如何将风向划分为角度，以绘图方式创建windrose python

2024-06-02 09:26:45 发布

您现在位置：Python中文网/ 问答频道 /正文
2512 3

在这里，我将风向（0到360度）分为16组（“N’、‘NNE’、‘NE’、‘ENE’、‘E’、‘ESE’、‘SE’、‘SSW’、‘SW’、‘WSW’、‘W’、‘WNW’、‘NW’、‘NNW’）。在我的代码中，N是-1到22.5，但实际上它应该在348.75（360-11.25）和11.25之间。为了检查我的结果，我使用Python windrose包和Python plotly px.bar_polar绘制了windrose

带Windrose软件包的Windrose（pip安装Windrose）：

ws=df.WS
wd=df.WD

ax = WindroseAxes.from_ax()
ax.bar(wd, ws, normed=True, opening=0.8, edgecolor='white')
ax.set_legend()

输出风玫瑰：

enter image description here

但当我使用plotly（px.bar\u polar）绘制windrose时，它给出**不同的windrose（**，用于相同的数据）：

# populate values in new columns
df['WindSpeedkm/h'] = df['WS'] * 3.6

bins = [-1, 10, 20, 30, 40,50, np.inf]
names = ['0-10 km/h', '10-20 km/h', '20-30 km/h', '30-40 km/h', '40-50 km/h','>50 km/h']
df['speed_kmhRange'] = pd.cut(df['WindSpeedkm/h'], bins, labels=names)


bins = [-1, 22.5,45,67.5,90,112.5,135,157.5,180,202.5,225,247.5,270,292.5,315,337.5,361]
names = ['N','NNE','NE','ENE','E','ESE','SE','SSE','S','SSW','SW','WSW','W','WNW','NW','NNW']

输出风玫瑰： enter image description here

我知道这个错误是由于我前面提到的对角度进行分组造成的，但是我怎样才能解决这个问题呢？我需要使用python来实现所需的输出

DF:

{'WS': {0: 4.87,
  1: 4.87,
  2: 4.91,
  3: 4.97,
  4: 5.06,
  6: 0.21,
  7: 7.06,
  8: 6.62,
  10: 6.69,
  11: 7.0,
  12: 7.37,
  13: 7.41,
  14: 7.44,
  15: 7.41,
  16: 7.16,
  17: 6.69,
  18: 6.19,
  19: 5.91,
  20: 5.81,
  21: 5.75,
  22: 5.72,
  23: 5.72,
  24: 5.72,
  25: 0.37,
  26: 5.91,
  27: 6.09,
  28: 6.28,
  29: 6.28,
  30: 6.25,
  31: 6.03,
  32: 5.81,
  33: 5.59,
  34: 5.56,
  35: 5.34,
  36: 5.09,
  37: 4.87,
  38: 4.84,
  40: 4.84,
  41: 5.09,
  42: 5.69,
  43: 5.75,
  44: 5.78,
  45: 5.75,
  46: 5.5,
  47: 5.28,
  48: 5.09,
  49: 5.09,
  50: 5.12,
  51: 5.12,
  52: 5.12,
  53: 5.12,
  54: 5.22,
  55: 5.37,
  56: 5.56,
  57: 5.56,
  59: 5.53,
  60: 5.5,
  61: 5.53,
  62: 5.62,
  63: 5.69,
  64: 5.69,
  65: 5.62,
  66: 5.34,
  67: 5.09,
  68: 4.75,
  69: 4.69,
  70: 4.72,
  71: 4.75,
  72: 4.75,
  73: 0.0,
  74: 4.72,
  75: 4.72,
  76: 4.91,
  77: 5.16,
  78: 5.41,
  79: 5.44,
  80: 5.5,
  81: 5.53,
  82: 5.44,
  83: 5.34,
  84: 5.31,
  85: 5.25,
  86: 5.19,
  87: 5.12,
  89: 5.09,
  90: 5.06,
  91: 5.12,
  92: 5.41,
  93: 5.69,
  94: 6.0,
  95: 6.09,
  96: 6.12,
  97: 6.09,
  98: 6.09,
  99: 6.28,
  100: 6.5,
  101: 6.69,
  102: 6.5,
  104: 6.34,
  105: 6.47,
  106: 6.78,
  107: 7.03,
  108: 7.03,
  109: 7.0,
  111: 7.06,
  112: 7.03,
  113: 6.81,
  114: 6.56,
  115: 6.34,
  116: 6.28,
  117: 6.25,
  118: 6.25,
  120: 6.34,
  121: 6.41,
  122: 6.44,
  123: 6.44,
  124: 6.47,
  125: 6.81,
  126: 7.09,
  127: 7.16,
  128: 6.97,
  129: 6.75,
  130: 6.75,
  131: 7.0,
  132: 7.28,
  133: 7.69,
  135: 7.91,
  136: 8.06,
  137: 7.78,
  138: 6.78,
  139: 6.5,
  140: 6.22,
  141: 5.56,
  142: 4.91,
  143: 4.75,
  144: 5.03,
  145: 5.31,
  146: 5.37,
  147: 5.41,
  148: 5.44,
  149: 0.29,
  150: 5.72,
  151: 5.97,
  152: 6.28,
  153: 6.25,
  154: 6.22,
  155: 6.22,
  156: 6.25,
  157: 6.31,
  158: 6.34,
  159: 6.37,
  160: 6.16,
  161: 5.87,
  162: 5.62,
  163: 5.78,
  165: 5.94,
  166: 5.81,
  167: 5.53,
  168: 5.28,
  169: 5.22,
  170: 5.22,
  171: 5.22,
  172: 5.22,
  173: 5.28,
  174: 5.34,
  175: 5.44,
  176: 5.69,
  177: 5.91,
  178: 5.87,
  179: 0.29,
  180: 5.53,
  181: 5.56,
  182: 5.59,
  183: 5.62,
  184: 5.66,
  185: 5.69,
  186: 5.72,
  187: 5.75,
  188: 5.75,
  189: 5.78,
  190: 5.81,
  191: 5.84,
  192: 5.87,
  193: 6.0,
  194: 6.03,
  195: 6.03,
  196: 6.06,
  197: 6.12,
  198: 6.19,
  199: 6.41,
  200: 6.62,
  201: 6.81,
  202: 6.87,
  203: 6.91,
  204: 6.97,
  206: 7.22,
  207: 8.31,
  208: 8.69,
  209: 8.69,
  210: 8.25},
 'WD': {0: 358.56,
  1: 1.16,
  2: 2.87,
  3: 4.31,
  4: 5.55,
  6: 281.27,
  7: 1.02,
  8: 359.84,
  10: 357.78,
  11: 357.2,
  12: 356.2,
  13: 357.82,
  14: 1.04,
  15: 2.93,
  16: 3.83,
  17: 4.85,
  18: 6.42,
  19: 8.91,
  20: 10.37,
  21: 9.92,
  22: 8.24,
  23: 6.57,
  24: 6.36,
  25: 270.67,
  26: 8.14,
  27: 10.49,
  28: 11.55,
  29: 11.76,
  30: 13.27,
  31: 15.64,
