%matplotlib inline
import xarray as xr
import geoviews as gv
gv.extension('bokeh')
tiles = gv.tile_sources.ESRI
rasm = xr.tutorial.load_dataset('rasm')
rasm
<xarray.Dataset> Dimensions: (time: 36, x: 275, y: 205) Coordinates: * time (time) datetime64[ns] 1980-09-16T12:00:00 1980-10-17 ... xc (y, x) float64 189.2 189.4 189.6 189.7 189.9 190.1 190.2 190.4 ... yc (y, x) float64 16.53 16.78 17.02 17.27 17.51 17.76 18.0 18.25 ... Dimensions without coordinates: x, y Data variables: Tair (time, y, x) float64 nan nan nan nan nan nan nan nan nan nan ... Attributes: title: /workspace/jhamman/processed/R1002RBRxaaa01a/l... institution: U.W. source: RACM R1002RBRxaaa01a output_frequency: daily output_mode: averaged convention: CF-1.4 references: Based on the initial model of Liang et al., 19... comment: Output from the Variable Infiltration Capacity... nco_openmp_thread_number: 1 NCO: "4.6.0" history: Tue Dec 27 14:15:22 2016: ncatted -a dimension...
rasm.Tair
<xarray.DataArray 'Tair' (time: 36, y: 205, x: 275)> array([[[ nan, nan, ..., nan, nan], [ nan, nan, ..., nan, nan], ..., [ nan, nan, ..., 26.802619, 27.086035], [ nan, nan, ..., 26.564739, 26.730649]], [[ nan, nan, ..., nan, nan], [ nan, nan, ..., nan, nan], ..., [ nan, nan, ..., 24.29624 , 24.614224], [ nan, nan, ..., 24.299677, 24.454399]], ..., [[ nan, nan, ..., nan, nan], [ nan, nan, ..., nan, nan], ..., [ nan, nan, ..., 27.311049, 27.673872], [ nan, nan, ..., 27.008894, 27.23018 ]], [[ nan, nan, ..., nan, nan], [ nan, nan, ..., nan, nan], ..., [ nan, nan, ..., 28.422736, 28.687212], [ nan, nan, ..., 28.185955, 28.20753 ]]]) Coordinates: * time (time) datetime64[ns] 1980-09-16T12:00:00 1980-10-17 ... xc (y, x) float64 189.2 189.4 189.6 189.7 189.9 190.1 190.2 190.4 ... yc (y, x) float64 16.53 16.78 17.02 17.27 17.51 17.76 18.0 18.25 ... Dimensions without coordinates: y, x Attributes: units: C long_name: Surface air temperature type_preferred: double time_rep: instantaneous
qmeshes = gv.Dataset(rasm.Tair[::4, ::3, ::3]).to(gv.QuadMesh, groupby='time')
print(qmeshes)
:HoloMap [time] :QuadMesh [xc,yc] (Tair)
%%opts QuadMesh [width=700 height=350]
tiles * qmeshes
url = 'https://gamone.whoi.edu/thredds/dodsC/coawst_4/use/fmrc/coawst_4_use_best.ncd'
ds = xr.open_dataset(url)
ds.Hwave
<xarray.DataArray 'Hwave' (time: 44053, eta_rho: 336, xi_rho: 896)> [377518080 values with dtype=float32] Coordinates: lon_rho (eta_rho, xi_rho) float64 ... lat_rho (eta_rho, xi_rho) float64 ... * time (time) datetime64[ns] 2013-01-03T01:00:00 2013-01-03T02:00:00 ... time_run (time) datetime64[ns] ... Dimensions without coordinates: eta_rho, xi_rho Attributes: units: meter long_name: wind-induced significant wave height time: ocean_time field: Hwave, scalar, series _ChunkSizes: [ 1 336 896] standard_name: sea_surface_wave_significant_height
qmeshes2 = gv.Dataset(ds.Hwave[0:2,::4,::4]).to(gv.QuadMesh, groupby='time')
print(qmeshes2)
:HoloMap [time] :QuadMesh [lon_rho,lat_rho] (Hwave)
%%opts QuadMesh [width=700 height=350]
tiles * qmeshes2