################################################# ## ## 这个文件包含了数值相对论所需要的格点 ## 小曲 ## 2024/03/20 ## 2025/01/21 修改 ## ################################################# import numpy ## 导入 numpy 包 import matplotlib.pyplot as plt ## 导入 matplotlib 包中的 pyplot 函数，并用 plt 名称代表 matplotlib.pyplot import os ## 导入 os 包进行系统操作 import AMSS_NCKU_Input as input_data ## 导入程序输入文件 ## import print_information ################################################# # 设置黑洞 puncture （穿刺法）的信息 puncture = numpy.zeros( (input_data.puncture_number,3) ) # 初始化每个 puncture 的位置 print( ) print( " 正在设定 Puncture 位置和动量参数" ) print( ) ################################################# ## 设置 puncture 的位置 ## 如果设置 puncture data 的方式选为 TwoPuncture，则读入重设后的 puncture 位置 if (input_data.puncture_data_set == "TwoPuncture" ): import generate_TwoPuncture_input for i in range(input_data.puncture_number): if (i<=1): puncture[i] = generate_TwoPuncture_input.position_BH[i] else: puncture[i] = input_data.position_BH[i] ## 如果如果设置 puncture data 的方式选为 Manually，则直接读入 puncture 位置 elif (input_data.puncture_data_set == "Manually" ): puncture = input_data.position_BH ################################################# ## 输出网格的信息 print( ) print( " Wirte Down The Grid Information " ) print( ) print( " Number of Total Grid Level = ", input_data.grid_level ) ## 输出网格的总层数 print( " Number of Static Grid Level = ", input_data.static_grid_level ) ## 输出静态网格的层数 print( " Number of Moving Grid Level = ", input_data.moving_grid_level ) ## 输出可移动网格的层数 ## print( " Number of Points in Each Grid Level = ", input_data.grid_number ) ## 输出每一层每一维数的网格格点数目 print( ) ################################################# print( ) print( " 正在设定计算网格 " ) print( ) ################################################# ## 初始化网格信息 ## 初始化网格坐标最小值、最大值、格点数目 Grid_X_Min = numpy.zeros( (input_data.grid_level) ) # 定义实数数组，作为每一层网格 X 坐标的最小值 Grid_X_Max = numpy.zeros( (input_data.grid_level) ) # 定义实数数组，作为每一层网格 X 坐标的最大值 Grid_Y_Min = numpy.zeros( (input_data.grid_level) ) # 定义实数数组，作为每一层网格 Y 坐标的最小值 Grid_Y_Max = numpy.zeros( (input_data.grid_level) ) # 定义实数数组，作为每一层网格 Y 坐标的最大值 Grid_Z_Min = numpy.zeros( (input_data.grid_level) ) # 定义实数数组，作为每一层网格 Z 坐标的最小值 Grid_Z_Max = numpy.zeros( (input_data.grid_level) ) # 定义实数数组，作为每一层网格 Z 坐标的最大值 Grid_Resolution = numpy.zeros( input_data.grid_level ) # 定义每层网格分辨率 static_grid_number = input_data.static_grid_number # 定义整数数组，作为每一层固定网格每一维的格点数 moving_grid_number = input_data.moving_grid_number # 定义整数数组，作为每一层可移动网格每一维的格点数 ## 初始化固定网格 ## 定义实数数组，维度 grid_number * static_grid_level，分别作为每一层固定网格的 X Y Z 坐标 Static_Grid_X = numpy.zeros( (input_data.static_grid_level, input_data.static_grid_number+1) ) Static_Grid_Y = numpy.zeros( (input_data.static_grid_level, input_data.static_grid_number+1) ) Static_Grid_Z = numpy.zeros( (input_data.static_grid_level, input_data.static_grid_number+1) ) ## 初始化可移动格点 ## 定义实数数组，维度 grid_number * puncture_number * moving_grid_level，作为每一层可移动网格的 X Y Z 坐标 Moving_Grid_X = numpy.zeros( (input_data.moving_grid_level, input_data.puncture_number, input_data.moving_grid_number+1) ) Moving_Grid_Y = numpy.zeros( (input_data.moving_grid_level, input_data.puncture_number, input_data.moving_grid_number+1) ) Moving_Grid_Z = numpy.zeros( (input_data.moving_grid_level, input_data.puncture_number, input_data.moving_grid_number+1) ) ## 初始化可移动网格的坐标最小值、最大值 Moving_Grid_X_Min = numpy.zeros( (input_data.moving_grid_level, input_data.puncture_number) ) Moving_Grid_X_Max = numpy.zeros( (input_data.moving_grid_level, input_data.puncture_number) ) Moving_Grid_Y_Min = numpy.zeros( (input_data.moving_grid_level, input_data.puncture_number) ) Moving_Grid_Y_Max = numpy.zeros( (input_data.moving_grid_level, input_data.puncture_number) ) Moving_Grid_Z_Min = numpy.zeros( (input_data.moving_grid_level, input_data.puncture_number) ) Moving_Grid_Z_Max = numpy.zeros( (input_data.moving_grid_level, input_data.puncture_number) ) ################################################# ################################################# ## 根据输入文件，设置每层固定网格坐标的最小值和最大值 ## 设置第一层固定网格的坐标最小值与最大值 Grid_X_Min[0] = input_data.grid_minvalue Grid_X_Max[0] = input_data.grid_maxvalue Grid_Y_Min[0] = input_data.grid_minvalue Grid_Y_Max[0] = input_data.grid_maxvalue Grid_Z_Min[0] = input_data.grid_minvalue Grid_Z_Max[0] = input_data.grid_maxvalue ## 设置其它层固定网格的坐标最大值与最小值 for i in range(input_data.static_grid_level-1) : Grid_X_Min[i+1] = Grid_X_Min[i] / input_data.devide_factor Grid_X_Max[i+1] = Grid_X_Max[i] / input_data.devide_factor Grid_Y_Min[i+1] = Grid_Y_Min[i] / input_data.devide_factor Grid_Y_Max[i+1] = Grid_Y_Max[i] / input_data.devide_factor Grid_Z_Min[i+1] = Grid_Z_Min[i] / input_data.devide_factor Grid_Z_Max[i+1] = Grid_Z_Max[i] / input_data.devide_factor ## 每层固定网格最小值（或最大值）为上一层固定网格最小值（或最大值）除以因子 devide_factor ## 固定网格与可移动网格的格点数目不同，为保证相同分辨率，需要引入网格大小调节因子 adjust_factor = input_data.moving_grid_number / input_data.static_grid_number ## 设置第一层可移动网格的坐标最大值与最小值 i = input_data.static_grid_level Grid_X_Min[i] = ( Grid_X_Min[i-1] / input_data.devide_factor ) * adjust_factor Grid_X_Max[i] = ( Grid_X_Max[i-1] / input_data.devide_factor ) * adjust_factor Grid_Y_Min[i] = ( Grid_Y_Min[i-1] / input_data.devide_factor ) * adjust_factor Grid_Y_Max[i] = ( Grid_Y_Max[i-1] / input_data.devide_factor ) * adjust_factor Grid_Z_Min[i] = ( Grid_Z_Min[i-1] / input_data.devide_factor ) * adjust_factor Grid_Z_Max[i] = ( Grid_Z_Max[i-1] / input_data.devide_factor ) * adjust_factor ## 设置其它层可移动网格的坐标最大值与最小值 for j in range(input_data.moving_grid_level-1) : k = input_data.static_grid_level + j Grid_X_Min[k+1] = Grid_X_Min[k] / input_data.devide_factor Grid_X_Max[k+1] = Grid_X_Max[k] / input_data.devide_factor Grid_Y_Min[k+1] = Grid_Y_Min[k] / input_data.devide_factor Grid_Y_Max[k+1] = Grid_Y_Max[k] / input_data.devide_factor