[yt-svn] commit/yt: 3 new changesets
commits-noreply at bitbucket.org
commits-noreply at bitbucket.org
Wed Jul 23 17:14:22 PDT 2014
3 new commits in yt:
https://bitbucket.org/yt_analysis/yt/commits/3ba0536c72c7/
Changeset: 3ba0536c72c7
Branch: yt-3.0
User: mzingale
Date: 2014-07-23 17:26:50
Summary: origin="upper" to origin="lower". This change makes volume renders appear
righthanded.
Affected #: 1 file
diff -r 6521bd42b10d93b208a4b64144216f855f386f84 -r 3ba0536c72c7c8854ddf4bc4732ff5223f568b01 yt/visualization/volume_rendering/camera.py
--- a/yt/visualization/volume_rendering/camera.py
+++ b/yt/visualization/volume_rendering/camera.py
@@ -649,7 +649,7 @@
del nz
else:
nim = im
- ax = self._pylab.imshow(nim[:,:,:3]/nim[:,:,:3].max(), origin='upper')
+ ax = self._pylab.imshow(nim[:,:,:3]/nim[:,:,:3].max(), origin='lower')
return ax
def draw(self):
https://bitbucket.org/yt_analysis/yt/commits/a8bea59fcaab/
Changeset: a8bea59fcaab
Branch: yt-3.0
User: mzingale
Date: 2014-07-23 17:27:33
Summary: merge
Affected #: 13 files
diff -r 3ba0536c72c7c8854ddf4bc4732ff5223f568b01 -r a8bea59fcaab5bbbd2e217d3cea16c3eea2b2141 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
@@ -2,185 +2,135 @@
Clump Finding
=============
-.. sectionauthor:: Britton Smith <britton.smith at colorado.edu>
-``yt`` has the ability to identify topologically disconnected structures based in a dataset using
-any field available. This is powered by a contouring algorithm that runs in a recursive
-fashion. The user specifies the initial data object in which the clump-finding will occur,
-the field over which the contouring will be done, the upper and lower limits of the
-initial contour, and the contour increment.
+The clump finder uses a contouring algorithm to identified topologically
+disconnected structures within a dataset. This works by first creating a
+single contour over the full range of the contouring field, then continually
+increasing the lower value of the contour until it reaches the maximum value
+of the field. As disconnected structures are identified as separate contoures,
+the routine continues recursively through each object, creating a hierarchy of
+clumps. Individual clumps can be kept or removed from the hierarchy based on
+the result of user-specified functions, such as checking for gravitational
+boundedness. A sample recipe can be found in :ref:`cookbook-find_clumps`.
-The clump finder begins by creating a single contour of the specified field over the entire
-range given. For every isolated contour identified in the initial iteration, contouring is
-repeated with the same upper limit as before, but with the lower limit increased by the
-specified increment. This repeated for every isolated group until the lower limit is equal
-to the upper limit.
+The clump finder requires a data container and a field over which the
+contouring is to be performed.
-Often very tiny clumps can appear as groups of only a few cells that happen to be slightly
-overdense (if contouring over density) with respect to the surrounding gas. The user may
-specify criteria that clumps must meet in order to be kept. The most obvious example is
-selecting only those clumps that are gravitationally bound.
+.. code:: python
-Once the clump-finder has finished, the user can write out a set of quantities for each clump in the
-index. Additional info items can also be added. We also provide a recipe
-for finding clumps in :ref:`cookbook-find_clumps`.
+ import yt
+ from yt.analysis_modules.level_sets.api import *
-Treecode Optimization
----------------------
+ ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030")
-.. sectionauthor:: Stephen Skory <s at skory.us>
-.. versionadded:: 2.1
+ data_source = ds.disk([0.5, 0.5, 0.5], [0., 0., 1.],
+ (8, 'kpc'), (1, 'kpc'))
-As mentioned above, the user has the option to limit clumps to those that are
-gravitationally bound.
-The correct and accurate way to calculate if a clump is gravitationally
-bound is to do the full double sum:
+ master_clump = Clump(data_source, ("gas", "density"))
-.. math::
+At this point, every isolated contour will be considered a clump,
+whether this is physical or not. Validator functions can be added to
+determine if an individual contour should be considered a real clump.
+These functions are specified with the ``Clump.add_validator`` function.
