Merge pull request #201 from ACCLAB/feat-update-dependencies

Update dependencies, Python compatibility, and various doc updates

Author: JAnns98

.github/workflows/test-pytest.yaml

@@ -8,7 +8,7 @@ jobs:
       - uses: actions/checkout@v3
       - uses: actions/setup-python@v4
         with:
-          python-version: 3.9
+          python-version: 3.11
           cache: "pip"
           cache-dependency-path: settings.ini
       - name: Run pytest

CHANGELOG.md

@@ -2,6 +2,50 @@
 
 <!-- do not remove -->
 
+## v2025.03.14
+
+### New Features
+
+- **Horizontal Plots**: This new feature allows users to create horizontal plots instead of the regular vertical plots, providing a more compact visualization of data. This can be utilized by setting `horizontal=True` in the `plot()` method.
+
+- **Forest Plots**: This new feature allows users to create forest plots! Forest plots provide a simple and intuitive way to visualize many delta-delta (or Deltas' g) or mini-meta effect sizes at once from multiple different dabest objects without presenting the raw data.
+
+- **Gridkey**: This new feature allows users to create a gridkey to represent the labels of the groups in the plot. This can be utilized with the `gridkey_rows` argument in the `plot()` method.
+
+- **Aesthetic Updates**: We have made several aesthetic improvements to the plots, including:
+   - **Swarm, Contrast, and Summary bars**: We have added bars to better highlight the various groups and their differences. These bars can be customized to suit the user's needs. The swarm and contrast bars are provided by default, while the summary bars can be added by the user.
+  
+   - **Delta-Delta Plots**: We have modified the delta-delta plot format to be more compact and easier to read. The new format brings the delta-delta (or Deltas' g) effect size closer to the regular effect sizes. In addition, a gap has been added to the zeroline to separate the delta-delta and regular effect sizes.
+
+   - **Delta-delta Effect Sizes for Proportion Plots**: Delta-delta effect sizes for proportion plots are now available.
+  
+   - **Mini-Meta Plots**: We have modified the mini-meta plot format to be more compact and easier to read. The new format brings the mini-meta effect size closer to the regular effect sizes.
+
+   - **Proportion Plots Sample Sizes**: We have updated the proportion plots to show sample sizes for each group. These can be toggled on or off via the `prop_sample_counts` parameter.
+
+   - **Effect Size Lines for Paired Plots**: Effect size lines for paired plots are now available. These can be toggled on or off via the `es_paired_lines` parameter.
+
+   - **Baseline Error Curves**: Plots now include a baseline error dot and curve to show the error of the baseline/control group. By default, the dot is shown, while the curve can be added by the user (via the `show_baseline_ec` parameter).
+
+   - **Delta Text**: There is now an option to display delta text on the contrast axes. It displays the effect size of the contrast group relative to the reference group. This can be toggled on or off via the `delta_text` parameter. It is on by default.
+
+   - **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.
+
+### Enhancement
+
+- **Python 3.13 Support**: DABEST now supports Python 3.10-3.13.
+
+- **Numba for Speed Improvements**: We have included Numba to speed up the various calculations in DABEST. This should make the calculations faster and more efficient. Importing DABEST may take a little longer than before, and a progress bar will appear during the import process to show the calculations being performed. Once imported, loading and plotting data should now be faster.
+
+- **Terminology Updates**: We have made several updates to the documentation and terminology to improve clarity and consistency. For example:
+    - The method to utilise the Deltas' g effect size is now via the `.hedges_g.plot()` method now 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 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.
+
+
+
 ## v2024.03.29
 
