1from __future__ import division 2from __future__ import print_function 3 4import os 5import sys 6 7import numpy as np 8 9MAX_INPUT_SIZE = int(1e6) 10MAX_FLOAT32 = np.finfo(np.float32).max 11 12def IsValidSize(n): 13 if n == 0: 14 return False 15 # PFFFT only supports transforms for inputs of length N of the form 16 # N = (2^a)*(3^b)*(5^c) where a >= 5, b >=0, c >= 0. 17 FACTORS = [2, 3, 5] 18 factorization = [0, 0, 0] 19 for i, factor in enumerate(FACTORS): 20 while n % factor == 0: 21 n = n // factor 22 factorization[i] += 1 23 return factorization[0] >= 5 and n == 1 24 25 26def main(): 27 if len(sys.argv) < 2: 28 print('Usage: %s <path to output directory>' % sys.argv[0]) 29 sys.exit(1) 30 31 output_path = sys.argv[1] 32 if not os.path.exists(output_path): 33 print('The output path does not exists.') 34 sys.exit(2) 35 36 # List of valid input sizes. 37 N = [n for n in range(MAX_INPUT_SIZE) if IsValidSize(n)] 38 39 # Set the seed to always generate the same random data. 40 np.random.seed(0) 41 42 # Generate different types of input arrays for each target length. 43 for n in N: 44 # Zeros. 45 z = np.zeros(n, np.float32) 46 z.tofile(os.path.join(output_path, 'zeros_%d' % n)) 47 # Max float 32. 48 m = np.ones(n, np.float32) * MAX_FLOAT32 49 m.tofile(os.path.join(output_path, 'max_%d' % n)) 50 # Random values in the s16 range. 51 rnd_s16 = 32768.0 * 2.0 * (np.random.rand(n) - 1.0) 52 rnd_s16 = rnd_s16.astype(np.float32) 53 rnd_s16.tofile(os.path.join(output_path, 'rnd_s16_%d' % n)) 54 55 sys.exit(0) 56 57 58if __name__ == '__main__': 59 main() 60