Update print statements

compass-stc · Jan 29, 2024 · 582aea1 · 582aea1
1 parent 0f74495
commit 582aea1
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Showing 3 changed files with 18 additions and 89 deletions.
diff --git a/StructuralGT/base.py b/StructuralGT/base.py
@@ -268,7 +268,6 @@ def gsd_to_G(gsd_name, sub=False, _2d=False, crop=None):
                            for i in list(range(min(positions.shape)))))
     canvas[tuple(list(positions.T))] = 1
     canvas = canvas.astype(int)
-    print('gsd_to_G canvas has shape ', canvas.shape)
 
     G = sknwEdits.build_sknw(canvas)
 
@@ -279,31 +278,16 @@ def gsd_to_G(gsd_name, sub=False, _2d=False, crop=None):
 
 #Function generates largest connected induced subgraph. Node and edge numbers are reset such that they are consecutive integers, starting from 0
 def sub_G(G): 
-    print('pre sub has ', G.vcount(), ' nodes')
+    print(f'Before removing smaller components, graph has {G.vcount()}  nodes')
     components = G.connected_components()
     G = components.giant() 
-    print('post sub has ', G.vcount(), ' nodes')
+    print(f'After removing smaller components, graph has {G.vcount()}  nodes')
 
    # G_sub  = G.subgraph(max(nx.connected_components(G), key=len).copy())
    # G = nx.relabel.convert_node_labels_to_integers(G_sub)
 
     return G
 
-#GT_Params_noGUI is a modified copy of the original SGT .py file, with the GUI modules removed    
-def write_averaged(gsd_name):
-    import GT_Params_noGUI
-
-    start = time.time()
-    G = gsd_to_G(gsd_name)
-    end = time.time()
-    G = sub_G(G)
-
-    start = time.time()
-    data = GT_Params_noGUI.run_GT_calcs(G,1,1,1,1,1,1,1,1,0,1,1,0)
-    end = time.time()
-    print('Ran GT_Params in', end-start, 'for a graph with ', G.vcount(), 'nodes')
-    datas = pd.DataFrame(data)
-    datas.to_csv(gsd_name + 'Averaged_indices.csv')
 
 def debubble(g, elements):
     if not isinstance(elements,list): raise error.StructuralElementError
@@ -333,7 +317,7 @@ def debubble(g, elements):
         s.particles.typeid = ['0']*s.particles.N
         f.append(s)
     end = time.time()
-    print('Ran debubble in ', end-start, 'for an image with shape ', g._skeleton_3d.shape)
+    print(f'Ran debubble in {end-start} for an image with shape {g._skeleton_3d.shape}')
 
     return g
 
@@ -361,7 +345,7 @@ def merge_nodes(g, disk_size):
         s.particles.typeid = ['0']*s.particles.N
         f.append(s)
     end = time.time()
-    print('Ran merge in ', end-start, 'for an image with shape ', g._skeleton_3d.shape)
+    print(f'Ran merge in {end-start} for an image with shape {g._skeleton_3d.shape}')
 
     return g
 
@@ -388,7 +372,7 @@ def prune(g, size):
         s.particles.typeid = ['0']*s.particles.N
         f.append(s)
     end = time.time()
-    print('Ran prune in ', end-start, 'for an image with shape ', g._skeleton_3d.shape)
+    print(f'Ran prune in {end-start} for an image with shape {g._skeleton_3d.shape}')
 
     return g
 
@@ -415,29 +399,10 @@ def remove_objects(g, size):
         s.particles.typeid = ['0']*s.particles.N
         f.append(s)
     end = time.time()
-    print('Ran remove objects in ', end-start, 'for an image with shape ', g._skeleton_3d.shape)
+    print(f'Ran remove objects in {end-start} for an image with shape {g._skeleton_3d.shape})
 
     return g
 
-def igraph_ANC(directory, I):
-    start = time.time()
-    vclist = []
-
-    for node_i in I.vs:
-        for node_j in I.vs:
-            if node_i.index == node_j.index: continue
-            if I.are_connected(node_i, node_j): continue
-            cut = I.vertex_connectivity(source=node_i.index, target = node_j.index)
-            vclist.append(cut)
-
-    ANC = np.mean(np.asarray(vclist))
-    end = time.time()
-    np.savetxt(directory+'/ANC.csv',ANC)
-    print('ANC calculated as ', ANC, ' in ', end-start) 
-
-    return ANC
-
-
 def add_weights(g, weight_type=None, R_j=0, rho_dim=1):
     if not isinstance(weight_type,list) and weight_type is not None: raise TypeError('weight_type must be list, even if single element')
     for _type in weight_type:
@@ -451,40 +416,6 @@ def add_weights(g, weight_type=None, R_j=0, rho_dim=1):
 
