Python bokeh.plotting.show() Examples

def plot_performance_vs_trials(results, output_directory, save_file="PerformanceVsTrials.png", plot_title=""):
    try:
        import matplotlib.pyplot as plt
        matplotlib_imported = True
    except ImportError:
        matplotlib_imported = False
    
    if not matplotlib_imported:
        warnings.warn('AutoGluon summary plots cannot be created because matplotlib is not installed. Please do: "pip install matplotlib"')
        return None
    
    ordered_trials = sorted(list(results['trial_info'].keys()))
    ordered_val_perfs = [results['trial_info'][trial_id][results['reward_attr']] for trial_id in ordered_trials]
    x = range(1, len(ordered_trials)+1)
    y = []
    for i in x:
        y.append(max([ordered_val_perfs[j] for j in range(i)])) # best validation performance in trials up until ith one (assuming higher = better)
    fig, ax = plt.subplots()
    ax.plot(x, y)
    ax.set(xlabel='Completed Trials', ylabel='Best Performance', title = plot_title)
    if output_directory is not None:
        outputfile = os.path.join(output_directory, save_file)
        fig.savefig(outputfile)
        print("Plot of HPO performance saved to file: %s" % outputfile)
    plt.show() 

def timeseries():
    # Get data
    df = pd.read_csv('data/Land_Ocean_Monthly_Anomaly_Average.csv')
    df['datetime'] = pd.to_datetime(df['datetime'])
    df = df[['anomaly','datetime']]
    df['moving_average'] = pd.rolling_mean(df['anomaly'], 12)
    df = df.fillna(0)
    
    # List all the tools that you want in your plot separated by comas, all in one string.
    TOOLS="crosshair,pan,wheel_zoom,box_zoom,reset,hover,previewsave"

    # New figure
    t = figure(x_axis_type = "datetime", width=1000, height=200,tools=TOOLS)

    # Data processing
    # The hover tools doesn't render datetime appropriately. We'll need a string. 
    # We just want dates, remove time
    f = lambda x: str(x)[:7]
    df["datetime_s"]=df[["datetime"]].applymap(f)
    source = ColumnDataSource(df)

    # Create plot
    t.line('datetime', 'anomaly', color='lightgrey', legend='anom', source=source)
    t.line('datetime', 'moving_average', color='red', legend='avg', source=source, name="mva")

    # Style
    xformatter = DatetimeTickFormatter(formats=dict(months=["%b %Y"], years=["%Y"]))
    t.xaxis[0].formatter = xformatter
    t.xaxis.major_label_orientation = math.pi/4
    t.yaxis.axis_label = 'Anomaly(ºC)'
    t.legend.orientation = "bottom_right"
    t.grid.grid_line_alpha=0.2
    t.toolbar_location=None

    # Style hover tool
    hover = t.select(dict(type=HoverTool))
    hover.tooltips = """
        <div>
            <span style="font-size: 15px;">Anomaly</span>
            <span style="font-size: 17px;  color: red;">@anomaly</span>
        </div>
        <div>
            <span style="font-size: 15px;">Month</span>
            <span style="font-size: 10px; color: grey;">@datetime_s</span>
        </div>
        """
    hover.renderers = t.select("mva")

    # Show plot
    #show(t)
    return t

# Add title 

def show(plot_to_show):
    """Display a plot, either interactive or static.

    Parameters
    ----------
    plot_to_show: Output of a plotting command (matplotlib axis or bokeh figure)
        The plot to show

    Returns
    -------
    None
    """
    if isinstance(plot_to_show, plt.Axes):
        show_static()
    elif isinstance(plot_to_show, bpl.Figure):
        show_interactive(plot_to_show)
    else:
        raise ValueError(
            "The type of ``plot_to_show`` was not valid, or not understood."
        ) 

def jitterCurve(phmi_breakPtsNeg,landmarks_coordi,PHMI_coordi,plot_x): #LandmarkMap_dic,PHMI_dic
    output_file("PHmi_01.html")
    source=ColumnDataSource(data=dict(
        x=landmarks_coordi[0].tolist(),
        y=landmarks_coordi[1].tolist(),
        desc=[str(i) for i in list(range(landmarks_coordi[0].shape[0]))],
    ))
    TOOLTIPS=[
        ("index", "$index"),
        ("(x,y)", "($x, $y)"),
        ("desc", "@desc"),
    ]

    p=figure(plot_width=1800, plot_height=320, tooltips=TOOLTIPS,title="partition")
    p.circle('y','x',  size=5, source=source)
    p.line(PHMI_coordi[1],PHMI_coordi[0],line_color="coral", line_dash="dotdash", line_width=2)

