diff --git a/src/MetaWinAnalysis.py b/src/MetaWinAnalysis.py
index 9a4a09f..09860fc 100644
--- a/src/MetaWinAnalysis.py
+++ b/src/MetaWinAnalysis.py
@@ -1798,37 +1798,36 @@ def do_meta_analysis(data, options, decimal_places: int = 4, alpha: float = 0.05
     output, all_citations = options.report_choices()
     output_blocks.extend(output)
     if options.structure == SIMPLE_MA:
-        (output, figure, chart_data, analysis_values,
+        (output, chart_data, analysis_values,
          citations) = MetaWinAnalysisFunctions.simple_meta_analysis(data, options, decimal_places, alpha, norm_ci,
                                                                     sender=sender)
     elif options.structure == GROUPED_MA:
-        (output, figure, chart_data, analysis_values,
+        (output, chart_data, analysis_values,
          citations) = MetaWinAnalysisFunctions.grouped_meta_analysis(data, options, decimal_places, alpha, norm_ci,
                                                                      sender=sender)
     elif options.structure == CUMULATIVE_MA:
-        output, figure, chart_data = MetaWinAnalysisFunctions.cumulative_meta_analysis(data, options, decimal_places,
-                                                                                       alpha, norm_ci, sender=sender)
+        output, chart_data = MetaWinAnalysisFunctions.cumulative_meta_analysis(data, options, decimal_places, alpha,
+                                                                               norm_ci, sender=sender)
         analysis_values = None
         citations = []
     elif options.structure == REGRESSION_MA:
-        (output, figure, chart_data, analysis_values,
+        (output, chart_data, analysis_values,
          citations) = MetaWinAnalysisFunctions.regression_meta_analysis(data, options, decimal_places, alpha, norm_ci,
                                                                         sender=sender)
     elif options.structure == COMPLEX_MA:
         output, analysis_values, citations = MetaWinAnalysisFunctions.complex_meta_analysis(data, options,
                                                                                             decimal_places, alpha,
                                                                                             norm_ci, sender=sender)
-        figure = None
         chart_data = None
     elif options.structure == NESTED_MA:
-        (output, figure, chart_data, analysis_values,
+        (output, chart_data, analysis_values,
          citations) = MetaWinAnalysisFunctions.nested_meta_analysis(data, options, decimal_places, alpha, norm_ci,
                                                                     sender=sender)
     elif options.structure == TRIM_FILL:
-        (output, figure, chart_data, analysis_values,
+        (output, chart_data, analysis_values,
          citations) = MetaWinAnalysisFunctions.trim_and_fill_analysis(data, options, decimal_places, alpha, norm_ci)
     elif options.structure == JACKKNIFE:
-        (output, figure,  chart_data,
+        (output, chart_data,
          citations) = MetaWinAnalysisFunctions.jackknife_meta_analysis(data, options, decimal_places, alpha, norm_ci,
                                                                        sender=sender)
         analysis_values = None
@@ -1836,24 +1835,21 @@ def do_meta_analysis(data, options, decimal_places: int = 4, alpha: float = 0.05
         output, citations = MetaWinAnalysisFunctions.phylogenetic_meta_analysis(data, options, tree, decimal_places,
                                                                                 alpha, norm_ci, sender=sender)
         analysis_values = None
-        figure = None
         chart_data = None
     elif options.structure == RANKCOR:
         output, citations = MetaWinAnalysisFunctions.rank_correlation_analysis(data, options, decimal_places,
                                                                                sender=sender)
-        figure = None
         chart_data = None
         analysis_values = None
     else:
         output = []
         analysis_values = None
-        figure = None
         chart_data = None
         citations = []
     all_citations.extend(citations)
     output_blocks.extend(output)
     output_blocks.extend(create_reference_list(all_citations))
-    return output_blocks, figure, chart_data, analysis_values
+    return output_blocks, chart_data, analysis_values
 
 
 def meta_analysis(sender, data, last_effect, last_var, decimal_places: int = 4, alpha: float = 0.05,
@@ -1909,9 +1905,9 @@ def meta_analysis(sender, data, last_effect, last_var, decimal_places: int = 4,
                 meta_analysis_options.structure = None
 
         if meta_analysis_options.structure is not None:
-            output, figure, chart_data, _ = do_meta_analysis(data, meta_analysis_options, decimal_places, alpha, tree,
-                                                             norm_ci, sender=sender)
+            output, chart_data, _ = do_meta_analysis(data, meta_analysis_options, decimal_places, alpha, tree, norm_ci,
+                                                     sender=sender)
             sender.last_effect = meta_analysis_options.effect_data
             sender.last_var = meta_analysis_options.effect_vars
-            return output, figure, chart_data
-    return None, None, None
+            return output, chart_data
+    return None, None
diff --git a/src/MetaWinAnalysisFunctions.py b/src/MetaWinAnalysisFunctions.py
index f1b7872..6b82b84 100644
--- a/src/MetaWinAnalysisFunctions.py
+++ b/src/MetaWinAnalysisFunctions.py
@@ -601,7 +601,6 @@ def simple_meta_analysis(data, options, decimal_places: int = 4, alpha: float =
 
     output_blocks = output_filtered_bad(filtered, bad_data)
 
-    figure = None
     chart_data = None
     n = len(e_data)
     citations = []
@@ -670,15 +669,15 @@ def simple_meta_analysis(data, options, decimal_places: int = 4, alpha: float =
         citations.extend(new_cites)
 
         if options.create_graph:
-            figure, chart_data = MetaWinCharts.chart_forest_plot("basic analysis", effect_sizes.label, forest_data,
-                                                                 alpha, options.bootstrap_mean, normal_ci=norm_ci)
+            chart_data = MetaWinCharts.chart_forest_plot("basic analysis", effect_sizes.label, forest_data,
+                                                         alpha, options.bootstrap_mean, normal_ci=norm_ci)
 
     else:
         output_blocks.append([get_text("Fewer than two studies were valid for analysis")])
         qt, df, p, pooled_var, i2 = None, None, None, None, None
         mean_data = None
 
-    return output_blocks, figure, chart_data, simple_ma_values(mean_data, pooled_var, qt, df, p, i2), citations
+    return output_blocks, chart_data, simple_ma_values(mean_data, pooled_var, qt, df, p, i2), citations
 
 
 # ---------- grouped meta-analysis ----------
@@ -740,7 +739,6 @@ def grouped_meta_analysis(data, options, decimal_places: int = 4, alpha: float =
 
     output_blocks = output_filtered_bad(filtered, bad_data)
 
-    figure = None
     chart_data = None
     n = len(e_data)
     g_cnt = len(group_names)
@@ -905,9 +903,8 @@ def grouped_meta_analysis(data, options, decimal_places: int = 4, alpha: float =
         citations.extend(new_cites)
 
         if options.create_graph:
-            figure, chart_data = MetaWinCharts.chart_forest_plot("grouped analysis", effect_sizes.label, forest_data,
-                                                                 alpha, options.bootstrap_mean, groups.label,
-                                                                 normal_ci=norm_ci)
+            chart_data = MetaWinCharts.chart_forest_plot("grouped analysis", effect_sizes.label, forest_data, alpha,
+                                                         options.bootstrap_mean, groups.label, normal_ci=norm_ci)
 
         global_values = simple_ma_values(global_mean_data, pooled_var, qt, df, pqt, i2)
     else:
@@ -917,7 +914,7 @@ def grouped_meta_analysis(data, options, decimal_places: int = 4, alpha: float =
         model_het = None
         error_het = None
 
-    return (output_blocks, figure, chart_data,
+    return (output_blocks, chart_data,
             group_ma_values(global_values, group_mean_values, group_het_values, model_het, error_het), citations)
 
 
@@ -945,7 +942,6 @@ def cumulative_meta_analysis(data, options, decimal_places: int = 4, alpha: floa
 
     output_blocks = output_filtered_bad(filtered, bad_data)
 
-    figure = None
     chart_data = None
     n = len(tmp_data)
     if n > 1:
@@ -1016,14 +1012,14 @@ def cumulative_meta_analysis(data, options, decimal_places: int = 4, alpha: floa
                              decimal_places, alpha, options.log_transformed))
 
         if options.create_graph:
-            figure, chart_data = MetaWinCharts.chart_forest_plot("cumulative analysis", effect_sizes.label,
-                                                                 cumulative_means, alpha, options.bootstrap_mean,
-                                                                 order.label, normal_ci=norm_ci)
+            chart_data = MetaWinCharts.chart_forest_plot("cumulative analysis", effect_sizes.label,
+                                                         cumulative_means, alpha, options.bootstrap_mean, order.label,
+                                                         normal_ci=norm_ci)
 
     else:
         output_blocks.append([get_text("Fewer than two studies were valid for analysis")])
 
-    return output_blocks, figure, chart_data
+    return output_blocks, chart_data
 
 
 # ---------- simple regression meta-analysis ----------
@@ -1076,7 +1072,6 @@ def regression_meta_analysis(data, options, decimal_places: int = 4, alpha: floa
 
     output_blocks = output_filtered_bad(filtered, bad_data)
 
-    figure = None
     chart_data = None
     model_het = None
     error_het = None
@@ -1205,14 +1200,13 @@ def regression_meta_analysis(data, options, decimal_places: int = 4, alpha: floa
                                               get_citation("Greenland_1987"))
                 model = get_text("fixed effects")
                 fig_citations = ["Hedges_Olkin_1985", "Greenland_1987"]
-            figure, chart_data = MetaWinCharts.chart_regression(options.independent_variable.label, effect_sizes.label,
-                                                                x_data, e_data, b1_slope, b0_intercept, model,
-                                                                ref_list, fig_citations)
+            chart_data = MetaWinCharts.chart_regression(options.independent_variable.label, effect_sizes.label, x_data,
+                                                        e_data, b1_slope, b0_intercept, model, ref_list, fig_citations)
 
     else:
         output_blocks.append([get_text("Fewer than two studies were valid for analysis")])
 
-    return output_blocks, figure, chart_data, reg_ma_values(global_values, model_het, error_het, predictors), citations
+    return output_blocks, chart_data, reg_ma_values(global_values, model_het, error_het, predictors), citations
 
 
 # ---------- complex (glm) meta-analysis ----------
@@ -1665,7 +1659,6 @@ def nested_meta_analysis(data, options, decimal_places: int = 4, alpha: float =
 
     n = len(e_data)
 
-    figure = None
     chart_data = None
     group_het_values = None
     group_mean_values = None
@@ -1810,11 +1803,11 @@ def nested_meta_analysis(data, options, decimal_places: int = 4, alpha: float =
         citations.extend(new_cites)
 
         if options.create_graph:
-            figure, chart_data = MetaWinCharts.chart_forest_plot("nested analysis", effect_sizes.label, forest_data,
-                                                                 alpha, options.bootstrap_mean, normal_ci=norm_ci)
+            chart_data = MetaWinCharts.chart_forest_plot("nested analysis", effect_sizes.label, forest_data, alpha,
+                                                         options.bootstrap_mean, normal_ci=norm_ci)
 
-    return (output_blocks, figure, chart_data, group_ma_values(global_values, group_mean_values, group_het_values,
-                                                               model_het_values, error_het_values), citations)
+    return (output_blocks, chart_data, group_ma_values(global_values, group_mean_values, group_het_values,
+                                                       model_het_values, error_het_values), citations)
 
 
 # ---------- trim-and-fill analysis ----------
@@ -1847,7 +1840,6 @@ def trim_and_fill_analysis(data, options, decimal_places: int = 4, alpha: float
 
     output_blocks = output_filtered_bad(filtered, bad_data)
 
-    figure = None
     chart_data = None
     n = len(e_data)
     citations = []
@@ -1969,13 +1961,12 @@ def trim_and_fill_analysis(data, options, decimal_places: int = 4, alpha: float
                              decimal_places, alpha, options.log_transformed))
 
         if options.create_graph:
-            figure, chart_data = MetaWinCharts.chart_trim_fill_plot(effect_sizes.label, tmp_data, n,  original_mean,
-                                                                    mean_e)
+            chart_data = MetaWinCharts.chart_trim_fill_plot(effect_sizes.label, tmp_data, n,  original_mean, mean_e)
 
     else:
         output_blocks.append([get_text("Fewer than two studies were valid for analysis")])
 
-    return output_blocks, figure, chart_data, None, citations
+    return output_blocks, chart_data, None, citations
 
 
 # ---------- phylogenetic meta-analysis ----------
@@ -2275,7 +2266,6 @@ def jackknife_meta_analysis(data, options, decimal_places: int = 4, alpha: float
 
     output_blocks = output_filtered_bad(filtered, bad_data)
 
-    figure = None
     chart_data = None
     n = len(e_data)
     citations = []
@@ -2380,12 +2370,12 @@ def jackknife_meta_analysis(data, options, decimal_places: int = 4, alpha: float
         citations.extend(new_cites)
 
         if options.create_graph:
-            figure, chart_data = MetaWinCharts.chart_forest_plot("jackknife analysis", effect_sizes.label, forest_data,
-                                                                 alpha, options.bootstrap_mean, normal_ci=norm_ci)
+            chart_data = MetaWinCharts.chart_forest_plot("jackknife analysis", effect_sizes.label, forest_data, alpha,
+                                                         options.bootstrap_mean, normal_ci=norm_ci)
     else:
         output_blocks.append([get_text("Fewer than two studies were valid for analysis")])
 
-    return output_blocks, figure, chart_data, citations
+    return output_blocks, chart_data, citations
 
 
 # ---------- rank correlation analysis ----------
diff --git a/src/MetaWinCharts.py b/src/MetaWinCharts.py
index 3a07a9a..0083453 100644
--- a/src/MetaWinCharts.py
+++ b/src/MetaWinCharts.py
@@ -3,13 +3,13 @@
 """
 
 import math
-from typing import Optional, Tuple, Union
+from typing import Optional, Union
 
 from matplotlib import patches
 from matplotlib.backends.backend_qtagg import FigureCanvasQTAgg
+from matplotlib.figure import Figure
 # the following import is necessary to force pyinstaller to include these backends for vector output when packaging
 from matplotlib.backends import backend_svg, backend_ps, backend_pgf, backend_pdf
-from matplotlib.figure import Figure
 from matplotlib.colors import XKCD_COLORS, hex2color
 import numpy
 import scipy.stats
@@ -46,11 +46,6 @@
                     "tick down", "upward caret", "downward caret", "left caret", "right caret",
                     "centered upward caret", "centered downward caret", "centered left caret", "centered right caret"}
 
-# I need a single hook to the FigureCanvasQTAgg that I can use to erase existing figures prior
-# to creating new ones, otherwise the save figure function will try to serially save every
-# figure that had been created. This is currently the cleanest solution I can find.
-FIGURE_CANVAS = FigureCanvasQTAgg(Figure(figsize=(8, 6)))
-
 
 # ---------- Chart Data Classes ---------- #
 class BaseChartData:
@@ -759,19 +754,20 @@ def export_to_list(self):
         return outlist
 
 
-def base_figure():
+def base_figure(figure_canvas):
     """
     create the baseline figure used for all plots
     """
-    figure_canvas = FIGURE_CANVAS
+    # figure_canvas = FIGURE_CANVAS
     figure_canvas.figure.clf()  # clean any existing figures
     faxes = figure_canvas.figure.subplots()
     faxes.spines["right"].set_visible(False)
     faxes.spines["top"].set_visible(False)
-    return figure_canvas, faxes
+    # return figure_canvas, faxes
+    return faxes
 
 
-def create_figure(chart_data):
+def create_figure(chart_data, figure_canvas):
     """
     create a figure given pre-determined chart data
 
@@ -779,7 +775,7 @@ def create_figure(chart_data):
     which subsequently can allow user modification of plot styles and redrawing of the figure w/o needing to
     recalculate anything
     """
-    figure_canvas, faxes = base_figure()
+    faxes = base_figure(figure_canvas)
     faxes.set_ylabel(chart_data.y_label)
     faxes.set_xlabel(chart_data.x_label)
     for data in chart_data.data:
@@ -815,12 +811,10 @@ def create_figure(chart_data):
     if chart_data.rescale_x is not None:
         faxes.set_xlim(chart_data.rescale_x[0], chart_data.rescale_x[1])
 
-    return figure_canvas
-
 
 def chart_forest_plot(analysis_type: str, effect_name, forest_data, alpha: float = 0.05,
                       bootstrap_n: Optional[int] = None, extra_name: Optional[str] = None,
-                      normal_ci: bool = True) -> Tuple[FigureCanvasQTAgg, ChartData]:
+                      normal_ci: bool = True) -> ChartData:
     chart_data = ChartData(analysis_type)
     chart_data.caption.e_label = effect_name
     chart_data.caption.alpha = alpha
@@ -888,8 +882,7 @@ def chart_forest_plot(analysis_type: str, effect_name, forest_data, alpha: float
                                                          bias_cis, ci_y_data, marker=7, zorder=4, color="#d62728",
                                                          label="{:0.0%} CI (bias-corrected bootstrap)".format(1-alpha))
 
-    figure_canvas = create_figure(chart_data)
-    return figure_canvas, chart_data
+    return chart_data
 
 
 def add_regression_to_chart(x_name: str, y_name: str, x_data, y_data, slope: float, intercept: float,
@@ -905,7 +898,7 @@ def add_regression_to_chart(x_name: str, y_name: str, x_data, y_data, slope: flo
 
 
 def chart_regression(x_name, y_name, x_data, y_data, slope, intercept, model, ref_list,
-                     citations) -> Tuple[FigureCanvasQTAgg, ChartData]:
+                     citations) -> ChartData:
     x_min = numpy.min(x_data)
     x_max = numpy.max(x_data)
     chart_data = ChartData("regression")
@@ -917,8 +910,7 @@ def chart_regression(x_name, y_name, x_data, y_data, slope, intercept, model, re
 
     add_regression_to_chart(x_name, y_name, x_data, y_data, slope, intercept, x_min, x_max, chart_data)
 
-    figure_canvas = create_figure(chart_data)
-    return figure_canvas, chart_data
+    return chart_data
 
 
 def add_quantile_axes_to_chart(x_data, y_data, slope: float, intercept: float, chart_data, alpha: float = 0.05) -> None:
@@ -956,15 +948,14 @@ def add_quantile_axes_to_chart(x_data, y_data, slope: float, intercept: float, c
 
 
 def chart_normal_quantile(x_name, y_name, x_data, y_data, slope, intercept,
-                          alpha: float = 0.05) -> Tuple[FigureCanvasQTAgg, ChartData]:
+                          alpha: float = 0.05) -> ChartData:
     chart_data = ChartData("normal quantile")
     add_quantile_axes_to_chart(x_data, y_data, slope, intercept, chart_data, alpha)
     x_min = numpy.min(x_data)
     x_max = numpy.max(x_data)
     add_regression_to_chart(x_name, y_name, x_data, y_data, slope, intercept, x_min, x_max, chart_data)
 
-    figure_canvas = create_figure(chart_data)
-    return figure_canvas, chart_data
+    return chart_data
 
 
 def add_radial_curve_to_chart(effect_label: str, r: float, min_e: float, max_e: float, chart_data,
@@ -1042,8 +1033,7 @@ def add_radial_curve_to_chart(effect_label: str, r: float, min_e: float, max_e:
     chart_data.add_line(get_text("Vertical Axis Zero Line"), 0, 0, r+1, 0, color="silver", linestyle="dotted")
 
 
-def chart_radial(e_name, x_data, y_data, slope, min_e, max_e, is_log: bool = False) -> Tuple[FigureCanvasQTAgg,
-                                                                                             ChartData]:
+def chart_radial(e_name, x_data, y_data, slope, min_e, max_e, is_log: bool = False) -> ChartData:
     chart_data = ChartData("radial")
     chart_data.caption.e_label = e_name
     max_d = numpy.max(numpy.sqrt(numpy.square(x_data) + numpy.square(y_data)))+1
@@ -1054,12 +1044,10 @@ def chart_radial(e_name, x_data, y_data, slope, min_e, max_e, is_log: bool = Fal
     add_radial_curve_to_chart(e_name, max_d, min_e, max_e, chart_data, is_log)
     chart_data.rescale_x = (0, max_d+2)
 
-    figure_canvas = create_figure(chart_data)
-    return figure_canvas, chart_data
+    return chart_data
 
 
-def chart_histogram(e_data, w_data, n_bins, e_label,
-                    weighted: int = WEIGHT_NONE) -> Tuple[FigureCanvasQTAgg, ChartData]:
+def chart_histogram(e_data, w_data, n_bins, e_label, weighted: int = WEIGHT_NONE) -> ChartData:
     if weighted == WEIGHT_NONE:
         cnts, bins = numpy.histogram(e_data, n_bins)
         y_label = get_text("Count")
@@ -1075,8 +1063,7 @@ def chart_histogram(e_data, w_data, n_bins, e_label,
     chart_data.y_label = y_label
     chart_data.add_histogram("Bin Counts", cnts, bins, edgecolor="black", linewidth=1)
 
-    figure_canvas = create_figure(chart_data)
-    return figure_canvas, chart_data
+    return chart_data
 
 
 def draw_tree(faxes, tree, minx, maxx, miny, maxy, scale, draw_labels: bool = False, draw_branch_lengths: bool = False):
@@ -1175,7 +1162,7 @@ def chart_phylogeny(root) -> FigureCanvasQTAgg:
     return figure_canvas
 
 
-def chart_scatter(x_data, y_data, x_label: str = "x", y_label: str = "y") -> Tuple[FigureCanvasQTAgg, ChartData]:
+def chart_scatter(x_data, y_data, x_label: str = "x", y_label: str = "y") -> ChartData:
     chart_data = ChartData("scatter plot")
     chart_data.caption.x_label = x_label
     chart_data.caption.y_label = y_label
@@ -1183,11 +1170,10 @@ def chart_scatter(x_data, y_data, x_label: str = "x", y_label: str = "y") -> Tup
     chart_data.y_label = y_label
     chart_data.add_scatter(get_text("Point Data"), x_data, y_data)
 
