[yt-svn] commit/yt: 3 new changesets

commits-noreply at bitbucket.org commits-noreply at bitbucket.org
Wed Mar 2 09:26:45 PST 2016


3 new commits in yt:

https://bitbucket.org/yt_analysis/yt/commits/f76d7177e56b/
Changeset:   f76d7177e56b
Branch:      yt
User:        MatthewTurk
Date:        2016-02-26 21:57:48+00:00
Summary:     Move quantities to be on the top level of YTSelection.
Affected #:  1 file

diff -r 7130b7cef71f9422cc6191b755e1bd5ca96fbaa0 -r f76d7177e56b525a92ad0d335ad8ad2f0a3dea9e yt/data_objects/data_containers.py
--- a/yt/data_objects/data_containers.py
+++ b/yt/data_objects/data_containers.py
@@ -1054,6 +1054,7 @@
                                    "of lower dimensionality (%u vs %u)" %
                                     (data_source._dimensionality, self._dimensionality))
             self.field_parameters.update(data_source.field_parameters)
+        self.quantities = DerivedQuantityCollection(self)
 
     @property
     def selector(self):
@@ -1457,7 +1458,6 @@
         self._set_center(center)
         self.coords = None
         self._grids = None
-        self.quantities = DerivedQuantityCollection(self)
 
     def cut_region(self, field_cuts, field_parameters=None):
         """


https://bitbucket.org/yt_analysis/yt/commits/069de0afd8c5/
Changeset:   069de0afd8c5
Branch:      yt
User:        MatthewTurk
Date:        2016-02-26 22:08:12+00:00
Summary:     Adding slices to the derived quantities tests.
Affected #:  1 file

diff -r f76d7177e56b525a92ad0d335ad8ad2f0a3dea9e -r 069de0afd8c56ab3fc6f3b732db128d3042d72e5 yt/data_objects/tests/test_derived_quantities.py
--- a/yt/data_objects/tests/test_derived_quantities.py
+++ b/yt/data_objects/tests/test_derived_quantities.py
@@ -13,109 +13,109 @@
     for nprocs in [1, 2, 4, 8]:
         ds = fake_random_ds(16, nprocs = nprocs, fields = ("density",
                 "velocity_x", "velocity_y", "velocity_z"))
-        sp = ds.sphere("c", (0.25, 'unitary'))
-        mi, ma = sp.quantities["Extrema"]("density")
-        yield assert_equal, mi, np.nanmin(sp["density"])
-        yield assert_equal, ma, np.nanmax(sp["density"])
-        dd = ds.all_data()
-        mi, ma = dd.quantities["Extrema"]("density")
-        yield assert_equal, mi, np.nanmin(dd["density"])
-        yield assert_equal, ma, np.nanmax(dd["density"])
-        sp = ds.sphere("max", (0.25, 'unitary'))
-        yield assert_equal, np.any(np.isnan(sp["radial_velocity"])), False
-        mi, ma = dd.quantities["Extrema"]("radial_velocity")
-        yield assert_equal, mi, np.nanmin(dd["radial_velocity"])
-        yield assert_equal, ma, np.nanmax(dd["radial_velocity"])
+        for sp in [ds.sphere("c", (0.25, 'unitary')), ds.r[0.5,:,:]]:
+            mi, ma = sp.quantities["Extrema"]("density")
+            yield assert_equal, mi, np.nanmin(sp["density"])
+            yield assert_equal, ma, np.nanmax(sp["density"])
+            dd = ds.all_data()
+            mi, ma = dd.quantities["Extrema"]("density")
+            yield assert_equal, mi, np.nanmin(dd["density"])
+            yield assert_equal, ma, np.nanmax(dd["density"])
+            sp = ds.sphere("max", (0.25, 'unitary'))
+            yield assert_equal, np.any(np.isnan(sp["radial_velocity"])), False
+            mi, ma = dd.quantities["Extrema"]("radial_velocity")
+            yield assert_equal, mi, np.nanmin(dd["radial_velocity"])
+            yield assert_equal, ma, np.nanmax(dd["radial_velocity"])
 
 def test_average():
     for nprocs in [1, 2, 4, 8]:
         ds = fake_random_ds(16, nprocs = nprocs, fields = ("density",))
-        ad = ds.all_data()
+        for ad in [ds.all_data(), ds.r[0.5, :, :]]:
         