    colors=('aliceblue', 'antiquewhite', 'aqua', 'aquamarine', 'azure', 'beige', 'bisque', 'black', 'blanchedalmond', 'blue', 'blueviolet', 'brown', 'burlywood', 'cadetblue', 'chartreuse', 'chocolate', 'coral', 'cornflowerblue', 'cornsilk', 'crimson', 'cyan', 'darkblue', 'darkcyan', 'darkgoldenrod', 'darkgray', 'darkgreen', 'darkgrey', 'darkkhaki', 'darkmagenta', 'darkolivegreen', 'darkorange', 'darkorchid', 'darkred', 'darksalmon', 'darkseagreen', 'darkslateblue', 'darkslategray', 'darkslategrey', 'darkturquoise', 'darkviolet', 'deeppink', 'deepskyblue', 'dimgray', 'dimgrey', 'dodgerblue', 'firebrick', 'floralwhite', 'forestgreen', 'fuchsia', 'gainsboro', 'ghostwhite', 'gold', 'goldenrod', 'gray', 'green', 'greenyellow', 'grey', 'honeydew', 'hotpink', 'indianred', 'indigo', 'ivory', 'khaki', 'lavender', 'lavenderblush', 'lawngreen', 'lemonchiffon', 'lightblue', 'lightcoral', 'lightcyan', 'lightgoldenrodyellow', 'lightgray', 'lightgreen', 'lightgrey', 'lightpink', 'lightsalmon', 'lightseagreen', 'lightskyblue', 'lightslategray', 'lightslategrey', 'lightsteelblue', 'lightyellow', 'lime', 'limegreen', 'linen', 'magenta', 'maroon', 'mediumaquamarine', 'mediumblue', 'mediumorchid', 'mediumpurple', 'mediumseagreen', 'mediumslateblue', 'mediumspringgreen', 'mediumturquoise', 'mediumvioletred', 'midnightblue', 'mintcream', 'mistyrose', 'moccasin', 'navajowhite', 'navy', 'oldlace', 'olive', 'olivedrab', 'orange', 'orangered', 'orchid', 'palegoldenrod', 'palegreen', 'paleturquoise', 'palevioletred', 'papayawhip', 'peachpuff', 'peru', 'pink', 'plum', 'powderblue', 'purple', 'red', 'rosybrown', 'royalblue', 'saddlebrown', 'salmon', 'sandybrown', 'seagreen', 'seashell', 'sienna', 'silver', 'skyblue', 'slateblue', 'slategray', 'slategrey', 'snow', 'springgreen', 'steelblue', 'tan', 'teal', 'thistle', 'tomato', 'turquoise', 'violet', 'wheat', 'white', 'whitesmoke', 'yellow', 'yellowgreen')
    colors=colors*5
    ScalePhmi=math.pow(10,1)
    i=0
    for val,idx in zip(phmi_breakPtsNeg, plot_x):
        p.line(idx,np.array(val)*ScalePhmi,line_color=colors[i])
        i+=1

    show(p)

#04-network show between PHMI and landmarks 网络分析
#extract landmarks corresponding to the AVs' position along a route 

def singleBoxplot(array):
    import plotly.express as px
    import pandas as pd
    df=pd.DataFrame(array,columns=["value"])
    fig = px.box(df, y="value",points="all")
    # fig.show() #show in Jupyter
    import plotly
    plotly.offline.plot (fig) #works in spyder

#03-split curve into continuous parts based on the jumping position 使用1维卷积的方法，在曲线跳变点切分曲线 

def colorMeshShow(histogram2dDic_part,patternDic_part,Phmi_part,condi):
    use_svg_display()
    xiArray=np.array([histogram2dDic_part[key]["xi"].tolist() for key in histogram2dDic_part.keys()])
    yiArray=np.array([histogram2dDic_part[key]["yi"].tolist() for key in histogram2dDic_part.keys()])
    ziArray=np.array([histogram2dDic_part[key]["ziMasked"].tolist() for key in histogram2dDic_part.keys()])
    
    xArray=np.array([patternDic_part[key]["x"].tolist() for key in patternDic_part.keys()])
    yArray=np.array([patternDic_part[key]["y"].tolist() for key in patternDic_part.keys()])
    zArray=np.array([patternDic_part[key]["z"] for key in patternDic_part.keys()])
    
    # print(xiArray)
    
    width=int(round(math.sqrt(len(xArray)),2))  
    # print("+"*50)
    # print(width)
    fig, axs = plt.subplots(width, width, figsize=(10, 10),constrained_layout=True)
    for ax, xi, yi, zi, x, y,z,titleV,key in zip(axs.flat, xiArray,yiArray,ziArray,xArray, yArray,zArray,Phmi_part,condi):

        ax.pcolormesh(xi, yi, zi, edgecolors='black')
        scat = ax.scatter(x, y, c="r", s=15) #c=z
        fig.colorbar(scat)
        ax.margins(0.05)
        
        ax.set_title("PHmi_%d:%f"%(key,titleV))
        ax.axes.get_xaxis().set_visible(False)
        ax.axes.get_yaxis().set_visible(False)

    plt.show() 

def colorMeshShow(histogram2dDic_part,patternDic_part,Phmi_part,condi):
    use_svg_display()
    xiArray=np.array([histogram2dDic_part[key]["xi"].tolist() for key in histogram2dDic_part.keys()])
    yiArray=np.array([histogram2dDic_part[key]["yi"].tolist() for key in histogram2dDic_part.keys()])
    ziArray=np.array([histogram2dDic_part[key]["ziMasked"].tolist() for key in histogram2dDic_part.keys()])
    
    xArray=np.array([patternDic_part[key]["x"].tolist() for key in patternDic_part.keys()])
    yArray=np.array([patternDic_part[key]["y"].tolist() for key in patternDic_part.keys()])
    zArray=np.array([patternDic_part[key]["z"] for key in patternDic_part.keys()])
    
    # print(xiArray)
    
    width=int(round(math.sqrt(len(xArray)),2))  
    # print("+"*50)
    # print(width)
    fig, axs = plt.subplots(width, width, figsize=(10, 10),constrained_layout=True)
    for ax, xi, yi, zi, x, y,z,titleV,key in zip(axs.flat, xiArray,yiArray,ziArray,xArray, yArray,zArray,Phmi_part,condi):

        ax.pcolormesh(xi, yi, zi, edgecolors='black')
        scat = ax.scatter(x, y, c="r", s=15) #c=z
        fig.colorbar(scat)
        ax.margins(0.05)
        
        ax.set_title("PHmi_%d:%f"%(key,titleV))
        ax.axes.get_xaxis().set_visible(False)
        ax.axes.get_yaxis().set_visible(False)

    plt.show() 

