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

[Bitbucket]

2 new commits in yt:


https://bitbucket.org/yt_analysis/yt/changeset/f0dec88aea40/
changeset:   f0dec88aea40
branch:      yt
user:        chummels
date:        2011-12-07 00:52:57
summary:     Modifying how smoothed covering grids calculate (and save) their various field values.  Previously, they threw the field information away after each calculation, but I found a slow down of a factor of 50 in building such grids for my datasets.  So I've modified it so that it keeps the information after it calculates.  It may result in a factor of 2x memory consumption, but that is OK by me.
affected #:  1 file

diff -r a37631cda71afd84fba5b7ef36d127109c8fb356 -r f0dec88aea405c09fc33847b58b8dee7932041cb yt/data_objects/data_containers.py
--- a/yt/data_objects/data_containers.py
+++ b/yt/data_objects/data_containers.py
@@ -88,6 +88,20 @@
         return tr
     return save_state
 
+def restore_field_information_state(func):
+    """
+    A decorator that takes a function with the API of (self, grid, field)
+    and ensures that after the function is called, the field_parameters will
+    be returned to normal.
+    """
+    def save_state(self, grid, field=None, *args, **kwargs):
+        old_params = grid.field_parameters
+        grid.field_parameters = self.field_parameters
+        tr = func(self, grid, field, *args, **kwargs)
+        grid.field_parameters = old_params
+        return tr
+    return save_state
+
 def cache_mask(func):
     """
     For computationally intensive indexing operations, we can cache
@@ -3467,7 +3481,7 @@
                                    output_field, output_left)
             self.field_data[field] = output_field
 
-    @restore_grid_state
+    @restore_field_information_state
     def _get_data_from_grid(self, grid, fields):
         fields = ensure_list(fields)
         g_fields = [grid[field].astype("float64") for field in fields]



https://bitbucket.org/yt_analysis/yt/changeset/3ec2c245c827/
changeset:   3ec2c245c827
branch:      yt
user:        chummels
date:        2011-12-07 20:21:03
summary:     Merging.
affected #:  1 file

diff -r f0dec88aea405c09fc33847b58b8dee7932041cb -r 3ec2c245c82714557c87d8bd6fe4d06fbcb83b3c yt/frontends/flash/data_structures.py
--- a/yt/frontends/flash/data_structures.py
+++ b/yt/frontends/flash/data_structures.py
@@ -265,6 +265,7 @@
     def _find_parameter(self, ptype, pname, scalar = False):
         nn = "/%s %s" % (ptype,
                 {False: "runtime parameters", True: "scalars"}[scalar])
+        if nn not in self._handle: raise KeyError(nn)
         for tpname, pval in self._handle[nn][:]:
             if tpname.strip() == pname:
                 return pval
@@ -285,20 +286,26 @@
             [self._find_parameter("real", "%smin" % ax) for ax in 'xyz'])
         self.domain_right_edge = na.array(
             [self._find_parameter("real", "%smax" % ax) for ax in 'xyz'])
-        self.dimensionality = self._find_parameter("integer", "dimensionality",
-                                scalar = True)
 
         # Determine domain dimensions
         try:
             nxb = self._find_parameter("integer", "nxb", scalar = True)
             nyb = self._find_parameter("integer", "nyb", scalar = True)
             nzb = self._find_parameter("integer", "nzb", scalar = True)
+            dimensionality = self._find_parameter("integer", "dimensionality",
+                                    scalar = True)
         except KeyError:
             nxb, nyb, nzb = [int(self._handle["/simulation parameters"]['n%sb' % ax])
                               for ax in 'xyz']
+            dimensionality = 3
+            if nzb == 1: dimensionality = 2
+            if nyb == 1: dimensionality = 1
+            if dimensionality < 3:
+                mylog.warning("Guessing dimensionality as %s", dimensionality)
         nblockx = self._find_parameter("integer", "nblockx")
         nblocky = self._find_parameter("integer", "nblockx")
         nblockz = self._find_parameter("integer", "nblockx")
+        self.dimensionality = dimensionality
         self.domain_dimensions = \
             na.array([nblockx*nxb,nblocky*nyb,nblockz*nzb])

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

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