt.analysis_modules.halo_analysis.api import HaloCatalog
+ data_ds = load('Enzo_64/RD0006/RedshiftOutput0006')
+ hc = HaloCatalog(data_ds=data_ds, finder_method='hop')
+ hc.create()
+ ad = hc.all_data()
+ masses = ad['particle_mass'][:]
+
+
+Prefiltering Halos
+------------------
+
+Prefiltering halos before analysis takes place is now done
+by adding a filter before the call to create. An example
+is shown below
+
+.. code-block:: python
+ from yt.mods import *
+ from yt.analysis_modules.halo_analysis.api import HaloCatalog
+ data_ds = load('Enzo_64/RD0006/RedshiftOutput0006')
+ hc = HaloCatalog(data_ds=data_ds, finder_method='hop')
+ hc.add_filter("quantity_value", "particle_mass", ">", 1e13, "Msun")
+ hc.create()
+
+Profiling Halos
+---------------
+
+The halo profiler available in yt-2.x has been removed, and
+profiling functionality is now completely contained within the
+halo catalog. A complete example of how to profile halos by
+radius using the new infrastructure is given in
+:ref:`halo_analysis_example`.
+
+Plotting Halos
+--------------
+
+Annotating halo locations onto a slice or projection works in
+the same way as in yt-2.x, but now a halo catalog must be
+passed to the annotate halo call rather than a halo list.
+
+.. code-block:: python
+ from yt.mods import *
+ from yt.analysis_modules.halo_analysis.api import HaloCatalog
+
+ data_ds = load('Enzo_64/RD0006/RedshiftOutput0006')
+ hc = HaloCatalog(data_ds=data_ds, finder_method='hop')
+ hc.create()
+
+ prj = ProjectionPlot(data_ds, 'z', 'density')
+ prj.annotate_halos(hc)
+ prj.save()
+
+Written Data
+------------
+
+Data is now written out in the form of h5 files rather than
+text files. The directory they are written out to is
+controlled by the keyword ``output_dir``. Each quantity
+is a field in the file.
diff -r 317470ca82f580407f932c66cc6eb6ad964a68df -r 74f5a294c4dcc113b0941ea30ee4ba81f4f6998d doc/source/analyzing/analysis_modules/hmf_howto.rst
--- a/doc/source/analyzing/analysis_modules/hmf_howto.rst
+++ b/doc/source/analyzing/analysis_modules/hmf_howto.rst
@@ -27,8 +27,8 @@
.. code-block:: python
from yt.mods import *
- pf = load("data0001")
- halo_list = HaloFinder(pf)
+ ds = load("data0001")
+ halo_list = HaloFinder(ds)
halo_list.write_out("HopAnalysis.out")
The only important columns of data in the text file ``HopAnalysis.out``
@@ -79,8 +79,8 @@
from yt.mods import *
from yt.analysis_modules.halo_mass_function.api import *
- pf = load("data0001")
- hmf = HaloMassFcn(pf, halo_file="VirialHaloes.out",
+ ds = load("data0001")
+ hmf = HaloMassFcn(ds, halo_file="VirialHaloes.out",
sigma8input=0.9, primordial_index=1., omega_baryon0=0.06,
fitting_function=4, mass_column=5, num_sigma_bins=200)
hmf.write_out(prefix='hmf')
@@ -107,9 +107,9 @@
from yt.analysis_modules.halo_mass_function.api import *
# If desired, start loop here.
- pf = load("data0001")
+ ds = load("data0001")
- halo_list = HaloFinder(pf)
+ halo_list = HaloFinder(ds)
halo_list.write_out("HopAnalysis.out")
hp = HP.HaloProfiler("data0001", halo_list_file='HopAnalysis.out')
@@ -120,7 +120,7 @@
virial_quantities=['TotalMassMsun','RadiusMpc'])
hp.make_profiles(filename="VirialHaloes.out")
- hmf = HaloMassFcn(pf, halo_file="VirialHaloes.out",
+ hmf = HaloMassFcn(ds, halo_file="VirialHaloes.out",
sigma8input=0.9, primordial_index=1., omega_baryon0=0.06,
fitting_function=4, mass_column=5, num_sigma_bins=200)
hmf.write_out(prefix='hmf')
diff -r 317470ca82f580407f932c66cc6eb6ad964a68df -r 74f5a294c4dcc113b0941ea30ee4ba81f4f6998d doc/source/analyzing/analysis_modules/light_cone_generator.rst
--- a/doc/source/analyzing/analysis_modules/light_cone_generator.rst
+++ b/doc/source/analyzing/analysis_modules/light_cone_generator.rst
@@ -2,15 +2,15 @@
Light Cone Generator
====================
-.. sectionauthor:: Britton Smith <brittonsmith at gmail.com>
-Light cones are projections made by stacking multiple datasets together to
-continuously span a given redshift interval. The width of individual
-projection slices is adjusted such that each slice has the same angular size.
