old_contents: import numpy as np
  import numba as nb
  
  from numpy.random import PCG64
  from timeit import timeit
  
  bit_gen = PCG64()
  next_d = bit_gen.cffi.next_double
  state_addr = bit_gen.cffi.state_address
  
  def normals(n, state):
      out = np.empty(n)
      for i in range((n + 1) // 2):
          x1 = 2.0 * next_d(state) - 1.0
          x2 = 2.0 * next_d(state) - 1.0
          r2 = x1 * x1 + x2 * x2
          while r2 >= 1.0 or r2 == 0.0:
              x1 = 2.0 * next_d(state) - 1.0
              x2 = 2.0 * next_d(state) - 1.0
              r2 = x1 * x1 + x2 * x2
          f = np.sqrt(-2.0 * np.log(r2) / r2)
          out[2 * i] = f * x1
          if 2 * i + 1 < n:
              out[2 * i + 1] = f * x2
      return out
  
  # Compile using Numba
  normalsj = nb.jit(normals, nopython=True)
  # Must use state address not state with numba
  n = 10000
  
  def numbacall():
      return normalsj(n, state_addr)
  
  rg = np.random.Generator(PCG64())
  
  def numpycall():
      return rg.normal(size=n)
  
  # Check that the functions work
  r1 = numbacall()
  r2 = numpycall()
  assert r1.shape == (n,)
  assert r1.shape == r2.shape
  
  t1 = timeit(numbacall, number=1000)
  +print(f'{t1:.2f} secs for {n} PCG64 (Numba/PCG64) gaussian randoms')
  t2 = timeit(numpycall, number=1000)
  print(f'{t2:.2f} secs for {n} PCG64 (NumPy/PCG64) gaussian randoms')
  
  # example 2
  
  next_u32 = bit_gen.ctypes.next_uint32
  ctypes_state = bit_gen.ctypes.state
  
  @nb.jit(nopython=True)
  def bounded_uint(lb, ub, state):
      mask = delta = ub - lb
      mask |= mask >> 1
      mask |= mask >> 2
      mask |= mask >> 4
      mask |= mask >> 8
      mask |= mask >> 16
  
      val = next_u32(state) & mask
      while val > delta:
          val = next_u32(state) & mask
  
      return lb + val
  
  
  print(bounded_uint(323, 2394691, ctypes_state.value))
  
  
  @nb.jit(nopython=True)
  def bounded_uints(lb, ub, n, state):
      out = np.empty(n, dtype=np.uint32)
      for i in range(n):
          out[i] = bounded_uint(lb, ub, state)
  
  
  bounded_uints(323, 2394691, 10000000, ctypes_state.value)
  
  
  
new_contents: import numpy as np
  import numba as nb
  
  from numpy.random import PCG64
  from timeit import timeit
  
  bit_gen = PCG64()
  next_d = bit_gen.cffi.next_double
  state_addr = bit_gen.cffi.state_address
  
  def normals(n, state):
      out = np.empty(n)
      for i in range((n + 1) // 2):
          x1 = 2.0 * next_d(state) - 1.0
          x2 = 2.0 * next_d(state) - 1.0
          r2 = x1 * x1 + x2 * x2
          while r2 >= 1.0 or r2 == 0.0:
              x1 = 2.0 * next_d(state) - 1.0
              x2 = 2.0 * next_d(state) - 1.0
              r2 = x1 * x1 + x2 * x2
          f = np.sqrt(-2.0 * np.log(r2) / r2)
          out[2 * i] = f * x1
          if 2 * i + 1 < n:
              out[2 * i + 1] = f * x2
      return out
  
  # Compile using Numba
  normalsj = nb.jit(normals, nopython=True)
  # Must use state address not state with numba
  n = 10000
  
  def numbacall():
      return normalsj(n, state_addr)
  
  rg = np.random.Generator(PCG64())
  
  def numpycall():
      return rg.normal(size=n)
  
  # Check that the functions work
  r1 = numbacall()
  r2 = numpycall()
  assert r1.shape == (n,)
  assert r1.shape == r2.shape
  
  t1 = timeit(numbacall, number=1000)
  print(f'{t1:.2f} secs for {n} PCG64 (Numba/PCG64) gaussian randoms')
  t2 = timeit(numpycall, number=1000)
  print(f'{t2:.2f} secs for {n} PCG64 (NumPy/PCG64) gaussian randoms')
  
  # example 2
  
  next_u32 = bit_gen.ctypes.next_uint32
  ctypes_state = bit_gen.ctypes.state
  
  @nb.jit(nopython=True)
  def bounded_uint(lb, ub, state):
      mask = delta = ub - lb
      mask |= mask >> 1
      mask |= mask >> 2
      mask |= mask >> 4
      mask |= mask >> 8
      mask |= mask >> 16
  
      val = next_u32(state) & mask
      while val > delta:
          val = next_u32(state) & mask
  
      return lb + val
  
  
  print(bounded_uint(323, 2394691, ctypes_state.value))
  
  
  @nb.jit(nopython=True)
  def bounded_uints(lb, ub, n, state):
      out = np.empty(n, dtype=np.uint32)
      for i in range(n):
          out[i] = bounded_uint(lb, ub, state)
  
  
  bounded_uints(323, 2394691, 10000000, ctypes_state.value)
  
  
  
subject: remove stray '+' from f-string upgrade