+Current, two validators exist: a minimum number of cells and gravitational
+boundedness.
- PE = \Sigma_{i=1}^N \Sigma_{j=i}^N \frac{G M_i M_j}{r_{ij}}
+.. code:: python
-where :math:`PE` is the gravitational potential energy of :math:`N` cells,
-:math:`G` is the
-gravitational constant, :math:`M_i` is the mass of cell :math:`i`,
-and :math:`r_{ij}` is the distance
-between cell :math:`i` and :math:`j`.
-The number of calculations required for this calculation
-grows with the square of :math:`N`. Therefore, for large clumps with many cells, the
-test for boundedness can take a significant amount of time.
+ master_clump.add_validator("min_cells", 20)
-An effective way to greatly speed up this calculation with minimal error
-is to use the treecode approximation pioneered by
-`Barnes and Hut (1986) <http://adsabs.harvard.edu/abs/1986Natur.324..446B>`_.
-This method of calculating gravitational potentials works by
-grouping individual masses that are located close together into a larger conglomerated
-mass with a geometric size equal to the distribution of the individual masses.
-For a mass cell that is sufficiently distant from the conglomerated mass,
-the gravitational calculation can be made using the conglomerate, rather than
-each individual mass, which saves time.
+ master_clump.add_validator("gravitationally_bound", use_particles=False)
-The decision whether or not to use a conglomerate depends on the accuracy control
-parameter ``opening_angle``. Using the small-angle approximation, a conglomerate
-may be used if its geometric size subtends an angle no greater than the
-``opening_angle`` upon the remote mass. The default value is
-``opening_angle = 1``, which gives errors well under 1%. A value of
-``opening_angle = 0`` is identical to the full O(N^2) method, and larger values
-will speed up the calculation and sacrifice accuracy (see the figures below).
+As many validators as desired can be added, and a clump is only kept if all
+return True. If not, a clump is remerged into its parent. Custom validators
+can easily be added. A validator function must only accept a ``Clump`` object
+and either return True or False.
-The treecode method is iterative. Conglomerates may themselves form larger
-conglomerates. And if a larger conglomerate does not meet the ``opening_angle``
-criterion, the smaller conglomerates are tested as well. This iteration of
-conglomerates will
-cease once the level of the original masses is reached (this is what happens
-for all pair calculations if ``opening_angle = 0``).
+.. code:: python
-Below are some examples of how to control the usage of the treecode.
+ def _minimum_gas_mass(clump, min_mass):
+ return (clump["gas", "cell_mass"].sum() >= min_mass)
+ add_validator("minimum_gas_mass", _minimum_gas_mass)
-This example will calculate the ratio of the potential energy to kinetic energy
-for a spherical clump using the treecode method with an opening angle of 2.
-The default opening angle is 1.0:
+The ``add_validator`` function adds the validator to a registry that can
+be accessed by the clump finder. Then, the validator can be added to the
+clump finding just like the others.
-.. code-block:: python
-
- from yt.mods import *
-
- ds = load("DD0000")
- sp = ds.sphere([0.5, 0.5, 0.5], radius=0.1)
-
- ratio = sp.quantities.is_bound(truncate=False, include_thermal_energy=True,
- treecode=True, opening_angle=2.0)
+.. code:: python
-This example will accomplish the same as the above, but will use the full
-N^2 method.
+ master_clump.add_validator("minimum_gas_mass", ds.quan(1.0, "Msun"))
-.. code-block:: python
-
- from yt.mods import *
-
- ds = load("DD0000")
- sp = ds.sphere([0.5, 0.5, 0.5], radius=0.1)
-
- ratio = sp.quantities.is_bound(truncate=False, include_thermal_energy=True,
- treecode=False)
+The clump finding algorithm accepts the ``Clump`` object, the initial minimum
+and maximum of the contouring field, and the step size. The lower value of the
+contour finder will be continually multiplied by the step size.
-Here the treecode method is specified for clump finding (this is default).
-Please see the link above for the full example of how to find clumps (the
-trailing backslash is important!):
+.. code:: python
-.. code-block:: python
-
- function_name = 'self.data.quantities.is_bound(truncate=True, \
- include_thermal_energy=True, treecode=True, opening_angle=2.0) > 1.0'
- master_clump = amods.level_sets.Clump(data_source, None, field,
- function=function_name)
+ c_min = data_source["gas", "density"].min()
+ c_max = data_source["gas", "density"].max()
+ step = 2.0
+ find_clumps(master_clump, c_min, c_max, step)
-To turn off the treecode, of course one should turn treecode=False in the
-example above.
+After the clump finding has finished, the master clump will represent the top
+of a hierarchy of clumps. The ``children`` attribute within a ``Clump`` object
+contains a list of all sub-clumps. Each sub-clump is also a ``Clump`` object
+with its own ``children`` attribute, and so on.
-Treecode Speedup and Accuracy Figures
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+A number of helper routines exist for examining the clump hierarchy.
-Two datasets are used to make the three figures below. Each is a zoom-in
-simulation with high resolution in the middle with AMR, and then lower
-resolution static grids on the periphery. In this way they are very similar to
-a clump in a full-AMR simulation, where there are many AMR levels stacked
-around a density peak. One dataset has a total of 3 levels of AMR, and
-the other has 10 levels, but in other ways are very similar.
+.. code:: python
-The first figure shows the effect of varying the opening angle on the speed
-and accuracy of the treecode. The tests were performed using the L=10
-dataset on a clump with approximately 118,000 cells. The speedup of up the
-treecode is in green, and the accuracy in blue, with the opening angle
-on the x-axis.
+ # Write a text file of the full hierarchy.
+ write_clump_index(master_clump, 0, "%s_clump_hierarchy.txt" % ds)
-With an ``opening_angle`` = 0, the accuracy is perfect, but the treecode is
-less than half as fast as the brute-force method. However, by an
-``opening_angle`` of 1, the treecode is now nearly twice as fast, with
-about 0.2% error. This trend continues to an ``opening_angle`` 8, where
-large opening angles have no effect due to geometry.
+ # Write a text file of only the leaf nodes.
+ write_clumps(master_clump,0, "%s_clumps.txt" % ds)
-.. image:: _images/TreecodeOpeningAngleBig.png
- :width: 450
- :height: 400
+ # Get a list of just the leaf nodes.
+ leaf_clumps = get_lowest_clumps(master_clump)
-Note that the accuracy is always below 1. The treecode will always underestimate
-the gravitational binding energy of a clump.
+``Clump`` objects can be used like all other data containers.
-In this next figure, the ``opening_angle`` is kept constant at 1, but the
-number of cells is varied on the L=3 dataset by slowly expanding a spherical
-region of analysis. Up to about 100,000 cells,
-the treecode is actually slower than the brute-force method. This is due to
-the fact that with fewer cells, smaller geometric distances,
-and a shallow AMR index, the treecode
-method has very little chance to be applied. The calculation is overall
-slower due to the overhead of the treecode method & startup costs. This
-explanation is further strengthened by the fact that the accuracy of the
-treecode method stay perfect for the first couple thousand cells, indicating
-that the treecode method is not being applied over that range.
+.. code:: python
-Once the number of cells gets high enough, and the size of the region becomes
-large enough, the treecode method can work its magic and the treecode method
-becomes advantageous.
+ print leaf_clumps[0]["gas", "density"]
+ print leaf_clumps[0].quantities.total_mass()
-.. image:: _images/TreecodeCellsSmall.png
- :width: 450
- :height: 400
+The writing functions will write out a series or properties about each
+clump by default. Additional properties can be appended with the
+``Clump.add_info_item`` function.
-The saving grace to the figure above is that for small clumps, a difference of
-50% in calculation time is on the order of a second or less, which is tiny
-compared to the minutes saved for the larger clumps where the speedup can
-be greater than 3.
+.. code:: python
-The final figure is identical to the one above, but for the L=10 dataset.
-Due to the higher number of AMR levels, which translates into more opportunities
-for the treecode method to be applied, the treecode becomes faster than the
-brute-force method at only about 30,000 cells. The accuracy shows a different
-behavior, with a dip and a rise, and overall lower accuracy. However, at all
-times the error is still well under 1%, and the time savings are significant.
+ master_clump.add_info_item("total_cells")
-.. image:: _images/TreecodeCellsBig.png
- :width: 450
- :height: 400
+Just like the validators, custom info items can be added by defining functions
+that minimally accept a ``Clump`` object and return a string to be printed.