-Each projection slice is randomly shifted and projected along a random axis to
-ensure that the same structures are not sampled multiple times. Since deeper
-images sample earlier epochs of the simulation, light cones represent the
-closest thing to synthetic imaging observations.
+Light cones are created by stacking multiple datasets together to
+continuously span a given redshift interval. To make a projection of a
+field through a light cone, the width of individual slices is adjusted
+such that each slice has the same angular size.
+Each slice is randomly shifted and projected along a random axis to
+ensure that the same structures are not sampled multiple times. A
+recipe for creating a simple light cone projection can be found in
+the cookbook under :ref:`cookbook-light_cone`.
.. image:: _images/LightCone_full_small.png
:width: 500
@@ -23,49 +23,44 @@
Configuring the Light Cone Generator
------------------------------------
-A recipe for creating a simple light cone projection can be found in the
-cookbook. The required arguments to instantiate a ``LightCone`` objects are
+The required arguments to instantiate a ``LightCone`` object are
the path to the simulation parameter file, the simulation type, the nearest
redshift, and the furthest redshift of the light cone.
.. code-block:: python
- from yt.analysis_modules.api import LightCone
+ from yt.analysis_modules.cosmological_observation.api import \
+ LightCone
lc = LightCone('enzo_tiny_cosmology/32Mpc_32.enzo',
'Enzo', 0., 0.1)
The additional keyword arguments are:
- * **field_of_view_in_arcminutes** (*float*): The field of view of the image
- in units of arcminutes. Default: 600.0.
-
- * **image_resolution_in_arcseconds** (*float*): The size of each image pixel
- in units of arcseconds. Default: 60.0.
-
- * **use_minimum_datasets** (*bool*): If True, the minimum number of datasets
- is used to connect the initial and final redshift. If false, the light
- cone solution will contain as many entries as possible within the redshift
- interval. Default: True.
+ * **use_minimum_datasets** (*bool*): If True, the minimum number of
+ datasets is used to connect the initial and final redshift. If False,
+ the light cone solution will contain as many entries as possible within
+ the redshift interval. Default: True.
* **deltaz_min** (*float*): Specifies the minimum Delta-z between
consecutive datasets in the returned list. Default: 0.0.
- * **minimum_coherent_box_fraction** (*float*): Used with use_minimum_datasets
- set to False, this parameter specifies the fraction of the total box size
- to be traversed before rerandomizing the projection axis and center. This
- was invented to allow light cones with thin slices to sample coherent large
- scale structure, but in practice does not work so well. Try setting this
- parameter to 1 and see what happens. Default: 0.0.
+ * **minimum_coherent_box_fraction** (*float*): Used with
+ **use_minimum_datasets** set to False, this parameter specifies the
+ fraction of the total box size to be traversed before rerandomizing the
+ projection axis and center. This was invented to allow light cones with
+ thin slices to sample coherent large cale structure, but in practice does
+ not work so well. Try setting this parameter to 1 and see what happens.
+ Default: 0.0.
* **time_data** (*bool*): Whether or not to include time outputs when
gathering datasets for time series. Default: True.
- * **redshift_data** (*bool*): Whether or not to include redshift outputs when
- gathering datasets for time series. Default: True.
+ * **redshift_data** (*bool*): Whether or not to include redshift outputs
+ when gathering datasets for time series. Default: True.
* **set_parameters** (*dict*): Dictionary of parameters to attach to
- pf.parameters. Default: None.
+ ds.parameters. Default: None.
* **output_dir** (*string*): The directory in which images and data files
will be written. Default: 'LC'.
