FIX: Found a few bugs/ inconsistencies that need fixing.

act/plotting/distributiondisplay.py

@@ -192,7 +192,6 @@ def plot_stacked_bar(
             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:
@@ -330,7 +329,6 @@ def plot_size_distribution(
             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:
@@ -578,7 +576,7 @@ def plot_heatmap(
         if x_bins is not None and isinstance(x_bins, int):
             x_bins = np.linspace(xdata.values.min(), xdata.values.max(), x_bins)
 
-        if y_bins is not None and isinstance(x_bins, int):
+        if y_bins is not None and isinstance(y_bins, int):
             y_bins = np.linspace(ydata.values.min(), ydata.values.max(), y_bins)
 
         if x_bins is not None and y_bins is None:
@@ -825,7 +823,7 @@ def plot_violin(
                 'or more datasets in the TimeSeriesDisplay '
                 'object.'
             )
-        if dsname is None:
+        elif dsname is None:
             dsname = list(self._ds.keys())[0]
 
         ds = self._get_data(dsname, field)

examples/plotting/plot_stripes.py

@@ -14,9 +14,24 @@
 
 # SGP E13 MET data has already been processed to yearly averages,
 # removing data flagged by embedded qc and DQRs
-url = 'https://raw.githubusercontent.com/AdamTheisen/ARM-Climatologies/refs/heads/main/results/sgpmetE13.b1_temp_mean_Y.csv'
-col_names = ['time', 'temperature', 'count']
-ds = act.io.read_csv(url, column_names=col_names, index_col=0, parse_dates=True)
+url = 'https://raw.githubusercontent.com/AdamTheisen/ARM-Climatologies/refs/heads/main/results/sgpmetE13.b1_temp_mean_YS.csv'
+col_names = [
+    'time',
+    'temperature',
+    'count',
+    'minimum',
+    'maximum',
+    'standard_deviation',
+    'standard_error',
+]
+ds = act.io.read_csv(
+    url,
+    column_names=col_names,
+    index_col=0,
+    parse_dates=['time'],
+    date_format="%Y-%m-%dT%H:%M:%S",
+    header=0,
+)
 
 # Drop years with less than 500000 samples
 ds = ds.where(ds['count'] > 500000)