-        my_mean = ad.quantities["WeightedAverageQuantity"]("density", "ones")
-        yield assert_rel_equal, my_mean, ad["density"].mean(), 12
+            my_mean = ad.quantities["WeightedAverageQuantity"]("density", "ones")
+            yield assert_rel_equal, my_mean, ad["density"].mean(), 12
 
-        my_mean = ad.quantities["WeightedAverageQuantity"]("density", "cell_mass")
-        a_mean = (ad["density"] * ad["cell_mass"]).sum() / ad["cell_mass"].sum()
-        yield assert_rel_equal, my_mean, a_mean, 12
+            my_mean = ad.quantities["WeightedAverageQuantity"]("density", "cell_mass")
+            a_mean = (ad["density"] * ad["cell_mass"]).sum() / ad["cell_mass"].sum()
+            yield assert_rel_equal, my_mean, a_mean, 12
 
 def test_variance():
     for nprocs in [1, 2, 4, 8]:
         ds = fake_random_ds(16, nprocs = nprocs, fields = ("density", ))
-        ad = ds.all_data()
+        for ad in [ds.all_data(), ds.r[0.5, :, :]]:
         
-        my_std, my_mean = ad.quantities["WeightedVariance"]("density", "ones")
-        yield assert_rel_equal, my_mean, ad["density"].mean(), 12
-        yield assert_rel_equal, my_std, ad["density"].std(), 12
+            my_std, my_mean = ad.quantities["WeightedVariance"]("density", "ones")
+            yield assert_rel_equal, my_mean, ad["density"].mean(), 12
+            yield assert_rel_equal, my_std, ad["density"].std(), 12
 
-        my_std, my_mean = ad.quantities["WeightedVariance"]("density", "cell_mass")        
-        a_mean = (ad["density"] * ad["cell_mass"]).sum() / ad["cell_mass"].sum()
-        yield assert_rel_equal, my_mean, a_mean, 12
-        a_std = np.sqrt((ad["cell_mass"] * (ad["density"] - a_mean)**2).sum() / 
-                        ad["cell_mass"].sum())
-        yield assert_rel_equal, my_std, a_std, 12
+            my_std, my_mean = ad.quantities["WeightedVariance"]("density", "cell_mass")        
+            a_mean = (ad["density"] * ad["cell_mass"]).sum() / ad["cell_mass"].sum()
+            yield assert_rel_equal, my_mean, a_mean, 12
+            a_std = np.sqrt((ad["cell_mass"] * (ad["density"] - a_mean)**2).sum() / 
+                            ad["cell_mass"].sum())
+            yield assert_rel_equal, my_std, a_std, 12
 
 def test_max_location():
     for nprocs in [1, 2, 4, 8]:
         ds = fake_random_ds(16, nprocs = nprocs, fields = ("density", ))
-        ad = ds.all_data()
+        for ad in [ds.all_data(), ds.r[0.5, :, :]]:
 
-        mv, x, y, z = ad.quantities.max_location(("gas", "density"))
+            mv, x, y, z = ad.quantities.max_location(("gas", "density"))
 
-        yield assert_equal, mv, ad["density"].max()
+            yield assert_equal, mv, ad["density"].max()
 
-        mi = np.argmax(ad["density"])
+            mi = np.argmax(ad["density"])
 
-        yield assert_equal, ad["x"][mi], x
-        yield assert_equal, ad["y"][mi], y
-        yield assert_equal, ad["z"][mi], z
+            yield assert_equal, ad["x"][mi], x
+            yield assert_equal, ad["y"][mi], y
+            yield assert_equal, ad["z"][mi], z
 
 def test_min_location():
     for nprocs in [1, 2, 4, 8]:
         ds = fake_random_ds(16, nprocs = nprocs, fields = ("density", ))
-        ad = ds.all_data()
+        for ad in [ds.all_data(), ds.r[0.5, :, :]]:
 
-        mv, x, y, z = ad.quantities.min_location(("gas", "density"))
+            mv, x, y, z = ad.quantities.min_location(("gas", "density"))
 
-        yield assert_equal, mv, ad["density"].min()
+            yield assert_equal, mv, ad["density"].min()
 
-        mi = np.argmin(ad["density"])
+            mi = np.argmin(ad["density"])
 
