# 1.去掉重叠部分
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# 2.将数据和标准数据对比，旋转角度和平移使之初步重合
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import pandas as pd
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import numpy as np
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import scipy.linalg as linalg
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import scipy.spatial.distance as dis
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import matplotlib.pyplot as plt
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import math
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import cv2
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# 计算距离矩阵
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def EuclideanDistances(A, B):
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BT = B.transpose()
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vecProd = A * BT
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SqA = A.getA()**2
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sumSqA = np.matrix(np.sum(SqA, axis=1))
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sumSqAEx = np.tile(sumSqA.transpose(), (1, vecProd.shape[1]))
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SqB = B.getA()**2
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sumSqB = np.sum(SqB, axis=1)
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sumSqBEx = np.tile(sumSqB, (vecProd.shape[0], 1))
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SqED = sumSqBEx + sumSqAEx - 2*vecProd
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ED = (SqED.getA())**0.5
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return np.matrix(ED)
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standard = pd.read_csv("D:\\standard.csv")
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standardArray = np.array(standard).transpose()
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data = pd.read_csv("D:\\Level1.csv")
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# **************************去掉重叠部分*******************************
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A = np.matrix(np.array(data[(data["0"].index % 2 == 0) & (data["0"] > 4000) & (data["0"] <61000)])[:,1:4:2])
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B = np.matrix(np.array(data[(data["0"].index % 2 == 1) & (data["0"] > 4000) & (data["0"] <61000)])[:,1:4:2])
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C = np.matrix(np.array(data[(data["0"] <= 4000) | (data["0"] >=61000)])[:,1:4:2])
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# 根据距离计算
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#distanceMatrix = EuclideanDistances(A , B)
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#print(distanceMatrix)
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#print(distanceMatrix.shape)
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#overlapIndex = np.argwhere(distanceMatrix.diagonal()<5)
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# 根据Y方向偏差计算
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if(A.shape[0] > B.shape[0]):
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A = np.delete(A,A.shape[0]-1,0)
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else:
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if(A.shape[0] < B.shape[0]):
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B = np.delete(B,B.shape[0]-1,0)
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yoffset = (A - B)[:,1]
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overlapIndex =np.argwhere(np.abs(yoffset)>2.5)[:,0]
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A = np.delete(A,overlapIndex,0)
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D = np.r_[A,B,C]
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#plt.scatter(D[:,0].getA(),D[:,1].getA(),color='r')
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#plt.grid()
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#plt.show()
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# *********************************************************************
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#dataArray = np.array(data).transpose()[1:4:2,:]/1000
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dataArray = np.array(D).transpose()/1000
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mirroring = np.array([[1,0],[0,-1]])
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dataMirroring = mirroring.dot(dataArray)
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# 只取第一个数据x方向
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mean_x = dataMirroring.mean(axis = 1)[0]
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mean_x_s = standardArray.mean(axis = 1)[0]
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dataFrameMirroring = pd.DataFrame(dataMirroring.transpose())
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dataLine = np.array(dataFrameMirroring[(dataFrameMirroring[0] > 20) & (dataFrameMirroring[0] < 50)]).transpose()
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dataLine_s = np.array(standard[(standard['0'] > 260) & (standard['0'] < 290)]).transpose()
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# 只取y方向
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mean_y = dataLine.mean(axis =1)[1]
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mean_y_s = dataLine_s.mean(axis = 1)[1]
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p = np.poly1d(np.polyfit(dataLine[0,:],dataLine[1,:],1))
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yval = p(dataLine[0,:])
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p_s = np.poly1d(np.polyfit(dataLine_s[0,:],dataLine_s[1,:],1))
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phi = math.atan(p[1])*180/math.pi
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rotateMatrix = cv2.getRotationMatrix2D((mean_x,mean_y),phi,1)
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dataTrans1 = rotateMatrix.dot(np.r_[dataMirroring,np.ones(dataMirroring.shape[1]).reshape(1,dataMirroring.shape[1])])
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transMatrix = np.array([[1,0,mean_x_s - mean_x],[0,1,mean_y_s - mean_y]])
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dataTrans2 = transMatrix.dot(np.r_[dataTrans1,np.ones(dataTrans1.shape[1]).reshape(1,dataTrans1.shape[1])])
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dataFrameTrans2 = pd.DataFrame(dataTrans2.transpose())
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dataFrameTrans2[dataFrameTrans2[1]>443.71].to_csv("D:\\Level1Trans.csv")
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plt.figure()
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plt.scatter(dataTrans2[0,:] , dataTrans2[1,:])
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plt.plot(standardArray[0,:] , standardArray[1,:],color='r')
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plt.show()
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