We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
Error plotting a single variable with plot_density and bokeh backend
**Describe the bug**
Over in ArviZ.jl, we use the Julia equivalent to the below snippet to test Bokeh integration for `plot_density`. It worked fine until recently, where we now get an error with bokeh only but not matplotlib, though I'm not certain whether a change in arviz or bokeh is responsible.

**To Reproduce**
```python
>>> import arviz
>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> arr1 = np.random.randn(4, 100)
>>> arr2 = np.random.randn(4, 100)
>>> arviz.plot_density([{"x": arr1}, {"x": arr2}], var_names = ["x"]) # matplotlib works fine
>>> plt.show()
```
<img src=https://user-images.githubusercontent.com/8673634/94775414-9bce2480-0374-11eb-8938-f74a486f97de.png width=400></img>
```python
>>> arviz.plot_density([{"x": arr1}, {"x": arr2}], var_names = ["x"], backend="bokeh") # errors
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/Users/saxen/.julia/conda/3/lib/python3.8/site-packages/arviz/plots/densityplot.py", line 252, in plot_density
    ax = plot(**plot_density_kwargs)
  File "/Users/saxen/.julia/conda/3/lib/python3.8/site-packages/arviz/plots/backends/bokeh/densityplot.py", line 74, in plot_density
    for label, ax_ in zip(all_labels, (item for item in ax.flatten() if item is not None))
AttributeError: 'Figure' object has no attribute 'flatten'
```

**Additional context**
Relevant package versions in the conda environment used:
```
arviz                     0.10.0                     py_0    conda-forge
bokeh                     2.2.1            py38h32f6830_0    conda-forge
matplotlib                3.1.3                    py38_0    conda-forge
numpy                     1.19.1           py38h3b9f5b6_0  
```

--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `arviz/plots/backends/bokeh/densityplot.py`
Content:
```
1 """Bokeh Densityplot."""
2 from collections import defaultdict
3 from itertools import cycle
4 
5 import matplotlib.pyplot as plt
6 import numpy as np
7 from bokeh.models.annotations import Legend, Title
8 
9 from ....stats import hdi
10 from ....stats.density_utils import get_bins, histogram, kde
11 from ...plot_utils import _scale_fig_size, calculate_point_estimate, make_label, vectorized_to_hex
12 from .. import show_layout
13 from . import backend_kwarg_defaults, create_axes_grid
14 
15 
16 def plot_density(
17     ax,
18     all_labels,
19     to_plot,
20     colors,
21     bw,
22     circular,
23     figsize,
24     length_plotters,
25     rows,
26     cols,
27     textsize,
28     hdi_prob,
29     point_estimate,
30     hdi_markers,
31     outline,
32     shade,
33     n_data,
34     data_labels,
35     backend_kwargs,
36     show,
37 ):
38     """Bokeh density plot."""
39     if backend_kwargs is None:
40         backend_kwargs = {}
41 
42     backend_kwargs = {
43         **backend_kwarg_defaults(),
44         **backend_kwargs,
45     }
46 
47     if colors == "cycle":
48         colors = [
49             prop
50             for _, prop in zip(
51                 range(n_data), cycle(plt.rcParams["axes.prop_cycle"].by_key()["color"])
52             )
53         ]
54     elif isinstance(colors, str):
55         colors = [colors for _ in range(n_data)]
56     colors = vectorized_to_hex(colors)
57 
58     (figsize, _, _, _, line_width, markersize) = _scale_fig_size(figsize, textsize, rows, cols)
59 
60     if ax is None:
61         ax = create_axes_grid(
62             length_plotters,
63             rows,
64             cols,
65             figsize=figsize,
66             squeeze=True,
67             backend_kwargs=backend_kwargs,
68         )
69     else:
70         ax = np.atleast_2d(ax)
71 
72     axis_map = {
73         label: ax_
74         for label, ax_ in zip(all_labels, (item for item in ax.flatten() if item is not None))
75     }
76     if data_labels is None:
77         data_labels = {}
78 
79     legend_items = defaultdict(list)
80     for m_idx, plotters in enumerate(to_plot):
81         for var_name, selection, values in plotters:
82             label = make_label(var_name, selection)
83 
84             if data_labels:
85                 data_label = data_labels[m_idx]
86             else:
87                 data_label = None
88 
89             plotted = _d_helper(
90                 values.flatten(),
91                 label,
92                 colors[m_idx],
93                 bw,
94                 circular,
95                 line_width,
96                 markersize,
97                 hdi_prob,
98                 point_estimate,
