[yt-svn] commit/yt: MatthewTurk: Merged in ngoldbaum/yt (pull request #1242)

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1 new commit in yt:

https://bitbucket.org/yt_analysis/yt/commits/5ba254a71989/
Changeset:   5ba254a71989
Branch:      yt
User:        MatthewTurk
Date:        2014-10-10 10:23:53+00:00
Summary:     Merged in ngoldbaum/yt (pull request #1242)

Generic data loading fixes
Affected #:  1 file

diff -r 88b61bedaddf676bf3499a6c52e4a14aa28e1ee9 -r 5ba254a71989d47a89c40e81c3cdefd710ee8341 yt/frontends/stream/data_structures.py
--- a/yt/frontends/stream/data_structures.py
+++ b/yt/frontends/stream/data_structures.py
@@ -364,8 +364,10 @@
     _additional_fields = ()
 
     @property
-    def all_fields(self): 
-        fields = list(self._additional_fields) + self[0].keys()
+    def all_fields(self):
+        self_fields = chain.from_iterable(s.keys() for s in self.values())
+        self_fields = list(set(self_fields))
+        fields = list(self._additional_fields) + self_fields
         fields = list(set(fields))
         return fields
 
@@ -471,8 +473,6 @@
             field_units[k] = v.units
             new_data[k] = v.copy().d
         data = new_data
-    elif all([isinstance(val, np.ndarray) for val in data.values()]):
-        field_units = {field:'' for field in data.keys()}
     elif all([(len(val) == 2) for val in data.values()]):
         new_data, field_units = {}, {}
         for field in data:
@@ -489,6 +489,9 @@
                 raise RuntimeError("The data dict appears to be invalid.\n" +
                                    str(e))
         data = new_data
+    elif all([iterable(val) for val in data.values()]):
+        field_units = {field:'' for field in data.keys()}
+        data = dict((field, np.array(val)) for field, val in data.iteritems())
     else:
         raise RuntimeError("The data dict appears to be invalid. "
                            "The data dictionary must map from field "
@@ -763,12 +766,13 @@
     ...          dimensions = [32, 32, 32],
     ...          number_of_particles = 0)
     ... ]
-    ... 
+    ...
     >>> for g in grid_data:
-    ...     g["Density"] = np.random.random(g["dimensions"]) * 2**g["level"]
+    ...     g["density"] = np.random.random(g["dimensions"]) * 2**g["level"]
     ...
-    >>> units = dict(Density='g/cm**3')
-    >>> ds = load_amr_grids(grid_data, [32, 32, 32], 1.0)
+    >>> units = dict(density='g/cm**3')
+    >>> ds = load_amr_grids(grid_data, [8, 8, 8], field_units=units,
+    ...                     length_unit=1.0)
     """
 
     domain_dimensions = np.array(domain_dimensions)
@@ -822,6 +826,11 @@
     if magnetic_unit is None:
         magnetic_unit = 'code_magnetic'
 
+    particle_types = {}
+
+    for grid in grid_data:
+        particle_types.update(set_particle_types(grid))
+
     handler = StreamHandler(
         grid_left_edges,
         grid_right_edges,
@@ -833,7 +842,7 @@
         sfh,
         field_units,
         (length_unit, mass_unit, time_unit, velocity_unit, magnetic_unit),
-        particle_types=set_particle_types(grid_data[0])
+        particle_types=particle_types
     )
 
     handler.name = "AMRGridData"
@@ -1018,7 +1027,7 @@
     magnetic_unit : float
         Conversion factor from simulation magnetic units to gauss
     bbox : array_like (xdim:zdim, LE:RE), optional
-        Size of computational domain in units sim_unit_to_cm
+        Size of computational domain in units of the length_unit
     sim_time : float, optional
         The simulation time in seconds
     periodicity : tuple of booleans
@@ -1191,10 +1200,8 @@
     coordinates : array_like
         This should be of size (M,3) where M is the number of vertices
         indicated in the connectivity matrix.
-    sim_unit_to_cm : float
-        Conversion factor from simulation units to centimeters
     bbox : array_like (xdim:zdim, LE:RE), optional
-        Size of computational domain in units sim_unit_to_cm
+        Size of computational domain in units of the length unit.
     sim_time : float, optional
         The simulation time in seconds
     mass_unit : string

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

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