Adjusted delta g label, fixed a sankey bug, and trimmed some plot tools code

> Trimmed some plot tools functions
> Adjusted delta g label name in code and tutorials
> two group sankey now doesnt allow flow=False if there's only two-groups (very buggy).

Author: JAnns98

README.md

@@ -18,7 +18,7 @@ We are proud to announce **DABEST Version Dadar (v2025.03.27)** This new
 version of the DABEST Python library includes several new features and
 performance improvements. It’s a big one!
 
-1.  **Python 3.13 Support**: DABEST now supports Python 3.10-3.13.
+1.  **Python 3.13 Support**: DABEST now supports Python 3.10—3.13.
 
 2.  **Horizontal Plots**: Users can now create horizontal layout plots,
     providing compact data visualization. This can be achieved by
@@ -27,19 +27,18 @@ performance improvements. It’s a big one!
     for more details.
 
 3.  **Forest Plots**: Forest plots provide a simple and intuitive way to
-    visualize many delta-delta (or Deltas’ g), mini-meta, or regular
+    visualize many delta-delta (or delta *g*), mini-meta, or regular
     delta effect sizes at once from multiple different dabest objects
     without presenting the raw data. See the [Forest
     Plots](../tutorials/07-forest_plot.html) tutorial for more details.
 
-4.  **Gridkey**: Users can now represent their experimental labels in a
-    gridkey format. This can be accessed with the `gridkey` argument in
-    the `plot()` method. See the gridkey section in the [Plot
+4.  **Gridkey**: Users can now represent experimental labels in a
+    ‘gridkey’ table. This can be accessed with the `gridkey` parameter
+    in the `plot()` method. See the gridkey section in the [Plot
     Aesthetics](../tutorials/09-plot_aesthetics.html) tutorial for more
     details.
 
-5.  **Aesthetic Updates**: We have made several aesthetic improvements
-    to the plots, including:
+5.  **Other Visualization Improvements**:
 
     - **Raw, Contrast, and Summary bars**: We added bars highlighting
       the various groups’ differences. These bars can be customized to
@@ -49,32 +48,41 @@ performance improvements. It’s a big one!
       Aesthetics](../tutorials/09-plot_aesthetics.html) tutorial for
       more details.
 
-    - **Delta-Delta and Mini-Meta Plots**: We have adjusted the spacing
-      of delta-delta and mini-meta plots to reduce whitespace. The new
-      format brings the added effect size closer to the regular effect
-      sizes. In addition, delta-delta plots now have a gap in the
-      zeroline to separate the delta-delta and regular effect sizes.
+    - **Tighter spacing in Delta-Delta and Mini-Meta Plots**: We have
+      adjusted the spacing of delta-delta and mini-meta plots to reduce
+      whitespace. The new format brings the overall effect size closer
+      to the two-groups effect sizes. In addition, delta-delta plots now
+      have a gap in the zero line to separate the delta-delta from the ∆
+      effect sizes.
 
-    - **Delta-Delta Effect Sizes for Proportion Plots**: Delta-delta
-      experimental plotting now supports binary data.
+    - **Delta-Delta Effect Sizes for Proportion Plots**: In addition to
+      continuous data, delta-delta plots now support binary data
+      (proportions). This means that 2-way designs for binary outcomes
+      can be analyzed with DABEST.
 
     - **Proportion Plots Sample Sizes**: The sample size of each binary
       option for each group can now be displayed. These can be toggled
       on or off via the `prop_sample_counts` parameter.
 
-    - **Effect Size Lines for Paired Plots**: Paired plots now display
-      lines linking the effect sizes within a group together in the
-      contrast axes. These can be toggled on or off via the
+    - **Effect Size Lines for Paired Plots**: Along with lines
+      connecting paired observed values, the paired plots now also
+      display lines linking the effect sizes within a group in the
+      contrast axes. These lines can be toggled on or off via the
       `contrast_paired_lines` parameter.
 
-    - **Baseline Error Curves**: Baseline error dot and curve are now
-      available to represent the baseline/control group in the contrast
-      axes. The dot is shown by default, while the curve can be toggled
-      on/off by the user (via the `show_baseline_ec` parameter).
+    - **Baseline Error Curves**: To represent the baseline/control group
+      in the contrast axes, it is now possible to plot the baseline dot
+      and the baseline error curve. The dot is shown by default, while
+      the curve can be toggled on/off by the user (via the
+      `show_baseline_ec` parameter). This dot helps make it clear where
+      the baseline comes from i.e. the control minus itself. The
+      baseline error curve can be used to show that the baseline itself
+      is an estimate inferred from the observed values of the control
+      data.
 
