data/tools/support/max_distance.py

+from itertools import combinations
+
+
+def square_distance(x, y):
+    return sum([(xi-yi)**2 for xi, yi in zip(x, y)])
+
+
+def max_distance(points):
+    max_square_distance = 0
+    max_pair = (0, 0)
+    for pair in combinations(points, 2):
+        sq_dist = square_distance(*pair)
+
+        if sq_dist > max_square_distance:
+            max_square_distance = sq_dist
+            max_pair = pair
+    return max_pair

data/tools/support/max_distance.py~

+from itertools import combinations
+
+
+def square_distance(x, y):
+    return sum([(xi-yi)**2 for xi, yi in zip(x, y)])
+
+
+def max_distance(points):
+    max_square_distance = 0
+    max_pair = (0, 0)
+    for pair in combinations(points, 2):
+        sq_dist = square_distance(*pair)
+        print(sq_dist)
+        if sq_dist > max_square_distance:
+            max_square_distance = sq_dist
+            max_pair = pair
+    return max_pair

data/tools/support/max_velocity.py

+import numpy as np
+
+def max_velocity(x) :
+    return maximum(norm(x))
\ No newline at end of file

data/tools/support/max_velocity.py~

+import numpy as np
+
+def max_velocity(x) :
+    size = x.shape 
+    res = np.zeros(size[0])
+
+    for time_step in range(size[0]) :
+        for vertex in range(size[1]) :
+            res[time_step] = max(res[time_step], np.linalg.norm(x[time_step, vertex, :]))
+
+    return res
\ No newline at end of file

data/tools/support/maximum.py

+import numpy as np
+
+
+def maximum(x):
+    size = x.shape
+    res = np.zeros(size[0])
+
+    for time_step in range(size[0]):
+        res[time_step] = max(res[time_step], max(x[time_step, :]))
+
+    return res

data/tools/support/maximum.py~

+import numpy as np
+
+def maximum(x) :
+    size = x.shape 
+    res = np.zeros(size[0])
+
+    for time_step in range(size[0]) :
+        for vertex in range(size[1]) :
+            res[time_step] = max(res[time_step], max(x[time_step, vertex]))
+
+    return res
\ No newline at end of file

data/tools/support/norm.py

+import numpy as np
+
+
+def norm(x):
+    size = x.shape
+    res = np.zeros((size[0], size[1]))
+
+    for time_step in range(size[0]):
+        for vertex in range(size[1]):
+            res[time_step, vertex] = np.linalg.norm(x[time_step, vertex, :])
+
+    return res

data/tools/support/norm.py~

+import numpy as np
+
+def norm(x) :
+    size = x.shape 
+    res = np.zeros(size[0], size[1])
+
+    for time_step in range(size[0]) :
+        for vertex in range(size[1]) :
+            res[time_step, vertex] = np.linalg.norm(x[time_step, vertex, :])
+
+    return res 

data/tools/support/slip_beginnings.py

+import array as ar
+
+
+def slip_beginnings(x):
+    # returns indicies i for which x[i-1]=0 and x[i]>0
+    starts = ar.array('i', (0 for i in range(len(x))))
+    prev = False
+
+    length = 0
+    for i in range(len(x)):
+        if (not prev and x[i]):
+            starts[length] = i
+            length += 1
+        prev = x[i]
+    return starts[:length]

data/tools/support/slip_beginnings.py~

+import numpy as np
+
+def slip_beginnings(x) :
+    # returns indicies i for which x[i-1]=0 and x[i]>0  
+    starts = []
+    prev = False
+    for i in range(len(x)) :
+        if (not prev and x[i]) :
+            starts.append(i)
+            prev = x[i]
+    return starts

data/tools/support/slip_endings.py

+import array as ar
+
+
+def slip_endings(x):
+    # returns indicies i for which x[i-1]>0 and x[i]=0
+    ends = ar.array('i', (0 for i in range(len(x))))
+    prev = False
+
+    length = 0
+    for i in range(len(x)):
+        if (prev and not x[i]):
+            ends[length] = i
+            length += 1
+        prev = x[i]
+
+    return ends[:length]

data/tools/support/slip_endings.py~

+import numpy as np
+
+def slip_endings(x) :
+    # returns indicies i for which x[i-1]>0 and x[i]=0
+    starts = np.array([])
+    prev = False
+    for i in range(len(x)) :
+        if (prev and not x[i]) :
+            starts.append(i)
+            prev = x[i]
+    return starts
\ No newline at end of file

data/tools/support/trapezoidal.py

+# x: vector of domain values
+# y: vector of function values f(x)
+def trapezoidal(x, y) :
+    # returns integral of (x, y = f(x)) calculated with trapezoidal rule 
+	res = 0.0
+	for i in range(1, len(x)) :
+		res += (x[i] - x[i - 1]) * (y[i] + y[i - 1]) / 2.0
+	return res
\ No newline at end of file

data/tools/support/trapezoidal.py~

+# x: vector of domain values
+# y: vector of function values f(x)
+def trapezoidal(x, y) :
+	# returns integral of (x, y = f(x)) calculated with trapezoidal rule 
+	res = 0.0
+	for i in range(1, len(x)) :
+		res += (x[i] - x[i - 1]) * (y[i] + y[i - 1]) / 2.0
+	return res
\ No newline at end of file