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This article mainly explains "how to draw a map of China by python". The content of the explanation is simple and clear, and it is easy to learn and understand. Please follow the editor's train of thought to study and learn "how to draw a map of China by python".

Preface

The visualization of maps is indispensable to the analysis of meteorology, ocean and geoscience.

The library I often use for mapping and spatial information analysis in Python is Cartopy.

Cartopy has a very serious problem, that is, there is a problem with its own Chinese border data, which is also a common problem in many foreign open source databases.

In the analysis of the Chinese region, because the position of the nine-dash line is very southward, the most standard approach is to draw a submap of the South China Sea at the same time.

When doing some site presentation, if you only draw a few sites alone, you always feel ugly, you can add some terrain background.

To sum up, today I would like to use a small example to solve these three problems:

Correct China's national boundary and nine-dash line drawing South China Sea mini-map drawing global topographic map addition preparation to obtain the correct Chinese vector file: official account backstage message "China Administrative Division"

(this vector file comes from the resource and environment platform and is consistent with the standard map of the authoritative organization.) Get global terrain image: official account backstage message "Global terrain"

(the tif image with a global resolution of 50m is provided. If the resolution is not high, you can directly use stock_img () code #-*-coding: utf-8-*-

Import numpy as np

Import pandas as pd

Import cartopy

Import cartopy.crs as ccrs

Import cartopy.feature as cfeat

From cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER

From cartopy.io.shapereader import Reader, natural_earth

Import matplotlib.pyplot as plt

Import matplotlib.ticker as mticker

From matplotlib.image import imread

Def create_map ():

Shp_path ='. / cn_shp/Province_9/'

#-create a drawing space

Proj = ccrs.PlateCarree () # create coordinate system

Fig = plt.figure (figsize= (6,8), dpi=400) # create page

Ax = fig.subplots (1,1, subplot_kw= {'projection': proj})

#-set map properties

Provinces = cfeat.ShapelyFeature (

Reader (shp_path + 'Province_9.shp'). Geometries ()

Proj, edgecolor='k'

Facecolor='none'

)

# load provincial boundaries

Ax.add_feature (provinces, linewidth=0.6, zorder=2)

# load a coastline with a resolution of 50

Ax.add_feature (cfeat.COASTLINE.with_scale ('50m'), linewidth=0.6, zorder=10)

# load a river with a resolution of 50 ~

Ax.add_feature (cfeat.RIVERS.with_scale ('50m'), zorder=10)

# load a lake with a resolution of 50

Ax.add_feature (cfeat.LAKES.with_scale ('50m'), zorder=10)

Ax.set_extent ([105,133,15,45])

# ax.stock_img ()

Ax.imshow (

Imread ('. / NE1_50M_SR_W.tif')

Origin='upper'

Transform=proj

Extent= [- 180,180,90,90]

)

#-set grid point properties

Gl = ax.gridlines (

Crs=ccrs.PlateCarree ()

Draw_labels=True

Linewidth=1.2

Color='k'

Alpha=0.5

Linestyle='--'

)

Gl.xlabels_top = False # turn off the latitude and longitude label at the top

Gl.ylabels_right = False # turn off the latitude and longitude label on the right

Gl.xformatter = LONGITUDE_FORMATTER # x axis is formatted as longitude

Gl.yformatter = LATITUDE_FORMATTER # y axis is formatted as latitude

Gl.xlocator = mticker.FixedLocator (np.arange (95,145 + 5,5))

Gl.ylocator = mticker.FixedLocator (np.arange (- 5,45 + 5,5))

#-set up Mini Map

Left, bottom, width, height = 0.67,0.15,0.23,0.27

Ax2 = fig.add_axes (

[left, bottom, width, height]

Projection=proj

)

Ax2.add_feature (provinces, linewidth=0.6, zorder=2)

Ax2.add_feature (cfeat.COASTLINE.with_scale ('50m'), linewidth=0.6, zorder=10)

Ax2.add_feature (cfeat.RIVERS.with_scale ('50m'), zorder=10)

Ax2.add_feature (cfeat.LAKES.with_scale ('50m'), zorder=10)

Ax2.set_extent ([105,125,0,25])

# ax2.stock_img ()

Ax2.imshow (

Imread ('. / NE1_50M_SR_W.tif')

Origin='upper'

Transform=proj

Extent= [- 180,180,90,90]

)

Return ax

Def main ():

Ax = create_map ()

Title = f'distribution of station around China'

Ax.set_title (title, fontsize=18)

Df = pd.read_csv ('buyo_position.csv')

Df ['lon'] = df [' lon'] .astype (np.float64)

Df ['lat'] = df [' lat'] .astype (np.float64)

Ax.scatter (

Df ['lon']. Values

Df ['lat']. Values

Marker='o'

Slug 10

Color = "blue"

)

For I, j, k in list (zip (df ['lon'] .values, df [' lat'] .values, df ['name'] .values):

Ax.text (I-0.8,j + 0.2k, fontsize=6)

Plt.savefig ('station_distribute_map.png')

If _ _ name__ = ='_ _ main__':

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