99                 hdi_markers,
100                 outline,
101                 shade,
102                 axis_map[label],
103             )
104             if data_label is not None:
105                 legend_items[axis_map[label]].append((data_label, plotted))
106 
107     for ax1, legend in legend_items.items():
108         legend = Legend(
109             items=legend,
110             location="center_right",
111             orientation="horizontal",
112         )
113         ax1.add_layout(legend, "above")
114         ax1.legend.click_policy = "hide"
115 
116     show_layout(ax, show)
117 
118     return ax
119 
120 
121 def _d_helper(
122     vec,
123     vname,
124     color,
125     bw,
126     circular,
127     line_width,
128     markersize,
129     hdi_prob,
130     point_estimate,
131     hdi_markers,
132     outline,
133     shade,
134     ax,
135 ):
136 
137     extra = dict()
138     plotted = []
139 
140     if vec.dtype.kind == "f":
141         if hdi_prob != 1:
142             hdi_ = hdi(vec, hdi_prob, multimodal=False)
143             new_vec = vec[(vec >= hdi_[0]) & (vec <= hdi_[1])]
144         else:
145             new_vec = vec
146 
147         x, density = kde(new_vec, circular=circular, bw=bw)
148         density *= hdi_prob
149         xmin, xmax = x[0], x[-1]
150         ymin, ymax = density[0], density[-1]
151 
152         if outline:
153             plotted.append(ax.line(x, density, line_color=color, line_width=line_width, **extra))
154             plotted.append(
155                 ax.line(
156                     [xmin, xmin],
157                     [-ymin / 100, ymin],
158                     line_color=color,
159                     line_dash="solid",
160                     line_width=line_width,
161                     muted_color=color,
162                     muted_alpha=0.2,
163                 )
164             )
165             plotted.append(
166                 ax.line(
167                     [xmax, xmax],
168                     [-ymax / 100, ymax],
169                     line_color=color,
170                     line_dash="solid",
171                     line_width=line_width,
172                     muted_color=color,
173                     muted_alpha=0.2,
174                 )
175             )
176 
177         if shade:
178             plotted.append(
179                 ax.patch(
180                     np.r_[x[::-1], x, x[-1:]],
181                     np.r_[np.zeros_like(x), density, [0]],
182                     fill_color=color,
183                     fill_alpha=shade,
184                     muted_color=color,
185                     muted_alpha=0.2,
186                     **extra
187                 )
188             )
189 
190     else:
191         xmin, xmax = hdi(vec, hdi_prob, multimodal=False)
192         bins = get_bins(vec)
193 
194         _, hist, edges = histogram(vec, bins=bins)
195 
196         if outline:
197             plotted.append(
198                 ax.quad(
199                     top=hist,
200                     bottom=0,
201                     left=edges[:-1],
202                     right=edges[1:],
203                     line_color=color,
204                     fill_color=None,
205                     muted_color=color,
206                     muted_alpha=0.2,
207                     **extra
208                 )
209             )
210         else:
211             plotted.append(
212                 ax.quad(
213                     top=hist,
214                     bottom=0,
215                     left=edges[:-1],
216                     right=edges[1:],
217                     line_color=color,
218                     fill_color=color,
219                     fill_alpha=shade,
220                     muted_color=color,
221                     muted_alpha=0.2,
222                     **extra
223                 )
224             )
225 
226     if hdi_markers:
227         plotted.append(ax.diamond(xmin, 0, line_color="black", fill_color=color, size=markersize))
228         plotted.append(ax.diamond(xmax, 0, line_color="black", fill_color=color, size=markersize))
229 
230     if point_estimate is not None:
231         est = calculate_point_estimate(point_estimate, vec, bw, circular)
232         plotted.append(ax.circle(est, 0, fill_color=color, line_color="black", size=markersize))
233 
234     _title = Title()
235     _title.text = vname
236     ax.title = _title
237     ax.title.text_font_size = "13pt"
238 
239     return plotted
240
```
--- END FILES ---

Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.

Here is an example:

```diff
diff --git a/examples/server_async.py b/examples/server_async.py
--- a/examples/server_async.py
+++ b/examples/server_async.py
@@ -313,4 +313,4 @@
 
 
 if __name__ == "__main__":
-    asyncio.run(run_async_server("."), debug=True)
+    asyncio.run(run_async_server(), debug=True)
diff --git a/examples/server_sync.py b/examples/server_sync.py
--- a/examples/server_sync.py
+++ b/examples/server_sync.py
@@ -313,5 +313,5 @@
 
 
 if __name__ == "__main__":
-    server = run_sync_server(".")
+    server = run_sync_server()
     server.shutdown()

```

diff --git a/arviz/plots/backends/bokeh/densityplot.py b/arviz/plots/backends/bokeh/densityplot.py
--- a/arviz/plots/backends/bokeh/densityplot.py
+++ b/arviz/plots/backends/bokeh/densityplot.py
@@ -63,7 +63,7 @@
             rows,
             cols,
             figsize=figsize,
-            squeeze=True,
+            squeeze=False,
             backend_kwargs=backend_kwargs,
         )
     else:

{"golden_diff": "diff --git a/arviz/plots/backends/bokeh/densityplot.py b/arviz/plots/backends/bokeh/densityplot.py\n--- a/arviz/plots/backends/bokeh/densityplot.py\n+++ b/arviz/plots/backends/bokeh/densityplot.py\n@@ -63,7 +63,7 @@\n             rows,\n             cols,\n             figsize=figsize,\n-            squeeze=True,\n+            squeeze=False,\n             backend_kwargs=backend_kwargs,\n         )\n     else:\n", "issue": "Error plotting a single variable with plot_density and bokeh backend\n**Describe the bug**\r\nOver in ArviZ.jl, we use the Julia equivalent to the below snippet to test Bokeh integration for `plot_density`. It worked fine until recently, where we now get an error with bokeh only but not matplotlib, though I'm not certain whether a change in arviz or bokeh is responsible.\r\n\r\n**To Reproduce**\r\n```python\r\n>>> import arviz\r\n>>> import numpy as np\r\n>>> import matplotlib.pyplot as plt\r\n>>> arr1 = np.random.randn(4, 100)\r\n>>> arr2 = np.random.randn(4, 100)\r\n>>> arviz.plot_density([{\"x\": arr1}, {\"x\": arr2}], var_names = [\"x\"]) # matplotlib works fine\r\n>>> plt.show()\r\n```\r\n<img src=https://user-images.githubusercontent.com/8673634/94775414-9bce2480-0374-11eb-8938-f74a486f97de.png width=400></img>\r\n```python\r\n>>> arviz.plot_density([{\"x\": arr1}, {\"x\": arr2}], var_names = [\"x\"], backend=\"bokeh\") # errors\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 1, in <module>\r\n  File \"/Users/saxen/.julia/conda/3/lib/python3.8/site-packages/arviz/plots/densityplot.py\", line 252, in plot_density\r\n    ax = plot(**plot_density_kwargs)\r\n  File \"/Users/saxen/.julia/conda/3/lib/python3.8/site-packages/arviz/plots/backends/bokeh/densityplot.py\", line 74, in plot_density\r\n    for label, ax_ in zip(all_labels, (item for item in ax.flatten() if item is not None))\r\nAttributeError: 'Figure' object has no attribute 'flatten'\r\n```\r\n\r\n**Additional context**\r\nRelevant package versions in the conda environment used:\r\n```\r\narviz                     0.10.0                     py_0    conda-forge\r\nbokeh                     2.2.1            py38h32f6830_0    conda-forge\r\nmatplotlib                3.1.3                    py38_0    conda-forge\r\nnumpy                     1.19.1           py38h3b9f5b6_0  \r\n```\n", "before_files": [{"content": "\"\"\"Bokeh Densityplot.\"\"\"\nfrom collections import defaultdict\nfrom itertools import cycle\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom bokeh.models.annotations import Legend, Title\n\nfrom ....stats import hdi\nfrom ....stats.density_utils import get_bins, histogram, kde\nfrom ...plot_utils import _scale_fig_size, calculate_point_estimate, make_label, vectorized_to_hex\nfrom .. import show_layout\nfrom . import backend_kwarg_defaults, create_axes_grid\n\n\ndef plot_density(\n    ax,\n    all_labels,\n    to_plot,\n    colors,\n    bw,\n    circular,\n    figsize,\n    length_plotters,\n    rows,\n    cols,\n    textsize,\n    hdi_prob,\n    point_estimate,\n    hdi_markers,\n    outline,\n    shade,\n    n_data,\n    data_labels,\n    backend_kwargs,\n    show,\n):\n    \"\"\"Bokeh density plot.