Repository: PMEAL/OpenPNM
Pull request #1833 (2021-02-02 05:13:03)

Fixing the is_fully_connected check in alg.run


Patch:
diff --git a/examples/paper_recreations/Tranter et al. (2017)/Tranter et al. (2017) - Part A.ipynb b/examples/paper_recreations/Tranter et al. (2017)/Tranter et al. (2017) - Part A.ipynb
index 31f5d9825..09a76866a 100644
--- a/examples/paper_recreations/Tranter et al. (2017)/Tranter et al. (2017) - Part A.ipynb
+++ b/examples/paper_recreations/Tranter et al. (2017)/Tranter et al. (2017) - Part A.ipynb
@@ -151,7 +151,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -374,7 +374,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -450,20 +450,20 @@
      "output_type": "stream",
      "text": [
       "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
-      "OpenPNM Version 2.6.1 Workspace\n",
+      "OpenPNM Version 2.5.1 Workspace\n",
       "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
       " proj_01\n",
       "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
       " Object Name     Object ID                                                        \n",
       "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n",
-      " net_01          <openpnm.network.GenericNetwork object at 0x1d736f66860>         \n",
-      " geo_01          <openpnm.geometry.Imported object at 0x1d7393dc270>              \n",
-      " air             <openpnm.phases.Air object at 0x1d7394757c0>                     \n",
-      " water           <openpnm.phases.Water object at 0x1d7393ebcc0>                   \n",
-      " phys_air        <openpnm.physics.Standard object at 0x1d7394b2860>               \n",
-      " phys_water      <openpnm.physics.Standard object at 0x1d739458680>               \n",
-      " injection       <openpnm.algorithms.MixedInvasionPercolation object at 0x1d73a203900>\n",
-      " withdrawal      <openpnm.algorithms.MixedInvasionPercolationCoop object at 0x1d739d0eae0>\n",
+      " net_01          <openpnm.network.GenericNetwork object at 0x7f198ee25ea0>        \n",
+      " geo_01          <openpnm.geometry.Imported object at 0x7f198ef73680>             \n",
+      " air             <openpnm.phases.Air object at 0x7f198ed8b630>                    \n",
+      " water           <openpnm.phases.Water object at 0x7f198ed9a630>                  \n",
+      " phys_air        <openpnm.physics.Standard object at 0x7f198ed35b30>              \n",
+      " phys_water      <openpnm.physics.Standard object at 0x7f198ed18ef0>              \n",
+      " injection       <openpnm.algorithms.MixedInvasionPercolation object at 0x7f198e014590>\n",
+      " withdrawal      <openpnm.algorithms.MixedInvasionPercolationCoop object at 0x7f1991f0b9a0>\n",
       "――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――――\n"
      ]
     }
@@ -486,7 +486,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "ws.settings['loglevel'] = 60  # Disable critical warnings about non-serializable attrs.\n",
     "ws.save_project(prj, 'hysteresis_paper_project')"
    ]
   }
@@ -507,7 +506,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.8.3"
   }
  },
  "nbformat": 4,
diff --git a/openpnm/algorithms/GenericTransport.py b/openpnm/algorithms/GenericTransport.py
index 16386b741..bc8fda587 100644
--- a/openpnm/algorithms/GenericTransport.py
+++ b/openpnm/algorithms/GenericTransport.py
@@ -6,7 +6,7 @@ from numpy.linalg import norm
 import scipy.sparse.csgraph as spgr
 from scipy.spatial import ConvexHull
 from scipy.spatial import cKDTree
-from openpnm.topotools import iscoplanar
+from openpnm.topotools import iscoplanar, is_fully_connected
 from openpnm.algorithms import GenericAlgorithm
 from openpnm.utils import logging, Docorator, GenericSettings
 # Uncomment this line when we stop supporting Python 3.6
@@ -776,7 +776,8 @@ class GenericTransport(GenericAlgorithm):
         physics = prj.physics().values()
 
