2to3, blackify code...

Author: daedalus

DoubleAndAdd.py

@@ -9,6 +9,7 @@ def bits(n):
         yield n & 1
         n >>= 1
 
+
 def double_and_add(n, x):
     """
     Returns the result of n * x, computed using

FormalDerivative.py

@@ -1,12 +1,13 @@
 def FormalDerivative(Fx):
-  return [(x[0] * x[1], x[1] - 1) for x in Fx if x[1] > 0]
+    return [(x[0] * x[1], x[1] - 1) for x in Fx if x[1] > 0]
 
 
 def polyrep(Fx):
-  s = ["%dx ^ %d" % x for x in Fx]
-  return " + ".join(s)
+    s = ["%dx ^ %d" % x for x in Fx]
+    return " + ".join(s)
 
-fx = [(-1,6),(1,0)]
-print(polyrep(fx))
+
+fx = [(-1, 6), (1, 0)]
+print((polyrep(fx)))
 fx = FormalDerivative(fx)
-print(polyrep(fx))
+print((polyrep(fx)))

GaussGF2.py

@@ -2,24 +2,25 @@
 # Author Dario Clavijo 2021
 # based on https://www.cs.umd.edu/~gasarch/TOPICS/factoring/fastgauss.pdf, page2
 
+
 def Gauss_GF2(A):
-  h = len(A)
-  m = len(A[0])
-  marks = [False] * h
-  for j in range(0,m):
-    for i in range(0,h):
-      if A[i][j] == 1:
-          marks[i] = True
-          for k in range(j+1,m):
-            if A[i][k] == 1:
-              A[i][k] = (A[i][j] ^ A[i][k]) 
-          break
-  return marks, A
-              
-if __name__ == "__main__":
-  A  = [[1,1,0,0],[1,1,0,1],[0,1,1,1],[0,0,1,0],[0,0,0,1]]
-  marks,A = Gauss_GF2(A)
-  print(marks)
-  for row in A:
-    print(row)
+    h = len(A)
+    m = len(A[0])
+    marks = [False] * h
+    for j in range(0, m):
+        for i in range(0, h):
+            if A[i][j] == 1:
+                marks[i] = True
+                for k in range(j + 1, m):
+                    if A[i][k] == 1:
+                        A[i][k] = A[i][j] ^ A[i][k]
+                break
+    return marks, A
 
+
+if __name__ == "__main__":
+    A = [[1, 1, 0, 0], [1, 1, 0, 1], [0, 1, 1, 1], [0, 0, 1, 0], [0, 0, 0, 1]]
+    marks, A = Gauss_GF2(A)
+    print(marks)
+    for row in A:
+        print(row)

anomaly_detection.py

@@ -107,14 +107,14 @@ def test():
     MAD anomaly detection: [(5, 12, 3.8333333333333344)]
     """
     S = [2, 3, 5, 2, 3, 12, 5, 3, 4]
-    print("Series:", S)
-    print("Mean:", mean(S))
-    print("StdDev:", stddev(S))
-    print("Bounds:", bounds(S))
-    print("simple anomaly detection:", list(simple_anonaly_detection(S)))
-    print("zvalues:", list(zvalue(S)))
-    print("zvalue anomaly detection:", list(zvalue_anomaly_detection(S)))
-    print("MAD anomaly detection:", list(MAD_anomaly_detection(S)))
+    print(("Series:", S))
+    print(("Mean:", mean(S)))
+    print(("StdDev:", stddev(S)))
+    print(("Bounds:", bounds(S)))
+    print(("simple anomaly detection:", list(simple_anonaly_detection(S))))
+    print(("zvalues:", list(zvalue(S))))
+    print(("zvalue anomaly detection:", list(zvalue_anomaly_detection(S))))
+    print(("MAD anomaly detection:", list(MAD_anomaly_detection(S))))
 
 
 if __name__ == "__main__":

archimedes_pi.py

@@ -14,13 +14,13 @@ def approximation(sides):
     pi_guess = (inside + outside) / 2
     accuracy = (1 - (pi_guess - pi) / pi) * 100
 
-    print "sides:", sides
-    print "angle:", angle
-    print "inside:", inside
-    print "outside:", outside,
-    print "pi_guess:", pi_guess
-    print "accuracy:", accuracy
+    print("sides:", sides)
+    print("angle:", angle)
+    print("inside:", inside)
+    print("outside:", outside, end=" ")
+    print("pi_guess:", pi_guess)
+    print("accuracy:", accuracy)
 