@@ -76,10 +71,10 @@
Creating Light Cone Solutions
-----------------------------
-A light cone solution consists of a list of datasets and the width, depth,
-center, and axis of the projection to be made for that slice. The
-:meth:`LightCone.calculate_light_cone_solution` function is used to
-calculate the random shifting and projection axis:
+A light cone solution consists of a list of datasets spanning a redshift
+interval with a random orientation for each dataset. A new solution
+is calcuated with the :meth:`LightCone.calculate_light_cone_solution`
+function:
.. code-block:: python
@@ -87,70 +82,39 @@
The keyword argument are:
- * **seed** (*int*): the seed for the random number generator. Any light cone
- solution can be reproduced by giving the same random seed. Default: None
- (each solution will be distinct).
+ * **seed** (*int*): the seed for the random number generator. Any light
+ cone solution can be reproduced by giving the same random seed.
+ Default: None.
* **filename** (*str*): if given, a text file detailing the solution will be
written out. Default: None.
-If a new solution for the same LightCone object is desired, the
-:meth:`rerandomize_light_cone_solution` method should be called in place of
-:meth:`calculate_light_cone_solution`:
-
-.. code-block:: python
-
- new_seed = 987654321
- lc.rerandomize_light_cone_solution(new_seed, Recycle=True,
- filename='new_lightcone.dat')
-
-Additional keyword arguments are:
-
- * **recycle** (*bool*): if True, the new solution will have the same shift in
- the line of sight as the original solution. Since the projections of each
- slice are serialized and stored for the entire width of the box (even if
- the width used is left than the total box), the projection data can be
- deserialized instead of being remade from scratch. This can greatly speed
- up the creation of a large number of light cone projections. Default: True.
-
- * **filename** (*str*): if given, a text file detailing the solution will be
- written out. Default: None.
-
-If :meth:`rerandomize_light_cone_solution` is used, the LightCone object will
-keep a copy of the original solution that can be returned to at any time by
-calling :meth:`restore_master_solution`:
-
-.. code-block:: python
-
- lc.restore_master_solution()
-
-.. note:: All light cone solutions made with the above method will still use
- the same list of datasets. Only the shifting and projection axis will be
- different.
-
Making a Light Cone Projection
------------------------------
-With the light cone solution set, projections can be made of any available
-field:
+With the light cone solution in place, projections with a given field of
+view and resolution can be made of any available field:
.. code-block:: python
field = 'density'
- lc.project_light_cone(field , weight_field=None,
+ field_of_view = (600.0, "arcmin")
+ resolution = (60.0, "arcsec")
+ lc.project_light_cone(field_of_vew, resolution,
+ field , weight_field=None,
save_stack=True,
save_slice_images=True)
+The field of view and resolution can be specified either as a tuple of
+value and unit string or as a unitful ``YTQuantity``.
Additional keyword arguments:
- * **weight_field** (*str*): the weight field of the projection. This has the
- same meaning as in standard projections. Default: None.
+ * **weight_field** (*str*): the weight field of the projection. This has
+ the same meaning as in standard projections. Default: None.
- * **apply_halo_mask** (*bool*): if True, a boolean mask is apply to the light
- cone projection. See below for a description of halo masks. Default: False.
-
- * **node** (*str*): a prefix to be prepended to the node name under which the
- projection data is serialized. Default: None.
+ * **photon_field** (*bool*): if True, the projection data for each slice is
+ decremented by 4 pi R :superscript:`2` , where R is the luminosity
+ distance between the observer and the slice redshift. Default: False.
* **save_stack** (*bool*): if True, the unflatted light cone data including
each individual slice is written to an hdf5 file. Default: True.
@@ -161,13 +125,7 @@
* **save_slice_images** (*bool*): save images for each individual projection
slice. Default: False.
- * **flatten_stack** (*bool*): if True, the light cone stack is continually
- flattened each time a slice is added in order to save memory. This is
- generally not necessary. Default: False.
-
- * **photon_field** (*bool*): if True, the projection data for each slice is
- decremented by 4 pi R :superscript:`2` , where R is the luminosity
- distance between the observer and the slice redshift. Default: False.
+ * **cmap_name** (*string*): color map for images. Default: "algae".
* **njobs** (*int*): The number of parallel jobs over which the light cone
projection will be split. Choose -1 for one processor per individual
@@ -177,34 +135,4 @@
* **dynamic** (*bool*): If True, use dynamic load balancing to create the
projections. Default: False.