Grid_Z_Min[k+1] = Grid_Z_Min[k] / input_data.devide_factor Grid_Z_Max[k+1] = Grid_Z_Max[k] / input_data.devide_factor ## 设置每层网格的网格分辨率 for i in range(input_data.static_grid_level) : Grid_Resolution[i] = ( Grid_X_Max[i] - Grid_X_Min[i] ) / input_data.static_grid_number for j in range(input_data.moving_grid_level) : i = j + input_data.static_grid_level Grid_Resolution[i] = ( Grid_X_Max[i] - Grid_X_Min[i] ) / input_data.moving_grid_number ## 设置最外层球壳格点 shell patch 的坐标最大值和最小值 Shell_R_Resolution = Grid_Resolution[0] Shell_R_Min = input_data.grid_maxvalue Shell_R_Max = Shell_R_Min + Grid_Resolution[0] * input_data.shell_grid_number[2] ################################################# ################################################# ## 设置每层网格的坐标值 ################################################# ## 设置固定网格的格点坐标坐标 ## 线性格点 if input_data.static_grid_type == 'Linear' : for i in range(input_data.static_grid_level): Static_Grid_X[i] = numpy.linspace( Grid_X_Min[i], Grid_X_Max[i], static_grid_number+1 ) Static_Grid_Y[i] = numpy.linspace( Grid_Y_Min[i], Grid_Y_Max[i], static_grid_number+1 ) Static_Grid_Z[i] = numpy.linspace( Grid_Z_Min[i], Grid_Z_Max[i], static_grid_number+1 ) # 利用 numpy 设置线性格点，参数表示起始值 Rmin，最大值 Rmax，格点数目 Rnum # 注意，如果是线性格点，则格点坐标最大值为 GridMax；如果是对数格点，则格点坐标最大值为 e^{GridMax} else: print( ) print( " Static Grid Error: Grid Type is Undifined !!!!!!! " ) print( ) ################################################# ## 设置可移动网格的格点坐标坐标 print( " 正在调整移动网格中心的位置，以便同时与 Puncture 位置以及上一层粗网格相匹配 " ) print( ) ## 为了让每一层可移动网格的边界与上一层网格重合，要对 Puncture 的位置进行调整 adjust_puncture = numpy.zeros( (input_data.puncture_number, 3) ) ## 线性格点 if ( input_data.moving_grid_type == "Linear" ): ## 对可移动网格层数进行循环 for j in range(input_data.moving_grid_level) : i = j + input_data.static_grid_level ## 对 Puncture 数目进行循环 for k in range(input_data.puncture_number) : ## 将 Puncture 的位置进行调整 if j==0 : level0 = input_data.static_grid_level - 1 ## 引入新变量，避免代码过长 for m in range(static_grid_number) : if ( Static_Grid_X[level0, m] <= puncture[k,0] <= Static_Grid_X[level0, m+1] ): if ( abs( puncture[k,0] - Static_Grid_X[level0, m] ) < ( Grid_Resolution[i]/2.0 ) ): adjust_puncture[k,0] = Static_Grid_X[level0, m] elif ( abs( puncture[k,0] - Static_Grid_X[level0, m+1 ] ) < ( Grid_Resolution[i]/2.0 ) ): adjust_puncture[k,0] = Static_Grid_X[level0, m+1 ] else: adjust_puncture[k,0] = ( Static_Grid_X[level0, m] + Static_Grid_X[level0, m+1] ) / 2.0 if ( Static_Grid_Y[level0, m] <= puncture[k,1] <= Static_Grid_Y[level0, m+1] ): if ( abs( puncture[k,1] - Static_Grid_Y[level0, m] ) < ( Grid_Resolution[i]/2.0 ) ): adjust_puncture[k,1] = Static_Grid_Y[level0, m] elif ( abs( puncture[k,1] - Static_Grid_Y[level0, m+1] ) < ( Grid_Resolution[i]/2.0 ) ): adjust_puncture[k,1] = Static_Grid_Y[level0, m+1] else: adjust_puncture[k,1] = ( Static_Grid_Y[level0, m] + Static_Grid_Y[level0, m+1] ) / 