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

# # Simple `hvplot` demo on ocean model data
# [hvplot](https://hvplot.pyviz.org) is a high-level plotting API for pandas, dask, streamz and xarray built on HoloViews. I learned about it in [James Bednar's pyviz talk at SciPy 2018](https://www.youtube.com/watch?v=DGLi-UWReM8) and I've been using it since.  It's a young project, but super useful. 
# 
# The great thing is you can just replace `.plot()` with `.hvplot()` on `xarray` and `pandas` objects and get interactive plots in your browser. 
# 
# It can be installed from the conda-forge channel: `conda install -c conda-forge hvplot`

# In[1]:


import xarray as xr


# Let's open some global wave forecast data using an OPeNDAP link from Unidata's THREDDS server

# In[2]:


url = 'http://thredds.ucar.edu/thredds/dodsC/grib/FNMOC/WW3/Global_1p0deg/Best'


# In[3]:


ds = xr.open_dataset(url)


# In[4]:


ds


# Let's look at significant wave height

# In[5]:


var = 'sig_wav_ht_surface'


# In[6]:


ds[var]


# For this dataset, the time dimension is sometimes `time`, sometimes `time1` or `time2`, so determine it here:

# In[7]:


ds[var].dims


# In[8]:


time_dim = ds[var].dims[0]


# `hvplot` currently has a known issue that is gets confused by extra time variables, so drop the associated `reftime` variable:

# In[9]:


ds = ds.drop('ref'+time_dim)


# Now we are ready to try `hvplot`:

# In[10]:


import hvplot.xarray  


# We tell `hvplot` to group on time so that the time dimension appears on a slider (otherwise we get a histogram of all data):

# In[11]:


ds[var].hvplot(groupby=time_dim)


# We can customize the plot via additional parameters, and because there is not yet a way to set the aspect ratio, set the size and width by eye to get a better aspect ratio:

# In[12]:


ds[var].hvplot(groupby=time_dim, clim=(0,5), label=var, rasterize=True,  width=700, height=400, cmap='viridis')


# Now let's extract a time series at a specified location 
# using xarray's selection by coordinate variable:

# In[13]:


lon = 302.5
lat = 25.23
label = f'{var} at lon: {lon}, lat: {lat}'

d1d = ds[var].sel(lon=lon, lat=lat, method='nearest')


# In[14]:


type(d1d)


# Using `hvplot` on this 1D `DataArray` does the right thing:

# In[15]:


d1d.hvplot()


# Note that `hvplot` also works with `pandas`:

# In[16]:


import hvplot.pandas


# In[17]:


df = d1d.to_pandas()


# In[18]:


df.hvplot()