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

# # What's new in xarray?
# 
# An udpate for PyData NYC 2019.

# In[1]:


import xarray as xr
xr.__version__


# ## Rich HTML repr

# In[2]:


xr.set_options(display_style="html")
xr.tutorial.load_dataset('rasm').chunk()


# 
#  
# 
#  
# 
#  
# 
#  
# 
#  
# 
#  
# 
#  
# 
# ## Flexible Array Support (NEP18)
# 
# Create sparse array and put it in an xarray Dataset.

# In[3]:


import sparse
coords = [[0, 1, 2, 3, 4],
          [0, 1, 2, 3, 4]]
data = [10, 20, 30, 40, 50]
s = sparse.COO(coords, data, shape=(5, 5))
das = xr.DataArray(s, dims=['lat', 'lon'])
das


# In[4]:


das.mean(dim='lon')


# Put it inside a dask array.

# In[5]:


das.chunk()


# Create sparse array from pandas multiindex.

# In[6]:


import pandas as pd
import numpy as np
tuples = [('a', 0), ('a', 2), ('b', 1), ('c', 3)]
index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second'])
s = pd.Series(np.zeros(len(index)), index=index)
s


# In[7]:


xr.DataArray.from_series(s)


# In[8]:


das = xr.DataArray.from_series(s, sparse=True)
das


# In[9]:


das.sel(first='a', second=0).data.todense()


# Hypothetically should work for `cupy` arrays, `pint` arrays, etc.

# 
#  
# 
#  
# 
#  
# 
#  
# 
#  
# 
#  
# 
#  
# 
# ## CFTimeIndex
# 
# For non-standard calendars.

# In[10]:


day = np.arange(0, 360*10)
ds = xr.Dataset(coords={'time': ('time', day,
                                 {'units': 'days since 4000-01-01',
                                  'calendar': '360_day'})})
ds = xr.decode_cf(ds)
ds


# In[11]:


ds.indexes


# In[12]:


ds.groupby('time.month')


# In[ ]: