diff --git a/act/plotting/histogramdisplay.py b/act/plotting/histogramdisplay.py
deleted file mode 100644
index e65baf98c9..0000000000
--- a/act/plotting/histogramdisplay.py
+++ /dev/null
@@ -1,606 +0,0 @@
-""" Module for Histogram Plotting. """
-
-import matplotlib.pyplot as plt
-import numpy as np
-import xarray as xr
-import warnings
-
-from ..utils import datetime_utils as dt_utils
-from .plot import Display
-
-
-class HistogramDisplay(Display):
-    """
-    This class is used to make histogram plots. It is inherited from Display
-    and therefore contains all of Display's attributes and methods.
-
-    Examples
-    --------
-    To create a TimeSeriesDisplay with 3 rows, simply do:
-
-    .. code-block:: python
-
-        ds = act.io.read_arm_netcdf(the_file)
-        disp = act.plotting.HistogramDisplay(ds, subplot_shape=(3,), figsize=(15, 5))
-
-    The HistogramDisplay constructor takes in the same keyword arguments as
-    plt.subplots. For more information on the plt.subplots keyword arguments,
-    see the `matplotlib documentation
-    <https://matplotlib.org/api/_as_gen/matplotlib.pyplot.subplots.html>`_.
-    If no subplot_shape is provided, then no figure or axis will be created
-    until add_subplots or plots is called.
-
-    """
-
-    def __init__(self, ds, subplot_shape=(1,), ds_name=None, **kwargs):
-        super().__init__(ds, subplot_shape, ds_name, **kwargs)
-
-        message = 'HistogramDisplay will be retired in version 2.0.0.  Please use DistributionDisplay instead.'
-        warnings.warn(message, DeprecationWarning, 2)
-
-    def set_xrng(self, xrng, subplot_index=(0,)):
-        """
-        Sets the x range of the plot.
-
-        Parameters
-        ----------
-        xrng : 2 number array
-            The x limits of the plot.
-        subplot_index : 1 or 2D tuple, list, or array
-            The index of the subplot to set the x range of.
-
-        """
-        if self.axes is None:
-            raise RuntimeError('set_xrng requires the plot to be displayed.')
-
-        if not hasattr(self, 'xrng') and len(self.axes.shape) == 2:
-            self.xrng = np.zeros((self.axes.shape[0], self.axes.shape[1], 2), dtype='datetime64[D]')
-        elif not hasattr(self, 'xrng') and len(self.axes.shape) == 1:
-            self.xrng = np.zeros((self.axes.shape[0], 2), dtype='datetime64[D]')
-
-        self.axes[subplot_index].set_xlim(xrng)
-        self.xrng[subplot_index, :] = np.array(xrng)
-
-    def set_yrng(self, yrng, subplot_index=(0,)):
-        """
-        Sets the y range of the plot.
-
-        Parameters
-        ----------
-        yrng : 2 number array
-            The y limits of the plot.
-        subplot_index : 1 or 2D tuple, list, or array
-            The index of the subplot to set the x range of.
-
-        """
-        if self.axes is None:
-            raise RuntimeError('set_yrng requires the plot to be displayed.')
-
-        if not hasattr(self, 'yrng') and len(self.axes.shape) == 2:
-            self.yrng = np.zeros((self.axes.shape[0], self.axes.shape[1], 2))
-        elif not hasattr(self, 'yrng') and len(self.axes.shape) == 1:
-            self.yrng = np.zeros((self.axes.shape[0], 2))
-
-        if yrng[0] == yrng[1]:
-            yrng[1] = yrng[1] + 1
-
-        self.axes[subplot_index].set_ylim(yrng)
-        self.yrng[subplot_index, :] = yrng
-
-    def _get_data(self, dsname, fields):
-        if isinstance(fields, str):
-            fields = [fields]
-        return self._ds[dsname][fields].dropna('time')
-
-    def plot_stacked_bar_graph(
-        self,
-        field,
-        dsname=None,
-        bins=10,
-        sortby_field=None,
-        sortby_bins=None,
-        subplot_index=(0,),
-        set_title=None,
-        density=False,
-        hist_kwargs=dict(),
-        **kwargs,
-    ):
-        """
-        This procedure will plot a stacked bar graph of a histogram.
