全局图的动态可视化

Question

我制作了 17 个全球图，显示了 1850-2015 年最大地表臭氧的十年平均值。我不想单独绘制它们，而是希望创建一个循环遍历它们的动画（几乎像 gif），即始终具有相同的海岸线、轴和颜色条，但将绘制的内容更改为等高线。

任何有关如何调整我的代码以执行此操作的帮助将不胜感激 - 提前致谢！！

import numpy as np
import netCDF4 as n4
import matplotlib.pyplot as plt
from matplotlib import colorbar, colors
import matplotlib.cm as cm

import cartopy as cart
import cartopy.crs as ccrs
from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
import cartopy.feature as cfeature

nc = n4.Dataset('datafile.nc','r')

# daily maximum O3 VMR (units: mol mol-1)
sfo3max = nc.variables['sfo3max']
lon = nc.variables['lon'] # longitude
lat = nc.variables['lat'] # latitude

# (I manipulate the data to produce 17 arrays containing the decadal average O3 VMR which are
#  listed below in sfo3max_avg)

sfo3max_avg = [sfo3max_1850_1860_avg, sfo3max_1860_1870_avg, sfo3max_1870_1880_avg,
               sfo3max_1880_1890_avg, sfo3max_1890_1900_avg, sfo3max_1900_1910_avg,
               sfo3max_1910_1920_avg, sfo3max_1920_1930_avg, sfo3max_1930_1940_avg,
               sfo3max_1940_1950_avg, sfo3max_1950_1960_avg, sfo3max_1960_1970_avg,
               sfo3max_1970_1980_avg, sfo3max_1980_1990_avg, sfo3max_1990_2000_avg,
               sfo3max_2000_2010_avg, sfo3max_2010_2015_avg]

# find overall min & max values for colour bar in plots
min_sfo3max_avg = np.array([])
for i in sfo3max_avg:
    sfo3max_avg_min = np.amin(i)
    min_sfo3max_avg = np.append(min_sfo3max_avg, sfo3max_avg_min)
overall_min_sfo3max_avg = np.amin(min_sfo3max_avg)

max_sfo3max_avg = np.array([])
for i in sfo3max_avg:
    sfo3max_avg_max = np.amax(i)
    max_sfo3max_avg = np.append(max_sfo3max_avg, sfo3max_avg_max)
overall_max_sfo3max_avg = np.amax(max_sfo3max_avg)

# finally plot the 17 global plots of sfo3max_avg
for k in sfo3max_avg:
    fig = plt.figure()
    ax = plt.axes(projection=ccrs.PlateCarree())
    ax.coastlines() # Adding coastlines
    cs = ax.contourf(lon[:], lat[:], k[:], cmap='magma')
    ax.set_title('Decadal Average of Maximum O3 Volume Mixing Ratio')

    m = plt.cm.ScalarMappable(cmap=cm.magma)
    m.set_array(i[:])
    m.set_clim(overall_min_sfo3max_avg, overall_max_sfo3max_avg)

    # Additional necessary information
    cbar = plt.colorbar(m, boundaries=np.arange(overall_min_sfo3max_avg, overall_max_sfo3max_avg
                        + 0.5e-08, 0.5e-08))
    cbar.set_label('mol mol-1')

    # Adding axis labels - latitude & longitude
    gridl = ax.gridlines(color="black", linestyle="dotted", draw_labels=True) 
    gridl.xformatter=LONGITUDE_FORMATTER
    gridl.yformatter=LATITUDE_FORMATTER
    gridl.xlabels_top = False
    gridl.ylabels_right = False

    fig.set_size_inches(w=20,h=10)
    plt.show() # show global plot

Answer 1

绘图中的几个元素可以避免循环，因为它们只需要设置一次。设置绘图元素后，您可以通过遍历列表来更新绘图和动画。这可以通过使用 matplotlib 的交互模式来实现，如下面的代码所示：

import numpy as np
import netCDF4 as n4
import matplotlib
matplotlib.use("nbagg")

import matplotlib.pyplot as plt
from matplotlib import colorbar, colors
import matplotlib.cm as cm

import cartopy as cart
import cartopy.crs as ccrs
from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
import cartopy.feature as cfeature

nc = n4.Dataset('datafile.nc','r')

# daily maximum O3 VMR (units: mol mol-1)
sfo3max = nc.variables['sfo3max']
lon = nc.variables['lon'] # longitude
lat = nc.variables['lat'] # latitude

# (I manipulate the data to produce 17 arrays containing the decadal average O3 VMR which are
#  listed below in sfo3max_avg)

sfo3max_avg = [sfo3max_1850_1860_avg, sfo3max_1860_1870_avg, sfo3max_1870_1880_avg,
               sfo3max_1880_1890_avg, sfo3max_1890_1900_avg, sfo3max_1900_1910_avg,
               sfo3max_1910_1920_avg, sfo3max_1920_1930_avg, sfo3max_1930_1940_avg,
               sfo3max_1940_1950_avg, sfo3max_1950_1960_avg, sfo3max_1960_1970_avg,
               sfo3max_1970_1980_avg, sfo3max_1980_1990_avg, sfo3max_1990_2000_avg,
               sfo3max_2000_2010_avg, sfo3max_2010_2015_avg]

# find overall min & max values for colour bar in plots
min_sfo3max_avg = np.array([])
for i in sfo3max_avg:
    sfo3max_avg_min = np.amin(i)
    min_sfo3max_avg = np.append(min_sfo3max_avg, sfo3max_avg_min)
overall_min_sfo3max_avg = np.amin(min_sfo3max_avg)

max_sfo3max_avg = np.array([])
for i in sfo3max_avg:
    sfo3max_avg_max = np.amax(i)
    max_sfo3max_avg = np.append(max_sfo3max_avg, sfo3max_avg_max)
overall_max_sfo3max_avg = np.amax(max_sfo3max_avg)

#setup the plot elements
fig = plt.figure()
fig.set_size_inches(w=20,h=10)
ax = plt.axes(projection=ccrs.PlateCarree())
ax.coastlines() # Adding coastlines
ax.set_title('Decadal Average of Maximum O3 Volume Mixing Ratio')

m = plt.cm.ScalarMappable(cmap=cm.magma)
m.set_array(i[:])
m.set_clim(overall_min_sfo3max_avg, overall_max_sfo3max_avg)


# Additional necessary information
cbar = plt.colorbar(m, boundaries=np.arange(overall_min_sfo3max_avg, overall_max_sfo3max_avg
                    + 0.5e-08, 0.5e-08))
cbar.set_label('mol mol-1')

# plot here only the 1st item in your sfo3max_avg list.
cs = ax.contourf(lon[:], lat[:], sfo3max_avg[0][:], cmap='magma')

# Adding axis labels - latitude & longitude
gridl = ax.gridlines(color="black", linestyle="dotted", draw_labels=True) 
gridl.xformatter=LONGITUDE_FORMATTER
gridl.yformatter=LATITUDE_FORMATTER
gridl.xlabels_top = False
gridl.ylabels_right = False


plt.ion()   # set interactive mode
plt.show()

# finally plot the 17 global plots of sfo3max_avg
for k in sfo3max_avg:
    cs = ax.contourf(lon[:], lat[:], k[:], cmap='magma')
    plt.gcf().canvas.draw()
    plt.pause(1) #control the interval between successive displays, currently set to 1 sec.

全局图的动态可视化

Dynamic Visualisation of Global Plots

python

matplotlib

cartopy

matplotlib-animation