-The figures above show that the treecode method is generally very advantageous,
-and that the error introduced is minimal.
+.. code:: python
+
+ def _mass_weighted_jeans_mass(clump):
+ jeans_mass = clump.data.quantities.weighted_average_quantity(
+ "jeans_mass", ("gas", "cell_mass")).in_units("Msun")
+ return "Jeans Mass (mass-weighted): %.6e Msolar." % jeans_mass
+ add_clump_info("mass_weighted_jeans_mass", _mass_weighted_jeans_mass)
+
+Then, add it to the list:
+
+.. code:: python
+
+ master_clump.add_info_item("mass_weighted_jeans_mass")
+
+By default, the following info items are activated: **total_cells**,
+**cell_mass**, **mass_weighted_jeans_mass**, **volume_weighted_jeans_mass**,
+**max_grid_level**, **min_number_density**, **max_number_density**, and
+**distance_to_main_clump**.
+
+Clumps can be visualized using the ``annotate_clumps`` callback.
+
+.. code:: python
+
+ prj = yt.ProjectionPlot(ds, 2, ("gas", "density"),
+ center='c', width=(20,'kpc'))
+ prj.annotate_clumps(leaf_clumps)
+ prj.save('clumps')
diff -r 3ba0536c72c7c8854ddf4bc4732ff5223f568b01 -r a8bea59fcaab5bbbd2e217d3cea16c3eea2b2141 doc/source/cookbook/amrkdtree_downsampling.py
--- a/doc/source/cookbook/amrkdtree_downsampling.py
+++ b/doc/source/cookbook/amrkdtree_downsampling.py
@@ -1,6 +1,3 @@
-### THIS RECIPE IS CURRENTLY BROKEN IN YT-3.0
-### DO NOT TRUST THIS RECIPE UNTIL THIS LINE IS REMOVED
-
# Using AMRKDTree Homogenized Volumes to examine large datasets
# at lower resolution.
@@ -13,15 +10,15 @@
import yt
from yt.utilities.amr_kdtree.api import AMRKDTree
-# Load up a dataset
+# Load up a dataset and define the kdtree
ds = yt.load('IsolatedGalaxy/galaxy0030/galaxy0030')
-
kd = AMRKDTree(ds)
# Print out specifics of KD Tree
print "Total volume of all bricks = %i" % kd.count_volume()
print "Total number of cells = %i" % kd.count_cells()
+# Define a camera and take an volume rendering.
tf = yt.ColorTransferFunction((-30, -22))
cam = ds.camera([0.5, 0.5, 0.5], [0.2, 0.3, 0.4], 0.10, 256,
tf, volume=kd)
diff -r 3ba0536c72c7c8854ddf4bc4732ff5223f568b01 -r a8bea59fcaab5bbbd2e217d3cea16c3eea2b2141 doc/source/cookbook/find_clumps.py
--- a/doc/source/cookbook/find_clumps.py
+++ b/doc/source/cookbook/find_clumps.py
@@ -3,18 +3,16 @@
import yt
from yt.analysis_modules.level_sets.api import *
-fn = "IsolatedGalaxy/galaxy0030/galaxy0030" # dataset to load
-# this is the field we look for contours over -- we could do
-# this over anything. Other common choices are 'AveragedDensity'
-# and 'Dark_Matter_Density'.
-field = "density"
-
-step = 2.0 # This is the multiplicative interval between contours.
-
-ds = yt.load(fn) # load data
+ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030")
data_source = ds.disk([0.5, 0.5, 0.5], [0., 0., 1.],
- (8, 'kpc'), (1, 'kpc'))
+ (1, 'kpc'), (1, 'kpc'))
+
+# the field to be used for contouring
+field = ("gas", "density")
+
+# This is the multiplicative interval between contours.
+step = 2.0
# Now we set some sane min/max values between which we want to find contours.
# This is how we tell the clump finder what to look for -- it won't look for
@@ -23,25 +21,19 @@
c_max = 10**np.floor(np.log10(data_source[field]).max()+1)
# Now find get our 'base' clump -- this one just covers the whole domain.
-master_clump = Clump(data_source, None, field)
+master_clump = Clump(data_source, field)
# Add a "validator" to weed out clumps with less than 20 cells.
# As many validators can be added as you want.
master_clump.add_validator("min_cells", 20)
-# This next command accepts our base clump and we say the range between which
-# we want to contour. It recursively finds clumps within the master clump, at
-# intervals defined by the step size we feed it. The current value is
-# *multiplied* by step size, rather than added to it -- so this means if you
-# want to look in log10 space intervals, you would supply step = 10.0.