-
-        Parameters
-        ----------
-        field : str
-            The name of the field to take the histogram of.
-        dsname : str or None
-            The name of the datastream the field is contained in. Set
-            to None to let ACT automatically determine this.
-        bins : array-like or int
-            The histogram bin boundaries to use. If not specified, numpy's
-            default 10 is used.
-        sortby_field : str or None
-            Set this option to a field name in order to sort the histograms
-            by a given field parameter. For example, one can sort histograms of CO2
-            concentration by temperature.
-        sortby_bins : array-like or None
-            The bins to sort the histograms by.
-        subplot_index : tuple
-            The subplot index to place the plot in
-        set_title : str
-            The title of the plot.
-        density : bool
-            Set to True to plot a p.d.f. instead of a frequency histogram.
-        hist_kwargs : dict
-            Additional keyword arguments to pass to numpy histogram.
-
-        Other keyword arguments will be passed into :func:`matplotlib.pyplot.bar`.
-
-        Returns
-        -------
-        return_dict : dict
-            A dictionary containing the plot axis handle, bin boundaries, and
-            generated histogram.
-
-        """
-        if dsname is None and len(self._ds.keys()) > 1:
-            raise ValueError(
-                'You must choose a datastream when there are 2 '
-                + 'or more datasets in the TimeSeriesDisplay '
-                + 'object.'
-            )
-        elif dsname is None:
-            dsname = list(self._ds.keys())[0]
-
-        if sortby_field is not None:
-            ds = self._get_data(dsname, [field, sortby_field])
-            xdata, ydata = ds[field], ds[sortby_field]
-        else:
-            xdata = self._get_data(dsname, field)[field]
-
-        if 'units' in xdata.attrs:
-            xtitle = ''.join(['(', xdata.attrs['units'], ')'])
-        else:
-            xtitle = field
-
-        if sortby_bins is None and sortby_field is not None:
-            # We will defaut the y direction to have the same # of bins as x
-            sortby_bins = np.linspace(ydata.values.min(), ydata.values.max(), len(bins))
-
-        # Get the current plotting axis, add day/night background and plot data
-        if self.fig is None:
-            self.fig = plt.figure()
-
-        if self.axes is None:
-            self.axes = np.array([plt.axes()])
-            self.fig.add_axes(self.axes[0])
-
-        if sortby_field is not None:
-            if 'units' in ydata.attrs:
-                ytitle = ''.join(['(', ydata.attrs['units'], ')'])
-            else:
-                ytitle = field
-            if sortby_bins is None:
-                my_hist, x_bins, y_bins = np.histogram2d(
-                    xdata.values.flatten(), ydata.values.flatten(), density=density,
-                    bins=bins,
-                    **hist_kwargs)
-            else:
-                my_hist, x_bins, y_bins = np.histogram2d(
-                    xdata.values.flatten(),
-                    ydata.values.flatten(),
-                    density=density,
-                    bins=[bins, sortby_bins],
-                    **hist_kwargs)
-            x_inds = (x_bins[:-1] + x_bins[1:]) / 2.0
-            self.axes[subplot_index].bar(
-                x_inds,
-                my_hist[:, 0].flatten(),
-                label=(str(y_bins[0]) + ' to ' + str(y_bins[1])),
-                **kwargs,
-            )
-            for i in range(1, len(y_bins) - 1):
-                self.axes[subplot_index].bar(
-                    x_inds,
-                    my_hist[:, i].flatten(),
-                    bottom=my_hist[:, i - 1],
-                    label=(str(y_bins[i]) + ' to ' + str(y_bins[i + 1])),
-                    **kwargs,
-                )
-            self.axes[subplot_index].legend()
-        else:
-            my_hist, bins = np.histogram(xdata.values.flatten(), bins=bins,
-                                         density=density, **hist_kwargs)
-            x_inds = (bins[:-1] + bins[1:]) / 2.0
-            self.axes[subplot_index].bar(x_inds, my_hist)
-
-        # Set Title
-        if set_title is None:
-            set_title = ' '.join(
-                [
-                    dsname,
-                    field,
-                    'on',
-                    dt_utils.numpy_to_arm_date(self._ds[dsname].time.values[0]),
-                ]
-            )
-        self.axes[subplot_index].set_title(set_title)
-        self.axes[subplot_index].set_ylabel('count')
-        self.axes[subplot_index].set_xlabel(xtitle)
-
-        return_dict = {}
-        return_dict['plot_handle'] = self.axes[subplot_index]
-        if 'x_bins' in locals():
-            return_dict['x_bins'] = x_bins
-            return_dict['y_bins'] = y_bins
-        else:
-            return_dict['bins'] = bins
-        return_dict['histogram'] = my_hist
-
-        return return_dict
-
-    def plot_size_distribution(
-        self, field, bins, time=None, dsname=None, subplot_index=(0,), set_title=None, **kwargs
-    ):
-        """
-        This procedure plots a stairstep plot of a size distribution. This is
-        useful for plotting size distributions and waveforms.
