Update systematics plots

py/saga/plot.py

@@ -161,6 +161,40 @@ def plot_watermark(
     )
 
 
+def plot_label(
+    ax1,
+    plot_label_args=None,
+):
+    """
+    Parameters
+    ----------
+    ax1 : matplotlib.axes._axes.Axes, required
+        Matplotlib.pyplot figure axis
+    plot_label_args : list, optional
+        List of positional arguments for plot label
+
+    Description
+    -----------
+    Plot a text label.
+    """
+    if plot_label_args is None:
+        plot_label_args = [0.95, 0.15, None]
+    if (
+        isinstance(plot_label_args, tuple)
+        or isinstance(plot_label_args, list)
+        and len(plot_label_args) == 3
+        and isinstance(plot_label_args[-1], str)
+    ):
+        ax1.text(
+            *plot_label_args,
+            transform=ax1.transAxes,
+            fontsize=plt.rcParams["axes.titlesize"],
+            fontweight="bold",
+            va="top",
+            ha="right",
+        )
+
+
 def plot_vlines(
     hist,
     binlims=None,
@@ -704,7 +738,9 @@ def plot_systematics(
     watermark="CLAS12 Preliminary",
     watermark_kwargs=None,
     use_default_plt_settings=True,
-    legend_loc="upper left",
+    title_pad=20,
+    lg_kwargs=None,
+    plot_label_args=None,
     axlinewidth=1.0,
     log=False,
     figsize=(16, 10),
@@ -742,8 +778,12 @@ def plot_systematics(
         Optional key word arguments for :meth:`plot_watermark`
     use_default_plt_settings : bool, optional
         Option to use default font and tick parameter style settings
-    legend_loc : str, optional
-        Matplotlib.pyplot legend location string, will not be plotted if set to :obj:`None` or :obj:`''`
+    title_pad : int, optional
+        Title padding
+    lg_kwargs : dict, optional
+        Matplotlib.pyplot legend keyword arguments
+    plot_label_args : list, optional
+        List of positional arguments for plot label
     axlinewidth : float, optional
         Axis line and injected asymmetries line width
     log : bool, optional
@@ -757,6 +797,14 @@ def plot_systematics(
     Save systematics breakdowns to CSV in :obj:`<outpath>.csv`.  Note that this does **not** allow for asymmetric errors.
     """
 
+    # Check arguments
+    if lg_kwargs is None:
+        lg_kwargs = {
+            "loc": "upper left",
+            "bbox_to_anchor": (1.05, 1.0),
+            "frameon": False,
+        }
+
     # Set color palette
     sbn.set_palette(palette)
 
@@ -768,7 +816,7 @@ def plot_systematics(
     f1, ax1 = plt.subplots(figsize=figsize)
     plt.xlim(*xlims)
     plt.ylim(*ylims)
-    plt.title(title, usetex=True)
+    plt.title(title, usetex=True, pad=title_pad)
     plt.xlabel(xtitle, usetex=True)
     plt.ylabel(ytitle, usetex=True)
 
@@ -797,8 +845,11 @@ def plot_systematics(
         plot_watermark(ax1, watermark=watermark, **watermark_kwargs)
 
     # Plot legend
-    if legend_loc is not None and legend_loc != "":
-        ax1.legend(loc=legend_loc)
+    if isinstance(lg_kwargs, dict):
+        ax1.legend(**lg_kwargs)
+
+    # Plot label
+    plot_label(ax1, plot_label_args)
 
