Wind Rose

A wind rose is a graphic tool used by meteorologists to give a succinct view of how wind speed and direction are typically distributed at a particular location. Historically, wind roses were predecessors of the compass rose, as there was no differentiation between a cardinal direction and the wind which blew from such a direction. Using a polar coordinate system of gridding, the frequency of winds over a time period is plotted by wind direction, with color bands showing wind speed ranges. The direction of the longest spoke shows the wind direction with the greatest frequency (https://en.wikipedia.org/wiki/Wind_rose).

This notebook is extracted from the python package of windrose with a little bit modification. Please refer to the original windrose’s documentation for more information.

1. Load all needed libraries



In [2]:

    
%matplotlib inline
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
import matplotlib.cm as cm
import numpy as np
from math import pi

from windrose import WindroseAxes, WindAxes, plot_windrose

from pylab import rcParams
rcParams['figure.figsize'] = 6, 6

2. Load wind time series data



In [3]:

    
df = pd.read_csv("data/sample_wind_poitiers.csv", parse_dates=['Timestamp'])
df = df.set_index('Timestamp')
df.head()









    Out[3]:







  
    
      
      speed
      direction
    
    
      Timestamp
      
      
    
  
  
    
      2011-03-07 17:30:00
      3.6
      91.0
    
    
      2011-03-07 17:40:00
      5.6
      92.0
    
    
      2011-03-07 17:50:00
      4.9
      92.0
    
    
      2011-03-07 18:00:00
      2.3
      113.0
    
    
      2011-03-07 18:10:00
      3.2
      84.0



In [4]:

    
df.index[0:5]









    Out[4]:





DatetimeIndex(['2011-03-07 17:30:00', '2011-03-07 17:40:00',
               '2011-03-07 17:50:00', '2011-03-07 18:00:00',
               '2011-03-07 18:10:00'],
              dtype='datetime64[ns]', name=u'Timestamp', freq=None)

2.1 Convert wind from speed and direction to u-wind and v-wind



In [5]:

    
df['speed_x'] = df['speed'] * np.sin(df['direction'] * pi / 180.0)
df['speed_y'] = df['speed'] * np.cos(df['direction'] * pi / 180.0)

3. Visualization

3.1 Have a quick shot



In [6]:

    
fig, ax = plt.subplots()
x0, x1 = ax.get_xlim()
y0, y1 = ax.get_ylim()
ax.set_aspect('equal')
_ = df.plot(kind='scatter', x='speed_x', y='speed_y', alpha=0.25, ax=ax)
Vw = 60
_ = ax.set_xlim([-Vw, Vw])
_ = ax.set_ylim([-Vw, Vw])

3.2 Stacked histogram with normed (displayed in percent)



In [7]:

    
ax = WindroseAxes.from_ax()
ax.bar(df.direction.values, df.speed.values, normed=True, bins=np.arange(0.01,8,1), cmap=cm.RdYlBu_r, lw=3)
ax.set_legend()









    



C:\Anaconda2\lib\site-packages\numpy\core\fromnumeric.py:57: RuntimeWarning: invalid value encountered in rint
  return getattr(obj, method)(*args, **kwds)






    Out[7]:





<matplotlib.legend.Legend at 0x9dccc50>

3.3 Contour representation with normed (displayed in percent)



In [8]:

    
_ = plot_windrose(df, kind='contour', normed=True, bins=np.arange(0.01,8,1), cmap=cm.RdYlBu_r, lw=3)

3.4 Probability density function (pdf) and fitting Weibull distribution



In [9]:

    
bins = np.arange(0,30+1,1)
bins = bins[1:]
bins









    Out[9]:





array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,
       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30])



In [10]:

    
_ = plot_windrose(df, kind='pdf', bins=np.arange(0.01,30,1))



In [11]:

    
data = np.histogram(df['speed'], bins=bins)[0]
data









    Out[11]:





array([15377, 20041, 23987, 24356, 24041, 21238, 18007, 14144, 11328,
        8461,  6551,  4625,  3265,  2523,  1749,  1294,   922,   686,
         463,   362,   267,   195,   128,   117,    77,    52,    52,
          40,    29], dtype=int64)

3.5 Wind rose for a specific month



In [12]:

    
def plot_month(df, t_year_month, *args, **kwargs):
    by = 'year_month'
    df[by] = df.index.map(lambda x: x.year*100+x.month) 
    df_month = df[df[by] == t_year_month[0]*100+t_year_month[1]]
    ax = plot_windrose(df_month, *args, **kwargs)
    return ax









    Out[12]:





<windrose.windrose.WindroseAxes at 0xe5ccd30>

3.5.1 July 2014



In [ ]:

    
plot_month(df, (2014, 7), kind='contour', normed=True,bins=np.arange(0, 10, 1), cmap=cm.RdYlBu_r)

3.5.2 August 2014



In [13]:

    
plot_month(df, (2014, 8), kind='contour', normed=True,bins=np.arange(0, 10, 1), cmap=cm.RdYlBu_r)









    Out[13]:





<windrose.windrose.WindroseAxes at 0xeb883c8>

3.5.3 Septemer 2014



In [14]:

    
plot_month(df, (2014, 9), kind='contour', normed=True, bins=np.arange(0, 10, 1), cmap=cm.RdYlBu_r)









    Out[14]:





<windrose.windrose.WindroseAxes at 0xf7d5c50>

References

Dan Reboussin (2005). Wind Rose. University of Florida. Retrieved on 2009-04-26.

John D. Hunter. Matplotlib: A 2D Graphics Environment, Computing in Science & Engineering, 9, 90-95 (2007), DOI:10.1109/MCSE.2007.55

Stéfan van der Walt, S. Chris Colbert and Gaël Varoquaux. The NumPy Array: A Structure for Efficient Numerical Computation, Computing in Science & Engineering, 13, 22-30 (2011), DOI:10.1109/MCSE.2011.37



In [ ]:

	speed	direction
Timestamp
2011-03-07 17:30:00	3.6	91.0
2011-03-07 17:40:00	5.6	92.0
2011-03-07 17:50:00	4.9	92.0
2011-03-07 18:00:00	2.3	113.0
2011-03-07 18:10:00	3.2	84.0