2.0 if ( Static_Grid_Z[level0, m] <= puncture[k,2] <= Static_Grid_Z[level0, m+1] ): if ( abs( puncture[k,2] - Static_Grid_Z[level0, m] ) < ( Grid_Resolution[i]/2.0 ) ): adjust_puncture[k,2] = Static_Grid_Z[level0, m] elif ( abs( puncture[k,2] - Static_Grid_Z[level0, m+1] ) < ( Grid_Resolution[i]/2.0 ) ): adjust_puncture[k,2] = Static_Grid_Z[level0, m+1] else: adjust_puncture[k,2] = ( Static_Grid_Z[level0, m] + Static_Grid_Z[level0, m+1] ) / 2.0 elif j>0 : for m in range(moving_grid_number) : if ( Moving_Grid_X[j-1,k,m] <= puncture[k,0] <= Moving_Grid_X[j-1,k,m+1] ): if ( abs( puncture[k,0] - Moving_Grid_X[j-1,k,m] ) < ( Grid_Resolution[i]/2.0 ) ): adjust_puncture[k,0] = Moving_Grid_X[j-1,k,m] elif ( abs( puncture[k,0] - Moving_Grid_X[j-1,k,m+1] ) < ( Grid_Resolution[i]/2.0 ) ): adjust_puncture[k,0] = Moving_Grid_X[j-1,k,m+1] else: adjust_puncture[k,0] = ( Moving_Grid_X[j-1,k,m] + Moving_Grid_X[j-1,k,m+1] ) / 2.0 if ( Moving_Grid_Y[j-1,k,m] <= puncture[k,1] <= Moving_Grid_Y[j-1,k,m+1] ): if ( abs( puncture[k,1] - Moving_Grid_Y[j-1,k,m] ) < ( Grid_Resolution[i]/2.0 ) ): adjust_puncture[k,1] = Moving_Grid_Y[j-1,k,m] elif ( abs( puncture[k,1] - Moving_Grid_Y[j-1,k,m+1] ) < ( Grid_Resolution[i]/2.0 ) ): adjust_puncture[k,1] = Moving_Grid_Y[j-1,k,m+1] else: adjust_puncture[k,1] = ( Moving_Grid_Y[j-1,k,m] + Moving_Grid_Y[j-1,k,m+1] ) / 2.0 if ( Moving_Grid_Z[j-1,k,m] <= puncture[k,2] <= Moving_Grid_Z[j-1,k,m+1] ): if ( abs( puncture[k,2] - Moving_Grid_Z[j-1,k,m] ) < ( Grid_Resolution[i]/2.0 ) ): adjust_puncture[k,2] = Moving_Grid_Z[j-1,k,m] elif ( abs( puncture[k,2] - Moving_Grid_Z[j-1,k,m+1] ) < ( Grid_Resolution[i]/2.0 ) ): adjust_puncture[k,2] = Moving_Grid_Z[j-1,k,m+1] else: adjust_puncture[k,2] = ( Moving_Grid_Z[j-1,k,m] + Moving_Grid_Z[j-1,k,m+1] ) / 2.0 else: print( " Adjusting puncture position to compatable with coaser grid ! Error !!! " ) ## Puncture 的位置调整完毕 ## 为了防止 C++ 程度读入数据出错 ## 如果遇到很小的数 1e-10，保留 2 位小数，不使用科学技术法 if ( abs(adjust_puncture[k,0]) < 1e-10 ): adjust_puncture[k,0] = 0.00 # adjust_puncture[k,0] = f"{ adjust_puncture[k,0]:.2f } if ( abs(adjust_puncture[k,1]) < 1e-10 ): adjust_puncture[k,1] = 0.00 # adjust_puncture[k,1] = f"{ adjust_puncture[k,1]:.2f } if ( abs(adjust_puncture[k,2]) < 1e-10 ): adjust_puncture[k,2] = 0.00 # adjust_puncture[k,2] = f"{ adjust_puncture[k,2]:.2f } # 第 j 层可移动网格的 XYZ 坐标最小值（或最大值） = 第 i 层网格的 XYZ 坐标最小值（或最大值） + 第 k 个 puncture 的 XYZ 坐标 Moving_Grid_X_Min[j,k] = adjust_puncture[k,0] + Grid_X_Min[i] Moving_Grid_X_Max[j,k] = adjust_puncture[k,0] + Grid_X_Max[i] Moving_Grid_Y_Min[j,k] = adjust_puncture[k,1] + Grid_Y_Min[i] Moving_Grid_Y_Max[j,k] = adjust_puncture[k,1] + Grid_Y_Max[i] Moving_Grid_Z_Min[j,k] = adjust_puncture[k,2] + Grid_Z_Min[i] Moving_Grid_Z_Max[j,k] = adjust_puncture[k,2] + Grid_Z_Max[i] ## 为了防止 C++ 程度读入数据出错 ## 如果遇到很小的数 1e-10，保留 2 