CunrenLiang
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2637bb6a9e
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@ -755,146 +755,3 @@ def reformatMaskedAreas(maskedAreas, length, width):
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return maskedAreasReformated
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########################################################################################################
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# The following functions are not used anywhere, and can be deleted.
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########################################################################################################
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def fit_surface(x, y, z, wgt, order):
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# x: x coordinate, a column vector
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# y: y coordinate, a column vector
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# z: z coordinate, a column vector
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# wgt: weight of the data points, a column vector
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#number of data points
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m = x.shape[0]
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l = np.ones((m,1), dtype=np.float64)
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# #create polynomial
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# if order == 1:
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# #order of estimated coefficents: 1, x, y
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# a1 = np.concatenate((l, x, y), axis=1)
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# elif order == 2:
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# #order of estimated coefficents: 1, x, y, x*y, x**2, y**2
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# a1 = np.concatenate((l, x, y, x*y, x**2, y**2), axis=1)
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# elif order == 3:
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# #order of estimated coefficents: 1, x, y, x*y, x**2, y**2, x**2*y, y**2*x, x**3, y**3
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# a1 = np.concatenate((l, x, y, x*y, x**2, y**2, x**2*y, y**2*x, x**3, y**3), axis=1)
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# else:
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# raise Exception('order not supported yet\n')
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if order < 1:
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raise Exception('order must be larger than 1.\n')
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#create polynomial
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a1 = l;
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for i in range(1, order+1):
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for j in range(i+1):
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a1 = np.concatenate((a1, x**(i-j)*y**(j)), axis=1)
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#number of variable to be estimated
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n = a1.shape[1]
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#do the least squares
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a = a1 * np.matlib.repmat(np.sqrt(wgt), 1, n)
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b = z * np.sqrt(wgt)
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c = np.linalg.lstsq(a, b, rcond=-1)[0]
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#type: <class 'numpy.ndarray'>
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return c
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def cal_surface(x, y, c, order):
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#x: x coordinate, a row vector
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#y: y coordinate, a column vector
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#c: coefficients of polynomial from fit_surface
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#order: order of polynomial
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if order < 1:
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raise Exception('order must be larger than 1.\n')
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#number of lines
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length = y.shape[0]
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#number of columns, if row vector, only one element in the shape tuple
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#width = x.shape[1]
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width = x.shape[0]
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x = np.matlib.repmat(x, length, 1)
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y = np.matlib.repmat(y, 1, width)
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z = c[0] * np.ones((length,width), dtype=np.float64)
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index = 0
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for i in range(1, order+1):
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for j in range(i+1):
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index += 1
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z += c[index] * x**(i-j)*y**(j)
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return z
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def weight_fitting(ionos, weight, width, length, nrli, nali, nrlo, nalo, order):
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'''
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ionos: input ionospheric phase
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weight: weight
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width: file width
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length: file length
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nrli: number of range looks of the input interferograms
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nali: number of azimuth looks of the input interferograms
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nrlo: number of range looks of the output ionosphere phase
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nalo: number of azimuth looks of the ioutput ionosphere phase
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order: the order of the polynomial for fitting ionosphere phase estimates
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'''
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from isceobj.Alos2Proc.Alos2ProcPublic import create_multi_index2
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lengthi = int(length/nali)
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widthi = int(width/nrli)
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lengtho = int(length/nalo)
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widtho = int(width/nrlo)
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#calculate output index
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rgindex = create_multi_index2(widtho, nrli, nrlo)
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azindex = create_multi_index2(lengtho, nali, nalo)
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#look for data to use
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flag = (weight!=0)*(ionos!=0)
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point_index = np.nonzero(flag)
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m = point_index[0].shape[0]
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#calculate input index matrix
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x0=np.matlib.repmat(np.arange(widthi), lengthi, 1)
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y0=np.matlib.repmat(np.arange(lengthi).reshape(lengthi, 1), 1, widthi)
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x = x0[point_index].reshape(m, 1)
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y = y0[point_index].reshape(m, 1)
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z = ionos[point_index].reshape(m, 1)
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w = weight[point_index].reshape(m, 1)
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#convert to higher precision type before use
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x=np.asfarray(x,np.float64)
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y=np.asfarray(y,np.float64)
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z=np.asfarray(z,np.float64)
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w=np.asfarray(w,np.float64)
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coeff = fit_surface(x, y, z, w, order)
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#convert to higher precision type before use
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rgindex=np.asfarray(rgindex,np.float64)
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azindex=np.asfarray(azindex,np.float64)
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phase_fit = cal_surface(rgindex, azindex.reshape(lengtho, 1), coeff, order)
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#format: widtho, lengtho, single band float32
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return phase_fit
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