我们从Python开源项目中，提取了以下9个代码示例，用于说明如何使用scipy.ceil()。
def lambda_interp(lambdau, s): # lambda is the index sequence that is produced by the model # s is the new vector at which evaluations are required. # the value is a vector of left and right indices, and a vector of fractions. # the new values are interpolated bewteen the two using the fraction # Note: lambda decreases. you take: # sfrac*left+(1-sfrac*right) if len(lambdau) == 1: nums = len(s) left = scipy.zeros([nums, 1], dtype = scipy.integer) right = left sfrac = scipy.zeros([nums, 1], dtype = scipy.float64) else: s[s > scipy.amax(lambdau)] = scipy.amax(lambdau) s[s < scipy.amin(lambdau)] = scipy.amin(lambdau) k = len(lambdau) sfrac = (lambdau[0] - s)/(lambdau[0] - lambdau[k - 1]) lambdau = (lambdau[0] - lambdau)/(lambdau[0] - lambdau[k - 1]) coord = scipy.interpolate.interp1d(lambdau, range(k))(sfrac) left = scipy.floor(coord).astype(scipy.integer, copy = False) right = scipy.ceil(coord).astype(scipy.integer, copy = False) # tf = left != right sfrac[tf] = (sfrac[tf] - lambdau[right[tf]])/(lambdau[left[tf]] - lambdau[right[tf]]) sfrac[~tf] = 1.0 #if left != right: # sfrac = (sfrac - lambdau[right])/(lambdau[left] - lambdau[right]) #else: # sfrac[left == right] = 1.0 result = dict() result['left'] = left result['right'] = right result['frac'] = sfrac return(result) # end of lambda_interp # =========================================
def __init__( self, w, h, r, n ): # w and h are the width and height of the field self.w = w self.h = h # n is the number of test points self.n = n self.r2 = r**2.0 self.A = 3.0*self.r2 # cs is the cell size self.cs = r / scipy.sqrt(2) # gw and gh are the number of grid cells self.gw = int( scipy.ceil( self.w/self.cs ) ) self.gh = int( scipy.ceil( self.h/self.cs ) ) # create a grid and a queue self.grid = [ None ] * self.gw * self.gh self.queue = list() # set the queue size and sample size to zero self.qs, self.ss = 0, 0
def kde(data, N=None, MIN=None, MAX=None): # Parameters to set up the mesh on which to calculate N = 2**14 if N is None else int(2**sci.ceil(sci.log2(N))) if MIN is None or MAX is None: minimum = min(data) maximum = max(data) Range = maximum - minimum MIN = minimum - Range/10 if MIN is None else MIN MAX = maximum + Range/10 if MAX is None else MAX # Range of the data R = MAX-MIN # Histogram the data to get a crude first approximation of the density M = len(data) DataHist, bins = sci.histogram(data, bins=N, range=(MIN,MAX)) DataHist = DataHist/M DCTData = scipy.fftpack.dct(DataHist, norm=None) I = [iN*iN for iN in xrange(1, N)] SqDCTData = (DCTData[1:]/2)**2 # The fixed point calculation finds the bandwidth = t_star guess = 0.1 try: t_star = scipy.optimize.brentq(fixed_point, 0, guess, args=(M, I, SqDCTData)) except ValueError: print 'Oops!' return None # Smooth the DCTransformed data using t_star SmDCTData = DCTData*sci.exp(-sci.arange(N)**2*sci.pi**2*t_star/2) # Inverse DCT to get density density = scipy.fftpack.idct(SmDCTData, norm=None)*N/R mesh = [(bins[i]+bins[i+1])/2 for i in xrange(N)] bandwidth = sci.sqrt(t_star)*R density = density/sci.trapz(density, mesh) return bandwidth, mesh, density
def minmax(arr): minv = arr[0] maxv = arr[0] for val in arr: if val > maxv: maxv = val elif val < minv: minv = val return int(sp.floor(minv)), int(sp.ceil(maxv))
def ellipse2bbox(a, b, angle, cx, cy): a, b = max(a, b), min(a, b) ca = sp.cos(angle) sa = sp.sin(angle) if sa == 0.0: cta = 2.0 / sp.pi else: cta = ca / sa if ca == 0.0: ta = sp.pi / 2.0 else: ta = sa / ca x = lambda t: cx + a * sp.cos(t) * ca - b * sp.sin(t) * sa y = lambda t: cy + b * sp.sin(t) * ca + a * sp.cos(t) * sa # x = cx + a * cos(t) * cos(angle) - b * sin(t) * sin(angle) # tan(t) = -b * tan(angle) / a tx1 = sp.arctan(-b * ta / a) tx2 = tx1 - sp.pi x1, y1 = x(tx1), y(tx1) x2, y2 = x(tx2), y(tx2) # y = cy + b * sin(t) * cos(angle) + a * cos(t) * sin(angle) # tan(t) = b * cot(angle) / a ty1 = sp.arctan(b * cta / a) ty2 = ty1 - sp.pi x3, y3 = x(ty1), y(ty1) x4, y4 = x(ty2), y(ty2) minx, maxx = Util.minmax([x1, x2, x3, x4]) miny, maxy = Util.minmax([y1, y2, y3, y4]) return sp.floor(minx), sp.floor(miny), sp.ceil(maxx), sp.ceil(maxy)
def stft_mc(x,N=1024,hop=None,window='hann'): # N=1024 if hop is None: hop=N/2 S=x.shape if len(S)==1: nch=1 nsampl=len(x) x=np.reshape(x,(1,nsampl)) else: nch=S[0] nsampl=S[1] xdtype=x.dtype nfram=int(scipy.ceil(float(nsampl)/float(hop))) npad=int(nfram)*hop-nsampl pad=np.zeros((nch,npad)).astype(xdtype) x=np.concatenate((x,pad),axis=1) #pad the edges to avoid window taper effects pad=np.zeros((nch,N)).astype(xdtype) x=np.concatenate((pad,x,pad),axis=1) for ich in range(0,nch): x0=x[ich,:] if not x0.flags.c_contiguous: x0=x0.copy(order='C') X0=librosa.core.stft(x0,n_fft=N,hop_length=hop,window=window,center=False,dtype=np.complex64) if ich==0: X=np.zeros((N/2+1,X0.shape[1],nch)).astype(np.complex64) X[:,:,0]=X0 else: X[:,:,ich]=X0 return X
