Python pywt.dwt函数代码示例

def collect(S, wavelet, mode, level):
    '''
    Returns the full binary tree of wavelet packets.
    @param S:         Input signal.
                      Both single and double precision floating-point data types are supported
                      and the output type depends on the input type. If the input data is not
                      in one of these types it will be converted to the default double precision
                      data format before performing computations.
    @param wavelet:   Wavelet to use in the transform. 
                      This must be a name of the wavelet from the wavelist() list.
    @param mode:      Signal extension mode to deal with the border distortion problem.
    @param level:     Number of decomposition steps to perform. If the level is None, then the
                      full decomposition up to the level computed with dwt_max_level() function for
                      the given data and wavelet lengths is performed.
    @return:          The full binary tree of wavelet packets.
    '''
    Nodes = [[] for i in range(level)]
    (Cl, Cr) = pywt.dwt(S, wavelet=wavelet, mode=mode)
    Nodes[0] = [node.Node(Cl, 0, 0), node.Node(Cr, 0, 1)]
    for l in range(0, level-1):
        Parents = Nodes[l]
        Childs = []
        for p in range(len(Parents)):
            (Cl, Cr) = pywt.dwt(Parents[p].C, wavelet=wavelet, mode=mode)
            Childs.append(node.Node(Cl, l+1, 2*p))
            Childs.append(node.Node(Cr, l+1, 2*p+1))
        Nodes[l+1] = Childs 
    return Nodes

def ecg_plot(data, ax1,signum):
	x, y = [], []
	print("Tworze wykres numer %d" % signum)
	#for row in arr[2:]:
	#	x.append(row[0])
	#	y.append(row[signum])
	x, y =[], []
	for index, cell in enumerate(data[signum-1]):
		x.append(index)
		y.append(float(cell)/mV)
		
	global X
	global Y
	X.append(x)
	Y.append(y)
	if(signum==1):
		find_QRS(y)
	ax1.plot(X[signum-1],Y[signum-1])
	fftax = np.fft.fft(y)/ylen
	fftax = fftax[0:int(ylen/2)]

	(wtaxA, wtaxD) = pywt.dwt((y),'db1') ##wt approximation + detail coefficients cokolwiek to znaczy
	if(WT_times > 1):
		for i in range(WT_times-1):
			(wtaxA,wtaxD) = pywt.dwt((wtaxA),'db1')

	global FFTaxes
	global WTaxesA
	global WTaxesD
	FFTaxes[signum-1] = fftax
	WTaxesA[signum-1] = wtaxA
	WTaxesD[signum-1] = wtaxD

def test_dwt_axis_arg():
    x = [[3, 7, 1, 1], [-2, 5, 4, 6]]

    cA_, cD_ = pywt.dwt(x, "db2", axis=-1)
    cA, cD = pywt.dwt(x, "db2", axis=1)

    assert_allclose(cA_, cA)
    assert_allclose(cD_, cD)

def test_default_mode():
    # The default mode should be 'symmetric'
    x = [1, 2, 1, 5, -1, 8, 4, 6]
    cA, cD = pywt.dwt(x, 'db2')
    cA2, cD2 = pywt.dwt(x, 'db2', mode='symmetric')
    assert_allclose(cA, cA2)
    assert_allclose(cD, cD2)
    assert_allclose(pywt.idwt(cA, cD, 'db2'), x)

def extractFeatureFromWave(wave):
    cA, cD = pywt.dwt(wave, 'dmey')
    counter = 1
    while counter <= 4:
        print cA.shape
        cA, cD = pywt.dwt(cA, 'dmey')
        counter = counter + 1
    return cA, cD

def bpm_detector(data,fs):
    cA = []
    cD = []
    correl = []
    cD_sum = []
    levels = 4
    max_decimation = 2**(levels-1);
    min_ndx = 60./ 220 * (fs/max_decimation)
    max_ndx = 60./ 40 * (fs/max_decimation)

    for loop in range(0,levels):
        cD = []
        # 1) DWT
        if loop == 0:
            [cA,cD] = pywt.dwt(data,'db4');
            cD_minlen = len(cD)/max_decimation+1;
            cD_sum = numpy.zeros(cD_minlen);
        else:
            [cA,cD] = pywt.dwt(cA,'db4');
        # 2) Filter
        cD = signal.lfilter([0.01],[1 -0.99],cD);

