How to use the bokeh.plotting.figure function in bokeh

def test_plot_dict_returned_when_wrap_plot_info_is_false(self, mock_make_id):
        doc = Document()
        plot1 = figure()
        plot1.circle([], [])
        doc.add_root(plot1)

        plot2 = figure()
        plot2.circle([], [])
        doc.add_root(plot2)

        expected_plotdict_1 = RenderRoot(elementid="ID", id="ID")
        expected_plotdict_2 = RenderRoot(elementid="ID", id="ID")

        _, plotdict = bes.components(plot1, wrap_plot_info=False)
        assert plotdict == expected_plotdict_1

        _, plotids = bes.components((plot1, plot2), wrap_plot_info=False)
        assert plotids == (expected_plotdict_1, expected_plotdict_2)

ymin.append(min_value)
         ymax.append(max_value)
      date = datetime(data[varindex['track_date']])

      datasource = dict(date=date,
                           sdate=data[varindex['track_date']],
                           lat=data[varindex['track_lat']],
                           lon=data[varindex['track_lon']],
                           value=data[varindex['data_value']])

      sources.append(ColumnDataSource(data=datasource))

   zf.close()
   shutil.rmtree(csv_dir)

   ts_plot = figure(title=plot_title, x_axis_type="datetime", y_axis_type = plot_scale, width=1000, logo=None,
              height=400, responsive=True
   )

   tooltips = [("Date", "@sdate")]
   tooltips.append(("Value", "@value{0.000}"))
   tooltips.append(("Latitude", "@lat{1.1}"))
   tooltips.append(("Longitude", "@lon{1.1}"))

   ts_plot.add_tools(CrosshairTool())

   ts_plot.xaxis.axis_label = 'Date'
   ts_plot.title.text_font_size = "14pt"
   ts_plot.xaxis.axis_label_text_font_size = "12pt"
   ts_plot.yaxis.axis_label_text_font_size = "12pt"
   # Set up the axis label here as it writes to all y axes so overwrites the right hand one
   # if we run it later.

xr = Range1d(start=-10, end=10)
yr = Range1d(start=-10, end=10)

# EXERCISE: Plot all the sets of points on different plots p1, p2, p3. Use the
# ranges above for `x_range` and `y_range` for each figure. Set different colors
# as well. Try setting line_color and fill_color instead of just color. You can
# also set alpha, line_alpha, and fill_alpha if you like. Set tools to TOOLS on
# the figures. Change the value of the 'marker' parameter, "circle", "square",
# "triangle", etc. One example is given
p1 = figure(x_range=xr, y_range=yr, tools=TOOLS, plot_width=300, plot_height=300)
p1.scatter(x1, y1, size=12, color="red", alpha=0.5)

p2 = figure(x_range=xr, y_range=yr, tools=TOOLS, plot_width=300, plot_height=300)
p2.scatter(x2, y2, size=12, color="blue", fill_alpha=0.5, marker="square")

p3 = figure(x_range=xr, y_range=yr, tools=TOOLS, plot_width=300, plot_height=300)
p3.scatter(x3, y3, size=12, fill_color="green", line_color="orange", marker="triangle")

# EXERCISE: Try panning and zooming one of the plots with another one visible!
# Set the plot_width and plot_height to smaller if necessary

# EXERCISE: create a new figure p4
p4 = figure(title="Colorful Scatter")

# Lets plot 4000 circles, you can play around with this if you like
N = 4000

# Create a bunch of random points, radii and colors for plotting
x = np.random.random(size=N) * 100
y = np.random.random(size=N) * 100
radii = np.random.random(size=N) * 1.5
colors = [

