#!/usr/bin/env python
# coding: utf-8

# # Try geoviews quadmesh 

# In[1]:


get_ipython().run_line_magic('matplotlib', 'inline')

import xarray as xr
import geoviews as gv
gv.extension('bokeh')


# ### 1. Try example from http://geo.holoviews.org/gallery/bokeh/xarray_quadmesh.html

# In[2]:


tiles = gv.tile_sources.ESRI


# In[3]:


rasm = xr.tutorial.load_dataset('rasm')


# In[4]:


rasm


# In[5]:


rasm.Tair


# In[6]:


qmeshes = gv.Dataset(rasm.Tair[::4, ::3, ::3]).to(gv.QuadMesh, groupby='time')


# In[7]:


print(qmeshes)


# In[8]:


rasm.Tair[0, ::3, ::3].size


# In[9]:


get_ipython().run_cell_magic('opts', 'QuadMesh [width=700 height=350]', 'tiles * qmeshes\n')


# ### 2. Try using ROMS curvilinear ocean model output

# In[10]:


url = 'https://gamone.whoi.edu/thredds/dodsC/coawst_4/use/fmrc/coawst_4_use_best.ncd'


# In[11]:


ds = xr.open_dataset(url)


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ds.Hwave


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qmeshes2 = gv.Dataset(ds.Hwave[0:2,::10,::10]).to(gv.QuadMesh, groupby='time')


# In[14]:


print(qmeshes2)


# In[15]:


ds.Hwave[0,::10,::10].size


# In[ ]:


get_ipython().run_cell_magic('opts', 'QuadMesh [width=700 height=350]', 'tiles * qmeshes2\n')