Merge pull request materialsinnovation#524 from SvenPVoigt/pair-correlations

Pair correlations

Author: wd15

doc/API.rst

@@ -6,7 +6,7 @@ API
 
 .. jinja::
 
-   {% set functions = ['plot_microstructures', 'generate_delta', 'generate_multiphase', 'generate_checkerboard', 'solve_cahn_hilliard', 'solve_fe', 'coeff_to_real'] | sort %}
+   {% set functions = ['plot_microstructures', 'generate_delta', 'generate_multiphase', 'generate_checkerboard', 'solve_cahn_hilliard', 'solve_fe', 'coeff_to_real', 'paircorr_from_twopoint'] | sort %}
 
    {% set classes = ['PrimitiveTransformer', 'LegendreTransformer', 'TwoPointCorrelation', 'FlattenTransformer', 'LocalizationRegressor', 'ReshapeTransformer'] | sort %}
 

notebooks/cahn_hilliard.ipynb

@@ -226,6 +226,8 @@
     }
    ],
    "source": [
+    "#PYTEST_VALIDATE_IGNORE_OUTPUT\n",
+    "\n",
     "x_data"
    ]
   },
@@ -315,6 +317,8 @@
     }
    ],
    "source": [
+    "#PYTEST_VALIDATE_IGNORE_OUTPUT\n",
+    "\n",
     "y_data"
    ]
   },

notebooks/fiber.ipynb

@@ -213,6 +213,8 @@
     }
    ],
    "source": [
+    "#PYTEST_VALIDATE_IGNORE_OUTPUT\n",
+    "\n",
     "x_data"
    ]
   },
@@ -278,6 +280,8 @@
     }
    ],
    "source": [
+    "#PYTEST_VALIDATE_IGNORE_OUTPUT\n",
+    "\n",
     "y_data"
    ]
   },

notebooks/intro.ipynb

@@ -234,6 +234,8 @@
     }
    ],
    "source": [
+    "#PYTEST_VALIDATE_IGNORE_OUTPUT\n",
+    "\n",
     "x_data"
    ]
   },
@@ -297,6 +299,8 @@
     }
    ],
    "source": [
+    "#PYTEST_VALIDATE_IGNORE_OUTPUT\n",
+    "\n",
     "y_data"
    ]
   },

notebooks/stress.ipynb

@@ -25,6 +25,7 @@
 from .fmks.correlations import FlattenTransformer
 from .fmks.correlations import TwoPointCorrelation
 from .fmks.data.checkerboard import generate_checkerboard
+from .fmks.pair_correlations import paircorr_from_twopoint
 
 try:
     import sfepy  # noqa: F401
@@ -105,4 +106,5 @@ def get_version():
     "FlattenTransformer",
     "TwoPointCorrelation",
     "generate_checkerboard",
+    "paircorr_from_twopoint",
 ]

pymks/__init__.py

@@ -245,8 +245,10 @@ def correlations_multiple(data, correlations, periodic_boundary=True, cutoff=Non
 
     >>> data = np.arange(18).reshape(1, 3, 3, 2)
     >>> out = correlations_multiple(data, [[0, 1], [1, 1]])
-    >>> out
-    dask.array<stack, shape=(1, 3, 3, 2), dtype=float64, chunksize=(1, 3, 3, 1)>
+    >>> out.shape
+    (1, 3, 3, 2)
+    >>> out.chunks
+    ((1,), (3,), (3,), (2,))
     >>> answer = np.array([[[58, 62, 58], [94, 98, 94], [58, 62, 58]]]) + 1. / 3.
     >>> assert(out.compute()[...,0], answer)
     """

pymks/fmks/correlations.py

@@ -283,7 +283,7 @@ def wrapper(arr, *args, **kwargs):
             chunks = arr.chunks
         return func(rechunk(chunks, arr), *args, **kwargs)
 
-    return wrapper
+    return wraps(func)(wrapper)
 
 
 @curry
@@ -452,3 +452,58 @@ def wrapper(*args, **kwargs):
         return func(*args, **kwargs)
 
     return wrapper
+
+
+def dist_mesh(shape):
+    """Calculate a mesh of distances
+
+    Assume dx=1 and the center pixel is shifted to the left for odd
+    shaped axes.
+
+    Args:
+      shape: the shape of the domain
+
+    Returns:
+      an array of distances from the center
+
+    >>> assert np.allclose(
+    ...    dist_mesh((3, 2, 4)),
+    ...    np.sqrt(np.array(
+    ...     [[[6, 3, 2, 3],
+    ...       [5, 2, 1, 2]],
+    ...      [[5, 2, 1, 2],
+    ...       [4, 1, 0, 1]],
+    ...      [[6, 3, 2, 3],
+    ...       [5, 2, 1, 2]]]
+    ...     ))
+    ... )
+
+    >>> dist_mesh((3, 5, 4)).shape
+    (3, 5, 4)
+
+    """
+
+    center = lambda x: np.reshape(np.array(x) // 2, (len(x),) + (1,) * len(x))
+
+    return sequence(
+        fmap(lambda x: np.linspace(0, x - 1, x)),
+        lambda x: np.array(np.meshgrid(*x, indexing="ij")),
+        lambda x: x - center(x.shape[1:]),
+        lambda x: np.linalg.norm(x, axis=0),
+    )(shape)
+
+
+def sort_array(arr):
+    """Functional sort
+
+    Args:
+      arr: array to sort
+
+    Returns:
+      tuple of the sorted array and the index ordering
+
+    >>> sort_array(np.array([2, 1, 3]))
+    (array([1, 2, 3]), array([1, 0, 2]))
+
+    """
+    return sequence(np.argsort, lambda x: (arr[x], x))(arr)