def create_hgt_figure(sh_df, sz_df):
    if sh_df is None or sz_df is None: return figure()
    data = {
        'date': sh_df.date.tolist(),
        'index': sh_df.index.tolist(),
        'cum_buy': ((sh_df['cum_buy'] + sz_df['cum_buy'])).round(2).tolist(),
        'net_buy': (10 * (sh_df['net_buy'] + sz_df['net_buy'])).round(2).tolist()
    }
    source = ColumnDataSource(data)
    yval_max = max(data['cum_buy'])
    p = figure(tools="", toolbar_location=None, x_range=(0, len(data['date'])), y_range=(-yval_max * 0.2, yval_max * 1.3))
    mapper = linear_cmap(field_name='net_buy', palette=['red', 'green'], low=0, high=0, low_color = 'green', high_color = 'red')
    p.line(x = 'index', y = 'cum_buy', line_width=2, color=Spectral11[0], alpha=0.8, legend_label="沪港通买入累计余额", source = source)
    p.vbar(x = 'index', bottom=0, top='net_buy', color=mapper, width=1, legend_label='融资变化', source = source)
    p.add_tools(HoverTool(tooltips=[("value", "@cum_buy"), ("date", "@date")]))
    p.legend.location = "top_left"
    p.legend.click_policy = "hide"
    p.legend.orientation = "horizontal"
    p.yaxis.axis_label = "沪港通数据概况"
    p.xaxis.major_label_overrides = {i: mdate for i, mdate in enumerate(data['date'])}
    return p

measured = np.random.lognormal(mu, sigma, 1000)
hist, edges = np.histogram(measured, density=True, bins=50)

# compute ideal values
x = np.linspace(0, 8.0, 1000)
pdf = 1/(x* sigma * np.sqrt(2*np.pi)) * np.exp(-(np.log(x)-mu)**2 / (2*sigma**2))
cdf = (1+scipy.special.erf((np.log(x)-mu)/(np.sqrt(2)*sigma)))/2

# EXERCISE: recreate the first plot for this new data

# EXERCISE: (optional): Add new plots for the following distributions:
# * Gamma
# * Beta
# * Weibull
# The numerical code is included, you will need to add renderers to the figures
p3 = figure(title="Gamma Distribution (k=1, θ=2)",
            background_fill="#E8DDCB", tools="")

k, theta = 1.0, 2.0

# sample the distribution
measured = np.random.gamma(k, theta, 1000)
hist, edges = np.histogram(measured, density=True, bins=50)

# compute ideal values
x = np.linspace(0, 20.0, 1000)
pdf = x**(k-1) * np.exp(-x/theta) / (theta**k * scipy.special.gamma(k))
cdf = scipy.special.gammainc(k, x/theta) / scipy.special.gamma(k)



p4 = figure(title="Beta Distribution (α=2, β=2)",

('Value', '@y{0.2f}')],
                                        formatters={'x': 'datetime'}))

        # Set the legend
        legend_plot = Legend(items=[("Data  ", [self.plot_data]),
                                    ("Series Average  ", [self.plot_avg]),
                                    ("Rolling Average  ", [self.plot_trend]),
                                    ("Percentile Region  ", [self.plot_patch])],
                             orientation='horizontal')

        # Add the layout outside the plot, clicking legend item hides the line
        self.figure.add_layout(legend_plot, 'above')
        self.figure.legend.click_policy = "hide"

        tools = "pan,wheel_zoom,box_zoom,reset,crosshair,save"
        self.histograms = figure(plot_width=plot_width, plot_height=275, tools=tools, active_drag="box_zoom")
        self.histograms.xaxis.axis_label_text_font_size = options.PLOT_AXIS_LABEL_FONT_SIZE
        self.histograms.yaxis.axis_label_text_font_size = options.PLOT_AXIS_LABEL_FONT_SIZE
        self.histograms.xaxis.major_label_text_font_size = options.PLOT_AXIS_MAJOR_LABEL_FONT_SIZE
        self.histograms.yaxis.major_label_text_font_size = options.PLOT_AXIS_MAJOR_LABEL_FONT_SIZE
        self.histograms.min_border_left = options.MIN_BORDER
        self.histograms.min_border_bottom = options.MIN_BORDER
        self.vbar = self.histograms.vbar(x='x', width='width', bottom=0, top='top', source=self.source['hist'],
                                         color=options.PLOT_COLOR, alpha=options.HISTOGRAM_ALPHA)