#07相关性分析
#classification PHMI with percentile 

def readMatLabFig_LandmarkMap(LandmarkMap_fn):
    LandmarkMap=loadmat(LandmarkMap_fn, squeeze_me=True, struct_as_record=False)
    y=loadmat(LandmarkMap_fn)
    print(sorted(LandmarkMap.keys()))
        
    LandmarkMap_dic={} #提取.fig值
    for object_idx in range(LandmarkMap['hgS_070000'].children.children.shape[0]):
        # print(object_idx)
        try:
            X=LandmarkMap['hgS_070000'].children.children[object_idx].properties.XData #good
            Y=LandmarkMap['hgS_070000'].children.children[object_idx].properties.YData 
            LandmarkMap_dic[object_idx]=(X,Y)
        except:
            pass
    
    # print(LandmarkMap_dic)
    fig= plt.figure(figsize=(130,20))
    colors=['#7f7f7f','#d62728','#1f77b4','','','']
    markers=['.','+','o','','','']
    dotSizes=[200,3000,3000,0,0,0]
    linewidths=[2,10,10,0,0,0]
    i=0
    for key in LandmarkMap_dic.keys():
        plt.scatter(LandmarkMap_dic[key][1],LandmarkMap_dic[key][0], s=dotSizes[i],marker=markers[i], color=colors[i],linewidth=linewidths[i])
        i+=1
    plt.tick_params(axis='both',labelsize=80)
    plt.show()
    return LandmarkMap_dic

#02-read PHMI values for evaluation of AVs' on-board lidar navigation 读取PHMI值
#the PHMI value less than pow(10,-5) is scaled to show clearly------on; the PHMI value larger than pow(10,-5) is scaled to show clearly------on
#数据类型A 

def location_landmarks_network(targetPts_idx,locations,landmarks):   
    LMs=np.stack((landmarks[0], landmarks[1]), axis=-1)
    LCs=np.stack((locations[0], locations[1]), axis=-1)
    ptsMerge=np.vstack((LMs,LCs))
    print("LMs shape:%s, LCs shaoe:%s, ptsMerge shape:%s"%(LMs.shape, LCs.shape,ptsMerge.shape))
    targetPts_idx_adj={}
    for key in targetPts_idx.keys():
        targetPts_idx_adj[key+LMs.shape[0]]=targetPts_idx[key]
    edges=[[(key,i) for i in targetPts_idx_adj[key]] for key in targetPts_idx_adj.keys()]
    edges=flatten_lst(edges)
    
    G=nx.Graph()
    G.position={}
    # G.targetPtsNum={}    
    i=0
    for pts in ptsMerge:
        G.add_node(i)
        G.position[i]=(pts[1],pts[0])        
        # G.targetPtsNum[LM]=(len(targetPts[key]))
        i+=1
    
    G.add_edges_from(edges)
    
    plt.figure(figsize=(130,20))
    nx.draw(G,G.position,linewidths=1,edge_color='gray')
    plt.show()
    return G
#网络交互图表 

def plot_bokeh_history(solvers, x, y, x_arrays, y_arrays, mins, legends,
                       log_scale, show):
    import bokeh.plotting as bk

    min_x, max_x, min_y, max_y = mins
    if log_scale:
        # Bokeh has a weird behaviour when using logscale with 0 entries...
        # We use the difference between smallest value of second small
        # to set the range of y
        all_ys = np.hstack(y_arrays)
        y_range_min = np.min(all_ys[all_ys != 0])
        if y_range_min < 0:
            raise ValueError("Cannot plot negative values on a log scale")

        fig = bk.Figure(plot_height=300, y_axis_type="log",
                        y_range=[y_range_min, max_y])
    else:
        fig = bk.Figure(plot_height=300, x_range=[min_x, max_x],
                        y_range=[min_y, max_y])

    for i, (solver, x_array, y_array, legend) in enumerate(
            zip(solvers, x_arrays, y_arrays, legends)):
        color = get_plot_color(i)
        fig.line(x_array, y_array, line_width=3, legend=legend, color=color)
    fig.xaxis.axis_label = x
    fig.yaxis.axis_label = y
    fig.xaxis.axis_label_text_font_size = "12pt"
    fig.yaxis.axis_label_text_font_size = "12pt"
    if show:
        bk.show(fig)
        return None
    else:
        return fig 

def singleBoxplot(array):
    import plotly.express as px
    import pandas as pd
    df=pd.DataFrame(array,columns=["value"])
    fig = px.box(df, y="value",points="all")
    # fig.show() #show in Jupyter
    import plotly
    plotly.offline.plot (fig) #works in spyder

#04-split curve into continuous parts based on the jumping position 使用1维卷积的方法，在曲线跳变点切分曲线 

def singleBoxplot(array):
    import plotly.express as px
    import pandas as pd
    df=pd.DataFrame(array,columns=["value"])
    fig = px.box(df, y="value",points="all")
    # fig.show() #show in Jupyter
    import plotly
    plotly.offline.plot (fig) #works in spyder

#03-split curve into continuous parts based on the jumping position 使用1维卷积的方法，在曲线跳变点切分曲线 

def readMatLabFig_LandmarkMap(LandmarkMap_fn):
    LandmarkMap=loadmat(LandmarkMap_fn, squeeze_me=True, struct_as_record=False)
    y=loadmat(LandmarkMap_fn)
    print(sorted(LandmarkMap.keys()))
        
