add json saving/loading to surrogate models, remove old surrogate models

examples/02_Multicomponent_Precipitation.ipynb

@@ -137,13 +137,13 @@
       "\tFCC_L12\t0.000e+00\t\t0.0000\t\t0.0000e+00\t2.4244e+02\n",
       "\n",
       "N\tTime (s)\tSim Time (s)\tTemperature (K)\tAL\tCR\t\n",
-      "5000\t1.3e+04\t\t17.2\t\t1073\t\t8.8453\t8.5602\t\n",
+      "5000\t1.3e+04\t\t13.2\t\t1073\t\t8.8453\t8.5602\t\n",
       "\n",
       "\tPhase\tPrec Density (#/m3)\tVolume Frac\tAvg Radius (m)\tDriving Force (J/mol)\n",
       "\tFCC_L12\t6.195e+20\t\t11.2806\t\t3.2974e-08\t9.0518e+00\n",
       "\n",
       "N\tTime (s)\tSim Time (s)\tTemperature (K)\tAL\tCR\t\n",
-      "7691\t1.0e+06\t\t25.7\t\t1073\t\t8.8171\t8.5673\t\n",
+      "7691\t1.0e+06\t\t20.3\t\t1073\t\t8.8171\t8.5673\t\n",
       "\n",
       "\tPhase\tPrec Density (#/m3)\tVolume Frac\tAvg Radius (m)\tDriving Force (J/mol)\n",
       "\tFCC_L12\t8.583e+18\t\t11.6262\t\t1.3883e-07\t2.1529e+00\n",
@@ -232,13 +232,13 @@
       "\tFCC_L12\t0.000e+00\t\t0.0000\t\t0.0000e+00\t2.4244e+02\n",
       "\n",
       "N\tTime (s)\tSim Time (s)\tTemperature (K)\tAL\tCR\t\n",
-      "5000\t1.3e+04\t\t14.1\t\t1073\t\t8.8606\t8.5200\t\n",
+      "5000\t1.3e+04\t\t13.3\t\t1073\t\t8.8606\t8.5200\t\n",
       "\n",
       "\tPhase\tPrec Density (#/m3)\tVolume Frac\tAvg Radius (m)\tDriving Force (J/mol)\n",
       "\tFCC_L12\t6.242e+20\t\t11.4616\t\t3.3070e-08\t9.0275e+00\n",
       "\n",
       "N\tTime (s)\tSim Time (s)\tTemperature (K)\tAL\tCR\t\n",
-      "7688\t1.0e+06\t\t21.5\t\t1073\t\t8.8335\t8.5244\t\n",
+      "7688\t1.0e+06\t\t20.5\t\t1073\t\t8.8335\t8.5244\t\n",
       "\n",
       "\tPhase\tPrec Density (#/m3)\tVolume Frac\tAvg Radius (m)\tDriving Force (J/mol)\n",
       "\tFCC_L12\t8.686e+18\t\t11.8159\t\t1.3905e-07\t2.1499e+00\n",

examples/10_Surrogates.ipynb

@@ -102,7 +102,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -124,7 +124,6 @@
     "precipitate = PrecipitateParameters('AL3ZR')\n",
     "precipitate.gamma = 0.1\n",
     "precipitate.volume.setVolume(1e-5, 'VM', 4)\n",
-    "precipitate.setup()\n",
     "R = np.linspace(1e-10, 5e-9, 100)\n",
     "G = precipitate.computeGibbsThomsonContribution(R)\n",
     "\n",
@@ -160,7 +159,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -208,7 +207,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -232,7 +231,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -285,7 +284,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -312,7 +311,6 @@
     "precipitate = PrecipitateParameters('FCC_L12')\n",
     "precipitate.gamma = 0.023\n",
     "precipitate.volume.setVolume(1e-5, 'VM', 4)\n",
-    "precipitate.setup()\n",
     "Rtest = np.linspace(1e-10, 3e-8, 300)\n",
     "Gtest = precipitate.computeGibbsThomsonContribution(Rtest)\n",
     "\n",
@@ -364,9 +362,9 @@
     "\n",
     "The surrogates can be saved and loaded for later usage. These will not retain the thermodynamic functions used for the training, so re-training of the surrogate cannot be done after saving/loading; however, the hyperparameters can still be changed.\n",
     "\n",
-    "$ Surrogate.save(filename) $\n",
+    "$ Surrogate.toJson(filename) $\n",
     "\n",
-    "$ surr = Surrogate.load(filename) $"
+    "$ Surrogate.fromJson(filename) $"
    ]
   },
   {

