prompt: We are currently solving the following issue within our repository. Here is the issue text:
  --- BEGIN ISSUE ---
  Inconsistent capitalization in `plot_hdi` docstring
  **Describe the bug**
  Docs have inconsistent capitalization
  ![image](https://user-images.githubusercontent.com/7213793/82233398-d020d700-98e4-11ea-812c-588628ed753e.png)
  
  
  **To Reproduce**
  https://arviz-devs.github.io/arviz/generated/arviz.plot_hdi.html#arviz.plot_hdi
  
  **Expected behavior**
  Consistency across arg docstrings
  
  
  --- 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/hdiplot.py`
  Content:
  ```
  1 """Plot highest density intervals for regression data."""
  2 import warnings
  3 
  4 import numpy as np
  5 from scipy.interpolate import griddata
  6 from scipy.signal import savgol_filter
  7 
  8 from ..stats import hdi
  9 from .plot_utils import get_plotting_function, matplotlib_kwarg_dealiaser
  10 from ..rcparams import rcParams
  11 from ..utils import credible_interval_warning
  12 
  13 
  14 def plot_hdi(
  15     x,
  16     y,
  17     hdi_prob=None,
  18     color="C1",
  19     circular=False,
  20     smooth=True,
  21     smooth_kwargs=None,
  22     fill_kwargs=None,
  23     plot_kwargs=None,
  24     ax=None,
  25     backend=None,
  26     backend_kwargs=None,
  27     show=None,
  28     credible_interval=None,
  29 ):
  30     r"""
  31     Plot hdi intervals for regression data.
  32 
  33     Parameters
  34     ----------
  35     x : array-like
  36         Values to plot
  37     y : array-like
  38         values from which to compute the hdi. Assumed shape (chain, draw, \*shape).
  39     hdi_prob : float, optional
  40         Probability for the highest density interval. Defaults to 0.94.
  41     color : str
  42         Color used for the limits of the hdi and fill. Should be a valid matplotlib color
  43     circular : bool, optional
  44         Whether to compute the hdi taking into account `x` is a circular variable
  45         (in the range [-np.pi, np.pi]) or not. Defaults to False (i.e non-circular variables).
  46     smooth : boolean
  47         If True the result will be smoothed by first computing a linear interpolation of the data
  48         over a regular grid and then applying the Savitzky-Golay filter to the interpolated data.
  49         Defaults to True.
  50     smooth_kwargs : dict, optional
  51         Additional keywords modifying the Savitzky-Golay filter. See Scipy's documentation for
  52         details
  53     fill_kwargs : dict
  54         Keywords passed to `fill_between` (use fill_kwargs={'alpha': 0} to disable fill).
  55     plot_kwargs : dict
  56         Keywords passed to hdi limits
  57     ax: axes, optional
  58         Matplotlib axes or bokeh figures.
  59     backend: str, optional
  60         Select plotting backend {"matplotlib","bokeh"}. Default "matplotlib".
  61     backend_kwargs: bool, optional
  62         These are kwargs specific to the backend being used. For additional documentation
  63         check the plotting method of the backend.
  64     show : bool, optional
  65         Call backend show function.
  66     credible_interval: float, optional
  67         deprecated: Please see hdi_prob
  68 
  69     Returns
  70     -------
  71     axes : matplotlib axes or bokeh figures
  72     """
  73     if credible_interval:
  74         hdi_prob = credible_interval_warning(credible_interval, hdi_prob)
  75 
  76     plot_kwargs = matplotlib_kwarg_dealiaser(plot_kwargs, "plot")
  77     plot_kwargs.setdefault("color", color)
  78     plot_kwargs.setdefault("alpha", 0)
  79 
  80     fill_kwargs = matplotlib_kwarg_dealiaser(fill_kwargs, "hexbin")
  81     fill_kwargs.setdefault("color", color)
  82     fill_kwargs.setdefault("alpha", 0.5)
  83 
  84     x = np.asarray(x)
  85     y = np.asarray(y)
  86 
  87     x_shape = x.shape
  88     y_shape = y.shape
  89     if y_shape[-len(x_shape) :] != x_shape:
  90         msg = "Dimension mismatch for x: {} and y: {}."