    LandmarkMap_dic={} #提取.fig值
    for object_idx in range(LandmarkMap['hgS_070000'].children.children.shape[0]):
        # print(object_idx)
        try:
            X=LandmarkMap['hgS_070000'].children.children[object_idx].properties.XData #good
            Y=LandmarkMap['hgS_070000'].children.children[object_idx].properties.YData 
            LandmarkMap_dic[object_idx]=(X,Y)
        except:
            pass
    
    # print(LandmarkMap_dic)
    fig= plt.figure(figsize=(130,20))
    colors=['#7f7f7f','#d62728','#1f77b4','','','']
    markers=['.','+','o','','','']
    dotSizes=[200,3000,3000,0,0,0]
    linewidths=[2,10,10,0,0,0]
    i=0
    for key in LandmarkMap_dic.keys():
        plt.scatter(LandmarkMap_dic[key][1],LandmarkMap_dic[key][0], s=dotSizes[i],marker=markers[i], color=colors[i],linewidth=linewidths[i])
        i+=1
    plt.tick_params(axis='both',labelsize=80)
    plt.show()
    return LandmarkMap_dic

#03-read PHMI values for evaluation of AVs' on-board lidar navigation 读取PHMI值
#the PHMI value less than pow(10,-5) is scaled to show clearly------on; the PHMI value larger than pow(10,-5) is scaled to show clearly------on
#数据类型A 

def plot_simple_two_stock(two_stcok_dict):
    """
    将两个金融时间序列收盘价格缩放到一个价格水平后，可视化价格变动
    :param two_stcok_dict: 字典形式，key将做为lable进行可视化使用，元素为金融时间序列
    """
    if not isinstance(two_stcok_dict, dict) or len(two_stcok_dict) != 2:
        print('two_stcok_dict type must dict! or len(two_stcok_dict) != 2')
        return

    label_arr = [s_name for s_name in two_stcok_dict.keys()]
    x, y = ABuScalerUtil.scaler_xy(two_stcok_dict[label_arr[0]].close, two_stcok_dict[label_arr[1]].close)
    plt.plot(x, label=label_arr[0])
    plt.plot(y, label=label_arr[1])
    plt.legend(loc=2)
    plt.show() 

def plot_simple_multi_stock(multi_kl_pd):
    """
    将多个金融时间序列收盘价格缩放到一个价格水平后，可视化价格变动
    :param multi_kl_pd: 可迭代的序列，元素为金融时间序列
    """
    rg_ret = ABuScalerUtil.scaler_matrix([kl_pd.close for kl_pd in multi_kl_pd])
    for i, kl_pd in enumerate(multi_kl_pd):
        plt.plot(kl_pd.index, rg_ret[i])
    plt.show() 

def jitterCurve(phmi_breakPtsNeg,landmarks_coordi,PHMI_coordi,plot_x): #LandmarkMap_dic,PHMI_dic
    output_file("PHmi_01.html")
    source=ColumnDataSource(data=dict(
        x=landmarks_coordi[0].tolist(),
        y=landmarks_coordi[1].tolist(),
        desc=[str(i) for i in list(range(landmarks_coordi[0].shape[0]))],
    ))
    TOOLTIPS=[
        ("index", "$index"),
        ("(x,y)", "($x, $y)"),
        ("desc", "@desc"),
    ]

    p=figure(plot_width=1800, plot_height=320, tooltips=TOOLTIPS,title="partition")
    p.circle('y','x',  size=5, source=source)
    p.line(PHMI_coordi[1],PHMI_coordi[0],line_color="coral", line_dash="dotdash", line_width=2)

    colors=('aliceblue', 'antiquewhite', 'aqua', 'aquamarine', 'azure', 'beige', 'bisque', 'black', 'blanchedalmond', 'blue', 'blueviolet', 'brown', 'burlywood', 'cadetblue', 'chartreuse', 'chocolate', 'coral', 'cornflowerblue', 'cornsilk', 'crimson', 'cyan', 'darkblue', 'darkcyan', 'darkgoldenrod', 'darkgray', 'darkgreen', 'darkgrey', 'darkkhaki', 'darkmagenta', 'darkolivegreen', 'darkorange', 'darkorchid', 'darkred', 'darksalmon', 'darkseagreen', 'darkslateblue', 'darkslategray', 'darkslategrey', 'darkturquoise', 'darkviolet', 'deeppink', 'deepskyblue', 'dimgray', 'dimgrey', 'dodgerblue', 'firebrick', 'floralwhite', 'forestgreen', 'fuchsia', 'gainsboro', 'ghostwhite', 'gold', 'goldenrod', 'gray', 'green', 'greenyellow', 'grey', 'honeydew', 'hotpink', 'indianred', 'indigo', 'ivory', 'khaki', 'lavender', 'lavenderblush', 'lawngreen', 'lemonchiffon', 'lightblue', 'lightcoral', 'lightcyan', 'lightgoldenrodyellow', 'lightgray', 'lightgreen', 'lightgrey', 'lightpink', 'lightsalmon', 'lightseagreen', 'lightskyblue', 'lightslategray', 'lightslategrey', 'lightsteelblue', 'lightyellow', 'lime', 'limegreen', 'linen', 'magenta', 'maroon', 'mediumaquamarine', 'mediumblue', 'mediumorchid', 'mediumpurple', 'mediumseagreen', 'mediumslateblue', 'mediumspringgreen', 'mediumturquoise', 'mediumvioletred', 'midnightblue', 'mintcream', 'mistyrose', 'moccasin', 'navajowhite', 'navy', 'oldlace', 'olive', 'olivedrab', 'orange', 'orangered', 'orchid', 'palegoldenrod', 'palegreen', 'paleturquoise', 'palevioletred', 'papayawhip', 'peachpuff', 'peru', 'pink', 'plum', 'powderblue', 'purple', 'red', 'rosybrown', 'royalblue', 'saddlebrown', 'salmon', 'sandybrown', 'seagreen', 'seashell', 'sienna', 'silver', 'skyblue', 'slateblue', 'slategray', 'slategrey', 'snow', 'springgreen', 'steelblue', 'tan', 'teal', 'thistle', 'tomato', 'turquoise', 'violet', 'wheat', 'white', 'whitesmoke', 'yellow', 'yellowgreen')
    colors=colors*5
    ScalePhmi=math.pow(10,1)
    i=0
    for val,idx in zip(phmi_breakPtsNeg, plot_x):
        p.line(idx,np.array(val)*ScalePhmi,line_color=colors[i])
        i+=1

    show(p)

