Fixes to Python wrapper examples. Changes to work with Python 3.12

Author: christophe0606

PythonWrapper/build/create.bat

@@ -1,7 +1,7 @@
 cmake -DHOST=YES ^
   -DLOOPUNROLL=ON ^
   -DWRAPPER=YES ^
-  -DCMSISDSP=".." ^
+  -DCMSISDSP="path to CMSIS-DSP folder" ^
   -DCMAKE_C_FLAGS_RELEASE="-std=c11 -Ofast -ffast-math -DNDEBUG -Wall -Wextra" ^
   -DCMAKE_CXX_FLAGS_RELEASE="-fno-rtti -std=c++11 -Ofast -ffast-math -DNDEBUG -Wall -Wextra -Wno-unused-parameter" ^
   -G "Unix Makefiles" ..

PythonWrapper/cmsisdsp_pkg/src/cmsisdsp_module.h

@@ -32,7 +32,7 @@
 #include <numpy/numpyconfig.h>
 
 // API version used on google colab
-// List in https://github.com/numpy/numpy numpyconfig.h
+// https://github.com/numpy/numpy/blob/main/numpy/_core/include/numpy/numpyconfig.h
 #if (NPY_API_VERSION != 0x0000000F )
 //#error("Error building with wrong NumPy API version")
 #endif

PythonWrapper/examples/debug.py

@@ -9,6 +9,7 @@
 import colorama
 from colorama import init,Fore, Back, Style
 from numpy.testing import assert_allclose
+import scipy.spatial.distance as d 
 
 init()
 
@@ -19,63 +20,61 @@ def printSubTitle(s):
     print("\n" + Style.BRIGHT + s + Style.RESET_ALL)
 
 
-def chop(A, eps = 1e-6):
-    B = np.copy(A)
-    B[np.abs(A) < eps] = 0
-    return B
+def packset(a):
+    b = np.packbits(a)
+    newSize = int(np.ceil(b.shape[0] / 4.0)) * 4
+    c = np.copy(b).astype(np.uint32)
+    c.resize(newSize)
+    #print(c)
+    vecSize = round(newSize/4)
+    c=c.reshape(vecSize,4)
+    #print(c)
+    r = np.zeros(vecSize)
+    result = []
+    for i in range(0,vecSize):
+        print(c[i,:])
+        #print("%X %X %X %X" % (c[i,0],c[i,1],c[i,2],c[i,3]))
+        d = (c[i,0] << 24) | (c[i,1] << 16) | (c[i,2] << 8) | c[i,3] 
+        result.append(np.uint32(d))
+    return(result) 
 
-nb = 32
-signal = np.cos(2 * np.pi * np.arange(nb) / nb)*np.cos(0.2*2 * np.pi * np.arange(nb) / nb)
+nb = 34
+#va = np.random.choice([0,1],nb)
+# Array of word32 containing all of our bits
+#pva = packset(va)
 
-ref=scipy.fft.rfft(signal)
-invref = scipy.fft.irfft(ref)
 
-print(f"ref length = {len(ref)}")
-print(ref)
+#vb = np.random.choice([0,1],nb)
+# Array of word32 containing all of our bits
+#pvb = packset(vb)
+#
+va=[1, 0, 1, 0, 1, 1, 1, 0 ,0, 1, 1, 0, 1, 0, 0, 0, 0, 1,0,0,0,1,1,0,1,0,1,0,0,1,1,1,1,1]
+vb=[0,1,0,0,1,0,0,0,1,0,0,0,0,1,1,0,0,1,0,0,0,1,0,1,1,0,0,1,1,0,0,0,1,0]
+
+va = np.array(va)
+vb = np.array(vb)
+
+pva=packset(va)
+pvb=packset(vb)
+
+#pva = [np.uint32(167), np.uint32(0)]
+#pvb = [np.uint32(152), np.uint32(0)]
+
+#print(va,pva)
+#print(vb,pvb)
+
+ctt=1.0*np.count_nonzero((va==1) & (vb==1))
+ctf=1.0*np.count_nonzero((va==1) & (vb==0))
+cft=1.0*np.count_nonzero((va==0) & (vb==1))
+
+res=(cft+ctf)/(2*ctt+cft+ctf)
 
