Remove changes to approx_cell1_size

astropy · Nov 27, 2019 · 4fb90c7 · 4fb90c7
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commit 4fb90c7
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Showing 7 changed files with 7 additions and 7 deletions.
diff --git a/halotools/mock_observables/isolation_functions/conditional_cylindrical_isolation.py b/halotools/mock_observables/isolation_functions/conditional_cylindrical_isolation.py
@@ -105,7 +105,7 @@ def conditional_cylindrical_isolation(sample1, sample2, rp_max, pi_max,
         The optimum choice unavoidably depends on the specs of your machine.
         Default choice is to use ``rp_max``/10 in the xy-dimensions
         and ``pi_max``/10 in the z-dimension,
-        which will return reasonable performance for most use-cases.
+        which will return reasonable result performance for most use-cases.
         Performance can vary sensitively with this parameter, so it is highly
         recommended that you experiment with this parameter when carrying out
         performance-critical calculations.

diff --git a/halotools/mock_observables/isolation_functions/spherical_isolation.py b/halotools/mock_observables/isolation_functions/spherical_isolation.py
@@ -76,7 +76,7 @@ def spherical_isolation(sample1, sample2, r_max, period=None,
         will be apportioned into subvolumes of the simulation box.
         The optimum choice unavoidably depends on the specs of your machine.
         Default choice is to use ``r_max``/10 in each dimension,
-        which will return reasonable performance for most use-cases.
+        which will return reasonable result performance for most use-cases.
         Performance can vary sensitively with this parameter, so it is highly
         recommended that you experiment with this parameter when carrying out
         performance-critical calculations.

diff --git a/halotools/mock_observables/large_scale_density/large_scale_density_spherical_annulus.py b/halotools/mock_observables/large_scale_density/large_scale_density_spherical_annulus.py
@@ -75,7 +75,7 @@ def large_scale_density_spherical_annulus(sample, tracers, inner_radius, outer_r
         will be apportioned into subvolumes of the simulation box.
         The optimum choice unavoidably depends on the specs of your machine.
         Default choice is to use Lbox/10 in each dimension,
-        which will return reasonable performance for most use-cases.
+        which will return reasonable result performance for most use-cases.
         Performance can vary sensitively with this parameter, so it is highly
         recommended that you experiment with this parameter when carrying out
         performance-critical calculations.

diff --git a/halotools/mock_observables/large_scale_density/large_scale_density_spherical_volume.py b/halotools/mock_observables/large_scale_density/large_scale_density_spherical_volume.py
@@ -75,7 +75,7 @@ def large_scale_density_spherical_volume(sample, tracers, radius,
         will be apportioned into subvolumes of the simulation box.
         The optimum choice unavoidably depends on the specs of your machine.
         Default choice is to use Lbox/10 in each dimension,
-        which will return reasonable performance for most use-cases.
+        which will return reasonable result performance for most use-cases.
         Performance can vary sensitively with this parameter, so it is highly
         recommended that you experiment with this parameter when carrying out
         performance-critical calculations.

diff --git a/halotools/mock_observables/pair_counters/marked_npairs_3d.py b/halotools/mock_observables/pair_counters/marked_npairs_3d.py
@@ -88,7 +88,7 @@ def marked_npairs_3d(sample1, sample2, rbins, weight_func_id,
         will be apportioned into subvolumes of the simulation box.
         The optimum choice unavoidably depends on the specs of your machine.
         Default choice is to use Lbox/10 in each dimension,
-        which will return reasonable performance for most use-cases.
+        which will return reasonable result performance for most use-cases.
         Performance can vary sensitively with this parameter, so it is highly
         recommended that you experiment with this parameter when carrying out
         performance-critical calculations.

diff --git a/halotools/mock_observables/pair_counters/npairs_3d.py b/halotools/mock_observables/pair_counters/npairs_3d.py
@@ -67,7 +67,7 @@ def npairs_3d(sample1, sample2, rbins, period=None,
         will be apportioned into subvolumes of the simulation box.
         The optimum choice unavoidably depends on the specs of your machine.
         Default choice is to use Lbox/10 in each dimension,
-        which will return reasonable performance for most use-cases.
+        which will return reasonable result performance for most use-cases.
         Performance can vary sensitively with this parameter, so it is highly
         recommended that you experiment with this parameter when carrying out
         performance-critical calculations.

diff --git a/halotools/mock_observables/tensor_calculations/inertia_tensor.py b/halotools/mock_observables/tensor_calculations/inertia_tensor.py
@@ -93,7 +93,7 @@ def inertia_tensor_per_object(sample1, sample2, weights2, smoothing_scale,
 
         The optimum choice unavoidably depends on the specs of your machine.
         Default choice is to use Lbox/10 in each dimension,
-        which will return reasonable performance for most use-cases.
+        which will return reasonable result performance for most use-cases.
         Performance can vary sensitively with this parameter, so it is highly
         recommended that you experiment with this parameter when carrying out
         performance-critical calculations.