#04-network show between PHMI and landmarks 网络分析
#extract landmarks corresponding to the AVs' position along a route 

def colorMeshShow(histogram2dDic_part,patternDic_part,Phmi_part,condi):
    use_svg_display()
    xiArray=np.array([histogram2dDic_part[key]["xi"].tolist() for key in histogram2dDic_part.keys()])
    yiArray=np.array([histogram2dDic_part[key]["yi"].tolist() for key in histogram2dDic_part.keys()])
    ziArray=np.array([histogram2dDic_part[key]["ziMasked"].tolist() for key in histogram2dDic_part.keys()])
    
    xArray=np.array([patternDic_part[key]["x"].tolist() for key in patternDic_part.keys()])
    yArray=np.array([patternDic_part[key]["y"].tolist() for key in patternDic_part.keys()])
    zArray=np.array([patternDic_part[key]["z"] for key in patternDic_part.keys()])
    
    # print(xiArray)
    
    width=int(round(math.sqrt(len(xArray)),2))  
    # print("+"*50)
    # print(width)
    fig, axs = plt.subplots(width, width, figsize=(10, 10),constrained_layout=True)
    for ax, xi, yi, zi, x, y,z,titleV,key in zip(axs.flat, xiArray,yiArray,ziArray,xArray, yArray,zArray,Phmi_part,condi):

        ax.pcolormesh(xi, yi, zi, edgecolors='black')
        scat = ax.scatter(x, y, c="r", s=15) #c=z
        fig.colorbar(scat)
        ax.margins(0.05)
        
        ax.set_title("PHmi_%d:%f"%(key,titleV))
        ax.axes.get_xaxis().set_visible(False)
        ax.axes.get_yaxis().set_visible(False)

    plt.show() 

def timeseries():
    # Get data
    df = pd.read_csv('data/Land_Ocean_Monthly_Anomaly_Average.csv')
    df['datetime'] = pd.to_datetime(df['datetime'])
    df = df[['anomaly','datetime']]
    df['moving_average'] = pd.rolling_mean(df['anomaly'], 12)
    df = df.fillna(0)
    
    # List all the tools that you want in your plot separated by comas, all in one string.
    TOOLS="crosshair,pan,wheel_zoom,box_zoom,reset,hover,previewsave"

    # New figure
    t = figure(x_axis_type = "datetime", width=1000, height=200,tools=TOOLS)

    # Data processing
    # The hover tools doesn't render datetime appropriately. We'll need a string. 
    # We just want dates, remove time
    f = lambda x: str(x)[:7]
    df["datetime_s"]=df[["datetime"]].applymap(f)
    source = ColumnDataSource(df)

    # Create plot
    t.line('datetime', 'anomaly', color='lightgrey', legend='anom', source=source)
    t.line('datetime', 'moving_average', color='red', legend='avg', source=source, name="mva")

    # Style
    xformatter = DatetimeTickFormatter(formats=dict(months=["%b %Y"], years=["%Y"]))
    t.xaxis[0].formatter = xformatter
    t.xaxis.major_label_orientation = math.pi/4
    t.yaxis.axis_label = 'Anomaly(ºC)'
    t.legend.orientation = "bottom_right"
    t.grid.grid_line_alpha=0.2
    t.toolbar_location=None

    # Style hover tool
    hover = t.select(dict(type=HoverTool))
    hover.tooltips = """
        <div>
            <span style="font-size: 15px;">Anomaly</span>
            <span style="font-size: 17px;  color: red;">@anomaly</span>
        </div>
        <div>
            <span style="font-size: 15px;">Month</span>
            <span style="font-size: 10px; color: grey;">@datetime_s</span>
        </div>
        """
    hover.renderers = t.select("mva")

    # Show plot
    #show(t)
    return t 

def _show(f):
    if _figures is None:
        if _static_images:
            _show_static_images(f)
        else:
            _process_canvas([])
            _bplt.show(f)
            _process_canvas([f])
    else:
        _figures[-1].append(f) 

def __exit__(self, *args):
        global _figures, _figsize
        if len(_figures) > 1 or len(_figures[0]) > 0:
            f = _bplt.gridplot(_figures, merge_tools=False)
            if _static_images:
                _show_static_images(f)
            else:
                _process_canvas([])
                _bplt.show(f)
                _process_canvas([item for sublist in _figures for item in sublist])
        _figures = None
        _figsize = self.ofigsize 

def location_landmarks_network(targetPts_idx,locations,landmarks):   
    LMs=np.stack((landmarks[0], landmarks[1]), axis=-1)
    LCs=np.stack((locations[0], locations[1]), axis=-1)
    ptsMerge=np.vstack((LMs,LCs))
    print("LMs shape:%s, LCs shaoe:%s, ptsMerge shape:%s"%(LMs.shape, LCs.shape,ptsMerge.shape))
    targetPts_idx_adj={}
    for key in targetPts_idx.keys():
        targetPts_idx_adj[key+LMs.shape[0]]=targetPts_idx[key]
    edges=[[(key,i) for i in targetPts_idx_adj[key]] for key in targetPts_idx_adj.keys()]
    edges=flatten_lst(edges)
    