\"\"\"\n    if backend_kwargs is None:\n        backend_kwargs = {}\n\n    backend_kwargs = {\n        **backend_kwarg_defaults(),\n        **backend_kwargs,\n    }\n\n    if colors == \"cycle\":\n        colors = [\n            prop\n            for _, prop in zip(\n                range(n_data), cycle(plt.rcParams[\"axes.prop_cycle\"].by_key()[\"color\"])\n            )\n        ]\n    elif isinstance(colors, str):\n        colors = [colors for _ in range(n_data)]\n    colors = vectorized_to_hex(colors)\n\n    (figsize, _, _, _, line_width, markersize) = _scale_fig_size(figsize, textsize, rows, cols)\n\n    if ax is None:\n        ax = create_axes_grid(\n            length_plotters,\n            rows,\n            cols,\n            figsize=figsize,\n            squeeze=True,\n            backend_kwargs=backend_kwargs,\n        )\n    else:\n        ax = np.atleast_2d(ax)\n\n    axis_map = {\n        label: ax_\n        for label, ax_ in zip(all_labels, (item for item in ax.flatten() if item is not None))\n    }\n    if data_labels is None:\n        data_labels = {}\n\n    legend_items = defaultdict(list)\n    for m_idx, plotters in enumerate(to_plot):\n        for var_name, selection, values in plotters:\n            label = make_label(var_name, selection)\n\n            if data_labels:\n                data_label = data_labels[m_idx]\n            else:\n                data_label = None\n\n            plotted = _d_helper(\n                values.flatten(),\n                label,\n                colors[m_idx],\n                bw,\n                circular,\n                line_width,\n                markersize,\n                hdi_prob,\n                point_estimate,\n                hdi_markers,\n                outline,\n                shade,\n                axis_map[label],\n            )\n            if data_label is not None:\n                legend_items[axis_map[label]].append((data_label, plotted))\n\n    for ax1, legend in legend_items.items():\n        legend = Legend(\n            items=legend,\n            location=\"center_right\",\n            orientation=\"horizontal\",\n        )\n        ax1.add_layout(legend, \"above\")\n        ax1.legend.click_policy = \"hide\"\n\n    show_layout(ax, show)\n\n    return ax\n\n\ndef _d_helper(\n    vec,\n    vname,\n    color,\n    bw,\n    circular,\n    line_width,\n    markersize,\n    hdi_prob,\n    point_estimate,\n    hdi_markers,\n    outline,\n    shade,\n    ax,\n):\n\n    extra = dict()\n    plotted = []\n\n    if vec.dtype.kind == \"f\":\n        if hdi_prob != 1:\n            hdi_ = hdi(vec, hdi_prob, multimodal=False)\n            new_vec = vec[(vec >= hdi_[0]) & (vec <= hdi_[1])]\n        else:\n            new_vec = vec\n\n        x, density = kde(new_vec, circular=circular, bw=bw)\n        density *= hdi_prob\n        xmin, xmax = x[0], x[-1]\n        ymin, ymax = density[0], density[-1]\n\n        if outline:\n            plotted.append(ax.line(x, density, line_color=color, line_width=line_width, **extra))\n            plotted.append(\n                ax.line(\n                    [xmin, xmin],\n                    [-ymin / 100, ymin],\n                    line_color=color,\n                    line_dash=\"solid\",\n                    line_width=line_width,\n                    muted_color=color,\n                    muted_alpha=0.2,\n                )\n            )\n            plotted.append(\n                ax.line(\n                    [xmax, xmax],\n                    [-ymax / 100, ymax],\n                    line_color=color,\n                    line_dash=\"solid\",\n                    line_width=line_width,\n                    muted_color=color,\n                    muted_alpha=0.2,\n                )\n            )\n\n        if shade:\n            plotted.append(\n                ax.patch(\n                    np.r_[x[::-1], x, x[-1:]],\n                    np.r_[np.zeros_like(x), density, [0]],\n                    fill_color=color,\n                    fill_alpha=shade,\n                    muted_color=color,\n                    muted_alpha=0.2,\n                    **extra\n                )\n            )\n\n    else:\n        xmin, xmax = hdi(vec, hdi_prob, multimodal=False)\n        bins = get_bins(vec)\n\n        _, hist, edges = histogram(vec, bins=bins)\n\n        if outline:\n            plotted.append(\n                ax.quad(\n                    top=hist,\n                    bottom=0,\n                    left=edges[:-1],\n                    right=edges[1:],\n                    line_color=color,\n                    fill_color=None,\n                    muted_color=color,\n                    muted_alpha=0.2,\n                    **extra\n                )\n            )\n        else:\n            plotted.append(\n                ax.quad(\n                    top=hist,\n                    bottom=0,\n                    left=edges[:-1],\n                    right=edges[1:],\n                    line_color=color,\n                    fill_color=color,\n                    fill_alpha=shade,\n                    muted_color=color,\n                    muted_alpha=0.2,\n                    **extra\n                )\n            )\n\n    if hdi_markers:\n        plotted.append(ax.diamond(xmin, 0, line_color=\"black\", fill_color=color, size=markersize))\n        plotted.append(ax.diamond(xmax, 0, line_color=\"black\", fill_color=color, size=markersize))\n\n    if point_estimate is not None:\n        est = calculate_point_estimate(point_estimate, vec, bw, circular)\n        plotted.append(ax.circle(est, 0, fill_color=color, line_color=\"black\", size=markersize))\n\n    _title = Title()\n    _title.text = vname\n    ax.title = _title\n    ax.title.text_font_size = \"13pt\"\n\n    return plotted\n", "path": "arviz/plots/backends/bokeh/densityplot.py"}], "after_files": [{"content": "\"\"\"Bokeh Densityplot.\"\"\"\nfrom collections import defaultdict\nfrom itertools import cycle\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom bokeh.models.annotations import Legend, Title\n\nfrom ....stats import hdi\nfrom ....stats.density_utils import get_bins, histogram, kde\nfrom ...plot_utils import _scale_fig_size, calculate_point_estimate, make_label, vectorized_to_hex\nfrom .. import show_layout\nfrom . import backend_kwarg_defaults, create_axes_grid\n\n\ndef plot_density(\n    ax,\n    all_labels,\n    to_plot,\n    colors,\n    bw,\n    circular,\n    figsize,\n    length_plotters,\n    rows,\n    cols,\n    textsize,\n    hdi_prob,\n    point_estimate,\n    hdi_markers,\n    outline,\n    shade,\n    n_data,\n    data_labels,\n    backend_kwargs,\n    show,\n):\n    \"\"\"Bokeh density plot.\"\"\"\n    if backend_kwargs is None:\n        backend_kwargs = {}\n\n    backend_kwargs = {\n        **backend_kwarg_defaults(),\n        **backend_kwargs,\n    }\n\n    if colors == \"cycle\":\n        colors = [\n            prop\n            for _, prop in zip(\n                range(n_data), cycle(plt.rcParams[\"axes.prop_cycle\"].by_key()[\"color\"])\n            )\n        ]\n    elif isinstance(colors, str):\n        colors = [colors for _ in range(n_data)]\n    colors = vectorized_to_hex(colors)\n\n    (figsize, _, _, _, line_width, markersize) = _scale_fig_size(figsize, textsize, rows, cols)\n\n    if ax is None:\n        ax = create_axes_grid(\n            length_plotters,\n            rows,\n            cols,\n            figsize=figsize,\n            squeeze=False,\n            backend_kwargs=backend_kwargs,\n        )\n    else:\n        ax = np.atleast_2d(ax)\n\n    axis_map = {\n        label: ax_\n        for label, ax_ in zip(all_labels, (item for item in ax.flatten() if item is not None))\n    }\n    if data_labels is None:\n        data_labels = {}\n\n    legend_items = defaultdict(list)\n    for m_idx, plotters in enumerate(to_plot):\n        for var_name, selection, values in plotters:\n            label = make_label(var_name, selection)\n\n            if data_labels:\n                data_label = data_labels[m_idx]\n            else:\n                data_label = None\n\n            plotted = _d_helper(\n                values.flatten(),\n                label,\n                colors[m_idx],\n                bw,\n                circular,\n                line_width,\n                markersize,\n                hdi_prob,\n                point_estimate,\n                hdi_markers,\n                outline,\n                shade,\n                axis_map[label],\n            )\n            if data_label is not None:\n                legend_items[axis_map[label]].append((data_label, plotted))\n\n    for ax1, legend in legend_items.items():\n        legend = Legend(\n            items=legend,\n            location=\"center_right\",\n            orientation=\"horizontal\",\n        )\n        ax1.add_layout(legend, \"above\")\n        ax1.legend.click_policy = \"hide\"\n\n    show_layout(ax, show)\n\n    return ax\n\n\ndef _d_helper(\n    vec,\n    vname,\n    color,\n    bw,\n    circular,\n    line_width,\n    markersize,\n    hdi_prob,\n    point_estimate,\n    hdi_markers,\n    outline,\n    shade,\n    ax,\n):\n\n    extra = dict()\n    plotted = []\n\n    if vec.dtype.kind == \"f\":\n        if hdi_prob != 1:\n            hdi_ = hdi(vec, hdi_prob, multimodal=False)\n            new_vec = vec[(vec >= hdi_[0]) & (vec <= hdi_[1])]\n        else:\n            new_vec = vec\n\n        x, density = kde(new_vec, circular=circular, bw=bw)\n        density *= hdi_prob\n        xmin, xmax = x[0], x[-1]\n        ymin, ymax = density[0], density[-1]\n\n        if outline:\n            plotted.append(ax.line(x, density, line_color=color, line_width=line_width, **extra))\n            plotted.append(\n                ax.line(\n                    [xmin, xmin],\n                    [-ymin / 100, ymin],\n                    line_color=color,\n                    line_dash=\"solid\",\n                    line_width=line_width,\n                    muted_color=color,\n                    muted_alpha=0.2,\n                )\n            )\n            plotted.append(\n                ax.line(\n                    [xmax, xmax],\n                    [-ymax / 100, ymax],\n                    line_color=color,\n                    line_dash=\"solid\",\n                    line_width=line_width,\n                    muted_color=color,\n                    muted_alpha=0.2,\n                )\n            )\n\n        if shade:\n            plotted.append(\n                ax.patch(\n                    np.r_[x[::-1], x, x[-1:]],\n                    np.r_[np.zeros_like(x), density, [0]],\n                    fill_color=color,\n                    fill_alpha=shade,\n                    muted_color=color,\n                    muted_alpha=0.2,\n                    **extra\n                )\n            )\n\n    else:\n        xmin, xmax = hdi(vec, hdi_prob, multimodal=False)\n        bins = get_bins(vec)\n\n        _, hist, edges = histogram(vec, bins=bins)\n\n        if outline:\n            plotted.append(\n                ax.quad(\n                    top=hist,\n                    bottom=0,\n                    left=edges[:-1],\n                    right=edges[1:],\n                    line_color=color,\n                    fill_color=None,\n                    muted_color=color,\n                    muted_alpha=0.2,\n                    **extra\n                )\n            )\n        else:\n            plotted.append(\n                ax.quad(\n                    top=hist,\n                    bottom=0,\n                    left=edges[:-1],\n                    right=edges[1:],\n                    line_color=color,\n                    fill_color=color,\n                    fill_alpha=shade,\n                    muted_color=color,\n                    muted_alpha=0.2,\n                    **extra\n                )\n            )\n\n    if hdi_markers:\n        plotted.append(ax.diamond(xmin, 0, line_color=\"black\", fill_color=color, size=markersize))\n        plotted.append(ax.diamond(xmax, 0, line_color=\"black\", fill_color=color, size=markersize))\n\n    if point_estimate is not None:\n        est = calculate_point_estimate(point_estimate, vec, bw, circular)\n        plotted.append(ax.circle(est, 0, fill_color=color, line_color=\"black\", size=markersize))\n\n    _title = Title()\n    _title.text = vname\n    ax.title = _title\n    ax.title.text_font_size = \"13pt\"\n\n    return plotted\n", "path": "arviz/plots/backends/bokeh/densityplot.py"}]}