kawin/precipitation/KWNEuler.py

@@ -415,6 +415,7 @@ def _calcNucleationSites(self, t, x, p):
             nucleationSites += self.matrixParameters.nucleationSites.GBareaN0 - boundPrec * (AVOGADROS_NUMBER / self.matrixParameters.volume.Vm)**(2/3)
 
         elif isinstance(nucParams[p].description, GrainEdgeDescription):
+            # TODO: where did sqrt(1-GBk^2) come from?
             edgePrec = np.sum([np.sqrt(1 - nucParams[p2].GBk**2) * self.PBM[p2].FirstMomentFromN(x[p2]) for p2 in range(len(self.phases)) if isinstance(nucParams[p2].description, GrainEdgeDescription)])
             nucleationSites += self.matrixParameters.nucleationSites.GBedgeN0 - edgePrec * (AVOGADROS_NUMBER / self.matrixParameters.volume.Vm)**(1/3)
 

kawin/precipitation/parameters/ElasticFactors.py

@@ -595,7 +595,6 @@ def strainEnergyBohm(self, radius):
 
         V = 4*np.pi/3 * np.prod(radius)
         S = self.Sijmn(self.Dijkl(radius, cM4))
-        #invTerm = np.linalg.tensorinv(self._multiply(self._c4Prec - self._c4, S) + self._c4)
         invTerm = invert4rankTensor(self._multiply(cP4 - cM4, S) + cM4)
         multTerm = self._multiply(invTerm, cP4)
         stressC = self._multiply(cM4, self._multiply(self._multiply(S, multTerm), eigenstrain))
@@ -636,16 +635,28 @@ def __init__(self, shape='constant'):
         self._unrotated_cPrec_4th = np.zeros((3,3,3,3))
         self.rotation = np.eye(3)
         self.rotationPrec = np.eye(3)
-
+        
         #Cached values for calculating equilibrium aspect ratio
         self.ifmethod = 1
         self._aspectRatios = None
         self._normEnergies = None
         self._cachedRange = 5
         self._cachedIntervals = 100
 
+        self._description = ConstantEnergyDescription()
+        self._updateCallbacks = []
         self.setShape(shape)
 
+    @property
+    def description(self):
+        return self._description
+
+    @description.setter
+    def description(self, value):
+        self._description = value
+        for callback in self._updateCallbacks:
+            callback()
+
     @property
     def unrotated_cMatrix_4th(self):
         return self._unrotated_cMatrix_4th
@@ -891,6 +902,8 @@ def setAppliedStress(self, stress):
         Thus, this is only valid if the following conditions are met:
             The matrix phase has a single orientation (either as a single crystal or highly textured)
             Precipitates will form only in a single orientation with respect to the matrix
+        
+        NOTE: this is only available in EllipsoidalEnergyDescription.strainEnergyEllipsoidWithStress and is currently not used in the KWNModel
 
         TODO: It will be nice to expand on this.
             For polycrystalline matrix and randomly oriented precipitates, it should be possible

kawin/tests/test_surrogate.py

@@ -105,7 +105,6 @@ def test_Surr_binary_Diff_output():
     #Compare to Thermodynamics, high tolerance since we're just checking that functions are interchangeable
     assert_allclose(dnkj, dnkjT, atol=0, rtol=1e-1)
 
-@pytest.mark.skip(reason="Saving/loading not implemented in new surrogate yet")
 def test_Surr_binary_save():
     '''
     Checks that binary surrogate can be saved and loaded to get same values
@@ -119,42 +118,49 @@ def test_Surr_binary_save():
     gExtra = np.linspace(100, 1000, 5)
     surr.trainInterfacialComposition(T, gExtra)
 
-    surr.trainInterdiffusivity(xtrain, [T, T + 100])
+    surr.trainDiffusivity(xtrain, [T, T + 100])
 
     a, b = surr.getDrivingForce(0.004, T)
     c, d = surr.getInterfacialComposition(T, 500)
     e = surr.getInterdiffusivity(0.1, T + 50)
 
-    surr.save('kawin/tests/alzr')
+    surr.toJson('kawin/tests/alzr.json')
 
-    surr2 = BinarySurrogate.load('kawin/tests/alzr')
+    #surr2 = BinarySurrogate.load('kawin/tests/alzr')
+    surr2 = BinarySurrogate(AlZrTherm)
+    surr2.fromJson('kawin/tests/alzr.json')
     a2, b2 = surr2.getDrivingForce(0.004, T)
     c2, d2 = surr2.getInterfacialComposition(T, 500)
     e2 = surr2.getInterdiffusivity(0.1, T + 50)
 
-    os.remove('kawin/tests/alzr')
+    os.remove('kawin/tests/alzr.json')
 