     return g.Gr
 
-
-def gyration_moments_3(G, sampling=1, weighted=True):
-    Ax=0
-    Ay=0
-    Axy=0
-    node_count = np.asarray(list(range(G.vcount())))
-    mask = np.random.rand(G.vcount()) > (1-sampling)
-    trimmed_node_count = node_count[mask]
-    for i in trimmed_node_count:
-        for j in trimmed_node_count:
-            if i >= j:    #Symetric matrix
-                continue
-
-            if weighted:
-                path = G.get_shortest_paths(i,to=j, weights='Resistance')
-            else:
-                path = G.get_shortest_paths(i,to=j)
-            Ax_term  = 0
-            Ay_term  = 0
-            Axy_term = 0
-            for hop_s,hop_t in zip(path[0][0:-1],path[0][1::]):
-                if weighted:
-                    weight = G.es[G.get_eid(hop_s,hop_t)]['Conductance']
-                else:
-                    weight = 1
-                Ax_term  = Ax_term  + weight*(((G.vs[hop_s]['o'][0]).astype(float) - (G.vs[hop_t]['o'][0]).astype(float))**2)
-                Ay_term  = Ay_term  + weight*(((G.vs[hop_s]['o'][1]).astype(float) - (G.vs[hop_t]['o'][1]).astype(float))**2)
-                Axy_term = Axy_term + weight*(((G.vs[hop_s]['o'][1]).astype(float) - (G.vs[hop_t]['o'][1].astype(float))) * ((G.vs[hop_s]['o'][0]).astype(float) - (G.vs[hop_t]['o'][0]).astype(float)))
-            Ax  = Ax  + (Ax_term)
-            Ay  = Ay  + (Ay_term)
-            Axy = Axy + (Axy_term)
-            A = np.array([[Ax,Axy,0],[Axy,Ay,0],[0,0,0]])/(len(trimmed_node_count)**2)
-    return A
-
 def quadrupletise(i):
     if len(str(i))==4: return str(i)
     elif len(str(i))==3: return '0' + str(i)

diff --git a/StructuralGT/electronic.py b/StructuralGT/electronic.py
@@ -37,7 +37,7 @@ def compute(self, network, R_j, axis, boundary_conditions, source=-1,
         sink_id = source_id + 1
         network.graph_connected.add_vertices(2)
 
-        print("Graph has max ", network.shape)
+        print("Graph has shape ", network.shape)
         axes = np.array([0, 1, 2])[0 : network.dim]
         indices = axes[axes != axis]
         axis_centre1 = np.zeros(network.dim, dtype=int)
@@ -49,15 +49,11 @@ def compute(self, network, R_j, axis, boundary_conditions, source=-1,
         axis_centre2[axis] = network.shape[axis]
         source_coord = axis_centre1 - delta
         sink_coord = axis_centre2 + delta
-        print("source coord is ", source_coord)
-        print("sink coord is ", sink_coord)
+        print("Source coordinate is ", source_coord)
+        print("Sink coordinate is ", sink_coord)
         network.graph_connected.vs[source_id]["o"] = source_coord
         network.graph_connected.vs[sink_id]["o"] = sink_coord
 
-        print(
-            "Before connecting external nodes, G has vcount ",
-            network.graph_connected.vcount(),
-        )
         for node in network.graph_connected.vs:
             if node["o"][axis] >= boundary1[0] and node["o"][axis] <= boundary1[1]:
                 network.graph_connected.add_edges([(node.index, source_id)])
@@ -77,10 +73,6 @@ def compute(self, network, R_j, axis, boundary_conditions, source=-1,
                 ] = base.connector(sink_coord, node["o"])
 
         # Write skeleton connected to external node
-        print(network.graph_connected.is_connected(), " connected")
-        print(
-            "After connecting external nodes, G has vcount ", network.graph_connected.vcount()
-        )
         connected_name = (
             os.path.split(network.gsd_name)[0]
             + "/connected_"

diff --git a/StructuralGT/networks.py b/StructuralGT/networks.py
@@ -351,11 +351,11 @@ def img_to_skel(
 
         end = time.time()
         print(
-            "Ran stack_to_gsd() in ",
+            "Ran img_to_skel() in ",
             end - start,
-            "for gsd with ",
+            "for skeleton with ",
             len(positions),
-            "particles",
+            "voxels",
         )
 
         if debubble is not None:
@@ -593,6 +593,12 @@ def graph(self):
         skeleton"""
         return self.Gr
 
+    @property
+    def image(self):
+        """:class:`np.ndarray`: The original image used to obtain the graph."""
+        if self._2d: return self.image_stack[0][0]
+        else: return self.image_stack[:][0]
+
 def from_gsd(_dir, filename, frame=0, depth=None):
     """
     Function returns a Network object stored in a given .gsd file