         # Validate network topology health
-        if not prj.network._is_fully_connected():
+        Ps = (self['pore.bc_rate'] > 0) + (self['pore.bc_value'] > 0)
+        if not is_fully_connected(network=self.network, pores_BC=Ps):
             msg = (
                 "Your network is clustered. Run h = net.check_network_health()"
                 " followed by op.topotools.trim(net, pores=h['trim_pores'])"
diff --git a/openpnm/io/PNM.py b/openpnm/io/PNM.py
index 6eb0cdc9f..6ffe4dd79 100644
--- a/openpnm/io/PNM.py
+++ b/openpnm/io/PNM.py
@@ -37,9 +37,9 @@ class PNM(GenericIO):
                 foreign_attrs = found_attrs.difference(known_attrs)
                 if len(foreign_attrs) > 0:
                     line_break = f"\n{'':13}"
-                    logger.critical(f"{obj.name} has the following attributes that will"
-                                    + f" not be saved: {[i for i in foreign_attrs]}"
-                                    + f"{line_break}Consider using Pickle instead")
+                    logger.warning(f"{obj.name} has the following attributes that will"
+                                   + f" not be saved: {[i for i in foreign_attrs]}"
+                                   + f"{line_break}Consider using Pickle instead")
                 item = root.create_group(obj.name)
                 for arr in obj.keys():  # Store data
                     try:
diff --git a/openpnm/network/GenericNetwork.py b/openpnm/network/GenericNetwork.py
index 3e56ff3d0..a6d4d1af5 100644
--- a/openpnm/network/GenericNetwork.py
+++ b/openpnm/network/GenericNetwork.py
@@ -919,13 +919,3 @@ class GenericNetwork(Base, ModelsMixin):
         health = self.project.check_network_health()
 
         return health
-
-    def _is_fully_connected(self):
-        r"""
-        Checks whether network is fully connected, i.e. not clustered.
-        """
-        import scipy.sparse.csgraph as csgraph
-
-        am = self.get_adjacency_matrix(fmt='coo')
-        temp = csgraph.connected_components(am, directed=False)[1]
-        return False if np.unique(temp).size > 1 else True
diff --git a/openpnm/topotools/__init__.py b/openpnm/topotools/__init__.py
index 85d8c248c..2eaee1ca3 100644
--- a/openpnm/topotools/__init__.py
+++ b/openpnm/topotools/__init__.py
@@ -21,6 +21,7 @@ from .topotools import find_pore_to_pore_distance
 from .topotools import generate_base_points
 from .topotools import iscoplanar
 from .topotools import isoutside
+from .topotools import is_fully_connected
 from .topotools import label_faces
 from .topotools import merge_networks
 from .topotools import merge_pores
diff --git a/openpnm/topotools/topotools.py b/openpnm/topotools/topotools.py
index f57c95320..caf9561f9 100644
--- a/openpnm/topotools/topotools.py
+++ b/openpnm/topotools/topotools.py
@@ -1756,3 +1756,36 @@ def iscoplanar(coords):
     n_dot = np.dot(n, r)
 
     return bool(np.sum(np.absolute(n_dot)) == 0)
+
+
+def is_fully_connected(network, pores_BC=None):
+    r"""
+    Checks whether network is fully connected, i.e. not clustered.
+
+    Parameters
+    ----------
+    network : GenericNetwork
+        The network whose connectivity to check.
+    pores_BC : array_like (optional)
+        The pore indices of boundary conditions (inlets/outlets).
+
+    Returns
+    -------
+    bool
+        If ``pores_BC`` is not specified, then returns ``True`` only if
+        the entire network is connected to the same cluster. If
+        ``pores_BC`` is given, then returns ``True`` only if all clusters
+        are connected to the given boundary condition pores.
+    """
+    am = network.get_adjacency_matrix(fmt='lil').copy()
+    temp = csgraph.connected_components(am, directed=False)[1]
+    is_connected = np.unique(temp).size == 1
+    # Ensure all clusters are part of pores, if given
+    if not is_connected and pores_BC is not None:
+        am.resize(network.Np + 1, network.Np + 1)
+        pores_BC = network._parse_indices(pores_BC)
+        am.rows[-1] = pores_BC.tolist()
+        am.data[-1] = np.arange(network.Nt, network.Nt + len(pores_BC)).tolist()
+        temp = csgraph.connected_components(am, directed=False)[1]
+        is_connected = np.unique(temp).size == 1
+    return is_connected