-# Convert ref to CMSIS-DSP format 
-referenceFloat=np.zeros(2*len(ref))
-print(f"referenceFloat length = {len(referenceFloat)}")
-# Replace complex datatype by real datatype
-referenceFloat[0::2] = np.real(ref)
-referenceFloat[1::2] = np.imag(ref)
-# Copy Nyquist frequency value into first 
-# sample.This is just a storage trick so that the
-# output of the RFFT has same length as input
-# It is legacy behavior that we need to keep
-# for backward compatibility but it is not
-# very pretty
-#referenceFloat[1] = np.real(ref[-1])
-
-rifftQ31=dsp.arm_rfft_instance_q31()
-status=dsp.arm_rfft_init_q31(rifftQ31,nb,1,1)
-# Apply CMSIS-DSP scaling
-referenceQ31 = f.toQ31(referenceFloat / nb) 
-
-resultQ31 = dsp.arm_rfft_q31(rifftQ31,referenceQ31)
-resultF = f.Q31toF32(resultQ31)
-
-print(f"resultF length = {len(resultF)}")
-assert_allclose(invref/nb,resultF,atol=1e-6)
-
-signalQ31 = f.toQ31(signal)
-rfftQ31=dsp.arm_rfft_instance_q31()
-status=dsp.arm_rfft_init_q31(rfftQ31,nb,0,1)
-resultQ31 = dsp.arm_rfft_q31(rfftQ31,signalQ31)
-print(len(resultQ31))
-print(2*nb)
-resultF = f.Q31toF32(resultQ31) * nb
-
-def compareWithConjugatePart(r):
-    res = r[0::2] + 1j * r[1::2]
-    conjPart = res[nb:nb//2:-1].conj()
-    refPart = res[1:nb//2]
-    assert(np.equal(refPart , conjPart).all())
-
-compareWithConjugatePart(resultF)
-
-res = resultF[0::2] + 1j * resultF[1::2]
 print(res)
 
-print(res[0:nb//2+1])
-print(res[0:nb//2+1].shape)
+
+print("\nDice")
+ref=d.dice(va,vb)
+res=dsp.arm_dice_distance(pva,pvb,nb)
+print(ref)
+print(res)
+assert_allclose(ref,res,1e-6)

PythonWrapper/examples/example_1_11.py

@@ -122,26 +122,28 @@ def printSubTitle(s):
 
 printSubTitle("With a window")
 
+nan = np.nan 
+
 referenceDistance = 0.617099940776825
-referenceCost=np.array([[9.1612804e-01, 9.9920368e-01, np.NAN, np.NAN,
-        np.NAN],
-       [1.2353053e+00, 1.6792301e+00, np.NAN, np.NAN,
-        np.NAN],
-       [1.3028694e+00, 2.3696373e+00, 4.4372001e+00, np.NAN,
-        np.NAN],
-       [np.NAN, 3.0795674e+00, 4.9687119e+00, np.NAN,
-        np.NAN],
-       [np.NAN, 3.5039051e+00, 4.9290380e+00, 5.3565612e+00,
-        np.NAN],
-       [np.NAN, np.NAN, 4.8520918e+00, 5.1756082e+00,
-        np.NAN],
-       [np.NAN, np.NAN, 5.0427418e+00, 5.8497019e+00,
+referenceCost=np.array([[9.1612804e-01, 9.9920368e-01, nan, nan,
+        nan],
+       [1.2353053e+00, 1.6792301e+00, nan, nan,
+        nan],
+       [1.3028694e+00, 2.3696373e+00, 4.4372001e+00, nan,
+        nan],
+       [nan, 3.0795674e+00, 4.9687119e+00, nan,
+        nan],
+       [nan, 3.5039051e+00, 4.9290380e+00, 5.3565612e+00,
+        nan],
+       [nan, nan, 4.8520918e+00, 5.1756082e+00,
+        nan],
+       [nan, nan, 5.0427418e+00, 5.8497019e+00,
         7.6590457e+00],
-       [np.NAN, np.NAN, np.NAN, 6.7571073e+00,
+       [nan, nan, nan, 6.7571073e+00,
         8.6668968e+00],
-       [np.NAN, np.NAN, np.NAN, 7.3949833e+00,
+       [nan, nan, nan, 7.3949833e+00,
         9.0352430e+00],
-       [np.NAN, np.NAN, np.NAN, np.NAN,
+       [nan, nan, nan, nan,
         9.2564993e+00]], dtype=np.float32)
 
 

PythonWrapper/examples/testdistance.py

@@ -6,6 +6,14 @@
 a=[1,2,3]
 b=[1,5,2]
 