-    - **Delta Text**: Effect size deltas are now displayed as text next
-      to their respective effect size. This can be toggled on or off via
-      the `delta_text` parameter.
+    - **Delta Text**: Effect-size deltas (e.g. mean differences) are now
+      displayed as numerals next to their respective effect size. This
+      can be toggled on or off via the `delta_text` parameter.
 
     - **Empty Circle Color Palette**: A new swarmplot color palette
       modification is available for unpaired plots via the
@@ -84,26 +92,27 @@ performance improvements. It’s a big one!
 
 6.  **Miscellaneous Improvements & Adjustments**
 
-    - **Numba for Speed Improvements**: We have included Numba to speed
-      up the various calculations in DABEST. Precalculations will be
-      performed during import, which will help speed up the subsequent
-      loading and plotting of data.
+    - **Numba for Speed Improvements**: We have added
+      [Numba](https://numba.pydata.org/) to speed up the various
+      calculations in DABEST. Precalculations will be performed during
+      import, which will help speed up the subsequent loading and
+      plotting of data.
 
-    - **Terminology Updates**: We have made several updates to the
-      documentation and terminology to improve clarity and consistency.
-      For example:
+    - **Terminology/Naming Updates**: During the refactoring of the
+      code, we have made several updates to the documentation and
+      terminology to improve clarity and consistency. For example:
 
-      - Plot arguments have been adjusted to bring more clarity and
+      - Many plot arguments have been adjusted to bring more clarity and
         consistency in naming. Arguments relating to the rawdata plot
-        axis will now be typically referred to with ‘raw’ while
+        axis will now be typically referred to with `raw` while
         arguments relating to the contrast axis will be referred to with
-        ‘contrast’. For example, ‘raw_label’ replaces ‘swarm_label’ and
-        ‘bar_label’. The various kwargs relating to each different type
-        of plot (e.g., swarmplot_kwargs) remain unchanged.
-      - The method to utilise the Deltas’ g effect size is now via the
+        `contrast`. For example, `raw_label` replaces `swarm_label` and
+        `bar_label`. The various kwargs relating to each different type
+        of plot (e.g., `swarmplot_kwargs`) remain unchanged.
+      - The method to utilise the Delta *g* effect size is now via the
         .hedges_g.plot() method rather than creating a whole new Delta_g
         object as before. The functionality remains the same, it plots
-        hedges_g effect sizes and then the Deltas’ g effect size
+        hedges_g effect sizes and then the Delta *g* effect size
         alongside these (if a delta-delta experiment was loaded
         correctly).
 

dabest/_dabest_object.py

@@ -248,9 +248,9 @@ def cliffs_delta(self):
     @property
     def delta_g(self):
         """
-        Returns an :py:class:`EffectSizeDataFrame` for deltas' g, its confidence interval, and relevant statistics, for all comparisons as indicated via the `idx` and `paired` argument in `dabest.load()`.
+        Returns an :py:class:`EffectSizeDataFrame` for delta g, its confidence interval, and relevant statistics, for all comparisons as indicated via the `idx` and `paired` argument in `dabest.load()`.
         """
-        raise DeprecationWarning("delta_g has been depreciated  - Please use hedges_g (with delta2=True) for deltas' g experiments")
+        raise DeprecationWarning("delta_g has been depreciated  - Please use hedges_g (with delta2=True) for delta g experiments")
 
 
     @property

dabest/_delta_objects.py

@@ -33,7 +33,7 @@ class DeltaDelta(object):
 
     $$\Delta_{\Delta} = \Delta_{2} - \Delta_{1}$$
 
-    and a deltas' g value is calculated as the mean difference between the two primary deltas divided by
+    and a delta g value is calculated as the mean difference between the two primary deltas divided by
     the standard deviation of the delta-delta value, which is calculated from a pooled variance of the 4 samples:
 
     $$\Delta_{g} = \frac{\Delta_{\Delta}}{s_{\Delta_{\Delta}}}$$
@@ -63,7 +63,7 @@ def __init__(
         self.__control = self.__dabest_obj.experiment_label[0]
         self.__test = self.__dabest_obj.experiment_label[1]
 