     # Save figure
     f1.savefig(outpath)
@@ -856,7 +907,9 @@ def plot_results(
     watermark="CLAS12 Preliminary",
     watermark_kwargs=None,
     show_injected_asymmetries=False,
-    legend_loc="upper left",
+    title_pad=20,
+    lg_kwargs=None,
+    plot_label_args=None,
     ecolor="black",
     elinewidth=2.0,
     capsize=18,
@@ -956,8 +1009,12 @@ def plot_results(
         Optional key word arguments for :meth:`plot_watermark`
     show_injected_asymmetries : bool, optional
         Option to show injected signal and background asymmetries
-    legend_loc : str, optional
-        Matplotlib.pyplot legend location string, will not be plotted if set to :obj:`None` or :obj:`''`
+    title_pad : int, optional
+        Title padding
+    lg_kwargs : dict, optional
+        Matplotlib.pyplot legend keyword arguments
+    plot_label_args : list, optional
+        List of positional arguments for plot label
     ecolor : str, optional
         Error line color
     ecolor : float, optional
@@ -1049,6 +1106,8 @@ def plot_results(
         sgasyms = [0.10]
     if sg_colors is None:
         sg_colors = ["blue"]
+    if lg_kwargs is None:
+        lg_kwargs = {"loc": "best", "frameon": False}
 
     # Rescale graph
     scaling, acceptanceratio = None, None
@@ -1093,7 +1152,7 @@ def plot_results(
     # Set up plot
     ax1.set_xlim(*xlims)
     ax1.set_ylim(*ylims)
-    ax1.set_title(title, usetex=True)
+    ax1.set_title(title, usetex=True, pad=title_pad)
     ax1.set_xlabel(xlabel, usetex=True)
     ax1.set_ylabel(ylabel, usetex=True)
 
@@ -1207,8 +1266,11 @@ def plot_results(
         plot_watermark(ax1, watermark=watermark, **watermark_kwargs)
 
     # Plot legend
-    if legend_loc is not None and legend_loc != "":
-        ax1.legend(loc=legend_loc)
+    if isinstance(lg_kwargs, dict):
+        ax1.legend(**lg_kwargs)
+
+    # Plot label
+    plot_label(ax1, plot_label_args)
 
     # Check whether you have graph data to save to CSV
     if ct_mean is None:

py/saga/rescale.py

@@ -135,8 +135,10 @@ def rescale_graph_data(
     y_mean = new_sim_graph["y"]
 
     # Set y values to constant and update scaled y errors if requested
-    scaled_y_mean = y_mean if yvalue < -1 else [np.abs(yvalue) for i in range(len(y_mean))]
-    if yvalue >= -1 and yvalue<0:
+    scaled_y_mean = (
+        y_mean if yvalue < -1 else [np.abs(yvalue) for i in range(len(y_mean))]
+    )
+    if yvalue >= -1 and yvalue < 0:
         scaled_yerr_mean = (
             np.divide(1.0, np.sqrt(scaled_ct_mean)) * 1.0 / (tpol_factor * tdil_factor)
         ) * np.sqrt(1 - np.square(yvalue * tpol_factor * tdil_factor))
@@ -281,9 +283,11 @@ def rescale_csv_data(
         if yvalue < -1
         else [np.abs(yvalue) for i in range(len(old_dat_df[y_key]))]
     )
-    if yvalue >= -1 and yvalue<0:
+    if yvalue >= -1 and yvalue < 0:
         scaled_yerrs = (
-            np.divide(1.0, np.sqrt(new_dat_df_count)) * 1.0 / (tpol_factor * tdil_factor)
+            np.divide(1.0, np.sqrt(new_dat_df_count))
+            * 1.0
+            / (tpol_factor * tdil_factor)
         ) * np.sqrt(1 - np.square(yvalue * tpol_factor * tdil_factor))
     elif yvalue >= 0 and yvalue <= 1:
         scaled_yerrs *= np.sqrt(1 - np.square(yvalue * tpol_factor * tdil_factor))

tutorials/aggregate_jobs_nested_kinematics2d.py

@@ -43,7 +43,7 @@
     'hist_density':False,
     'axlinewidth':0,
     'hist_dim':2,
-    'legend_loc':None #NOTE: Do not plot a legend since you are using 2d hists.
+    'lg_kwargs':None #NOTE: Do not plot a legend since you are using 2d hists.
 }
 
 # Set additional kwargs