+# Begin clump finding.
find_clumps(master_clump, c_min, c_max, step)
-# As it goes, it appends the information about all the sub-clumps to the
-# master-clump. Among different ways we can examine it, there's a convenience
-# function for outputting the full hierarchy to a file.
+# Write out the full clump hierarchy.
write_clump_index(master_clump, 0, "%s_clump_hierarchy.txt" % ds)
-# We can also output some handy information, as well.
+# Write out only the leaf nodes of the hierarchy.
write_clumps(master_clump,0, "%s_clumps.txt" % ds)
# We can traverse the clump hierarchy to get a list of all of the 'leaf' clumps
diff -r 3ba0536c72c7c8854ddf4bc4732ff5223f568b01 -r a8bea59fcaab5bbbd2e217d3cea16c3eea2b2141 doc/source/cookbook/opaque_rendering.py
--- a/doc/source/cookbook/opaque_rendering.py
+++ b/doc/source/cookbook/opaque_rendering.py
@@ -1,6 +1,3 @@
-### THIS RECIPE IS CURRENTLY BROKEN IN YT-3.0
-### DO NOT TRUST THIS RECIPE UNTIL THIS LINE IS REMOVED
-
import yt
import numpy as np
diff -r 3ba0536c72c7c8854ddf4bc4732ff5223f568b01 -r a8bea59fcaab5bbbd2e217d3cea16c3eea2b2141 doc/source/cookbook/rendering_with_box_and_grids.py
--- a/doc/source/cookbook/rendering_with_box_and_grids.py
+++ b/doc/source/cookbook/rendering_with_box_and_grids.py
@@ -1,6 +1,3 @@
-### THIS RECIPE IS CURRENTLY BROKEN IN YT-3.0
-### DO NOT TRUST THIS RECIPE UNTIL THIS LINE IS REMOVED
-
import yt
import numpy as np
diff -r 3ba0536c72c7c8854ddf4bc4732ff5223f568b01 -r a8bea59fcaab5bbbd2e217d3cea16c3eea2b2141 doc/source/cookbook/simple_profile.py
--- a/doc/source/cookbook/simple_profile.py
+++ b/doc/source/cookbook/simple_profile.py
@@ -1,6 +1,3 @@
-### THIS RECIPE IS CURRENTLY BROKEN IN YT-3.0
-### DO NOT TRUST THIS RECIPE UNTIL THIS LINE IS REMOVED
-
import yt
# Load the dataset.
diff -r 3ba0536c72c7c8854ddf4bc4732ff5223f568b01 -r a8bea59fcaab5bbbd2e217d3cea16c3eea2b2141 yt/analysis_modules/level_sets/clump_handling.py
--- a/yt/analysis_modules/level_sets/clump_handling.py
+++ b/yt/analysis_modules/level_sets/clump_handling.py
@@ -17,16 +17,16 @@
import numpy as np
import uuid
-from .clump_info_items import \
- clump_info_registry
-from .clump_validators import \
- clump_validator_registry
-
-from .contour_finder import \
- identify_contours
-
from yt.fields.derived_field import \
ValidateSpatial
+from yt.funcs import mylog
+
+from .clump_info_items import \
+ clump_info_registry
+from .clump_validators import \
+ clump_validator_registry
+from .contour_finder import \
+ identify_contours
def add_contour_field(ds, contour_key):
def _contours(field, data):
@@ -46,22 +46,22 @@
class Clump(object):
children = None
- def __init__(self, data, parent, field, cached_fields = None,
+ def __init__(self, data, field, parent=None,
clump_info=None, validators=None):
+ self.data = data
+ self.field = field
self.parent = parent
- self.data = data
self.quantities = data.quantities
- self.field = field
self.min_val = self.data[field].min()
self.max_val = self.data[field].max()
- self.cached_fields = cached_fields
# List containing characteristics about clumps that are to be written
# out by the write routines.
if clump_info is None:
self.set_default_clump_info()
else:
- # Clump info will act the same if add_info_item is called before or after clump finding.
