Final changes

- updated tutorial images
- updated test images
- updated readme, about, and changelog

Author: JAnns98

CHANGELOG.md

@@ -2,6 +2,22 @@
 
 <!-- do not remove -->
 
+## v2025.10.17
+
+### New Features
+
+1. **Group Summaries for Paired Plots**: Slopegraphs now support group summaries, showing summary statistics for each group. By default, a line connects group means, with vertical bars indicating standard deviation. Users can choose the summary type via the `group_summaries` argument in `.plot()` — options include `'mean_sd'`, `'median_quartiles'`, or `None`. Appearance can be customized using `group_summaries_kwargs`.
+
+2. **Fixed Mini-meta Weighted Delta Calculation**: The weighted delta calculation in mini-meta plots has been modified to ensure accurate reporting of the weighted delta.
+
+3. **Whorlmaps**: A new plot type for visualizing high-dimensional data in a compact heatmap-like format. TBC
+
+4. **Miscellaneous Improvements & Adjustments**:
+    - **Additional color options for barplots (unpaired proportional)**: `custom_palette` dict can now take 0 and 1 as keys to color the filled and unfilled portions of the plots.
+    - **Custom palette usage for slopegraphs**: The `custom_palette` can now be used for paired (non proportional) plots to color the contrast bars and effect size curves.
+
+
+
 ## v2025.03.27
 
 ### New Features
@@ -69,6 +85,7 @@
 ## v2023.03.29
 
 ### New Features
+
 - **Repeated measures**: Augments the prior function for plotting (independent) multiple test groups versus a shared control; it can now do the same for repeated-measures experimental designs. Thus, together, these two methods can be used to replace both flavors of the 1-way ANOVA with an estimation analysis.
 
 - **Proportional data**: Generates proportional bar plots, proportional differences, and calculates Cohen’s h. Also enables plotting Sankey diagrams for paired binary data. This is the estimation equivalent to a bar chart with Fischer’s exact test.

README.md

@@ -1,5 +1,5 @@
-DABEST-Python
-================
+# DABEST-Python
+
 
 <!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->
 
@@ -14,139 +14,42 @@ citation](https://zenodo.org/badge/DOI/10.1038/s41592-019-0470-3.svg)](https://r
 
 ## Recent Version Update
 
-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.
-
-2.  **Horizontal Plots**: Users can now create horizontal layout plots,
-    providing compact data visualization. This can be achieved by
-    setting `horizontal=True` in the `.plot()` method. See the
-    [Horizontal Plots
-    tutorial](https://acclab.github.io/DABEST-python/tutorials/08-horizontal_plot.html)
-    for more details.
-
-3.  **Forest Plots**: Forest plots provide a simple and intuitive way to
-    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
-    tutorial](https://acclab.github.io/DABEST-python/tutorials/07-forest_plot.html)
-    for more details.
-
-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
+We are proud to announce **DABEST Version Dadar (v2025.10.17)** This new
+version of the DABEST Python library includes new features, updated
+graphics, and improved calculations.
+
+1.  **Group Summaries for Paired Plots**: Slopegraphs now support group
+    summaries, showing summary statistics for each group. By default, a
+    line connects group means, with vertical bars indicating standard
+    deviation. Users can choose the summary type via the
+    `group_summaries` argument in `.plot()` — options include
+    `'mean_sd'`, `'median_quartiles'`, or `None`. Appearance can be
+    customized using `group_summaries_kwargs`. See the group summaries
+    section in the [Plot Aesthetics
     tutorial](https://acclab.github.io/DABEST-python/tutorials/09-plot_aesthetics.html)
     for more details.
 