位小数，不使用科学技术法 if ( abs(Moving_Grid_X_Min[j,k]) < 1e-10 ): Moving_Grid_X_Min[j,k] = 0.00 if ( abs(Moving_Grid_X_Max[j,k]) < 1e-10 ): Moving_Grid_X_Max[j,k] = 0.00 if ( abs(Moving_Grid_Y_Min[j,k]) < 1e-10 ): Moving_Grid_Y_Min[j,k] = 0.00 if ( abs(Moving_Grid_Y_Max[j,k]) < 1e-10 ): Moving_Grid_Y_Max[j,k] = 0.00 if ( abs(Moving_Grid_Z_Min[j,k]) < 1e-10 ): Moving_Grid_Z_Min[j,k] = 0.00 if ( abs(Moving_Grid_Z_Max[j,k]) < 1e-10 ): Moving_Grid_Z_Max[j,k] = 0.00 print( f" adjust_puncture[{i},{k},0] = { adjust_puncture[k,0] } " ) print( f" adjust_puncture[{i},{k},1] = { adjust_puncture[k,1] } " ) print( f" adjust_puncture[{i},{k},2] = { adjust_puncture[k,2] } " ) ## 利用 numpy 设置线性格点，参数表示起始值 Rmin，最大值 Rmax，格点数目 Rnum Moving_Grid_X[j,k] = numpy.linspace( Moving_Grid_X_Min[j,k], Moving_Grid_X_Max[j,k], moving_grid_number + 1 ) Moving_Grid_Y[j,k] = numpy.linspace( Moving_Grid_Y_Min[j,k], Moving_Grid_Y_Max[j,k], moving_grid_number + 1 ) Moving_Grid_Z[j,k] = numpy.linspace( Moving_Grid_Z_Min[j,k], Moving_Grid_Z_Max[j,k], moving_grid_number + 1 ) else: print( ) print( " Moving Grid Error: Grid Type is Undifined !!!!!!! " ) print( ) print( ) print( " 移动网格中心位置调整完毕 " ) print( ) ################################################# ################################################# ## 该函数画出初始的网格 def plot_initial_grid(): ## 依次画出最后的网格 if (input_data.static_grid_level > 0): X0, Y0 = numpy.meshgrid( Static_Grid_X[0], Static_Grid_Y[0] ) plt.plot( X0, Y0, # 画出第 0 层静态格点 color='brown', # 全部点设置为红棕色 marker='.', # 点的形状为圆点 linestyle='' ) # 线型为空，也即点与点之间不用线连接 if (input_data.static_grid_level > 1): X1, Y1 = numpy.meshgrid( Static_Grid_X[1], Static_Grid_Y[1] ) plt.plot( X1, Y1, # 画出第 1 层静态格点 color='red', # 全部点设置为红色 marker='.', # 点的形状为圆点 linestyle='' ) # 线型为空，也即点与点之间不用线连接 if (input_data.static_grid_level > 2): X2, Y2 = numpy.meshgrid( Static_Grid_X[2], Static_Grid_Y[2] ) plt.plot( X2, Y2, # 画出第 2 层静态格点 color='orange', # 全部点设置为橙色 marker='.', # 点的形状为圆点 linestyle='' ) # 线型为空，也即点与点之间不用线连接 if (input_data.static_grid_level > 3): X3, Y3 = numpy.meshgrid( Static_Grid_X[3], Static_Grid_Y[3] ) plt.plot( X3, Y3, # 画出第 3 层静态格点 color='yellow', # 全部点设置为黄色 marker='.', # 点的形状为圆点 linestyle='' ) # 线型为空，也即点与点之间不用线连接 if (input_data.static_grid_level > 4): X4, Y4 = numpy.meshgrid( Static_Grid_X[4], Static_Grid_Y[4] ) plt.plot( X4, Y4, # 画出第 4 层静态格点 color='greenyellow', # 全部点设置为黄绿色 marker='.', # 点的形状为圆点 linestyle='' ) # 线型为空，也即点与点之间不用线连接 ## 依次画出可移动网格 if (input_data.moving_grid_level > 0): for k in range(input_data.puncture_number): Xk0, Yk0 = numpy.meshgrid( Moving_Grid_X[0,k], Moving_Grid_Y[0,k] ) plt.plot( Xk0, Yk0, # 画出第 0 层可移动格点 color='cyan', # 全部点设置为青蓝色 marker='.', # 点的形状为圆点 linestyle='' ) # 线型为空，也即点与点之间不用线连接 if (input_data.moving_grid_level > 1): for k in range(input_data.puncture_number): Xk1, Yk1 = numpy.meshgrid( Moving_Grid_X[1,k], Moving_Grid_Y[1,k] ) plt.plot( Xk1, Yk1, # 