-    figure_canvas = create_figure(chart_data)
-    return figure_canvas, chart_data
+    return chart_data
 
 
-def chart_trim_fill_plot(effect_label, data, n, original_mean, new_mean) -> Tuple[FigureCanvasQTAgg, ChartData]:
+def chart_trim_fill_plot(effect_label, data, n, original_mean, new_mean) -> ChartData:
     chart_data = ChartData("trim and fill")
     chart_data.caption.e_label = effect_label
     chart_data.x_label = effect_label
@@ -1214,8 +1200,7 @@ def chart_trim_fill_plot(effect_label, data, n, original_mean, new_mean) -> Tupl
     chart_data.caption.inferred_mean = chart_data.add_line(get_text("Inferred Mean"), new_mean, y_min, new_mean, y_max,
                                                            color="red", linestyle="dashed", zorder=1)
 
-    figure_canvas = create_figure(chart_data)
-    return figure_canvas, chart_data
+    return chart_data
 
 
 def find_color_name(color: str) -> str:
diff --git a/src/MetaWinDraw.py b/src/MetaWinDraw.py
index bc53f10..82ee083 100644
--- a/src/MetaWinDraw.py
+++ b/src/MetaWinDraw.py
@@ -370,10 +370,8 @@ def draw_scatter_plot(data, x_data_col, y_data_col):
     if len(x_data) > 0:
         x_data = numpy.array(x_data)
         y_data = numpy.array(y_data)
-        figure, chart_data = MetaWinCharts.chart_scatter(x_data, y_data, x_data_col.label, y_data_col.label)
-        # fig_caption = get_text("Scatter plot of {} vs. {}.").format(y_data_col.label, x_data_col.label)
-        return figure, chart_data
-    return None, None
+        return MetaWinCharts.chart_scatter(x_data, y_data, x_data_col.label, y_data_col.label)
+    return None
 
 
 def draw_scatter_dialog(sender, data):
@@ -381,12 +379,12 @@ def draw_scatter_dialog(sender, data):
     if sender.draw_dialog.exec():
         x_data_col = sender.draw_dialog.columns[sender.draw_dialog.x_box.currentIndex()]
         y_data_col = sender.draw_dialog.columns[sender.draw_dialog.y_box.currentIndex()]
-        figure, chart_data = draw_scatter_plot(data, x_data_col, y_data_col)
-        if figure is not None:
-            return figure, chart_data
+        chart_data = draw_scatter_plot(data, x_data_col, y_data_col)
+        if chart_data is not None:
+            return chart_data
         else:
             MetaWinMessages.report_critical(sender, "Error", "No valid data found for given options.")
-    return None, None
+    return None
 
 
 def calculate_regression(x: numpy.array, y: numpy.array) -> Tuple[float, float]:
@@ -429,14 +427,9 @@ def draw_normal_quantile_plot(data, e_data_col, v_data_col, alpha: float = 0.05)
         y_data = numpy.array(y_data)
         slope, intercept = calculate_regression(x_data, y_data)
 
-        figure, chart_data = MetaWinCharts.chart_normal_quantile(get_text("Normal Quantile"),
-                                                                 get_text("Standardized Effect Size"),
-                                                                 x_data, y_data, slope, intercept, alpha)
-        # fig_caption = get_text("normal_quantile_caption").format(get_citation("Wang_and_Bushman_1998")) + \
-        #               create_reference_list(["Wang_and_Bushman_1998"], True)
-
-        return figure, chart_data
-    return None, None
+        return MetaWinCharts.chart_normal_quantile(get_text("Normal Quantile"), get_text("Standardized Effect Size"),
+                                                   x_data, y_data, slope, intercept, alpha)
+    return None
 
 
 def draw_normal_quantile_dialog(sender, data, last_effect, last_var, alpha: float = 0.05):
@@ -444,12 +437,12 @@ def draw_normal_quantile_dialog(sender, data, last_effect, last_var, alpha: floa
     if sender.draw_dialog.exec():
         e_data_col = sender.draw_dialog.columns[sender.draw_dialog.effect_size_box.currentIndex()]
         v_data_col = sender.draw_dialog.columns[sender.draw_dialog.variance_box.currentIndex()]
-        figure, chart_data = draw_normal_quantile_plot(data, e_data_col, v_data_col, alpha)
-        if figure is not None:
-            return figure, chart_data
+        chart_data = draw_normal_quantile_plot(data, e_data_col, v_data_col, alpha)
+        if chart_data is not None:
+            return chart_data
         else:
             MetaWinMessages.report_critical(sender, "Error", "No valid data found for given options.")
-    return None, None
+    return None
 
 
 def draw_radial_plot(data, e_data_col, v_data_col, is_log: bool = False):
@@ -480,12 +473,8 @@ def draw_radial_plot(data, e_data_col, v_data_col, is_log: bool = False):
         # calculate a regression through the origin, rather than with an intercept
         slope_through_origin = numpy.sum(x_data*y_data)/numpy.sum(numpy.square(x_data))
 
-        figure, chart_data = MetaWinCharts.chart_radial(e_data_col.label, x_data, y_data, slope_through_origin, min_e,
-                                                        max_e, is_log)
-        # fig_caption = get_text("Radial_chart_caption").format(e_data_col.label)
-        # fig_caption += create_reference_list(["Galbraith_1988", "Galbraith_1994"], True)
-        return figure, chart_data
-    return None, None
+        return MetaWinCharts.chart_radial(e_data_col.label, x_data, y_data, slope_through_origin, min_e, max_e, is_log)
+    return None
 
 
 def draw_radial_dialog(sender, data, last_effect, last_var):
@@ -494,12 +483,12 @@ def draw_radial_dialog(sender, data, last_effect, last_var):
         e_data_col = sender.draw_dialog.columns[sender.draw_dialog.effect_size_box.currentIndex()]
         v_data_col = sender.draw_dialog.columns[sender.draw_dialog.variance_box.currentIndex()]
         is_log = sender.draw_dialog.log_transform_box.isChecked()
-        figure, chart_data = draw_radial_plot(data, e_data_col, v_data_col, is_log)
-        if figure is not None:
-            return figure, chart_data
+        chart_data = draw_radial_plot(data, e_data_col, v_data_col, is_log)
+        if chart_data is not None:
+            return chart_data
         else:
             MetaWinMessages.report_critical(sender, "Error", "No valid data found for given options.")
-    return None, None
+    return None
 
 
 def draw_histogram_plot(data, e_data_col, w_data_col, weight_type, n_bins: int):
@@ -533,9 +522,8 @@ def draw_histogram_plot(data, e_data_col, w_data_col, weight_type, n_bins: int):
     if len(e_data) > 0:
         e_data = numpy.array(e_data)
         w_data = numpy.array(w_data)
-        figure, chart_data = MetaWinCharts.chart_histogram(e_data, w_data, n_bins, e_data_col.label, weight_type)
-        return figure, chart_data
-    return None, None
+        return MetaWinCharts.chart_histogram(e_data, w_data, n_bins, e_data_col.label, weight_type)
+    return None
 
 
 def draw_histogram_dialog(sender, data, last_effect, last_var):
@@ -550,12 +538,12 @@ def draw_histogram_dialog(sender, data, last_effect, last_var):
         else:
             weight_type = MetaWinCharts.WEIGHT_NONE
         n_bins = int(sender.draw_dialog.bin_edit.text())
-        figure, chart_data = draw_histogram_plot(data, e_data_col, w_data_col, weight_type, n_bins)
-        if figure is not None:
-            return figure, chart_data
+        chart_data = draw_histogram_plot(data, e_data_col, w_data_col, weight_type, n_bins)
+        if chart_data is not None:
+            return chart_data
         else:
             MetaWinMessages.report_critical(sender, "Error", "No valid data found for given options.")
-    return None, None
+    return None
 
 
 def draw_forest_plot(data, e_data_col, v_data_col, alpha: float = 0.05):
@@ -576,11 +564,7 @@ def draw_forest_plot(data, e_data_col, v_data_col, alpha: float = 0.05):
         else:
             filtered.append(row.label)
 