-5.  **Other Visualization Improvements**:
-
-    - **Comparing means and effect sizes**: The estimation plots now
-      include three types of customizable visual features to enhance
-      contextualization and comparison of means and effect sizes:
-
-      - **Bars for the mean of the observed values (`raw_bars`)**:
-        Colored rectangles that extend from the zero line to the mean of
-        each group’s raw data. These bars visually highlight the central
-        tendency of the raw data.
-
-      - **Bars for effect size/s (`contrast_bars`)**: Similar to raw
-        bars, these highlight the effect-size difference between two
-        groups (typically test and control) in the contrast axis. They
-        provide a visual representation of the differences between
-        groups.
-
-      - **Summary bands (`reference_band`)**: An optional band or ribbon
-        that can be added to emphasize a specific effect size’s
-        confidence interval that is used as a reference range across the
-        entire contrast axis. Unlike raw and contrast bars, these span
-        horizontally (or vertically if `horizontal=True`) and are not
-        displayed by default.
-
-        Raw and contrast bars are shown by default. Users can customize
-        these bars and add summary bands as needed. For detailed
-        customization instructions, please refer to the [Plot Aesthetics
-        tutorial](https://acclab.github.io/DABEST-python/tutorials/09-plot_aesthetics.html).
-
-    - **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**: 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/off via the `prop_sample_counts` parameter.
-
-    - **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/off via the
-      `contrast_paired_lines` 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 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 (e.g. mean differences) are now
-      displayed as numerals next to their respective effect size. This
-      can be toggled on/off via the `delta_text` parameter.
-
-    - **Empty circle color palette**: A new swarmplot color palette
-      modification is available for unpaired plots via the
-      `empty_circle` parameter in the `.plot()` method. This option
-      modifies the two-group swarmplots to have empty circles for the
-      control group and filled circles for the experimental group.
-
-6.  **Miscellaneous Improvements & Adjustments**
-
-    - **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/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
-        consistency in naming. Arguments relating to the rawdata plot
-        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 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 Delta *g* effect size
-        alongside these (if a delta-delta experiment was loaded
-        correctly).
-
-    - **Updated tutorial pages**: We have updated the tutorial pages to
-      reflect the new features and changes. The tutorial pages are now
-      more comprehensive and (hopefully!) more intuitive!
-
-    - **Results dataframe for delta-delta and mini-meta plots**: A
-      results dataframe can now be extracted for both the delta-delta
-      and mini-meta effect size data (similar to the results dataframe
-      for the regular effect sizes). These can be found via the
-      `.results` attribute of the `.delta_delta` or `.mini_meta` object.
+2.  **Fixed Mini-meta Weighted Delta Calculation**: The weighted delta
+    calculation in mini-meta plots has been modified to ensure accurate
+    reporting of the weighted delta.
+
+3.  **Whorlmaps**: A new plot type for visualizing high-dimensional data
+    in a compact heatmap-like format. TBC
+
+4.  **Miscellaneous Improvements & Adjustments**:
+
+    - **Additional color options for barplots (unpaired proportional)**:
+      `custom_palette` dict can now take 0 and 1 as keys to color the
+      filled and unfilled portions of the plots. See the custom palette
+      section in the [Plot Aesthetics
+      tutorial](https://acclab.github.io/DABEST-python/tutorials/09-plot_aesthetics.html)
+      for more details.
+    - **Custom palette usage for slopegraphs**: The `custom_palette` can
+      now be used for paired (non proportional) plots to color the
+      contrast bars and effect size curves. See the custom palette
+      section in the [Plot Aesthetics
+      tutorial](https://acclab.github.io/DABEST-python/tutorials/09-plot_aesthetics.html)
+      for more details.
 
 ## Contents
 

dabest/__init__.py

@@ -12,4 +12,4 @@
     if not _NUMBA_COMPILED:
         precompile_all()
 
-__version__ = "2025.03.27"
+__version__ = "2025.10.17"

dabest/forest_plot.py

@@ -645,8 +645,8 @@ def forest_plot(
     ## y-label 
     if ylabel is None:
         effect_attr_map = {
-            "mean_diff": "Mean Difference",
-            "median_diff": "Median Difference", 
+            "mean_diff": "Mean difference",
+            "median_diff": "Median difference", 
             "cohens_d": "Cohen's d",
             "cohens_h": "Cohen's h",
             "cliffs_delta": "Cliff's delta",

dabest/misc_tools.py

@@ -951,24 +951,24 @@ def initialize_fig(
 
     # Set contrast axes y-label.
     contrast_label_dict = {
-        "mean_diff": "Mean difference",
-        "median_diff": "Median difference",
+        "mean_diff": "mean difference",
+        "median_diff": "median difference",
         "cohens_d": "Cohen's d",
         "hedges_g": "Hedges' g",
         "cliffs_delta": "Cliff's delta",
         "cohens_h": "Cohen's h",
     }
 