 
-for i in xrange(10):
-    approximation(10 ** i)
+for i in range(10):
+    approximation(10**i)

barret.py

@@ -1,26 +1,27 @@
-
-class BarretReduccer():
+class BarretReduccer:
     """
     https://en.wikipedia.org/wiki/Barrett_reduction
     """
+
     def __init__(self, m):
-        if m <=0: raise ValueError("Modulus must be positive")
-        if m & (m - 1) == 0: raise ValueError("Modulus must not be a power of 2")
+        if m <= 0:
+            raise ValueError("Modulus must be positive")
+        if m & (m - 1) == 0:
+            raise ValueError("Modulus must not be a power of 2")
         self.m = m
         self.shift = m.bit_length() << 1
         self.q = (1 << self.shift) // m
 
     def reduce(self, a):
-        #if 0 <= a <= self.m**2: raise ValueError("a must be >0 and < n^2")
+        # if 0 <= a <= self.m**2: raise ValueError("a must be >0 and < n^2")
         a -= ((a * self.q) >> self.shift) * self.m
-        if a >= self.m: a -= self.m
+        if a >= self.m:
+            a -= self.m
         return a
 
-def test():
-  br = BarretReduccer(12)
-  print(br.reduce(6))
-  print(br.reduce(13))
-  print(br.reduce(18))
 
-
-  
+def test():
+    br = BarretReduccer(12)
+    print((br.reduce(6)))
+    print((br.reduce(13)))
+    print((br.reduce(18)))

binGCD.py

@@ -18,9 +18,9 @@ def gcd(a, b):
 
 
 def test():
-    print(gcd(115, 5))
-    print(gcd(5, 7))
-    print(gcd(10, 4))
+    print((gcd(115, 5)))
+    print((gcd(5, 7)))
+    print((gcd(10, 4)))
 
     a = (
         37975227936943673922808872755445627854565536638199
@@ -30,7 +30,7 @@ def test():
         40094690950920881030683735292761468389214899724061
         * 5846418214406154678836553182979162384198610505601062333
     )
-    print(gcd(a, b))
+    print((gcd(a, b)))
 
 
 if __name__ == "__main__":

binary_polinomial_factoring.sage

@@ -64,7 +64,7 @@ def test():
                 ff += 1
             else:
                 nf += 1
-            print(n, i, ff, nf, f)
+            print((n, i, ff, nf, f))
         n += 1
 
 

birthdayparadox.py

@@ -1,11 +1,11 @@
-def bpp(g,w):
-  """
-  Compute the probability of all elements in group g in the whole set w to have unique birthdays.
-  """
-  proba = 1
-  for i in range(w, w - g, -1):
-    proba *= (i/w)
-  return proba
+def bpp(g, w):
+    """
+    Compute the probability of all elements in group g in the whole set w to have unique birthdays.
+    """
+    proba = 1
+    for i in range(w, w - g, -1):
+        proba *= i / w
+    return proba
 
 
-print(bpp(23,365))
+print((bpp(23, 365)))

chirp_zeta_transform.py

@@ -1,6 +1,6 @@
 # Given the following paper: https://www.nature.com/articles/s41598-019-50234-9
 
-#TODO: Implimentand test functions
+# TODO: Implimentand test functions
 # Found function implementation: ToeplitzMultiplyE
 # Suplemental paper (at bottom of previous link):
 # https://static-content.springer.com/esm/art%3A10.1038%2Fs41598-019-50234-9/MediaObjects/41598_2019_50234_MOESM1_ESM.pdf
@@ -13,36 +13,42 @@
 #
 # Depends on FFT and IFFT (import fftw)
 
-def CZT(x,M,W,A):
+
+def CZT(x, M, W, A):
     N = len(x)
-    X, r , c = [] * N, [] * N, [] * M
+    X, r, c = [] * N, [] * N, [] * M
     for k in range(0, N - 1):
         k2 = k * k
         X[k] = W[((k2) >> 1)] * A[-k] * x[k]
         r[k] = W[-((k2) >> 1)]
-    for k in range(0,M-1):
+    for k in range(0, M - 1):
         c[k] = W[-((k * k) >> 1)]
-    X = ToeplitzMultiplyE(r,c,X)
+    X = ToeplitzMultiplyE(r, c, X)
     for k in range(0, M - 1):
         X[k] = W[((k * k) >> 1)] * X[k]
     return X
 
