【python海洋专题八】Cartopy画地形水深图的contourf填充间隔数调整

【python海洋专题八】Cartopy画地形水深图的contourf填充间隔数调整
article
有时候想把contourf的画面变得更细

此时，就需要增加填充间隔数

本期内容

1：contourf的填充个数改变

cf = ax.contourf(lon, lat, ele[:, :], levels=np.linspace(-9000,0,60),extend='both',cmap=cmap_r1, transform=ccrs.PlateCarree())
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levels=np.linspace(-9000,0,60)：调整60即可，数字越大说明画的条数越多。自然填色越多。

问题：

填色变密之后的colorbar设置也会随着改变。

解决

固定colorbar的刻度：

cb = plt.colorbar(cf, ax=ax, extend='both', orientation='vertical',ticks=np.linspace(-9000, 0, 10))
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【python海洋专题一】查看数据nc文件的属性并输出属性到txt文件

【python海洋专题二】读取水深nc文件并水深地形图
【python海洋专题三】图像修饰之画布和坐标轴

【Python海洋专题四】之水深地图图像修饰

【Python海洋专题五】之水深地形图海岸填充

【Python海洋专题六】之Cartopy画地形水深图

【python海洋专题】测试数据

【Python海洋专题七】Cartopy画地形水深图的陆地填充

参考文献及其在本文中的作用

1：Matplotlib 系列：colorbar 的设置-CSDN博客

全文代码

1：# -*- coding: utf-8 -*-
# %%
# Importing related function packages
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import cartopy.feature as feature
import numpy as np
import matplotlib.ticker as ticker
from cartopy import mpl
from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter
from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
from matplotlib.font_manager import FontProperties
from netCDF4 import Dataset
from palettable.cmocean.diverging import Delta_4
from palettable.colorbrewer.sequential import GnBu_9
from palettable.colorbrewer.sequential import Blues_9
from palettable.scientific.diverging import Roma_20
from pylab import *
def reverse_colourmap(cmap, name='my_cmap_r'):
    reverse = []
    k = []

    for key in cmap._segmentdata:
        k.append(key)
        channel = cmap._segmentdata[key]
        data = []

        for t in channel:
            data.append((1 - t[0], t[2], t[1]))
        reverse.append(sorted(data))

    LinearL = dict(zip(k, reverse))
    my_cmap_r = mpl.colors.LinearSegmentedColormap(name, LinearL)
    return my_cmap_r

cmap = Blues_9.mpl_colormap
cmap_r = reverse_colourmap(cmap)
cmap1 = GnBu_9.mpl_colormap
cmap_r1 = reverse_colourmap(cmap1)
cmap2 = Roma_20.mpl_colormap
cmap_r2 = reverse_colourmap(cmap2)
# read data
a = Dataset('D:\pycharm_work\data\scs_etopo.nc')
print(a)
lon = a.variables['lon'][:]
lat = a.variables['lat'][:]
ele = a.variables['elevation'][:]
# ele[ele > 0] = 0
# plot
# 图三
# 设置地图全局属性
scale = '50m'
plt.rcParams['font.sans-serif'] = ['Times New Roman']  # 设置整体的字体为Times New Roman
fig = plt.figure(dpi=300, figsize=(3, 2), facecolor='w', edgecolor='blue')#设置一个画板，将其返还给fig
ax = fig.add_axes([0.05, 0.08, 0.92, 0.8], projection=ccrs.PlateCarree(central_longitude=180))
ax.set_extent([105, 125, 0, 25], crs=ccrs.PlateCarree())# 设置显示范围
land = feature.NaturalEarthFeature('physical', 'land', scale, edgecolor='face',
                                    facecolor=feature.COLORS['land'])
ax.add_feature(land, facecolor='0.6')
ax.add_feature(feature.COASTLINE.with_scale('50m'), lw=0.4)#添加海岸线：关键字lw设置线宽;linestyle设置线型
cf = ax.contourf(lon, lat, ele[:, :], levels=np.linspace(-9000,0,6),extend='both',cmap=cmap_r1, transform=ccrs.PlateCarree())
# ------colorbar设置
cb = plt.colorbar(cf, ax=ax, extend='both', orientation='vertical',ticks=np.linspace(-9000, 0, 10))
cb.set_label('depth', fontsize=4, color='k')#设置colorbar的标签字体及其大小
cb.ax.tick_params(labelsize=4, direction='in') #设置colorbar刻度字体大小。
# 添加标题
ax.set_title('Etopo', fontsize=4)
# 利用Formatter格式化刻度标签
ax.set_xticks(np.arange(107, 125, 4), crs=ccrs.PlateCarree())#添加经纬度
ax.set_xticklabels(np.arange(107, 125, 4), fontsize=4)
ax.set_yticks(np.arange(0, 25, 2), crs=ccrs.PlateCarree())
ax.set_yticklabels(np.arange(0, 25, 2), fontsize=4)
ax.xaxis.set_major_formatter(LongitudeFormatter())
ax.yaxis.set_major_formatter(LatitudeFormatter())
ax.tick_params(color='k', direction='in')#更改刻度指向为朝内，颜色设置为蓝色
gl = ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=False, xlocs=np.arange(107, 125, 4), ylocs=np.arange(0, 25, 2),
        linewidth=0.25, linestyle='--', color='k', alpha=0.8)#添加网格线
gl.top_labels, gl.bottom_labels, gl.right_labels, gl.left_labels = False, False, False, False
plt.savefig('scs_elevation20.jpg', dpi=600, bbox_inches='tight', pad_inches=0.1)  # 输出地图，并设置边框空白紧密
plt.show()
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