+    # assert that models will generated from json data
+    assert 'FCC_A1' in surr2.diffusivityModels
+    assert 'AL3ZR' in surr2.drivingForceModels
     assert_allclose([a, b, c, d, e], [a2, b2, c2, d2, e2], rtol=1e-3)
 
-@pytest.mark.skip(reason="Saving/loading not implemented in new surrogate yet")
 def test_Surr_binary_save_missing():
     '''
     Checks that load function will not fail if one of the three surrogates are not trained yet
     '''
     surr = BinarySurrogate(AlZrTherm)
     T = 673.15
     xtrain = np.logspace(-5, -2, 5)
-    surr.trainDrivingForce(xtrain, T, scale='log')
+    surr.trainDrivingForce(xtrain, T, logX=True)
 
     a, b = surr.getDrivingForce(0.004, T)
 
-    surr.save('kawin/tests/alzr')
+    surr.toJson('kawin/tests/alzr.json')
 
-    surr2 = BinarySurrogate.load('kawin/tests/alzr')
+    #surr2 = BinarySurrogate.load('kawin/tests/alzr')
+    surr2 = BinarySurrogate(AlZrTherm)
+    surr2.fromJson('kawin/tests/alzr.json')
     a2, b2 = surr2.getDrivingForce(0.004, T)
 
-    os.remove('kawin/tests/alzr')
+    os.remove('kawin/tests/alzr.json')
 
+    assert 'AL3ZR' in surr2.drivingForceModels
     assert_allclose(a, a2, atol=0, rtol=1e-3)
 
 def test_Surr_ternary_DG_output():
@@ -220,7 +226,6 @@ def test_Surr_ternary_IC_output():
     assert_allclose(ca, caT, atol=0, rtol=1e-1)
     assert_allclose(cb, cbT, atol=0, rtol=1e-1)
 
-@pytest.mark.skip(reason="Saving/loading not implemented in new surrogate yet")
 def test_Surr_ternary_save():
     '''
     Checks that multicomponent surrogate can be saved and loaded
@@ -236,18 +241,21 @@ def test_Surr_ternary_save():
     g, ca, cb, _, _ = surr.getGrowthAndInterfacialComposition([0.08, 0.1], T[0]+25, 900, 1e-9, 1000)
     beta = surr.impingementFactor([0.08, 0.1], T[0]+25)
 
-    surr.save('kawin/tests/nicral')
+    surr.toJson('kawin/tests/nicral.json')
 
-    surr2 = MulticomponentSurrogate.load('kawin/tests/nicral')
+    #surr2 = MulticomponentSurrogate.load('kawin/tests/nicral')
+    surr2 = MulticomponentSurrogate(NiCrAlTherm)
+    surr2.fromJson('kawin/tests/nicral.json')
     a2, b2 = surr2.getDrivingForce([0.08, 0.1], T[0]+25)
     g2, ca2, cb2, _, _ = surr2.getGrowthAndInterfacialComposition([0.08, 0.1], T[0]+25, 900, 1e-9, 1000)
     beta2 = surr2.impingementFactor([0.08, 0.1], T[0]+25)
 
-    os.remove('kawin/tests/nicral')
+    os.remove('kawin/tests/nicral.json')
 
+    assert 'FCC_L12' in surr2.drivingForceModels
+    assert 'FCC_L12' in surr2.curvatureModels
     assert_allclose([a, b[0], b[1], g, ca[0], ca[1], cb[0], cb[1], beta], [a2, b2[0], b2[1], g2, ca2[0], ca2[1], cb2[0], cb2[1], beta2], atol=0, rtol=1e-3)
 
-@pytest.mark.skip(reason="Saving/loading not implemented in new surrogate yet")
 def test_Surr_ternary_save_missing():
     '''
     Checks that load function will not fail if one of the three surrogates are not trained yet
@@ -258,10 +266,13 @@ def test_Surr_ternary_save_missing():
     surr.trainDrivingForce(x, T)
 
     a, b = surr.getDrivingForce([0.08, 0.1], T[0]+25)
-    surr.save('kawin/tests/nicral')
+    surr.toJson('kawin/tests/nicral.json')
 
-    surr2 = MulticomponentSurrogate.load('kawin/tests/nicral')
+    #surr2 = MulticomponentSurrogate.load('kawin/tests/nicral')
+    surr2 = MulticomponentSurrogate(NiCrAlTherm)
+    surr2.fromJson('kawin/tests/nicral.json')
     a2, b2 = surr2.getDrivingForce([0.08, 0.1], T[0]+25)
-    os.remove('kawin/tests/nicral')
+    os.remove('kawin/tests/nicral.json')
 
+    assert 'FCC_L12' in surr2.drivingForceModels
     assert_allclose([a, b[0], b[1]], [a2, b2[0], b2[1]], atol=0, rtol=1e-3)