  91         msg += " y-dimensions should be (chain, draw, *x.shape) or"
  92         msg += " (draw, *x.shape)"
  93         raise TypeError(msg.format(x_shape, y_shape))
  94 
  95     if len(y_shape[: -len(x_shape)]) > 1:
  96         new_shape = tuple([-1] + list(x_shape))
  97         y = y.reshape(new_shape)
  98 
  99     if hdi_prob is None:
  100         hdi_prob = rcParams["stats.hdi_prob"]
  101     else:
  102         if not 1 >= hdi_prob > 0:
  103             raise ValueError("The value of hdi_prob should be in the interval (0, 1]")
  104 
  105     hdi_ = hdi(y, hdi_prob=hdi_prob, circular=circular, multimodal=False)
  106 
  107     if smooth:
  108         if smooth_kwargs is None:
  109             smooth_kwargs = {}
  110         smooth_kwargs.setdefault("window_length", 55)
  111         smooth_kwargs.setdefault("polyorder", 2)
  112         x_data = np.linspace(x.min(), x.max(), 200)
  113         x_data[0] = (x_data[0] + x_data[1]) / 2
  114         hdi_interp = griddata(x, hdi_, x_data)
  115         y_data = savgol_filter(hdi_interp, axis=0, **smooth_kwargs)
  116     else:
  117         idx = np.argsort(x)
  118         x_data = x[idx]
  119         y_data = hdi_[idx]
  120 
  121     hdiplot_kwargs = dict(
  122         ax=ax,
  123         x_data=x_data,
  124         y_data=y_data,
  125         plot_kwargs=plot_kwargs,
  126         fill_kwargs=fill_kwargs,
  127         backend_kwargs=backend_kwargs,
  128         show=show,
  129     )
  130 
  131     if backend is None:
  132         backend = rcParams["plot.backend"]
  133     backend = backend.lower()
  134 
  135     # TODO: Add backend kwargs
  136     plot = get_plotting_function("plot_hdi", "hdiplot", backend)
  137     ax = plot(**hdiplot_kwargs)
  138     return ax
  139 
  140 
  141 def plot_hpd(*args, **kwargs):  # noqa: D103
  142     warnings.warn("plot_hdi has been deprecated, please use plot_hdi", DeprecationWarning)
  143     return plot_hdi(*args, **kwargs)
  144
  ```
  --- 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()
  
  ```
  
golden_diff: diff --git a/arviz/plots/hdiplot.py b/arviz/plots/hdiplot.py
  --- a/arviz/plots/hdiplot.py
  +++ b/arviz/plots/hdiplot.py
  @@ -33,13 +33,13 @@
       Parameters
       ----------
       x : array-like
  -        Values to plot
  +        Values to plot.
       y : array-like
  -        values from which to compute the hdi. Assumed shape (chain, draw, \*shape).
  +        Values from which to compute the hdi. Assumed shape (chain, draw, \*shape).
       hdi_prob : float, optional
           Probability for the highest density interval. Defaults to 0.94.
       color : str
  -        Color used for the limits of the hdi and fill. Should be a valid matplotlib color
  +        Color used for the limits of the hdi and fill. Should be a valid matplotlib color.
       circular : bool, optional
           Whether to compute the hdi taking into account `x` is a circular variable
           (in the range [-np.pi, np.pi]) or not. Defaults to False (i.e non-circular variables).
  @@ -49,11 +49,11 @@
           Defaults to True.
       smooth_kwargs : dict, optional
           Additional keywords modifying the Savitzky-Golay filter. See Scipy's documentation for
  -        details
  +        details.
       fill_kwargs : dict
           Keywords passed to `fill_between` (use fill_kwargs={'alpha': 0} to disable fill).
       plot_kwargs : dict
  -        Keywords passed to hdi limits
  +        Keywords passed to hdi limits.
       ax: axes, optional
           Matplotlib axes or bokeh figures.
       backend: str, optional
  @@ -64,7 +64,7 @@
       show : bool, optional
           Call backend show function.
