def test(file_name, threshold): #读取测试文件地址 path = r'C:\Users\Tyler\Documents\Tencent Files\383746778\FileRecv\new_version\10-binary_file' #读取统计结果各小时矩阵 label_path = r'C:\Users\Tyler\Documents\Tencent Files\383746778\FileRecv\new_version\result' f = open(os.path.join(path, file_name),'r') s = f.read() paraDict,matrix = cleanUpPic.resolveRadar(s) xsize = paraDict['xsize'] ysize = paraDict['ysize'] img_origin = np.reshape(matrix, (ysize, xsize)) hour = file_name[-4:-2] label_data = np.loadtxt(os.path.join(label_path,'final_matrix_'+hour)) img_bool = np.zeros((ysize, xsize)) for i in xrange(ysize): for j in xrange(xsize): dzb = img_origin[i][j] if dzb!=-1: label_test_data = np.reshape(label_data[dzb], (421,561)) if float(label_test_data[i][j]) < threshold: img_bool[i][j] = 1 final_image = img_origin * img_bool plt.subplot(2,1,1) plt.imshow(img_origin) plt.subplot(2,1,2) plt.imshow(final_image) plt.show()
def statistics_history_distribution(): #读取历史数据的地址 path = r'C:\Users\Tyler\Documents\Tencent Files\383746778\FileRecv\new_version\10-binary_file\test' #保存结果的地址 save_path = r'C:\Users\Tyler\Documents\Tencent Files\383746778\FileRecv\new_version\result' y_size = 421 x_size = 561 total_matrix = np.zeros((24,16,421,561), dtype= np.int) #各小时数据和统计 count = np.zeros(24) file_list=os.listdir(path) for file_item in file_list: f = open(os.path.join(path, file_item),'r') s = f.read() paraDict,matrix = cleanUpPic.resolveRadar(s) xsize = paraDict['xsize'] ysize = paraDict['ysize'] img_origin = np.reshape(matrix, (ysize, xsize)) hour = int(file_item[-4:-2]) count[hour] +=1 for i in xrange(16): total_matrix[hour][i] += (img_origin ==i)*1 #每个小时的矩阵转成2维发送 for i in xrange(24): final_matrix = total_matrix[i]*(1.0/count[i]) final_2d_matrix = np.reshape(final_matrix, (16, ysize*xsize)) np.savetxt(os.path.join(save_path,"final_matrix_"+str(i)), final_2d_matrix, fmt = '%1.7f') #total总和矩阵转成二维发送 total_2d_matrix = np.reshape(total_matrix, (24*16, ysize*xsize)) np.savetxt(os.path.join(save_path,"total_matrix"),total_2d_matrix, fmt = '%d') return total_matrix
#list all of the documents list_doc = [] for item in os.listdir(savePath): list_doc.append(item) list_doc.sort() count = 0 #print list_doc for item in list_doc: #read the image f=open(os.path.join(savePath,item),'r') s= f.read() #resolve it read the whole image paraDict,matrix=cleanUpPic.resolveRadar(s) xsize=paraDict['xsize'] ysize=paraDict['ysize'] matrix=np.reshape(matrix,(ysize,xsize)) print xsize, ysize normal_matrix.append(matrix) times_list.append(Time_Trans(paraDict['timeStr'])) #prepic cleanup the noise of the image pic=cleanUpPic.PrePic() pic.Load_Pic_Matrix(matrix) pic.calc_from_DB() bool_matrix.append(pic.pic_bool) all_matrix.append(pic.pic_radar)