+def kulsinski(va,vb):
+    n = len(va)
+    ctt=1.0*np.count_nonzero((va==1) & (vb==1))
+    ctf=1.0*np.count_nonzero((va==1) & (vb==0))
+    cft=1.0*np.count_nonzero((va==0) & (vb==1))
+    return(1.0*(ctf + cft - ctt+n)/(cft + ctf + n))
+
+
 print("\nBray-Curtis")
 ref=d.braycurtis(a,b)
 res=dsp.arm_braycurtis_distance_f32(a,b)
@@ -96,7 +104,7 @@
 def packset(a):
     b = np.packbits(a)
     newSize = int(np.ceil(b.shape[0] / 4.0)) * 4
-    c = np.copy(b)
+    c = np.copy(b).astype(np.uint32)
     c.resize(newSize)
     #print(c)
     vecSize = round(newSize/4)
@@ -105,7 +113,7 @@ def packset(a):
     r = np.zeros(vecSize)
     result = []
     for i in range(0,vecSize):
-        #print(c[i,:])
+        print(c[i,:])
         #print("%X %X %X %X" % (c[i,0],c[i,1],c[i,2],c[i,3]))
         d = (c[i,0] << 24) | (c[i,1] << 16) | (c[i,2] << 8) | c[i,3] 
         result.append(np.uint32(d))
@@ -143,7 +151,7 @@ def packset(a):
 assert_allclose(ref,res,1e-6)
 
 print("\nKulsinski")
-ref=d.kulsinski(va,vb)
+ref=kulsinski(va,vb)
 res=dsp.arm_kulsinski_distance(pva,pvb,nb)
 print(ref)
 print(res)

PythonWrapper/examples/testdsp6.py

@@ -169,10 +169,14 @@
 supportVectors = supportVectors.reshape(nbSupportVectors * VECDIM)
 
 svmInst=dsp.arm_svm_polynomial_instance_f32() 
+
+
 dsp.arm_svm_polynomial_init_f32(svmInst,nbSupportVectors,vectorDimensions,
-    intercept,dualCoefs,supportVectors,
+    intercept[0],dualCoefs,supportVectors,
     [0,1],degree,coef0,gamma)
 
+exit(0)
+
 test1 = np.array([0.4,0.1])
 predicted1 = dsp.arm_svm_polynomial_predict_f32(svmInst,test1)
 print(predicted1)

PythonWrapper/examples/testmfcc.py

@@ -17,7 +17,7 @@
 freq_high = sample_rate / 2
 numOfMelFilters = 20
 
-window = sig.hamming(FFTSize, sym=False)
+window = sig.windows.hamming(FFTSize, sym=False)
 
 filtLen,filtPos,packedFilters = mfcc.melFilterMatrix(F32,freq_min, freq_high, numOfMelFilters,sample_rate,FFTSize)
 

PythonWrapper/examples/testmfccq15.py

@@ -17,7 +17,7 @@
 freq_high = sample_rate / 2
 numOfMelFilters = 20
 
-windowQ15 = dt.convert(sig.hamming(FFTSize, sym=False),Q15)
+windowQ15 = dt.convert(sig.windows.hamming(FFTSize, sym=False),Q15)
 filtLen,filtPos,packedFiltersQ15 = mfcc.melFilterMatrix(Q15,freq_min, freq_high, numOfMelFilters,sample_rate,FFTSize)
 dctMatrixFiltersQ15 = mfcc.dctMatrix(Q15,numOfDctOutputs, numOfMelFilters)
 

PythonWrapper/examples/testmfccq31.py

@@ -17,7 +17,7 @@
 freq_high = sample_rate / 2
 numOfMelFilters = 20
 
-windowQ31 = dt.convert(sig.hamming(FFTSize, sym=False),Q31)
+windowQ31 = dt.convert(sig.windows.hamming(FFTSize, sym=False),Q31)
 filtLen,filtPos,packedFiltersQ31 = mfcc.melFilterMatrix(Q31,freq_min, freq_high, numOfMelFilters,sample_rate,FFTSize)
 dctMatrixFiltersQ31 = mfcc.dctMatrix(Q31,numOfDctOutputs, numOfMelFilters)
 

PythonWrapper_README.md

@@ -234,6 +234,9 @@ MEL filters are represented as 3 arrays to encode a sparse array.
 
 # Change history
 
+## Version 1.9.9:
+* Supports Python 3.12
+
 ## Version 1.9.8:
 * Compute graph API has been removed
 * Dependency on numpy 1.22 has been lifted, tested through numpy 1.26