Kwargs optimization of high dimensional plots (#28)

UniStuttgart-VISUS · Oct 21, 2024 · a789df4 · a789df4
1 parent 3b096b1
commit a789df4
Show file tree

Hide file tree

Showing 3 changed files with 45 additions and 63 deletions.
diff --git a/uadapy/plotting/plots1D.py b/uadapy/plotting/plots1D.py
@@ -43,11 +43,9 @@ def _setup_plot(distributions, n_samples, seed, fig=None, axs=None, colors=None,
         Axes object(s) to use for plotting. If None, new axes will be created.
     colors : list or None, optional
         List of colors to use for each distribution. If None, Glasbey colors will be used.
-    **kwargs : additional keyword arguments
-        Additional optional arguments.
-        - colorblind_safe : bool, optional
-            If True, the plot will use colors suitable for colorblind individuals.
-            Default is False.
+    colorblind_safe : bool, optional
+        If True, the plot will use colors suitable for colorblind individuals.
+        Default is False.
 
     Returns
     -------
@@ -122,7 +120,7 @@ def plot_1d_distribution(
         distrib_colors=None,
         vert=True,
         colorblind_safe=False,
-        show_plot=True,
+        show_plot=False,
         dot_size=0,
         **kwargs):
     """
@@ -316,7 +314,7 @@ def generate_boxplot(distributions,
         distrib_colors=None,
         vert=True,
         colorblind_safe=False,
-        show_plot=True,
+        show_plot=False,
         **kwargs):
     """
     Plot box plots for samples drawn from given distributions.
@@ -375,7 +373,7 @@ def generate_violinplot(distributions,
         distrib_colors=None,
         vert=True,
         colorblind_safe=False,
-        show_plot=True,
+        show_plot=False,
         **kwargs):
     """
     Plot violin plots for samples drawn from given distributions.
@@ -433,7 +431,7 @@ def generate_dotplot(distributions,
         distrib_colors=None,
         vert=True,
         colorblind_safe=False,
-        show_plot=True,
+        show_plot=False,
         dot_size=0):
     """
     Plot dot plots for samples drawn from given distributions.
@@ -491,7 +489,7 @@ def generate_stripplot(distributions,
         distrib_colors=None,
         vert=True,
         colorblind_safe=False,
-        show_plot=True,
+        show_plot=False,
         dot_size=0):
     """
     Plot strip plots for samples drawn from given distributions.
@@ -551,7 +549,7 @@ def generate_swarmplot(distributions,
         distrib_colors=None,
         vert=True,
         colorblind_safe=False,
-        show_plot=True,
+        show_plot=False,
         dot_size=0):
     """
     Plot swarm plots for samples drawn from given distributions.

diff --git a/uadapy/plotting/plots2D.py b/uadapy/plotting/plots2D.py
@@ -4,7 +4,7 @@
 from numpy import ma
 from matplotlib import ticker
 
-def plot_samples(distributions, n_samples, seed=55, **kwargs):
+def plot_samples(distributions, n_samples, seed=55, xlabel=None, ylabel=None, title=None, show_plot=False):
     """
     Plot samples from the given distribution. If several distributions should be
     plotted together, an array can be passed to this function.
@@ -17,15 +17,15 @@ def plot_samples(distributions, n_samples, seed=55, **kwargs):
         Number of samples per distribution.
     seed : int
         Seed for the random number generator for reproducibility. It defaults to 55 if not provided.
-    **kwargs : additional keyword arguments
-        Additional optional plotting arguments.
-        - xlabel : string, optional
-            label for x-axis.
-        - ylabel : string, optional
-            label for y-axis.
-        - show_plot : bool, optional
-            If True, display the plot.
-            Default is False.
+    xlabel : string, optional
+        label for x-axis.
+    ylabel : string, optional
+        label for y-axis.
+    title : string, optional
+        title for the plot.
+    show_plot : bool, optional
+        If True, display the plot.
+        Default is False.
 
     Returns
     -------
@@ -40,25 +40,24 @@ def plot_samples(distributions, n_samples, seed=55, **kwargs):
     for d in distributions:
         samples = d.sample(n_samples, seed)
         plt.scatter(x=samples[:,0], y=samples[:,1])
-    if 'xlabel' in kwargs:
-        plt.xlabel(kwargs['xlabel'])
-    if 'ylabel' in kwargs:
-        plt.ylabel(kwargs['ylabel'])
-    if 'title' in kwargs:
-        plt.title(kwargs['title'])
+    if xlabel:
+        plt.xlabel(xlabel)
+    if ylabel:
+        plt.ylabel(ylabel)
+    if title:
+        plt.title(title)
 
     # Get the current figure and axes
     fig = plt.gcf()
     axs = plt.gca()
 
-    show_plot = kwargs.get('show_plot', False)
     if show_plot:
         fig.tight_layout()
         plt.show()
 
     return fig, axs
 
-def plot_contour(distributions, resolution=128, ranges=None, quantiles:list=None, seed=55, **kwargs):
+def plot_contour(distributions, resolution=128, ranges=None, quantiles:list=None, seed=55, show_plot=False):
     """
     Plot contour plots for samples drawn from given distributions.
 
@@ -74,11 +73,9 @@ def plot_contour(distributions, resolution=128, ranges=None, quantiles:list=None
         List of quantiles to use for determining isovalues. If None, the 99.7%, 95%, and 68% quantiles are used.
     seed : int
         Seed for the random number generator for reproducibility. It defaults to 55 if not provided.
-    **kwargs : additional keyword arguments
-        Additional optional plotting arguments.
-        - show_plot : bool, optional
-            If True, display the plot.
-            Default is False.
+    show_plot : bool, optional
+        If True, display the plot.
+        Default is False.
 