-def ICZT(X,N,W,A):
+
+def ICZT(X, N, W, A):
     M = len(X)
     if M != N:
-        raise("M must == to N")
+        raise ("M must == to N")
     n = N
     x, p, u, z = [] * n, [] * n, [] * n, [] * n
     for k in range(0, n - 1):
         x[k] = W[((k * k) >> 1)] * X[k]
     p[0] = 1
-    for k in range(0,n-1):
+    for k in range(0, n - 1):
         p[k] = (p[k - 1]) * (W[k] - 1)
-    for k in range(0, n-1, 2):
-        u[k] = ((W[((k * k) << 1) - ((n << 1) - 1) + n * (n - 1)]) / (p[n - k - 1] * p[k]))
-    for k in range(1, n-1, 2):
-        u[k] = ((W[((k * k) << 1) - ((n << 1) - 1) + n * (n - 1)]) / (p[n - k - 1] * p[k])) * -1
-    for j = range(n-1,0,-1):
+    for k in range(0, n - 1, 2):
+        u[k] = (W[((k * k) << 1) - ((n << 1) - 1) + n * (n - 1)]) / (
+            p[n - k - 1] * p[k]
+        )
+    for k in range(1, n - 1, 2):
+        u[k] = (
+            (W[((k * k) << 1) - ((n << 1) - 1) + n * (n - 1)]) / (p[n - k - 1] * p[k])
+        ) * -1
+    for j in range(n - 1, 0, -1):
         u1[k] = u[k - j]
     u2 = u[0] + [0] * (n - 1)
     x1 = ToeplitzMultiplyE(u1, z, x)
@@ -54,5 +60,3 @@ def ICZT(X,N,W,A):
     for k in range(0, n - 1):
         x[k] = A[k] * W[-((k * k) >> 1)] * x[k]
     return x
-
-

contfrac.py

@@ -17,5 +17,5 @@ def cont_frac(a, b):
     return coef
 
 
-print(cont_frac(649, 200))
-print(cont_frac(17993, 90581))
+print((cont_frac(649, 200)))
+print((cont_frac(17993, 90581)))

dft.py

@@ -14,13 +14,13 @@ def dft_slow(signal):
 
     def Fk(signal, k):
         Freq = 0.0
-        for n in xrange(0, N):
+        for n in range(0, N):
             coef = math.exp((-2 * math.pi * k * n) / N)
             Freq += signal[n] * coef
         return Freq
 
     histogram = []
-    for k in xrange(0, N):
+    for k in range(0, N):
         histogram.append(Fk(signal, k))
     return histogram
 
@@ -30,4 +30,4 @@ def nyquist_norm(histogram):
     return [histogram[n] * 2 for n in range(0, N / 2)]
 
 
-print nyquist_norm(dft_slow(signal))
+print((nyquist_norm(dft_slow(signal))))

dx.py

@@ -20,7 +20,7 @@ def dx(x):
 
 
 def g(x):
-    return x ** 2
+    return x**2
 
 
 dg = derivative(g)
@@ -42,10 +42,10 @@ def intf(b, a=0):
     return intf
 
 
-print "g(x)=", [g(x) for x in range(11)]
+print(("g(x)=", [g(x) for x in range(11)]))
 
-print "df g(x)=", [dg(x) for x in range(11)]
+print(("df g(x)=", [dg(x) for x in range(11)]))
 
 inv1 = integral(derivative(g))
 
-print "integral(df g(x))=", [inv1(x) for x in range(11)]
+print(("integral(df g(x))=", [inv1(x) for x in range(11)]))

e.py

@@ -18,7 +18,7 @@ def direct_e():
 
 
 def taylor_e():
-    return sum(1.0 / (math.factorial(n)) for n in xrange(0, 15))
+    return sum(1.0 / (math.factorial(n)) for n in range(0, 15))
 
 
 # by limits definition:
@@ -30,29 +30,31 @@ def taylor_e():
 def lim_ddx_e(x):
     h = 1.0 / (534645555)
     return (
-        (math.e ** x * math.e ** h) - math.e ** x
+        (math.e**x * math.e**h) - math.e**x
     ) / h  # = math.e * ((math.e ** h - 1.0)/h)
 
 
 def test():
-  print(math.e)
-  print(direct_e())
-  print(taylor_e())
-  print(lim_ddx_e(1))
+    print((math.e))
+    print((direct_e()))
+    print((taylor_e()))
+    print((lim_ddx_e(1)))
 
 
-i = complex(0,1)
+i = complex(0, 1)
 pi = math.pi
 
+
 def exp(n, precision=100):
-    return sum((n ** x) / math.factorial(x) for x in range(0, precision))
+    return sum((n**x) / math.factorial(x) for x in range(0, precision))
+
 
 def test_exp():
-  print(exp(0))
-  print(exp(0.5))
-  print(exp(1))
-  print(exp(pi))
-  print(exp(i*pi))
+    print((exp(0)))
+    print((exp(0.5)))
+    print((exp(1)))
+    print((exp(pi)))
+    print((exp(i * pi)))
 
 
 test_exp()