前言

風玫瑰是由氣象學家用於給出如何風速和風向在特定位置通常分佈的簡明檢視的圖形工具。它也可以用來描述空氣質量汙染源。

風玫瑰工具使用Matplotlib作為後端。

安裝方式直接使用pip install windrose

匯入模組

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import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
import matplotlib.cm as cm
from math import pi
import windrose
from
 
 windrose import WindroseAxes, WindAxes, plot_windrose
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
import cartopy.crs as ccrs
import cartopy.io.img_tiles as cimgt

讀取資料

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

計算風速的u、v分量

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

uv風速散點圖(含透明度)

fig, ax = plt.subplots(figsize=(8, 8), dpi=80)
x0, x1 = ax.get_xlim()
y0, y1 = ax.get_ylim()
ax.set_aspect(abs(x1-x0)/abs(y1-y0))
ax.set_aspect(
 
'equal')
ax.scatter(df['speed_x'], df['speed_y'], alpha=0.25)
df.plot(kind='scatter', x='speed_x', y='speed_y', alpha=0.05, ax=ax)
Vw = 80
ax.set_xlim([-Vw, Vw])
ax.set_ylim([-Vw, Vw])

風玫瑰圖(多種形式)

ax = WindroseAxes.from_ax()
ax.bar(df.direction.values, df.speed.values, bins=np.arange(0.01,10,1), cmap=cm.hot, lw=3)
ax.set_legend()

ax = WindroseAxes.from_ax()
ax.box(df.direction.values, df.speed.values, bins=np.arange(0.01,10,1), cmap=cm.hot, lw=3)
ax.set_legend()

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

繪製特定月份風玫瑰圖

def plot_month(df, t_year_month, *args, **kwargs):
    by = 'year_month'
    df[by] = df.index.map(lambda dt: (dt.year, dt.month))
    df_month = df[df[by] == t_year_month]
    ax = plot_windrose(df_month, *args, **kwargs)
    return ax
plot_month(df, (2014, 7), kind='contour', bins=np.arange(0, 10, 1), cmap=cm.hot)

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

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

繪製風速頻率直方圖

bins = np.arange(0,30+1,1)
bins = bins[1:]
plot_windrose(df, kind='pdf', bins=np.arange(0.01,30,1),normed=True)

在地圖上繪製風玫瑰圖

proj = ccrs.PlateCarree()

fig = plt.figure(figsize=(12, 6))
minlon, maxlon, minlat, maxlat = (6.5, 7.0, 45.85, 46.05)

main_ax = fig.add_subplot(1, 1, 1, projection=proj)
main_ax.set_extent([minlon, maxlon, minlat, maxlat], crs=proj)
main_ax.gridlines(draw_labels=True)

main_ax.add_wms(wms='http://vmap0.tiles.osgeo.org/wms/vmap0',layers=['basic'])

cham_lon, cham_lat = (6.8599, 45.9259)
passy_lon, passy_lat = (6.7, 45.9159)



wrax_cham = inset_axes(main_ax,
        width=1,   
        height=1, 
        loc='center',  
        bbox_to_anchor=(cham_lon, cham_lat), 
        bbox_transform=main_ax.transData,  
        axes_class=windrose.WindroseAxes, 
        )


height_deg = 0.1
wrax_passy = inset_axes(main_ax,
        width="100%",                        
        height="100%",                       
        bbox_to_anchor=(passy_lon-height_deg/2, passy_lat-height_deg/2, height_deg, height_deg),
        bbox_transform=main_ax.transData,
        axes_class=windrose.WindroseAxes,
        )

wrax_cham.bar(df.direction.values, df.speed.values,bins=np.arange(0.01,10,1), lw=3)
wrax_passy.bar(df.direction.values, df.speed.values,bins=np.arange(0.01,10,1), lw=3)

for ax in [wrax_cham, wrax_passy]:
        ax.tick_params(labelleft=False, labelbottom=False)

最後

這樣繪製出來的風玫瑰看起來還是很漂亮的，並且也能夠大大提高工作效率，對於那些科研人員是很有幫助的。程式碼以及圖片效

果就放在上面了。