-    figure, chart_data = MetaWinCharts.chart_forest_plot("forest plot", e_data_col.label, data_list, alpha, None)
-
-    # fig_caption = get_text("Forest plot of individual effect sizes for each study.") + \
-    #               MetaWinCharts.common_forest_plot_caption(e_data_col.label, alpha, inc_median=False)
-    return figure, chart_data
+    return MetaWinCharts.chart_forest_plot("forest plot", e_data_col.label, data_list, alpha, None)
 
 
 def draw_forest_dialog(sender, data, last_effect, last_var, alpha: float = 0.05):
@@ -588,16 +572,16 @@ def draw_forest_dialog(sender, data, last_effect, last_var, alpha: float = 0.05)
     if sender.draw_dialog.exec():
         e_data_col = sender.draw_dialog.columns[sender.draw_dialog.effect_size_box.currentIndex()]
         v_data_col = sender.draw_dialog.columns[sender.draw_dialog.variance_box.currentIndex()]
-        figure, chart_data = draw_forest_plot(data, e_data_col, v_data_col, alpha)
-        if figure is not None:
-            return figure, chart_data
+        chart_data = draw_forest_plot(data, e_data_col, v_data_col, alpha)
+        if chart_data is not None:
+            return chart_data
         else:
             MetaWinMessages.report_critical(sender, "Error", "No valid data found for given options.")
-    return None, None
+    return None
 
 
 def edit_figure(sender, chart_data):
     sender.edit_fig_dialog = MetaAnalysisEditFigure(chart_data)
     if sender.edit_fig_dialog.exec():
-        return MetaWinCharts.create_figure(chart_data)
+        return chart_data
     return None
diff --git a/src/MetaWinMain.py b/src/MetaWinMain.py
index 6a92426..c879ba1 100644
--- a/src/MetaWinMain.py
+++ b/src/MetaWinMain.py
@@ -11,7 +11,8 @@
     QWidgetAction, QComboBox
 from PyQt6.QtGui import QIcon, QColor, QAction, QActionGroup, QTextCursor
 from PyQt6 import QtCore
-from matplotlib.backends.backend_qtagg import NavigationToolbar2QT
+from matplotlib.backends.backend_qtagg import NavigationToolbar2QT, FigureCanvasQTAgg
+from matplotlib.figure import Figure
 
 import MetaWinCharts
 from MetaWinAbout import MetaWinAbout
@@ -77,6 +78,8 @@ def __init__(self, config: dict):
         self.empty_col_num = 10
         self.empty_row_num = 15
         self.chart_data = None
+        self.figure_canvas = None
+        self.caption_box = None
         self.init_ui()
 
     def init_ui(self):
@@ -338,6 +341,12 @@ def init_ui(self):
         self.graph_toolbar.addAction(export_graph_data_action)
         self.graph_toolbar.addAction(edit_graph_action)
         graph_master_layout.addWidget(self.graph_toolbar)
+        self.figure_canvas = FigureCanvasQTAgg(Figure(figsize=(8, 6)))
+        toolbar = NavigationToolbar2QT(self.figure_canvas, None)
+        self.save_graph_action.triggered.connect(toolbar.save_figure)
+        self.caption_box = QTextEdit()
+        self.graph_layout.addWidget(self.figure_canvas, stretch=8)
+        self.graph_layout.addWidget(self.caption_box, stretch=1)
         graph_master_layout.addLayout(self.graph_layout)
         self.graph_area.setLayout(graph_master_layout)
         self.main_area.addTab(self.graph_area, QIcon(MetaWinConstants.graph_icon), get_text("Graph"))
@@ -711,35 +720,26 @@ def meta_analysis(self) -> None:
                 norm_ci = False
             else:
                 norm_ci = True
-            output, figure, chart_data = MetaWinAnalysis.meta_analysis(self, self.data, self.last_effect,
-                                                                       self.last_var, self.output_decimals,
-                                                                       self.alpha, self.phylogeny, norm_ci)
+            output, chart_data = MetaWinAnalysis.meta_analysis(self, self.data, self.last_effect, self.last_var,
+                                                               self.output_decimals, self.alpha, self.phylogeny,
+                                                               norm_ci)
             if output is not None:
                 self.write_multi_output_blocks(output)
                 self.main_area.setCurrentIndex(1)
-                if figure is not None:
-                    self.show_figure(figure, chart_data)
+                if chart_data is not None:
+                    self.show_figure(chart_data)
         else:
             MetaWinMessages.report_warning(self, get_text("Warning"), get_text("No data has been loaded."))
 
-    def show_figure(self, figure, chart_data) -> None:
+    def show_figure(self, chart_data) -> None:
         """
-        Replace the current figure in the graphics tab with a new figure, toolbar, and caption
+        Create a new figure based on the chart_data, display it and its caption
         """
         self.main_area.setTabVisible(2, True)
-        # erase existing figure elements
-        for i in reversed(range(self.graph_layout.count())):
-            self.graph_layout.itemAt(i).widget().setParent(None)
-
-        # create new figure
-        toolbar = NavigationToolbar2QT(figure, None)
-        self.save_graph_action.triggered.connect(toolbar.save_figure)
-        caption_box = QTextEdit()
-        caption_box.setText(chart_data.caption_text())
-        self.graph_layout.addWidget(figure, stretch=8)
-        self.graph_layout.addWidget(caption_box, stretch=1)
+        self.caption_box.setText(chart_data.caption_text())
         self.chart_data = chart_data
-        figure.draw()
+        MetaWinCharts.create_figure(chart_data, self.figure_canvas)
+        self.figure_canvas.draw()
 
     def load_phylogeny(self) -> None:
         inname = QFileDialog.getOpenFileName(self, get_text("Load Phylogeny"))
@@ -759,39 +759,37 @@ def refresh_tree_panel(self) -> None:
 
     def draw_scatter_plot(self) -> None:
         if self.data is not None:
-            figure, chart_data = MetaWinDraw.draw_scatter_dialog(self, self.data)
-            if figure is not None:
-                self.show_figure(figure, chart_data)
+            chart_data = MetaWinDraw.draw_scatter_dialog(self, self.data)
+            if chart_data is not None:
+                self.show_figure(chart_data)
                 self.main_area.setCurrentIndex(2)
 
     def draw_histogram(self) -> None:
         if self.data is not None:
-            figure, chart_data = MetaWinDraw.draw_histogram_dialog(self, self.data, self.last_effect, self.last_var)
-            if figure is not None:
-                self.show_figure(figure, chart_data)
+            chart_data = MetaWinDraw.draw_histogram_dialog(self, self.data, self.last_effect, self.last_var)
+            if chart_data is not None:
+                self.show_figure(chart_data)
                 self.main_area.setCurrentIndex(2)
 