    G=nx.Graph()
    G.position={}
    # G.targetPtsNum={}    
    i=0
    for pts in ptsMerge:
        G.add_node(i)
        G.position[i]=(pts[1],pts[0])        
        # G.targetPtsNum[LM]=(len(targetPts[key]))
        i+=1
    
    G.add_edges_from(edges)
    
    plt.figure(figsize=(130,20))
    nx.draw(G,G.position,linewidths=1,edge_color='gray')
    plt.show()
    return G
#网络交互图表 

def show_layout(ax, show=True, force_layout=False):
    """Create a layout and call bokeh show."""
    if show is None:
        show = rcParams["plot.bokeh.show"]
    if show:
        import bokeh.plotting as bkp

        layout = create_layout(ax, force_layout=force_layout)
        bkp.show(layout) 

def despline(ax=None):
    import matplotlib.pyplot as plt

    ax = plt.gca() if ax is None else ax
    # Hide the right and top spines
    ax.spines["right"].set_visible(False)
    ax.spines["top"].set_visible(False)
    # Only show ticks on the left and bottom spines
    ax.yaxis.set_ticks_position("left")
    ax.xaxis.set_ticks_position("bottom") 

def some_activities(constraint):

    f'''
    # Some Activities: {constraint}

    This displays thumbnails of routes that match the query over statistics.  For example,

        Active Distance > 40 & Active Distance < 60

    will show all activities with a distance between 40 and 60 km.
    '''

    '''
    $contents
    '''

    '''
    ## Build Maps
    
    Loop over activities, retrieve data, and construct maps. 
    '''

    s = session('-v2')
    maps = [map_thumbnail(100, 120, data)
            for data in (activity_statistics(s, SPHERICAL_MERCATOR_X, SPHERICAL_MERCATOR_Y,
                                             ACTIVE_DISTANCE, TOTAL_CLIMB,
                                             activity_journal=aj)
                         for aj in constrained_sources(s, constraint))
            if len(data[SPHERICAL_MERCATOR_X].dropna()) > 10]
    print(f'Found {len(maps)} activities')

    '''
    ## Display Maps
    '''

    output_notebook()
    show(htile(maps, 8)) 

def get_jupyter(self):
        output_notebook()
        show(self.plot()) 

def _do_plot_candle_html(date, p_open, high, low, close, symbol, save):
    """
    bk绘制可交互的k线图
    :param date: 融时间序列交易日时间，pd.DataFrame.index对象
    :param p_open: 金融时间序列开盘价格序列，np.array对象
    :param high: 金融时间序列最高价格序列，np.array对象
    :param low: 金融时间序列最低价格序列，np.array对象
    :param close: 金融时间序列收盘价格序列，np.array对象
    :param symbol: symbol str对象
    :param save: 是否保存可视化结果在本地
    """
    mids = (p_open + close) / 2
    spans = abs(close - p_open)

    inc = close > p_open
    dec = p_open > close

    w = 24 * 60 * 60 * 1000

    t_o_o_l_s = "pan,wheel_zoom,box_zoom,reset,save"

    p = bp.figure(x_axis_type="datetime", tools=t_o_o_l_s, plot_width=1280, title=symbol)
    p.xaxis.major_label_orientation = pi / 4
    p.grid.grid_line_alpha = 0.3

    p.segment(date.to_datetime(), high, date.to_datetime(), low, color="black")
    # noinspection PyUnresolvedReferences
    p.rect(date.to_datetime()[inc], mids[inc], w, spans[inc], fill_color=__colorup__, line_color=__colorup__)
    # noinspection PyUnresolvedReferences
    p.rect(date.to_datetime()[dec], mids[dec], w, spans[dec], fill_color=__colordown__, line_color=__colordown__)

    bp.show(p)
    if save:
        save_dir = os.path.join(K_SAVE_CACHE_HTML_ROOT, ABuDateUtil.current_str_date())
        html_name = os.path.join(save_dir, symbol + ".html")
        ABuFileUtil.ensure_dir(html_name)
        bp.output_file(html_name, title=symbol) 

def con_1_dim(data,loc_x):
    kernel_conv=[-1,2,-1] #卷积核，类似2维提取图像的边缘
    result_conv=npConv(data,kernel_conv,'same')
    plt.figure(figsize=(130, 20))
    # print(result_conv)
    z=np.abs(stats.zscore(result_conv))
    z_=stats.zscore(result_conv)
    # print(z)
    #print(len(z))
    threshold=1
    breakPts=np.where(z > threshold)
    breakPts_=np.where(z_ < -threshold)

    con_breakPtsNeg=lindexsplit(result_conv.tolist(), breakPts_[0].tolist())
    phmi_breakPtsNeg=lindexsplit(data, breakPts_[0].tolist())
    phmi_breakIdx=lindexsplit(list(range(len(data))), breakPts_[0].tolist())
    x=lindexsplit(loc_x.tolist(),breakPts_[0].tolist())

    plt.scatter(loc_x, [abs(v) for v in result_conv],s=1) #[abs(v) for v in discretizeIdx]

    for idx in range(len(phmi_breakPtsNeg)-1):
        phmi_breakPtsNeg[idx+1].insert(0,phmi_breakPtsNeg[idx][-1])
    phmi_breakPtsNeg.insert(0,phmi_breakPtsNeg[0])
    
    for idx in range(len(phmi_breakIdx)-1):
        phmi_breakIdx[idx+1].insert(0,phmi_breakIdx[idx][-1])
    phmi_breakIdx.insert(0,phmi_breakIdx[0])    
    
    for idx in range(len(x)-1):
        x[idx+1].insert(0,x[idx][-1])
    x.insert(0,x[0])      
        