画出第 1 层可移动格点 color='blue', # 全部点设置为蓝色 marker='.', # 点的形状为圆点 linestyle='' ) # 线型为空，也即点与点之间不用线连接 if (input_data.moving_grid_level > 2): for k in range(input_data.puncture_number): Xk2, Yk2 = numpy.meshgrid( Moving_Grid_X[2,k], Moving_Grid_Y[2,k] ) plt.plot( Xk2, Yk2, # 画出第 1 层可移动格点 color='navy', # 全部点设置为深蓝色 marker='.', # 点的形状为圆点 linestyle='' ) # 线型为空，也即点与点之间不用线连接 if (input_data.moving_grid_level > 3): for k in range(input_data.puncture_number): Xk3, Yk3 = numpy.meshgrid( Moving_Grid_X[3,k], Moving_Grid_Y[3,k] ) plt.plot( Xk3, Yk3, # 画出第 1 层可移动格点 color='gray', # 全部点设置为灰色 marker='.', # 点的形状为圆点 linestyle='' ) # 线型为空，也即点与点之间不用线连接 if (input_data.moving_grid_level > 4): for k in range(input_data.puncture_number): Xk4, Yk4 = numpy.meshgrid( Moving_Grid_X[4,k], Moving_Grid_Y[4,k] ) plt.plot( Xk4, Yk4, # 画出第 1 层可移动格点 color='black', # 全部点设置为灰色 marker='.', # 点的形状为圆点 linestyle='' ) # 线型为空，也即点与点之间不用线连接 plt.grid(True) ## plt.show() plt.savefig( os.path.join(input_data.File_directionary, "Initial_Grid.jpeg") ) plt.savefig( os.path.join(input_data.File_directionary, "Initial_Grid.pdf") ) ################################################# ################################################# ## 将以上格点信息写入 AMSS-NCKU 程序的输入文件 def append_AMSSNCKU_cgh_input(): file1 = open( os.path.join(input_data.File_directionary, "AMSS-NCKU.input"), "a") # "a" 表示追加输出 ## 输出 cgh 的相关设定 print( file=file1 ) print( "cgh::moving levels start from = ", input_data.static_grid_level, file=file1 ) print( "cgh::levels = ", input_data.grid_level, file=file1) ## 输出固定网格的信息 for i in range(input_data.static_grid_level): print( f"cgh::grids[{i}] = 1", file=file1 ) if ( input_data.Symmetry == "octant-symmetry" ): print( f"cgh::shape[{i}][0][0] = { static_grid_number//2 } ", file=file1 ) print( f"cgh::shape[{i}][0][1] = { static_grid_number//2 } ", file=file1 ) else: print( f"cgh::shape[{i}][0][0] = { static_grid_number } ", file=file1 ) print( f"cgh::shape[{i}][0][1] = { static_grid_number } ", file=file1 ) if ( input_data.Symmetry == "octant-symmetry" ): print( f"cgh::shape[{i}][0][2] = { static_grid_number//2 } ", file=file1 ) elif ( input_data.Symmetry == "equatorial-symmetry" ): print( f"cgh::shape[{i}][0][2] = { static_grid_number//2 } ", file=file1 ) elif ( input_data.Symmetry == "no-symmetry" ): print( f"cgh::shape[{i}][0][2] = { static_grid_number } ", file=file1 ) else: print( " Symmetry Setting Error " ) if ( input_data.Symmetry == "octant-symmetry" ): print( f"cgh::bbox[{i}][0][0] = 0.0 ", file=file1 ) print( f"cgh::bbox[{i}][0][1] = 0.0 ", file=file1 ) else: print( f"cgh::bbox[{i}][0][0] = { Grid_X_Min[i] } ", file=file1 ) print( f"cgh::bbox[{i}][0][1] = { Grid_Y_Min[i] } ", file=file1 ) if ( input_data.Symmetry == "octant-symmetry" ): print( f"cgh::bbox[{i}][0][2] = 0.0 ", file=file1 ) elif ( input_data.Symmetry == "equatorial-symmetry" ): print( f"cgh::bbox[{i}][0][2] = 