chore: black formatting

Author: Lachlan Grose

LoopStructural/modelling/intrusions/__init__.py

@@ -1,4 +1,4 @@
 from .intrusion_frame import IntrusionBuilder
 from .intrusion_network import IntrusionNetwork
 from .intrusion_body import IntrusionBody
-from .intrusion_feature import IntrusionFeature
+from .intrusion_feature import IntrusionFeature

LoopStructural/modelling/intrusions/geom_conceptual_models.py

@@ -2,113 +2,140 @@
 import numpy as np
 import pandas as pd
 
-def ellipse_function(lateral_contact_data, test = 0, minP=None, maxP=None, minS=None, maxS=None):
+
+def ellipse_function(
+    lateral_contact_data, test=0, minP=None, maxP=None, minS=None, maxS=None
+):
     import math
-    if minP==None: 
-        minP = lateral_contact_data['coord1'].min()
-    if maxP==None: 
-        maxP = lateral_contact_data['coord1'].max()
-    if minS==None: 
-        minS = lateral_contact_data['coord2'].abs().min()
-    if maxS==None: 
-        maxS = lateral_contact_data['coord2'].max()
-            
-    a = (maxP-minP)/2
-    b = (maxS-minS)/10
-#     b = abs(minS)
-
-    po = minP + (maxP-minP)/2
-    
-    p_locations = lateral_contact_data.loc[:,'coord1'].copy().to_numpy()
-#     s_values = lateral_contact_data.loc[:,'coord2'].copy().to_numpy()
-    
-    s = np.zeros([len(p_locations),2])
-    
+
+    if minP == None:
+        minP = lateral_contact_data["coord1"].min()
+    if maxP == None:
+        maxP = lateral_contact_data["coord1"].max()
+    if minS == None:
+        minS = lateral_contact_data["coord2"].abs().min()
+    if maxS == None:
+        maxS = lateral_contact_data["coord2"].max()
+
+    a = (maxP - minP) / 2
+    b = (maxS - minS) / 10
+    #     b = abs(minS)
+
+    po = minP + (maxP - minP) / 2
+
+    p_locations = lateral_contact_data.loc[:, "coord1"].copy().to_numpy()
+    #     s_values = lateral_contact_data.loc[:,'coord2'].copy().to_numpy()
+
+    s = np.zeros([len(p_locations), 2])
+
     for i in range(len(p_locations)):
         if minP < p_locations[i] < maxP:
-            s[i,0] = b*math.sqrt(1-pow((p_locations[i] - po)/a , 2)) # max side
-            s[i,1] = -b*math.sqrt(1-pow((p_locations[i] - po)/a , 2)) # min side
-#         elif test == 1:
-#             s[i,:] = lateral_contact_data.loc[i,'coord2']
+            s[i, 0] = b * math.sqrt(1 - pow((p_locations[i] - po) / a, 2))  # max side
+            s[i, 1] = -b * math.sqrt(1 - pow((p_locations[i] - po) / a, 2))  # min side
+        #         elif test == 1:
+        #             s[i,:] = lateral_contact_data.loc[i,'coord2']
         else:
-            s[i,:] = 0
+            s[i, :] = 0
 
     return s
 
-def rectangle_function(lateral_contact_data, minP=None, maxP=None, minS=None, maxS=None):
+
+def rectangle_function(
+    lateral_contact_data, minP=None, maxP=None, minS=None, maxS=None
+):
     import math
-    if minP==None: 
-        minP = lateral_contact_data['coord1'].min()
-    if maxP==None: 
-        maxP = lateral_contact_data['coord1'].max()
-    if minS==None: 
-        minS = lateral_contact_data['coord2'].min()
-    if maxS==None: 
-        maxS = lateral_contact_data['coord2'].max()
-            
-    p_locations = lateral_contact_data.loc[:,'coord1'].copy().to_numpy()
-    s = np.zeros([len(p_locations),2])
-    
+
+    if minP == None:
+        minP = lateral_contact_data["coord1"].min()
+    if maxP == None:
+        maxP = lateral_contact_data["coord1"].max()
+    if minS == None:
+        minS = lateral_contact_data["coord2"].min()
+    if maxS == None:
+        maxS = lateral_contact_data["coord2"].max()
+
+    p_locations = lateral_contact_data.loc[:, "coord1"].copy().to_numpy()
+    s = np.zeros([len(p_locations), 2])
+
     for i in range(len(p_locations)):
         if minP < p_locations[i] < maxP:
-            s[i,0] = maxS # max side
-            s[i,1] = minS # min side
+            s[i, 0] = maxS  # max side
+            s[i, 1] = minS  # min side
         else:
-            s[i,:] = 0
+            s[i, :] = 0
 