 ### New Features

README.md

@@ -4,7 +4,7 @@
 <!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->
 
 [![minimal Python
-version](https://img.shields.io/badge/Python%3E%3D-3.9-6666ff.svg)](https://www.anaconda.com/distribution/)
+version](https://img.shields.io/badge/Python%3E%3D-3.10-6666ff.svg)](https://www.anaconda.com/distribution/)
 [![PyPI
 version](https://badge.fury.io/py/dabest.svg)](https://badge.fury.io/py/dabest)
 [![Downloads](https://img.shields.io/pepy/dt/dabest.svg)](https://pepy.tech/project/dabest)
@@ -14,37 +14,110 @@ 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 Ondeh (v2024.03.29)**. This
-new version of the DABEST Python library provides several new features
-and includes performance improvements.
-
-1.  **New Paired Proportion Plot**: This feature builds upon the
-    existing proportional analysis capabilities by introducing advanced
-    aesthetics and clearer visualization of changes in proportions
-    between different groups, inspired by the informative nature of
-    Sankey Diagrams. It’s particularly useful for studies that require
-    detailed examination of how proportions shift in paired
-    observations.
-
-2.  **Customizable Swarm Plot**: Enhancements allow for tailored swarm
-    plot aesthetics, notably the adjustment of swarm sides to produce
-    asymmetric swarm plots. This customization enhances data
-    representation, making visual distinctions more pronounced and
-    interpretations clearer.
-
-3.  **Standardized Delta-delta Effect Size**: We added a new metric akin
-    to a Hedges’ g for delta-delta effect size, which allows comparisons
-    between delta-delta effects generated from metrics with different
-    units.
-
-4.  **Miscellaneous Improvements**: This version also encompasses a
-    broad range of miscellaneous enhancements, including bug fixes,
-    Bootstrapping speed improvements, new templates for raising issues,
-    and updated unit tests. These improvements are designed to
-    streamline the user experience, increase the software’s stability,
-    and expand its versatility. By addressing user feedback and
-    identified issues, DABEST continues to refine its functionality and
-    reliability.
+We are proud to announce **DABEST Version TBC (v2025.03.14)** This new
+version of the DABEST Python library provides several new features and
+includes performance improvements. It’s a big one!
+
+1.  **Python 3.13 Support**: DABEST now supports Python 3.10-3.13.
+
+2.  **Horizontal Plots**: This new feature allows users to create
+    horizontal plots instead of the regular vertical plots, providing a
+    more compact visualization of data. This can be utilized by setting
+    `horizontal=True` in the `plot()` method. See the [Horizontal
+    Plots](../tutorials/08-horizontal_plot.html) tutorial for more
+    details.
+
+3.  **Forest Plots**: This new feature allows users to create forest
+    plots! Forest plots provide a simple and intuitive way to visualize
+    many delta-delta (or Deltas’ g) or mini-meta 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**: This new feature allows users to create a gridkey to
+    represent the labels of the groups in the plot. This can be utilized
+    with the `gridkey_rows` argument 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:
+
+    - **Swarm, Contrast, and Summary bars**: We have added bars to
+      better highlight the various groups and their differences. These
+      bars can be customized to suit the user’s needs. The swarm and
+      contrast bars are provided by default, while the summary bars can
+      be added by the user. See the relevant sections in the [Plot
+      Aesthetics](../tutorials/09-plot_aesthetics.html) tutorial for
+      more details.
+
+    - **Delta-Delta Plots**: We have modified the delta-delta plot
+      format to be more compact and easier to read. The new format
+      brings the delta-delta (or Deltas’ g) effect size closer to the
+      regular effect sizes. In addition, a gap has been added to the
+      zeroline to separate the delta-delta and regular effect sizes.
+
+    - **Delta-delta Effect Sizes for Proportion Plots**: Delta-delta
+      effect sizes for proportion plots are now available.
+
+    - **Mini-Meta Plots**: We have modified the mini-meta plot format to
+      be more compact and easier to read. The new format brings the
+      mini-meta effect size closer to the regular effect sizes.
+
+    - **Proportion Plots Sample Sizes**: We have updated the proportion
+      plots to show sample sizes for each group. These can be toggled on
+      or off via the `prop_sample_counts` parameter.
+
+    - **Effect Size Lines for Paired Plots**: Effect size lines for
+      paired plots are now available. These can be toggled on or off via
+      the `es_paired_lines` parameter.
+
+    - **Baseline Error Curves**: Plots now include a baseline error dot
+      and curve to show the error of the baseline/control group. By
+      default, the dot is shown, while the curve can be added by the
+      user (via the `show_baseline_ec` parameter).
+
+    - **Delta Text**: There is now an option to display delta text on
+      the contrast axes. It displays the effect size of the contrast
+      group relative to the reference group. This can be toggled on or
+      off via the `delta_text` parameter. It is on by default.
+
+    - **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 included Numba to speed
+      up the various calculations in DABEST. This should make the
+      calculations faster and more efficient. Importing DABEST may take
+      a little longer than before, and a progress bar will appear during
+      the import process to show the calculations being performed. Once
+      imported, loading and plotting data should now be faster.
+
+    - **Terminology Updates**: We have made several updates to the
+      documentation and terminology to improve clarity and consistency.
+      For example:
+
+      - The method to utilise the Deltas’ g effect size is now via the
+        `.hedges_g.plot()` method now 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 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.
 
 ## Contents
 
@@ -92,7 +165,7 @@ allowing everyone access to high-quality estimation plots.
 
 ## Installation
 
-This package is tested on Python 3.8 and onwards. It is highly
+This package is tested on Python 3.10 and onwards. It is highly
 recommended to download the [Anaconda
 distribution](https://www.continuum.io/downloads) of Python in order to
 obtain the dependencies easily.
@@ -161,17 +234,17 @@ page](https://github.com/ACCLAB/DABEST-python/issues/new).
 ## Contributing
 
 All contributions are welcome; please read the [Guidelines for
-contributing](CONTRIBUTING.md) first.
+contributing](../CONTRIBUTING.md) first.
 
