2to3, blackify code...

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()