        # 4) Subtractargs.filename out the mean.

        # 5) Decimate for reconstruction later.
        cD = abs(cD[::(2**(levels-loop-1))]);
        cD = cD - numpy.mean(cD);
        # 6) Recombine the signal before ACF
        #    essentially, each level I concatenate
        #    the detail coefs (i.e. the HPF values)
        #    to the beginning of the array
        cD_sum = cD[0:cD_minlen] + cD_sum;

    if [b for b in cA if b != 0.0] == []:
        return no_audio_data()
    # adding in the approximate data as well...
    cA = signal.lfilter([0.01],[1 -0.99],cA);
    cA = abs(cA);
    cA = cA - numpy.mean(cA);
    cD_sum = cA[0:cD_minlen] + cD_sum;

    # ACF
    correl = numpy.correlate(cD_sum,cD_sum,'full')

    midpoint = len(correl) / 2
    correl_midpoint_tmp = correl[midpoint:]
    peak_ndx = peak_detect(correl_midpoint_tmp[min_ndx:max_ndx]);
    if len(peak_ndx) > 1:
        return no_audio_data()

    peak_ndx_adjusted = peak_ndx[0]+min_ndx;
    bpm = 60./ peak_ndx_adjusted * (fs/max_decimation)
    global bpm_list
    bpm_list.append(bpm.item(0))
    #print bpm
    return bpm,correl

def swt(data, wavelet, level=None):
    """
    Stationary Wavelet Transform

    This version is 2 orders of magnitude faster than the one in pywt
    even though it uses pywt for all the calculations.
    
      Input parameters: 

        data
          One-dimensional data to transform
        wavelet
          Either the name of a wavelet or a Wavelet object
        level
          Number of levels

    """
    if level is None:
        level = pywt.swt_max_level(len(data))
    num_levels = level
    idata = data.copy()
    res = []
    for j in range(1,num_levels+1): 
        step_size = int(math.pow(2, j-1))
        last_index = step_size
        # allocate
        cA = np.empty_like(data)
        cD = np.empty_like(data)
        for first in xrange(last_index): # 0 to last_index - 1
            # Getting the indices that we will transform 
            indices = np.arange(first, len(cD), step_size)

            # select the even indices
            even_indices = indices[0::2] 
            # select the odd indices
            odd_indices = indices[1::2] 
            
            # get the even
            (cA1,cD1) = pywt.dwt(idata[indices], wavelet, 'per')
            cA[even_indices] = cA1
            cD[even_indices] = cD1

            # then the odd
            (cA1,cD1) = pywt.dwt(np.roll(idata[indices],-1), wavelet, 'per')
            cA[odd_indices] = cA1
            cD[odd_indices] = cD1

        # set the data for the next loop
        idata = cA

        # prepend the result
        res.insert(0,(cA,cD))

    return res

def zdwtfun(data, wname):
    matSize = np.shape(data)
    testL, testH = pywt.dwt(data[0, 0, :], wname)
    L = np.zeros((matSize[0], matSize[1], testL.shape[0]), dtype='float64')
    H = np.zeros((matSize[0], matSize[1], testH.shape[0]), dtype='float64')
    for i in range(0, matSize[0]):
        for j in range(0, matSize[1]):
            line = np.float64(data[i, j, :])
            cL, cH = pywt.dwt(line, wname)
            L[i, j, :] = cL
            H[i, j, :] = cH
    return L, H