       credible_interval: float, optional
  -        deprecated: Please see hdi_prob
  +        Deprecated: Please see hdi_prob
   
       Returns
       -------
  
verification_info: {"golden_diff": "diff --git a/arviz/plots/hdiplot.py b/arviz/plots/hdiplot.py\n--- a/arviz/plots/hdiplot.py\n+++ b/arviz/plots/hdiplot.py\n@@ -33,13 +33,13 @@\n     Parameters\n     ----------\n     x : array-like\n-        Values to plot\n+        Values to plot.\n     y : array-like\n-        values from which to compute the hdi. Assumed shape (chain, draw, \\*shape).\n+        Values from which to compute the hdi. Assumed shape (chain, draw, \\*shape).\n     hdi_prob : float, optional\n         Probability for the highest density interval. Defaults to 0.94.\n     color : str\n-        Color used for the limits of the hdi and fill. Should be a valid matplotlib color\n+        Color used for the limits of the hdi and fill. Should be a valid matplotlib color.\n     circular : bool, optional\n         Whether to compute the hdi taking into account `x` is a circular variable\n         (in the range [-np.pi, np.pi]) or not. Defaults to False (i.e non-circular variables).\n@@ -49,11 +49,11 @@\n         Defaults to True.\n     smooth_kwargs : dict, optional\n         Additional keywords modifying the Savitzky-Golay filter. See Scipy's documentation for\n-        details\n+        details.\n     fill_kwargs : dict\n         Keywords passed to `fill_between` (use fill_kwargs={'alpha': 0} to disable fill).\n     plot_kwargs : dict\n-        Keywords passed to hdi limits\n+        Keywords passed to hdi limits.\n     ax: axes, optional\n         Matplotlib axes or bokeh figures.\n     backend: str, optional\n@@ -64,7 +64,7 @@\n     show : bool, optional\n         Call backend show function.\n     credible_interval: float, optional\n-        deprecated: Please see hdi_prob\n+        Deprecated: Please see hdi_prob\n \n     Returns\n     -------\n", "issue": "Inconsistent capitalization in `plot_hdi` docstring\n**Describe the bug**\r\nDocs have inconsistent capitalization\r\n![image](https://user-images.githubusercontent.com/7213793/82233398-d020d700-98e4-11ea-812c-588628ed753e.png)\r\n\r\n\r\n**To Reproduce**\r\nhttps://arviz-devs.github.io/arviz/generated/arviz.plot_hdi.html#arviz.plot_hdi\r\n\r\n**Expected behavior**\r\nConsistency across arg docstrings\r\n\n", "before_files": [{"content": "\"\"\"Plot highest density intervals for regression data.\"\"\"\nimport warnings\n\nimport numpy as np\nfrom scipy.interpolate import griddata\nfrom scipy.signal import savgol_filter\n\nfrom ..stats import hdi\nfrom .plot_utils import get_plotting_function, matplotlib_kwarg_dealiaser\nfrom ..rcparams import rcParams\nfrom ..utils import credible_interval_warning\n\n\ndef plot_hdi(\n    x,\n    y,\n    hdi_prob=None,\n    color=\"C1\",\n    circular=False,\n    smooth=True,\n    smooth_kwargs=None,\n    fill_kwargs=None,\n    plot_kwargs=None,\n    ax=None,\n    backend=None,\n    backend_kwargs=None,\n    show=None,\n    credible_interval=None,\n):\n    r\"\"\"\n    Plot hdi intervals for regression data.\n\n    Parameters\n    ----------\n    x : array-like\n        Values to plot\n    y : array-like\n        values from which to compute the hdi. Assumed shape (chain, draw, \\*shape).\n    hdi_prob : float, optional\n        Probability for the highest density interval. Defaults to 0.94.\n    color : str\n        Color used for the limits of the hdi and fill. Should be a valid matplotlib color\n    circular : bool, optional\n        Whether to compute the hdi taking into account `x` is a circular variable\n        (in the range [-np.pi, np.pi]) or not. Defaults to False (i.e non-circular variables).\n    smooth : boolean\n        If True the result will be smoothed by first computing a linear interpolation of the data\n        over a regular grid and then applying the Savitzky-Golay filter to the interpolated data.\n        Defaults to True.