我们从Python开源项目中，提取了以下15个代码示例，用于说明如何使用numpy.piecewise()。
def get_LUT_value(self, data, window, level): """Apply the RGB Look-Up Table for the given data and window/level value.""" if not have_numpy: raise ImportError("Numpy is not available. See http://numpy.scipy.org/ to download and install") if isinstance(window, list): window = window[0] if isinstance(level, list): level = level[0] return np.piecewise(data, [data <= (level - 0.5 - (window - 1) / 2), data > (level - 0.5 + (window - 1) / 2)], [0, 255, lambda data: ((data - (level - 0.5)) / (window - 1) + 0.5) * (255 - 0)] ) # ----------------------------------------------------------- # ImFrame.loadPIL_LUT(dataset) # Display an image using the Python Imaging Library (PIL) # -----------------------------------------------------------
def cosine_kernel(x): ''' Raised cosine basis kernel, normalized such that it integrates to 1 centered at zero. Time is rescaled so that the kernel spans from -2 to 2 Parameters ---------- x : vector Returns ------- vector $\\tfrac 1 4 + \\tfrac 1 2 cos(x)$ if $x\in[-\pi,\pi]$, otherwise 0. ''' x = np.float64(np.abs(x))/2.0*np.pi return np.piecewise(x,[x<=np.pi],[lambda x:(np.cos(x)+1)/4.0])
def safe_inverse_root(d): if (d < 0).any(): raise ValueError return np.piecewise(d, [d>0, d==0], [lambda x: x**(-0.5), lambda x: x])
def get_LUT_value(data, window, level): """Apply the RGB Look-Up Table for the given data and window/level value.""" if not have_numpy: raise ImportError("Numpy is not available. See http://numpy.scipy.org/" " to download and install") return np.piecewise(data, [data <= (level - 0.5 - (window - 1) / 2), data > (level - 0.5 + (window - 1) / 2)], [0, 255, lambda data: ((data - (level - 0.5)) / (window - 1) + 0.5) * (255 - 0)])
def breakpoint_linear(x, ts, k1, k2, c1): '''Function representing a step-wise linear curve with one breakpoint located at ts. ''' return np.piecewise(x, [x < ts], [lambda x: k1 * x + c1, lambda x: k2 * x + (k1 - k2) * ts + c1])
def nfw_cumulative(self,R): ''' Cumulative radial NFW distribution @param R: Radius @return float: Probability of being within R ''' R0 = self.__R0 norm = self.__norm Rs = self.__Rs Rcore = self.__Rcore def integrate(z): return quad(nfw,0,z,args=(norm,Rs,Rcore))[0] if np.isscalar(R): R = np.array([float(R)]) else: R = R.astype(np.float) res = np.piecewise(R, [R < 0.0, np.logical_and(R >= 0.0, R < R0), R >= R0], [lambda z: z, lambda z: np.fromiter(map(integrate,z),np.float), lambda z: z/R0]) if len(res) == 1: return res[0] else: return res
def coordinate_Newton(losses, indice, grad, hess, batch_size, mt_arr, vt_arr, real_modifier, up, down, lr, adam_epoch, beta1, beta2, proj): # def sign(x): # return np.piecewise(x, [x < 0, x >= 0], [-1, 1]) cur_loss = losses[0] for i in range(batch_size): grad[i] = (losses[i*2+1] - losses[i*2+2]) / 0.0002 hess[i] = (losses[i*2+1] - 2 * cur_loss + losses[i*2+2]) / (0.0001 * 0.0001) # print("New epoch:") # print('grad', grad) # print('hess', hess) # hess[hess < 0] = 1.0 # hess[np.abs(hess) < 0.1] = sign(hess[np.abs(hess) < 0.1]) * 0.1 # negative hessian cannot provide second order information, just do a gradient descent hess[hess < 0] = 1.0 # hessian too small, could be numerical problems hess[hess < 0.1] = 0.1 # print(hess) m = real_modifier.reshape(-1) old_val = m[indice] old_val -= lr * grad / hess # set it back to [-0.5, +0.5] region if proj: old_val = np.maximum(np.minimum(old_val, up[indice]), down[indice]) # print('delta', old_val - m[indice]) m[indice] = old_val # print(m[indice])
def predict(weight, inp): inp = modulate(inp) net = np.dot(weight, inp) predOtp = np.piecewise(net, [net<0, net>0], [-1, 1]) return demodulate(predOtp)
def continuum_kernel(x): ''' limit of continuum magic kernel as a piecewise function. See http://johncostella.webs.com/magic/ Parameters ---------- Returns ------- ''' x = np.float64(abs(x)) return np.piecewise(x,[x>=1.5,(x>=0.5)&(x<1.5),(x>=0.0)&(x<0.5)],\ [lambda x:0, lambda x:0.5*(x-1.5)**2, lambda x:0.75-x**2])
