177 lines
6.1 KiB
Cython
177 lines
6.1 KiB
Cython
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# distutils: language = c++
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# cython: boundscheck = False
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# cython: wraparound = False
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# cython: cdivision = True
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import numpy as np
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cimport numpy as cnp
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from libc.math cimport exp, cos, tanh, log10, pow,atan2,sqrt
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from cython.parallel import prange
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cdef double windspeedcmd5n(double angle, double incidenceangle, double sigma0) nogil:
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"""
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CMOD5N模型计算风速 - Cython优化版本
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输入参数：
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angle - 风向角度(度)
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incidenceangle - 入射角(度)
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sigma0 - 后向散射系数(dB)
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输出参数：
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U - 计算得到的风速(m/s)
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"""
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# 声明C类型变量
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# cdef double U=0
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cdef int i=0
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cdef double y0, n, a, b, x, a0, a1, a2, gamma, s0, s, g, alpha, f
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cdef double b0, b1, b2, angle_rad, sigma0_model, v0, d1, d2, y, v2
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# CMOD5N模型系数 - 使用C数组存储以提高性能
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cdef double[29] cmod5_c = [
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0, -0.6878, -0.7957, 0.3380, -0.1728, 0.0000, 0.0040, 0.1103, 0.0159,
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6.7329, 2.7713, -2.2885, 0.4971, -0.7250, 0.0450,
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0.0066, 0.3222, 0.0120, 22.7000, 2.0813, 3.0000, 8.3659,
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-3.3428, 1.3236, 6.2437, 2.3893, 0.3249, 4.1590, 1.6930
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]
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# 确保入射角不小于16度
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if incidenceangle <= 16.0:
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incidenceangle = 16.0
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i=0
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cdef int start = 0
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cdef int end = 2601
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# 在0-26m/s范围内搜索风速
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for i in range(start, end): # 0到26.0，步长0.01，共2601次迭代
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U_actual = i * 0.01
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# 计算归一化参数
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y0 = cmod5_c[19] # 索引从0开始
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n = cmod5_c[20]
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a = y0 - (y0 - 1.0) / n
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b = 1.0 / (n * pow(y0 - 1.0, n - 1.0))
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# 归一化入射角
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x = (incidenceangle - 40.0) / 25.0
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# 计算基础后向散射系数
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a0 = (cmod5_c[1] + cmod5_c[2] * x + cmod5_c[3] * x * x +
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cmod5_c[4] * x * x * x)
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a1 = cmod5_c[5] + cmod5_c[6] * x
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a2 = cmod5_c[7] + cmod5_c[8] * x
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gamma = (cmod5_c[9] + cmod5_c[10] * x +
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cmod5_c[11] * x * x)
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s0 = cmod5_c[12] + cmod5_c[13] * x
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s = a2 * U_actual
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# 计算饱和函数
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if s < s0:
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g = 1.0 / (1.0 + exp(-s0))
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alpha = s0 * (1.0 - g)
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f = pow(s / s0, alpha) * g
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else:
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f = 1.0 / (1.0 + exp(-s))
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# 计算各向同性部分
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b0 = pow(f, gamma) * pow(10.0, a0 + a1 * U_actual)
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# 计算一阶调和系数
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b1 = (cmod5_c[15] * U_actual *
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(0.5 + x - tanh(4.0 * (x + cmod5_c[16] + cmod5_c[17] * U_actual))))
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b1 = ((cmod5_c[14] * (1.0 + x) - b1) /
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(exp(0.34 * (U_actual - cmod5_c[18])) + 1.0))
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# 计算二阶调和系数
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v0 = cmod5_c[21] + cmod5_c[22] * x + cmod5_c[23] * x * x
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d1 = cmod5_c[24] + cmod5_c[25] * x + cmod5_c[26] * x * x
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d2 = cmod5_c[27] + cmod5_c[28] * x
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y = (U_actual / v0 + 1.0)
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if y < y0:
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v2 = a + b * pow(y - 1.0, n)
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else:
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v2 = y
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b2 = (-d1 + d2 * v2) * exp(-v2)
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# 计算模拟后向散射系数并与实测值比较
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angle_rad = angle * 3.141592653589793 / 180.0
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sigma0_model = (10.0 * log10(b0 *
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pow(1.0 + b1 * cos(angle_rad) + b2 * cos(2.0 * angle_rad), 1.6)))
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if (sigma0_model - sigma0) >= 0.0: # 函数为单调的
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return U_actual
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# 如果未找到匹配的风速，返回最大值
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return 26.0
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cdef void windspeedCmod5n_parallel(float[:, :] wind_dir,
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float[:, :] inc_angle,
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float[:, :] sigma0,
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double[:, :] out_wind_speed) nogil:
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cdef int rowcount = wind_dir.shape[0]
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cdef int colcount = wind_dir.shape[1]
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cdef int rowid, colid
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for rowid in prange(rowcount, nogil=True, schedule='static'): # 使用prange
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for colid in range(colcount):
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# 通过内存视图访问数据是纯C操作，无需GIL
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out_wind_speed[rowid, colid] = windspeedcmd5n(
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wind_dir[rowid, colid],
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inc_angle[rowid, colid],
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sigma0[rowid, colid]
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)
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cdef void ERA5_windAngle_parallel(float[:, :] wind_U10,
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float[:, :] wind_V10,
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float[:, :] out_wind_angle) nogil:
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cdef int rowcount = wind_U10.shape[0]
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cdef int colcount = wind_U10.shape[1]
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cdef int rowid, colid
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cdef double V=0;
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cdef double sinValue=0;
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cdef double cosValue=0;
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cdef double U10=0
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cdef double V10=0
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for rowid in prange(rowcount, nogil=True, schedule='static'):
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for colid in range(colcount):
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U10=wind_U10[rowid,colid]
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V10=wind_V10[rowid,colid]
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V=sqrt(U10*U10+V10*V10)
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sinValue=-U10/V
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cosValue=-V10/V
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out_wind_angle[rowid, colid] =<float>atan2(sinValue,cosValue)
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def python_ERA5_windAngle(cnp.ndarray[float,ndim=2] wind_U10, cnp.ndarray[float,ndim=2] wind_V10):
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cdef float[:, :] out_wind_angle = np.empty((wind_U10.shape[0], wind_U10.shape[1]), dtype=np.float32)
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cdef float[:, :] wind_U10_view = wind_U10
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cdef float[:, :] wind_V10_view = wind_V10
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with nogil:
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ERA5_windAngle_parallel(wind_U10_view, wind_V10_view,out_wind_angle)
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return np.asarray(out_wind_angle)
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# 4. 提供一个Python包装函数（如果需要从Python调用）
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def python_windspeedCmod5n(cnp.ndarray[float,ndim=2] wind_dir, cnp.ndarray[float,ndim=2] inc_angle, cnp.ndarray[float,ndim=2] sigma0):
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# 1. 创建输出数组
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cdef double[:, :] out_speed = np.empty((wind_dir.shape[0], wind_dir.shape[1]), dtype=np.float64)
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# 2. 将输入的numpy数组转换为内存视图
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cdef float[:, :] wind_dir_view = wind_dir
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cdef float[:, :] inc_angle_view = inc_angle
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cdef float[:, :] sigma0_view = sigma0
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# 3. 在nogil上下文中执行核心计算
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with nogil:
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windspeedCmod5n_parallel(wind_dir_view, inc_angle_view, sigma0_view, out_speed)
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# 4. 将结果内存视图转换回numpy数组并返回
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return np.asarray(out_speed)
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