     def draw_normal_quantile_plot(self) -> None:
         if self.data is not None:
-            figure, chart_data = MetaWinDraw.draw_normal_quantile_dialog(self, self.data, self.last_effect,
-                                                                         self.last_var)
-            if figure is not None:
-                self.show_figure(figure, chart_data)
+            chart_data = MetaWinDraw.draw_normal_quantile_dialog(self, self.data, self.last_effect, self.last_var)
+            if chart_data is not None:
+                self.show_figure(chart_data)
                 self.main_area.setCurrentIndex(2)
 
     def draw_radial_plot(self) -> None:
         if self.data is not None:
-            figure, chart_data = MetaWinDraw.draw_radial_dialog(self, self.data, self.last_effect, self.last_var)
-            if figure is not None:
-                self.show_figure(figure, chart_data)
+            chart_data = MetaWinDraw.draw_radial_dialog(self, self.data, self.last_effect, self.last_var)
+            if chart_data is not None:
+                self.show_figure(chart_data)
                 self.main_area.setCurrentIndex(2)
 
     def draw_forest_plot(self) -> None:
         if self.data is not None:
-            figure, chart_data = MetaWinDraw.draw_forest_dialog(self, self.data, self.last_effect, self.last_var,
-                                                                self.alpha)
-            if figure is not None:
-                self.show_figure(figure, chart_data)
+            chart_data = MetaWinDraw.draw_forest_dialog(self, self.data, self.last_effect, self.last_var, self.alpha)
+            if chart_data is not None:
+                self.show_figure(chart_data)
                 self.main_area.setCurrentIndex(2)
 
     def clear_filters(self) -> None:
@@ -862,9 +860,9 @@ def export_graph_data(self):
 
     def edit_graph(self):
         if self.chart_data is not None:
-            figure = MetaWinDraw.edit_figure(self, self.chart_data)
-            if figure is not None:
-                self.show_figure(figure, self.chart_data)
+            chart_data = MetaWinDraw.edit_figure(self, self.chart_data)
+            if chart_data is not None:
+                self.show_figure(chart_data)
 
     def change_conf_int_distribution(self):
         if self.confidence_interval_dist == "Normal":
diff --git a/tests/test_metawin.py b/tests/test_metawin.py
index 4cb021c..8c936b6 100644
--- a/tests/test_metawin.py
+++ b/tests/test_metawin.py
@@ -16,6 +16,8 @@
 
 from PyQt6.QtWidgets import QDialog, QVBoxLayout, QFrame, QPushButton, QTextEdit
 import matplotlib.colors as mcolors
+from matplotlib.backends.backend_qtagg import FigureCanvasQTAgg
+from matplotlib.figure import Figure
 
 # note these may be marked by the IDE as unknown modules, but pytest.ini will resolve the errors when tests
 # are actually executed
@@ -30,20 +32,32 @@
 
 
 TEST_FIGURES = True
+# if the following line is not present, the tests with figures all crash for no obvious
+# reason. The call must be preloading something
+FIGURE_CANVAS = FigureCanvasQTAgg(Figure(figsize=(8, 6)))
 
 
 class TestFigureDialog(QDialog):
-    def __init__(self, figure, caption):
+    def __init__(self, chart_data, figure=None):
         super().__init__()
-        self.init_ui(figure, caption)
+        self.init_ui(chart_data, figure)
 
-    def init_ui(self, figure, caption):
+    def init_ui(self, chart_data, prefigure=None):
         ok_button = QPushButton("Ok")
         ok_button.clicked.connect(self.accept)
         figure_layout = QVBoxLayout()
+        if prefigure is None:
+            figure = FigureCanvasQTAgg(Figure(figsize=(8, 6)))
+            MetaWinCharts.create_figure(chart_data, figure)
+        else:
+            figure = prefigure
+        figure.draw()
         figure_layout.addWidget(figure)
         caption_area = QTextEdit()
-        caption_area.setText(caption)
+        if isinstance(chart_data, str):
+            caption_area.setText(chart_data)
+        else:
+            caption_area.setText(chart_data.caption_text())
         main_frame = QFrame()
         main_frame.setLayout(figure_layout)
         main_layout = QVBoxLayout()
@@ -292,11 +306,11 @@ def test_simple_meta_analysis():
     options.rosenberg_failsafe = 0.05
     options.create_graph = True
 
-    output, figure, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
+    output, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
     print_test_output(output)
 
     if TEST_FIGURES:
-        test_win = TestFigureDialog(figure, chart_data.caption_text())
+        test_win = TestFigureDialog(chart_data)
         test_win.exec()
 
     mean_values = analysis_values.mean_data
@@ -329,11 +343,11 @@ def test_simple_meta_analysis_lep():
     options.effect_vars = data.cols[5]
     options.create_graph = True
 
-    output, figure, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
+    output, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
     print_test_output(output)
 
     if TEST_FIGURES:
-        test_win = TestFigureDialog(figure, chart_data.caption_text())
+        test_win = TestFigureDialog(chart_data)
         test_win.exec()
 
     mean_values = analysis_values.mean_data
@@ -357,7 +371,7 @@ def test_i2_confidence_interval():
     options.effect_vars = data.cols[1]
     options.create_graph = False
 
-    output, figure, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
+    output, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
     print_test_output(output)
 
 
@@ -379,11 +393,11 @@ def test_simple_meta_analysis_lep_randeff():
     options.effect_vars = data.cols[5]
     options.create_graph = True
 
-    output, figure, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
+    output, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
     print_test_output(output)
 
     if TEST_FIGURES:
-        test_win = TestFigureDialog(figure, chart_data.caption_text())
+        test_win = TestFigureDialog(chart_data)
         test_win.exec()
 
     mean_values = analysis_values.mean_data
@@ -419,7 +433,7 @@ def test_simple_meta_analysis_random_effects():
     options.rosenthal_failsafe = 0.05
     options.rosenberg_failsafe = 0.05
 
-    output, _, _, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
+    output, _, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
     print_test_output(output)
     mean_values = analysis_values.mean_data
 
@@ -457,10 +471,10 @@ def test_simple_meta_analysis_bootstrap():
     options.bootstrap_mean = 9999
     options.create_graph = True
 
-    output, figure, chart_data, _ = MetaWinAnalysis.do_meta_analysis(data, options, 4)
+    output, chart_data, _ = MetaWinAnalysis.do_meta_analysis(data, options, 4)
     print_test_output(output)
     if TEST_FIGURES:
-        test_win = TestFigureDialog(figure, chart_data.caption_text())
+        test_win = TestFigureDialog(chart_data)
         test_win.exec()
 
 
@@ -497,11 +511,11 @@ def test_group_meta_analysis_lep_suborders():
     options.groups = data.cols[1]
     options.create_graph = True
 
-    output, figure, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
+    output, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
     print_test_output(output)
 
     if TEST_FIGURES:
-        test_win = TestFigureDialog(figure, chart_data.caption_text())
+        test_win = TestFigureDialog(chart_data)
         test_win.exec()
 
     global_values = analysis_values.global_values
@@ -551,11 +565,11 @@ def test_group_meta_analysis_lep_suborders_rand_eff():
     options.random_effects = True
     options.create_graph = True
 