-Sampling Unique Light Cone Volumes
-----------------------------------
-
-When making a large number of light cones, particularly for statistical
-analysis, it is important to have a handle on the amount of sampled volume in
-common from one projection to another. Any statistics may untrustworthy if a
-set of light cones have too much volume in common, even if they may all be
-entirely different in appearance. LightCone objects have the ability to
-calculate the volume in common between two solutions with the same dataset
-ist. The :meth:`find_unique_solutions` and
-:meth:`project_unique_light_cones` functions can be used to create a set of
-light cone solutions that have some maximum volume in common and create light
-cone projections for those solutions. If specified, the code will attempt to
-use recycled solutions that can use the same serialized projection objects
-that have already been created. This can greatly increase the speed of making
-multiple light cone projections. See the cookbook for an example of doing this.
-
-Making Light Cones with a Halo Mask
------------------------------------
-
-The situation may arise where it is necessary or desirable to know the
-location of halos within the light cone volume, and specifically their
-location in the final image. This can be useful for developing algorithms to
-find galaxies or clusters in image data. The light cone generator does this
-by running the HaloProfiler (see :ref:`halo_profiling`) on each of the
-datasets used in the light cone and shifting them accordingly with the light
-cone solution. The ability also exists to create a boolean mask with the
-dimensions of the final light cone image that can be used to mask out the
-halos in the image. It is left as an exercise to the reader to find a use for
-this functionality. This process is somewhat complicated, but not terribly.
-See the recipe in the cookbook for an example of this functionality.
+.. note:: As of :code:`yt-3.0`, the halo mask and unique light cone functionality no longer exist. These are still available in :code:`yt-2.x`. If you would like to use these features in :code:`yt-3.x`, help is needed to port them over. Contact the yt-users mailing list if you are interested in doing this.
\ No newline at end of file
diff -r 317470ca82f580407f932c66cc6eb6ad964a68df -r 74f5a294c4dcc113b0941ea30ee4ba81f4f6998d doc/source/analyzing/analysis_modules/light_ray_generator.rst
--- a/doc/source/analyzing/analysis_modules/light_ray_generator.rst
+++ b/doc/source/analyzing/analysis_modules/light_ray_generator.rst
@@ -1,20 +1,21 @@
.. _light-ray-generator:
Light Ray Generator
-====================
-.. sectionauthor:: Britton Smith <brittonsmith at gmail.com>
+===================
Light rays are similar to light cones (:ref:`light-cone-generator`) in how
they stack multiple datasets together to span a redshift interval. Unlike
-light cones, which which stack randomly oriented projections from each
+light cones, which stack randomly oriented projections from each
dataset to create synthetic images, light rays use thin pencil beams to
-simulate QSO sight lines.
+simulate QSO sight lines. A sample script can be found in the cookbook
+under :ref:`cookbook-light_ray`.
.. image:: _images/lightray.png
-A ray segment records the information of all grid cells intersected by the ray
-as well as the path length, dl, of the ray through the cell. Column densities
-can be calculated by multiplying physical densities by the path length.
+A ray segment records the information of all grid cells intersected by the
+ray as well as the path length, dl, of the ray through the cell. Column
+densities can be calculated by multiplying physical densities by the path
+length.
Configuring the Light Ray Generator
-----------------------------------
@@ -25,7 +26,7 @@
.. code-block:: python
- from yt.analysis_modules.api import LightRay
+ from yt.analysis_modules.cosmological_observation.api import LightRay
lr = LightRay("enzo_tiny_cosmology/32Mpc_32.enzo",
'Enzo', 0.0, 0.1)
@@ -36,22 +37,22 @@
ray solution will contain as many entries as possible within the redshift
interval. Default: True.
- * **deltaz_min** (*float*): Specifies the minimum Delta-z between consecutive
- datasets in the returned list. Default: 0.0.
+ * **deltaz_min** (*float*): Specifies the minimum Delta-z between
+ consecutive datasets in the returned list. Default: 0.0.
- * **minimum_coherent_box_fraction** (*float*): Used with use_minimum_datasets
- set to False, this parameter specifies the fraction of the total box size
- to be traversed before rerandomizing the projection axis and center. This
- was invented to allow light rays with thin slices to sample coherent large
- scale structure, but in practice does not work so well. Try setting this
- parameter to 1 and see what happens. Default: 0.0.
+ * **minimum_coherent_box_fraction** (*float*): Used with
+ **use_minimum_datasets** set to False, this parameter specifies the
+ fraction of the total box size to be traversed before rerandomizing the
+ projection axis and center. This was invented to allow light rays with
+ thin slices to sample coherent large scale structure, but in practice
+ does not work so well. Try setting this parameter to 1 and see what
+ happens. Default: 0.0.
