利用Python绘制多种风玫瑰图利用Python绘制多种风玫瑰图

前言
导入模块
读取数据
计算风速的u、v分量
uv风速散点图(含透明度)
风玫瑰图(多种形式)
绘制特定月份风玫瑰图
绘制风速频率直方图
在地图上绘制风玫瑰图

前言风玫瑰是由气象学家用于给出如何风速和风向在特定位置通常分布的简明视图的图形工具。它也可以用来描述空气质量污染源。风玫瑰工具使用Matplotlib作为后端。
安装方式直接使用：

pip install windrose

导入模块

import pandas as pdimport numpy as npfrom matplotlib import pyplot as pltimport matplotlib.cm as cmfrom math import piimport windrosefrom windrose import WindroseAxes, WindAxes, plot_windrosefrom mpl_toolkits.axes_grid1.inset_locator import inset_axesimport cartopy.crs as ccrsimport cartopy.io.img_tiles as cimgt

读取数据

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

计算风速的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风速散点图(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 = 80ax.set_xlim([-Vw, Vw])ax.set_ylim([-Vw, Vw])

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风玫瑰图(多种形式)

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()

【利用Python绘制多种风玫瑰图】

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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()

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

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绘制特定月份风玫瑰图

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 axplot_month(df, (2014, 7), kind='contour', bins=np.arange(0, 10, 1), cmap=cm.hot)

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

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

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绘制风速频率直方图

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

在地图上绘制风玫瑰图

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 = 0.1wrax_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)