box_plot.py

box_plot.py

from math import pi
from bokeh.models.tools import CustomJSHover, HoverTool

import pandas as pd

from bokeh.io import show
from bokeh.models import BasicTicker, ColorBar, LinearColorMapper, Label
from bokeh.plotting import figure
from bokeh.sampledata.unemployment1948 import data
from bokeh.layouts import column

import numpy as np
from bokeh.models.tickers import FixedTicker

import colorcet as cc


def get_heatmap_bar():
    min_value = 1
    int_value = 5
    max_value = 10
    ticks = [min_value, int_value, max_value]
    top_labels = [None, None, None]
    heatmap_label_center =  (int_value + max_value) / 2
    return get_heatmap_bar_aux(ticks, top_labels, heatmap_label_center)


def get_heatmap_bar_aux(ticks, top_labels, heatmap_label_center):
    from bokeh.models.callbacks import CustomJS
    from bokeh.models import Label
    PLOT_HEIGHT = 5
    LABEL_HEIGHT = 4.988
    TOP_LABEL_HEIGHT = 5.005
    BOX_Y_DIFF = 0.0025
    box_width = (ticks[-1] - heatmap_label_center) * 2 - 0.05
    

    b = figure(title="World score", tools="", toolbar_location=None, plot_height=100)

    b.segment(x0=ticks[0], y0=PLOT_HEIGHT, x1=ticks[-1], y1=PLOT_HEIGHT, line_color="#808080", line_width=3)
    b.vbar(x=heatmap_label_center, top=PLOT_HEIGHT + BOX_Y_DIFF, bottom=PLOT_HEIGHT - BOX_Y_DIFF, 
    width=box_width, fill_color="black", line_color="black")

    for i, tick in enumerate(ticks):
        b.rect(x=tick, y=PLOT_HEIGHT, width=0.01, height=0.01, line_color="#808080")
        l = Label(x=tick - _get_x_diff(tick), y=LABEL_HEIGHT, text="{:.2f}".format(tick), render_mode='css',
        border_line_color=None, border_line_alpha=1.0, text_color='black', text_font_size='8pt')
        b.add_layout(l)
        if top_labels[i] != None:
            lt = Label(x=tick-_get_x_diff(top_labels[i]), y=TOP_LABEL_HEIGHT, text=top_labels[i], render_mode='css',
            border_line_color=None, border_line_alpha=1.0, text_color='black', text_font_size='8pt')
            b.add_layout(lt)

    heatmap_label = Label(x=heatmap_label_center-_get_x_diff('Heatmap'), y=TOP_LABEL_HEIGHT, text='Heatmap', render_mode='css',
    border_line_color=None, border_line_alpha=1.0, text_color='black', text_font_size='8pt')
    b.add_layout(heatmap_label)
    
    b.title.text_font_style='italic'
    #b.title.text_line_height=0.3
    #b.title.text_font_size='10px'
    #b.title.font = 'roboto'

    b.xgrid.grid_line_color = None
    b.ygrid.grid_line_color = None
    b.yaxis.visible = False
    b.xaxis.visible = False
    b.outline_line_color = None
    b.x_range.range_padding = None
    b.y_range.range_padding = 1
    b.background_fill_color = None
    b.border_fill_color = None


            
    callback = CustomJS(args=dict(xr=b.x_range), code="""

    console.log("Hello")

    """)
    
    #b.js_on_event(PlotEvent, callback)
    b.js_on_change('plot_width', callback)



    return b


def _get_x_diff(word):
    LETTER_SPACE = 0.06
    return len(str(word)) * LETTER_SPACE


data['Year'] = data['Year'].astype(str)
data = data.set_index('Year')
data.drop('Annual', axis=1, inplace=True)
data.columns.name = 'Month'

years = list(data.index)
months = list(data.columns)

# reshape to 1D array or rates with a month and year for each row.
df = pd.DataFrame(data.stack(), columns=['rate']).reset_index()

mapper = LinearColorMapper(palette=cc.palette['diverging_tritanopic_cwr_75_98_c20'], low=3, high=df.rate.max())
mapper_world = LinearColorMapper(palette=cc.palette['linear_blue_95_50_c20'], low=0, high=16)

TOOLS = "hover,save,pan,box_zoom,reset,wheel_zoom"

p = figure(title="US Unemployment ({0} - {1})".format(years[0], years[-1]),
           x_range=years, y_range=list(reversed(months)),
           x_axis_location="above", plot_width=900, plot_height=400,
           tools=TOOLS, toolbar_location='above',
           tooltips=[])


customTooltips = CustomJSHover(code='''
        var value;
        var modified;
        modified = value + "Test"
        return modified.toString()
''')

p.add_tools(
       HoverTool(
       tooltips=[
        ( 'rate', '@rate'),
        ( 'year', '@Year{custom}'),
        ],
        formatters={
        'rate' : 'numeral',
        '@Year' : customTooltips,
    },
))

p.grid.grid_line_color = None
p.axis.axis_line_color = None
p.axis.major_tick_line_color = None
p.axis.major_label_text_font_size = "7px"
p.axis.major_label_standoff = 0
p.xaxis.major_label_orientation = pi / 3
p.xaxis.axis_label = 'Time'
p.yaxis.axis_label = 'Value'

p.rect(x="Year", y="Month", width=1, height=1,
       source=df,
       fill_color={'field': 'rate', 'transform': mapper},
       line_color=None)

n_ticks = 5
ticks = np.linspace(df.rate.min(), df.rate.max(), n_ticks).round(1)  
color_ticks = FixedTicker(ticks=ticks)

color_bar = ColorBar(color_mapper=mapper, major_label_text_font_size="7px",
                     ticker=color_ticks, title="Gene expression (Score)",
                     label_standoff=6, border_line_color=None, location=(0, 0), orientation='horizontal')
                     

b = get_heatmap_bar()

show(column(p, b), sizing_mode='stretch_width')