如何在 xarray 图中调整 Matplotlib 颜色条范围？

Question

我有一个看起来像这样的情节

我不明白如何手动更改或设置颜色条的数据值范围。我想根据图中显示的数据值试验范围并将颜色条更改为（-4,4）。我看到plt.clim、vmin 和vmax 是可能使用的函数。

这是我的代码：

import cdsapi
import xarray as xr
import matplotlib.pyplot as plt
import numpy as np
import cartopy.crs as ccrs
# Also requires cfgrib library.

c = cdsapi.Client()

url = c.retrieve(
    'reanalysis-era5-single-levels-monthly-means',
    {
        'product_type': 'monthly_averaged_reanalysis',
        'format': 'grib',
        'variable': ['100m_u_component_of_wind','100m_v_component_of_wind'],
        'year': ['2006','2007','2008','2009','2010','2011','2012','2013','2014','2015','2016','2017','2018','2019','2020','2021'],
        'month': ['01','02','03','04','05','06','07','08','09','10','11','12'],
        'time': '00:00',
        'grid': [0.25, 0.25],
        'area': [70.00, -180.00, -40.00, 180.00],
    },
    "C:\\Users\\U321103\\.spyder-py3\\ERA5_MAPPING\\100m_wind_U_V.grib")
path = "C:\\Users\\U321103\\.spyder-py3\\ERA5_MAPPING\\100m_wind_U_V.grib"
ds = xr.load_dataset(path, engine='cfgrib')

wind_abs = np.sqrt(ds.u100**2 + ds.v100**2)
monthly_means = wind_abs.mean(dim='time')
wind_abs_clim = wind_abs.sel(time=slice('2006-01','2020-12')).groupby('time.month').mean(dim='time') # select averaging period

wind_abs_anom = ((wind_abs.groupby('time.month') / wind_abs_clim))-1 #deviation from climo

fg = wind_abs_anom.sel(time=slice('2021-01',None)).groupby('time.month').mean(dim='time').plot(col='month',
                        col_wrap=3,transform=ccrs.PlateCarree(),
                        cbar_kwargs={'orientation':'horizontal','shrink':0.6, 'aspect':40,'label':'Percent Deviation'},robust=False,subplot_kws={'projection': ccrs.Mercator()})

fg.map(lambda: plt.gca().coastlines())                                                                                               

Answer 1

我能够重现你的图，发现我可以添加vmin和vmax，如下所示。出于某种原因，这意味着我还必须指定颜色图，否则我最终会得到viridis。但是下面的代码对我有用（在我让它工作时进行了一些重构——这里唯一的重大变化是在底部的绘图部分）。

首先，加载数据：

import cdsapi

c = cdsapi.Client()
params = {
    'product_type': 'monthly_averaged_reanalysis',
    'format': 'grib',
    'variable': ['100m_u_component_of_wind', '100m_v_component_of_wind'],
    'year': [f'{n}' for n in range(2006, 2022)],
    'month': [f'{n:02d}' for n in range(1, 13)],
    'time': '00:00',
    'grid': [0.25, 0.25],
    'area': [70.00, -180.00, -40.00, 180.00],
}
path = '100m_wind_U_V.grib'
url = c.retrieve('reanalysis-era5-single-levels-monthly-means',
                 params,
                 path,
                )

然后是数据管道：

import xarray as xr
import numpy as np
# Also need cfgrib library.

ds = xr.load_dataset(path, engine='cfgrib')
wind_abs = np.sqrt(ds.u100**2 + ds.v100**2)
monthly_means = wind_abs.mean(dim='time')
wind_abs_clim = (wind_abs.sel(time=slice('2006-01','2020-12'))
                         .groupby('time.month')
                         .mean(dim='time'))
wind_abs_anom = ((wind_abs.groupby('time.month') / wind_abs_clim)) - 1

终于出图了：

import cartopy.crs as ccrs
import matplotlib.pyplot as plt

cbar_kwargs = {'orientation':'horizontal', 'shrink':0.6, 'aspect':40, 'label':'Percent Deviation'}
subplot_kws = {'projection': ccrs.Mercator()}
fg = (wind_abs_anom.sel(time=slice('2021-01', None))
                   .groupby('time.month')
                   .mean(dim='time')
                   .plot(col='month',
                         col_wrap=3,
                         transform=ccrs.PlateCarree(),
                         cmap='RdBu_r', vmin=-3, vmax=3,  # <-- New bit.
                         cbar_kwargs=cbar_kwargs,
                         robust=False,
                         subplot_kws=subplot_kws
                        ))
fg.map(lambda: plt.gca().coastlines())

有时我会使用百分位数来自动控制vmin 和vmax 的值，例如max_ = np.percentile(data, 99)，然后是vmin=-max_, vmax=max_。这很好地处理了拉伸颜色图的异常值，但它要求您能够在绘制图之前计算这些值。

如果您想开始对绘图有更多控制权，最好停止使用xarray 绘图界面并直接使用matplotlib 和cartopy。下面是它的样子（替换上面的所有绘图代码）：

import cartopy.crs as ccrs
import matplotlib.pyplot as plt

sel = wind_abs_anom.sel(time=slice('2021-01', None))

left, *_, right = wind_abs_anom.longitude
top, *_, bottom = wind_abs_anom.latitude  # Min and max latitude.
extent = [left, right, bottom, top]

fig, axs = plt.subplots(nrows=2, ncols=3,
                        figsize=(15, 6),
                        subplot_kw={'projection': ccrs.PlateCarree()},
                       )

for ax, (month, group) in zip(axs.flat, sel.groupby('time.month')):
    mean = group.mean(dim='time')
    im = ax.imshow(mean,
                   transform=ccrs.PlateCarree(),
                   extent=extent,
                   cmap='RdBu_r', vmin=-3, vmax=3)
    ax.set_title(f'month = {month}')
    ax.coastlines()

cbar_ax = fig.add_axes([0.2, 0.0, 0.6, 0.05])  # Left, bottom, width, height.
cbar = fig.colorbar(im, cax=cbar_ax, extend='both', orientation='horizontal')
cbar.set_label('Percent deviation')
    
plt.show()

由于某种原因，当我尝试将ccra.Mercator() 用于地图时，数据会失真；也许你可以弄清楚这一点。