-
-        Parameters
-        ----------
-        field : str
-            The name of the field to plot the spectrum from.
-        bins : str or array-like
-            The name of the field that stores the bins for the spectra.
-        time : none or datetime
-            If None, spectra to plot will be automatically determined.
-            Otherwise, specify this field for the time period to plot.
-        dsname : str
-            The name of the Dataset to plot. Set to None to have
-            ACT automatically determine this.
-        subplot_index : tuple
-            The subplot index to place the plot in.
-        set_title : str or None
-            Use this to set the title.
-
-        Additional keyword arguments will be passed into :func:`matplotlib.pyplot.step`
-
-        Returns
-        -------
-        ax : matplotlib axis handle
-            The matplotlib axis handle referring to the plot.
-
-        """
-        if dsname is None and len(self._ds.keys()) > 1:
-            raise ValueError(
-                'You must choose a datastream when there are 2 '
-                + 'or more datasets in the TimeSeriesDisplay '
-                + 'object.'
-            )
-        elif dsname is None:
-            dsname = list(self._ds.keys())[0]
-
-        xdata = self._get_data(dsname, field)[field]
-
-        if isinstance(bins, str):
-            bins = self._ds[dsname][bins]
-        else:
-            bins = xr.DataArray(bins)
-
-        if 'units' in bins.attrs:
-            xtitle = ''.join(['(', bins.attrs['units'], ')'])
-        else:
-            xtitle = 'Bin #'
-
-        if 'units' in xdata.attrs:
-            ytitle = ''.join(['(', xdata.attrs['units'], ')'])
-        else:
-            ytitle = field
-
-        if len(xdata.dims) > 1 and time is None:
-            raise ValueError(
-                'Input data has more than one dimension, ' + 'you must specify a time to plot!'
-            )
-        elif len(xdata.dims) > 1:
-            xdata = xdata.sel(time=time, method='nearest')
-
-        if len(bins.dims) > 1 or len(bins.values) != len(xdata.values):
-            raise ValueError(
-                'Bins must be a one dimensional field whose '
-                + 'length is equal to the field length!'
-            )
-
-        # Get the current plotting axis, add day/night background and plot data
-        if self.fig is None:
-            self.fig = plt.figure()
-
-        if self.axes is None:
-            self.axes = np.array([plt.axes()])
-            self.fig.add_axes(self.axes[0])
-
-        # Set Title
-        if set_title is None:
-            set_title = ' '.join(
-                [
-                    dsname,
-                    field,
-                    'on',
-                    dt_utils.numpy_to_arm_date(self._ds[dsname].time.values[0]),
-                ]
-            )
-
-        self.axes[subplot_index].set_title(set_title)
-        self.axes[subplot_index].step(bins.values, xdata.values, **kwargs)
-        self.axes[subplot_index].set_xlabel(xtitle)
-        self.axes[subplot_index].set_ylabel(ytitle)
-
-        return self.axes[subplot_index]
-
-    def plot_stairstep_graph(
-        self,
-        field,
-        dsname=None,
-        bins=10,
-        sortby_field=None,
-        sortby_bins=None,
-        subplot_index=(0,),
-        set_title=None,
-        density=False,
-        hist_kwargs=dict(),
-        **kwargs,
-    ):
-        """
-        This procedure will plot a stairstep plot of a histogram.
-
-        Parameters
-        ----------
-        field : str
-            The name of the field to take the histogram of.
-        dsname : str or None
-            The name of the datastream the field is contained in. Set
-            to None to let ACT automatically determine this.