def makeWT(y,times): ## robi pare razy WT na approx.
	(wtaxA, wtaxD) = pywt.dwt((y),'db1')
	if(times > 1):
		for i in range(times-1):
			(wtaxA,wtaxD) = pywt.dwt((wtaxA),'db1')
	ymin = min(wtaxD)
	for index,x in enumerate(wtaxD):
		wtaxD[index-1]=wtaxD[index-1]-ymin
	ymin = min(wtaxA)
	for index,x in enumerate(wtaxA):
		wtaxA[index-1]=wtaxA[index-1] - ymin
	
	return wtaxA,wtaxD

def test_mode_equivalence():
    old_new = [('zpd', 'zero'),
               ('cpd', 'constant'),
               ('sym', 'symmetric'),
               ('ppd', 'periodic'),
               ('sp1', 'smooth'),
               ('per', 'periodization')]
    x = np.arange(8.)
    with warnings.catch_warnings():
        warnings.simplefilter('ignore', DeprecationWarning)
        for old, new in old_new:
            assert_array_equal(pywt.dwt(x, 'db2', mode=old),
                               pywt.dwt(x, 'db2', mode=new))

def test_dwt_single_axis():
    x = [[3, 7, 1, 1], [-2, 5, 4, 6]]

    cA, cD = pywt.dwt(x, "db2", axis=-1)

    cA0, cD0 = pywt.dwt(x[0], "db2")
    cA1, cD1 = pywt.dwt(x[1], "db2")

    assert_allclose(cA[0], cA0)
    assert_allclose(cA[1], cA1)

    assert_allclose(cD[0], cD0)
    assert_allclose(cD[1], cD1)

def coiflets_wavelets_transf(X, parameter1=1):
    parameter1 = parameter1 if parameter1 in range(1, 6) else 1
    funct = 'coif'+str(parameter1)
    funct = funct if funct in pywt.wavelist('coif') else 'coif1'
    Xt = np.array([np.concatenate(pywt.dwt(X[:, i], funct))
                   for i in range(X.shape[1])])
    return Xt

def symlets_wavelets_transf(X, parameter1=2):
    parameter1 = parameter1 if parameter1 in range(2, 21) else 2
    funct = 'sym'+str(parameter1)
    funct = funct if funct in pywt.wavelist('sym') else 'sym2'
    Xt = np.array([np.concatenate(pywt.dwt(X[:, i], funct))
                  for i in range(X.shape[1])])
    return Xt

def daubechies_wavelet_transf(X, parameter1=1):
    parameter1 = parameter1 if parameter1 in range(1, 21) else 1
    funct = 'db'+str(parameter1)
    funct = funct if funct in pywt.wavelist('db') else 'db1'
    Xt = np.array([np.concatenate(pywt.dwt(X[:, i], funct))
                   for i in range(X.shape[1])])
    return Xt

    def wavelet(self, column, name):
        sample_size = self.sample_size
        sc = self.sc
        link = self.file
        length = self.file_size

        tab = []
        for i in range(0, length):
            tab.append(length - i)

        def get_key(iterator, size):
            key = int(iterator/size)
            iterator += 1
            return key

        rdd = sc\
            .textFile(link)\
            .filter(lambda line: name not in line)\
            .map(lambda line: (get_key(tab.pop(), sample_size), re.split(r';', line)[column]))\
            .groupByKey().mapValues(list)\
            .map(lambda line: (line[0], pywt.dwt(line[1], 'db1')[1]))

        def get_previous_line(line):
            iterator = line[0]
            if iterator == 0:
                prev = rdd.filter(lambda my_line: my_line[0] == iterator).collect()[0][1]
            else:
                prev = rdd.filter(lambda my_line: my_line[0] == iterator - 1).collect()[0][1]
            d = distance.euclidean(line[1], prev)
            return d

        return rdd\
            .map(lambda line: get_previous_line(line))\
            .collect()