-        # Compute the bootstrap delta-delta or deltas' g and the true dela-delta based on the raw data
+        # Compute the bootstrap delta-delta or delta g and the true dela-delta based on the raw data
         if self.__effect_size == "mean_diff":
             self.__bootstraps_delta_delta = bootstraps_delta_delta[2]
             self.__difference = (
@@ -157,7 +157,7 @@ def __repr__(self, header=True, sigfig=3):
         if self.__effect_size == "mean_diff":
             out1 = "The delta-delta between {control} and {test} ".format(**first_line)
         else:
-            out1 = "The deltas' g between {control} and {test} ".format(**first_line)
+            out1 = "The delta g between {control} and {test} ".format(**first_line)
 
         base_string_fmt = "{:." + str(sigfig) + "}"
         if "." in str(self.__ci):

dabest/forest_plot.py

@@ -647,12 +647,12 @@ def forest_plot(
         effect_attr_map = {
             "mean_diff": "Mean Difference",
             "hedges_g": "Hedges' g",
-            "delta_g": "Deltas' g"
+            "delta_g": "Delta g"
         }
         if contrast_type=='delta2' and idx is None and effect_size == "hedges_g":
-            ylabel = "Deltas' g"
+            ylabel = "Delta g"
         elif contrast_type=='delta2' and idx is not None and (effect_size == "delta_g" or effect_size == "hedges_g"):
-            ylabel = "Hedges' g with Deltas' g"
+            ylabel = "Hedges' g with Delta g"
         else:
             ylabel = effect_attr_map[effect_size]
     lim_key = ax.set_xlabel if horizontal else ax.set_ylabel

dabest/misc_tools.py

@@ -193,11 +193,13 @@ def get_params(
         else "right" if not horizontal
         else "left"
     )  
+    # Whether to show sample sizes with ticklabels
+    show_sample_size = plot_kwargs["show_sample_size"]
 
     return (dabest_obj, plot_data, xvar, yvar, is_paired, effect_size, proportional, all_plot_groups, 
             idx, show_delta2, show_mini_meta, float_contrast, show_pairs, group_summaries, 
             horizontal, results, ci_type, x1_level, experiment_label, show_baseline_ec, 
-            one_sankey, two_col_sankey, asymmetric_side)
+            one_sankey, two_col_sankey, asymmetric_side, show_sample_size)
 
 def get_kwargs(
         plot_kwargs: dict, 
@@ -219,6 +221,7 @@ def get_kwargs(
     default_swarmplot_kwargs = {
         "size": plot_kwargs["raw_marker_size"],
         "alpha": plot_kwargs["raw_alpha"],
+        "fontsize": plot_kwargs.get("fontsize_rawxlabel"),
     }
     if plot_kwargs["swarmplot_kwargs"] is None:
         swarmplot_kwargs = default_swarmplot_kwargs
@@ -234,6 +237,7 @@ def get_kwargs(
         "width": plot_kwargs["bar_width"],
         "alpha": plot_kwargs["raw_alpha"],
         "err_kws": {'color': 'black'},
+        "fontsize": plot_kwargs["fontsize_rawxlabel"]
     }
     if plot_kwargs["barplot_kwargs"] is None:
         barplot_kwargs = default_barplot_kwargs
@@ -251,6 +255,7 @@ def get_kwargs(
         "alpha": plot_kwargs['raw_alpha'],
         "rightColor": False,
         "bar_width": 0.2,
+        "fontsize": plot_kwargs.get("fontsize_rawxlabel")
     }
     if plot_kwargs["sankey_kwargs"] is None:
         sankey_kwargs = default_sankey_kwargs
@@ -280,7 +285,7 @@ def get_kwargs(
         "linewidth": 1, 
         "alpha": plot_kwargs["raw_alpha"],
         'jitter': 0, 
-        'jitter_seed': 9876543210
+        'jitter_seed': 9876543210,
     }
     if plot_kwargs["slopegraph_kwargs"] is None:
         slopegraph_kwargs = default_slopegraph_kwargs
@@ -1066,12 +1071,12 @@ def lookup_value(text):
         else:
             ticks_with_counts.append(f"{t}\n(N={value})")
 
-    fontsize_rawxlabel = plot_kwargs.get("fontsize_rawxlabel")
     set_major_loc_method(plt.FixedLocator(get_ticks()))
-    if plot_kwargs['show_sample_size']:
-        set_label(ticks_with_counts, fontsize=fontsize_rawxlabel)
-    else:
-        set_label(get_label(), fontsize=fontsize_rawxlabel)
+
+    # label = ticks_with_counts if plot_kwargs['show_sample_size'] else get_label()
+    # set_label(label, fontsize=plot_kwargs.get("fontsize_rawxlabel"))
+
+    set_label(ticks_with_counts, fontsize=plot_kwargs.get("fontsize_rawxlabel"))
 
     # Ensure ticks are at the correct locations
     set_major_loc_method(plt.FixedLocator(get_ticks()))