     if proportional and effect_size_type != "cohens_h":
-        default_contrast_label = "Proportion difference"
+        default_contrast_label = "proportion difference"
     else:
         default_contrast_label = contrast_label_dict[effect_size_type]
 
     if plot_kwargs["contrast_label"] is None:
         if is_paired:
             contrast_label = "Paired\n{}".format(default_contrast_label)
         else:
-            contrast_label = default_contrast_label
+            contrast_label = default_contrast_label.capitalize()
     else:
         contrast_label = plot_kwargs["contrast_label"]
 

nbs/02-about.ipynb

@@ -17,6 +17,8 @@
     "\n",
     "DABEST is written in Python by [Joses W. Ho](https://twitter.com/jacuzzijo), with design and input from [Adam Claridge-Chang](https://twitter.com/adamcchang) and other [lab members](https://www.claridgechang.net/people.html).\n",
     "\n",
+    "Features in v2025.10.17 were added by [Jonathan Anns](https://github.com/JAnns98), [Zinan Lu](https://github.com/Jacobluke-), [Yishan Mai](https://github.com/maiyishan), and [Sangyu Xu](https://github.com/sangyu).\n",
+    "\n",
     "Features in v2025.03.27 were added by [Jonathan Anns](https://github.com/JAnns98), [Zinan Lu](https://github.com/Jacobluke-), [Kah Seng Lian](https://github.com/sunroofgod), [Yishan Mai](https://github.com/maiyishan), [Sangyu Xu](https://github.com/sangyu), and [Lucas Wang Zhuoyu](https://github.com/Lucas1213WZY).\n",
     "\n",
     "Features in v2024.03.29 were added by [Zinan Lu](https://github.com/Jacobluke-), [Kah Seng Lian](https://github.com/sunroofgod), [Ana Rosa Castillo](https://github.com/cyberosa).\n",

nbs/API/forest_plot.ipynb

@@ -705,8 +705,8 @@
     "    ## y-label \n",
     "    if ylabel is None:\n",
     "        effect_attr_map = {\n",
-    "            \"mean_diff\": \"Mean Difference\",\n",
-    "            \"median_diff\": \"Median Difference\", \n",
+    "            \"mean_diff\": \"Mean difference\",\n",
+    "            \"median_diff\": \"Median difference\", \n",
     "            \"cohens_d\": \"Cohen's d\",\n",
     "            \"cohens_h\": \"Cohen's h\",\n",
     "            \"cliffs_delta\": \"Cliff's delta\",\n",

nbs/API/misc_tools.ipynb

@@ -1004,24 +1004,24 @@
     "\n",
     "    # Set contrast axes y-label.\n",
     "    contrast_label_dict = {\n",
-    "        \"mean_diff\": \"Mean difference\",\n",
-    "        \"median_diff\": \"Median difference\",\n",
+    "        \"mean_diff\": \"mean difference\",\n",
+    "        \"median_diff\": \"median difference\",\n",
     "        \"cohens_d\": \"Cohen's d\",\n",
     "        \"hedges_g\": \"Hedges' g\",\n",
     "        \"cliffs_delta\": \"Cliff's delta\",\n",
     "        \"cohens_h\": \"Cohen's h\",\n",
     "    }\n",
     "\n",
     "    if proportional and effect_size_type != \"cohens_h\":\n",
-    "        default_contrast_label = \"Proportion difference\"\n",
+    "        default_contrast_label = \"proportion difference\"\n",
     "    else:\n",
     "        default_contrast_label = contrast_label_dict[effect_size_type]\n",
     "\n",
     "    if plot_kwargs[\"contrast_label\"] is None:\n",
     "        if is_paired:\n",
     "            contrast_label = \"Paired\\n{}\".format(default_contrast_label)\n",
     "        else:\n",
-    "            contrast_label = default_contrast_label\n",
+    "            contrast_label = default_contrast_label.capitalize()\n",
     "    else:\n",
     "        contrast_label = plot_kwargs[\"contrast_label\"]\n",
     "\n",