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利用Python繪制多種風(fēng)玫瑰圖

更新時(shí)間：2022年05月08日 10:04:14 作者：螞蟻ailing

這篇文章主要介紹了利用Python繪制多種風(fēng)玫瑰圖，風(fēng)玫瑰是由氣象學(xué)家用于給出如何風(fēng)速和風(fēng)向在特定位置通常分布的簡明視圖的圖形工具，下文繪制實(shí)現(xiàn)詳情，需要的小伙伴可以參考一下

前言

風(fēng)玫瑰是由氣象學(xué)家用于給出如何風(fēng)速和風(fēng)向在特定位置通常分布的簡明視圖的圖形工具。它也可以用來描述空氣質(zhì)量污染源。風(fēng)玫瑰工具使用Matplotlib作為后端。

安裝方式直接使用：

pip install windrose

導(dǎo)入模塊

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

讀取數(shù)據(jù)

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

計(jì)算風(fēng)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(fēng)速散點(diǎn)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(fēng)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)

繪制特定月份風(fēng)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)

繪制風(fēng)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)

在地圖上繪制風(fēng)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)