Add example

Example/AgNWN/1_175_6/1_175_6_Results.csv

@@ -0,0 +1,37 @@
+,Theta,O_eff
+0,0.0,132.57072688353588
+1,10.0,134.9301966929933
+2,20.0,125.95388312874962
+3,30.0,105.9930473677451
+4,40.0,81.87194619754801
+5,50.0,60.31469076515667
+6,60.0,46.32175945873722
+7,70.0,38.9090421640372
+8,80.0,34.10182433856702
+9,90.0,32.016375613444
+10,100.0,32.68944594570813
+11,110.0,34.537476926411436
+12,120.0,40.500038745055484
+13,130.0,53.71422627746765
+14,140.0,69.99170431620517
+15,150.0,90.89873768877797
+16,160.0,108.19992220950888
+17,170.0,122.15791623707804
+18,180.0,132.67697629043923
+19,190.0,133.92895577409672
+20,200.0,125.47752302002705
+21,210.0,105.90636039743487
+22,220.0,81.75009236941338
+23,230.0,60.63752207855191
+24,240.0,46.205582224026045
+25,250.0,38.95952409951256
+26,260.0,33.986665846403525
+27,270.0,32.00350478163239
+28,280.0,32.531519736571695
+29,290.0,34.79470123200407
+30,300.0,40.35336259431624
+31,310.0,53.79040660719977
+32,320.0,69.54383946079517
+33,330.0,90.47407922315519
+34,340.0,108.5561376967024
+35,350.0,121.46018755685213

Example/AgNWN/1_175_6/img_options.json

@@ -0,0 +1 @@
+{"Thresh_method":1, "gamma": 0.8, "md_filter": 1, "g_blur":1, "autolvl": 0,"fg_color":0, "laplacian": 0, "scharr": 0, "sobel":0 , "lowpass": 0, "asize": 23, "bsize":31, "wsize":21, "thresh": 100}

Example/AgNWN/1_175_6/params.json

@@ -0,0 +1,6 @@
+{
+    "pix_per_m":1.37E8,
+    "crop":1765,
+    "O_x": 38,
+    "O_y": 144
+}

Example/config/freud.ini

@@ -0,0 +1,40 @@
+
+[LAYOUT]
+# Width of sidebar that contains server names
+server_width = 15
+# Height of container that holds headers
+header_height = 10
+# Height of container that shows server summary
+summary_height = 10
+
+[KEYS]
+new_server = n
+edit_server = e
+send_request = r
+edit_authentication = a
+edit_headers = h
+edit_body = b
+delete_server = d
+open_response_body = o
+sort_servers = s
+key_quick_ref = c-f
+
+[DB]
+filename = requests.db
+
+[JSON]
+indentation = 2
+
+[SORT_BY]
+# Column options: name, timestamp, url, method, body, authtype, authuser,
+# authpass, headers
+# Order options: asc, desc
+column = timestamp
+order = asc
+
+[STYLE]
+# More styles here:
+# https://bitbucket.org/birkenfeld/pygments-main/src/stable/pygments/styles
+theme = default
+separator_line_fg = gray
+separator_line_bg = black

StructuralGT/base.py

@@ -308,24 +308,24 @@ def debubble(g, elements):
         canvas = skeletonize_3d(canvas)/255
         canvas = binary_closing(canvas, footprint=elem)
 
-    g.skeleton = skeletonize_3d(canvas)/255
+    g._skeleton = skeletonize_3d(canvas)/255
 
     if g._2d:
-        g.skeleton_3d = np.swapaxes(np.array([g.skeleton]), 2, 1)
-        g.skeleton_3d = np.asarray([g.skeleton])
+        g._skeleton_3d = np.swapaxes(np.array([g.skeleton]), 2, 1)
+        g._skeleton_3d = np.asarray([g.skeleton])
     else:
-        g.skeleton_3d = np.asarray(g.skeleton)
+        g._skeleton_3d = np.asarray(g.skeleton)
 
-    positions = np.asarray(np.where(g.skeleton_3d!=0)).T
+    positions = np.asarray(np.where(g._skeleton_3d!=0)).T
     with gsd.hoomd.open(name=g.gsd_name, mode='w') as f:
         s = gsd.hoomd.Frame()
-        s.particles.N = int(sum(g.skeleton_3d.ravel()))
+        s.particles.N = int(sum(g._skeleton_3d.ravel()))
         s.particles.position = positions 
         s.particles.types = ['A']
         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('Ran debubble in ', end-start, 'for an image with shape ', g._skeleton_3d.shape)
 
     return g
 
@@ -335,25 +335,25 @@ def merge_nodes(g, disk_size):
     start = time.time()
     g.gsd_name = g.gsd_dir + '/merged_' + os.path.split(g.gsd_name)[1]
 
-    canvas = g.skeleton
-    g.skeleton = skel_ID.merge_nodes(canvas, disk_size)
+    canvas = g._skeleton
+    g._skeleton = skel_ID.merge_nodes(canvas, disk_size)
 
     if g._2d:
-        g.skeleton_3d = np.swapaxes(np.array([g.skeleton]), 2, 1)
-        g.skeleton_3d = np.asarray([g.skeleton])
+        g._skeleton_3d = np.swapaxes(np.array([g.skeleton]), 2, 1)
+        g._skeleton_3d = np.asarray([g.skeleton])
     else:
-        g.skeleton_3d = np.asarray(g.skeleton)
+        g._skeleton_3d = np.asarray(g.skeleton)
 
-    positions = np.asarray(np.where(g.skeleton_3d!=0)).T
+    positions = np.asarray(np.where(g._skeleton_3d!=0)).T
     with gsd.hoomd.open(name=g.gsd_name, mode='w') as f:
         s = gsd.hoomd.Frame()
-        s.particles.N = int(sum(g.skeleton_3d.ravel()))
+        s.particles.N = int(sum(g._skeleton_3d.ravel()))
         s.particles.position = positions 
         s.particles.types = ['A']
         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('Ran merge in ', end-start, 'for an image with shape ', g._skeleton_3d.shape)
 
     return g
 
@@ -362,25 +362,25 @@ def prune(g, size):
     start = time.time()
     g.gsd_name = g.gsd_dir + '/pruned_' + os.path.split(g.gsd_name)[1]
 
-    canvas = g.skeleton
-    g.skeleton = skel_ID.pruning(canvas, size)
+    canvas = g._skeleton
+    g._skeleton = skel_ID.pruning(canvas, size)
 
     if g._2d:
-        g.skeleton_3d = np.swapaxes(np.array([g.skeleton]), 2, 1)
-        g.skeleton_3d = np.asarray([g.skeleton])
+        g._skeleton_3d = np.swapaxes(np.array([g.skeleton]), 2, 1)
+        g._skeleton_3d = np.asarray([g.skeleton])
     else:
-        g.skeleton_3d = np.asarray(g.skeleton)
+        g._skeleton_3d = np.asarray(g.skeleton)
 
-    positions = np.asarray(np.where(g.skeleton_3d!=0)).T
+    positions = np.asarray(np.where(g._skeleton_3d!=0)).T
     with gsd.hoomd.open(name=g.gsd_name, mode='w') as f:
         s = gsd.hoomd.Frame()
-        s.particles.N = int(sum(g.skeleton_3d.ravel()))
+        s.particles.N = int(sum(g._skeleton_3d.ravel()))
         s.particles.position = positions 
         s.particles.types = ['A']
         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('Ran prune in ', end-start, 'for an image with shape ', g._skeleton_3d.shape)
 
     return g
 
@@ -389,27 +389,27 @@ def remove_objects(g, size):
     start = time.time()
     g.gsd_name = g.gsd_dir + '/cleaned_' + os.path.split(g.gsd_name)[1]
 
-    canvas = g.skeleton
-    g.skeleton = remove_small_objects(canvas, size, connectivity=2)
+    canvas = g._skeleton
+    g._skeleton = remove_small_objects(canvas, size, connectivity=2)
 
     if g._2d:
-        g.skeleton_3d = np.swapaxes(np.array([g.skeleton]), 2, 1)
-        g.skeleton_3d = np.asarray([g.skeleton])
+        g._skeleton_3d = np.swapaxes(np.array([g.skeleton]), 2, 1)
+        g._skeleton_3d = np.asarray([g.skeleton])
     else:
-        g.skeleton_3d = np.asarray(g.skeleton)
+        g._skeleton_3d = np.asarray(g.skeleton)
 
-    positions = np.asarray(np.where(g.skeleton_3d!=0)).T
+    positions = np.asarray(np.where(g._skeleton_3d!=0)).T
     with gsd.hoomd.open(name=g.gsd_name, mode='w') as f:
         s = gsd.hoomd.Frame()
-        s.particles.N = int(sum(g.skeleton_3d.ravel()))
+        s.particles.N = int(sum(g._skeleton_3d.ravel()))
         s.particles.position = positions 
         s.particles.types = ['A']
         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('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()

StructuralGT/interaction_networks.py

@@ -1,12 +1,12 @@
-from StructuralGT import util, base
+from StructuralGT import util, base, networks
 import numpy as np
 import freud
 import igraph as ig
 import os
 from skimage.measure import regionprops, label
 import gsd.hoomd
 
-class InteractionNetwork(util.Network):
+class InteractionNetwork(networks.Network):
     """Class for extracting bond networks from particle images. In this class,
     nodes are particles, and edges connect neighbouring particles.
     Neighbours are found using freudCITE.

StructuralGT/networks.py

@@ -9,19 +9,19 @@
 import gsd.hoomd
 import warnings
 
-from util import *
+from StructuralGT.util import _image_stack, _cropper, _domain, _fname
 
 class Network:
     """Generic class to represent networked image data. Should not be
     instantiated by the user.
 
-    Children of this class will hold igraph :class:`Graph` attributes and
+    Subclasses will hold :class:`graph` attributes 
     holds additional attributes and methods for supporting geometric features
     associated images, dimensionality etc.
 
     Args:
         directory (str):
-            The (absolute or relative) pathname for the stack of images to be
+            The (absolute or relative) pathname for the image(s) to be
             analysed. Where 2D analysis is concerned, the directory should
             contain a single image.
         child_dir (str):
@@ -94,7 +94,7 @@ def binarize(self, options_dict=None):
     def set_img_bin(self, crop):
         """Sets the :attr:`img_bin` and :attr:`img_bin_3d` attributes, which
         are numpy arrays of pixels and voxels which represent the binarized 
-        image. Called internally by child classes of :class:`Network`.
+        image. Called internally by subclasses of :class:`Network`.
 
         Args:
             crop (list):