- * **time_data** (*bool*): Whether or not to include time outputs when gathering
- datasets for time series. Default: True.
-
- * **redshift_data** (*bool*): Whether or not to include redshift outputs when
+ * **time_data** (*bool*): Whether or not to include time outputs when
gathering datasets for time series. Default: True.
+ * **redshift_data** (*bool*): Whether or not to include redshift outputs
+ when gathering datasets for time series. Default: True.
Making Light Ray Data
---------------------
@@ -74,7 +75,21 @@
* **seed** (*int*): Seed for the random number generator. Default: None.
- * **fields** (*list*): A list of fields for which to get data. Default: None.
+ * **start_position** (*list* of floats): Used only if creating a light ray
+ from a single dataset. The coordinates of the starting position of the
+ ray. Default: None.
+
+ * **end_position** (*list* of floats): Used only if creating a light ray
+ from a single dataset. The coordinates of the ending position of the ray.
+ Default: None.
+
+ * **trajectory** (*list* of floats): Used only if creating a light ray
+ from a single dataset. The (r, theta, phi) direction of the light ray.
+ Use either **end_position** or **trajectory**, not both.
+ Default: None.
+
+ * **fields** (*list*): A list of fields for which to get data.
+ Default: None.
* **solution_filename** (*string*): Path to a text file where the
trajectories of each subray is written out. Default: None.
@@ -83,51 +98,17 @@
Default: None.
* **get_los_velocity** (*bool*): If True, the line of sight velocity is
- calculated for each point in the ray. Default: False.
+ calculated for each point in the ray. Default: True.
- * **get_nearest_halo** (*bool*): If True, the HaloProfiler will be used to
- calculate the distance and mass of the nearest halo for each point in the
- ray. This option requires additional information to be included. See
- the cookbook for an example. Default: False.
-
- * **nearest_halo_fields** (*list*): A list of fields to be calculated for the
- halos nearest to every pixel in the ray. Default: None.
-
- * **halo_list_file** (*str*): Filename containing a list of halo properties to be used
- for getting the nearest halos to absorbers. Default: None.
-
- * **halo_profiler_parameters** (*dict*): A dictionary of parameters to be
- passed to the HaloProfiler to create the appropriate data used to get
- properties for the nearest halos. Default: None.
-
- * **njobs** (*int*): The number of parallel jobs over which the slices for the
- halo mask will be split. Choose -1 for one processor per individual slice
- and 1 to have all processors work together on each projection. Default: 1
+ * **njobs** (*int*): The number of parallel jobs over which the slices for
+ the halo mask will be split. Choose -1 for one processor per individual
+ slice and 1 to have all processors work together on each projection.
+ Default: 1
* **dynamic** (*bool*): If True, use dynamic load balancing to create the
projections. Default: False.
-Getting The Nearest Galaxies
-----------------------------
-
-The light ray tool will use the HaloProfiler to calculate the distance and
-mass of the nearest halo to that pixel. In order to do this, a dictionary
-called halo_profiler_parameters is used to pass instructions to the
-HaloProfiler. This dictionary has three additional keywords:
-
- * **halo_profiler_kwargs** (*dict*): A dictionary of standard HaloProfiler
- keyword arguments and values to be given to the HaloProfiler.
-
- * **halo_profiler_actions** (*list*): A list of actions to be performed by
- the HaloProfiler. Each item in the list should be a dictionary with the
- following entries: "function", "args", and "kwargs", for the function to
- be performed, the arguments supplied to that function, and the keyword
- arguments.
-
- * **halo_list** (*string*): 'all' to use the full halo list, or 'filtered'
- to use the filtered halo list created after calling make_profiles.
-
-See the recipe in the cookbook for am example.
+.. note:: As of :code:`yt-3.0`, the functionality for recording properties of the nearest halo to each element of the ray no longer exists. This is still available in :code:`yt-2.x`. If you would like to use this feature in :code:`yt-3.x`, help is needed to port it over. Contact the yt-users mailing list if you are interested in doing this.
What Can I do with this?