+ # Clump info will act the same if add_info_item is called
+ # before or after clump finding.
self.clump_info = copy.deepcopy(clump_info)
if validators is None:
@@ -105,7 +105,10 @@
self.add_info_item("max_number_density")
def clear_clump_info(self):
- "Clears the clump_info array and passes the instruction to its children."
+ """
+ Clears the clump_info array and passes the instruction to its
+ children.
+ """
self.clump_info = []
if self.children is None: return
@@ -121,7 +124,8 @@
def find_children(self, min_val, max_val = None):
if self.children is not None:
- print "Wiping out existing children clumps.", len(self.children)
+ mylog.info("Wiping out existing children clumps: %d.",
+ len(self.children))
self.children = []
if max_val is None: max_val = self.max_val
nj, cids = identify_contours(self.data, self.field, min_val, max_val)
@@ -141,15 +145,18 @@
["obj['contours_%s'] == %s" % (contour_key, cid)],
{('contour_slices_%s' % contour_key): cids})
if new_clump["ones"].size == 0:
- # This is to skip possibly duplicate clumps. Using "ones" here
- # will speed things up.
+ # This is to skip possibly duplicate clumps.
+ # Using "ones" here will speed things up.
continue
- self.children.append(Clump(new_clump, self, self.field,
- self.cached_fields,validators=self.validators,
- clump_info=self.clump_info))
+ self.children.append(Clump(new_clump, self.field, parent=self,
+ clump_info=self.clump_info,
+ validators=self.validators))
def pass_down(self,operation):
- "Performs an operation on a clump with an exec and passes the instruction down to clump children."
+ """
+ Performs an operation on a clump with an exec and passes the
+ instruction down to clump children.
+ """
# Call if callable, otherwise do an exec.
if callable(operation):
@@ -179,12 +186,14 @@
def __reduce__(self):
return (_reconstruct_clump,
(self.parent, self.field, self.min_val, self.max_val,
- self.valid, self.children, self.data, self.clump_info, self.function))
+ self.valid, self.children, self.data, self.clump_info,
+ self.function))
def __getitem__(self,request):
return self.data[request]
-def _reconstruct_clump(parent, field, mi, ma, valid, children, data, clump_info,
+def _reconstruct_clump(parent, field, mi, ma, valid, children,
+ data, clump_info,
function=None):
obj = object.__new__(Clump)
if iterable(parent):
@@ -193,7 +202,8 @@
except KeyError:
parent = parent
if children is None: children = []
- obj.parent, obj.field, obj.min_val, obj.max_val, obj.valid, obj.children, obj.clump_info, obj.function = \
+ obj.parent, obj.field, obj.min_val, obj.max_val, \
+ obj.valid, obj.children, obj.clump_info, obj.function = \
parent, field, mi, ma, valid, children, clump_info, function
# Now we override, because the parent/child relationship seems a bit
# unreliable in the unpickling
@@ -204,7 +214,8 @@
return obj
def find_clumps(clump, min_val, max_val, d_clump):
- print "Finding clumps: min: %e, max: %e, step: %f" % (min_val, max_val, d_clump)
+ mylog.info("Finding clumps: min: %e, max: %e, step: %f" %
+ (min_val, max_val, d_clump))
if min_val >= max_val: return
clump.find_children(min_val)
@@ -213,7 +224,7 @@
elif (len(clump.children) > 0):
these_children = []
- print "Investigating %d children." % len(clump.children)
+ mylog.info("Investigating %d children." % len(clump.children))
for child in clump.children:
find_clumps(child, min_val*d_clump, max_val, d_clump)
