Python signal.argrelmin方法代码示例

# 需要导入模块: from scipy import signal [as 别名]
# 或者: from scipy.signal import argrelmin [as 别名]
def voltage_threshold(recording, threshold, order=5):

    T, C = recording.shape
    spike_index = np.zeros((0, 2), 'int32')
    energy = np.zeros(0, 'float32')
    for c in range(C):
        single_chan_rec = recording[:, c]
        index = argrelmin(single_chan_rec, order=order)[0]
        index = index[single_chan_rec[index] < -threshold]
        spike_index_temp = np.vstack((index,
                                      np.ones(len(index), 'int32')*c)).T
        spike_index = np.concatenate((spike_index, spike_index_temp), axis=0)
        energy_ = np.abs(single_chan_rec[index])
        energy = np.hstack((energy, energy_))

    return spike_index, energy 

# 需要导入模块: from scipy import signal [as 别名]
# 或者: from scipy.signal import argrelmin [as 别名]
def estimate_actions(self, episode_data):
        """
        Estimates hold/buy/sell signals based on local peaks filtered by time horizon and signal amplitude.

        Args:
            episode_data:   1D np.array of unscaled [but possibly resampled] price values in OHL[CV] format

        Returns:
            1D vector of signals of same length as episode_data
        """
        # Find local maxima and minima indices within time horizon:
        max_ind = signal.argrelmax(episode_data, order=self.time_threshold)
        min_ind = signal.argrelmin(episode_data, order=self.time_threshold)
        indices = np.append(max_ind, min_ind)
        # Append first and last points:
        indices = np.append(indices, [0, episode_data.shape[0] - 1])
        indices = np.sort(indices)

        indices_and_values = []

        for i in indices:
            indices_and_values.append([episode_data[i], i])

        # Filter by value:
        indices_and_values = self.filter_by_margine(indices_and_values, self.value_threshold)

        #print('filtered_indices_and_values:', indices_and_values)

        # Estimate advised actions (no 'close' btw):
        # Assume all 'hold':
        advice = np.ones(episode_data.shape[0], dtype=np.uint32) * self.action_space[0]

        for num, (v, i) in enumerate(indices_and_values[:-1]):
            if v < indices_and_values[num + 1][0]:
                advice[i] = self.action_space[1]

            else:
                advice[i] = self.action_space[2]

        return advice 

# 需要导入模块: from scipy import signal [as 别名]
# 或者: from scipy.signal import argrelmin [as 别名]
def find_extrema(signal):
    signal = np.array(signal)
    extrema_index = np.sort(np.unique(np.concatenate((argrelmax(signal)[0], argrelmin(signal)[0]))))
    extrema = signal[extrema_index]
    return zip(extrema_index.tolist(), extrema.tolist()) 

# 需要导入模块: from scipy import signal [as 别名]
# 或者: from scipy.signal import argrelmin [as 别名]
def _getDotDistance(self, dots, axis):
        """ calculates dot distance from dist. between all neighbouring dots
        """
        # create distance matrix distM of dots
        z = np.array([complex(d[0],d[1]) for d in dots])
        #z = dots[:,axis] # TODO: test, ob alles auch hiermit funktionieren würde
        try:
            distM = abs(z[..., np.newaxis] - z)
        except MemoryError:
            self._raise(TooManyDotsException,self.ydPerInch)
        del z
        distM[distM==0] = np.max(distM)+1
        neighbours = np.argmin(distM, axis=0)
        #distances = distM.flat
        #distances = [abs(dots[i,axis]-dots[j,axis]) for i,j in enumerate(neighbours) if dots[i,axis]!=dots[j,axis]]
        distances = np.abs(dots[:,axis]-dots[neighbours,axis])
        distances = distances[distances>0]
        
        f = np.bincount(np.array(distances,dtype=np.uint16))
        #print(f[:15])
        #return np.argmax(f)
        self._print(3,"\n%s\n"%f[:30])
        maxima = np.array(argrelmax(f.argsort().argsort())[0])
        #maxima = [m for m in maxima if np.argmax(f)%m==0]
        MIN_DOT_DISTANCE = 0.01 # inches
        minDist = int(MIN_DOT_DISTANCE*self.imgDpi)
        if len(f[minDist:]) == 0: 
            self._raise(YDExtractingException,"Dot distances below minimum")
        maximum = np.argmax(f[minDist:])+minDist
        distance = maximum/2 if maximum/2.0 in maxima and float(maximum)/self.imgDpi==0.04 else maximum # because of Ricoh's marking dot
        """
        maxima_vals = f[maxima]
        threshold = np.percentile(f,93) #TODO schreiben TODO: oder percentile(f,...)?
        maxima = maxima[maxima_vals>threshold]
        maxima = [x for x in maxima if x<2 or x>len(f)-1-2 or not(f[x-2]<f[x] and f[x+2]>f[x] or f[x-2]>f[x] and f[x+2]<f[x])] #exclude hills on the way to maximum  #TODO: schreiben removed
        # a=f[x-2]-f[x]; b=f[x+2]-f[x]; a<0 and b>0 or a>0 and b<0 gdw a*b<0
        # pitch1 = 
        distance = maxima[1]
        """
        return distance

        """
        # calculate dmin from distribution of $distances 
        # distances for each dot to its closest one
        distances = np.min(distM, axis=0)
        del distM
        #div=100.0 #FIXME: depends on scaling? (hier: float2int)
        div = 1
        distances = np.array((distances*div).round(),dtype=np.int32)
        count = np.bincount(distances)
        maxima = argrelmax(count.argsort().argsort())[0]
        minima = argrelmin(count.argsort().argsort())[0]
        #dMin = minima[0]
        MIN_DOT_DISTANCE = 4/300.0 # inches # TODO: automatic detection of dublicate dots instead
        minDist = int(MIN_DOT_DISTANCE*self.imgDpi*div)
        import ipdb;ipdb.set_trace()
        distance = (np.argmax(count[minDist:])+minDist)/div
        return distance
        return maxima[1]
        """ 

# 需要导入模块: from scipy import signal [as 别名]
# 或者: from scipy.signal import argrelmin [as 别名]
def findPatternLen(self, m):
        distM = np.zeros(shape=(m.shape[0],m.shape[0]))
        z = m.copy()
        z[z==0] = -1
        z[z==.5] = 0
        for i in xrange(m.shape[0]):
            a = z[i]*z
            total = np.sum(a!=0,axis=1)
            a[a==1] = 0
            distM[i] = np.abs(np.sum(a,axis=1))/total
        # remove cols without ones:
        ones = np.sum(z==1,axis=1)
        remove = ones<=0
        distM[:,remove] = np.nan
        distM[remove,:] = np.nan
        
        div=50.0
        f = np.bincount(np.array((distM[np.isnan(distM)==False]*div).round(),dtype=np.int32).flat)
        minima = argrelmin(f.argsort().argsort())[0]
        if len(minima) > 1:
            threshold = minima[0]/div
        elif len(f)>0:
            threshold = f.argmax()/2/div
        else: threshold = 0
        self._print(3,"threshold=%f\n"%threshold)

        X1, X2 = np.nonzero(distM<=threshold)
        #func = lambda arr: np.argmax(np.bincount(arr))
        func = np.median
        #func = np.average
        #func = lambda x: x
        distances = [
            func(self._distances(X1[X2==i]))
            for i in xrange(m.shape[0])
            if len(X1[X2==i])>1
            #and 1 in m[i]
            ]
        distFreq = np.bincount(np.array(distances,dtype=np.int32))
        self._print(3,"Pattern length frequencies: \n%s\n"%repr(distFreq))
        MIN_MATRIX_PATTERN_LENGTH = 5 #4
        MAX_MATRIX_PATTERN_LENGTH = None # 30
        distFreqWindow = distFreq[MIN_MATRIX_PATTERN_LENGTH:MAX_MATRIX_PATTERN_LENGTH]
        if len(distFreqWindow)==0:
            self._print(0,"WARNING: Cannot detect pattern length.\n")
            return -1
        period = np.argmax(distFreqWindow)+MIN_MATRIX_PATTERN_LENGTH
        return period