    #for val,idx in zip(phmi_breakPtsNeg, phmi_breakIdx):
    #根据跳变点切分的曲线赋予不同的颜色打印
    for val,idx in zip(phmi_breakPtsNeg, x):
        # print(val,idx)
        # break
        # x, y = zip(start, stop)
        plt.plot(idx, val, color=uniqueish_color())
        
    #plt.scatter(list(range(len(result_conv))), [abs(v) for v in result_conv],s=1) #[abs(v) for v in discretizeIdx]    
    
    plt.show()             
    return con_breakPtsNeg,phmi_breakPtsNeg,phmi_breakIdx,x

#合并landmarks，locations和曲线跳变点切分，打印 

def readMatLabFig_PHMI_A(PHMI_fn,LandmarkMap_dic):
    PHMI=loadmat(PHMI_fn, squeeze_me=True, struct_as_record=False)
    x=loadmat(PHMI_fn)
    print(sorted(PHMI.keys()))
    
    PHMI_dic={} #提取MatLab的.fig值
    ax1=[c for c in PHMI['hgS_070000'].children if c.type == 'axes']
    if(len(ax1) > 0):
        ax1 = ax1[0]
    i=0
    for line in ax1.children:
        # print(line)
    # for object_idx in range(PHMI['hgS_070000'].children.children.shape[0]):
        # print(object_idx)
        try:
            X=line.properties.XData #good   
            Y=line.properties.YData 
            Z=line.properties.ZData
            PHMI_dic[i]=(X,Y,Z)
        except:
            pass
        i+=1
    
    # print(PHMI2_dic)
    fig= plt.figure(figsize=(130,20)) #figsize=(20,130)
    colors=['#7f7f7f','#d62728','#1f77b4','','','']
    markers=['.','+','o','','','']
    dotSizes=[200,3000,3000,0,0,0]
    linewidths=[2,10,10,0,0,0]
    
    ScalePhmi=math.pow(10,1)    
    plt.plot(PHMI_dic[0][1],PHMI_dic[0][0],marker=markers[0], color=colors[0],linewidth=linewidths[0])  
    ref=math.pow(10,-5)
    
    #for display clearly
    PHmiValue=PHMI_dic[1][2]
    replaceValue=np.extract(PHmiValue<ref,PHmiValue)*-math.pow(10,5)
    PHmiValue[PHmiValue<ref]=replaceValue
    plt.plot( PHMI_dic[0][1],PHmiValue*ScalePhmi,marker=markers[0], color=colors[1],linewidth=1)
    
    # plt.plot(PHMI_dic[1][2]*ScalePhmi, PHMI_dic[0][1],marker=markers[0], color=colors[1],linewidth=1)
    #plt.axvline(x=ref*ScalePhmi)
    plt.axhline(y=ref*ScalePhmi)
    
    plt.scatter(LandmarkMap_dic[1][1],LandmarkMap_dic[1][0],marker=markers[1], s=dotSizes[1],color=colors[2],linewidth=10)
    
    plt.tick_params(axis='both',labelsize=80)
    plt.show()
    
    return PHMI_dic
#数据类型B 

def interactiveG(G):
    from bokeh.models.graphs import NodesAndLinkedEdges,from_networkx
    from bokeh.models import Circle, HoverTool, MultiLine,Plot,Range1d,StaticLayoutProvider
    from bokeh.plotting import figure, output_file, show, ColumnDataSource
    from bokeh.io import output_notebook, show
    output_notebook()
    # We could use figure here but don't want all the axes and titles  
    #plot=Plot(plot_width=1600, plot_height=300, tooltips=TOOLTIPS,title="PHmi+landmarks+route+power(10,-5)",x_range=Range1d(-1.1,1.1), y_range=Range1d(-1.1,1.1))
    
    output_file("PHMI_network")
    source=ColumnDataSource(data=dict(
        x=locations[0].tolist(),
        #x=[idx for idx in range(len(PHMIList))],
        #y=locations[1].tolist(),
        y=PHMIList,
        #desc=[str(i) for i in PHMIList],
        #PHMI_value=PHMI_dic[0][0].tolist(),    
    ))
    TOOLTIPS=[
        ("index", "$index"),
        ("(x,y)", "($x, $y)"),
        #("desc", "@desc"),
        #("PHMI", "$PHMI_value"),
    ]
    
    
    plot=figure(x_range=Range1d(-1.1,1.1), y_range=Range1d(-1.1,1.1),plot_width=2200, plot_height=500,tooltips=TOOLTIPS,title="PHMI_network")
    
    #G_position={key:(G.position[key][1],G.position[key][0]) for key in G.position.keys()}
    graph = from_networkx(G,nx.spring_layout,scale=1, center=(0,0))  
    #plot.renderers.append(graph) 
    
    fixed_layout_provider = StaticLayoutProvider(graph_layout=G.position)
    graph.layout_provider = fixed_layout_provider
    plot.renderers.append(graph)
    
    # Blue circles for nodes, and light grey lines for edges  
    graph.node_renderer.glyph = Circle(size=5, fill_color='#2b83ba')  
    graph.edge_renderer.glyph = MultiLine(line_color="#cccccc", line_alpha=0.8, line_width=2)  
      