  32: 17.75,
  33: 20.26,
  34: 22.77,
  35: 24.25,
  36: 23.99,
  37: 21.69,
  38: 19.18,
  40: 17.07,
  41: 16.1,
  42: 15.73,
  43: 14.93,
  44: 13.6,
  45: 13.07,
  46: 13.27,
  47: 14.32,
  48: 16.79,
  49: 19.34,
  50: 19.89,
  51: 17.7,
  52: 14.05,
  53: 11.39,
  54: 13.13,
  55: 15.91,
  56: 17.48,
  57: 17.82,
  59: 17.57,
  60: 17.23,
  61: 16.72,
  62: 14.69,
  63: 12.13,
  64: 10.01,
  65: 8.81,
  66: 7.51,
  67: 5.69,
  68: 4.01,
  69: 6.87,
  70: 10.57,
  71: 13.5,
  72: 14.15,
  73: 3.11,
  74: 13.04,
  75: 10.81,
  76: 7.27,
  77: 3.99,
  78: 1.13,
  79: 359.69,
  80: 359.27,
  81: 359.09,
  82: 1.77,
  83: 3.01,
  84: 2.77,
  85: 1.24,
  86: 359.84,
  87: 359.04,
  89: 358.02,
  90: 359.94,
  91: 358.87,
  92: 357.51,
  93: 357.85,
  94: 359.33,
  95: 0.54,
  96: 1.54,
  97: 2.19,
  98: 3.58,
  99: 5.73,
  100: 7.51,
  101: 8.63,
  102: 9.29,
  104: 10.89,
  105: 13.9,
  106: 18.03,
  107: 21.25,
  108: 22.94,
  109: 22.73,
  111: 16.53,
  112: 12.06,
  113: 8.73,
  114: 7.4,
  115: 8.46,
  116: 10.71,
  117: 12.32,
  118: 13.99,
  120: 16.41,
  121: 21.16,
  122: 26.01,
  123: 29.35,
  124: 29.24,
  125: 13.66,
  126: 7.49,
  127: 8.61,
  128: 9.95,
  129: 10.22,
  130: 8.68,
  131: 5.71,
  132: 2.72,
  133: 359.88,
  135: 357.9,
  136: 355.18,
  137: 353.98,
  138: 355.36,
  139: 355.09,
  140: 353.57,
  141: 352.17,
  142: 355.87,
  143: 357.23,
  144: 359.79,
  145: 359.94,
  146: 358.07,
  147: 358.04,
  148: 1.58,
  149: 279.41,
  150: 4.73,
  151: 7.84,
  152: 10.57,
  153: 10.43,
  154: 9.5,
  155: 7.4,
  156: 5.26,
  157: 3.31,
  158: 1.75,
  159: 358.62,
  160: 350.52,
  161: 345.59,
  162: 341.33,
  163: 350.82,
  165: 359.53,
  166: 3.81,
  167: 2.9,
  168: 3.86,
  169: 13.1,
  170: 23.17,
  171: 31.06,
  172: 30,
  173: 25.04,
  174: 19.94,
  175: 15.79,
  176: 14.27,
  177: 13.06,
  178: 11.6,
  179: 279.44,
  180: 9.74,
  181: 10.06,
  182: 10.05,
  183: 8.97,
  184: 6.87,
  185: 4.44,
  186: 2.6,
  187: 1.47,
  188: 1.27,
  189: 2.29,
  190: 3.58,
  191: 3.14,
  192: 0.98,
  193: 359.71,
  194: 2.94,
  195: 6.95,
  196: 11.27,
  197: 14.19,
  198: 15.72,
  199: 15.46,
  200: 14.33,
  201: 12.98,
  202: 11.27,
  203: 9.73,
  204: 8.73,
  206: 8.59,
  207: 8.51,
  208: 7.7,
  209: 7.22,
  210: 7.27}}

Tags： dfwsnamesbaraxneesese
1条回答
网友
1楼 · 发布于 2024-06-02 09:26:45

如果我理解正确，您的问题是创建16个箱子，其中一个箱子的范围为348.75到11.25

要解决此问题，您可以创建17个垃圾箱。第一个箱子的范围从0到11.25，最后一个箱子的范围从348.75到360

由于pd.cut()要求每个箱子都有一个唯一的标签，因此，尽管最后一个箱子代表相同的方向，但您必须将其命名为不同于第一个箱子，即，将其标记为N和N2。 在binning之后的另一个步骤中，您可以将N2重命名为N

bins = np.linspace(0, 360, 17)+11.25
bins = np.insert(bins, 0, 0)
names = ['N','NNE','NE','ENE','E','ESE','SE','SSE','S','SSW','SW','WSW','W','WNW','NW','NNW', 'N2']
df['wind_dir_Range'] = pd.cut(df['WD'], bins, labels=names)
df['wind_dir_Range'] = df['wind_dir_Range'].replace('N2', 'N')

相关问题 更多 >

    编程相关推荐