0.0 ", file=file1 ) elif ( input_data.Symmetry == "no-symmetry" ): print( f"cgh::bbox[{i}][0][2] = { Grid_Z_Min[i] } ", file=file1 ) else: print( " Symmetry Setting Error " ) print( f"cgh::bbox[{i}][0][3] = { Grid_X_Max[i] } ", file=file1 ) print( f"cgh::bbox[{i}][0][4] = { Grid_Y_Max[i] } ", file=file1 ) print( f"cgh::bbox[{i}][0][5] = { Grid_Z_Max[i] } ", file=file1 ) ## 输出可移动网格的信息 ## 对可移动网格层数进行循环 for i in range(input_data.moving_grid_level): j = i + input_data.static_grid_level print( f"cgh::grids[{j}] = { input_data.puncture_number }", file=file1 ) ## 对 Puncture 数目进行循环 for k in range(input_data.puncture_number): print( f"cgh::shape[{j}][{k}][0] = { moving_grid_number } ", file=file1 ) print( f"cgh::shape[{j}][{k}][1] = { moving_grid_number } ", file=file1 ) if ( input_data.Symmetry == "octant-symmetry" ): print( f"cgh::shape[{j}][{k}][2] = { moving_grid_number//2 } ", file=file1 ) elif ( input_data.Symmetry == "equatorial-symmetry" ): print( f"cgh::shape[{j}][{k}][2] = { moving_grid_number//2 } ", file=file1 ) elif ( input_data.Symmetry == "no-symmetry" ): print( f"cgh::shape[{j}][{k}][2] = { moving_grid_number } ", file=file1 ) else: print( " Symmetry Setting Error" ) print( f"cgh::bbox[{j}][{k}][0] = { Moving_Grid_X_Min[i,k] } ", file=file1 ) print( f"cgh::bbox[{j}][{k}][1] = { Moving_Grid_Y_Min[i,k] } ", file=file1 ) if ( input_data.Symmetry == "octant-symmetry" ): print( f"cgh::bbox[{j}][{k}][2] = 0.0", file=file1 ) elif ( input_data.Symmetry == "equatorial-symmetry" ): print( f"cgh::bbox[{j}][{k}][2] = 0.0", file=file1 ) elif ( input_data.Symmetry == "no-symmetry" ): print( f"cgh::bbox[{j}][{k}][2] = { Moving_Grid_Z_Min[i,k] } ", file=file1 ) else: print( " Symmetry Setting Error" ) print( f"cgh::bbox[{j}][{k}][3] = { Moving_Grid_X_Max[i,k] } ", file=file1 ) print( f"cgh::bbox[{j}][{k}][4] = { Moving_Grid_Y_Max[i,k] } ", file=file1 ) print( f"cgh::bbox[{j}][{k}][5] = { Moving_Grid_Z_Max[i,k] } ", file=file1 ) ## 输出 BSSN 的相关设定 print( file=file1 ) print( "############ for shell-box coulping set this exactly to box boundary", file=file1 ) print( "BSSN::Shell shape[0] = ", input_data.shell_grid_number[0], file=file1 ) print( "BSSN::Shell shape[1] = ", input_data.shell_grid_number[1], file=file1 ) print( "BSSN::Shell shape[2] = ", input_data.shell_grid_number[2], file=file1 ) print( "BSSN::Shell R range[0] = ", Shell_R_Min, file=file1 ) print( "BSSN::Shell R range[1] = ", Shell_R_Max, file=file1 ) print( file=file1 ) file1.close() return file1 ################################################# ## ------------------------------------------------------------------------------------------------------------------------------------------------- ## -------------------------------------------------------------------------------------------------------------------------------------------------