-We also have a [Code of Conduct](CODE_OF_CONDUCT.md) to foster an
+We also have a [Code of Conduct](../CODE_OF_CONDUCT.md) to foster an
 inclusive and productive space.
 
 ### A wish list for new features
 
 If you have any specific comments and ideas for new features that you
 would like to share with us, please read the [Guidelines for
-contributing](CONTRIBUTING.md), create a new issue using Feature request
-template or create a new post in [our Google
+contributing](../CONTRIBUTING.md), create a new issue using Feature
+request template or create a new post in [our Google
 Group](https://groups.google.com/g/estimationstats).
 
 ## Acknowledgements
@@ -198,7 +271,7 @@ The test suite ensures that the bootstrapping functions and the plotting
 functions perform as expected.
 
 For detailed information, please refer to the [test
-folder](nbs/tests/README.md)
+folder](../nbs/tests/README.md)
 
 ## DABEST in other languages
 

dabest/__init__.py

@@ -11,4 +11,4 @@
     if not _NUMBA_COMPILED:
         precompile_all()
 
-__version__ = "2024.03.30"
+__version__ = "2025.03.14"

dabest/_delta_objects.py

@@ -17,7 +17,7 @@
 
 # %% ../nbs/API/delta_objects.ipynb 6
 class DeltaDelta(object):
-    """
+    r"""
     A class to compute and store the delta-delta statistics for experiments with a 2-by-2 arrangement where two independent variables, A and B, each have two categorical values, 1 and 2. The data is divided into two pairs of two groups, and a primary delta is first calculated as the mean difference between each of the pairs:
 
 

dabest/forest_plot.py

@@ -223,7 +223,7 @@ def get_kwargs(
         "widths": 0.5,
         "showextrema": False,
         "showmedians": False,
-        "vert": not horizontal
+        "vert": not horizontal,
     }
     if violin_kwargs is None:
         violin_kwargs = default_violin_kwargs

dabest/misc_tools.py

@@ -259,7 +259,7 @@ def get_kwargs(
     # Violinplot kwargs.
     default_violinplot_kwargs = {
         "widths": 0.5,
-        "vert": True,
+        "vert": 'vertical',
         "showextrema": False,
         "showmedians": False,
 

dabest/plot_tools.py

@@ -2703,7 +2703,7 @@ def _swarm(
                         bad_x_offsets.append(True)
                     else:
                         bad_x_offsets.append(False)
-                potential_x_offsets[bad_x_offsets] = np.infty
+                potential_x_offsets[bad_x_offsets] = np.inf
                 abs_potential_x_offsets = [abs(_) for _ in potential_x_offsets]
                 valid_x_offset = potential_x_offsets[
                     abs_potential_x_offsets.index(min(abs_potential_x_offsets))

dabest/plotter.py

@@ -77,7 +77,6 @@ def effectsize_df_plotter(effectsize_df: object, **plot_kwargs) -> matplotlib.fi
         set_xaxis_ticks_and_lims,
         show_legend,
         gardner_altman_adjustments,
-        # Cumming_Plot_Aesthetic_Adjustments,
         extract_group_summaries,
         draw_zeroline,
         redraw_dependent_spines,

nbs/01-getting_started.ipynb

@@ -64,15 +64,16 @@
    "source": [
     "\n",
     "\n",
-    "Python 3.10 is strongly recommended. DABEST has also been tested with Python 3.8 and onwards.\n",
+    "Python 3.11 is strongly recommended. DABEST has also been tested with Python 3.10 and onwards.\n",
     "\n",
     "In addition, the following packages are also required (listed with their minimal versions):\n",
     "\n",
-    "* [numpy 1.23.5](https://www.numpy.org)\n",
-    "* [scipy 1.9.3](https://www.scipy.org)\n",
-    "* [matplotlib 3.6.3](https://www.matplotlib.org)\n",
-    "* [pandas 1.5.0](https://pandas.pydata.org)\n",
-    "* [seaborn 0.12.2](https://seaborn.pydata.org)\n",
+    "* [numpy 2.1.3](https://www.numpy.org)\n",
+    "* [scipy 1.15.2](https://www.scipy.org)\n",
+    "* [matplotlib 3.10.0](https://www.matplotlib.org)\n",
+    "* [pandas 2.2.3](https://pandas.pydata.org)\n",
+    "* [seaborn 0.13.2](https://seaborn.pydata.org)\n",
+    "* [numba 0.61.0](https://numba.pydata.org)\n",
     "* [lqrt 0.3.3](https://github.com/alyakin314/lqrt)\n",
     "\n",
     "To obtain these package dependencies easily, it is highly recommended to download the [Anaconda](https://www.continuum.io/downloads) distribution of Python.\n"