Lines Matching refs:np
2 import numpy as np namespace
25 ar=np.zeros(np.array(a.shape) * [1,2])
34 return(a/np.max(np.abs(a)))
37 result = np.correlate(x, x, mode='full')
42 a=np.array([1.,-3.,4.,0.,-10.,8.])
71 a=np.array([1.,-3.,4.,0.5,-10.,8.])
103 a=np.array([1.,-3.,4.,0.5,-10.,8.])
130 a=np.array([[1.,2,3,4],[5,6,7,8],[9,10,11,12]])
131 b=np.array([-2,-1,3,4])
133 c = np.dot(a,b)
139 normalizationFactor=2.0*np.sqrt(np.max(np.abs(c)))
142 print(np.dot(a,b))
157 a=np.array([[1.,2,3,4],[5,6,7,8],[9,10,11,12]])
158 b=np.array([[1.,2,3],[5.1,6,7],[9.1,10,11],[5,8,4]])
159 print(np.dot(a , b))
166 normalizationFactor=2.0*np.sqrt(np.max(np.abs(c[1])))
176 tmp=np.zeros(nbSamples)
190 tmp=np.zeros(nb)
200 tmp=np.zeros(nb)
207 a=np.array([[1.,2,3,4],[5,6,7,8],[9,10,11,12]])
208 normalizationFactor=np.max(np.abs(c[1]))
211 print(np.transpose(a))
233 a=np.full((nb,),v)
251 a=np.array([[1. + 0.0j ,2 + 1.0j,3 + 0.0j,4 + 2.0j],
254 normalizationFactor=np.max(np.abs(c[1]))
257 print(np.transpose(a))
275 s = np.random.randn(na+1)
307 return(np.random.randint(minVal, maxVal, size=nb))
316 ffff = (np.ones(NBSAMPLES)*(-1)).astype(int)
319 ref=np.bitwise_and(su32A, su32B)
324 ref=np.bitwise_or(su32A, su32B)
329 ref=np.bitwise_xor(su32A, su32B)
334 ref=np.bitwise_xor(ffff, su32A)
343 ffff = (np.ones(NBSAMPLES)*(-1)).astype(int)
346 ref=np.bitwise_and(su16A, su16B)
348 result=dsp.arm_and_u16(su16A, su16B).astype(np.short)
351 ref=np.bitwise_or(su16A, su16B)
353 result=dsp.arm_or_u16(su16A, su16B).astype(np.short)
356 ref=np.bitwise_xor(su16A, su16B)
358 result=dsp.arm_xor_u16(su16A, su16B).astype(np.short)
361 ref=np.bitwise_xor(ffff, su16A)
363 result=dsp.arm_not_u16(su16A).astype(np.short)
371 ref=np.bitwise_and(su8A, su8B)
373 result=dsp.arm_and_u8(su8A, su8B).astype(np.byte)
376 ref=np.bitwise_or(su8A, su8B)
378 result=dsp.arm_or_u8(su8A, su8B).astype(np.byte)
381 ref=np.bitwise_xor(su8A, su8B)
383 result=dsp.arm_xor_u8(su8A, su8B).astype(np.byte)
386 ref=np.bitwise_xor(ffff, su8A)
388 result=dsp.arm_not_u8(su8A).astype(np.byte)
395 return(np.array([list(x) for x in l]).reshape(4*len(l)))
398 return(np.array([list(x) for x in l]).reshape(9*len(l)))
456 c = np.array(a) * np.array(b)