Test patch:
diff --git a/tests/unit/network/GenericNetworkTest.py b/tests/unit/network/GenericNetworkTest.py
index d7726c8ee..92f02d64f 100644
--- a/tests/unit/network/GenericNetworkTest.py
+++ b/tests/unit/network/GenericNetworkTest.py
@@ -171,16 +171,6 @@ class GenericNetworkTest:
         assert np.size(a) == 17
         assert np.all(np.in1d([0, 1], a))
 
-    def test_is_fully_connected(self):
-        assert self.net._is_fully_connected()
-        # Disconnect pore 0 from the network
-        Ps = self.net.find_neighbor_pores(pores=0)
-        Ts = self.net.find_neighbor_throats(pores=0)
-        op.topotools.trim(self.net, throats=Ts)
-        assert not self.net._is_fully_connected()
-        # Reconnect pore 0 to the network
-        op.topotools.connect_pores(self.net, pores1=0, pores2=Ps)
-
 
 if __name__ == '__main__':
 
diff --git a/tests/unit/topotools/TopotoolsTest.py b/tests/unit/topotools/TopotoolsTest.py
index 7eff8c981..0bc033799 100644
--- a/tests/unit/topotools/TopotoolsTest.py
+++ b/tests/unit/topotools/TopotoolsTest.py
@@ -449,6 +449,33 @@ class TopotoolsTest:
         Ps = op.topotools.filter_pores_by_z(network=pn, pores=pn.Ps, z=4)
         assert_allclose(Ps, [5, 6, 9, 10])
 
+    def test_is_fully_connected(self):
+        pn = op.network.Cubic(shape=[4, 4, 1])
+        op.topotools.trim(network=pn, pores=[1, 5, 9, 13])
+        assert op.topotools.is_fully_connected(pn) is False
+        assert op.topotools.is_fully_connected(pn, pores_BC=[0, 1, 9, 10]) is True
+        assert op.topotools.is_fully_connected(pn, pores_BC=[0, 10]) is True
+        assert op.topotools.is_fully_connected(pn, pores_BC=[1, 10]) is False
+        assert op.topotools.is_fully_connected(pn, pores_BC=[0, 9]) is False
+        assert op.topotools.is_fully_connected(pn, pores_BC=[0]) is False
+        assert op.topotools.is_fully_connected(pn, pores_BC=[]) is False
+
+    def test_is_fully_connected_with_alg(self):
+        pn = op.network.Cubic(shape=[4, 4, 1])
+        Ts = pn.find_neighbor_throats(5)
+        op.topotools.trim(network=pn, throats=Ts)
+        assert op.topotools.is_fully_connected(pn) is False
+        phase = op.phases.GenericPhase(network=pn)
+        phase['throat.diffusive_conductance'] = 1.0
+        alg = op.algorithms.FickianDiffusion(network=pn, phase=phase)
+        alg.set_value_BC(pores=[0, 1, 2, 3], values=1.0)
+        alg.set_value_BC(pores=[12, 13, 14, 15], values=0.5)
+        with pytest.raises(Exception):
+            alg.run()
+        alg.set_value_BC(pores=5, values=1.0)
+        alg.run()
+        assert np.ptp(alg['pore.concentration']) == 0.5
+
 
 if __name__ == '__main__':