visuals.py 文件源码

def create_figure(df,x,y,discrete,quantileable,continuous,size,color,controls):
    xs = df[x.value].values
    ys = df[y.value].values

    # x_title = x.value.title()
    # y_title = y.value.title()
    x_title = "Marginal Effective Tax Rate"
    y_title = "Asset Category"

    source = ColumnDataSource(ColumnDataSource.from_df(df))

    kw = dict()
    if x.value in discrete:
        kw['x_range'] = sorted(set(xs))
    if y.value in discrete:
        kw['y_range'] = sorted(set(ys))
    # kw['title'] = "%s vs %s" % (x_title, y_title)
    #kw['title'] = "Marginal Effective Tax Rates on Typically Financed Corporate Investments, 2016 Law"
    # kw['title'] = "Marginal Effective Tax Rates on Corporate Investments, 2016 Law"
    kw['title'] = "METRs on Corporate Investments, 2016 Law"

    p = figure(plot_height=400, plot_width=600, tools='pan,box_zoom,reset,hover', **kw)
    p.xaxis.axis_label = x_title
    p.yaxis.axis_label = y_title

    hover = p.select(dict(type=HoverTool))
    hover.tooltips = [('Asset', '@Asset')]

    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 = "#73000A"
    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, source=source, color=c, size=sz, line_color="white", alpha=0.6, hover_color='white', hover_alpha=0.5)

    # p.title.text_color = "black"
    # p.title.text_font = "Georgia"

    return p