Complete the signature of plot methods with all their kind options

hvplot/plotting/core.py

@@ -799,7 +799,7 @@ def ohlc(self, x=None, y=None, **kwds):
         """
         return self(kind='ohlc', x=x, y=y, **kwds)
 
-    def heatmap(self, x=None, y=None, C=None, colorbar=True, **kwds):
+    def heatmap(self, x=None, y=None, C=None, colorbar=True, logz=False, **kwds):
         """
         `heatmap` visualises tabular data indexed by two key dimensions as a grid of colored values.
         This allows spotting correlations in multivariate data and provides a high-level overview
@@ -822,6 +822,7 @@ def heatmap(self, x=None, y=None, C=None, colorbar=True, **kwds):
             Whether to apply log scaling to the z-axis. Default is False.
         reduce_function : function, optional
             Function to compute statistics for heatmap, for example `np.mean`.
+            If omitted, no aggregation is applied and duplicate values are dropped.
         **kwds : optional
             Additional keywords arguments are documented in `hvplot.help('heatmap')`.
 
@@ -860,9 +861,19 @@ def heatmap(self, x=None, y=None, C=None, colorbar=True, **kwds):
         - Plotly: https://plotly.com/python/heatmaps/
         - Wiki: https://en.wikipedia.org/wiki/Heat_map
         """
-        return self(x, y, kind='heatmap', C=C, colorbar=colorbar, **kwds)
-
-    def hexbin(self, x=None, y=None, C=None, colorbar=True, **kwds):
+        return self(x, y, kind='heatmap', C=C, colorbar=colorbar, logz=logz, **kwds)
+
+    def hexbin(
+        self,
+        x=None,
+        y=None,
+        C=None,
+        colorbar=True,
+        gridsize=50,
+        logz=False,
+        min_count=None,
+        **kwds,
+    ):
         """
         The `hexbin` plot uses hexagons to split the area into several parts and attribute a color
         to it.
@@ -883,8 +894,12 @@ def hexbin(self, x=None, y=None, C=None, colorbar=True, **kwds):
             Whether to display a colorbar. Default is True.
         reduce_function : function, optional
             Function to compute statistics for hexbins, for example `np.mean`.
-        gridsize: int, optional
-            The number of hexagons in the x-direction
+            Default aggregation is a count of the values in the area.
+        gridsize: int or tuple, optional
+            Number of hexagonal bins along x- and y-axes. Defaults to uniform
+            sampling along both axes when setting and integer but independent
+            bin sampling can be specified a tuple of integers corresponding to
+            the number of bins along each axis. Default is 50.
         logz : bool
             Whether to apply log scaling to the z-axis. Default is False.
         min_count : number, optional
@@ -928,9 +943,21 @@ def hexbin(self, x=None, y=None, C=None, colorbar=True, **kwds):
         - Plotly: https://plotly.com/python/hexbin-mapbox/
         - Wiki: https://think.design/services/data-visualization-data-design/hexbin/
         """
-        return self(x, y, kind='hexbin', C=C, colorbar=colorbar, **kwds)
+        return self(
+            x,
+            y,
+            kind='hexbin',
+            C=C,
+            colorbar=colorbar,
+            gridsize=gridsize,
+            logz=logz,
+            min_count=min_count,
+            **kwds,
+        )
 
-    def bivariate(self, x=None, y=None, colorbar=True, **kwds):
+    def bivariate(
+        self, x=None, y=None, colorbar=True, bandwidth=None, cut=3, filled=False, levels=10, **kwds
+    ):
         """
         A bivariate, density plot uses nested contours (or contours plus colors) to indicate
         regions of higher local density.
@@ -950,12 +977,13 @@ def bivariate(self, x=None, y=None, colorbar=True, **kwds):
             Whether to display a colorbar
         bandwidth: int, optional
             The bandwidth of the kernel for the density estimate. Default is None.
-        cut: Integer, Optional
-            Draw the estimate to cut * bw from the extreme data points. Default is None.
+        cut: int, optional
+            Draw the estimate to cut * bw from the extreme data points. Default is 3.
         filled : bool, optional
-            If True the the contours will be filled. Default is False.
-        levels: int, optional
-            The number of contour lines to draw. Default is 10.
+            If True the contours will be filled. Default is False.
+        levels: int or list, optional
+            The number of contour lines to draw or a list of scalar values used
+            to specify the contour levels. Default is 10.
 
         **kwds : optional
             Additional keywords arguments are documented in `hvplot.help('bivariate')`.
@@ -1000,7 +1028,17 @@ def bivariate(self, x=None, y=None, colorbar=True, **kwds):
         - Seaborn: https://seaborn.pydata.org/generated/seaborn.kdeplot.html
         - Wiki: https://en.wikipedia.org/wiki/Bivariate_analysis
         """
-        return self(x, y, kind='bivariate', colorbar=colorbar, **kwds)
+        return self(
+            x,
+            y,
+            kind='bivariate',
+            colorbar=colorbar,
+            bandwidth=bandwidth,
+            cut=cut,
+            filled=filled,
+            levels=levels,
+            **kwds,
+        )
 
     def bar(self, x=None, y=None, stacked=False, **kwds):
         """
@@ -1289,7 +1327,9 @@ def violin(self, y=None, by=None, **kwds):
         """
         return self(kind='violin', x=None, y=y, by=by, **dict(kwds, hover=False))
 
-    def hist(self, y=None, by=None, **kwds):
+    def hist(
+        self, y=None, by=None, bins=20, bin_range=None, normed=False, cumulative=False, **kwds
+    ):
         """
         A `histogram` displays an approximate representation of the distribution of continuous data.
 
