如何调整 xarray 图中的 Matplotlib 颜色条范围？

Question

我有一个看起来像这样的情节

我无法理解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尝试范围，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，并发现我可以添加vmin和 ，vmax如下所示。由于某种原因，这意味着我还必须指定颜色图，否则我最终会得到viridis. 但下面的代码对我有用（当我让它工作时进行了一些重构 \xe2\x80\x94\xc2\xa0 这里唯一的实质性变化是在底部的绘图部分）。

\n

首先，加载数据：

\n

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

\n

然后是数据管道：

\n

import xarray as xr\nimport numpy as np\n# Also need cfgrib library.\n\nds = xr.load_dataset(path, engine='cfgrib')\nwind_abs = np.sqrt(ds.u100**2 + ds.v100**2)\nmonthly_means = wind_abs.mean(dim='time')\nwind_abs_clim = (wind_abs.sel(time=slice('2006-01','2020-12'))\n                         .groupby('time.month')\n                         .mean(dim='time'))\nwind_abs_anom = ((wind_abs.groupby('time.month') / wind_abs_clim)) - 1\n

\n

最后是绘图：

\n

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

\n

vmin有时我会使用百分位来自动控制和的值vmax，例如max_ = np.percentile(data, 99), then vmin=-max_, vmax=max_。这可以很好地处理拉伸颜色图的异常值，但它要求您能够在绘制绘图之前计算这些值。

\n

如果您想开始对绘图有更多控制，最好停止使用xarray绘图界面并直接使用matplotlib和cartopy。看起来可能是这样的（替换上面的所有绘图代码）：

\n

import cartopy.crs as ccrs\nimport matplotlib.pyplot as plt\n\nsel = wind_abs_anom.sel(time=slice('2021-01', None))\n\nleft, *_, right = wind_abs_anom.longitude\ntop, *_, bottom = wind_abs_anom.latitude  # Min and max latitude.\nextent = [left, right, bottom, top]\n\nfig, axs = plt.subplots(nrows=2, ncols=3,\n                        figsize=(15, 6),\n                        subplot_kw={'projection': ccrs.PlateCarree()},\n                       )\n\nfor ax, (month, group) in zip(axs.flat, sel.groupby('time.month')):\n    mean = group.mean(dim='time')\n    im = ax.imshow(mean,\n                   transform=ccrs.PlateCarree(),\n                   extent=extent,\n                   cmap='RdBu_r', vmin=-3, vmax=3)\n    ax.set_title(f'month = {month}')\n    ax.coastlines()\n\ncbar_ax = fig.add_axes([0.2, 0.0, 0.6, 0.05])  # Left, bottom, width, height.\ncbar = fig.colorbar(im, cax=cbar_ax, extend='both', orientation='horizontal')\ncbar.set_label('Percent deviation')\n    \nplt.show()\n

\n

由于某种原因，当我尝试用于ccra.Mercator()地图时，数据会失真；也许你能弄清楚这一点。

\n