\n    smooth_kwargs : dict, optional\n        Additional keywords modifying the Savitzky-Golay filter. See Scipy's documentation for\n        details\n    fill_kwargs : dict\n        Keywords passed to `fill_between` (use fill_kwargs={'alpha': 0} to disable fill).\n    plot_kwargs : dict\n        Keywords passed to hdi limits\n    ax: axes, optional\n        Matplotlib axes or bokeh figures.\n    backend: str, optional\n        Select plotting backend {\"matplotlib\",\"bokeh\"}. Default \"matplotlib\".\n    backend_kwargs: bool, optional\n        These are kwargs specific to the backend being used. For additional documentation\n        check the plotting method of the backend.\n    show : bool, optional\n        Call backend show function.\n    credible_interval: float, optional\n        deprecated: Please see hdi_prob\n\n    Returns\n    -------\n    axes : matplotlib axes or bokeh figures\n    \"\"\"\n    if credible_interval:\n        hdi_prob = credible_interval_warning(credible_interval, hdi_prob)\n\n    plot_kwargs = matplotlib_kwarg_dealiaser(plot_kwargs, \"plot\")\n    plot_kwargs.setdefault(\"color\", color)\n    plot_kwargs.setdefault(\"alpha\", 0)\n\n    fill_kwargs = matplotlib_kwarg_dealiaser(fill_kwargs, \"hexbin\")\n    fill_kwargs.setdefault(\"color\", color)\n    fill_kwargs.setdefault(\"alpha\", 0.5)\n\n    x = np.asarray(x)\n    y = np.asarray(y)\n\n    x_shape = x.shape\n    y_shape = y.shape\n    if y_shape[-len(x_shape) :] != x_shape:\n        msg = \"Dimension mismatch for x: {} and y: {}.\"\n        msg += \" y-dimensions should be (chain, draw, *x.shape) or\"\n        msg += \" (draw, *x.shape)\"\n        raise TypeError(msg.format(x_shape, y_shape))\n\n    if len(y_shape[: -len(x_shape)]) > 1:\n        new_shape = tuple([-1] + list(x_shape))\n        y = y.reshape(new_shape)\n\n    if hdi_prob is None:\n        hdi_prob = rcParams[\"stats.hdi_prob\"]\n    else:\n        if not 1 >= hdi_prob > 0:\n            raise ValueError(\"The value of hdi_prob should be in the interval (0, 1]\")\n\n    hdi_ = hdi(y, hdi_prob=hdi_prob, circular=circular, multimodal=False)\n\n    if smooth:\n        if smooth_kwargs is None:\n            smooth_kwargs = {}\n        smooth_kwargs.setdefault(\"window_length\", 55)\n        smooth_kwargs.setdefault(\"polyorder\", 2)\n        x_data = np.linspace(x.min(), x.max(), 200)\n        x_data[0] = (x_data[0] + x_data[1]) / 2\n        hdi_interp = griddata(x, hdi_, x_data)\n        y_data = savgol_filter(hdi_interp, axis=0, **smooth_kwargs)\n    else:\n        idx = np.argsort(x)\n        x_data = x[idx]\n        y_data = hdi_[idx]\n\n    hdiplot_kwargs = dict(\n        ax=ax,\n        x_data=x_data,\n        y_data=y_data,\n        plot_kwargs=plot_kwargs,\n        fill_kwargs=fill_kwargs,\n        backend_kwargs=backend_kwargs,\n        show=show,\n    )\n\n    if backend is None:\n        backend = rcParams[\"plot.backend\"]\n    backend = backend.lower()\n\n    # TODO: Add backend kwargs\n    plot = get_plotting_function(\"plot_hdi\", \"hdiplot\", backend)\n    ax = plot(**hdiplot_kwargs)\n    return ax\n\n\ndef plot_hpd(*args, **kwargs):  # noqa: D103\n    warnings.warn(\"plot_hdi has been deprecated, please use plot_hdi\", DeprecationWarning)\n    return plot_hdi(*args, **kwargs)\n", "path": "arviz/plots/hdiplot.py"}], "after_files": [{"content": "\"\"\"Plot highest density intervals for regression data.\"\"\"\nimport warnings\n\nimport numpy as np\nfrom scipy.interpolate import griddata\nfrom scipy.signal import savgol_filter\n\nfrom ..stats import hdi\nfrom .plot_utils import get_plotting_function, matplotlib_kwarg_dealiaser\nfrom ..rcparams import rcParams\nfrom ..utils import credible_interval_warning\n\n\ndef plot_hdi(\n    x,\n    y,\n    hdi_prob=None,\n    color=\"C1\",\n    circular=False,\n    smooth=True,\n    smooth_kwargs=None,\n    fill_kwargs=None,\n    plot_kwargs=None,\n    ax=None,\n    backend=None,\n    backend_kwargs=None,\n    show=None,\n    credible_interval=None,\n):\n    r\"\"\"\n    Plot hdi intervals for regression data.\n\n    Parameters\n    ----------\n    x : array-like\n        Values to plot.