     Returns
     -------
@@ -144,15 +141,14 @@ def plot_contour(distributions, resolution=128, ranges=None, quantiles:list=None
     fig = plt.gcf()
     axs = plt.gca()
 
-    show_plot = kwargs.get('show_plot', False)
     if show_plot:
         fig.tight_layout()
         plt.show()
 
     return fig, axs
 
 def plot_contour_bands(distributions, n_samples, resolution=128, ranges=None, quantiles: list = None, seed=55,
-                       **kwargs):
+                       show_plot=False):
     """
     Plot contour bands for samples drawn from given distributions.
 
@@ -170,11 +166,9 @@ def plot_contour_bands(distributions, n_samples, resolution=128, ranges=None, qu
         List of quantiles to use for determining isovalues. If None, the 99.7%, 95%, and 68% quantiles are used.
     seed : int
         Seed for the random number generator for reproducibility. It defaults to 55 if not provided.
-    **kwargs : additional keyword arguments
-        Additional optional plotting arguments.
-        - show_plot : bool, optional
-            If True, display the plot.
-            Default is False.
+    show_plot : bool, optional
+        If True, display the plot.
+        Default is False.
 
     Returns
     -------
@@ -247,7 +241,6 @@ def plot_contour_bands(distributions, n_samples, resolution=128, ranges=None, qu
     fig = plt.gcf()
     axs = plt.gca()
 
-    show_plot = kwargs.get('show_plot', False)
     if show_plot:
         fig.tight_layout()
         plt.show()

diff --git a/uadapy/plotting/plotsND.py b/uadapy/plotting/plotsND.py
@@ -3,7 +3,7 @@
 from uadapy import Distribution
 import uadapy.plotting.utils as utils
 
-def plot_samples(distributions, n_samples, seed=55, **kwargs):
+def plot_samples(distributions, n_samples, seed=55, show_plot=False):
     """
     Plot samples from the multivariate distribution as a SLOM.
 
@@ -15,11 +15,9 @@ def plot_samples(distributions, n_samples, seed=55, **kwargs):
         Number of samples per distribution.
     seed : int
         Seed for the random number generator for reproducibility. It defaults to 55 if not provided.
-    **kwargs : additional keyword arguments
-        Additional optional plotting arguments.
-        - show_plot : bool, optional
-            If True, display the plot.
-            Default is False.
+    show_plot : bool, optional
+        If True, display the plot.
+        Default is False.
 
     Returns
     -------
@@ -64,14 +62,13 @@ def plot_samples(distributions, n_samples, seed=55, **kwargs):
     fig = plt.gcf()
     axs = plt.gca()
 
-    show_plot = kwargs.get('show_plot', False)
     if show_plot:
         fig.tight_layout()
         plt.show()
 
     return fig, axs
 
-def plot_contour(distributions, n_samples, resolution=128, ranges=None, quantiles: list = None, seed=55, **kwargs):
+def plot_contour(distributions, n_samples, resolution=128, ranges=None, quantiles: list = None, seed=55, show_plot=False):
     """
     Visualizes a multidimensional distribution in a matrix of contour plots.
 
@@ -89,11 +86,9 @@ def plot_contour(distributions, n_samples, resolution=128, ranges=None, quantile
         List of quantiles to use for determining isovalues. If None, the 99.7%, 95%, and 68% quantiles are used.
     seed : int
         Seed for the random number generator for reproducibility. It defaults to 55 if not provided.
-    **kwargs : additional keyword arguments
-        Additional optional plotting arguments.
-        - show_plot : bool, optional
-            If True, display the plot.
-            Default is False.
+    show_plot : bool, optional
+        If True, display the plot.
+        Default is False.
 
     Returns
     -------
@@ -192,15 +187,14 @@ def plot_contour(distributions, n_samples, resolution=128, ranges=None, quantile
     fig = plt.gcf()
     axs = plt.gca()
 
-    show_plot = kwargs.get('show_plot', False)
     if show_plot:
         fig.tight_layout()
         plt.show()
 
     return fig, axs
 
 def plot_contour_samples(distributions, n_samples, resolution=128, ranges=None, quantiles: list = None, seed=55,
-                         **kwargs):
+                         show_plot=False):
     """
     Visualizes a multidimensional distribution in a matrix visualization where the
     upper diagonal contains contour plots and the lower diagonal contains scatterplots.
@@ -219,11 +213,9 @@ def plot_contour_samples(distributions, n_samples, resolution=128, ranges=None,
         List of quantiles to use for determining isovalues. If None, the 99.7%, 95%, and 68% quantiles are used.
     seed : int
         Seed for the random number generator for reproducibility. It defaults to 55 if not provided.
-    **kwargs : additional keyword arguments
-        Additional optional plotting arguments.
-        - show_plot : bool, optional
-            If True, display the plot.
-            Default is False.
+    show_plot : bool, optional
+        If True, display the plot.
+        Default is False.
 
     Returns
     -------
@@ -324,7 +316,6 @@ def plot_contour_samples(distributions, n_samples, resolution=128, ranges=None,
     fig = plt.gcf()
     axs = plt.gca()
 
-    show_plot = kwargs.get('show_plot', False)
     if show_plot:
         fig.tight_layout()
         plt.show()