I think such feature can be very useful specially when we have different variables (example Normalized Difference Vegetation Index (NDVI) and land surface temperature (LST) which have different values) rather than a common color bar.
%matplotlib inline
import numpy as np
import pandas as pd
import xarray as xr
import matplotlib.pyplot as plt
airtemps = xr.tutorial.open_dataset("air_temperature")
air = airtemps.air - 273.15
air.attrs = airtemps.air.attrs
air.attrs["units"] = "deg C"
aot = xr.concat([air.isel(time=0)* .01, air.isel(time=100)], "time")
# assume this is NDVI
(air.isel(time=0)* .01 ).plot()
# ndvi values cant be visualized when there is a common color bar with Faceting
aot.plot(col = 'time')
Describe the solution you'd like
# manual plot
fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(12, 5))
for i in [0,1]:
aot.isel(time=i).plot(ax = axes[i])
Rather than manual plot to adjust each color bar, it will be more efficient to integrate this option automatically when using Faceting
Is your feature request related to a problem?
I think such feature can be very useful specially when we have different variables (example Normalized Difference Vegetation Index (NDVI) and land surface temperature (LST) which have different values) rather than a common color bar.
Describe the solution you'd like
Rather than manual plot to adjust each color bar, it will be more efficient to integrate this option automatically when using Faceting
Describe alternatives you've considered
No response
Additional context
No response