-        bins : array-like or int
-            The histogram bin boundaries to use. If not specified, numpy's
-            default 10 is used.
-        sortby_field : str or None
-            Set this option to a field name in order to sort the histograms
-            by a given field parameter. For example, one can sort histograms of CO2
-            concentration by temperature.
-        sortby_bins : array-like or None
-            The bins to sort the histograms by.
-        subplot_index : tuple
-            The subplot index to place the plot in.
-        set_title : str
-            The title of the plot.
-        density : bool
-            Set to True to plot a p.d.f. instead of a frequency histogram.
-        hist_kwargs : dict
-            Additional keyword arguments to pass to numpy histogram.
-
-        Other keyword arguments will be passed into :func:`matplotlib.pyplot.step`.
-
-        Returns
-        -------
-        return_dict : dict
-             A dictionary containing the plot axis handle, bin boundaries, and
-             generated histogram.
-
-        """
-        if dsname is None and len(self._ds.keys()) > 1:
-            raise ValueError(
-                'You must choose a datastream when there are 2 '
-                + 'or more datasets in the TimeSeriesDisplay '
-                + 'object.'
-            )
-        elif dsname is None:
-            dsname = list(self._ds.keys())[0]
-
-        xdata = self._get_data(dsname, field)[field]
-
-        if 'units' in xdata.attrs:
-            xtitle = ''.join(['(', xdata.attrs['units'], ')'])
-        else:
-            xtitle = field
-
-        if sortby_field is not None:
-            ydata = self._ds[dsname][sortby_field]
-
-        if sortby_bins is None and sortby_field is not None:
-            # We will defaut the y direction to have the same # of bins as x
-            sortby_bins = np.linspace(ydata.values.min(), ydata.values.max(), len(bins))
-
-        # Get the current plotting axis, add day/night background and plot data
-        if self.fig is None:
-            self.fig = plt.figure()
-
-        if self.axes is None:
-            self.axes = np.array([plt.axes()])
-            self.fig.add_axes(self.axes[0])
-
-        if sortby_field is not None:
-            if 'units' in ydata.attrs:
-                ytitle = ''.join(['(', ydata.attrs['units'], ')'])
-            else:
-                ytitle = field
-            if sortby_bins is None:
-                my_hist, x_bins, y_bins = np.histogram2d(
-                    xdata.values.flatten(), ydata.values.flatten(), bins=bins,
-                    density=density, **hist_kwargs)
-            else:
-                my_hist, x_bins, y_bins = np.histogram2d(
-                    xdata.values.flatten(),
-                    ydata.values.flatten(),
-                    density=density,
-                    bins=[bins, sortby_bins],
-                    **hist_kwargs
-                )
-            x_inds = (x_bins[:-1] + x_bins[1:]) / 2.0
-            self.axes[subplot_index].step(
-                x_inds,
-                my_hist[:, 0].flatten(),
-                label=(str(y_bins[0]) + ' to ' + str(y_bins[1])),
-                **kwargs,
-            )
-            for i in range(1, len(y_bins) - 1):
-                self.axes[subplot_index].step(
-                    x_inds,
-                    my_hist[:, i].flatten(),
-                    label=(str(y_bins[i]) + ' to ' + str(y_bins[i + 1])),
-                    **kwargs,
-                )
-            self.axes[subplot_index].legend()
-        else:
-            my_hist, bins = np.histogram(xdata.values.flatten(), bins=bins,
-                                         density=density, **hist_kwargs)
-
-            x_inds = (bins[:-1] + bins[1:]) / 2.0
-            self.axes[subplot_index].step(x_inds, my_hist, **kwargs)
-
-        # Set Title
-        if set_title is None:
-            set_title = ' '.join(
-                [
-                    dsname,
-                    field,
-                    'on',
-                    dt_utils.numpy_to_arm_date(self._ds[dsname].time.values[0]),
-                ]
-            )
-        self.axes[subplot_index].set_title(set_title)
-        self.axes[subplot_index].set_ylabel('count')
-        self.axes[subplot_index].set_xlabel(xtitle)
-
-        return_dict = {}
-        return_dict['plot_handle'] = self.axes[subplot_index]
-        if 'x_bins' in locals():
-            return_dict['x_bins'] = x_bins
-            return_dict['y_bins'] = y_bins
-        else:
-            return_dict['bins'] = bins
-        return_dict['histogram'] = my_hist
-
-        return return_dict
-
-    def plot_heatmap(
-        self,
-        x_field,
-        y_field,
-        dsname=None,
-        x_bins=None,
-        y_bins=None,
-        subplot_index=(0,),
-        set_title=None,
-        density=False,
-        set_shading='auto',
-        hist_kwargs=dict(),
-        **kwargs,
-    ):
-        """
-        This procedure will plot a heatmap of a histogram from 2 variables.
