plotting quantified data
[1]:
import xarray as xr
# to be able to read unit attributes following the CF conventions
import cf_xarray.units # must be imported before pint_xarray
import pint_xarray
from pint_xarray import unit_registry as ureg
xr.set_options(display_expand_data=False)
[1]:
<xarray.core.options.set_options at 0x7f3b5d9727d0>
load the data
[2]:
ds = xr.tutorial.open_dataset("air_temperature")
data = ds.air
data
[2]:
<xarray.DataArray 'air' (time: 2920, lat: 25, lon: 53)> Size: 31MB
[3869000 values with dtype=float64]
Coordinates:
* lat (lat) float32 100B 75.0 72.5 70.0 67.5 65.0 ... 22.5 20.0 17.5 15.0
* lon (lon) float32 212B 200.0 202.5 205.0 207.5 ... 325.0 327.5 330.0
* time (time) datetime64[ns] 23kB 2013-01-01 ... 2014-12-31T18:00:00
Attributes:
long_name: 4xDaily Air temperature at sigma level 995
units: degK
precision: 2
GRIB_id: 11
GRIB_name: TMP
var_desc: Air temperature
dataset: NMC Reanalysis
level_desc: Surface
statistic: Individual Obs
parent_stat: Other
actual_range: [185.16 322.1 ]quantify the data
Note: this example uses the data provided by the xarray.tutorial functions. As such, the units attributes follow the CF conventions, which pint does not understand by default. To still be able to read them we are using the registry provided by cf-xarray.
[3]:
quantified = data.pint.quantify()
quantified
[3]:
<xarray.DataArray 'air' (time: 2920, lat: 25, lon: 53)> Size: 31MB
[K] 241.2 242.5 243.5 244.0 244.1 243.9 ... 297.9 297.4 297.2 296.5 296.2 295.7
Coordinates:
* lat (lat) float32 100B 75.0 72.5 70.0 67.5 65.0 ... 22.5 20.0 17.5 15.0
* lon (lon) float32 212B 200.0 202.5 205.0 207.5 ... 325.0 327.5 330.0
* time (time) datetime64[ns] 23kB 2013-01-01 ... 2014-12-31T18:00:00
Attributes:
long_name: 4xDaily Air temperature at sigma level 995
precision: 2
GRIB_id: 11
GRIB_name: TMP
var_desc: Air temperature
dataset: NMC Reanalysis
level_desc: Surface
statistic: Individual Obs
parent_stat: Other
actual_range: [185.16 322.1 ]work with the data
[4]:
monthly_means = quantified.pint.to("degC").sel(time="2013").groupby("time.month").mean()
monthly_means
[4]:
<xarray.DataArray 'air' (month: 12, lat: 25, lon: 53)> Size: 127kB
[°C] -28.68 -28.49 -28.48 -28.67 -28.99 -29.32 ... 24.73 24.77 24.35 24.26 24.22
Coordinates:
* lat (lat) float32 100B 75.0 72.5 70.0 67.5 65.0 ... 22.5 20.0 17.5 15.0
* lon (lon) float32 212B 200.0 202.5 205.0 207.5 ... 325.0 327.5 330.0
* month (month) int64 96B 1 2 3 4 5 6 7 8 9 10 11 12
Attributes:
long_name: 4xDaily Air temperature at sigma level 995
precision: 2
GRIB_id: 11
GRIB_name: TMP
var_desc: Air temperature
dataset: NMC Reanalysis
level_desc: Surface
statistic: Individual Obs
parent_stat: Other
actual_range: [185.16 322.1 ]Most operations will preserve the units but there are some which will drop them (see the duck array integration status page). To work around that there are unit-aware versions on the .pint accessor. For example, to select data use .pint.sel instead of .sel:
[5]:
monthly_means.pint.sel(
lat=ureg.Quantity(4350, "angular_minute"),
lon=ureg.Quantity(12000, "angular_minute"),
)
[5]:
<xarray.DataArray 'air' (month: 12)> Size: 96B
[°C] -26.08 -31.22 -22.49 -15.6 -5.43 ... 0.4102 -0.1338 -3.855 -14.51 -21.41
Coordinates:
lat float32 4B 4.35e+03
lon float32 4B 1.2e+04
* month (month) int64 96B 1 2 3 4 5 6 7 8 9 10 11 12
Attributes:
long_name: 4xDaily Air temperature at sigma level 995
precision: 2
GRIB_id: 11
GRIB_name: TMP
var_desc: Air temperature
dataset: NMC Reanalysis
level_desc: Surface
statistic: Individual Obs
parent_stat: Other
actual_range: [185.16 322.1 ]plot
xarray’s plotting functions will cast the data to numpy.ndarray, so we need to “dequantify” first.
[6]:
monthly_means.pint.dequantify(format="~P").plot.imshow(col="month", col_wrap=4)
[6]:
<xarray.plot.facetgrid.FacetGrid at 0x7f3b47e17750>