     return s
 
-def parallelepiped_function(othercontact_data, mean_growth=None, minP=None, maxP=None, minS=None, maxS=None, vertex=None):
-    
+
+def parallelepiped_function(
+    othercontact_data,
+    mean_growth=None,
+    minP=None,
+    maxP=None,
+    minS=None,
+    maxS=None,
+    vertex=None,
+):
+
     if mean_growth == None:
-        mean_growth = othercontact_data.loc[:,'coord1'].mean()
-        
-    data_ps = np.array([othercontact_data.loc[:,'coord1'], othercontact_data.loc[:,'coord2']]).T
-    
-    conceptual_growth = np.ones([len(data_ps),2]) * mean_growth
-    
+        mean_growth = othercontact_data.loc[:, "coord1"].mean()
+
+    data_ps = np.array(
+        [othercontact_data.loc[:, "coord1"], othercontact_data.loc[:, "coord2"]]
+    ).T
+
+    conceptual_growth = np.ones([len(data_ps), 2]) * mean_growth
+
     return conceptual_growth
 
 
-def obliquecone_function(othercontact_data, mean_growth=None, minP=None, maxP=None, minS=None, maxS=None, vertex=None): 
+def obliquecone_function(
+    othercontact_data,
+    mean_growth=None,
+    minP=None,
+    maxP=None,
+    minS=None,
+    maxS=None,
+    vertex=None,
+):
     import math
-    
-    ps_locations = othercontact_data.loc[:,['coord1','coord2']].to_numpy()
-    
-    a = (maxP-minP)/2 #semi-major axis
-    b = (maxS-minS)/2 #semi-minor axis
-    a2 = pow(a,2)
-    b2 = pow(b,2)
-    
-    po = minP + a #p coordinate of ellipsis centre
-    so = minS + b #s coordinate of ellipsis centre
-    
-    alpha = vertex[0] #p coordinate of vertex
-    beta = vertex[1] #g coordinate of vertex
-    gamma = vertex[2] #s coordinate of vertex
-    
-    growth = np.zeros([len(ps_locations),2]) #container for results
-    
+
+    ps_locations = othercontact_data.loc[:, ["coord1", "coord2"]].to_numpy()
+
+    a = (maxP - minP) / 2  # semi-major axis
+    b = (maxS - minS) / 2  # semi-minor axis
+    a2 = pow(a, 2)
+    b2 = pow(b, 2)
+
+    po = minP + a  # p coordinate of ellipsis centre
+    so = minS + b  # s coordinate of ellipsis centre
+
+    alpha = vertex[0]  # p coordinate of vertex
+    beta = vertex[1]  # g coordinate of vertex
+    gamma = vertex[2]  # s coordinate of vertex
+
+    growth = np.zeros([len(ps_locations), 2])  # container for results
+
     for i in range(len(ps_locations)):
-        p = ps_locations[i,0]
-        s = ps_locations[i,1]
-        
+        p = ps_locations[i, 0]
+        s = ps_locations[i, 1]
+
         A = alpha - po
-        B = beta*(p-alpha)
+        B = beta * (p - alpha)
         C = gamma - so
-        D = beta*(s-gamma)
-        
-        F = pow(A*b,2) + pow(C*a,2) - a2*b2
-        G = 2*(B*A*b2 + C*D*a2)
-        H = pow(b*B,2) + pow(a*D,2)
-        
+        D = beta * (s - gamma)
+
+        F = pow(A * b, 2) + pow(C * a, 2) - a2 * b2
+        G = 2 * (B * A * b2 + C * D * a2)
+        H = pow(b * B, 2) + pow(a * D, 2)
+
         constant_g2 = F
-        constant_g = -2*F*beta - G
-        constant_1 = F*pow(beta,2) + G*beta + H
-        
-        discriminant = pow(constant_g,2) - 4*constant_g2*constant_1
-        
-        growth[i,0] = -(constant_g + math.sqrt(discriminant))/(2*constant_g2)
-        growth[i,1] = -(constant_g - math.sqrt(discriminant))/(2*constant_g2)
-        
-    return growth
+        constant_g = -2 * F * beta - G
+        constant_1 = F * pow(beta, 2) + G * beta + H
+
+        discriminant = pow(constant_g, 2) - 4 * constant_g2 * constant_1
+
+        growth[i, 0] = -(constant_g + math.sqrt(discriminant)) / (2 * constant_g2)
+        growth[i, 1] = -(constant_g - math.sqrt(discriminant)) / (2 * constant_g2)
+
+    return growth