# 工具条  
TOOLS="hover,crosshair,pan,wheel_zoom,box_zoom,reset,tap,save,box_select,poly_select,lasso_select" 
p=figure(tools=TOOLS)  # 创建画布，伪代码

# HEX颜色，画布1，绘图1  
colors2 = ["#%02x%02x%02x" % (int(r), int(g), 150) for r, g in zip(50+2*x, 30+2*y)]
# 画布背景颜色
p = figure(title="log axis example", y_axis_type="log",  
           x_range=(0, 5), y_range=(0.001, 10**22),  
           background_fill_color="#fafafa")

p = figure(  
    tools="pan,box_zoom,reset,save",  
    y_axis_type="log", title="log axis example",  
    x_axis_label='sections', y_axis_label='particles',  
    width=700, height=350)  #坐标轴的数据格式 linear、log、datetime、mercator\(墨卡托)
from bokeh.models import Range1d,SingleIntervalTicker
xdr=Range1d(start=0,end=10) #早期版本需要预定义
p=figure(x_range=xdr) # x轴范围，新版本可以直接用列表
p.xaxis.ticker  = SingleIntervalTicker(interval=5,num_minor_ticks=0)  # 刻度间隔步距，间隔起点
from bokeh.models import ColumnDataSource, NumeralTickFormatter, SingleIntervalTicker  
p.xaxis.ticker = SingleIntervalTicker(interval=10, num_minor_ticks=0)  
p.yaxis.formatter = NumeralTickFormatter(format='0.0a') # 数据位数
p2.ygrid.band_fill_color = "olive"  # 自定义y轴数据间隔颜色  
p2.ygrid.band_fill_alpha = 0.1  
from bokeh.models import ColumnDataSource, FactorRange  

factors = [  
    ("Q1", "jan"), ("Q1", "feb"), ("Q1", "mar"),  
    ("Q2", "apr"), ("Q2", "may"), ("Q2", "jun"),  
    ("Q3", "jul"), ("Q3", "aug"), ("Q3", "sep"),  
    ("Q4", "oct"), ("Q4", "nov"), ("Q4", "dec"),  
  
]  
p = figure(x_range=FactorRange(*factors), plot_height=250)  # 功能上FactorRange映射较直接采用Range1d维度更多

p.arc(x, y, 20, 0.6, 4.1, radius_units="screen", color="#BEAED4", line_width=3)  # radius_units="screen" 屏幕像素坐标

from bokeh.models import ColumnDataSource, DatetimeAxis
# 设置时间轴  
plot.add_layout(DatetimeAxis(), 'below')  
plot.add_layout(DatetimeAxis(), 'left')  
from bokeh.models import ColumnDataSource, DatetimeAxis, DatetimeTickFormatter
xformatter = DatetimeTickFormatter(months="%b %d %Y")  
from time import mktime  
min_time = dt.datetime.min.time()  
xticker = FixedTicker(ticks=[  
    mktime(dt.datetime.combine(summer_start, min_time).timetuple()) * 1000, 
    mktime(dt.datetime.combine(summer_end, min_time).timetuple()) * 1000  
])  
xaxis = DatetimeAxis(formatter=xformatter, ticker=xticker)  
plot.add_layout(xaxis, 'below')  
yaxis = DatetimeAxis()  
yaxis.formatter.hours = ['%H:%M']  
plot.add_layout(yaxis, 'left')  
legend = Legend(items=[  
    LegendItem(label=value('sunset'), renderers=[sunset_line_renderer]),  
    LegendItem(label=value('sunrise'), renderers=[sunrise_line_renderer]),  
])  
plot.add_layout(legend)

# 机器学习分类颜色、标记自动映射
from bokeh.transform import factor_cmap, factor_mark
p.scatter("petal_length", "sepal_width", source=flowers, legend="species", fill_alpha=0.4, size=12,  
            marker=factor_mark('species', MARKERS, SPECIES),  
       color=factor_cmap('species', 'Category10_3', SPECIES))

p.legend.click_policy="hide" # 通过点击图例显示、隐藏图形  
from bokeh.models import BoxSelectTool, Div 
div = Div(text=""" 
            <p>Bokeh中的画布可通过多种布局方式进行显示；</p> 
            <p>通过配置参数BoxSelectTool，在图中用鼠标选择数据，采用不同方式进行交互。</p> 
            """) # HTML代码直接在作为一个图层显示，也可以整个HTML文档  
p2 = figure(title="selection on mousemove", **opts)  
p2.square(x, y, color="olive", size=6, alpha=0.6)  
p2.select_one(BoxSelectTool).select_every_mousemove = True # 使用鼠标选择数据点

layout = column(div,  # 直接显示html标签
                gridplot([[p1, p2], [p3, p4]], toolbar_location="right"),  
                sizing_mode="scale_width")  # sizing_mode 根据窗口宽度缩放图像  
# 绘图  
show(layout)  
from bokeh.io import push_notebook  
plot = figure()  
plot.circle([1,2,3], [4,6,5])  
handle = show(plot, notebook_handle=True)  
# 通过画布的句柄更新数据  
plot.title.text = "New Title"  
push_notebook(handle=handle)  
show(plot)  
# 通过画布直接关联不通过图件的数据
# 画布1  
s1 = figure(title=None)  
s1.circle(x, y0, size=3, color="navy", alpha=0.5)  
# 画布2，关联画布1的x,y轴范围  
s2 = figure(x_range=s1.x_range, y_range=s1.y_range, title=None)  
s2.circle(x, y1, size=3, color="firebrick", alpha=0.5)  
# 画布3，关联画布1的x轴范围，仅随着画布1的x轴方向拖动变化  
s3 = figure(x_range=s1.x_range, title=None)  
s3.circle(x, y2, size=3, color="olive", alpha=0.5)  
# 网格  
p = gridplot([[s1, s2, s3]], toolbar_location=None, plot_width=250, plot_height=250)  
# 控件及JS数据回调（略）

# 借鉴folium，通过Bokeh与Flask动态交互，或本地保存JPG
# tiles='http://webrd02.is.autonavi.com/appmaptile?lang=zh_cn&size=1&scale=1&style=7&x={x}&y={y}&z={z}', # 高德街道图
tiles='http://webst02.is.autonavi.com/appmaptile?style=8&x={x}&y={y}&z={z}', # 高德卫星图
# tiles='https://mt.google.com/vt/lyrs=s&x={x}&y={y}&z={z}', # google 卫星图
# tiles='https://mt.google.com/vt/lyrs=h&x={x}&y={y}&z={z}', # google 地图