        self.histograms.xaxis.axis_label = ""
        self.histograms.yaxis.axis_label = "Frequency"
        self.histograms.add_tools(HoverTool(show_arrow=True, line_policy='next',
                                            tooltips=[('x', '@x{0.2f}'),
                                                      ('Counts', '@top')]))

        self.bokeh_layout = column(self.figure, Div(text='<hr>', width=plot_width), self.histograms)

palette = [cc.rainbow[i] for i in range(len(step_data['mouse'].unique()))]
    palette = [cc.rainbow[i*15] for i in range(5)]

    mice = sorted(step_data['mouse'].unique())
    current_step = step_data.groupby('mouse').last().reset_index()
    current_step = current_step[['mouse','step']]

    plots = []
    p = figure(x_range=step_data['mouse'].unique(),title='Mouse Steps',
               plot_height=200)
    p.xaxis.major_label_orientation = np.pi / 2
    p.vbar(x=current_step['mouse'], top=current_step['step'], width=0.9)
    plots.append(p)
    for i, (mus, group) in enumerate(step_data.groupby('mouse')):
        meancx = group['correct'].ewm(span=100,ignore_na=True).mean()
        p = figure(plot_height=100,y_range=(0,1),title=mus)

        p.line(group['trial_num'], meancx, color=palette[group['step'].iloc[0]-1])
        plots.append(p)
    grid = gridplot(plots, ncols=1)
    show(grid)

# Get Name of x-axis data:
    if kind == "barh":
        xlabelname = (
            figure_options["y_axis_label"]
            if figure_options.get("y_axis_label", "") != ""
            else "x"
        )
    else:
        xlabelname = (
            figure_options["x_axis_label"]
            if figure_options.get("x_axis_label", "") != ""
            else "x"
        )

    # Create Figure for plotting:
    p = _figure(**figure_options)
    if "x_axis_type" not in figure_options:
        figure_options["x_axis_type"] = None

    # For categorical plots, set the xticks:
    if x_labels_dict is not None:
        p.xaxis.formatter = FuncTickFormatter(
            code="""
                                var labels = %s;
                                return labels[tick];
                                """
            % x_labels_dict
        )

    # Use figure when passed by user:
    if figure is not None:
        p = figure

def make_plot(x, y):
    p = figure(title = "Iris Morphology", sizing_mode="fixed", plot_width=400, plot_height=400)
    p.xaxis.axis_label = x
    p.yaxis.axis_label = y
    p.circle(flowers[x], flowers[y], color=colors, fill_alpha=0.2, size=10)
    return p

Parameters
    ----------
    conn : DBobject
        Connection object that represents database
    start : time
        Startlimit for x-axis and query (typ. datetime.now()- 4Months)
    end : time
        Endlimit for x-axis and query (typ. datetime.now())
    Return
    ------
    p : Plot object
        Bokeh plot
    '''

    # create a new plot with a title and axis labels
    p = figure(tools="pan,wheel_zoom,box_zoom,reset,save", \
               toolbar_location="above", \
               plot_width=1120, \
               plot_height=800, \
               y_range=[-6, 4], \
               x_range=utils.time_delta(Time(end)), \
               x_axis_type='datetime', \
               x_axis_label='Date', y_axis_label='ratio (normalized)')

    p.xaxis.formatter = copy.copy(utils.plot_x_axis_format)

    p.grid.visible = True
    p.title.text = "Filterwheel Ratio"
    pf.add_basic_layout(p)

    pf.add_to_wplot(p, "FND", "IMIR_HK_FW_POS_RATIO_FND", start, end, conn, mn.fw_nominals['FND'], color="green")
    pf.add_to_wplot(p, "OPAQUE", "IMIR_HK_FW_POS_RATIO_OPAQUE", start, end, conn, mn.fw_nominals['OPAQUE'], color="red")