import plotly
plotly.__version__

import plotly.plotly as py

from plotly.graph_objs import *

import numpy as np           
from scipy.io import netcdf  

from mpl_toolkits.basemap import Basemap

# Path the downloaded NetCDF file (different for each download)
f_path = '/home/etienne/Downloads/compday.Bo3cypJYyE.nc'

# Retrieve data from NetCDF file
with netcdf.netcdf_file(f_path, 'r') as f:
    lon = f.variables['lon'][::]    # copy as list
    lat = f.variables['lat'][::-1]  # invert the latitude vector -> South to North
    air = f.variables['air'][0,::-1,:]  # squeeze out the time dimension, 
                                        # invert latitude index

# Shift 'lon' from [0,360] to [-180,180], make numpy array
tmp_lon = np.array([lon[n]-360 if l>=180 else lon[n] 
                   for n,l in enumerate(lon)])  # => [0,180]U[-180,2.5]

i_east, = np.where(tmp_lon>=0)  # indices of east lon
i_west, = np.where(tmp_lon<0)   # indices of west lon
lon = np.hstack((tmp_lon[i_west], tmp_lon[i_east]))  # stack the 2 halves

# Correspondingly, shift the 'air' array
tmp_air = np.array(air)
air = np.hstack((tmp_air[:,i_west], tmp_air[:,i_east]))

trace1 = Contour(
    z=air,
    x=lon,
    y=lat,
    colorscale="RdBu",
    zauto=False,  # custom contour levels
    zmin=-5,      # first contour level
    zmax=5        # last contour level  => colorscale is centered about 0
)

# Make shortcut to Basemap object, 
# not specifying projection type for this example
m = Basemap()     

# Make trace-generating function (return a Scatter object)
def make_scatter(x,y):
    return Scatter(
        x=x,
        y=y,
        mode='lines',
        line=Line(color="black"),
        name=' '  # no name on hover
    )

# Functions converting coastline/country polygons to lon/lat traces
def polygons_to_traces(poly_paths, N_poly):
    ''' 
    pos arg 1. (poly_paths): paths to polygons
    pos arg 2. (N_poly): number of polygon to convert
    '''
    traces = []  # init. plotting list 

    for i_poly in range(N_poly):
        poly_path = poly_paths[i_poly]
        
        # get the Basemap coordinates of each segment
        coords_cc = np.array(
            [(vertex[0],vertex[1]) 
             for (vertex,code) in poly_path.iter_segments(simplify=False)]
        )
        
        # convert coordinates to lon/lat by 'inverting' the Basemap projection
        lon_cc, lat_cc = m(coords_cc[:,0],coords_cc[:,1], inverse=True)
        
        # add plot.ly plotting options
        traces.append(make_scatter(lon_cc,lat_cc))
     
    return traces

# Function generating coastline lon/lat traces
def get_coastline_traces():
    poly_paths = m.drawcoastlines().get_paths() # coastline polygon paths
    N_poly = 91  # use only the 91st biggest coastlines (i.e. no rivers)
    return polygons_to_traces(poly_paths, N_poly)

# Function generating country lon/lat traces
def get_country_traces():
    poly_paths = m.drawcountries().get_paths() # country polygon paths
    N_poly = len(poly_paths)  # use all countries
    return polygons_to_traces(poly_paths, N_poly)

# Get list of of coastline and country lon/lat traces
traces_cc = get_coastline_traces()+get_country_traces()

data = Data([trace1]+traces_cc)

title = u"Average daily surface air temperature anomalies [\u2103]<br> \
in July 2014 with respect to 1981-2010 climatology"

anno_text = "Data courtesy of \
<a href='http://www.esrl.noaa.gov/psd/data/composites/day/'>\
NOAA Earth System Research Laboratory</a>"

axis_style = dict(
    zeroline=False,
    showline=False,
    showgrid=False,
    ticks='',
    showticklabels=False,
)

layout = Layout(
    title=title,
    showlegend=False,
    hovermode="closest",        # highlight closest point on hover
    xaxis=XAxis(
        axis_style,
        range=[lon[0],lon[-1]]  # restrict y-axis to range of lon
    ),
    yaxis=YAxis(
        axis_style,
    ),
    annotations=Annotations([
        Annotation(
            text=anno_text,
            xref='paper',
            yref='paper',
            x=0,
            y=1,
            yanchor='bottom',
            showarrow=False
        )
    ]),
    autosize=False,
    width=1000,
    height=500,
)

fig = Figure(data=data, layout=layout)

py.iplot(fig, filename="maps", width=1000)

from IPython.display import display, HTML
import urllib2
url = 'https://raw.githubusercontent.com/plotly/python-user-guide/master/custom.css'
display(HTML(urllib2.urlopen(url).read()))