-        yield assert_equal, ad["x"][mi], x
-        yield assert_equal, ad["y"][mi], y
-        yield assert_equal, ad["z"][mi], z
+            yield assert_equal, ad["x"][mi], x
+            yield assert_equal, ad["y"][mi], y
+            yield assert_equal, ad["z"][mi], z
 
 def test_sample_at_min_field_values():
     for nprocs in [1, 2, 4, 8]:
         ds = fake_random_ds(16, nprocs = nprocs,
             fields = ("density", "temperature", "velocity_x"))
-        ad = ds.all_data()
+        for ad in [ds.all_data(), ds.r[0.5, :, :]]:
 
-        mv, temp, vm = ad.quantities.sample_at_min_field_values(
-            "density", ["temperature", "velocity_x"])
+            mv, temp, vm = ad.quantities.sample_at_min_field_values(
+                "density", ["temperature", "velocity_x"])
 
-        yield assert_equal, mv, ad["density"].min()
+            yield assert_equal, mv, ad["density"].min()
 
-        mi = np.argmin(ad["density"])
+            mi = np.argmin(ad["density"])
 
-        yield assert_equal, ad["temperature"][mi], temp
-        yield assert_equal, ad["velocity_x"][mi], vm
+            yield assert_equal, ad["temperature"][mi], temp
+            yield assert_equal, ad["velocity_x"][mi], vm
 
 def test_sample_at_max_field_values():
     for nprocs in [1, 2, 4, 8]:
         ds = fake_random_ds(16, nprocs = nprocs,
             fields = ("density", "temperature", "velocity_x"))
-        ad = ds.all_data()
+        for ad in [ds.all_data(), ds.r[0.5, :, :]]:
 
-        mv, temp, vm = ad.quantities.sample_at_max_field_values(
-            "density", ["temperature", "velocity_x"])
+            mv, temp, vm = ad.quantities.sample_at_max_field_values(
+                "density", ["temperature", "velocity_x"])
 
-        yield assert_equal, mv, ad["density"].max()
+            yield assert_equal, mv, ad["density"].max()
 
-        mi = np.argmax(ad["density"])
+            mi = np.argmax(ad["density"])
 
-        yield assert_equal, ad["temperature"][mi], temp
-        yield assert_equal, ad["velocity_x"][mi], vm
+            yield assert_equal, ad["temperature"][mi], temp
+            yield assert_equal, ad["velocity_x"][mi], vm
 
 if __name__ == "__main__":
     for i in test_extrema():


https://bitbucket.org/yt_analysis/yt/commits/bf1389cb5d09/
Changeset:   bf1389cb5d09
Branch:      yt
User:        jzuhone
Date:        2016-03-02 17:26:34+00:00
Summary:     Merged in MatthewTurk/yt (pull request #2006)

Move .quantities to top level of YTSelectionContainer
Affected #:  2 files

diff -r 764f5ed10e7ec972b70dbccbfb201c03d2657e58 -r bf1389cb5d0995907a78e24adc00dd6b5788ec03 yt/data_objects/data_containers.py
--- a/yt/data_objects/data_containers.py
+++ b/yt/data_objects/data_containers.py
@@ -1054,6 +1054,7 @@
                                    "of lower dimensionality (%u vs %u)" %
                                     (data_source._dimensionality, self._dimensionality))
             self.field_parameters.update(data_source.field_parameters)
+        self.quantities = DerivedQuantityCollection(self)
 
     @property
     def selector(self):
@@ -1457,7 +1458,6 @@
         self._set_center(center)
         self.coords = None
         self._grids = None
-        self.quantities = DerivedQuantityCollection(self)
 
     def cut_region(self, field_cuts, field_parameters=None):
         """