------------------------
diff -r 317470ca82f580407f932c66cc6eb6ad964a68df -r 74f5a294c4dcc113b0941ea30ee4ba81f4f6998d doc/source/analyzing/analysis_modules/merger_tree.rst
--- a/doc/source/analyzing/analysis_modules/merger_tree.rst
+++ b/doc/source/analyzing/analysis_modules/merger_tree.rst
@@ -2,8 +2,9 @@
Halo Merger Tree
================
-.. sectionauthor:: Stephen Skory <sskory at physics.ucsd.edu>
-.. versionadded:: 1.7
+
+.. note:: At the moment the merger tree is not yet implemented using new
+ halo catalog functionality.
The Halo Merger Tree extension is capable of building a database of halo mergers
over a set of time-ordered Enzo datasets. The fractional contribution of older
diff -r 317470ca82f580407f932c66cc6eb6ad964a68df -r 74f5a294c4dcc113b0941ea30ee4ba81f4f6998d doc/source/analyzing/analysis_modules/photon_simulator.rst
--- a/doc/source/analyzing/analysis_modules/photon_simulator.rst
+++ b/doc/source/analyzing/analysis_modules/photon_simulator.rst
@@ -48,7 +48,7 @@
.. code:: python
- pf = load("MHDSloshing/virgo_low_res.0054.vtk",
+ ds = load("MHDSloshing/virgo_low_res.0054.vtk",
parameters={"time_unit":(1.0,"Myr"),
"length_unit":(1.0,"Mpc"),
"mass_unit":(1.0e14,"Msun")})
@@ -386,7 +386,7 @@
from yt.mods import *
from yt.utilities.physical_constants import cm_per_kpc, K_per_keV, mp
from yt.utilities.cosmology import Cosmology
- from yt.analysis_modules.api import *
+ from yt.analysis_modules.photon_simulator.api import *
import aplpy
R = 1000. # in kpc
@@ -423,7 +423,7 @@
evacuated two "bubbles" of radius 30 kpc at a distance of 50 kpc from
the center.
-Now, we create a parameter file out of this dataset:
+Now, we create a yt Dataset object out of this dataset:
.. code:: python
@@ -445,7 +445,7 @@
.. code:: python
- sphere = ds.sphere(pf.domain_center, (1.0,"Mpc"))
+ sphere = ds.sphere(ds.domain_center, (1.0,"Mpc"))
A = 6000.
exp_time = 2.0e5
diff -r 317470ca82f580407f932c66cc6eb6ad964a68df -r 74f5a294c4dcc113b0941ea30ee4ba81f4f6998d doc/source/analyzing/analysis_modules/planning_cosmology_simulations.rst
--- a/doc/source/analyzing/analysis_modules/planning_cosmology_simulations.rst
+++ b/doc/source/analyzing/analysis_modules/planning_cosmology_simulations.rst
@@ -10,7 +10,7 @@
.. code-block:: python
- from yt.analysis_modules.api import CosmologySplice
+ from yt.analysis_modules.cosmological_observation.api import CosmologySplice
my_splice = CosmologySplice('enzo_tiny_cosmology/32Mpc_32.enzo', 'Enzo')
my_splice.plan_cosmology_splice(0.0, 0.1, filename='redshifts.out')
diff -r 317470ca82f580407f932c66cc6eb6ad964a68df -r 74f5a294c4dcc113b0941ea30ee4ba81f4f6998d doc/source/analyzing/analysis_modules/radial_column_density.rst
--- a/doc/source/analyzing/analysis_modules/radial_column_density.rst
+++ b/doc/source/analyzing/analysis_modules/radial_column_density.rst
@@ -41,15 +41,15 @@
from yt.mods import *
from yt.analysis_modules.radial_column_density.api import *
- pf = load("data0030")
+ ds = load("data0030")
- rcdnumdens = RadialColumnDensity(pf, 'NumberDensity', [0.5, 0.5, 0.5],
+ rcdnumdens = RadialColumnDensity(ds, 'NumberDensity', [0.5, 0.5, 0.5],
max_radius = 0.5)
def _RCDNumberDensity(field, data, rcd = rcdnumdens):
return rcd._build_derived_field(data)
add_field('RCDNumberDensity', _RCDNumberDensity, units=r'1/\rm{cm}^2')
- dd = pf.h.all_data()
+ dd = ds.all_data()
print dd['RCDNumberDensity']
The field ``RCDNumberDensity`` can be used just like any other derived field
This diff is so big that we needed to truncate the remainder.
https://bitbucket.org/yt_analysis/yt/commits/a2777e129e42/
Changeset: a2777e129e42
Branch: yt-3.0
User: chummels
Date: 2014-07-20 04:54:46
Summary: Fixing the "Projected" prepend to projected units.
