Question

我想在Jupyter笔记本的底图上叠加风向的颤动图。我有一个包含列的pandas数据框：纬度|经度|真风推断|

我已经使用geopandas创建一个geodataframe，并使用上下文（在下面的代码中）在osm底图上绘制gps跟踪数据。我还能够对纬度和经度进行分类，以获取地图上“盒子”的平均“真实风”推论（风向）。但是，我还没有找到任何有关如何在框中绘制归类的真实风的颤动图的示例。到目前为止，我仅作为散点图进行绘制，但是颜色图无法很好地可视化方向数据。

进口：

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# plot inline graphics
%pylab inline
import os.path
from shapely.geometry import Point
import geopandas as gpd
import contextily as ctx

示例数据框：

df[['Latitude', 'Longitude', 'True Wind Inferred', 'coords']].head()

    Latitude    Longitude   True Wind Inferred  coords
0   -31.991899  115.848825  173.835559  POINT (115.848825 -31.991899)
1   -31.992036  115.848873  182.620880  POINT (115.848873 -31.992036)
2   -31.992181  115.848895  192.140276  POINT (115.848895 -31.992181)
3   -31.992308  115.848832  206.655730  POINT (115.848832 -31.992308)
4   -31.992430  115.848784  218.656646  POINT (115.848784 -31.99243)

禁止数据框：

step = 0.005
to_bin = lambda x: np.floor(x / step) * step
dfLocBin['latbin'] = df['Latitude'].map(to_bin)
dfLocBin['lonbin'] = df['Latitude'].map(to_bin)
dfLocBin = df.groupby(['latbin', 'lonbin'])[['True Wind Inferred']].mean()
dfLocBin.reset_index(inplace=True)
dfLocBin['coords'] = list(zip(dfLocBin['lonbin'], dfLocBin['latbin']))
dfLocBin['coords'] = dfLocBin['coords'].apply(Point)
dfLocBin.head()

    latbin  lonbin  True Wind Inferred  coords
0   -32.015 115.790 223.149075  POINT (115.79 -32.015)
1   -32.015 115.795 222.242870  POINT (115.795 -32.015)
2   -32.015 115.800 223.710092  POINT (115.8 -32.015)
3   -32.015 115.805 225.887096  POINT (115.805 -32.015)
4   -32.015 115.810 225.298059  POINT (115.81 -32.015)

并绘制：

def add_basemap(ax, zoom, url='http://tile.stamen.com/terrain/tileZ/tileX/tileY.png'):
    xmin, xmax, ymin, ymax = ax.axis()
    basemap, extent = ctx.bounds2img(xmin, ymin, xmax, ymax, zoom=zoom, url=url)
    ax.imshow(basemap, extent=extent, interpolation='bilinear')
    # restore original x/y limits
    ax.axis((xmin, xmax, ymin, ymax))

geo_df = gpd.GeoDataFrame(
    dfLocBin, crs  ={'init': 'epsg:4326'},
    geometry = dfLocBin['coords']
).to_crs(epsg=3857)

ax = geo_df.plot(
    figsize= (20, 20),
    alpha  = 1,
    c=dfLocBin['True Wind Inferred']
)

add_basemap(ax, zoom=15, url=ctx.tile_providers.ST_TONER)
ax.set_axis_off()
plt.title('Binned True Wind Direction')
plt.show()

scatter plot

我想将绘图的类型从彩色散点图更改为带有箭头的风向图，箭头表示风的罗盘方向。

Answer 1

我解决了。颤动的X，Y必须来自地理数据框的geometry，才能在同一轴上正确绘制。地理数据框列如下所示：

geo_df.head()

latbin  lonbin  True Wind Inferred  coords  geometry
0   -32.014 115.798 220.492453  POINT (115.798 -32.014) POINT (12890574.39487949 -3765148.48502445)
1   -32.014 115.800 225.718756  POINT (115.8 -32.014)   POINT (12890797.03386108 -3765148.48502445)

工作代码：

# bin the coordinates and plot a vector field
step = 0.002
to_bin = lambda x: np.floor(x / step) * step
df['latbin'] = df['Latitude'].map(to_bin)
df['lonbin'] = df['Longitude'].map(to_bin)
dfLocBin = df.groupby(['latbin', 'lonbin'])[['True Wind Inferred']].mean()
dfLocBin.reset_index(inplace=True)
dfLocBin['coords'] = list(zip(dfLocBin['lonbin'], dfLocBin['latbin']))
dfLocBin['coords'] = dfLocBin['coords'].apply(Point)

# ... turn them into geodataframe, and convert our
# epsg into 3857, since web map tiles are typically
# provided as such.
geo_df = gpd.GeoDataFrame(
    dfLocBin, crs  ={'init': 'epsg:4326'},
    geometry = dfLocBin['coords']
).to_crs(epsg=3857)

# ... and make the plot
ax = geo_df.plot(
    figsize= (20, 20),
    alpha  = 1
)

geo_df['X'] = geo_df['geometry'].x
geo_df['Y'] = geo_df['geometry'].y

geo_df['U'] = np.cos(np.radians(geo_df['True Wind Inferred']))
geo_df['V'] = np.sin(np.radians(geo_df['True Wind Inferred']))

ax.quiver(geo_df['X'], 
          geo_df['Y'], 
          geo_df['U'], 
          geo_df['V'],
         color='deepskyblue')


add_basemap(ax, zoom=15, url=ctx.tile_providers.ST_TONER)

ax.set_axis_off()
plt.title('Binned True Wind Direction')
plt.show()

quiver on map

如何在python

1 个答案: