我有以下数据：
{'RH': {0: 58.9289, 1: 50.3455, 2: 50.8142, 3: 53.0112, 4: 55.4512, 5: 57.5399, 6: 59.4835,
7: 67.8222, 8: 70.6624, 9: 72.2602, 10: 73.3083, 11: 74.5302, 12: 75.4472, 13: 76.3634, 14:
77.0452, 15: 77.1069, 16: 77.0966, 17: 76.9039, 18: 72.3328, 19: 66.2169, 20: 62.0029, 21:
59.2981, 22: 57.6574, 23: 56.8761, 24: 56.6011, 25: 45.0752, 26: 43.0393, 27: 43.7027, 28:
45.9743, 29: 48.4454, 30: 49.8465, 31: 62.2785, 32: 66.3982, 33: 68.8178, 34: 69.8033, 35:
70.7879, 36: 71.8861, 37: 74.6885, 38: 75.5149, 39: 75.8017, 40: 75.8211, 41: 74.3816, 42:
70.3053, 43: 63.7589, 44: 59.6312, 45: 56.7952, 46: 54.9899, 47: 53.9745, 48: 53.5251, 49:
48.9537, 50: 49.2989, 51: 50.7327, 52: 52.5816, 53: 53.8873, 54: 55.1234, 55: 64.6119, 56:
66.6161, 57: 68.7152, 58: 70.1556, 59: 71.3258, 60: 72.2003, 61: 73.5831, 62: 74.1941, 63:
74.6312, 64: 74.9443, 65: 73.4724, 66: 68.891, 67: 61.5091, 68: 57.6684, 69: 55.2309, 70:
53.8839, 71: 53.1925, 72: 52.9267}, 'T': {0: 304.755, 1: 305.997, 2: 305.85, 3: 305.34, 4:
304.935, 5: 304.655, 6: 304.422, 7: 302.804, 8: 302.267, 9: 301.937, 10: 301.72, 11:
301.475, 12: 301.269, 13: 300.546, 14: 300.24, 15: 300.179, 16: 300.15, 17: 300.168, 18:
301.02, 19: 302.265, 20: 303.089, 21: 303.655, 22: 304.01, 23: 304.197, 24: 304.256, 25:
306.707, 26: 307.188, 27: 306.993, 28: 306.475, 29: 306.099, 30: 305.874, 31: 303.362, 32:
302.58, 33: 302.204, 34: 302.066, 35: 301.905, 36: 301.706, 37: 300.863, 38: 300.518, 39:
300.352, 40: 300.292, 41: 300.567, 42: 301.396, 43: 302.653, 44: 303.482, 45: 304.086, 46:
304.482, 47: 304.721, 48: 304.827, 49: 306.306, 50: 306.318, 51: 306.082, 52: 305.801, 53:
305.52, 54: 305.187, 55: 302.647, 56: 301.996, 57: 301.505, 58: 301.202, 59: 300.952, 60:
300.741, 61: 300.152, 62: 299.988, 63: 299.885, 64: 299.82, 65: 300.083, 66: 300.945, 67:
302.507, 68: 303.485, 69: 304.133, 70: 304.505, 71: 304.71, 72: 304.799}, 'T_V2': {0:
31.605000000000018, 1: 32.84700000000004, 2: 32.700000000000045, 3: 32.19, 4:
31.785000000000025, 5: 31.504999999999995, 6: 31.272000000000048, 7: 29.653999999999996, 8:
29.11700000000002, 9: 28.787000000000035, 10: 28.57000000000005, 11: 28.325000000000045,
12: 28.119000000000028, 13: 27.396000000000015, 14: 27.090000000000032, 15:
27.028999999999996, 16: 27.0, 17: 27.01800000000003, 18: 27.870000000000005, 19:
29.11500000000001, 20: 29.93900000000002, 21: 30.504999999999995, 22: 30.860000000000014,
23: 31.047000000000025, 24: 31.105999999999995, 25: 33.557000000000016, 26:
34.03800000000001, 27: 33.84300000000002, 28: 33.325000000000045, 29: 32.94900000000001,
30: 32.724000000000046, 31: 30.212000000000046, 32: 29.430000000000007, 33:
29.05400000000003, 34: 28.915999999999997, 35: 28.754999999999995, 36: 28.55600000000004,
37: 27.713000000000022, 38: 27.367999999999995, 39: 27.201999999999998, 40:
27.141999999999996, 41: 27.41700000000003, 42: 28.246000000000038, 43: 29.503000000000043,
44: 30.33200000000005, 45: 30.936000000000035, 46: 31.33200000000005, 47:
31.571000000000026, 48: 31.67700000000002, 49: 33.156000000000006, 50: 33.168000000000006,
51: 32.932000000000016, 52: 32.65100000000001, 53: 32.370000000000005, 54:
32.037000000000035, 55: 29.497000000000014, 56: 28.846000000000004, 57: 28.355000000000018,
58: 28.05200000000002, 59: 27.80200000000002, 60: 27.591000000000008, 61:
27.00200000000001, 62: 26.838000000000022, 63: 26.735000000000014, 64: 26.670000000000016,
65: 26.93300000000005, 66: 27.795000000000016, 67: 29.357000000000028, 68:
30.335000000000036, 69: 30.983000000000004, 70: 31.355000000000018, 71: 31.560000000000002,
72: 31.649}}
我正在尝试使用 python 计算热应力指数。上面的数据有三列RH、T、T_V2。
我将只使用 RH 和 T_V2。
我有以下脚本：
def calculate_heat_index(t,rh):
t_fahrenheit = t * (9./5.) + 32
heat_index_fahrenheit = -42.379 + (2.04901523 * t_fahrenheit) + (10.14333127 * rh) + \
(-0.22475541 * t_fahrenheit * rh) + (-0.006837837 * t_fahrenheit * t_fahrenheit) + \
(-0.05481717 * rh * rh) + (0.001228747 * t_fahrenheit * t_fahrenheit * rh) + \
(0.00085282 * t_fahrenheit * rh * rh) + (-0.00000199 * t_fahrenheit * t_fahrenheit * rh * rh)
locs = np.ma.where(np.ma.logical_and((rh < 13), (t_fahrenheit > 80), (t_fahrenheit < 112)))
if len(locs[0]) > 0:
heat_index_fahrenheit[locs] = heat_index_fahrenheit[locs] - (((13.- rh[locs]) / 4.) * np.ma.sqrt((17. - np.ma.abs(t_fahrenheit[locs] - 95.)) / 17.))
locs = np.ma.where(np.ma.logical_and((rh > 85), (t_fahrenheit > 80), (t_fahrenheit < 87)))
if len(locs[0]) > 0:
heat_index_fahrenheit[locs] = heat_index_fahrenheit[locs] - (((rh[locs ] - 85) / 10.) * ((87. - t_fahrenheit[locs]) / 5.))
locs = np.ma.where(heat_index_fahrenheit < 80)
if len(locs[0]) > 0:
heat_index_fahrenheit[locs] = 0.5 * (t_fahrenheit[locs] + 61. + ((t_fahrenheit[locs] - 68.) * 1.2) + (rh[locs] * 0.094))
heat_index = (heat_index_fahrenheit - 32) / (9./5.)
locs = np.ma.where(t < 26.6667) # 80F
if len(locs[0]) > 0:
heat_index[locs] = -99
locs = np.ma.where(rh < 40.0)
if len(locs[0]) > 0:
heat_index[locs] = -99
return heat_index
当我输入这个时：
HI=calculate_heat_index(df['T_V2'],df['RH'])
我收到以下错误：
RecursionError: maximum recursion depth exceeded while calling a Python object
这是方程的起源：
Rothfusz LP. 1990. The Heat Index Equation, SR Technical Attachment, 94-19, pp 6.
http://www.hpc.ncep.noaa.gov/html/heatindex_equation.shtml
我只想有一个包含以下列的数据框：
RH, T_V2, HI
附件：
这是数据和脚本的链接。
https://www.dropbox.com/sh/t88oab9txej73u1/AAAkDCcsTC3_eHg8uBc0vhtda?dl=0
有关如何解决此问题的任何建议？我将不胜感激。
抱歉，我是 Python 新手。
答案 0 :(得分：1)
logical_and 似乎只接受两个参数。纠正这一点会导致另一个错误，这对我来说很奇怪 SingleBlockManager has no method view，这听起来像是 Pandas 布尔系列和 numpy 数组之间的兼容性错误。
因此，以下更改似乎可以解决问题-
locs = np.ma.where(np.ma.logical_and(np.ma.logical_and(np.array((rh < 13)), np.array((t_fahrenheit > 80))), np.array(t_fahrenheit < 112)))
if len(locs[0]) > 0:
heat_index_fahrenheit[locs] = heat_index_fahrenheit[locs] - (
((13. - rh[locs]) / 4.) * np.ma.sqrt((17. - np.ma.abs(t_fahrenheit[locs] - 95.)) / 17.))
locs = np.ma.where(np.ma.logical_and(np.ma.logical_and(np.aaray(rh > 85), np.aaray(t_fahrenheit > 80)), np.array(t_fahrenheit < 87)))
告诉我它是否适合您