import pandas as pd
import os
from bokeh.layouts import row, widgetbox
from bokeh.models import Select
from bokeh.palettes import Spectral5
from bokeh.plotting import curdoc, figure
from bokeh.sampledata.autompg import autompg
from bokeh.io import show
import ontology_results_csv_formatter

def main():
    #dir = 'C:\\grinstein\\VAST\\2006\\Code\\data\\visualization'
    dir = os.getcwd() + "\data\\"
    dir += "visualization\\"
    ontology_results_csv_formatter.csv_formatter(dir)
    ontology_results_csv_formatter.csv_crossfilter(dir)
    df = pd.read_csv(os.path.join(dir, 'ont_res_csv.csv'))
    df['Filename'] = df['Filename'].astype(str)
    df['Date'] = df['Date'].astype(str)
    df['EntityType'] = df['EntityType'].astype(str)
    df['EntityValue'] = df['EntityValue'].astype(str)


    SIZES = list(range(6, 22, 3))
    print (SIZES)
    COLORS = Spectral5
    print (COLORS)
    #ORIGINS = ['PER', 'ORG', 'GPE']

    columns = sorted(df.columns)
    print (columns)
    discrete = [x for x in columns if df[x].dtype == object]
    #print(discrete)
    continuous = [x for x in columns if x not in discrete]
    #print (continuous)
    quantileable = [x for x in continuous if len(df[x].unique()) > 20]
    print (quantileable)

    def create_figure():
        xs = df[x.value].values
        #print (xs)
        ys = df[y.value].values
        #print (ys)
        x_title = x.value.title()
        #print (x_title)
        y_title = y.value.title()
        #print (y_title)

        kw = dict()
        #print (kw)
        if x.value in discrete:
            kw['x_range'] = sorted(set(xs))
            #print (kw)
        if y.value in discrete:
            kw['y_range'] = sorted(set(ys))
            #print (kw)
        kw['title'] = "%s vs %s" % (x_title, y_title)
        #print (kw)

        p = figure(plot_height=400, plot_width=400, tools='pan,box_zoom,reset', **kw)
        p.xaxis.axis_label = x_title
        p.yaxis.axis_label = y_title

        # if x.value in discrete:
        #     p.xaxis.major_label_orientation = pd.np.pi / 4

        # sz = 9
        # if size.value != 'None':
        #     groups = pd.qcut(df[size.value].values, len(SIZES))
        #     sz = [SIZES[xx] for xx in groups.codes]
        #
        # c = "#31AADE"
        # if color.value != 'None':
        #     groups = pd.qcut(df[color.value].values, len(COLORS))
        #     c = [COLORS[xx] for xx in groups.codes]
        # p.circle(x=xs, y=ys, color=c, size=sz, line_color="white", alpha=0.6, hover_color='white', hover_alpha=0.5)
        p.circle(x=xs, y=ys, line_color="white", alpha=0.6, hover_color='white', hover_alpha=0.5)
        show(p)
        return p


    def update(attr, old, new):
        layout.children[1] = create_figure()


    x = Select(title='X-Axis', value='Date', options=columns)
    #x.on_change('value', update)

    y = Select(title='Y-Axis', value='EntityValue', options=columns)
    #y.on_change('value', update)

    # size = Select(title='Size', value='None', options=['None'] + quantileable)
    # size.on_change('value', update)
    #
    # color = Select(title='Color', value='None', options=['None'] + quantileable)
    # color.on_change('value', update)

    #controls = widgetbox([x, y, color, size], width=200)
    controls = widgetbox([x, y], width=200)
    layout = row(controls, create_figure())

    curdoc().add_root(layout)
    curdoc().title = "Crossfilter New"

if __name__ == '__main__':
    main()