-
-        Parameters
-        ----------
-        x_field : str
-            The name of the field to take the histogram of on the X axis.
-        y_field : str
-            The name of the field to take the histogram of on the Y axis.
-        dsname : str or None
-            The name of the datastream the field is contained in. Set
-            to None to let ACT automatically determine this.
-        x_bins : array-like or None
-            The histogram bin boundaries to use for the variable on the X axis.
-            Set to None to use numpy's default boundaries.
-        y_bins : array-like or None
-            The histogram bin boundaries to use for the variable on the Y axis.
-            Set to None to use numpy's default boundaries.
-        subplot_index : tuple
-            The subplot index to place the plot in
-        set_title : str
-            The title of the plot.
-        density : bool
-            Set to True to plot a p.d.f. instead of a frequency histogram.
-        set_shading : string
-            Option to to set the matplotlib.pcolormesh shading parameter.
-            Default to 'auto'
-        hist_kwargs : Additional keyword arguments to pass to numpy histogram.
-
-        Other keyword arguments will be passed into :func:`matplotlib.pyplot.pcolormesh`.
-
-        Returns
-        -------
-        return_dict : dict
-            A dictionary containing the plot axis handle, bin boundaries, and
-            generated histogram.
-
-        """
-        if dsname is None and len(self._ds.keys()) > 1:
-            raise ValueError(
-                'You must choose a datastream when there are 2 '
-                'or more datasets in the TimeSeriesDisplay '
-                'object.'
-            )
-        elif dsname is None:
-            dsname = list(self._ds.keys())[0]
-
-        ds = self._get_data(dsname, [x_field, y_field])
-        xdata, ydata = ds[x_field], ds[y_field]
-
-        if 'units' in xdata.attrs:
-            xtitle = ''.join(['(', xdata.attrs['units'], ')'])
-        else:
-            xtitle = x_field
-
-        if x_bins is not None and y_bins is None:
-            # We will defaut the y direction to have the same # of bins as x
-            y_bins = np.linspace(ydata.values.min(), ydata.values.max(), len(x_bins))
-
-        # Get the current plotting axis, add day/night background and plot data
-        if self.fig is None:
-            self.fig = plt.figure()
-
-        if self.axes is None:
-            self.axes = np.array([plt.axes()])
-            self.fig.add_axes(self.axes[0])
-
-        if 'units' in ydata.attrs:
-            ytitle = ''.join(['(', ydata.attrs['units'], ')'])
-        else:
-            ytitle = y_field
-
-        if x_bins is None:
-            my_hist, x_bins, y_bins = np.histogram2d(
-                xdata.values.flatten(), ydata.values.flatten(), density=density,
-                **hist_kwargs)
-        else:
-            my_hist, x_bins, y_bins = np.histogram2d(
-                xdata.values.flatten(),
-                ydata.values.flatten(),
-                density=density,
-                bins=[x_bins, y_bins],
-                **hist_kwargs
-            )
-        x_inds = (x_bins[:-1] + x_bins[1:]) / 2.0
-        y_inds = (y_bins[:-1] + y_bins[1:]) / 2.0
-        xi, yi = np.meshgrid(x_inds, y_inds, indexing='ij')
-        mesh = self.axes[subplot_index].pcolormesh(xi, yi, my_hist, shading=set_shading, **kwargs)
-
-        # Set Title
-        if set_title is None:
-            set_title = ' '.join(
-                [
-                    dsname,
-                    'on',
-                    dt_utils.numpy_to_arm_date(self._ds[dsname].time.values[0]),
