import xarray as xr
import numpy as np
import holoviews as hv
from holoviews import opts
hv.extension('matplotlib')

opts.defaults(opts.Scatter3D(color='Value', cmap='fire', edgecolor='black', s=50))

img = hv.Image((range(10), range(5), np.random.rand(5, 10)), datatype=['grid'])
img

img.data

arr_img = img.clone(datatype=['image'])
print(type(arr_img.data))

try: 
    xr_img = img.clone(datatype=['xarray'])

    print(type(xr_img.data))    
except:
    print('xarray interface could not be imported.')

print("Array type: %s with bounds %s" % (type(arr_img.data), arr_img.bounds))

dataset3d = hv.Dataset((range(3), range(5), range(7), np.random.randn(7, 5, 3)),
                       ['x', 'y', 'z'], 'Value')
dataset3d

hv.Scatter3D(dataset3d)

dataset3d.select(x=1).to(hv.Image, ['y', 'z']) + hv.Scatter3D(dataset3d.select(x=1))

(dataset3d.to(hv.Image, ['y', 'z'], 'Value', ['x']) +
hv.HoloMap({x: hv.Scatter3D(dataset3d.select(x=x)) for x in range(3)}, kdims='x'))

hv.Image(dataset3d.aggregate(['x', 'y'], np.mean)) + hv.Image(dataset3d.reduce(z=np.mean))

hv.Spread(dataset3d.aggregate('z', np.mean, np.std)) * hv.Curve(dataset3d.aggregate('z', np.mean))

dataset3d.to(hv.BoxWhisker, 'x', 'Value', groupby=[])

dataset3d.to(hv.Distribution, 'Value', [], groupby='x').overlay()

heatmap = hv.HeatMap((['A', 'B', 'C'], ['a', 'b', 'c', 'd', 'e'], np.random.rand(5, 3)))
heatmap + hv.Table(heatmap)

n = 8  # Number of bins in each direction
xs = np.logspace(1, 3, n)
ys = np.linspace(1, 10, n)
zs = np.arange((n-1)**2).reshape(n-1, n-1)
print('Shape of x-coordinates:', xs.shape)
print('Shape of y-coordinates:', ys.shape)
print('Shape of value array:', zs.shape)
hv.QuadMesh((xs, ys, zs))

n=20
coords = np.linspace(-1.5,1.5,n)
X,Y = np.meshgrid(coords, coords);
Qx = np.cos(Y) - np.cos(X)
Qy = np.sin(Y) + np.sin(X)
Z = np.sqrt(X**2 + Y**2)

print('Shape of x-coordinates:', Qx.shape)
print('Shape of y-coordinates:', Qy.shape)
print('Shape of value array:', Z.shape)

qmesh = hv.QuadMesh((Qx, Qy, Z))
qmesh

xr_ds = xr.tutorial.open_dataset("air_temperature").load()
xr_ds

hv_ds = hv.Dataset(xr_ds)[:, :, "2013-01-01"]
print(hv_ds)

airtemp = hv_ds.to(hv.Image, kdims=["lon", "lat"], dynamic=False)
airtemp[:, 220:320, :].opts(colorbar=True, fig_size=200)

rasm = xr.tutorial.open_dataset("rasm").load()
rasm.coords

rasm = rasm.isel(time=0, x=slice(0, 200)).assign_coords(x=np.arange(200), y=np.arange(205))
rasm.coords

hv.QuadMesh(rasm, ['x', 'y']) + hv.QuadMesh(rasm, ['xc', 'yc'])

heatmap.dimension_values('x')

heatmap.dimension_values('x', expanded=False)

heatmap.dimension_values('x', flat=False)

heatmap.dimension_values('z')

heatmap.dimension_values('z', flat=False)