全局图的动态可视化

Question

我制作了17个全球图，显示了1850-2015年期间最大表面臭氧的十年平均值。我希望创建一个动画来循环遍历它们（几乎像一个gif），而不是单独绘制它们，即整个动画具有相同的海岸线，轴和颜色条，但是会更改绘制为轮廓的内容。

将非常感谢您提供有关如何调整我的代码以实现此目的的任何帮助-预先感谢您！！

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

可以将绘图中的几个元素排除在循环之外，因为它们只需要设置一次。设置绘图元素后，您可以遍历列表来更新绘图并设置动画。这可以通过使用matplotlib的交互模式来实现，如下代码所示：

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)

#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.