-                ]
-            )
-        self.axes[subplot_index].set_title(set_title)
-        self.axes[subplot_index].set_ylabel(ytitle)
-        self.axes[subplot_index].set_xlabel(xtitle)
-        self.add_colorbar(mesh, title='count', subplot_index=subplot_index)
-
-        return_dict = {}
-        return_dict['plot_handle'] = self.axes[subplot_index]
-        return_dict['x_bins'] = x_bins
-        return_dict['y_bins'] = y_bins
-        return_dict['histogram'] = my_hist
-
-        return return_dict
diff --git a/examples/discovery/plot_neon.py b/examples/discovery/plot_neon.py
index c894106174..038fa62778 100644
--- a/examples/discovery/plot_neon.py
+++ b/examples/discovery/plot_neon.py
@@ -21,7 +21,7 @@
 
 if token is not None and len(token) > 0:
     # Download ARM data if a username/token are set
-    files = act.discovery.download_data(username, token, 'nsametC1.b1', '2022-10-01', '2022-10-07')
+    files = act.discovery.download_arm_data(username, token, 'nsametC1.b1', '2022-10-01', '2022-10-07')
     ds = act.io.arm.read_arm_netcdf(files)
 
     # Download NEON Data
diff --git a/examples/plotting/plot_ceil.py b/examples/plotting/plot_ceil.py
index e5701480a6..f650b1a16f 100644
--- a/examples/plotting/plot_ceil.py
+++ b/examples/plotting/plot_ceil.py
@@ -21,7 +21,7 @@
     ceil_ds = act.io.arm.read_arm_netcdf(act.tests.sample_files.EXAMPLE_CEIL1, engine='netcdf4')
 else:
     # Example to show how easy it is to download ARM data if a username/token are set
-    results = act.discovery.download_data(username, token, 'sgpceilC1.b1', '2022-01-14', '2022-01-19')
+    results = act.discovery.download_arm_data(username, token, 'sgpceilC1.b1', '2022-01-14', '2022-01-19')
     ceil_ds = act.io.arm.read_arm_netcdf(results)
 
 # Adjust ceilometer data for plotting
diff --git a/examples/workflows/plot_aerioe_with_cbh.py b/examples/workflows/plot_aerioe_with_cbh.py
index afc30f0634..717d47933e 100644
--- a/examples/workflows/plot_aerioe_with_cbh.py
+++ b/examples/workflows/plot_aerioe_with_cbh.py
@@ -22,9 +22,9 @@
 if username is None or token is None or len(username) == 0 or len(token) == 0:
     pass
 else:
-    results = act.discovery.download_data(username, token, 'sgpaerioe1turnC1.c1', '2022-02-11', '2022-02-11')
+    results = act.discovery.download_arm_data(username, token, 'sgpaerioe1turnC1.c1', '2022-02-11', '2022-02-11')
     aerioe_ds = act.io.arm.read_arm_netcdf(results)
-    results = act.discovery.download_data(username, token, 'sgpceilC1.b1', '2022-02-11', '2022-02-11')
+    results = act.discovery.download_arm_data(username, token, 'sgpceilC1.b1', '2022-02-11', '2022-02-11')
     ceil_ds = act.io.arm.read_arm_netcdf(results)
 
     # There isn't information content from the AERI above 3 km
diff --git a/examples/workflows/plot_multiple_dataset.py b/examples/workflows/plot_multiple_dataset.py
index 04af726495..8ef0a7aa43 100644
--- a/examples/workflows/plot_multiple_dataset.py
+++ b/examples/workflows/plot_multiple_dataset.py
@@ -24,9 +24,9 @@
     met_ds = act.io.arm.read_arm_netcdf(act.tests.sample_files.EXAMPLE_MET1)
 else:
     # Download and read data
-    results = act.discovery.download_data(username, token, 'sgpceilC1.b1', '2022-01-01', '2022-01-07')
+    results = act.discovery.download_arm_data(username, token, 'sgpceilC1.b1', '2022-01-01', '2022-01-07')
     ceil_ds = act.io.arm.read_arm_netcdf(results)
-    results = act.discovery.download_data(username, token, 'sgpmetE13.b1', '2022-01-01', '2022-01-07')
+    results = act.discovery.download_arm_data(username, token, 'sgpmetE13.b1', '2022-01-01', '2022-01-07')
     met_ds = act.io.arm.read_arm_netcdf(results)
 
 # Read in CEIL data and correct it