[yt-svn] commit/yt: ngoldbaum: Minor fixes for load_particles

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1 new commit in yt:

https://bitbucket.org/yt_analysis/yt/commits/ed2b7fa425ca/
Changeset:   ed2b7fa425ca
Branch:      yt
User:        ngoldbaum
Date:        2017-01-09 22:02:31+00:00
Summary:     Minor fixes for load_particles
Affected #:  1 file

diff -r 462126c6d9aefcbb363814d3a406df4fa5ebe959 -r ed2b7fa425ca7aeaf6f4b9174e2787b342ad2ce5 yt/frontends/stream/data_structures.py
--- a/yt/frontends/stream/data_structures.py
+++ b/yt/frontends/stream/data_structures.py
@@ -1036,10 +1036,9 @@
     This should allow a collection of particle data to be loaded directly into
     yt and analyzed as would any others.  This comes with several caveats:
 
-    * Units will be incorrect unless the data has already been converted to
-      cgs.
-    * Some functions may behave oddly, and parallelism will be
-      disappointing or non-existent in most cases.
+    * There must be sufficient space in memory to contain both the particle
+      data and the octree used to index the particles.
+    * Parallelism will be disappointing or non-existent in most cases.
 
     This will initialize an Octree of data.  Note that fluid fields will not
     work yet, or possibly ever.
@@ -1047,9 +1046,9 @@
     Parameters
     ----------
     data : dict
-        This is a dict of numpy arrays, where the keys are the field names.
-        Particles positions must be named "particle_position_x",
-        "particle_position_y", "particle_position_z".
+        This is a dict of numpy arrays or (numpy array, unit name) tuples, 
+        where the keys are the field names. Particles positions must be named 
+        "particle_position_x", "particle_position_y", and "particle_position_z".
     length_unit : float
         Conversion factor from simulation length units to centimeters
     mass_unit : float
@@ -1087,6 +1086,8 @@
     nprocs = 1
     if bbox is None:
         bbox = np.array([[0.0, 1.0], [0.0, 1.0], [0.0, 1.0]], 'float64')
+    else:
+        bbox = np.array(bbox)
     domain_left_edge = np.array(bbox[:, 0], 'float64')
     domain_right_edge = np.array(bbox[:, 1], 'float64')
     grid_levels = np.zeros(nprocs, dtype='int32').reshape((nprocs,1))

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

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