-    output, figure, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
+    output, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
     print_test_output(output)
 
     if TEST_FIGURES:
-        test_win = TestFigureDialog(figure, chart_data.caption_text())
+        test_win = TestFigureDialog(chart_data)
         test_win.exec()
 
     global_values = analysis_values.global_values
@@ -632,11 +646,11 @@ def test_group_meta_analysis_lep_families():
     options.create_graph = True
     data.cols[2].group_filter = ["Yponomeutidae", "Lycaenidae", "Hesperiidae"]
 
-    output, figure, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
+    output, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
     print_test_output(output)
 
     if TEST_FIGURES:
-        test_win = TestFigureDialog(figure, chart_data.caption_text())
+        test_win = TestFigureDialog(chart_data)
         test_win.exec()
 
     global_values = analysis_values.global_values
@@ -675,10 +689,10 @@ def test_group_meta_analysis_bootstrap():
     options.bootstrap_mean = 9999
     options.create_graph = True
 
-    output, figure, chart_data, _ = MetaWinAnalysis.do_meta_analysis(data, options, 4)
+    output, chart_data, _ = MetaWinAnalysis.do_meta_analysis(data, options, 4)
     print_test_output(output)
     if TEST_FIGURES:
-        test_win = TestFigureDialog(figure, chart_data.caption_text())
+        test_win = TestFigureDialog(chart_data)
         test_win.exec()
 
 
@@ -695,10 +709,10 @@ def test_group_meta_analysis_lrr():
 
     options.create_graph = True
 
-    output, figure, chart_data, _ = MetaWinAnalysis.do_meta_analysis(data, options, 4)
+    output, chart_data, _ = MetaWinAnalysis.do_meta_analysis(data, options, 4)
     print_test_output(output)
     if TEST_FIGURES:
-        test_win = TestFigureDialog(figure, chart_data.caption_text())
+        test_win = TestFigureDialog(chart_data)
         test_win.exec()
 
 
@@ -727,10 +741,10 @@ def test_cumulative_meta_analysis():
 
     options.create_graph = True
 
-    output, figure, chart_data, _ = MetaWinAnalysis.do_meta_analysis(data, options, 4)
+    output, chart_data, _ = MetaWinAnalysis.do_meta_analysis(data, options, 4)
     print_test_output(output)
     if TEST_FIGURES:
-        test_win = TestFigureDialog(figure, chart_data.caption_text())
+        test_win = TestFigureDialog(chart_data)
         test_win.exec()
 
 
@@ -746,10 +760,10 @@ def test_cumulative_meta_analysis_bootstrap():
 
     options.create_graph = True
 
-    output, figure, chart_data, _ = MetaWinAnalysis.do_meta_analysis(data, options, 4)
+    output, chart_data, _ = MetaWinAnalysis.do_meta_analysis(data, options, 4)
     print_test_output(output)
     if TEST_FIGURES:
-        test_win = TestFigureDialog(figure, chart_data.caption_text())
+        test_win = TestFigureDialog(chart_data)
         test_win.exec()
 
 
@@ -775,11 +789,11 @@ def test_regression_meta_analysis_lep():
     options.independent_variable = data.cols[3]
     options.create_graph = True
 
-    output, figure, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
+    output, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
     print_test_output(output)
 
     if TEST_FIGURES:
-        test_win = TestFigureDialog(figure, chart_data.caption_text())
+        test_win = TestFigureDialog(chart_data)
         test_win.exec()
 
     global_values = analysis_values.global_values
@@ -821,11 +835,11 @@ def test_regression_meta_analysis_lep_randeff():
     options.random_effects = True
     options.create_graph = True
 
-    output, figure, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
+    output, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
     print_test_output(output)
 
     if TEST_FIGURES:
-        test_win = TestFigureDialog(figure, chart_data.caption_text())
+        test_win = TestFigureDialog(chart_data)
         test_win.exec()
 
     global_values = analysis_values.global_values
@@ -886,7 +900,7 @@ def test_complex_meta_analysis_lep_simple_regression():
     options.continuous_vars = [data.cols[3]]
     options.create_graph = True
 
-    output, _, _, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
+    output, _, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
     print_test_output(output)
 
     global_values = analysis_values.global_values
@@ -932,7 +946,7 @@ def test_complex_meta_analysis_lep_group():
     options.continuous_vars = []
     options.create_graph = True
 
-    output, _, _, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
+    output, _, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
     print_test_output(output)
 
     global_values = analysis_values.global_values
@@ -980,7 +994,7 @@ def test_complex_meta_analysis_lep():
     options.continuous_vars = [data.cols[3]]
     options.create_graph = True
 
-    output, _, _, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
+    output, _, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
     print_test_output(output)
 
     global_values = analysis_values.global_values
@@ -1039,7 +1053,7 @@ def test_complex_meta_analysis_lep_randomeff():
     options.random_effects = True
     options.create_graph = True
 
-    output, _, _, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
+    output, _, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
     print_test_output(output)
 
     global_values = analysis_values.global_values
@@ -1102,11 +1116,11 @@ def test_nested_meta_analysis_lep():
     options.create_graph = True
     data.cols[2].group_filter = ["Yponomeutidae", "Lycaenidae", "Hesperiidae"]
 
-    output, figure, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
+    output, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
     print_test_output(output)
 
     if TEST_FIGURES:
-        test_win = TestFigureDialog(figure, chart_data.caption_text())
+        test_win = TestFigureDialog(chart_data)
         test_win.exec()
 
     global_values = analysis_values.global_values
@@ -1205,7 +1219,7 @@ def test_tree_import():
 
     if TEST_FIGURES:
         figure = MetaWinCharts.chart_phylogeny(tree)
-        test_win = TestFigureDialog(figure, "imported phylogeny")
+        test_win = TestFigureDialog("imported phylogeny", figure)
         test_win.exec()
 
 
@@ -1214,8 +1228,8 @@ def test_scatter_plot():
     x_col = data.cols[11]
     y_col = data.cols[10]
     if TEST_FIGURES:
-        figure, chart_data = MetaWinDraw.draw_scatter_plot(data, x_col, y_col)
-        test_win = TestFigureDialog(figure, chart_data.caption_text())
+        chart_data = MetaWinDraw.draw_scatter_plot(data, x_col, y_col)
+        test_win = TestFigureDialog(chart_data)
         test_win.exec()
 
 
@@ -1224,8 +1238,8 @@ def test_normal_quantile_plot():
     e_col = data.cols[10]
     v_col = data.cols[11]
     if TEST_FIGURES:
-        figure, chart_data = MetaWinDraw.draw_normal_quantile_plot(data, e_col, v_col)
-        test_win = TestFigureDialog(figure, chart_data.caption_text())
+        chart_data = MetaWinDraw.draw_normal_quantile_plot(data, e_col, v_col)
+        test_win = TestFigureDialog(chart_data)
         test_win.exec()
 
 
@@ -1234,8 +1248,8 @@ def test_radial_plot_d():
     e_col = data.cols[10]
     v_col = data.cols[11]
     if TEST_FIGURES:
-        figure, chart_data = MetaWinDraw.draw_radial_plot(data, e_col, v_col, False)
-        test_win = TestFigureDialog(figure, chart_data.caption_text())
+        chart_data = MetaWinDraw.draw_radial_plot(data, e_col, v_col, False)
+        test_win = TestFigureDialog(chart_data)
         test_win.exec()
 
 
@@ -1244,8 +1258,8 @@ def test_radial_plot_lnrr():
     e_col = data.cols[10]
     v_col = data.cols[11]
     if TEST_FIGURES:
-        figure, chart_data = MetaWinDraw.draw_radial_plot(data, e_col, v_col, True)
-        test_win = TestFigureDialog(figure, chart_data.caption_text())
+        chart_data = MetaWinDraw.draw_radial_plot(data, e_col, v_col, True)
+        test_win = TestFigureDialog(chart_data)
         test_win.exec()
 
 
@@ -1254,8 +1268,8 @@ def test_histogram_d_unweighted():
     e_col = data.cols[10]
     v_col = data.cols[11]
     if TEST_FIGURES:
-        figure, chart_data = MetaWinDraw.draw_histogram_plot(data, e_col, v_col, 0, 10)
-        test_win = TestFigureDialog(figure, chart_data.caption_text())
+        chart_data = MetaWinDraw.draw_histogram_plot(data, e_col, v_col, 0, 10)
+        test_win = TestFigureDialog(chart_data)
         test_win.exec()
 
 
@@ -1264,8 +1278,8 @@ def test_histogram_d_weighted_invvar():
     e_col = data.cols[10]
     v_col = data.cols[11]
     if TEST_FIGURES:
-        figure, chart_data = MetaWinDraw.draw_histogram_plot(data, e_col, v_col, 1, 10)
-        test_win = TestFigureDialog(figure, chart_data.caption_text())
+        chart_data = MetaWinDraw.draw_histogram_plot(data, e_col, v_col, 1, 10)
+        test_win = TestFigureDialog(chart_data)
         test_win.exec()
 
 
@@ -1274,8 +1288,8 @@ def test_histogram_d_weighted_sample_size():
     e_col = data.cols[10]
     v_col = data.cols[2]
     if TEST_FIGURES:
-        figure, chart_data = MetaWinDraw.draw_histogram_plot(data, e_col, v_col, 2, 15)
-        test_win = TestFigureDialog(figure, chart_data.caption_text())
+        chart_data = MetaWinDraw.draw_histogram_plot(data, e_col, v_col, 2, 15)
+        test_win = TestFigureDialog(chart_data)
         test_win.exec()
 
 
@@ -1284,8 +1298,8 @@ def test_forest_plot():
     e_col = data.cols[10]
     v_col = data.cols[11]
     if TEST_FIGURES:
-        figure, chart_data = MetaWinDraw.draw_forest_plot(data, e_col, v_col)
-        test_win = TestFigureDialog(figure, chart_data.caption_text())
+        chart_data = MetaWinDraw.draw_forest_plot(data, e_col, v_col)
+        test_win = TestFigureDialog(chart_data)
         test_win.exec()
 
 
@@ -1310,11 +1324,11 @@ def test_trim_and_fill_analysis():
     options.create_graph = True
     options.k_estimator = "L"
 
-    output, figure, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
+    output, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
     print_test_output(output)
 
     if TEST_FIGURES:
-        test_win = TestFigureDialog(figure, chart_data.caption_text())
+        test_win = TestFigureDialog(chart_data)
         test_win.exec()
 
 
@@ -1339,11 +1353,11 @@ def test_trim_and_fill_analysis_negative_mean():
     options.create_graph = True
     options.k_estimator = "R"
 
-    output, figure, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
+    output, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
     print_test_output(output)
 
     if TEST_FIGURES:
-        test_win = TestFigureDialog(figure, chart_data.caption_text())
+        test_win = TestFigureDialog(chart_data)
         test_win.exec()
 
 
@@ -1368,11 +1382,11 @@ def test_jackknife():
     options.effect_vars = data.cols[5]
     options.create_graph = True
 
-    output, figure, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
+    output, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
     print_test_output(output)
 
     if TEST_FIGURES:
-        test_win = TestFigureDialog(figure, chart_data.caption_text())
+        test_win = TestFigureDialog(chart_data)
         test_win.exec()
 
 
@@ -1391,21 +1405,21 @@ def test_phylogenetic_simple_test():
     options.tip_names = data.cols[0]
     print("UNWEIGHTED")
     options.structure = MetaWinAnalysis.SIMPLE_MA
-    output, figure, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
+    output, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
     print_test_output(output)
 
     print()
     print()
     print("WEIGHTED")
     options.effect_vars = data.cols[2]
-    output, figure, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
+    output, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
     print_test_output(output)
 
     print()
     print()
     print("PHYLOGENETIC")
     options.structure = MetaWinAnalysis.PHYLOGENETIC_MA
-    output, figure, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4, tree=tree)
+    output, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4, tree=tree)
     print_test_output(output)
 
 
@@ -1422,11 +1436,11 @@ def test_phylogenetic_glm_simple():
     options.random_effects = True
 
     options.structure = MetaWinAnalysis.SIMPLE_MA
-    output, figure, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
+    output, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
     print_test_output(output)
     options.structure = MetaWinAnalysis.PHYLOGENETIC_MA
     print(data.column_labels()[1])
-    output, figure, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4, tree=tree)
+    output, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4, tree=tree)
     print_test_output(output)
 
     print()
@@ -1438,10 +1452,10 @@ def test_phylogenetic_glm_simple():
     options.effect_vars = data.cols[4]
 
     options.structure = MetaWinAnalysis.SIMPLE_MA
-    output, figure, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
+    output, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
     print_test_output(output)
     options.structure = MetaWinAnalysis.PHYLOGENETIC_MA
-    output, figure, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4, tree=tree)
+    output, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4, tree=tree)
     print_test_output(output)
 
     print()
@@ -1452,10 +1466,10 @@ def test_phylogenetic_glm_simple():
     options.effect_data = data.cols[5]
     options.effect_vars = data.cols[6]
     options.structure = MetaWinAnalysis.SIMPLE_MA
-    output, figure, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
+    output, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
     print_test_output(output)
     options.structure = MetaWinAnalysis.PHYLOGENETIC_MA
-    output, figure, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4, tree=tree)
+    output, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4, tree=tree)
     print_test_output(output)
 
     print()
@@ -1466,10 +1480,10 @@ def test_phylogenetic_glm_simple():
     options.effect_data = data.cols[7]
     options.effect_vars = data.cols[8]
     options.structure = MetaWinAnalysis.SIMPLE_MA
-    output, figure, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
+    output, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
     print_test_output(output)
     options.structure = MetaWinAnalysis.PHYLOGENETIC_MA
-    output, figure, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4, tree=tree)
+    output, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4, tree=tree)
     print_test_output(output)
 
     print()
@@ -1480,10 +1494,10 @@ def test_phylogenetic_glm_simple():
     options.effect_data = data.cols[9]
     options.effect_vars = data.cols[10]
     options.structure = MetaWinAnalysis.SIMPLE_MA
-    output, figure, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
+    output, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
     print_test_output(output)
     options.structure = MetaWinAnalysis.PHYLOGENETIC_MA
-    output, figure, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4, tree=tree)
+    output, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4, tree=tree)
     print_test_output(output)
 
     print()
@@ -1494,10 +1508,10 @@ def test_phylogenetic_glm_simple():
     options.effect_data = data.cols[11]
     options.effect_vars = data.cols[12]
     options.structure = MetaWinAnalysis.SIMPLE_MA
-    output, figure, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
+    output, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4)
     print_test_output(output)
     options.structure = MetaWinAnalysis.PHYLOGENETIC_MA
-    output, figure, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4, tree=tree)
+    output, chart_data, analysis_values = MetaWinAnalysis.do_meta_analysis(data, options, 4, tree=tree)
     print_test_output(output)