\n    y : array-like\n        Values from which to compute the hdi. Assumed shape (chain, draw, \\*shape).\n    hdi_prob : float, optional\n        Probability for the highest density interval. Defaults to 0.94.\n    color : str\n        Color used for the limits of the hdi and fill. Should be a valid matplotlib color.\n    circular : bool, optional\n        Whether to compute the hdi taking into account `x` is a circular variable\n        (in the range [-np.pi, np.pi]) or not. Defaults to False (i.e non-circular variables).\n    smooth : boolean\n        If True the result will be smoothed by first computing a linear interpolation of the data\n        over a regular grid and then applying the Savitzky-Golay filter to the interpolated data.\n        Defaults to True.\n    smooth_kwargs : dict, optional\n        Additional keywords modifying the Savitzky-Golay filter. See Scipy's documentation for\n        details.\n    fill_kwargs : dict\n        Keywords passed to `fill_between` (use fill_kwargs={'alpha': 0} to disable fill).\n    plot_kwargs : dict\n        Keywords passed to hdi limits.\n    ax: axes, optional\n        Matplotlib axes or bokeh figures.\n    backend: str, optional\n        Select plotting backend {\"matplotlib\",\"bokeh\"}. Default \"matplotlib\".\n    backend_kwargs: bool, optional\n        These are kwargs specific to the backend being used. For additional documentation\n        check the plotting method of the backend.\n    show : bool, optional\n        Call backend show function.\n    credible_interval: float, optional\n        Deprecated: Please see hdi_prob\n\n    Returns\n    -------\n    axes : matplotlib axes or bokeh figures\n    \"\"\"\n    if credible_interval:\n        hdi_prob = credible_interval_warning(credible_interval, hdi_prob)\n\n    plot_kwargs = matplotlib_kwarg_dealiaser(plot_kwargs, \"plot\")\n    plot_kwargs.setdefault(\"color\", color)\n    plot_kwargs.setdefault(\"alpha\", 0)\n\n    fill_kwargs = matplotlib_kwarg_dealiaser(fill_kwargs, \"hexbin\")\n    fill_kwargs.setdefault(\"color\", color)\n    fill_kwargs.setdefault(\"alpha\", 0.5)\n\n    x = np.asarray(x)\n    y = np.asarray(y)\n\n    x_shape = x.shape\n    y_shape = y.shape\n    if y_shape[-len(x_shape) :] != x_shape:\n        msg = \"Dimension mismatch for x: {} and y: {}.\"\n        msg += \" y-dimensions should be (chain, draw, *x.shape) or\"\n        msg += \" (draw, *x.shape)\"\n        raise TypeError(msg.format(x_shape, y_shape))\n\n    if len(y_shape[: -len(x_shape)]) > 1:\n        new_shape = tuple([-1] + list(x_shape))\n        y = y.reshape(new_shape)\n\n    if hdi_prob is None:\n        hdi_prob = rcParams[\"stats.hdi_prob\"]\n    else:\n        if not 1 >= hdi_prob > 0:\n            raise ValueError(\"The value of hdi_prob should be in the interval (0, 1]\")\n\n    hdi_ = hdi(y, hdi_prob=hdi_prob, circular=circular, multimodal=False)\n\n    if smooth:\n        if smooth_kwargs is None:\n            smooth_kwargs = {}\n        smooth_kwargs.setdefault(\"window_length\", 55)\n        smooth_kwargs.setdefault(\"polyorder\", 2)\n        x_data = np.linspace(x.min(), x.max(), 200)\n        x_data[0] = (x_data[0] + x_data[1]) / 2\n        hdi_interp = griddata(x, hdi_, x_data)\n        y_data = savgol_filter(hdi_interp, axis=0, **smooth_kwargs)\n    else:\n        idx = np.argsort(x)\n        x_data = x[idx]\n        y_data = hdi_[idx]\n\n    hdiplot_kwargs = dict(\n        ax=ax,\n        x_data=x_data,\n        y_data=y_data,\n        plot_kwargs=plot_kwargs,\n        fill_kwargs=fill_kwargs,\n        backend_kwargs=backend_kwargs,\n        show=show,\n    )\n\n    if backend is None:\n        backend = rcParams[\"plot.backend\"]\n    backend = backend.lower()\n\n    # TODO: Add backend kwargs\n    plot = get_plotting_function(\"plot_hdi\", \"hdiplot\", backend)\n    ax = plot(**hdiplot_kwargs)\n    return ax\n\n\ndef plot_hpd(*args, **kwargs):  # noqa: D103\n    warnings.warn(\"plot_hdi has been deprecated, please use plot_hdi\", DeprecationWarning)\n    return plot_hdi(*args, **kwargs)\n", "path": "arviz/plots/hdiplot.py"}]}