Bokeh Tutorial

1.4 Plotting - Climate

Exercise: Plot temperature anomaly monthly average for a given year and month by lat/lon with bokeh.plotting.image_rgba()

Data: 'data/Land_and_Ocean_LatLong1.nc'

Note: For your convienience, I'm providing an RGBAColorMapper



In [1]:

    
from utils.colormap import RGBAColorMapper



In [3]:

    
# Get the netCDF4 variable for temperature anomaly
import netCDF4

data = netCDF4.Dataset('data/Land_and_Ocean_LatLong1.nc')
t = data.variables['temperature']



In [4]:

    
# Initialize the colormap with a bokeh palette and the low and high values for the scale.
from bokeh.palettes import RdBu11
colormap = RGBAColorMapper(-6, 6, RdBu11)



In [5]:

    
# Write a function ``get_slice(temperature_variable, year, month)`` that given a temperature variable, year and month
# returns an array of rgba colors. (Hint: Use RGBAColorMapper.color()). 
def get_slice(t, year, month):
    i = (year - 1850)*12 + month - 1
    return colormap.color(t[i, :, :])



In [6]:

    
from bokeh.plotting import figure, output_notebook, show



In [7]:

    
# Set output option
output_notebook()









    




    
        
        
        
    
        
        BokehJS successfully loaded.



In [8]:

    
# Get image data for a given year and month using the ``get_slice`` function
image = get_slice(t, 1950, 1)



In [9]:

    
# Create plot
# Tip: Make sure to set x_range and y_range appropriately
p =  figure(width=900, height=500, x_axis_type=None, y_axis_type=None, x_range=[-180,180], y_range=[-90,90])

p.image_rgba(
    image=[image],
    x=[-180], y=[-90],
    dw=[360], dh=[180], name='image'
)









    Out[9]:





<bokeh.plotting.Figure at 0x109e49cd0>



In [10]:

    
# Show plot
show(p)



In [10]:

    
# Change the year and month and rerun the plot again.

Exercise: Overlay the worldmap boundaries in the temperature image plot



In [11]:

    
# Get worldmap data
import pandas as pd
import utils.world_countries as wc
world_countries = wc.data.copy()

worldmap = pd.DataFrame.from_dict(world_countries, orient='index')



In [12]:

    
# Create your plot
p =  figure(width=900, height=500, x_axis_type=None, y_axis_type=None, x_range=[-180,180], y_range=[-90,90])

p.image_rgba(
    image=[image],
    x=[-180], y=[-90],
    dw=[360], dh=[180], name='image'
)

p.patches(xs=worldmap['lons'], ys=worldmap['lats'], fill_color="white", fill_alpha=0,
    line_color="black", line_width=0.5)









    Out[12]:





<bokeh.plotting.Figure at 0x109f73190>



In [13]:

    
# Show plot
show(p)