def cosine_kernel(x): ''' raised cosine basis kernel, normalized such that it integrates to 1 centered at zero. Time is rescaled so that the kernel spans from -2 to 2 Parameters ---------- Returns ------- ''' x = np.float64(np.abs(x))/2.0*pi return np.piecewise(x,[x<=pi],[lambda x:(np.cos(x)+1)/4.0])
def stabilize(td): """Compensate for motion of the ATIS sensor during recording of the Neuromorphic datasets Applies to the N-MNIST and N-Caltech101 datasets. The image motion is originally induced by egorotation of the ATIS sensor td: eventvision.Events """ assert isinstance(td, ev.Events) def correct_saccade1(data): data.x -= np.rint(3.5 * data.ts / 105e3).astype(np.uint16) data.y -= np.rint(7 * data.ts / 105e3).astype(np.uint16) return data def correct_saccade2(data): data.x -= np.rint(3.5 + 3.5 * (data.ts - 105e3) / 105e3).astype(np.uint16) data.y -= np.rint(7 - 7 * (data.ts - 105e3) / 105e3).astype(np.uint16) return data def correct_saccade3(data): data.x -= np.rint(7 - 7 * (data.ts - 210e3) / 105e3).astype(np.uint16) return data copy = np.piecewise(td.data,\ [td.data.ts <= 105e3, (td.data.ts > 105e3) & (td.data.ts <= 210e3), (td.data.ts > 210e3)],\ [correct_saccade1, correct_saccade2, correct_saccade3]).view(np.recarray) # after saccades, we might end up with invalid x and y values, have to # correct these x_vals = copy.x y_vals = copy.y copy.x = np.piecewise(x_vals,\ [x_vals >= 65000, (x_vals < 65000) & (x_vals >= td.width), x_vals < td.width],\ [0, td.width - 1, lambda x: x]) copy.y = np.piecewise(y_vals,\ [y_vals >= 65000, (y_vals < 65000) & (y_vals >= td.height), y_vals < td.height],\ [0, td.height - 1, lambda y: y]) return copy
def show_em(self): """Displays the EM events (grayscale ATIS events)""" frame_length = 24e3 t_max = self.data.ts[-1] frame_start = self.data[0].ts frame_end = self.data[0].ts + frame_length max_val = 1.16e5 min_val = 1.74e3 val_range = max_val - min_val thr = np.rec.array(None, dtype=[('valid', np.bool_), ('low', np.uint64), ('high', np.uint64)], shape=(self.height, self.width)) thr.valid.fill(False) thr.low.fill(frame_start) thr.high.fill(0) def show_em_frame(frame_data): """Prepare and show a single frame of em data to be shown""" for datum in np.nditer(frame_data): ts_val = datum['ts'].item(0) thr_data = thr[datum['y'].item(0), datum['x'].item(0)] if datum['p'].item(0) == 0: thr_data.valid = 1 thr_data.low = ts_val elif thr_data.valid == 1: thr_data.valid = 0 thr_data.high = ts_val - thr_data.low img = 255 * (1 - (thr.high - min_val) / (val_range)) #thr_h = cv2.adaptiveThreshold(thr_h, 255, #cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 3, 0) img = np.piecewise(img, [img <= 0, (img > 0) & (img < 255), img >= 255], [0, lambda x: x, 255]) img = img.astype('uint8') cv2.imshow('img', img) cv2.waitKey(1) while frame_start < t_max: #with timer.Timer() as em_playback_timer: frame_data = self.data[(self.data.ts >= frame_start) & (self.data.ts < frame_end)] show_em_frame(frame_data) frame_start = frame_end + 1 frame_end += frame_length + 1 #print 'showing em frame took %s seconds' %em_playback_timer.secs cv2.destroyAllWindows() return
def show_td(self, wait_delay=1): """Displays the TD events (change detection ATIS or DVS events) waitDelay: milliseconds """ frame_length = 24e3 t_max = self.data.ts[-1] frame_start = self.data[0].ts frame_end = self.data[0].ts + frame_length td_img = np.ones((self.height, self.width), dtype=np.uint8) while frame_start < t_max: frame_data = self.data[(self.data.ts >= frame_start) & (self.data.ts < frame_end)] if frame_data.size > 0: td_img.fill(128) #with timer.Timer() as em_playback_timer: for datum in np.nditer(frame_data): td_img[datum['y'].item(0), datum['x'].item(0)] = datum['p'].item(0) #print 'prepare td frame by iterating events took %s seconds' #%em_playback_timer.secs td_img = np.piecewise(td_img, [td_img == 0, td_img == 1, td_img == 128], [0, 255, 128]) cv2.imshow('img', td_img) cv2.waitKey(wait_delay) frame_start = frame_end + 1 frame_end = frame_end + frame_length + 1 cv2.destroyAllWindows() return
