def confidence_map2d(data, alpha=1.5, beta=90, gamma=0.03, spacing=None, data_mode='B', solver_mode="cg"): """2d confidence map # Args data: 3d numpy array, RF mode data,(height, width) mode: string, 'RF' or 'B' mode data alpha: float, distance(vertical) penalty beta: float, Random walks parameter gamma: float, horizontal penalty """ data = data.astype('float') data = normalize_data(data) if data_mode == "RF": data = np.abs(hilbert2(data)) labels = np.zeros_like(data) labels[0, :] = 1 # 探头元素 labels[-1, :] = 2 # shadow元素 conf_map = confidence_map(data, labels, alpha, beta, gamma, True, mode=solver_mode, spacing=spacing) conf_map = conf_map[0, :, :] return conf_map
def test_hilbert2(self, num_samps): cpu_sig = np.random.rand(num_samps, num_samps) gpu_sig = cp.asarray(cpu_sig) cpu_hilbert2 = signal.hilbert2(cpu_sig) gpu_hilbert2 = cp.asnumpy(cusignal.hilbert2(gpu_sig)) assert array_equal(cpu_hilbert2, gpu_hilbert2)
def drawPinholeMap(self ): sig = self.WidePinHole[0:300, 0, 0] - np.mean(self.WidePinHole[:, 0, 0]) evp = signal.hilbert2(sig) envelope = np.abs(evp) # envelope = np.real( evp) print "evp max =", np.max(envelope), "arg max =", np.argmax(envelope) plt.close("all") print "drawCurrentPinhole" # plt.gray() # ax = plt.gca() z = plt.axvline(x=frameNo, linewidth=2, color='r') # ax.add_artist(z) plt.plot(envelope, color='r') plt.plot(-envelope, color='g') plt.plot(sig) # plt.plot(self.WidePinHole[:, 1, 0]) plt.show() from cStringIO import StringIO buffer_ = StringIO() plt.savefig(buffer_, format="png") buffer_.seek(0) image = Image.open(buffer_) from PIL.ImageQt import ImageQt qimage = ImageQt(image) buffer_.close() return qimage
def measure_energy1_2d(f): hil = hilbert2(f) energy2d = np.sqrt(np.square(np.real(hil)) + np.square(np.imag(hil))) #phibar = np.arctan(h/f) phibar = np.arctan2(np.imag(hil), np.real(hil)) return (energy2d, phibar)
def measure_energy2_2d(f, pbflag=True): h = np.imag(hilbert2(f)) #phibar = np.arctan(h/f) phibar = np.arctan2(h, f) energy2d = np.multiply((np.cos(phibar) - np.abs(np.sin(phibar))), f) if (pbflag): return (energy2d, phibar) else: return (energy2d)
def hilbert2(x, N=None): return signal.hilbert2(x, N=N)
def cpu_version(self, cpu_sig): return signal.hilbert2(cpu_sig)
def hilb2(x): return hilbert2(fDesign(x)) return hilb2
def filterData(R_Data, L_Data): R_filt = dsp.hilbert2(R_Data) return R_filt
def fvecfilt(fce): return hilbert2(filter_design(sf, timeL, n.array(fce), filtname=filtname, cycle=cycle, order=order, axis=axis)(x))
def hilb2(x): return hilbert2(fDesign(x))