    # green hover for both nodes and edges  
    graph.node_renderer.hover_glyph = Circle(size=25, fill_color='#abdda4')  
    graph.edge_renderer.hover_glyph = MultiLine(line_color='#abdda4', line_width=4)  
      
    # When we hover over nodes, highlight adjecent edges too  
    graph.inspection_policy = NodesAndLinkedEdges()  
      
    plot.add_tools(HoverTool(tooltips=None))  
     
    colors=('aliceblue', 'antiquewhite', 'aqua', 'aquamarine', 'azure', 'beige', 'bisque', 'black', 'blanchedalmond', 'blue', 'blueviolet', 'brown', 'burlywood', 'cadetblue', 'chartreuse', 'chocolate', 'coral', 'cornflowerblue', 'cornsilk', 'crimson', 'cyan', 'darkblue', 'darkcyan', 'darkgoldenrod', 'darkgray', 'darkgreen', 'darkgrey', 'darkkhaki', 'darkmagenta', 'darkolivegreen', 'darkorange', 'darkorchid', 'darkred', 'darksalmon', 'darkseagreen', 'darkslateblue', 'darkslategray', 'darkslategrey', 'darkturquoise', 'darkviolet', 'deeppink', 'deepskyblue', 'dimgray', 'dimgrey', 'dodgerblue', 'firebrick', 'floralwhite', 'forestgreen', 'fuchsia', 'gainsboro', 'ghostwhite', 'gold', 'goldenrod', 'gray', 'green', 'greenyellow', 'grey', 'honeydew', 'hotpink', 'indianred', 'indigo', 'ivory', 'khaki', 'lavender', 'lavenderblush', 'lawngreen', 'lemonchiffon', 'lightblue', 'lightcoral', 'lightcyan', 'lightgoldenrodyellow', 'lightgray', 'lightgreen', 'lightgrey', 'lightpink', 'lightsalmon', 'lightseagreen', 'lightskyblue', 'lightslategray', 'lightslategrey', 'lightsteelblue', 'lightyellow', 'lime', 'limegreen', 'linen', 'magenta', 'maroon', 'mediumaquamarine', 'mediumblue', 'mediumorchid', 'mediumpurple', 'mediumseagreen', 'mediumslateblue', 'mediumspringgreen', 'mediumturquoise', 'mediumvioletred', 'midnightblue', 'mintcream', 'mistyrose', 'moccasin', 'navajowhite', 'navy', 'oldlace', 'olive', 'olivedrab', 'orange', 'orangered', 'orchid', 'palegoldenrod', 'palegreen', 'paleturquoise', 'palevioletred', 'papayawhip', 'peachpuff', 'peru', 'pink', 'plum', 'powderblue', 'purple', 'red', 'rosybrown', 'royalblue', 'saddlebrown', 'salmon', 'sandybrown', 'seagreen', 'seashell', 'sienna', 'silver', 'skyblue', 'slateblue', 'slategray', 'slategrey', 'snow', 'springgreen', 'steelblue', 'tan', 'teal', 'thistle', 'tomato', 'turquoise', 'violet', 'wheat', 'white', 'whitesmoke', 'yellow', 'yellowgreen')
    ScalePhmi=math.pow(10,1)
    i=0
    for val,idx in zip(phmi_breakPtsNeg, plot_x):
        plot.line(idx,np.array(val)*ScalePhmi,line_color=colors[i])
        i+=1    
        
    show(plot)
    
#05-single landmarks pattern 无人车位置点与对应landmarks栅格图
#convert location and corresponding landmarks to raster data format using numpy.histogram2d 

def colorMesh_phmi(landmarks,locations,targetPts_idx,Phmi):
    patternValuesDic={}
    for key in targetPts_idx.keys():
        patternValuesDic[key]=[0.5]*len(targetPts_idx[key])
        patternValuesDic[key].append(0.9)
    
    patternDic={}
    for key in patternValuesDic.keys():
        patternDic[key]={"x":landmarks[0][targetPts_idx[key]],
                         "y":landmarks[1][targetPts_idx[key]],
                         "z":patternValuesDic[key]
                         }
        patternDic[key]["x"]=np.append(patternDic[key]["x"],locations[0][key])
        patternDic[key]["y"]=np.append(patternDic[key]["y"],locations[1][key])
        
    histogram2dDic={}
    binNumber=(32,32) #32,25,68,70
    for key in patternDic.keys():
        zi, yi, xi = np.histogram2d(patternDic[key]["y"], patternDic[key]["x"], bins=binNumber, weights=patternDic[key]["z"], normed=False)
        counts, _, _ = np.histogram2d(patternDic[key]["y"], patternDic[key]["x"],bins=binNumber)
        # print(counts)
        histogram2dDic[key]={"xi":xi,"yi":yi,"zi":zi,"count":counts,"ziCount":zi / counts,"ziMasked":np.ma.masked_invalid(zi)}
    
    
    for key in histogram2dDic.keys():
        xi=histogram2dDic[key]["xi"]
        yi=histogram2dDic[key]["yi"]
        zi=histogram2dDic[key]["ziMasked"]
        
        x=patternDic[key]["x"]
        y=patternDic[key]["y"]
        z=patternDic[key]["z"]
        # print(x,y,z)
        #plot it
        '''
        fig, ax = plt.subplots(figsize=(25,20))
        ax.pcolormesh(xi, yi, zi, edgecolors='black')
        scat = ax.scatter(x, y, c=z, s=30)
        fig.colorbar(scat)
        ax.margins(0.05)
        
        plt.title("PHmi_%d:%f"%(key,Phmi[key]))
        plt.show()        
        
        if key==20:
            break
        '''
    return histogram2dDic,patternDic

#show raster