if ((child.children is not None) and (len(child.children) > 0)):
@@ -221,15 +232,20 @@
elif (child._validate()):
these_children.append(child)
else:
- print "Eliminating invalid, childless clump with %d cells." % len(child.data["ones"])
+ mylog.info(("Eliminating invalid, childless clump with " +
+ "%d cells.") % len(child.data["ones"]))
if (len(these_children) > 1):
- print "%d of %d children survived." % (len(these_children),len(clump.children))
+ mylog.info("%d of %d children survived." %
+ (len(these_children),len(clump.children)))
clump.children = these_children
elif (len(these_children) == 1):
- print "%d of %d children survived, linking its children to parent." % (len(these_children),len(clump.children))
+ mylog.info(("%d of %d children survived, linking its " +
+ "children to parent.") %
+ (len(these_children),len(clump.children)))
clump.children = these_children[0].children
else:
- print "%d of %d children survived, erasing children." % (len(these_children),len(clump.children))
+ mylog.info("%d of %d children survived, erasing children." %
+ (len(these_children),len(clump.children)))
clump.children = []
def get_lowest_clumps(clump, clump_list=None):
diff -r 3ba0536c72c7c8854ddf4bc4732ff5223f568b01 -r a8bea59fcaab5bbbd2e217d3cea16c3eea2b2141 yt/frontends/sph/data_structures.py
--- a/yt/frontends/sph/data_structures.py
+++ b/yt/frontends/sph/data_structures.py
@@ -552,10 +552,10 @@
f.seek(0, os.SEEK_END)
fs = f.tell()
f.seek(0, os.SEEK_SET)
+ #Read in the header
+ t, n, ndim, ng, nd, ns = struct.unpack("<diiiii", f.read(28))
except IOError:
return False, 1
- #Read in the header
- t, n, ndim, ng, nd, ns = struct.unpack("<diiiii", f.read(28))
endianswap = "<"
#Check Endianness
if (ndim < 1 or ndim > 3):
diff -r 3ba0536c72c7c8854ddf4bc4732ff5223f568b01 -r a8bea59fcaab5bbbd2e217d3cea16c3eea2b2141 yt/frontends/stream/data_structures.py
--- a/yt/frontends/stream/data_structures.py
+++ b/yt/frontends/stream/data_structures.py
@@ -436,7 +436,7 @@
pts = MatchPointsToGrids(grid_tree, len(x), x, y, z)
particle_grid_inds = pts.find_points_in_tree()
idxs = np.argsort(particle_grid_inds)
- particle_grid_count = np.bincount(particle_grid_inds,
+ particle_grid_count = np.bincount(particle_grid_inds.astype("intp"),
minlength=num_grids)
particle_indices = np.zeros(num_grids + 1, dtype='int64')
if num_grids > 1 :
diff -r 3ba0536c72c7c8854ddf4bc4732ff5223f568b01 -r a8bea59fcaab5bbbd2e217d3cea16c3eea2b2141 yt/geometry/tests/test_particle_octree.py
--- a/yt/geometry/tests/test_particle_octree.py
+++ b/yt/geometry/tests/test_particle_octree.py
@@ -91,7 +91,7 @@
ds = load_particles(data, 1.0, bbox = bbox, n_ref = n_ref)
dd = ds.all_data()
bi = dd["io","mesh_id"]
- v = np.bincount(bi.astype("int64"))
+ v = np.bincount(bi.astype("intp"))
yield assert_equal, v.max() <= n_ref, True
bi2 = dd["all","mesh_id"]
yield assert_equal, bi, bi2
diff -r 3ba0536c72c7c8854ddf4bc4732ff5223f568b01 -r a8bea59fcaab5bbbd2e217d3cea16c3eea2b2141 yt/utilities/lib/alt_ray_tracers.pyx
--- a/yt/utilities/lib/alt_ray_tracers.pyx
+++ b/yt/utilities/lib/alt_ray_tracers.pyx
@@ -101,7 +101,7 @@
rleft, rright, zleft, zright, \
cleft, cright, thetaleft, thetaright, \
tmleft, tpleft, tmright, tpright, tsect
- cdef np.ndarray[np.int64_t, ndim=1] inds, tinds, sinds
+ cdef np.ndarray[np.intp_t, ndim=1] inds, tinds, sinds
cdef np.ndarray[np.float64_t, ndim=2] xyz, rztheta, ptemp, b1, b2, dsect
# set up points
@@ -126,7 +126,7 @@
bsqrd = b**2
# Compute positive and negative times and associated masks
- I = left_edges.shape[0]
+ I = np.intp(left_edges.shape[0])
tmleft = np.empty(I, dtype='float64')
tpleft = np.empty(I, dtype='float64')
tmright = np.empty(I, dtype='float64')
@@ -152,7 +152,7 @@
np.argwhere(tmmright).flat,
np.argwhere(tpmright).flat,]))
if 0 == inds.shape[0]:
- inds = np.arange(np.int64(I))
+ inds = np.arange(np.intp(I))
thetaleft = np.empty(I)
thetaleft.fill(p1[2])
thetaright = np.empty(I)
diff -r 3ba0536c72c7c8854ddf4bc4732ff5223f568b01 -r a8bea59fcaab5bbbd2e217d3cea16c3eea2b2141 yt/utilities/lib/misc_utilities.pyx
--- a/yt/utilities/lib/misc_utilities.pyx
+++ b/yt/utilities/lib/misc_utilities.pyx
@@ -27,7 +27,7 @@
@cython.boundscheck(False)
@cython.wraparound(False)
@cython.cdivision(True)
-def new_bin_profile1d(np.ndarray[np.int64_t, ndim=1] bins_x,
+def new_bin_profile1d(np.ndarray[np.intp_t, ndim=1] bins_x,
np.ndarray[np.float64_t, ndim=1] wsource,
np.ndarray[np.float64_t, ndim=2] bsource,
np.ndarray[np.float64_t, ndim=1] wresult,
@@ -58,8 +58,8 @@
@cython.boundscheck(False)
@cython.wraparound(False)
@cython.cdivision(True)
-def new_bin_profile2d(np.ndarray[np.int64_t, ndim=1] bins_x,
- np.ndarray[np.int64_t, ndim=1] bins_y,
+def new_bin_profile2d(np.ndarray[np.intp_t, ndim=1] bins_x,
+ np.ndarray[np.intp_t, ndim=1] bins_y,
np.ndarray[np.float64_t, ndim=1] wsource,
np.ndarray[np.float64_t, ndim=2] bsource,
np.ndarray[np.float64_t, ndim=2] wresult,
@@ -91,9 +91,9 @@
@cython.boundscheck(False)
@cython.wraparound(False)
@cython.cdivision(True)
-def new_bin_profile3d(np.ndarray[np.int64_t, ndim=1] bins_x,
- np.ndarray[np.int64_t, ndim=1] bins_y,
- np.ndarray[np.int64_t, ndim=1] bins_z,
+def new_bin_profile3d(np.ndarray[np.intp_t, ndim=1] bins_x,
+ np.ndarray[np.intp_t, ndim=1] bins_y,
+ np.ndarray[np.intp_t, ndim=1] bins_z,
np.ndarray[np.float64_t, ndim=1] wsource,
np.ndarray[np.float64_t, ndim=2] bsource,
np.ndarray[np.float64_t, ndim=3] wresult,
diff -r 3ba0536c72c7c8854ddf4bc4732ff5223f568b01 -r a8bea59fcaab5bbbd2e217d3cea16c3eea2b2141 yt/utilities/particle_generator.py
--- a/yt/utilities/particle_generator.py
+++ b/yt/utilities/particle_generator.py
@@ -104,7 +104,7 @@
self.particles[:,self.posx_index] = x[idxs]
self.particles[:,self.posy_index] = y[idxs]
self.particles[:,self.posz_index] = z[idxs]
- self.NumberOfParticles = np.bincount(particle_grid_inds,
+ self.NumberOfParticles = np.bincount(particle_grid_inds.astype("intp"),
minlength=self.num_grids)
if self.num_grids > 1 :
np.add.accumulate(self.NumberOfParticles.squeeze(),
https://bitbucket.org/yt_analysis/yt/commits/3caff11feb0c/
Changeset: 3caff11feb0c
Branch: yt-3.0
User: ngoldbaum
Date: 2014-07-24 02:14:16
Summary: Merged in mzingale/yt/yt-3.0 (pull request #1063)
Fix handedness for volume renders
Affected #: 1 file
diff -r 816e624eefe07e7b3fd08cdb4185ab76c78fccec -r 3caff11feb0cb0b1f30f67b4d2a3bb652d31dc98 yt/visualization/volume_rendering/camera.py
--- a/yt/visualization/volume_rendering/camera.py
+++ b/yt/visualization/volume_rendering/camera.py
@@ -649,7 +649,7 @@
del nz
else:
nim = im
- ax = self._pylab.imshow(nim[:,:,:3]/nim[:,:,:3].max(), origin='upper')
+ ax = self._pylab.imshow(nim[:,:,:3]/nim[:,:,:3].max(), origin='lower')
return ax
def draw(self):
Repository URL: https://bitbucket.org/yt_analysis/yt/
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