import bokeh
bokeh.print_versions()

from bokeh.plotting import *
from bokeh.objects import HoverTool
from collections import OrderedDict

output_notebook()

#5 datasets (rows)
#8 buckets (columns)

buckets = ['AMOUNT', 'BUYER', 'TENDER TRACKING', 'TENDER FEATURES', 'AWARD TRACKING', 'AWARD FEATURES', 'SYSTEM', 'SUPPLIER']
datasets = ['Canada', 'Turkey', 'France', 'UK', 'Moldova']
data = [
('Canada', [1, 2, 1, 2, 1, 2, 1, 2]),
('Turkey', [2, 2, 2, 2, 0, 0, 1, 1]),
('France', [3, 2, 1, 3, 2, 1, 3, 2]),
('UK', [1, 1, 1, 0, 0, 0, 0, 1]),
('Moldova', [2, 3, 4, 1, 1, 2, 6, 9]),
]

x = []
y = []
size = []
alpha = []

for i in xrange(len(datasets)):
    for j in xrange(len(buckets)):
        dataset = datasets[i]
        bucket = buckets[j]
        x.append(dataset)
        y.append(bucket)
        size.append(0.8) # Fixed size
        alpha.append(data[i][1][j]*0.1) # <-- alpha as a "intensity" measure.
        
line_alpha = [0.1 if a == 0 else a for a in alpha] # <-- to get borders over 0 "intensity" squares

source = ColumnDataSource(
    data=dict(
        x=x,
        y=y,
        size=size,
        alpha=alpha, # <-- passed alpha to the source to be showed (later) by the hover tool
    )
)

labels = [tup[1] for tup in data]
labels = [str(item) for sublist in labels for item in sublist]

# draw it
figure()
hold() # <-- so we can add our new text renderer

visual_properties = {
    'color': 'red', # <-- another color to try
    'fill_alpha': alpha, # <-- alpha "defined" by our data
    'line_color': 'red', # <-- add borders to the squares
    'line_alpha': line_alpha, # <-- borders with the same alpha except for the 0 "intensity" square (cosmetic)
    'plot_height': 1000,
    'plot_width': 800,
    'title': None,
}

rect('x', 'y', 'size', 'size',# <-- Passing "size" as width and height parameters from our `source`
       source=source,
       x_range=datasets, 
       y_range=buckets,
       tools="hover",  # <-- Added "hover" tool
       marker="square",# <-- No it's not pretty, but there are (just now discovered) issues with
                       #      hit-detection for the circle glyph on categorical axes.
       **visual_properties)

text(x, y,
     text=labels, # <-- labels!
     text_color="black",
     angle=0)

grid().grid_line_color = None

hover = [t for t in curplot().tools if isinstance(t, HoverTool)][0]

hover.tooltips = OrderedDict([
    ("Dataset", "@x"),
    ("Bucket", "@y"),
    ("Alpha", "@alpha"),
])

show()