diff -r 764f5ed10e7ec972b70dbccbfb201c03d2657e58 -r bf1389cb5d0995907a78e24adc00dd6b5788ec03 yt/data_objects/tests/test_derived_quantities.py
--- a/yt/data_objects/tests/test_derived_quantities.py
+++ b/yt/data_objects/tests/test_derived_quantities.py
@@ -13,109 +13,109 @@
     for nprocs in [1, 2, 4, 8]:
         ds = fake_random_ds(16, nprocs = nprocs, fields = ("density",
                 "velocity_x", "velocity_y", "velocity_z"))
-        sp = ds.sphere("c", (0.25, 'unitary'))
-        mi, ma = sp.quantities["Extrema"]("density")
-        yield assert_equal, mi, np.nanmin(sp["density"])
-        yield assert_equal, ma, np.nanmax(sp["density"])
-        dd = ds.all_data()
-        mi, ma = dd.quantities["Extrema"]("density")
-        yield assert_equal, mi, np.nanmin(dd["density"])
-        yield assert_equal, ma, np.nanmax(dd["density"])
-        sp = ds.sphere("max", (0.25, 'unitary'))
-        yield assert_equal, np.any(np.isnan(sp["radial_velocity"])), False
-        mi, ma = dd.quantities["Extrema"]("radial_velocity")
-        yield assert_equal, mi, np.nanmin(dd["radial_velocity"])
-        yield assert_equal, ma, np.nanmax(dd["radial_velocity"])
+        for sp in [ds.sphere("c", (0.25, 'unitary')), ds.r[0.5,:,:]]:
+            mi, ma = sp.quantities["Extrema"]("density")
+            yield assert_equal, mi, np.nanmin(sp["density"])
+            yield assert_equal, ma, np.nanmax(sp["density"])
+            dd = ds.all_data()
+            mi, ma = dd.quantities["Extrema"]("density")
+            yield assert_equal, mi, np.nanmin(dd["density"])
+            yield assert_equal, ma, np.nanmax(dd["density"])
+            sp = ds.sphere("max", (0.25, 'unitary'))
+            yield assert_equal, np.any(np.isnan(sp["radial_velocity"])), False
+            mi, ma = dd.quantities["Extrema"]("radial_velocity")
+            yield assert_equal, mi, np.nanmin(dd["radial_velocity"])
+            yield assert_equal, ma, np.nanmax(dd["radial_velocity"])
 
 def test_average():
     for nprocs in [1, 2, 4, 8]:
         ds = fake_random_ds(16, nprocs = nprocs, fields = ("density",))
-        ad = ds.all_data()
+        for ad in [ds.all_data(), ds.r[0.5, :, :]]:
         
-        my_mean = ad.quantities["WeightedAverageQuantity"]("density", "ones")
-        yield assert_rel_equal, my_mean, ad["density"].mean(), 12
+            my_mean = ad.quantities["WeightedAverageQuantity"]("density", "ones")
+            yield assert_rel_equal, my_mean, ad["density"].mean(), 12
 
-        my_mean = ad.quantities["WeightedAverageQuantity"]("density", "cell_mass")
-        a_mean = (ad["density"] * ad["cell_mass"]).sum() / ad["cell_mass"].sum()
-        yield assert_rel_equal, my_mean, a_mean, 12
+            my_mean = ad.quantities["WeightedAverageQuantity"]("density", "cell_mass")
+            a_mean = (ad["density"] * ad["cell_mass"]).sum() / ad["cell_mass"].sum()
+            yield assert_rel_equal, my_mean, a_mean, 12
 
 def test_variance():
     for nprocs in [1, 2, 4, 8]:
         ds = fake_random_ds(16, nprocs = nprocs, fields = ("density", ))
-        ad = ds.all_data()
+        for ad in [ds.all_data(), ds.r[0.5, :, :]]:
         
-        my_std, my_mean = ad.quantities["WeightedVariance"]("density", "ones")
-        yield assert_rel_equal, my_mean, ad["density"].mean(), 12
-        yield assert_rel_equal, my_std, ad["density"].std(), 12
+            my_std, my_mean = ad.quantities["WeightedVariance"]("density", "ones")
+            yield assert_rel_equal, my_mean, ad["density"].mean(), 12
+            yield assert_rel_equal, my_std, ad["density"].std(), 12
 
-        my_std, my_mean = ad.quantities["WeightedVariance"]("density", "cell_mass")        
-        a_mean = (ad["density"] * ad["cell_mass"]).sum() / ad["cell_mass"].sum()
-        yield assert_rel_equal, my_mean, a_mean, 12
-        a_std = np.sqrt((ad["cell_mass"] * (ad["density"] - a_mean)**2).sum() / 
-                        ad["cell_mass"].sum())
-        yield assert_rel_equal, my_std, a_std, 12
+            my_std, my_mean = ad.quantities["WeightedVariance"]("density", "cell_mass")        
+            a_mean = (ad["density"] * ad["cell_mass"]).sum() / ad["cell_mass"].sum()
+            yield assert_rel_equal, my_mean, a_mean, 12
+            a_std = np.sqrt((ad["cell_mass"] * (ad["density"] - a_mean)**2).sum() / 
+                            ad["cell_mass"].sum())
+            yield assert_rel_equal, my_std, a_std, 12
 
 def test_max_location():
     for nprocs in [1, 2, 4, 8]:
         ds = fake_random_ds(16, nprocs = nprocs, fields = ("density", ))
-        ad = ds.all_data()
+        for ad in [ds.all_data(), ds.r[0.5, :, :]]:
 
-        mv, x, y, z = ad.quantities.max_location(("gas", "density"))
+            mv, x, y, z = ad.quantities.max_location(("gas", "density"))
 
-        yield assert_equal, mv, ad["density"].max()
+            yield assert_equal, mv, ad["density"].max()
 
-        mi = np.argmax(ad["density"])
+            mi = np.argmax(ad["density"])
 
-        yield assert_equal, ad["x"][mi], x
-        yield assert_equal, ad["y"][mi], y
-        yield assert_equal, ad["z"][mi], z
+            yield assert_equal, ad["x"][mi], x
+            yield assert_equal, ad["y"][mi], y
+            yield assert_equal, ad["z"][mi], z
 
 def test_min_location():
     for nprocs in [1, 2, 4, 8]:
         ds = fake_random_ds(16, nprocs = nprocs, fields = ("density", ))
-        ad = ds.all_data()
+        for ad in [ds.all_data(), ds.r[0.5, :, :]]:
 
-        mv, x, y, z = ad.quantities.min_location(("gas", "density"))
+            mv, x, y, z = ad.quantities.min_location(("gas", "density"))
 
-        yield assert_equal, mv, ad["density"].min()
+            yield assert_equal, mv, ad["density"].min()
 
-        mi = np.argmin(ad["density"])
+            mi = np.argmin(ad["density"])
 
-        yield assert_equal, ad["x"][mi], x
-        yield assert_equal, ad["y"][mi], y
-        yield assert_equal, ad["z"][mi], z
+            yield assert_equal, ad["x"][mi], x
+            yield assert_equal, ad["y"][mi], y
+            yield assert_equal, ad["z"][mi], z
 
 def test_sample_at_min_field_values():
     for nprocs in [1, 2, 4, 8]:
         ds = fake_random_ds(16, nprocs = nprocs,
             fields = ("density", "temperature", "velocity_x"))
-        ad = ds.all_data()
+        for ad in [ds.all_data(), ds.r[0.5, :, :]]:
 
-        mv, temp, vm = ad.quantities.sample_at_min_field_values(
-            "density", ["temperature", "velocity_x"])
+            mv, temp, vm = ad.quantities.sample_at_min_field_values(
+                "density", ["temperature", "velocity_x"])
 
-        yield assert_equal, mv, ad["density"].min()
+            yield assert_equal, mv, ad["density"].min()
 
-        mi = np.argmin(ad["density"])
+            mi = np.argmin(ad["density"])
 
-        yield assert_equal, ad["temperature"][mi], temp
-        yield assert_equal, ad["velocity_x"][mi], vm
+            yield assert_equal, ad["temperature"][mi], temp
+            yield assert_equal, ad["velocity_x"][mi], vm
 
 def test_sample_at_max_field_values():
     for nprocs in [1, 2, 4, 8]:
         ds = fake_random_ds(16, nprocs = nprocs,
             fields = ("density", "temperature", "velocity_x"))
-        ad = ds.all_data()
+        for ad in [ds.all_data(), ds.r[0.5, :, :]]:
 
-        mv, temp, vm = ad.quantities.sample_at_max_field_values(
-            "density", ["temperature", "velocity_x"])
+            mv, temp, vm = ad.quantities.sample_at_max_field_values(
+                "density", ["temperature", "velocity_x"])
 
-        yield assert_equal, mv, ad["density"].max()
+            yield assert_equal, mv, ad["density"].max()
 
-        mi = np.argmax(ad["density"])
+            mi = np.argmax(ad["density"])
 
-        yield assert_equal, ad["temperature"][mi], temp
-        yield assert_equal, ad["velocity_x"][mi], vm
+            yield assert_equal, ad["temperature"][mi], temp
+            yield assert_equal, ad["velocity_x"][mi], vm
 
 if __name__ == "__main__":
     for i in test_extrema():

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

--

This is a commit notification from bitbucket.org. You are receiving
this because you have the service enabled, addressing the recipient of
this email.


More information about the yt-svn mailing list