@@ -1302,18 +1342,25 @@ def hist(self, y=None, by=None, **kwds):
             Please note the fields should contain continuous data. Not categorical.
         by : string or sequence
             Field(s) in the *long* data to group by.
-        bins : int, optional
-            The number of bins
+        bins : int or string or np.ndarray or list or tuple, optional
+            The number of bins in the histogram, or an explicit set of bin edges
+            or a method to find the optimal set of bin edges, e.g. 'auto', 'fd',
+            'scott' etc. For more documentation on these approaches see the
+            :class:`numpy:numpy.histogram_bin_edges` documentation. Default is 20.
         bin_range: tuple, optional
-            The lower and upper range of the bins. Default is None.
-        normed : bool, optional
-            If True the distribution will sum to 1. Default is False.
-        cumulative: bool, optional
-            If True, then a histogram is computed where each bin gives the counts in that bin plus
-            all bins for smaller values. The last bin gives the total number of datapoints.
+            The lower and upper range of the bins.
+            Default is the minimum and maximum values of the continuous data.
+        normed : str or bool, optional
+            Controls normalization behavior.  If ``True`` or ``'integral'``, then
+            ``density=True`` is passed to np.histogram, and the distribution
+            is normalized such that the integral is unity.  If ``False``,
+            then the frequencies will be raw counts. If ``'height'``, then the
+            frequencies are normalized such that the max bin height is unity.
             Default is False.
-        alpha : float, optional
-            An alpha value between 0.0 and 1.0 to better visualize multiple fields. Default is 1.0.
+        cumulative: bool, optional
+            If True, then a histogram is computed where each bin gives the counts
+            in that bin plus all bins for smaller values. The last bin gives the
+            total number of data points. Default is False.
         kwds : optional
             Additional keywords arguments are documented in `hvplot.help('hist')`.
 
@@ -1368,7 +1415,16 @@ def hist(self, y=None, by=None, **kwds):
         - Seaborn: https://seaborn.pydata.org/generated/seaborn.histplot.html
         - Wiki: https://en.wikipedia.org/wiki/Histogram
         """
-        return self(kind='hist', x=None, y=y, by=by, **kwds)
+        return self(
+            kind='hist',
+            x=None,
+            y=y,
+            by=by,
+            bins=bins,
+            normed=normed,
+            cumulative=cumulative,
+            **kwds,
+        )
 
     def kde(self, y=None, by=None, **kwds):
         """
@@ -2252,7 +2308,7 @@ def quadmesh(self, x=None, y=None, z=None, colorbar=True, **kwds):
         """
         return self(x, y, z=z, kind='quadmesh', colorbar=colorbar, **kwds)
 
-    def contour(self, x=None, y=None, z=None, colorbar=True, **kwds):
+    def contour(self, x=None, y=None, z=None, colorbar=True, levels=5, logz=False, **kwds):
         """
         Line contour plot
 
@@ -2266,10 +2322,13 @@ def contour(self, x=None, y=None, z=None, colorbar=True, **kwds):
             The coordinate variable along the y-axis
         z : string, optional
             The data variable to plot
-        levels: int, optional
-            The number of contour levels
         colorbar: boolean
             Whether to display a colorbar
+        levels: int or list, optional
+            The number of contour lines to draw or a list of scalar values used
+            to specify the contour levels. Default is 5
+        logz: bool, optional
+            Whether to apply log scaling to the z-axis. Default is False
         **kwds : optional
             Additional keywords arguments are documented in `hvplot.help('contour')`.
 
@@ -2313,9 +2372,9 @@ def contour(self, x=None, y=None, z=None, colorbar=True, **kwds):
         - Matplotlib: https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.contour.html
         - Plotly: https://plotly.com/python/contour-plots/
         """
-        return self(x, y, z=z, kind='contour', colorbar=colorbar, **kwds)
+        return self(x, y, z=z, kind='contour', colorbar=colorbar, levels=levels, logz=logz, **kwds)
 
-    def contourf(self, x=None, y=None, z=None, colorbar=True, **kwds):
+    def contourf(self, x=None, y=None, z=None, colorbar=True, levels=5, logz=False, **kwds):
         """
         Filled contour plot
 
@@ -2329,10 +2388,13 @@ def contourf(self, x=None, y=None, z=None, colorbar=True, **kwds):
             The coordinate variable along the y-axis
         z : string, optional
             The data variable to plot
-        levels: int, optional
-            The number of contour levels
         colorbar: boolean
             Whether to display a colorbar
+        levels: int, optional
+            The number of contour lines to draw or a list of scalar values used
+            to specify the contour levels. Default is 5
+        logz: bool, optional
+            Whether to apply log scaling to the z-axis. Default is False
         **kwds : optional
             Additional keywords arguments are documented in `hvplot.help('contourf')`.
 
@@ -2375,4 +2437,6 @@ def contourf(self, x=None, y=None, z=None, colorbar=True, **kwds):
         - Matplotlib: https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.contour.html
         - Plotly: https://plotly.com/python/contour-plots/
         """
-        return self(x, y, z=z, kind='contourf', colorbar=colorbar, **kwds)
+        return self(
+            x, y, z=z, kind='contourf', colorbar=colorbar, levels=levels, logz=logz, **kwds
+        )