Python绘制气象实用地图(附代码和测试数据) ！

前面的推文对于常用的Python绘图工具都有了一些介绍，在这里就不赘述了。本文主要就以下几个方面：“中国区域绘图”、“包含南海”、“兰伯特投影带经纬度标签”、“基于salm的mask方法”、“进阶中国区域mask方法”、“进阶省份mask方法”。对日常的实用需求能够在一定程度上满足。后续就Python在气象常用的统计方法(显著性检验)、合成分析、多变量叠加绘图再进行推送，敬请期待！

简单粗暴，Just show you my code!，细节暂不做过多分析，有问题可以探讨。数据、中文字体、地图shapefile文件、代码后文全部提供。使用建议，根据提示把缺失的库使用pip install xxx /conda install xxx /python setup.py install；安装完备，Python环境管理只推荐conda来统一管理。IDE推荐：PyCharm(有教育版)本地/服务器远程、Jupyter notebook。

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•绘制兰勃脱投影的中国区域(包含南海子图)

•Mask掉海洋部分的兰勃脱投影(包含南海子图)

•基于salm的白化

•中国区域白化(包含南海子图)

•单独省份区域白化

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1.绘制兰勃脱投影的中国区域(包含南海子图)：

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import numpy as np
import xarray as xr
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import cartopy.feature as cfeature
from copy import copy
from cartopy.mpl.gridliner import LATITUDE_FORMATTER, LONGITUDE_FORMATTER
import shapely.geometry as sgeom
def find_side(ls, side):
    """
 Given a shapely LineString which is assumed to be rectangular, return the
 line corresponding to a given side of the rectangle.
 """
    minx, miny, maxx, maxy = ls.bounds
    points = {'left': [(minx, miny), (minx, maxy)],
              'right': [(maxx, miny), (maxx, maxy)],
              'bottom': [(minx, miny), (maxx, miny)],
              'top': [(minx, maxy), (maxx, maxy)],}
    return sgeom.LineString(points[side])
def lambert_xticks(ax, ticks):
    """Draw ticks on the bottom x-axis of a Lambert Conformal projection."""
    te = lambda xy: xy[0]
    lc = lambda t, n, b: np.vstack((np.zeros(n) + t, np.linspace(b[2], b[3], n))).T
    xticks, xticklabels = _lambert_ticks(ax, ticks, 'bottom', lc, te)
    ax.xaxis.tick_bottom()
    ax.set_xticks(xticks)
    ax.set_xticklabels([ax.xaxis.get_major_formatter()(xtick) for xtick in xticklabels])
def lambert_yticks(ax, ticks):
    """Draw ricks on the left y-axis of a Lamber Conformal projection."""
    te = lambda xy: xy[1]
    lc = lambda t, n, b: np.vstack((np.linspace(b[0], b[1], n), np.zeros(n) + t)).T
    yticks, yticklabels = _lambert_ticks(ax, ticks, 'left', lc, te)
    ax.yaxis.tick_left()
    ax.set_yticks(yticks)
    ax.set_yticklabels([ax.yaxis.get_major_formatter()(ytick) for ytick in yticklabels])
def _lambert_ticks(ax, ticks, tick_location, line_constructor, tick_extractor):
    """Get the tick locations and labels for an axis of a Lambert Conformal projection."""
    outline_patch = sgeom.LineString(ax.outline_patch.get_path().vertices.tolist())
    axis = find_side(outline_patch, tick_location)
    n_steps = 30
    extent = ax.get_extent(ccrs.PlateCarree())
    _ticks = []
    for t in ticks:
        xy = line_constructor(t, n_steps, extent)
        proj_xyz = ax.projection.transform_points(ccrs.Geodetic(), xy[:, 0], xy[:, 1])
        xyt = proj_xyz[..., :2]
        ls = sgeom.LineString(xyt.tolist())
        locs = axis.intersection(ls)
        if not locs:
            tick = [None]
        else:
            tick = tick_extractor(locs.xy)
        _ticks.append(tick[0])
    # Remove ticks that aren't visible: 
    ticklabels = copy(ticks)
    while True:
        try:
            index = _ticks.index(None)
        except ValueError:
            break
        _ticks.pop(index)
        ticklabels.pop(index)
    return _ticks, ticklabels
# Load the border data, CN-border-La.dat is downloaded from
# https://gmt-china.org/data/CN-border-La.dat
with open('CN-border-La.dat') as src:
    context = src.read()
    blocks = [cnt for cnt in context.split('>') if len(cnt) > 0]
    borders = [np.fromstring(block, dtype=float, sep=' ') for block in blocks]
# Set figure size
proj = ccrs.LambertConformal(central_longitude=105, central_latitude=90,
                             false_easting=400000, false_northing=400000)#,standard_parallels=(46, 49))
fig = plt.figure(figsize=[10, 8],frameon=True)
# Set projection and plot the main figure
ax = fig.add_axes([0.08, 0.05, 0.8, 0.94], projection=proj)
# Set figure extent
ax.set_extent([80, 130, 15, 55],crs=ccrs.PlateCarree())
# Plot border lines
for line in borders:
    ax.plot(line[0::2], line[1::2], '-', lw=1.5, color='k',
            transform=ccrs.Geodetic())
# Add ocean, land, rivers and lakes
ax.add_feature(cfeature.OCEAN.with_scale('50m'))
ax.add_feature(cfeature.LAND.with_scale('50m'))
ax.add_feature(cfeature.RIVERS.with_scale('50m'))
ax.add_feature(cfeature.LAKES.with_scale('50m'))
# *must* call draw in order to get the axis boundary used to add ticks:
fig.canvas.draw()
# Define gridline locations and draw the lines using cartopy's b