Remove save memory

elaston/linear_elasticity/green.py

@@ -5,6 +5,7 @@
 import numpy as np
 from elaston.linear_elasticity import tools
 from tqdm.auto import tqdm
+from functools import cached_property
 
 __author__ = "Sam Waseda"
 __copyright__ = (
@@ -58,7 +59,7 @@ def _get_greens_function(self, r, derivative=0, fourier=False):
         )
 
     def get_greens_function(
-        self, r, derivative=0, fourier=False, check_unique=False, save_memory=False
+        self, r, derivative=0, fourier=False, check_unique=False
     ):
         """
         Args:
@@ -68,8 +69,6 @@ def get_greens_function(
             check_unique (bool): If `True`, pyiron checks whether there are
                 duplicate values and avoids calculating the Green's function
                 multiple times for the same values
-            save_memory (bool): If `True`, for loop will be used instead of
-                vector calculation, which saves the memory but makes it slow.
 
         Returns:
             (numpy.array): Green's function values. If `derivative=0` or `fourier=True`,
@@ -78,19 +77,9 @@ def get_greens_function(
         x = np.array(r)
         if check_unique:
             x, inv = np.unique(x.reshape(-1, 3), axis=0, return_inverse=True)
-        if save_memory:
-            g_tmp = np.array(
-                [
-                    self._get_greens_function(
-                        r=xx, derivative=derivative, fourier=fourier
-                    )
-                    for xx in tqdm(x)
-                ]
-            )
-        else:
-            g_tmp = self._get_greens_function(
-                r=x, derivative=derivative, fourier=fourier
-            )
+        g_tmp = self._get_greens_function(
+            r=x, derivative=derivative, fourier=fourier
+        )
         if check_unique:
             g_tmp = g_tmp[inv].reshape(np.asarray(r).shape + (derivative + 1) * (3,))
         return g_tmp
@@ -120,15 +109,13 @@ def __init__(self, poissons_ratio, shear_modulus, min_distance=0, optimize=True)
         self.shear_modulus = shear_modulus
         self.min_dist = min_distance
         self.optimize = optimize
-        self._A = None
         self._B = None
 
-    @property
+    # Rewrite A using cached_property
+    @cached_property
     def A(self):
         """First coefficient of the Green's function. For more, cf. DocString in the class level."""
-        if self._A is None:
-            self._A = (3 - 4 * self.poissons_ratio) * self.B
-        return self._A
+        return (3 - 4 * self.poissons_ratio) * self.B
 
     @property
     def B(self):

elaston/linear_elasticity/linear_elasticity.py

@@ -20,16 +20,6 @@
 __date__ = "Aug 21, 2021"
 
 
-def value_or_none(func):
-    def f(self):
-        if self.elastic_tensor is None:
-            return None
-        else:
-            return func(self)
-
-    return f
-
-
 point_defect_explanation = """
 According to the definition of the Green's function (cf. docstring of `get_greens_function`):
 
@@ -73,6 +63,18 @@ def f(self):
 job.run()
 dipole_tensor = -job.structure.get_volume()*job['output/generic/pressures'][-1]
 ```
+
+Instead of working with atomistic calculations, the dipole tensor can be calculated by the
+lambda tensor, which is defined as:
+
+.. math:
+    \\lambda_{ij} = \\frac{1]{V} \\frac{\\partial \\varepsilon_{ij}}{\\partial c}
+
+where :math:`c` is the concentration of the defect, :math:`V` is the volume
+and :math:`\\varepsilon` is the strain field. Then the dipole tensor is given by:
+
+.. math:
+    P_{ij} = VC_{ijkl}\\lambda_{kl}
 """
 
 
@@ -197,7 +199,6 @@ def elastic_tensor(self, C):
         self._elastic_tensor = C
 
     @property
-    @value_or_none
     def elastic_tensor_voigt(self):
         """
         Voigt notation of the elastic tensor, i.e. (i, j) = i, if i == j and
@@ -206,7 +207,6 @@ def elastic_tensor_voigt(self):
         return tools.C_to_voigt(self.elastic_tensor)
 
     @property
-    @value_or_none
     def compliance_matrix(self):
         """Compliance matrix in Voigt notation."""
         return np.linalg.inv(self.elastic_tensor_voigt)
@@ -273,6 +273,17 @@ def youngs_modulus(self):
         """
         Returns:
             ((3,)-array): xx-, yy-, zz-components of Young's modulus
+
+
+        Here is a list of Young's modulus of a few materials:
+
+        - Al: 70.4
+        - Cu: 170.0
+        - Fe: 211.0
+        - Mo: 442.0
+        - Ni: 248.0
+        - W: 630.0
+
         """
         return 1 / self.compliance_matrix[:3, :3].diagonal()
 
@@ -285,7 +296,6 @@ def get_greens_function(
         isotropic: bool = False,
         optimize: bool = True,
         check_unique: bool = False,
-        save_memory: bool = False,
     ):
         """
         Green's function of the equilibrium condition:
@@ -303,8 +313,6 @@ def get_greens_function(
                 is isotropic, it will automatically be set to isotropic=True
             optimize (bool): cf. `optimize` in `numpy.einsum`
             check_unique (bool): Whether to check the unique positions
-            save_memory (bool): Whether to save memory by using a for loop
-                instead of `numpy.einsum`
 
         Returns:
             ((n,3,3)-array): Green's function values for the given positions
@@ -320,7 +328,6 @@ def get_greens_function(
             derivative=derivative,
             fourier=fourier,
             check_unique=check_unique,
-            save_memory=save_memory,
         )
 
     get_greens_function.__doc__ += Green.__doc__
@@ -333,7 +340,6 @@ def get_point_defect_displacement(
         isotropic: bool = False,
         optimize: bool = True,
         check_unique: bool = False,
-        save_memory: bool = False,
     ):
         """
         Displacement field around a point defect
@@ -347,8 +353,6 @@ def get_point_defect_displacement(
                 is isotropic, it will automatically be set to isotropic=True
             optimize (bool): cf. `optimize` in `numpy.einsum`
             check_unique (bool): Whether to check the unique positions
-            save_memory (bool): Whether to save memory by using a for loop
-                instead of `numpy.einsum`
 
         Returns:
             ((n,3)-array): Displacement field
@@ -361,7 +365,6 @@ def get_point_defect_displacement(
             isotropic=isotropic,
             optimize=optimize,
             check_unique=check_unique,
-            save_memory=save_memory,
         )
         return -np.einsum("...ijk,...jk->...i", g_tmp, dipole_tensor)
 
@@ -375,7 +378,6 @@ def get_point_defect_strain(
         isotropic: bool = False,
         optimize: bool = True,
         check_unique: bool = False,
-        save_memory: bool = False,
     ):
         """
         Strain field around a point defect using the Green's function method
@@ -389,8 +391,6 @@ def get_point_defect_strain(
                 is isotropic, it will automatically be set to isotropic=True
             optimize (bool): cf. `optimize` in `numpy.einsum`
             check_unique (bool): Whether to check the unique positions
-            save_memory (bool): Whether to save memory by using a for loop
-                instead of `numpy.einsum`
 
         Returns:
             ((n,3,3)-array): Strain field
@@ -403,7 +403,6 @@ def get_point_defect_strain(
             isotropic=isotropic,
             optimize=optimize,
             check_unique=check_unique,
-            save_memory=save_memory,
         )
         v = -np.einsum("...ijkl,...kl->...ij", g_tmp, dipole_tensor)
         return 0.5 * (v + np.einsum("...ij->...ji", v))

tests/unit/elasticity/test_green.py

@@ -59,14 +59,8 @@ def test_memory(self):
         aniso = Anisotropic(create_random_C())
         positions = np.tile(np.random.random(3), 2).reshape(-1, 3)
         G_normal = aniso.get_greens_function(positions)
-        G_save = aniso.get_greens_function(positions, save_memory=True)
-        self.assertTrue(np.all(np.isclose(G_normal, G_save)))
         G_unique = aniso.get_greens_function(positions, check_unique=True)
         self.assertTrue(np.all(np.isclose(G_normal, G_unique)))
-        G = aniso.get_greens_function(
-            positions, check_unique=True, save_memory=True
-        )
-        self.assertTrue(np.all(np.isclose(G_normal, G)))
 
 
 if __name__ == "__main__":