Affected #: 1 file
diff -r 74f5a294c4dcc113b0941ea30ee4ba81f4f6998d -r a2777e129e42d6490e4fbaf0eda0e2b780853974 yt/visualization/plot_window.py
--- a/yt/visualization/plot_window.py
+++ b/yt/visualization/plot_window.py
@@ -1219,7 +1219,7 @@
ds = self.ds = ts[0]
axis = fix_axis(axis, ds)
# If a non-weighted integral projection, assure field-label reflects that
- if weight_field is None and proj_stype = "integrate":
+ if weight_field is None and proj_style == "integrate":
self.projected = True
(bounds, center) = get_window_parameters(axis, center, width, ds)
if field_parameters is None: field_parameters = {}
@@ -1430,7 +1430,7 @@
weight=weight_field, volume=volume, no_ghost=no_ghost,
le=le, re=re, north_vector=north_vector)
# If a non-weighted, integral projection, assure field-label reflects that
- if weight_field is None and proj_stype = "integrate":
+ if weight_field is None and OffAxisProj.proj_style == "integrate":
self.projected = True
# Hard-coding the origin keyword since the other two options
# aren't well-defined for off-axis data objects
https://bitbucket.org/yt_analysis/yt/commits/4e16b4a309f6/
Changeset: 4e16b4a309f6
Branch: yt-3.0
User: MatthewTurk
Date: 2014-07-20 16:17:45
Summary: Merged in chummels/yt/yt-3.0 (pull request #1020)
ProjectionPlots now include prepended "Projected" on color bar for whatever field is projected
Affected #: 1 file
diff -r 2f8bc1fbe63fa38f526a45d44a2da115c7e7487d -r 4e16b4a309f6a7ec765af203f815ec10f7604bb9 yt/visualization/plot_window.py
--- a/yt/visualization/plot_window.py
+++ b/yt/visualization/plot_window.py
@@ -269,8 +269,9 @@
"""
frb = None
def __init__(self, data_source, bounds, buff_size=(800,800), antialias=True,
- periodic=True, origin='center-window', oblique=False,
- window_size=8.0, fields=None, fontsize=18, aspect=None, setup=False):
+ periodic=True, origin='center-window', oblique=False,
+ window_size=8.0, fields=None, fontsize=18, aspect=None,
+ setup=False):
if not hasattr(self, "ds"):
self.ds = data_source.ds
ts = self._initialize_dataset(self.ds)
@@ -914,6 +915,11 @@
colorbar_label = image.info['label']
+ # If we're creating a plot of a projection, change the displayed
+ # field name accordingly.
+ if hasattr(self, 'projected'):
+ colorbar_label = "$\\rm{Projected }$ %s" % colorbar_label
+
# Determine the units of the data
units = Unit(self.frb[f].units, registry=self.ds.unit_registry)
units = units.latex_representation()
@@ -1212,13 +1218,17 @@
self.ts = ts
ds = self.ds = ts[0]
axis = fix_axis(axis, ds)
+ # If a non-weighted integral projection, assure field-label reflects that
+ if weight_field is None and proj_style == "integrate":
+ self.projected = True
(bounds, center) = get_window_parameters(axis, center, width, ds)
if field_parameters is None: field_parameters = {}
proj = ds.proj(fields, axis, weight_field=weight_field,
center=center, data_source=data_source,
field_parameters = field_parameters, style = proj_style)
PWViewerMPL.__init__(self, proj, bounds, fields=fields, origin=origin,
- fontsize=fontsize, window_size=window_size, aspect=aspect)
+ fontsize=fontsize, window_size=window_size,
+ aspect=aspect)
if axes_unit is None:
axes_unit = get_axes_unit(width, ds)
self.set_axes_unit(axes_unit)
@@ -1419,6 +1429,9 @@
center_rot, ds, normal, oap_width, fields, interpolated,
weight=weight_field, volume=volume, no_ghost=no_ghost,
le=le, re=re, north_vector=north_vector)
+ # If a non-weighted, integral projection, assure field-label reflects that
+ if weight_field is None and OffAxisProj.proj_style == "integrate":
+ self.projected = True
# Hard-coding the origin keyword since the other two options
# aren't well-defined for off-axis data objects
PWViewerMPL.__init__(
Repository URL: https://bitbucket.org/yt_analysis/yt/
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